Repository: LGE-ARC-AdvancedAI/auptimizer Branch: master Commit: 50f6e3b4e0cb Files: 892 Total size: 14.8 MB Directory structure: gitextract_g8wyt_2k/ ├── .coveragerc ├── .dockerignore ├── .gitignore ├── .travis.yml ├── CODE_OF_CONDUCT.md ├── Examples/ │ ├── .gitignore │ ├── 2dfunc_diff_mult_res/ │ │ ├── README.md │ │ ├── cpu.ini │ │ ├── exp_cpu.json │ │ └── rosenbrock_hpo.py │ ├── 2dfunc_diff_opt/ │ │ ├── .gitignore │ │ ├── History.ipynb │ │ ├── README.md │ │ ├── experiment_auto.json │ │ ├── experiment_bohb.json │ │ ├── experiment_hyperband.json │ │ ├── experiment_hyperopt.json │ │ ├── experiment_random.json │ │ ├── experiment_sequence.json │ │ ├── experiment_spearmint.json │ │ ├── rosenbrock_hpo.py │ │ └── rosenbrock_origin.py │ ├── 2dfunc_diff_res/ │ │ ├── README.md │ │ ├── aws.txt │ │ ├── cpu.ini │ │ ├── env_local_template.ini │ │ ├── env_user_template.ini │ │ ├── exp_aws.json │ │ ├── exp_cpu.json │ │ ├── exp_gpu.json │ │ ├── exp_node.json │ │ ├── exp_node_async.json │ │ ├── exp_passive.json │ │ ├── gpu.txt │ │ ├── node.txt │ │ ├── plainGPU.txt │ │ ├── rosenbrock_hpo.py │ │ └── singleGPU.txt │ ├── cai_eas/ │ │ ├── .gitignore │ │ ├── README.md │ │ ├── eas/ │ │ │ ├── client.py │ │ │ ├── data_providers/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_provider.py │ │ │ │ ├── cifar.py │ │ │ │ ├── downloader.py │ │ │ │ ├── svhn.py │ │ │ │ └── utils.py │ │ │ ├── expdir_monitor/ │ │ │ │ ├── __init__.py │ │ │ │ └── expdir_monitor.py │ │ │ ├── experiment.json │ │ │ └── models/ │ │ │ ├── __init__.py │ │ │ ├── basic_model.py │ │ │ ├── convnet.py │ │ │ ├── dense_net.py │ │ │ ├── layer_cascade.py │ │ │ ├── layer_multi_branch.py │ │ │ ├── layers.py │ │ │ └── utils.py │ │ └── start_nets/ │ │ └── start_net_convnet_small_C10+/ │ │ ├── init │ │ └── net.config │ ├── compression/ │ │ ├── mnist_pytorch/ │ │ │ ├── README.md │ │ │ ├── exp_activation_apoz_rank.json │ │ │ ├── exp_activation_apoz_rank_dependency_aware.json │ │ │ ├── exp_activation_mean_rank.json │ │ │ ├── exp_activation_mean_rank_dependency_aware.json │ │ │ ├── exp_admm.json │ │ │ ├── exp_agp.json │ │ │ ├── exp_amc.json │ │ │ ├── exp_auto_activation_apoz_rank.json │ │ │ ├── exp_auto_activation_mean_rank.json │ │ │ ├── exp_auto_admm.json │ │ │ ├── exp_auto_agp.json │ │ │ ├── exp_auto_fpgm_aup_args.json │ │ │ ├── exp_auto_fpgm_bohb.json │ │ │ ├── exp_auto_fpgm_hyperband.json │ │ │ ├── exp_auto_fpgm_hyperopt.json │ │ │ ├── exp_auto_fpgm_no_expand.json │ │ │ ├── exp_auto_fpgm_random.json │ │ │ ├── exp_auto_fpgm_random_no_expand.json │ │ │ ├── exp_auto_fpgm_sequence.json │ │ │ ├── exp_auto_fpgm_spearmint.json │ │ │ ├── exp_auto_l1filter.json │ │ │ ├── exp_auto_l2filter.json │ │ │ ├── exp_auto_level.json │ │ │ ├── exp_auto_lottery_ticket.json │ │ │ ├── exp_auto_taylor_fo.json │ │ │ ├── exp_autocompress.json │ │ │ ├── exp_fpgm.json │ │ │ ├── exp_fpgm_aup_args.json │ │ │ ├── exp_fpgm_dependency_aware.json │ │ │ ├── exp_l1filter.json │ │ │ ├── exp_l1filter_dependency_aware.json │ │ │ ├── exp_l2filter.json │ │ │ ├── exp_l2filter_dependency_aware.json │ │ │ ├── exp_level.json │ │ │ ├── exp_lottery_ticket.json │ │ │ ├── exp_net_adapt.json │ │ │ ├── exp_quantization_bnn.json │ │ │ ├── exp_quantization_dorefa.json │ │ │ ├── exp_quantization_naive.json │ │ │ ├── exp_quantization_qat.json │ │ │ ├── exp_sensitivity.json │ │ │ ├── exp_simulated_annealing.json │ │ │ ├── exp_taylor_fo.json │ │ │ ├── exp_taylor_fo_dependency_aware.json │ │ │ ├── mnist.py │ │ │ ├── mnist_admm.py │ │ │ ├── mnist_agp.py │ │ │ ├── mnist_amc.py │ │ │ ├── mnist_aup_args.py │ │ │ ├── mnist_autocompress.py │ │ │ ├── mnist_dependency_aware.py │ │ │ ├── mnist_lottery_ticket.py │ │ │ ├── mnist_net_adapt.py │ │ │ ├── mnist_no_speedup.py │ │ │ ├── mnist_pretrained.pth │ │ │ ├── mnist_pretrained.py │ │ │ ├── mnist_sensitivity.py │ │ │ └── mnist_simulated_annealing.py │ │ ├── mnist_tensorflow/ │ │ │ ├── README.md │ │ │ ├── exp_auto_level.json │ │ │ ├── exp_level.json │ │ │ ├── mnist.py │ │ │ └── mnist_pretrained.py │ │ └── utility_functions/ │ │ ├── README.md │ │ └── mnist.py │ ├── converter_examples/ │ │ ├── Convert_Benchmark/ │ │ │ ├── .gitignore │ │ │ ├── Benchmark.ipynb │ │ │ ├── README.md │ │ │ ├── repdata.py │ │ │ └── script_mobilenet.py │ │ ├── Convert_Profiler/ │ │ │ ├── .gitignore │ │ │ ├── README.md │ │ │ ├── convert_onnx.json │ │ │ ├── convert_tflite.json │ │ │ ├── download_test_models.py │ │ │ ├── env_onnx.template │ │ │ ├── env_tflite.template │ │ │ ├── model_names_tflite.txt │ │ │ ├── test_perf_onnx.py │ │ │ ├── test_perf_tflite.py │ │ │ └── tflite_output.txt │ │ ├── README.md │ │ ├── Tested_Models/ │ │ │ ├── .gitignore │ │ │ ├── README.md │ │ │ ├── conversion_jsons/ │ │ │ │ ├── convert_checkpoint_to_onnx.json │ │ │ │ ├── convert_checkpoint_to_tflite.json │ │ │ │ ├── convert_keras_to_onnx.json │ │ │ │ ├── convert_keras_to_tflite.json │ │ │ │ ├── convert_protobuf_to_onnx.json │ │ │ │ ├── convert_protobuf_to_tflite.json │ │ │ │ ├── convert_pytorch_to_onnx.json │ │ │ │ ├── convert_pytorch_to_tflite.json │ │ │ │ ├── convert_savedmodel_to_onnx.json │ │ │ │ └── convert_savedmodel_to_tflite.json │ │ │ ├── convert_pb_to_tflite.json │ │ │ ├── create_test_models.py │ │ │ ├── download_models.py │ │ │ └── download_urls.json │ │ └── dlconvert_requirements.txt │ ├── decorator_example/ │ │ ├── origin.py │ │ └── use_decorator.py │ ├── demo/ │ │ ├── .gitignore │ │ ├── demo.yml │ │ ├── experiment_wrapper.json │ │ ├── hpo_wrapper.py │ │ └── origin.py │ ├── early_stopping/ │ │ ├── README.md │ │ ├── mnist_keras/ │ │ │ ├── cpu.ini │ │ │ ├── exp_BOHB_bandit.json │ │ │ ├── exp_BOHB_curve_fitting.json │ │ │ ├── exp_BOHB_median.json │ │ │ ├── exp_BOHB_trunc.json │ │ │ ├── exp_random_bandit.json │ │ │ ├── exp_random_curve_fitting.json │ │ │ ├── exp_random_median.json │ │ │ ├── exp_random_trunc.json │ │ │ ├── exp_spearmint_bandit.json │ │ │ ├── exp_spearmint_curve_fitting.json │ │ │ ├── exp_spearmint_median.json │ │ │ ├── exp_spearmint_trunc.json │ │ │ └── mnist.py │ │ └── quad_equation_min/ │ │ ├── cpu.ini │ │ ├── quad_min.py │ │ ├── quad_min_BOHB_bandit.json │ │ ├── quad_min_BOHB_median.json │ │ ├── quad_min_BOHB_trunc.json │ │ ├── quad_min_random_bandit.json │ │ ├── quad_min_random_median.json │ │ ├── quad_min_random_trunc.json │ │ ├── quad_min_spearmint_bandit.json │ │ ├── quad_min_spearmint_median.json │ │ └── quad_min_spearmint_trunc.json │ ├── hpo_mnist/ │ │ ├── .gitignore │ │ ├── MNIST Hyperparameter Optimization Demo.ipynb │ │ ├── README.md │ │ ├── demo.json │ │ ├── exp_aws_async.json │ │ ├── exp_aws_demo.json │ │ ├── exp_aws_retry_job.json │ │ ├── exp_bohb.json │ │ ├── exp_hyperband.json │ │ ├── exp_hyperopt.json │ │ ├── exp_random.json │ │ ├── exp_random_async.json │ │ ├── exp_sequence.json │ │ ├── exp_spearmint.json │ │ ├── job_retries_random.json │ │ ├── mnist_hpo_demo.py │ │ └── mnist_hpo_fail.py │ ├── intermediate_results/ │ │ ├── README.md │ │ └── quad_equation_min/ │ │ ├── quad_min.py │ │ ├── quad_min_BOHB.json │ │ ├── quad_min_random.json │ │ └── quad_min_spearmint.json │ ├── job_failure_control/ │ │ ├── README.md │ │ ├── experiment_extra_argument.json │ │ ├── experiment_job_ignore_fail.json │ │ ├── experiment_job_retries.json │ │ ├── experiment_job_retries_ignore_fail.json │ │ ├── experiment_no_nsample.json │ │ ├── experiment_no_param_config.json │ │ ├── experiment_no_proposer.json │ │ ├── experiment_no_resource.json │ │ ├── experiment_no_script.json │ │ ├── experiment_test.json │ │ ├── rosenbrock_hpo.py │ │ └── test.py │ ├── mnist_keras_save_model/ │ │ ├── README.md │ │ ├── cpu.ini │ │ ├── exp_random_cpu.json │ │ ├── exp_random_node.json │ │ ├── mnist.py │ │ ├── mnist_wo_decorator.py │ │ └── node.txt │ ├── profiler_examples/ │ │ ├── README.md │ │ ├── bench/ │ │ │ ├── download.sh │ │ │ └── test_perf.py │ │ ├── env_benchmark.template │ │ ├── env_mnist.template │ │ ├── experiments/ │ │ │ └── Readme.md │ │ ├── internal/ │ │ │ └── ImageNet Experiments.ipynb │ │ ├── issues.md │ │ ├── mnist/ │ │ │ └── mnist.py │ │ └── model_names.txt │ ├── quad_equation_min/ │ │ ├── QUAD_MIN_Demo.ipynb │ │ ├── cpu.ini │ │ ├── quad_min.py │ │ ├── quad_min_BOHB.json │ │ ├── quad_min_random.json │ │ └── quad_min_spearmint.json │ ├── tf_flags/ │ │ ├── README.md │ │ ├── experiment.json │ │ ├── rosenbrock_hpo.py │ │ └── rosenbrock_tf.py │ └── tf_iris_diff_opt/ │ ├── History.ipynb │ ├── README.md │ ├── experiment_demo.json │ ├── experiment_hpo.json │ ├── experiment_hyperband.json │ ├── experiment_random.json │ ├── experiment_sequence.json │ ├── experiment_spearmint.json │ ├── iris_data.py │ ├── premade_estimator.py │ ├── premade_estimator_hpo.py │ ├── premade_estimator_hyper.py │ └── premade_estimator_wrapper.py ├── LICENSE ├── MANIFEST.in ├── R-src/ │ ├── README.md │ ├── Rpackage/ │ │ ├── DESCRIPTION │ │ ├── NAMESPACE │ │ ├── R/ │ │ │ └── auptimizer.R │ │ ├── man/ │ │ │ ├── get_config.Rd │ │ │ └── print_result.Rd │ │ ├── tests/ │ │ │ ├── testthat/ │ │ │ │ ├── test_IO.R │ │ │ │ └── test_io.json │ │ │ └── testthat.R │ │ └── vignettes/ │ │ ├── .gitignore │ │ └── auptimizer.Rmd │ └── examples/ │ ├── exp_ridge.R │ ├── exp_rosenbrock.R │ ├── ridge.json │ ├── ridgeRegression.R │ ├── rosenbrock.R │ └── rosenbrock.json ├── README.md ├── docs/ │ ├── Database/ │ │ ├── schema.dot │ │ ├── schema.sql │ │ └── sql_graphviz.py │ ├── Dockerfile │ ├── LICENSE │ ├── Makefile │ ├── README.rst │ ├── _static/ │ │ └── .gitkeep │ ├── algorithm.rst │ ├── archive/ │ │ ├── Auptimizer-1.0-py2-none-any.whl │ │ ├── Auptimizer-1.0-py3-none-any.whl │ │ ├── Auptimizer-1.1-py2-none-any.whl │ │ ├── Auptimizer-1.1-py3-none-any.whl │ │ ├── Auptimizer-1.2-py2-none-any.whl │ │ ├── Auptimizer-1.2-py3-none-any.whl │ │ ├── Auptimizer-1.3-py2-none-any.whl │ │ ├── Auptimizer-1.3-py3-none-any.whl │ │ ├── Auptimizer-1.4-py2.py3-none-any.whl │ │ ├── Auptimizer-2.0-py2.py3-none-any.whl │ │ └── aup.py │ ├── aup.EE.Experiment.rst │ ├── aup.EE.Job.rst │ ├── aup.EE.Resource.rst │ ├── aup.EE.rst │ ├── aup.ET.Connector.rst │ ├── aup.ET.rst │ ├── aup.Proposer.BOHBProposer.rst │ ├── aup.Proposer.EASProposer.rst │ ├── aup.Proposer.HyperbandProposer.rst │ ├── aup.Proposer.HyperoptProposer.rst │ ├── aup.Proposer.RandomProposer.rst │ ├── aup.Proposer.SequenceProposer.rst │ ├── aup.Proposer.SpearmintProposer.rst │ ├── aup.Proposer.rst │ ├── aup.__main__.rst │ ├── aup.compression.rst │ ├── aup.convert.rst │ ├── aup.dlconvert.rst │ ├── aup.dlconvert_API.rst │ ├── aup.init.rst │ ├── aup.profiler.rst │ ├── aup.rst │ ├── aup.setup.rst │ ├── aup.setupdb.rst │ ├── aup.setupdb.sqlite.rst │ ├── aup.setupdb_API.rst │ ├── aup.utils.rst │ ├── aup.visualize.rst │ ├── compression.rst │ ├── compression_main.rst │ ├── compression_utilities.rst │ ├── compressors.rst │ ├── conf.py │ ├── dashboard.rst │ ├── demo.rst │ ├── developer.rst │ ├── developer_guide.rst │ ├── dlconvert.rst │ ├── dlconvert_example.rst │ ├── dlconvert_readme.rst │ ├── early_stop.rst │ ├── edge.rst │ ├── environment.rst │ ├── errors.rst │ ├── experiment.rst │ ├── hpo.rst │ ├── index.rst │ ├── install.rst │ ├── prepare.sh │ ├── prof_example.rst │ ├── prof_readme.rst │ ├── profiler.rst │ ├── r_user.rst │ ├── requirements.txt │ ├── setup.rst │ └── version.rst ├── oss-package.info ├── publish.sh ├── requirements.txt ├── setup.py ├── src/ │ └── aup/ │ ├── EE/ │ │ ├── Experiment.py │ │ ├── Job.py │ │ ├── Resource/ │ │ │ ├── AWSResourceManager.py │ │ │ ├── AbstractResourceManager.py │ │ │ ├── CPUResourceManager.py │ │ │ ├── GPUResourceManager.py │ │ │ ├── PassiveResourceManager.py │ │ │ ├── SSHResourceManager.py │ │ │ ├── __init__.py │ │ │ └── utils/ │ │ │ ├── ResourceThreadPoolExecutor.py │ │ │ ├── __init__.py │ │ │ └── curve_fitting.py │ │ └── __init__.py │ ├── ET/ │ │ ├── Connector/ │ │ │ ├── AbstractConnector.py │ │ │ ├── SQLiteConnector.py │ │ │ └── __init__.py │ │ └── __init__.py │ ├── Proposer/ │ │ ├── AbstractProposer.py │ │ ├── BOHBProposer.py │ │ ├── EASProposer.py │ │ ├── HyperbandProposer.py │ │ ├── HyperoptProposer.py │ │ ├── Hyperopt_LICENSE │ │ ├── RandomProposer.py │ │ ├── SequenceProposer.py │ │ ├── SpearmintProposer.py │ │ ├── Spearmint_LICENSE │ │ ├── __init__.py │ │ ├── eas/ │ │ │ ├── LICENSE │ │ │ ├── __init__.py │ │ │ ├── arch_search/ │ │ │ │ ├── __init__.py │ │ │ │ ├── arch_search_convnet_net2net.py │ │ │ │ └── arch_search_densenet_net2net.py │ │ │ ├── arch_search.py │ │ │ ├── client.py │ │ │ ├── data_providers/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_provider.py │ │ │ │ ├── cifar.py │ │ │ │ ├── downloader.py │ │ │ │ ├── svhn.py │ │ │ │ └── utils.py │ │ │ ├── expdir_monitor/ │ │ │ │ ├── __init__.py │ │ │ │ ├── arch_manager.py │ │ │ │ ├── distributed.py │ │ │ │ └── expdir_monitor.py │ │ │ ├── main.py │ │ │ ├── meta_controller/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_controller.py │ │ │ │ └── rl_controller.py │ │ │ ├── models/ │ │ │ │ ├── __init__.py │ │ │ │ ├── basic_model.py │ │ │ │ ├── convnet.py │ │ │ │ ├── dense_net.py │ │ │ │ ├── layer_cascade.py │ │ │ │ ├── layer_multi_branch.py │ │ │ │ ├── layers.py │ │ │ │ └── utils.py │ │ │ ├── run_dense_net.py │ │ │ └── run_simple_convnet.py │ │ ├── hpbandster/ │ │ │ ├── __init__.py │ │ │ ├── core/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_config_generator.py │ │ │ │ ├── base_iteration.py │ │ │ │ ├── dispatcher.py │ │ │ │ ├── master.py │ │ │ │ ├── nameserver.py │ │ │ │ ├── result.py │ │ │ │ └── worker.py │ │ │ ├── examples/ │ │ │ │ ├── README.txt │ │ │ │ ├── __init__.py │ │ │ │ ├── commons.py │ │ │ │ ├── example_1_local_sequential.py │ │ │ │ ├── example_2_local_parallel_threads.py │ │ │ │ ├── example_3_local_parallel_processes.py │ │ │ │ ├── example_4_cluster.py │ │ │ │ ├── example_5_keras_worker.py │ │ │ │ ├── example_5_mnist.py │ │ │ │ ├── example_5_pytorch_worker.py │ │ │ │ ├── example_5_run/ │ │ │ │ │ ├── configs.json │ │ │ │ │ └── results.json │ │ │ │ ├── example_8_mnist_continued.py │ │ │ │ ├── plot_example_6_analysis.py │ │ │ │ └── plot_example_7_interactive_plot.py │ │ │ ├── optimizers/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bohb.py │ │ │ │ ├── config_generators/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── bohb.py │ │ │ │ │ ├── h2bo.py │ │ │ │ │ ├── kde.py │ │ │ │ │ ├── lcnet.py │ │ │ │ │ └── random_sampling.py │ │ │ │ ├── h2bo.py │ │ │ │ ├── hyperband.py │ │ │ │ ├── iterations/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── successivehalving.py │ │ │ │ │ └── successiveresampling.py │ │ │ │ ├── kde/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── kernels.py │ │ │ │ │ └── mvkde.py │ │ │ │ ├── lcnet.py │ │ │ │ ├── learning_curve_models/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── arif.py │ │ │ │ │ ├── base.py │ │ │ │ │ └── lcnet.py │ │ │ │ └── randomsearch.py │ │ │ ├── utils.py │ │ │ ├── visualization.py │ │ │ └── workers/ │ │ │ ├── __init__.py │ │ │ └── hpolibbenchmark.py │ │ ├── hpbandster_LICENSE │ │ ├── hyperband_LICENSE │ │ ├── hyperopt/ │ │ │ ├── LICENSE.txt │ │ │ ├── __init__.py │ │ │ ├── algobase.py │ │ │ ├── anneal.py │ │ │ ├── base.py │ │ │ ├── criteria.py │ │ │ ├── exceptions.py │ │ │ ├── fmin.py │ │ │ ├── graphviz.py │ │ │ ├── hp.py │ │ │ ├── ipy.py │ │ │ ├── main.py │ │ │ ├── mix.py │ │ │ ├── mongoexp.py │ │ │ ├── plotting.py │ │ │ ├── pyll/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ └── stochastic.py │ │ │ ├── pyll_utils.py │ │ │ ├── rand.py │ │ │ ├── rdists.py │ │ │ ├── tpe.py │ │ │ ├── utils.py │ │ │ └── vectorize.py │ │ └── spearmint/ │ │ ├── ExperimentGrid.py │ │ ├── Locker.py │ │ ├── __init__.py │ │ ├── chooser/ │ │ │ ├── CMAChooser.py │ │ │ ├── GPConstrainedEIChooser.py │ │ │ ├── GPEIChooser.py │ │ │ ├── GPEIOptChooser.py │ │ │ ├── GPEIperSecChooser.py │ │ │ ├── RandomChooser.py │ │ │ ├── RandomForestEIChooser.py │ │ │ ├── SequentialChooser.py │ │ │ ├── __init__.py │ │ │ └── cma.py │ │ ├── gp.py │ │ ├── helpers.py │ │ ├── runner.py │ │ ├── sobol_lib.py │ │ ├── spearmint_pb2.py │ │ └── util.py │ ├── RestAPI/ │ │ ├── __init__.py │ │ ├── server.py │ │ └── templates/ │ │ └── home.html │ ├── __init__.py │ ├── __main__.py │ ├── aup.py │ ├── compression/ │ │ ├── Compressor.py │ │ ├── NNI-LICENSE │ │ ├── __init__.py │ │ ├── __main__.py │ │ ├── tensorflow/ │ │ │ ├── __init__.py │ │ │ ├── compressor.py │ │ │ ├── default_layers.py │ │ │ └── pruning/ │ │ │ ├── __init__.py │ │ │ └── one_shot.py │ │ ├── torch/ │ │ │ ├── __init__.py │ │ │ ├── _graph_utils.py │ │ │ ├── compressor.py │ │ │ ├── default_layers.py │ │ │ ├── parameter_expressions.py │ │ │ ├── pruning/ │ │ │ │ ├── __init__.py │ │ │ │ ├── admm_pruner.py │ │ │ │ ├── agp.py │ │ │ │ ├── amc/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── amc_pruner.py │ │ │ │ │ ├── channel_pruning_env.py │ │ │ │ │ └── lib/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── agent.py │ │ │ │ │ ├── memory.py │ │ │ │ │ ├── net_measure.py │ │ │ │ │ └── utils.py │ │ │ │ ├── apply_compression.py │ │ │ │ ├── auto_compress_pruner.py │ │ │ │ ├── constants.py │ │ │ │ ├── constants_pruner.py │ │ │ │ ├── finegrained_pruning.py │ │ │ │ ├── lottery_ticket.py │ │ │ │ ├── net_adapt_pruner.py │ │ │ │ ├── one_shot.py │ │ │ │ ├── sensitivity_pruner.py │ │ │ │ ├── simulated_annealing_pruner.py │ │ │ │ ├── structured_pruning.py │ │ │ │ └── weight_masker.py │ │ │ ├── quantization/ │ │ │ │ ├── __init__.py │ │ │ │ └── quantizers.py │ │ │ ├── speedup/ │ │ │ │ ├── __init__.py │ │ │ │ ├── compress_modules.py │ │ │ │ ├── compressor.py │ │ │ │ └── infer_shape.py │ │ │ ├── torch_utils.py │ │ │ └── utils/ │ │ │ ├── __init__.py │ │ │ ├── config_validation.py │ │ │ ├── counter.py │ │ │ ├── mask_conflict.py │ │ │ ├── num_param_counter.py │ │ │ ├── sensitivity_analysis.py │ │ │ ├── shape_dependency.py │ │ │ └── utils.py │ │ └── utils.py │ ├── convert.py │ ├── dashboard/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── dashboard.py │ │ └── frontend/ │ │ ├── .browserslistrc │ │ ├── .editorconfig │ │ ├── .eslintrc.json │ │ ├── .gitignore │ │ ├── .prettierignore │ │ ├── .prettierrc │ │ ├── Angular-LICENSE │ │ ├── README.md │ │ ├── angular.json │ │ ├── e2e/ │ │ │ ├── protractor.conf.js │ │ │ ├── src/ │ │ │ │ ├── app.e2e-spec.ts │ │ │ │ └── app.po.ts │ │ │ └── tsconfig.json │ │ ├── febuild/ │ │ │ └── auptimizer-dashboard/ │ │ │ ├── 3rdpartylicenses.txt │ │ │ ├── 4.3bf463bd37e16d085b6c.js │ │ │ ├── 5.0e291298e9c49cb93c71.js │ │ │ ├── _redirects │ │ │ ├── index.html │ │ │ ├── main.09d22743789a55973001.js │ │ │ ├── polyfills.9cfb3f513e777138fb2c.js │ │ │ ├── runtime.29a333e72cc7398676d0.js │ │ │ └── styles.5a1d2b21684fc92c4f99.css │ │ ├── karma.conf.js │ │ ├── package.json │ │ ├── src/ │ │ │ ├── _redirects │ │ │ ├── app/ │ │ │ │ ├── @core/ │ │ │ │ │ ├── app-load.service.ts │ │ │ │ │ └── core.module.ts │ │ │ │ ├── app-routing.module.ts │ │ │ │ ├── app.component.html │ │ │ │ ├── app.component.scss │ │ │ │ ├── app.component.spec.ts │ │ │ │ ├── app.component.ts │ │ │ │ ├── app.module.ts │ │ │ │ ├── app.service.ts │ │ │ │ ├── appStore/ │ │ │ │ │ ├── app-state.model.ts │ │ │ │ │ ├── app.actions.ts │ │ │ │ │ └── app.store.ts │ │ │ │ ├── guards/ │ │ │ │ │ └── db.guard.ts │ │ │ │ ├── main/ │ │ │ │ │ ├── containers/ │ │ │ │ │ │ ├── index.ts │ │ │ │ │ │ └── main/ │ │ │ │ │ │ ├── main.component.html │ │ │ │ │ │ ├── main.component.scss │ │ │ │ │ │ ├── main.component.spec.ts │ │ │ │ │ │ └── main.component.ts │ │ │ │ │ ├── experiment/ │ │ │ │ │ │ ├── components/ │ │ │ │ │ │ │ ├── experiment-dropdown/ │ │ │ │ │ │ │ │ ├── experiment-dropdown.component.html │ │ │ │ │ │ │ │ ├── experiment-dropdown.component.scss │ │ │ │ │ │ │ │ ├── experiment-dropdown.component.spec.ts │ │ │ │ │ │ │ │ └── experiment-dropdown.component.ts │ │ │ │ │ │ │ ├── header/ │ │ │ │ │ │ │ │ ├── header.component.html │ │ │ │ │ │ │ │ ├── header.component.scss │ │ │ │ │ │ │ │ ├── header.component.spec.ts │ │ │ │ │ │ │ │ └── header.component.ts │ │ │ │ │ │ │ ├── index.ts │ │ │ │ │ │ │ ├── sidenav/ │ │ │ │ │ │ │ │ ├── sidenav.component.html │ │ │ │ │ │ │ │ ├── sidenav.component.scss │ │ │ │ │ │ │ │ ├── sidenav.component.spec.ts │ │ │ │ │ │ │ │ └── sidenav.component.ts │ │ │ │ │ │ │ └── sidenav-element/ │ │ │ │ │ │ │ ├── sidenav-element.component.html │ │ │ │ │ │ │ ├── sidenav-element.component.scss │ │ │ │ │ │ │ ├── sidenav-element.component.spec.ts │ │ │ │ │ │ │ └── sidenav-element.component.ts │ │ │ │ │ │ ├── containers/ │ │ │ │ │ │ │ ├── create-experiment/ │ │ │ │ │ │ │ │ ├── create-experiment.component.html │ │ │ │ │ │ │ │ ├── create-experiment.component.scss │ │ │ │ │ │ │ │ ├── create-experiment.component.spec.ts │ │ │ │ │ │ │ │ ├── create-experiment.component.ts │ │ │ │ │ │ │ │ └── exampleJSON.ts │ │ │ │ │ │ │ ├── experiment/ │ │ │ │ │ │ │ │ ├── experiment.component.html │ │ │ │ │ │ │ │ ├── experiment.component.scss │ │ │ │ │ │ │ │ ├── experiment.component.spec.ts │ │ │ │ │ │ │ │ └── experiment.component.ts │ │ │ │ │ │ │ ├── index.ts │ │ │ │ │ │ │ ├── initialize/ │ │ │ │ │ │ │ │ ├── initialize.component.html │ │ │ │ │ │ │ │ ├── initialize.component.scss │ │ │ │ │ │ │ │ ├── initialize.component.spec.ts │ │ │ │ │ │ │ │ └── initialize.component.ts │ │ │ │ │ │ │ ├── interm-results/ │ │ │ │ │ │ │ │ ├── interm-results.component.html │ │ │ │ │ │ │ │ ├── interm-results.component.scss │ │ │ │ │ │ │ │ ├── interm-results.component.spec.ts │ │ │ │ │ │ │ │ └── interm-results.component.ts │ │ │ │ │ │ │ ├── job-status/ │ │ │ │ │ │ │ │ ├── job-status.component.html │ │ │ │ │ │ │ │ ├── job-status.component.scss │ │ │ │ │ │ │ │ ├── job-status.component.spec.ts │ │ │ │ │ │ │ │ └── job-status.component.ts │ │ │ │ │ │ │ ├── list/ │ │ │ │ │ │ │ │ ├── list.component.html │ │ │ │ │ │ │ │ ├── list.component.scss │ │ │ │ │ │ │ │ ├── list.component.spec.ts │ │ │ │ │ │ │ │ └── list.component.ts │ │ │ │ │ │ │ ├── main/ │ │ │ │ │ │ │ │ ├── main.component.html │ │ │ │ │ │ │ │ ├── main.component.scss │ │ │ │ │ │ │ │ ├── main.component.spec.ts │ │ │ │ │ │ │ │ └── main.component.ts │ │ │ │ │ │ │ ├── multi-exp-comp/ │ │ │ │ │ │ │ │ ├── multi-exp-comp.component.html │ │ │ │ │ │ │ │ ├── multi-exp-comp.component.scss │ │ │ │ │ │ │ │ ├── multi-exp-comp.component.spec.ts │ │ │ │ │ │ │ │ └── multi-exp-comp.component.ts │ │ │ │ │ │ │ ├── notification/ │ │ │ │ │ │ │ │ ├── notification.component.html │ │ │ │ │ │ │ │ ├── notification.component.scss │ │ │ │ │ │ │ │ ├── notification.component.spec.ts │ │ │ │ │ │ │ │ └── notification.component.ts │ │ │ │ │ │ │ ├── overview/ │ │ │ │ │ │ │ │ ├── overview.component.html │ │ │ │ │ │ │ │ ├── overview.component.scss │ │ │ │ │ │ │ │ ├── overview.component.spec.ts │ │ │ │ │ │ │ │ └── overview.component.ts │ │ │ │ │ │ │ └── pcg/ │ │ │ │ │ │ │ ├── pcg.component.html │ │ │ │ │ │ │ ├── pcg.component.scss │ │ │ │ │ │ │ ├── pcg.component.spec.ts │ │ │ │ │ │ │ └── pcg.component.ts │ │ │ │ │ │ ├── experiment-routing.module.ts │ │ │ │ │ │ ├── experiment.module.ts │ │ │ │ │ │ ├── services/ │ │ │ │ │ │ │ └── experiment.service.ts │ │ │ │ │ │ └── store/ │ │ │ │ │ │ ├── experiment-state.model.ts │ │ │ │ │ │ ├── experiment.actions.ts │ │ │ │ │ │ └── experiment.store.ts │ │ │ │ │ ├── main-routing.module.ts │ │ │ │ │ └── main.module.ts │ │ │ │ ├── material/ │ │ │ │ │ └── material.module.ts │ │ │ │ ├── models/ │ │ │ │ │ ├── data/ │ │ │ │ │ │ ├── custom-icons.data.ts │ │ │ │ │ │ ├── graph-colors.data.ts │ │ │ │ │ │ ├── nav-items.ts │ │ │ │ │ │ └── plotly-hidden-displays.ts │ │ │ │ │ ├── enum/ │ │ │ │ │ │ ├── experiment-status.enum.ts │ │ │ │ │ │ ├── experiment-view-type.enum.ts │ │ │ │ │ │ ├── notification-type.enum.ts │ │ │ │ │ │ └── theme-option.enum.ts │ │ │ │ │ ├── experiment.model.ts │ │ │ │ │ ├── nav-items.model.ts │ │ │ │ │ ├── progress-bar.model.ts │ │ │ │ │ └── resource.model.ts │ │ │ │ ├── page-not-found/ │ │ │ │ │ ├── page-not-found.component.html │ │ │ │ │ ├── page-not-found.component.scss │ │ │ │ │ ├── page-not-found.component.spec.ts │ │ │ │ │ └── page-not-found.component.ts │ │ │ │ └── shared/ │ │ │ │ ├── dialogs/ │ │ │ │ │ ├── confirm/ │ │ │ │ │ │ ├── confirm-dialog.component.html │ │ │ │ │ │ ├── confirm-dialog.component.scss │ │ │ │ │ │ └── confirm-dialog.component.ts │ │ │ │ │ └── index.ts │ │ │ │ ├── pipes/ │ │ │ │ │ ├── experiment-status.pipe.ts │ │ │ │ │ ├── first-letter-uppercase/ │ │ │ │ │ │ ├── first-letter-uppercase.pipe.spec.ts │ │ │ │ │ │ └── first-letter-uppercase.pipe.ts │ │ │ │ │ ├── index.ts │ │ │ │ │ ├── min-to-hour/ │ │ │ │ │ │ ├── min-to-hour.pipe.spec.ts │ │ │ │ │ │ └── min-to-hour.pipe.ts │ │ │ │ │ ├── notification-icon.pipe.ts │ │ │ │ │ ├── progressBarWidth.pipe.ts │ │ │ │ │ ├── roudNumber.pipe.ts │ │ │ │ │ ├── sec-to-min/ │ │ │ │ │ │ ├── sec-to-min.pipe.spec.ts │ │ │ │ │ │ └── sec-to-min.pipe.ts │ │ │ │ │ └── truncate.pipe.ts │ │ │ │ ├── services/ │ │ │ │ │ ├── api.service.ts │ │ │ │ │ ├── color-scheme.service.ts │ │ │ │ │ ├── helper.service.ts │ │ │ │ │ ├── index.ts │ │ │ │ │ ├── snackbar.service.ts │ │ │ │ │ └── utils.service.ts │ │ │ │ ├── shared.module.ts │ │ │ │ └── validators/ │ │ │ │ ├── at-least.validator.ts │ │ │ │ └── index.ts │ │ │ ├── assets/ │ │ │ │ └── .gitkeep │ │ │ ├── environments/ │ │ │ │ ├── environment.prod.ts │ │ │ │ └── environment.ts │ │ │ ├── index.html │ │ │ ├── main.ts │ │ │ ├── polyfills.ts │ │ │ ├── scss/ │ │ │ │ ├── _colors.scss │ │ │ │ ├── _helpers.scss │ │ │ │ ├── _material-overrides.scss │ │ │ │ ├── jsoneditor-dark.scss │ │ │ │ ├── jsoneditor.scss │ │ │ │ ├── main.scss │ │ │ │ └── theme.scss │ │ │ ├── styles.scss │ │ │ └── test.ts │ │ ├── tailwind.config.js │ │ ├── tsconfig.app.json │ │ ├── tsconfig.json │ │ ├── tsconfig.spec.json │ │ └── webpack.config.js │ ├── dlconvert/ │ │ ├── .gitignore │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __main__.py │ │ ├── __version__.py │ │ ├── checkpoint_to_onnx.py │ │ ├── checkpoint_to_pb.py │ │ ├── checkpoint_to_tflite.py │ │ ├── keras_to_onnx.py │ │ ├── keras_to_pb.py │ │ ├── keras_to_tflite.py │ │ ├── pb_to_onnx.py │ │ ├── pb_to_tflite.py │ │ ├── pytorch_to_keras.py │ │ ├── pytorch_to_onnx.py │ │ ├── pytorch_to_tflite.py │ │ ├── savedmodel_to_onnx.py │ │ ├── savedmodel_to_tflite.py │ │ ├── spec_utils/ │ │ │ ├── __init__.py │ │ │ └── pb.py │ │ ├── to_frozen_pb.py │ │ ├── to_onnx.py │ │ ├── to_tflite.py │ │ └── utils.py │ ├── init.py │ ├── profiler/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __main__.py │ │ ├── calculate.py │ │ ├── compile_stats.py │ │ ├── issues.md │ │ ├── profiler.sh │ │ └── statscript.sh │ ├── setup.py │ ├── setupdb/ │ │ ├── __init__.py │ │ ├── __main__.py │ │ ├── reset.py │ │ └── sqlite.py │ ├── utils.py │ └── visualize.py └── tests/ ├── EE/ │ ├── Resource/ │ │ ├── .gitignore │ │ ├── __init__.py │ │ ├── test_AbstractResourceManager.py │ │ ├── test_CPUResourceManager.py │ │ ├── test_GPUResourceManager.py │ │ ├── test_PassiveResourceManager.py │ │ └── test_SSHResourceManager.py │ ├── __init__.py │ ├── test_Experiment.py │ └── test_Job.py ├── ET/ │ ├── Connector/ │ │ ├── __init__.py │ │ └── test_SQLiteConnector.py │ └── __init__.py ├── Proposer/ │ ├── __init__.py │ ├── test_AbstractProposer.py │ ├── test_BOHBProposer.py │ ├── test_HyperbandProposer.py │ ├── test_HyperoptProposer.py │ ├── test_RandomProposer.py │ ├── test_SequenceProposer.py │ └── test_SpearmintProposer.py ├── __init__.py ├── command/ │ ├── __init__.py │ ├── test_convert.py │ └── test_setup.py ├── compression/ │ ├── __init__.py │ ├── test_aup_compressor.py │ ├── test_compression_utils.py │ ├── test_compressor_tf.py │ ├── test_compressor_torch.py │ └── test_model_speedup.py ├── data/ │ ├── config/ │ │ ├── test_read.json │ │ └── test_wrong.json │ ├── exp4.json │ ├── exp4_no_name.json │ ├── exp4_no_param.json │ ├── exp4_no_script.json │ ├── exp5.json │ ├── exp6.json │ ├── exp7.json │ ├── gpus.txt │ ├── nodes.txt │ ├── plain_env.ini │ ├── target_gpu_env.ini │ ├── target_plain_env.ini │ ├── task1.py │ ├── task2.py │ ├── task3.py │ ├── task4.py │ ├── task5.py │ ├── task6.py │ ├── task7.py │ ├── task8.py │ ├── test_script.py │ ├── wrapper1.json │ └── wrapper2.json ├── dlconvert/ │ ├── .gitignore │ ├── Dockerfile_tf1 │ ├── Dockerfile_tf2 │ ├── README.md │ ├── __init__.py │ ├── data/ │ │ ├── .gitignore │ │ ├── create_test_model.py │ │ ├── evaluate_pred.py │ │ ├── flag_names.json │ │ ├── prepare.sh │ │ ├── prepare_docker.sh │ │ ├── pytorch_model.py │ │ └── repdata.py │ ├── dlconvert_requirements.txt │ ├── pytest.ini │ ├── test_checkpoint_to_onnx.py │ ├── test_checkpoint_to_pb.py │ ├── test_checkpoint_to_tflite.py │ ├── test_keras_to_onnx.py │ ├── test_keras_to_pb.py │ ├── test_keras_to_tflite.py │ ├── test_pb_to_onnx.py │ ├── test_pb_to_tflite.py │ ├── test_pytorch_to_keras.py │ ├── test_pytorch_to_onnx.py │ ├── test_pytorch_to_tflite.py │ ├── test_savedmodel_to_onnx.py │ ├── test_savedmodel_to_tflite.py │ ├── unittest_tf1.sh │ └── unittest_tf2.sh ├── setupdb/ │ ├── __init__.py │ └── test_sqlite.py ├── test_BasicConfig.py ├── test_utils.py └── test_wrapper.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .coveragerc ================================================ [run] omit = ./src/aup/Proposer/spearmint/* ./src/aup/Proposer/hyperopt/* ./src/aup/Proposer/eas/* ./src/aup/Proposer/hpbandster/* ./src/aup/Proposer/EASProposer.py ./src/aup/setupdb/reset.py ./src/aup/visualize.py ./src/aup/EE/Resource/AWSResourceManager.py ./src/aup/init.py */__main__.py */__init__.py ./src/aup/profiler/* ./src/aup/RestAPI/* ./src/aup/dashboard/* ./src/aup/Proposer/SpearmintProposer.py ./src/aup/Proposer/BOHBProposer.py ./src/aup/compression/torch/pruning/* [report] exclude_lines = pragma: no cover flags def main if __name__ == "__main__" @click raise NotImplementedError ================================================ FILE: .dockerignore ================================================ docs Examples/2d* Examples/dlconvert_examples/Convert* Examples/dlconvert_examples/Tested* Examples/demo Examples/cai_eas Examples/decorator_example Examples/hpo_mnist Examples/job* Examples/profiler* Examples/tf* build dist ================================================ FILE: .gitignore ================================================ Examples/demo/auto.py testenv/ .aup/ .idea/ *~ *.pkl tests/EE/jobs/ tests/data/jobs/ tests/EE/Resource/data .vscode/ analysis.html R-src/*.tar.gz R-src/R-src.Rproj # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # OS files .DS_Store .Rproj.user analysis_test jobs tests/mnist_compressed.pth dashboard_logs ================================================ FILE: .travis.yml ================================================ git: depth: false quiet: true branches: only: - master - release language: python python: - '3.7' env: global: - secure: 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 jobs: - TF2TEST=1 install: - pip install -r requirements.txt - pip install -r tests/dlconvert/dlconvert_requirements.txt - python setup.py install - docs/prepare.sh - tests/dlconvert/data/prepare.sh script: - pytest --cov-report=xml --cov=src/aup tests after_success: - codecov deploy: provider: pypi user: "__token__" password: $PYPITOKEN on: branch: release distributions: "sdist bdist_wheel" skip_existing: true after_deploy: - "./publish.sh" ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at auptimizer@lge.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq ================================================ FILE: Examples/.gitignore ================================================ jobs/ spearmint/ ================================================ FILE: Examples/2dfunc_diff_mult_res/README.md ================================================ # Return multiple results This feature allows the user to save and track multiple secondary results along with the primary result for the HPO experiment. Auptimizer still uses the primary result for the HPO algorithms, but saves the secondary results in the database under the table `multiple_results`. ## Usage The feature can be used by adding the following parameter to the experiment configuration file: "resource_args": { "multi_res_labels": ["x", "y", …] } In the above example, {"x", "y", …} are the secondary parameters the user wants to track and record. The user script would then return the results as a list including the primary result 'res' along with the secondary parameters as follows: @aup_args def HPO(): res = calculate_results() return [res, x, y, …] In the above example 'res' is the primary result which is always the first index of the returned array when using multiple results, and is used by HPO algorithm. The other indices are matched directly with the array provided in the `multi_res_labels` parameter. Hence, The length of the returned array from HPO script is '1 + length of multi_res_labels' parameter. ## Example code python -m aup.setup cpu.ini python -m aup exp_cpu.json The code will create the job configuration file, interactively ask you to run the script, and ask for the result from your training code. e.g. # Job running path is Examples/2dfunc_diff_mult_res # Config is at Examples/2dfunc_diff_mult_res/jobs/452.json Job command is: Examples/2dfunc_diff_mult_res/rosenbrock_hpo.py Return results: Then you should run the command in another terminal like: ./rosenbrock_hpo.py ./jobs/452.json You will see something like: #Auptimizer:232.78278278806914,-0.8297799529742598,2.2032449344215808 The first result is the main result, the next are "x" and "y" defined by multi_res_labels. Once you paste the result back to **Auptimizer**, it will ask you for the next one. ================================================ FILE: Examples/2dfunc_diff_mult_res/cpu.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/2dfunc_diff_mult_res/exp_cpu.json ================================================ { "name": "./2dfunc_diff_mult_res/exp_cpu.json", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 4, "target":"min", "resource_args": { "multi_res_labels": ["x", "y"] } } ================================================ FILE: Examples/2dfunc_diff_mult_res/rosenbrock_hpo.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys """ # ver 1.0 - modify existing code from aup import BasicConfig, print_result def rosenbrock(conf, a=1, b=100): x = conf.x y = conf.y return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) val = rosenbrock(config) print_result(val) """ from aup import aup_args @aup_args def rosenbrock(x, y, a=1, b=100): return [(a-x)*(a-x) + b*(y-x*x)*(y-x*x), x, y] if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) rosenbrock(sys.argv[1]) ================================================ FILE: Examples/2dfunc_diff_opt/.gitignore ================================================ spearmint/ rosenbrock_auto.py ================================================ FILE: Examples/2dfunc_diff_opt/History.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pylab as plt\n", "from aup.ET.Connector.SQLiteConnector import SQLiteConnector\n", "import json\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# connect to the result\n", "sql = SQLiteConnector(\"./.aup/sqlite3.db\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get all experiments (this run is for iris using hyperopt, sequence, spearmint, random)\n", "eids = sql.get_all_experiment()\n", "eids" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2,\n", " 1,\n", " 1564088286,\n", " 1564088287,\n", " '{\"proposer\": \"random\", \"n_samples\": 10, \"random_seed\": 1, \"script\": \"rosenbrock_hpo.py\", \"parameter_config\": [{\"name\": \"x\", \"range\": [-5, 5], \"type\": \"float\"}, {\"name\": \"y\", \"range\": [-5, 5], \"type\": \"float\"}], \"resource\": \"cpu\", \"n_parallel\": 2, \"target\": \"min\", \"workingdir\": \"/Users/jason.liu/PycharmProjects/CTE/Examples/2dfunc_diff_opt\"}')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show details in one experiment\n", "sql.cursor.execute(\"SELECT * FROM experiment where eid = ?\", (eids[1],))\n", "sql.cursor.fetchone()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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100 rows × 7 columns

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1564088283 \n", "19 20 83472.790123 1 4 1564088283 1564088283 \n", "20 21 77196.493827 1 3 1564088283 1564088283 \n", "21 22 32069.321140 1 2 1564088283 1564088283 \n", "22 23 11026.312452 1 1 1564088283 1564088283 \n", "23 24 3093.530864 1 4 1564088283 1564088283 \n", "24 25 955.018442 1 3 1564088283 1564088283 \n", "25 26 952.796220 1 2 1564088283 1564088283 \n", "26 27 3086.864198 1 1 1564088283 1564088283 \n", "27 28 11015.201341 1 4 1564088283 1564088283 \n", "28 29 32053.765585 1 3 1564088283 1564088283 \n", "29 30 77176.493827 1 2 1564088283 1564088283 \n", ".. ... ... ... ... ... ... \n", "70 71 49418.716049 1 1 1564088284 1564088284 \n", "71 72 15265.480262 1 4 1564088284 1564088284 \n", "72 73 2452.924249 1 3 1564088284 1564088284 \n", "73 74 7.111111 1 2 1564088284 1564088284 \n", "74 75 612.082914 1 1 1564088284 1564088284 \n", "75 76 609.860692 1 4 1564088284 1564088284 \n", "76 77 0.444444 1 3 1564088284 1564088284 \n", "77 78 2441.813138 1 2 1564088284 1564088284 \n", "78 79 15249.924707 1 1 1564088284 1564088284 \n", "79 80 49398.716049 1 4 1564088284 1564088284 \n", "80 81 44603.901235 1 3 1564088284 1564088285 \n", "81 82 12645.452827 1 2 1564088284 1564088285 \n", "82 83 1478.987349 1 1 1564088284 1564088285 \n", "83 84 130.567901 1 4 1564088284 1564088285 \n", "84 85 1284.236549 1 3 1564088285 1564088285 \n", "85 86 1282.014327 1 2 1564088285 1564088285 \n", "86 87 123.901235 1 1 1564088285 1564088285 \n", "87 88 1467.876238 1 4 1564088285 1564088285 \n", "88 89 12629.897272 1 3 1564088285 1564088285 \n", "89 90 44583.901235 1 2 1564088285 1564088285 \n", "90 91 40036.000000 1 1 1564088285 1564088285 \n", "91 92 10272.338973 1 4 1564088285 1564088285 \n", "92 93 751.964030 1 3 1564088285 1564088285 \n", "93 94 500.938272 1 2 1564088285 1564088285 \n", "94 95 2203.303765 1 1 1564088285 1564088285 \n", "95 96 2201.081542 1 4 1564088285 1564088285 \n", "96 97 494.271605 1 3 1564088285 1564088285 \n", "97 98 740.852919 1 2 1564088285 1564088285 \n", "98 99 10256.783417 1 1 1564088285 1564088285 \n", "99 100 40016.000000 1 4 1564088285 1564088285 \n", "\n", " job_config \n", "0 {'x': -5, 'y': -5} \n", "1 {'x': -3.888888888888889, 'y': -5} \n", "2 {'x': -2.7777777777777777, 'y': -5} \n", "3 {'x': -1.6666666666666665, 'y': -5} \n", "4 {'x': -0.5555555555555554, 'y': -5} \n", "5 {'x': 0.5555555555555558, 'y': -5} \n", "6 {'x': 1.666666666666667, 'y': -5} \n", "7 {'x': 2.777777777777778, 'y': -5} \n", "8 {'x': 3.8888888888888893, 'y': -5} \n", "9 {'x': 5.0, 'y': -5} \n", "10 {'x': -5, 'y': -3.888888888888889} \n", "11 {'x': -3.888888888888889, 'y': -3.888888888888... \n", "12 {'x': -2.7777777777777777, 'y': -3.88888888888... \n", "13 {'x': -1.6666666666666665, 'y': -3.88888888888... \n", "14 {'x': -0.5555555555555554, 'y': -3.88888888888... \n", "15 {'x': 0.5555555555555558, 'y': -3.888888888888... \n", "16 {'x': 1.666666666666667, 'y': -3.888888888888889} \n", "17 {'x': 2.777777777777778, 'y': -3.888888888888889} \n", "18 {'x': 3.8888888888888893, 'y': -3.888888888888... \n", "19 {'x': 5.0, 'y': -3.888888888888889} \n", "20 {'x': -5, 'y': -2.7777777777777777} \n", "21 {'x': -3.888888888888889, 'y': -2.777777777777... \n", "22 {'x': -2.7777777777777777, 'y': -2.77777777777... \n", "23 {'x': -1.6666666666666665, 'y': -2.77777777777... \n", "24 {'x': -0.5555555555555554, 'y': -2.77777777777... \n", "25 {'x': 0.5555555555555558, 'y': -2.777777777777... \n", "26 {'x': 1.666666666666667, 'y': -2.7777777777777... \n", "27 {'x': 2.777777777777778, 'y': -2.7777777777777... \n", "28 {'x': 3.8888888888888893, 'y': -2.777777777777... \n", "29 {'x': 5.0, 'y': -2.7777777777777777} \n", ".. ... \n", "70 {'x': -5, 'y': 2.777777777777778} \n", "71 {'x': -3.888888888888889, 'y': 2.777777777777778} \n", "72 {'x': -2.7777777777777777, 'y': 2.777777777777... \n", "73 {'x': -1.6666666666666665, 'y': 2.777777777777... \n", "74 {'x': -0.5555555555555554, 'y': 2.777777777777... \n", "75 {'x': 0.5555555555555558, 'y': 2.777777777777778} \n", "76 {'x': 1.666666666666667, 'y': 2.777777777777778} \n", "77 {'x': 2.777777777777778, 'y': 2.777777777777778} \n", "78 {'x': 3.8888888888888893, 'y': 2.777777777777778} \n", "79 {'x': 5.0, 'y': 2.777777777777778} \n", "80 {'x': -5, 'y': 3.8888888888888893} \n", "81 {'x': -3.888888888888889, 'y': 3.8888888888888... \n", "82 {'x': -2.7777777777777777, 'y': 3.888888888888... \n", "83 {'x': -1.6666666666666665, 'y': 3.888888888888... \n", "84 {'x': -0.5555555555555554, 'y': 3.888888888888... \n", "85 {'x': 0.5555555555555558, 'y': 3.8888888888888... \n", "86 {'x': 1.666666666666667, 'y': 3.8888888888888893} \n", "87 {'x': 2.777777777777778, 'y': 3.8888888888888893} \n", "88 {'x': 3.8888888888888893, 'y': 3.8888888888888... \n", "89 {'x': 5.0, 'y': 3.8888888888888893} \n", "90 {'x': -5, 'y': 5.0} \n", "91 {'x': -3.888888888888889, 'y': 5.0} \n", "92 {'x': -2.7777777777777777, 'y': 5.0} \n", "93 {'x': -1.6666666666666665, 'y': 5.0} \n", "94 {'x': -0.5555555555555554, 'y': 5.0} \n", "95 {'x': 0.5555555555555558, 'y': 5.0} \n", "96 {'x': 1.666666666666667, 'y': 5.0} \n", "97 {'x': 2.777777777777778, 'y': 5.0} \n", "98 {'x': 3.8888888888888893, 'y': 5.0} \n", "99 {'x': 5.0, 'y': 5.0} \n", "\n", "[100 rows x 7 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show details in job history for one experiment\n", "history = sql.get_all_history(1)\n", "history = pd.DataFrame(history)\n", "history.columns = ['jid', 'score','eid','rid','start_time','end_time','job_config']\n", "history" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,8))\n", "plt.subplot(121)\n", "for i in eids:\n", " history = sql.get_all_history(i)\n", " history = pd.DataFrame(history)\n", " history.columns = ['jid', 'score','eid','rid','start_time','end_time','job_config']\n", " sql.cursor.execute(\"SELECT * FROM experiment where eid = ?\", (i,))\n", " label = json.loads(sql.cursor.fetchone()[4])['proposer']\n", " plt.plot(history.score, label=label)\n", "plt.legend()\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.yscale(\"log\")\n", "\n", "plt.subplot(122)\n", "for i in eids:\n", " history = sql.get_all_history(i)\n", " history = pd.DataFrame(history)\n", " history.columns = ['jid', 'score','eid','rid','start_time','end_time','job_config']\n", " sql.cursor.execute(\"SELECT * FROM experiment where eid = ?\", (i,))\n", " label = json.loads(sql.cursor.fetchone()[4])['proposer']\n", " plt.plot(history.score.cummin(), label=label)\n", "plt.legend()\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Best Accuracy so far\")\n", "plt.yscale(\"log\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sql.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Examples/2dfunc_diff_opt/README.md ================================================ # Rosenbrock demo ## Setup python -m aup.setup To setup different resource configuration, see [resource example](../2dfunc_diff_res/README.md). ## Experiment python -m aup experiment_sequence.json --log warn python -m aup experiment_random.json python -m aup experiment_spearmint.json python -m aup experiment_hyperopt.json Results are presented in [History](History.ipynb) ## Experiment Auto-convert To convert the original `rosenbrock_origin.py` for *Auptimizer*, users can use python -m aup.convert rosenbrock_origin.py experiment_auto.json rosenbrock It will create `rosenbrock_auto.py` for user to use for *Auptimizer* experiment. ## Reset To clean the database, use python -m aup.setupdb.reset .aup/env.ini ================================================ FILE: Examples/2dfunc_diff_opt/experiment_auto.json ================================================ { "name": "./2dfunc_diff_opt/experiment_auto.json", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_auto.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 1, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_opt/experiment_bohb.json ================================================ { "name": "./2dfunc_diff_opt/experiment_bohb.json", "script": "rosenbrock_hpo.py", "resource": "cpu", "n_parallel": 5, "target": "min", "workingdir": "./", "proposer": "bohb", "n_iterations": 3, "min_points_in_model": "", "top_n_percent": 15, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 9, "parameter_config": [ { "name": "x", "range": [ 0, 1 ], "type": "float" }, { "name": "y", "range": [ 0, 1 ], "type": "float" } ] } ================================================ FILE: Examples/2dfunc_diff_opt/experiment_hyperband.json ================================================ { "name": "./2dfunc_diff_opt/experiment_hyperband.json", "proposer": "hyperband", "script": "rosenbrock_hpo.py", "resource": "cpu", "n_parallel": 1, "target":"max", "max_iter": 3, "engine": "random", "parameter_config": [ { "name": "x", "range": [-5, 5], "type": "float" }, { "name": "y", "range": [-5,5], "type": "float" } ] } ================================================ FILE: Examples/2dfunc_diff_opt/experiment_hyperopt.json ================================================ { "name": "./2dfunc_diff_opt/experiment_hyperopt.json", "proposer": "hyperopt", "random_seed": 1, "script": "rosenbrock_hpo.py", "engine":"tpe", "n_samples": 100, "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_opt/experiment_random.json ================================================ { "name": "./2dfunc_diff_opt/experiment_random.json", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 2, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_opt/experiment_sequence.json ================================================ { "name": "./2dfunc_diff_opt/experiment_sequence.json", "proposer": "sequence", "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float", "n": 10 }, { "name": "y", "range": [ -5, 5 ], "type": "float", "n": 10 } ], "resource": "cpu", "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_opt/experiment_spearmint.json ================================================ { "name": "./2dfunc_diff_opt/experiment_spearmint.json", "proposer": "spearmint", "n_samples": 100, "random_seed": 1, "script": "rosenbrock_hpo.py", "engine":"GPEIChooser", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float", "size": 1 }, { "name": "y", "range": [ -5, 5 ], "type": "float", "size": 1 } ], "resource": "cpu", "n_parallel": 2, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_opt/rosenbrock_hpo.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys """ # ver 1.0 - modify existing code from aup import BasicConfig, print_result def rosenbrock(conf, a=1, b=100): x = conf.x y = conf.y return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) val = rosenbrock(config) print_result(val) """ from aup import aup_args @aup_args def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) rosenbrock(sys.argv[1]) ================================================ FILE: Examples/2dfunc_diff_opt/rosenbrock_origin.py ================================================ """ Demonstration code for Rosenbrock function ========================================== .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) ================================================ FILE: Examples/2dfunc_diff_res/README.md ================================================ # Rosenbrock demo for different resources ## CPU python -m aup.setup cpu.ini python -m aup exp_cpu.json ## GPU Using a file contains the GPU ID per line will create GPU resources accordingly python -m aup.setup cpu.ini --gpu gpu.txt --overwrite python -m aup exp_gpu.json ## multi-node For multi node environment, first set up the ssh key (see [here](https://help.ubuntu.com/community/SSH/OpenSSH/Keys) as example), then either use IP for node, or use node name with IP in `/etc/hosts` file. In `node.txt`, each line contains `:[:port]`, where port number is optional (default 22). python -m aup.setup cpu.ini --node node.txt --overwrite python -m aup exp_node.json Also, it is important to add `workingdir` for remote execution. ## Passive mode Passive mode is used for debugging purpose. It helps users to run training code manually and check potential problem in the training code. python -m aup experiment_passive.json The code will create the job configuration file, interactively ask you to run the script, and ask for the result from your training code. e.g. # Job running path is Examples/2dfunc_diff_res # Config is at Examples/2dfunc_diff_res/jobs/452.json Job command is: Examples/2dfunc_diff_res/rosenbrock_hpo.py Return results: Then you should run the command in another terminal like: ./rosenbrock_hpo.py ./jobs/452.json You will see something like: #Auptimizer:232.78278278806914 Once you paste the result back to **Auptimizer**, it will ask you for the next one. ================================================ FILE: Examples/2dfunc_diff_res/aws.txt ================================================ AWS_EC2_ID SSH_KEY AWS_EC2_ID SSH_KEY ================================================ FILE: Examples/2dfunc_diff_res/cpu.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/2dfunc_diff_res/env_local_template.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/2dfunc_diff_res/env_user_template.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=~/.aup # Temp folder TMP_FOLDER=/tmp/aup # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/2dfunc_diff_res/exp_aws.json ================================================ { "name": "./2dfunc_diff_res/exp_aws.json", "workingdir": "/home/ubuntu/", "proposer": "random", "n_samples": 10, "resource_args": { "shutdown":true, "connection_retry": 5 }, "runtime_args": { "prescript": "source .profile", "postscript": "echo 'DONE'" }, "random_seed": 1, "script": "./rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "aws", "n_parallel": 1, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_res/exp_cpu.json ================================================ { "name": "./2dfunc_diff_res/exp_cpu.json", "proposer": "sequence", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 2, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_res/exp_gpu.json ================================================ { "name": "./2dfunc_diff_res/exp_gpu.json", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "gpu", "n_parallel": 2, "target":"min" } ================================================ FILE: Examples/2dfunc_diff_res/exp_node.json ================================================ { "name": "./2dfunc_diff_res/exp_node.json", "workingdir": "/home/ubuntu/aup_demo", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "runtime_args": { "prescript": "export CUDA_VISIBLE_DEVICES=-1", "postscript": "echo $CUDA_VISIBLE_DEVICES", "overwrite": true }, "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "node", "n_parallel": 2, "target":"min", "resource_args": { "max_retries": 2, "reconn_wait_time": 3 } } ================================================ FILE: Examples/2dfunc_diff_res/exp_node_async.json ================================================ { "name": "./2dfunc_diff_res/exp_node_async.json", "workingdir": "/home/paul/aup_demo", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "runtime_args": { "prescript": "export CUDA_VISIBLE_DEVICES=-1", "postscript": "echo $CUDA_VISIBLE_DEVICES", "overwrite": true }, "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "node", "n_parallel": 2, "target":"min", "resource_args": { "max_retries": 2, "reconn_wait_time": 3, "async_run": true, "async_reconnect": 1, "async_timeout": 100 } } ================================================ FILE: Examples/2dfunc_diff_res/exp_passive.json ================================================ { "name": "./2dfunc_diff_res/exp_passive.json", "proposer": "random", "n_samples": 5, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "passive", "target":"min" } ================================================ FILE: Examples/2dfunc_diff_res/gpu.txt ================================================ 0 1 ================================================ FILE: Examples/2dfunc_diff_res/node.txt ================================================ jasonliu@ubuntu1 jasonliu@ubuntu2 ================================================ FILE: Examples/2dfunc_diff_res/plainGPU.txt ================================================ 0 ================================================ FILE: Examples/2dfunc_diff_res/rosenbrock_hpo.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys """ # ver 1.0 - modify existing code from aup import BasicConfig, print_result def rosenbrock(conf, a=1, b=100): x = conf.x y = conf.y return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) val = rosenbrock(config) print_result(val) """ from aup import aup_args @aup_args def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) rosenbrock(sys.argv[1]) ================================================ FILE: Examples/2dfunc_diff_res/singleGPU.txt ================================================ 0 0 ================================================ FILE: Examples/cai_eas/.gitignore ================================================ net_pool arch_search ## Core latex/pdflatex auxiliary files: *.aux *.lof *.log *.lot *.fls *.out *.toc ## Intermediate documents: *.dvi *-converted-to.* # these rules might exclude image files for figures etc. # *.ps # *.eps # *.pdf /Datasets ## Bibliography auxiliary files (bibtex/biblatex/biber): *.bbl *.bcf *.blg *-blx.aux *-blx.bib *.brf *.run.xml ## Build tool auxiliary files: *.fdb_latexmk *.synctex *.synctex.gz *.synctex.gz(busy) *.pdfsync ## Auxiliary and intermediate files from other packages: # algorithms *.alg *.loa # achemso acs-*.bib # amsthm *.thm # beamer *.nav *.snm *.vrb #(e)ledmac/(e)ledpar *.end *.[1-9] *.[1-9][0-9] *.[1-9][0-9][0-9] *.[1-9]R *.[1-9][0-9]R *.[1-9][0-9][0-9]R *.eledsec[1-9] *.eledsec[1-9]R *.eledsec[1-9][0-9] *.eledsec[1-9][0-9]R *.eledsec[1-9][0-9][0-9] *.eledsec[1-9][0-9][0-9]R # glossaries *.acn *.acr *.glg *.glo *.gls # gnuplottex *-gnuplottex-* # hyperref # knitr *-concordance.tex *.tikz *-tikzDictionary # listings *.lol # makeidx *.idx *.ilg *.ind *.ist tex/rl-meta.pdf # minitoc *.maf *.mtc *.mtc[0-9] *.mtc[1-9][0-9] # minted _minted* *.pyg # morewrites *.mw # mylatexformat *.fmt # nomencl *.nlo # sagetex *.sagetex.sage *.sagetex.py *.sagetex.scmd # sympy *.sout *.sympy sympy-plots-for-*.tex/ # TikZ & PGF *.dpth *.md5 *.auxlock # todonotes *.tdo # xindy *.xdy # WinEdt *.bak *.sav *.DS_Store /data */.idea/ /output/ /exp/ /backup/ # python __pycache__ .pyc ================================================ FILE: Examples/cai_eas/README.md ================================================ # HPO for Deep Neural Networks Demonstration using https://github.com/han-cai/EAS The HPO engine is integrated into the Auptimizer. The running script is a modification of `client.py`. Run it as cd eas python -m aup experiment.json Notice that the start_nets is needed in this folder although the experiment is running in the eas folder ================================================ FILE: Examples/cai_eas/eas/client.py ================================================ #!/usr/bin/env python """ Modified from https://github.com/han-cai/EAS The file to run in the client side Train the network and return the validation performance """ import os from expdir_monitor.expdir_monitor import ExpdirMonitor import time import sys from aup import BasicConfig, print_result os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def run(expdir): start_time = time.time() expdir_monitor = ExpdirMonitor(expdir) valid_performance = expdir_monitor.run(pure=True, restore=False) end_time = time.time() print('running time: %s' % (end_time - start_time)) print('valid performance: %s' % valid_performance) print_result(valid_performance) def main(): print("Starting Client") config = BasicConfig().load(sys.argv[1]) run(config["expdir"]) if __name__ == "__main__": try: main() except KeyboardInterrupt: pass ================================================ FILE: Examples/cai_eas/eas/data_providers/__init__.py ================================================ ================================================ FILE: Examples/cai_eas/eas/data_providers/base_provider.py ================================================ import numpy as np class DataSet: """Class to represent some dataset: train, validation, test""" @property def num_examples(self): """Return qtty of examples in dataset""" raise NotImplementedError def next_batch(self, batch_size): """Return batch of required size of data, labels""" raise NotImplementedError class ImagesDataSet(DataSet): """Dataset for images that provide some often used methods""" @staticmethod def measure_mean_and_std(images): # for every channel in image means = [] stds = [] # for every channel in image(assume this is last dimension) for ch in range(images.shape[-1]): means.append(np.mean(images[:, :, :, ch])) stds.append(np.std(images[:, :, :, ch])) return means, stds @staticmethod def shuffle_images_and_labels(images, labels): rand_indexes = np.random.permutation(images.shape[0]) shuffled_images = images[rand_indexes] shuffled_labels = labels[rand_indexes] return shuffled_images, shuffled_labels @staticmethod def normalize_images(images, normalization_type, meanstd=None): """ Args: images: numpy 4D array normalization_type: `str`, available choices: - divide_255 - divide_256 - by_channels meanstd """ if normalization_type is not None: if normalization_type == 'divide_255': images = images / 255 elif normalization_type == 'divide_256': images = images / 256 elif normalization_type == 'by_channels': images = images.astype('float64') # for every channel in image(assume this is last dimension) means, stds = meanstd for i in range(images.shape[-1]): images[:, :, :, i] = ((images[:, :, :, i] - means[i]) / stds[i]) else: raise Exception('Unknown type of normalization') return images class DataProvider: _SEED = 88 @property def data_shape(self): """Return shape as python list of one data entry""" raise NotImplementedError @property def n_classes(self): """Return `int` of num classes""" raise NotImplementedError def labels_to_one_hot(self, labels): """Convert 1D array of labels to one hot representation Args: labels: 1D numpy array """ new_labels = np.zeros((labels.shape[0], self.n_classes)) new_labels[range(labels.shape[0]), labels] = np.ones(labels.shape) return new_labels @staticmethod def labels_from_one_hot(labels): """Convert 2D array of labels to 1D class based representation Args: labels: 2D numpy array """ return np.argmax(labels, axis=1) ================================================ FILE: Examples/cai_eas/eas/data_providers/cifar.py ================================================ import tempfile import os import pickle import random import numpy as np from data_providers.base_provider import ImagesDataSet, DataProvider from data_providers.downloader import download_data_url def augment_image(image, pad): """Perform zero padding, randomly crop image to original size, maybe mirror horizontally""" init_shape = image.shape new_shape = [init_shape[0] + pad * 2, init_shape[1] + pad * 2, init_shape[2]] zeros_padded = np.zeros(new_shape) zeros_padded[pad:init_shape[0] + pad, pad:init_shape[1] + pad, :] = image # randomly crop to original size init_x = np.random.randint(0, pad * 2) init_y = np.random.randint(0, pad * 2) cropped = zeros_padded[ init_x: init_x + init_shape[0], init_y: init_y + init_shape[1], :] flip = random.getrandbits(1) if flip: cropped = cropped[:, ::-1, :] return cropped def augment_all_images(initial_images, pad=4): new_images = np.zeros(initial_images.shape) for i in range(initial_images.shape[0]): new_images[i] = augment_image(initial_images[i], pad) return new_images class CifarDataSet(ImagesDataSet): def __init__(self, images, labels, n_classes, shuffle, normalization, augmentation, meanstd): """ Args: images: 4D numpy array labels: 2D or 1D numpy array n_classes: `int`, number of cifar classes - 10 or 100 shuffle: `str` or None None: no any shuffling once_prior_train: shuffle train data only once prior train every_epoch: shuffle train data prior every epoch normalization: `str` or None None: no any normalization divide_255: divide all pixels by 255 divide_256: divide all pixels by 256 by_channels: substract mean of every chanel and divide each chanel data by it's standard deviation augmentation: `bool` """ if shuffle is None: self.shuffle_every_epoch = False elif shuffle == 'once_prior_train': self.shuffle_every_epoch = False images, labels = self.shuffle_images_and_labels(images, labels) elif shuffle == 'every_epoch': self.shuffle_every_epoch = True else: raise Exception('Unknown type of shuffling') self._batch_counter, self.epoch_images, self.epoch_labels = 0, None, None self.images = images self.labels = labels self.n_classes = n_classes self.augmentation = augmentation self.normalization = normalization self.meanstd = meanstd self.images = self.normalize_images(images, self.normalization, self.meanstd) self.start_new_epoch() def start_new_epoch(self): self._batch_counter = 0 if self.shuffle_every_epoch: images, labels = self.shuffle_images_and_labels( self.images, self.labels) else: images, labels = self.images, self.labels if self.augmentation: images = augment_all_images(images, pad=4) self.epoch_images = images self.epoch_labels = labels @property def num_examples(self): return self.labels.shape[0] def next_batch(self, batch_size): start = self._batch_counter * batch_size end = (self._batch_counter + 1) * batch_size self._batch_counter += 1 images_slice = self.epoch_images[start: end] labels_slice = self.epoch_labels[start: end] if images_slice.shape[0] != batch_size: self.start_new_epoch() return self.next_batch(batch_size) else: return images_slice, labels_slice class CifarDataProvider(DataProvider): """Abstract class for cifar readers""" def __init__(self, save_path=None, validation_size=None, shuffle=None, normalization=None, one_hot=True, **kwargs): """ Args: save_path: `str` validation_set: `bool`. validation_split: `float` or None float: chunk of `train set` will be marked as `validation set`. None: if 'validation set' == True, `validation set` will be copy of `test set` shuffle: `str` or None None: no any shuffling once_prior_train: shuffle train data only once prior train every_epoch: shuffle train data prior every epoch normalization: `str` or None None: no any normalization divide_255: divide all pixels by 255 divide_256: divide all pixels by 256 by_channels: substract mean of every chanel and divide each chanel data by it's standard deviation one_hot: `bool`, return laels one hot encoded """ self._save_path = save_path self.one_hot = one_hot download_data_url(self.data_url, self.save_path) train_fnames, test_fnames = self.get_filenames(self.save_path) # add train and validations datasets images, labels = self.read_cifar(train_fnames) train_meanstd = ImagesDataSet.measure_mean_and_std(images) if validation_size is not None: np.random.seed(DataProvider._SEED) rand_indexes = np.random.permutation(images.shape[0]) valid_indexes = rand_indexes[:validation_size] train_indexes = rand_indexes[validation_size:] self.train = CifarDataSet( images=images[train_indexes], labels=labels[train_indexes], n_classes=self.n_classes, shuffle=shuffle, normalization=normalization, augmentation=self.data_augmentation, meanstd=train_meanstd) self.validation = CifarDataSet( images=images[valid_indexes], labels=labels[valid_indexes], n_classes=self.n_classes, shuffle=None, normalization=normalization, augmentation=False, meanstd=train_meanstd) else: self.train = CifarDataSet( images=images, labels=labels, n_classes=self.n_classes, shuffle=shuffle, normalization=normalization, augmentation=self.data_augmentation, meanstd=train_meanstd) # add test set images, labels = self.read_cifar(test_fnames) self.test = CifarDataSet( images=images, labels=labels, shuffle=None, n_classes=self.n_classes, normalization=normalization, augmentation=False, meanstd=train_meanstd) if validation_size is None: self.validation = self.test @property def save_path(self): if self._save_path is None: self._save_path = os.path.join( tempfile.gettempdir(), 'cifar%d' % self.n_classes) return self._save_path @property def data_url(self): """Return url for downloaded data depends on cifar class""" data_url = ('http://www.cs.toronto.edu/' '~kriz/cifar-%d-python.tar.gz' % self.n_classes) return data_url @property def data_shape(self): return 32, 32, 3 @property def n_classes(self): return self._n_classes def get_filenames(self, save_path): """Return two lists of train and test filenames for dataset""" raise NotImplementedError def read_cifar(self, filenames): if self.n_classes == 10: labels_key = b'labels' elif self.n_classes == 100: labels_key = b'fine_labels' images_res = [] labels_res = [] for fname in filenames: with open(fname, 'rb') as f: images_and_labels = pickle.load(f, encoding='bytes') images = images_and_labels[b'data'] images = images.reshape(-1, 3, 32, 32) images = images.swapaxes(1, 3).swapaxes(1, 2) images_res.append(images) labels_res.append(images_and_labels[labels_key]) images_res = np.vstack(images_res) labels_res = np.hstack(labels_res) if self.one_hot: labels_res = self.labels_to_one_hot(labels_res) return images_res, labels_res class Cifar10DataProvider(CifarDataProvider): _n_classes = 10 data_augmentation = False def get_filenames(self, save_path): sub_save_path = os.path.join(save_path, 'cifar-10-batches-py') train_filenames = [ os.path.join( sub_save_path, 'data_batch_%d' % i) for i in range(1, 6)] test_filenames = [os.path.join(sub_save_path, 'test_batch')] return train_filenames, test_filenames class Cifar100DataProvider(CifarDataProvider): _n_classes = 100 data_augmentation = False def get_filenames(self, save_path): sub_save_path = os.path.join(save_path, 'cifar-100-python') train_filenames = [os.path.join(sub_save_path, 'train')] test_filenames = [os.path.join(sub_save_path, 'test')] return train_filenames, test_filenames class Cifar10AugmentedDataProvider(Cifar10DataProvider): _n_classes = 10 data_augmentation = True class Cifar100AugmentedDataProvider(Cifar100DataProvider): _n_classes = 100 data_augmentation = True ================================================ FILE: Examples/cai_eas/eas/data_providers/downloader.py ================================================ import sys import os import urllib.request import tarfile import zipfile def report_download_progress(count, block_size, total_size): pct_complete = float(count * block_size) / total_size msg = '\r {0:.1%} already downloaded'.format(pct_complete) sys.stdout.write(msg) sys.stdout.flush() def download_data_url(url, download_dir): filename = url.split('/')[-1] file_path = os.path.join(download_dir, filename) if not os.path.exists(file_path): os.makedirs(download_dir, exist_ok=True) print('Download %s to %s' % (url, file_path)) file_path, _ = urllib.request.urlretrieve( url=url, filename=file_path, reporthook=report_download_progress) print('\nExtracting files') if file_path.endswith('.zip'): zipfile.ZipFile(file=file_path, mode='r').extractall(download_dir) elif file_path.endswith(('.tar.gz', '.tgz')): tarfile.open(name=file_path, mode='r:gz').extractall(download_dir) ================================================ FILE: Examples/cai_eas/eas/data_providers/svhn.py ================================================ import tempfile import os import scipy.io import numpy as np from data_providers.base_provider import ImagesDataSet, DataProvider from data_providers.downloader import download_data_url class SVHNDataSet(ImagesDataSet): n_classes = 10 def __init__(self, images, labels, shuffle, normalization): """ Args: images: 4D numpy array labels: 2D or 1D numpy array shuffle: `bool`, should shuffle data or not normalization: `str` or None None: no any normalization divide_255: divide all pixels by 255 divide_256: divide all pixels by 256 by_channels: substract mean of every chanel and divide each chanel data by it's standard deviation """ self._batch_counter, self.epoch_images, self.epoch_labels = 0, None, None self.shuffle = shuffle self.images = images self.labels = labels self.normalization = normalization self.start_new_epoch() def start_new_epoch(self): self._batch_counter = 0 if self.shuffle: self.epoch_images, self.epoch_labels = self.shuffle_images_and_labels( self.images, self.labels) else: self.epoch_images, self.epoch_labels = self.images, self.labels @property def num_examples(self): return self.labels.shape[0] def next_batch(self, batch_size): start = self._batch_counter * batch_size end = (self._batch_counter + 1) * batch_size self._batch_counter += 1 images_slice = self.epoch_images[start: end] labels_slice = self.epoch_labels[start: end] # due to memory error it should be done inside batch if self.normalization is not None: images_slice = self.normalize_images( images_slice, self.normalization) if images_slice.shape[0] != batch_size: self.start_new_epoch() return self.next_batch(batch_size) else: return images_slice, labels_slice class SVHNDataProvider(DataProvider): def __init__(self, save_path=None, validation_size=None, shuffle=False, normalization=None, one_hot=True, include_extra=True, **kwargs): """ Args: save_path: `str` validation_set: `bool`. validation_split: `int` or None float: chunk of `train set` will be marked as `validation set`. None: if 'validation set' == True, `validation set` will be copy of `test set` shuffle: `bool`, should shuffle data or not normalization: `str` or None None: no any normalization divide_255: divide all pixels by 255 divide_256: divide all pixels by 256 by_chanels: substract mean of every chanel and divide each chanel data by it's standart deviation one_hot: `bool`, return lasels one hot encoded """ self._save_path = save_path train_images = [] train_labels = [] if include_extra: train_data_src = ['train', 'extra'] else: train_data_src = ['train'] for part in train_data_src: images, labels = self.get_images_and_labels(part, one_hot) train_images.append(images) train_labels.append(labels) train_images = np.vstack(train_images) if one_hot: train_labels = np.vstack(train_labels) else: train_labels = np.hstack(train_labels) if validation_size is not None: np.random.seed(DataProvider._SEED) rand_indexes = np.random.permutation(train_images.shape[0]) valid_indexes = rand_indexes[:validation_size] train_indexes = rand_indexes[validation_size:] valid_images, valid_labels = train_images[valid_indexes], train_labels[valid_indexes] train_images, train_labels = train_images[train_indexes], train_labels[train_indexes] self.validation = SVHNDataSet( valid_images, valid_labels, False, normalization) self.train = SVHNDataSet( train_images, train_labels, shuffle, normalization) test_images, test_labels = self.get_images_and_labels('test', one_hot) self.test = SVHNDataSet(test_images, test_labels, False, normalization) if validation_size is None: self.validation = self.test def get_images_and_labels(self, name_part, one_hot=False): url = self.data_url + name_part + '_32x32.mat' download_data_url(url, self.save_path) filename = os.path.join(self.save_path, name_part + '_32x32.mat') data = scipy.io.loadmat(filename) images = data['X'].transpose(3, 0, 1, 2) labels = data['y'].reshape((-1)) labels[labels == 10] = 0 if one_hot: labels = self.labels_to_one_hot(labels) return images, labels @property def n_classes(self): return 10 @property def save_path(self): if self._save_path is None: self._save_path = os.path.join(tempfile.gettempdir(), 'svhn') return self._save_path @property def data_url(self): return 'http://ufldl.stanford.edu/housenumbers/' @property def data_shape(self): return 32, 32, 3 ================================================ FILE: Examples/cai_eas/eas/data_providers/utils.py ================================================ from data_providers.cifar import Cifar10DataProvider, Cifar100DataProvider, \ Cifar10AugmentedDataProvider, Cifar100AugmentedDataProvider from data_providers.svhn import SVHNDataProvider def get_data_provider_by_name(name, train_params): """Return required data provider class""" if name == 'C10': return Cifar10DataProvider(**train_params) if name == 'C10+': return Cifar10AugmentedDataProvider(**train_params) if name == 'C100': return Cifar100DataProvider(**train_params) if name == 'C100+': return Cifar100AugmentedDataProvider(**train_params) if name == 'SVHN': return SVHNDataProvider(**train_params) else: print('Sorry, data provider for `%s` dataset ' 'was not implemented yet' % name) exit() ================================================ FILE: Examples/cai_eas/eas/expdir_monitor/__init__.py ================================================ ================================================ FILE: Examples/cai_eas/eas/expdir_monitor/expdir_monitor.py ================================================ import json import os import subprocess from models.utils import RunConfig, get_model_config_by_name, get_model_by_name from data_providers.utils import get_data_provider_by_name import pickle class ExpdirMonitor: def __init__(self, expdir): self.expdir = os.path.realpath(expdir) os.makedirs(self.expdir, exist_ok=True) @property def logs(self): return '%s/logs' % self.expdir @property def checkpoint(self): return '%s/checkpoint' % self.expdir @property def snapshot(self): return '%s/snapshot' % self.expdir @property def output(self): return '%s/output' % self.expdir @property def init(self): return '%s/init' % self.expdir @property def run_config_path(self): return '%s/run.config' % self.expdir @property def net_config_path(self): return '%s/net.config' % self.expdir def load_run_config(self, print_info=False, dataset='C10+'): if os.path.isfile(self.run_config_path): run_config = json.load(open(self.run_config_path, 'r')) else: print('Use Default Run Config for %s' % dataset) run_config = RunConfig.get_default_run_config(dataset) if print_info: print('Run config:') for k, v in run_config.items(): print('\t%s: %s' % (k, v)) return RunConfig(**run_config) def load_init(self): init_path = '%s/init' % self.expdir if os.path.isfile(init_path): return pickle.load(open(self.init, 'rb')) else: return None def load_net_config(self, init, print_info=False): assert os.path.isfile(self.net_config_path), \ 'Net configs do not exist in the given expdir <%s>' % self.expdir net_config_json = json.load(open(self.net_config_path, 'r')) net_config = get_model_config_by_name(net_config_json['name'])() net_config.set_net_from_config(net_config_json, init=init, print_info=print_info) return net_config, net_config_json['name'] def run(self, pure=True, restore=False, test=False, valid=False, valid_size=-1): if not restore: _clear_files = ['logs', 'checkpoint', 'snapshot', 'output'] for file in _clear_files: subprocess.run(['rm', '-rf', os.path.join(self.expdir, file)]) init = self.load_init() dataset = 'C10+' if init is None else init.get('dataset', 'C10+') run_config = self.load_run_config(print_info=(not pure), dataset=dataset) run_config.renew_logs = False if valid_size > 0: run_config.validation_size = valid_size data_provider = get_data_provider_by_name(run_config.dataset, run_config.get_config()) net_config, model_name = self.load_net_config(init, print_info=(not pure)) model = get_model_by_name(model_name)(self.expdir, data_provider, run_config, net_config, pure=pure) start_epoch = 1 if restore: model.load_model() epoch_info_file = '%s/checkpoint/epoch.info' % self.expdir if os.path.isfile(epoch_info_file): start_epoch = json.load(open(epoch_info_file, 'r'))['epoch'] if not pure: print('start epoch: %d' % start_epoch) if test: print('Testing...') loss, accuracy = model.test(data_provider.test, batch_size=200) print('mean cross_entropy: %f, mean accuracy: %f' % (loss, accuracy)) json.dump({'test_loss': '%s' % loss, 'test_acc': '%s' % accuracy}, open(self.output, 'w')) elif valid: print('validating...') loss, accuracy = model.test(data_provider.validation, batch_size=200) print('mean cross_entropy: %f, mean accuracy: %f' % (loss, accuracy)) json.dump({'valid_loss': '%s' % loss, 'valid_acc': '%s' % accuracy}, open(self.output, 'w')) elif pure: model.pure_train() loss, accuracy = model.test(data_provider.validation, batch_size=200) json.dump({'valid_loss': '%s' % loss, 'valid_acc': '%s' % accuracy}, open(self.output, 'w')) model.save_init(self.snapshot, print_info=(not pure)) model.save_config(self.expdir, print_info=(not pure)) else: # train the model print('Data provider train images: ', data_provider.train.num_examples) model.train_all_epochs(start_epoch) print('Data provider test images: ', data_provider.test.num_examples) print('Testing...') loss, accuracy = model.test(data_provider.test, batch_size=200) print('mean cross_entropy: %f, mean accuracy: %f' % (loss, accuracy)) json.dump({'test_loss': '%s' % loss, 'test_acc': '%s' % accuracy}, open(self.output, 'w')) model.save_init(self.snapshot, print_info=(not pure)) model.save_config(self.expdir, print_info=(not pure)) return accuracy ================================================ FILE: Examples/cai_eas/eas/experiment.json ================================================ { "name": "./cai_eas/eas/experiment.json", "proposer": "eas", "script": "client.py", "resource": "gpu", "n_parallel": 2, "parameter_config": [ ], "target":"max" } ================================================ FILE: Examples/cai_eas/eas/models/__init__.py ================================================ ================================================ FILE: Examples/cai_eas/eas/models/basic_model.py ================================================ import os import shutil import tensorflow as tf import numpy as np import time from datetime import timedelta import json import pickle class BasicModel: def __init__(self, path, data_provider, run_config, net_config, pure=False, only_forward=False): if only_forward: pure = True self.graph = tf.Graph() self.data_provider = data_provider self._path = path self.run_config = run_config self.net_config = net_config self.data_shape = data_provider.data_shape self.n_classes = data_provider.n_classes self._save_path, self._logs_path = None, None self.batches_step = 0 self.cross_entropy, self.train_step, self.accuracy = None, None, None with self.graph.as_default(): self._define_inputs() self._build_graph(only_forward=only_forward) self.global_variables_initializer = tf.global_variables_initializer() if not pure: self._count_trainable_params() self.saver = tf.train.Saver() self._initialize_session(set_logs=(not pure)) @property def save_path(self): if self._save_path is None: save_path = '%s/checkpoint' % self._path os.makedirs(save_path, exist_ok=True) save_path = os.path.join(save_path, 'model.ckpt') self._save_path = save_path return self._save_path @property def logs_path(self): if self._logs_path is None: logs_path = '%s/logs' % self._path if self.run_config.renew_logs: shutil.rmtree(logs_path, ignore_errors=True) os.makedirs(logs_path, exist_ok=True) self._logs_path = logs_path return self._logs_path def _build_graph(self, only_forward=False): raise NotImplementedError def _define_inputs(self): shape = [None] shape.extend(self.data_shape) self.images = tf.placeholder( tf.float32, shape=shape, name='input_images') self.labels = tf.placeholder( tf.float32, shape=[None, self.n_classes], name='labels') self.learning_rate = tf.placeholder( tf.float32, shape=[], name='learning_rate') self.is_training = tf.placeholder(tf.bool, shape=[], name='is_training') def _initialize_session(self, set_logs=True): """Initialize session, variables""" config = tf.ConfigProto() # restrict model GPU memory utilization to min required config.gpu_options.allow_growth = True self.sess = tf.Session(graph=self.graph, config=config) self.sess.run(self.global_variables_initializer) if set_logs: logswriter = tf.summary.FileWriter self.summary_writer = logswriter(self.logs_path, graph=self.graph) def train_all_epochs(self, start_epoch=1): n_epochs = self.run_config.n_epochs learning_rate = self.run_config.init_lr batch_size = self.run_config.batch_size total_start_time = time.time() for epoch in range(start_epoch, n_epochs + 1): print('\n', '-' * 30, 'Train epoch: %d' % epoch, '-' * 30, '\n') start_time = time.time() new_lr = self.run_config.learning_rate(epoch) if new_lr != learning_rate: learning_rate = new_lr print('Decrease learning rate, new lr = %f' % learning_rate) print('Training...') loss, acc = self.train_one_epoch( self.data_provider.train, batch_size, learning_rate) # save logs about "loss" and "acc" if the option is true if self.run_config.should_save_logs: self.log_loss_accuracy(loss, acc, epoch, prefix='train') if self.run_config.validation_frequency and epoch % self.run_config.validation_frequency == 0: print('Validation...') loss, acc = self.test(self.data_provider.validation, batch_size) if self.run_config.should_save_logs: self.log_loss_accuracy(loss, acc, epoch, prefix='valid') if self.run_config.should_save_model: self.save_model() json.dump({'epoch': epoch + 1}, open('%s/checkpoint/epoch.info' % self._path, 'w')) time_per_epoch = time.time() - start_time seconds_left = int((n_epochs - epoch) * time_per_epoch) print('Time per epoch: %s, Est. complete in: %s' % ( str(timedelta(seconds=time_per_epoch)), str(timedelta(seconds=seconds_left)))) if self.run_config.should_save_model: self.save_model() total_training_time = time.time() - total_start_time print('\nTotal training time: %s' % str(timedelta( seconds=total_training_time))) def train_one_epoch(self, data, batch_size, learning_rate): num_examples = data.num_examples total_loss = [] total_accuracy = [] for i in range(num_examples // batch_size): batch = data.next_batch(batch_size) images, labels = batch feed_dict = { self.images: images, self.labels: labels, self.learning_rate: learning_rate, self.is_training: True, } fetches = [self.train_step, self.cross_entropy, self.accuracy] result = self.sess.run(fetches, feed_dict=feed_dict) _, loss, accuracy = result total_loss.append(loss) total_accuracy.append(accuracy) # save logs about "loss" and "acc" if the option is true if self.run_config.should_save_logs: self.batches_step += 1 self.log_loss_accuracy( loss, accuracy, self.batches_step, prefix='per_batch', should_print=False) mean_loss = np.mean(total_loss) mean_accuracy = np.mean(total_accuracy) return mean_loss, mean_accuracy def test(self, data, batch_size): num_examples = data.num_examples total_loss = [] total_accuracy = [] for i in range(num_examples // batch_size): batch = data.next_batch(batch_size) feed_dict = { self.images: batch[0], self.labels: batch[1], self.is_training: False, } fetches = [self.cross_entropy, self.accuracy] loss, accuracy = self.sess.run(fetches, feed_dict=feed_dict) total_loss.append(loss) total_accuracy.append(accuracy) mean_loss = np.mean(total_loss) mean_accuracy = np.mean(total_accuracy) remain_num = num_examples % batch_size if remain_num != 0: batch = data.next_batch(remain_num) feed_dict = { self.images: batch[0], self.labels: batch[1], self.is_training: False, } fetches = [self.cross_entropy, self.accuracy] loss, accuracy = self.sess.run(fetches, feed_dict=feed_dict) mean_loss = (mean_loss * (num_examples - remain_num) + loss * remain_num) / num_examples mean_accuracy = (mean_accuracy * (num_examples - remain_num) + accuracy * remain_num) / num_examples return mean_loss, mean_accuracy def save_config(self, save_path, print_info=True): os.makedirs(save_path, exist_ok=True) net_save_path = os.path.join(save_path, 'net.config') json.dump(self.net_config.get_config(), open(net_save_path, 'w'), indent=4) if print_info: print('Network configs dump to %s' % save_path) run_save_path = os.path.join(save_path, 'run.config') json.dump(self.run_config.get_config(), open(run_save_path, 'w'), indent=4) if print_info: print('Run configs dump to %s' % run_save_path) def save_init(self, save_path, print_info=True): os.makedirs(save_path, exist_ok=True) save_path = os.path.join(save_path, 'init') to_save_init = self.net_config.renew_init(self) to_save_init['dataset'] = self.run_config.dataset pickle.dump(to_save_init, open(save_path, 'wb')) if print_info: print('Network weights dump to %s' % save_path) def pure_train(self): n_epochs = self.run_config.n_epochs batch_size = self.run_config.batch_size for epoch in range(1, n_epochs + 1): learning_rate = self.run_config.learning_rate(epoch) # train one epoch data = self.data_provider.train num_examples = data.num_examples for i in range(num_examples // batch_size): batch = data.next_batch(batch_size) images, labels = batch feed_dict = { self.images: images, self.labels: labels, self.learning_rate: learning_rate, self.is_training: True, } fetches = self.train_step self.sess.run(fetches, feed_dict=feed_dict) def save_model(self, global_step=None): self.saver.save(self.sess, self.save_path, global_step=global_step) def load_model(self): try: self.saver.restore(self.sess, self.save_path) except Exception: raise IOError('Failed to to load model ' 'from save path: %s' % self.save_path) print('Successfully load model from save path: %s' % self.save_path) def log_loss_accuracy(self, loss, accuracy, epoch, prefix, should_print=True, write2file=True): if should_print: print('mean cross_entropy: %f, mean accuracy: %f' % (loss, accuracy)) summary = tf.Summary(value=[ tf.Summary.Value( tag='loss_%s' % prefix, simple_value=float(loss)), tf.Summary.Value( tag='accuracy_%s' % prefix, simple_value=float(accuracy)) ]) self.summary_writer.add_summary(summary, epoch) if write2file and prefix == 'valid': with open('%s/console.txt' % self.logs_path, 'a') as fout: fout.write('%d: mean cross_entropy: %f, mean accuracy: %f\n' % (epoch, loss, accuracy)) @staticmethod def _count_trainable_params(): total_parameters = 0 for variable in tf.trainable_variables(): shape = variable.get_shape() variable_parameters = 1 for dim in shape: variable_parameters *= dim.value total_parameters += variable_parameters print('Total training params: %.2fM' % (total_parameters / 1e6)) @staticmethod def dropout(_input, keep_prob, is_training): if keep_prob < 1: output = tf.cond( is_training, lambda: tf.nn.dropout(_input, keep_prob), lambda: _input ) else: output = _input return output @staticmethod def weight_variable(shape, name, initializer): return tf.get_variable( name, shape=shape, initializer=initializer, ) @staticmethod def avg_pool(_input, k=2, s=2): ksize = [1, k, k, 1] strides = [1, s, s, 1] padding = 'VALID' # if stride = 1, keep the image size unchanged if s == 1: padding = 'SAME' output = tf.nn.avg_pool(_input, ksize, strides, padding) return output @staticmethod def max_pool(_input, k=2, s=2): ksize = [1, k, k, 1] strides = [1, s, s, 1] padding = 'VALID' # if stride = 1, keep the image size unchanged if s == 1: padding = 'SAME' output = tf.nn.max_pool(_input, ksize, strides, padding) return output @staticmethod def conv2d(_input, out_features, kernel_size, strides=1, padding='SAME', param_initializer=None): if kernel_size == 1: padding = 'VALID' in_features = int(_input.get_shape()[-1]) if not param_initializer: param_initializer = {} kernel = BasicModel.weight_variable( [kernel_size, kernel_size, in_features, out_features], name='kernel', initializer=param_initializer.get('kernel', tf.contrib.layers.variance_scaling_initializer()) ) output = tf.nn.conv2d(_input, kernel, [1, strides, strides, 1], padding) return output @staticmethod def fc_layer(_input, out_units, use_bias=False, param_initializer=None): features_total = int(_input.get_shape()[-1]) if not param_initializer: param_initializer = {} W = BasicModel.weight_variable( [features_total, out_units], name='W', initializer=param_initializer.get('W', tf.contrib.layers.xavier_initializer()) ) output = tf.matmul(_input, W) if use_bias: bias = BasicModel.weight_variable( [out_units], name='bias', initializer=param_initializer.get('bias', tf.constant_initializer([0.0] * out_units)) ) output += bias return output @staticmethod def batch_norm(_input, is_training, epsilon=1e-3, decay=0.999, param_initializer=None): output = tf.contrib.layers.batch_norm( _input, scale=True, is_training=is_training, param_initializers=param_initializer, updates_collections=None, epsilon=epsilon, decay=decay) return output @staticmethod def activation(_input, activation='relu'): if activation == 'relu': return tf.nn.relu(_input) elif activation == 'tanh': return tf.tanh(_input) elif activation == 'sigmoid': return tf.sigmoid(_input) elif activation == 'softmax': return tf.nn.softmax(_input) elif activation is None: return _input else: raise ValueError('Do not support %s' % activation) @staticmethod def build_optimizer(learning_rate, opt_name, opt_param): if opt_name == 'momentum': return tf.train.MomentumOptimizer(learning_rate, **opt_param) elif opt_name == 'adam': return tf.train.AdamOptimizer(learning_rate, **opt_param) else: raise ValueError('Do not support the optimizer type: %s' % opt_name) @staticmethod def flatten(_input): input_shape = _input.shape.as_list() if len(input_shape) != 2: return tf.reshape(_input, [-1, np.prod(input_shape[1:])]) else: return _input ================================================ FILE: Examples/cai_eas/eas/models/convnet.py ================================================ from models.basic_model import BasicModel from data_providers.base_provider import DataProvider from models.layers import ConvLayer, PoolLayer, FCLayer from models.layer_cascade import LayerCascade import tensorflow as tf import numpy as np class SimpleConvnetConfig: def __init__(self): self.net_config = { 'weight_decay': None, 'bn_epsilon': None, 'bn_decay': None, 'drop_scheme': None, } self.layer_cascade = None @property def weight_decay(self): return self.net_config['weight_decay'] @property def bn_epsilon(self): return self.net_config['bn_epsilon'] @property def bn_decay(self): return self.net_config['bn_decay'] @property def drop_scheme(self): return self.net_config['drop_scheme'] @property def depth(self): return self.layer_cascade.depth def get_config(self): return { 'name': 'SimpleConvnet', **self.net_config, 'layer_cascade': self.layer_cascade.get_config() } def copy(self): net_config = SimpleConvnetConfig() net_config.set_net_from_config(self.get_config(), self.renew_init(None), print_info=False) return net_config def renew_init(self, convnet): return { 'layer_cascade': self.layer_cascade.renew_init(convnet) } def set_standard_convnet(self, data_provider: DataProvider, conv_blocks_config, fc_block_config, weight_decay, drop_scheme, bn_epsilon, bn_decay, print_info=True, **kwargs): self.net_config = { 'weight_decay': weight_decay, 'bn_epsilon': bn_epsilon, 'bn_decay': bn_decay, 'drop_scheme': drop_scheme, } image_size = data_provider.data_shape[0] layers = [] conv_id = 0 for _i, block_config in enumerate(conv_blocks_config): num_layers, kernel_size, filter_num = block_config for _j in range(num_layers): keep_prob = 1.0 if 'conv' in drop_scheme['type']: keep_prob = 1.0 if _i + _j == 0 else drop_scheme.get('conv_drop', 1.0) conv_layer = ConvLayer('conv_%d' % conv_id, filter_num, kernel_size=kernel_size, keep_prob=keep_prob, pre_activation=False) conv_id += 1 layers.append(conv_layer) if _i < len(conv_blocks_config) - 1: keep_prob = 1.0 if 'pool' in drop_scheme['type']: keep_prob = drop_scheme.get('pool_drop', 1.0) pool_layer = PoolLayer('pool_%d' % _i, 'max', keep_prob=keep_prob, pre_activation=False) layers.append(pool_layer) image_size = image_size // 2 global_avg_pool = PoolLayer('pool_%d' % len(conv_blocks_config), 'avg', kernel_size=image_size, strides=image_size, pre_activation=False) layers.append(global_avg_pool) for _i, units in enumerate(fc_block_config): keep_prob = 1.0 if 'fc' in drop_scheme['type']: keep_prob = drop_scheme.get('fc_drop', 1.0) fc_layer = FCLayer('fc_%d' % _i, units, keep_prob=keep_prob) layers.append(fc_layer) final_fc_layer = FCLayer('fc_%d' % len(fc_block_config), data_provider.n_classes, use_bn=False, use_bias=True, activation=None) layers.append(final_fc_layer) self.layer_cascade = LayerCascade('SimpleConvNet', layers) if print_info: pass return self def set_net_from_config(self, net_config_json, init=None, print_info=True): for key in self.net_config.keys(): self.net_config[key] = net_config_json[key] init = init['layer_cascade'] if init is not None else None self.layer_cascade = LayerCascade.set_from_config(net_config_json['layer_cascade'], init) if print_info: pass return self def widen(self, layer_idx, new_width, widen_type='output_dim', noise=None): change_out_dim, _, _ = self.layer_cascade.widen(layer_idx, new_width, widen_type, noise) if change_out_dim: raise ValueError('Can not change the final logits number') def deepen(self, layer_idx, new_layer_config): return self.layer_cascade.deepen(layer_idx, new_layer_config, None) def set_identity4deepen(self, to_set_layers, data_provider, batch_size, batch_num=1, strict=True, noise=None): """ to_set_layers = [(new_layer, prev_layer), ...] """ task_list = {} for new_layer, prev_layer in to_set_layers: if new_layer.ready: continue if new_layer.use_bn and strict: task_id = id(prev_layer) if task_id in task_list: task_list[task_id][1].append(new_layer) else: task_list[task_id] = (prev_layer, [new_layer]) else: new_layer.set_identity_layer(strict=strict, noise=noise) if len(task_list) > 0: model = SimpleConvnet(None, data_provider, None, net_config=self, only_forward=True) task_list = list(task_list.values()) fetches = [prev_layer.output_op for prev_layer, _ in task_list] statistics = [[0, 0] for _ in task_list] for _i in range(batch_num): input_images, _ = data_provider.train.next_batch(batch_size) outputs = model.sess.run(fetches, feed_dict={model.images: input_images, model.is_training: False}) for _j, out in enumerate(outputs): out = out.astype('float32') axis = tuple(range(len(out.shape) - 1)) mean = np.mean(out, axis=axis, keepdims=True) variance = np.mean(np.square(out - mean), axis=axis, keepdims=True) mean, variance = np.squeeze(mean), np.squeeze(variance) statistics[_j][0] += mean statistics[_j][1] += variance for _j, (prev_layer, new_layers) in enumerate(task_list): mean, variance = statistics[_j][0] / batch_num, statistics[_j][1] / batch_num for new_layer in new_layers: if new_layer.ready: continue param = { 'moving_mean': mean, 'moving_variance': variance, 'epsilon': self.bn_epsilon, } new_layer.set_identity_layer(strict=strict, param=param, noise=noise) class SimpleConvnet(BasicModel): def _build_graph(self, only_forward=False): _input = self.images output = _input output = self.net_config.layer_cascade.build(output, self, store_output_op=only_forward) if not only_forward: logits = output with tf.variable_scope('L2_Loss'): l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()]) prediction = tf.nn.softmax(logits) # losses cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=self.labels)) self.cross_entropy = cross_entropy # optimizer and train step optimizer = self.build_optimizer(self.learning_rate, self.run_config.opt_config[0], self.run_config.opt_config[1]) self.train_step = optimizer.minimize( cross_entropy + l2_loss * self.net_config.weight_decay) correct_prediction = tf.equal( tf.argmax(prediction, 1), tf.argmax(self.labels, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) ================================================ FILE: Examples/cai_eas/eas/models/dense_net.py ================================================ import tensorflow as tf from models.basic_model import BasicModel from models.layers import ConvLayer, FCLayer, PoolLayer, get_magnifier, apply_noise from data_providers.base_provider import DataProvider from models.layer_cascade import LayerCascade from models.layer_multi_branch import LayerMultiBranch import numpy as np def get_block_by_name(name): if name == 'transition': return TransitionBlock elif name == 'dense_block': return DenseBlock else: raise ValueError('Unsupported block type: %s' % name) class TransitionBlock(LayerCascade): def get_config(self): return { 'name': 'transition', **super(TransitionBlock, self).get_config(), } @staticmethod def set_from_config(config_json, init=None, return_class=True): _id, layers = LayerCascade.set_from_config(config_json, init, return_class=False) return TransitionBlock(_id, layers) def prev_widen(self, indices, magnifier, noise=None): super(TransitionBlock, self).prev_widen(indices, magnifier, noise=noise) return False, None, None def widen(self, loc, new_width, widen_type='output_dim', noise=None, input_dim=None): return super(TransitionBlock, self).widen(loc['layer'], new_width, widen_type, noise=noise) def deepen(self, loc, new_layer_config, input_dim): return super(TransitionBlock, self).deepen(loc['layer'], new_layer_config, input_dim) class DenseBlock: def __init__(self, _id, miniblocks): self._id = _id self.miniblocks = miniblocks self.output_op = None @property def id(self): return self._id @id.setter def id(self, value): self._id = value @property def depth(self): depth = 0 for miniblock in self.miniblocks: depth += miniblock.depth return depth def out_features_dim(self, in_features_dim): out_features_dim = in_features_dim for miniblock in self.miniblocks: out_features_dim += miniblock.out_features_dim return out_features_dim def build(self, _input, densenet, store_output_op=False): output = _input with tf.variable_scope(self._id): for miniblock in self.miniblocks: comp_out = miniblock.build(output, densenet, store_output_op=store_output_op) output = tf.concat(axis=3, values=(output, comp_out)) if store_output_op: self.output_op = output return output def get_config(self): return { 'name': 'dense_block', '_id': self._id, 'miniblocks': [miniblock.get_config() for miniblock in self.miniblocks] } def renew_init(self, densenet): return { '_id': self._id, 'miniblocks': [miniblock.renew_init(densenet) for miniblock in self.miniblocks] } @staticmethod def set_from_config(config_json, init=None): _id = config_json['_id'] miniblocks = [] for _i, miniblock_config in enumerate(config_json['miniblocks']): miniblock_init = init['miniblocks'][_i] if init is not None else None miniblock = LayerMultiBranch.set_from_config(miniblock_config, miniblock_init) miniblocks.append(miniblock) return DenseBlock(_id, miniblocks) """ Network Transformation Operations """ def insert_miniblock(self, idx, miniblock_config, input_dim, noise=None, scheme=0): assert 0 <= idx < len(self.miniblocks), 'Invalid miniblock index %d' % idx if miniblock_config['bc_mode']: # DenseNet-BC if scheme == 0: copy_idx = idx copy_miniblock = self.miniblocks[copy_idx] new_in_bottle = copy_miniblock.in_bottle.copy() new_in_layer = new_in_bottle.layers[0] pad_kernel_shape = list(new_in_layer.init['kernel'].shape) pad_kernel_shape[2] = copy_miniblock.out_features_dim new_in_layer.init['kernel'] = \ np.concatenate([new_in_layer.init['kernel'], np.zeros(pad_kernel_shape)], axis=2) if new_in_layer.pre_activation and new_in_layer.use_bn: new_in_layer.init['beta'] = \ np.concatenate([new_in_layer.init['beta'], np.zeros([copy_miniblock.out_features_dim])]) new_in_layer.init['gamma'] = \ np.concatenate([new_in_layer.init['gamma'], np.ones([copy_miniblock.out_features_dim])]) new_in_layer.init['moving_mean'] = \ np.concatenate([new_in_layer.init['moving_mean'], np.zeros([copy_miniblock.out_features_dim])]) new_in_layer.init['moving_variance'] = \ np.concatenate([new_in_layer.init['moving_variance'], np.ones([copy_miniblock.out_features_dim])]) new_in_layer.init['kernel'] = apply_noise(new_in_layer.init['kernel'], noise.get('wider')) if copy_miniblock.out_bottle is None: new_branches, indices = copy_miniblock.remapped_branches(noise=noise) new_miniblock = LayerMultiBranch('M_%d' % (idx + 2), new_branches, merge=copy_miniblock.merge, in_bottle=new_in_bottle) old_size = len(indices) indices = np.concatenate([np.arange(old_size), indices]) magnifier = get_magnifier(old_size, indices) prev_miniblock_out_dim = input_dim for _i in range(0, idx): prev_miniblock_out_dim += self.miniblocks[_i].out_features_dim indices = np.concatenate([ np.arange(prev_miniblock_out_dim), indices + prev_miniblock_out_dim, ]) magnifier = np.concatenate([ [1] * prev_miniblock_out_dim, magnifier, ]) prev_miniblock_out_dim += old_size for _i in range(idx + 1, len(self.miniblocks)): miniblock_out_dim = self.miniblocks[_i].out_features_dim self.miniblocks[_i].id = 'M_%d' % (_i + 2) self.miniblocks[_i].prev_widen(indices, magnifier, noise=noise) indices = np.concatenate([ indices, np.arange(prev_miniblock_out_dim, prev_miniblock_out_dim + miniblock_out_dim) ]) magnifier = np.concatenate([ magnifier, [1] * miniblock_out_dim, ]) prev_miniblock_out_dim += miniblock_out_dim self.miniblocks = self.miniblocks[:idx + 1] + [new_miniblock] + self.miniblocks[idx + 1:] return indices, magnifier else: raise NotImplementedError else: # identity scheme raise NotImplementedError else: # DenseNet without BC raise NotImplementedError def prev_widen(self, indices, magnifier, noise=None): old_size = np.max(indices) + 1 prev_miniblock_out_dim = old_size for miniblock in self.miniblocks: miniblock_out_dim = miniblock.out_features_dim miniblock.prev_widen(indices, magnifier, noise=noise) indices = np.concatenate([ indices, np.arange(prev_miniblock_out_dim, prev_miniblock_out_dim + miniblock_out_dim) ]) magnifier = np.concatenate([ magnifier, [1] * miniblock_out_dim, ]) prev_miniblock_out_dim += miniblock_out_dim return True, indices, magnifier def widen(self, loc, new_width, widen_type='output_dim', noise=None, input_dim=3): miniblock_idx = loc['miniblock'] miniblock = self.miniblocks[miniblock_idx] old_miniblock_out_dim = miniblock.out_features_dim change_out_dim, indices, magnifier = miniblock.widen(loc, new_width, widen_type, noise=noise) if change_out_dim: prev_miniblock_out_dim = input_dim for _i in range(0, miniblock_idx): prev_miniblock_out_dim += self.miniblocks[_i].out_features_dim indices = np.concatenate([ np.arange(prev_miniblock_out_dim), indices + prev_miniblock_out_dim, ]) magnifier = np.concatenate([ [1] * prev_miniblock_out_dim, magnifier, ]) prev_miniblock_out_dim += old_miniblock_out_dim for _i in range(miniblock_idx + 1, len(self.miniblocks)): miniblock_out_dim = self.miniblocks[_i].out_features_dim self.miniblocks[_i].prev_widen(indices, magnifier, noise=noise) indices = np.concatenate([ indices, np.arange(prev_miniblock_out_dim, prev_miniblock_out_dim + miniblock_out_dim) ]) magnifier = np.concatenate([ magnifier, [1] * miniblock_out_dim, ]) prev_miniblock_out_dim += miniblock_out_dim return True, indices, magnifier else: return False, None, None def deepen(self, loc, new_layer_config, input_dim): miniblock_idx = loc['miniblock'] for _i in range(0, miniblock_idx): input_dim += self.miniblocks[_i].out_features_dim return self.miniblocks[miniblock_idx].deepen(loc, new_layer_config, input_dim) class DenseNetConfig: def __init__(self): self.net_config = { 'model_type': None, 'weight_decay': None, 'first_ratio': None, 'reduction': None, 'bc_ratio': None, 'bn_epsilon': None, 'bn_decay': None, 'pre_activation': None, } self.blocks = None @property def model_type(self): return self.net_config['model_type'] @property def weight_decay(self): return self.net_config['weight_decay'] @property def first_ratio(self): return self.net_config['first_ratio'] @property def reduction(self): return self.net_config['reduction'] @property def bc_ratio(self): return self.net_config['bc_ratio'] @property def bn_epsilon(self): return self.net_config['bn_epsilon'] @property def bn_decay(self): return self.net_config['bn_decay'] @property def depth(self): depth = 0 for block in self.blocks: depth += block.depth return depth @property def average_growth_rate(self): growth_rate_list = [] for block in self.blocks: if isinstance(block, DenseBlock): for miniblock in block.miniblocks: growth_rate = miniblock.out_features_dim growth_rate_list.append(growth_rate) return np.mean(growth_rate_list) def copy(self): net_config = DenseNetConfig() net_config.set_net_from_config(self.get_config(), self.renew_init(None), print_info=False) return net_config def get_config(self): return { 'name': 'DenseNet', **self.net_config, 'blocks': [block.get_config() for block in self.blocks] } def renew_init(self, densenet): return { 'blocks': [block.renew_init(densenet) for block in self.blocks] } def set_standard_dense_net(self, data_provider: DataProvider, growth_rate, depth, total_blocks, keep_prob, weight_decay, model_type, first_ratio=2, reduction=1.0, bc_ratio=4, bn_epsilon=1e-5, bn_decay=0.9, print_info=True, pre_activation=True, **kwargs): self.net_config = { 'model_type': model_type, 'weight_decay': weight_decay, 'first_ratio': first_ratio, 'reduction': reduction, 'bc_ratio': bc_ratio, 'bn_epsilon': bn_epsilon, 'bn_decay': bn_decay, 'pre_activation': pre_activation, } image_size = data_provider.data_shape[0] first_output_features = growth_rate * first_ratio bc_mode = (model_type == 'DenseNet-BC') layers_per_block = (depth - (total_blocks + 1)) // total_blocks if bc_mode: layers_per_block = layers_per_block // 2 # initial conv if pre_activation: init_conv_layer = ConvLayer('conv_0', first_output_features, kernel_size=3, activation=None, use_bn=False) else: init_conv_layer = ConvLayer('conv_0', first_output_features, kernel_size=3, pre_activation=False) init_transition = TransitionBlock('T_0_first', [init_conv_layer]) self.blocks = [init_transition] # Dense Blocks in_features_dim = first_output_features for block_idx in range(1, total_blocks + 1): miniblocks = [] block_id = 'D_%d' % block_idx for miniblock_idx in range(1, layers_per_block + 1): miniblock_id = 'M_%d' % miniblock_idx in_bottle = None if bc_mode: bottelneck_layer = ConvLayer('conv_0', growth_rate * bc_ratio, kernel_size=1, keep_prob=keep_prob, pre_activation=pre_activation) in_bottle = LayerCascade('in_bottle', [bottelneck_layer]) branch_0 = LayerCascade('B_0', [ ConvLayer('conv_0', growth_rate, kernel_size=3, keep_prob=keep_prob, pre_activation=pre_activation) ]) miniblocks.append(LayerMultiBranch(miniblock_id, [branch_0], in_bottle=in_bottle)) dense_block = DenseBlock(block_id, miniblocks) self.blocks += [dense_block] out_features_dim = dense_block.out_features_dim(in_features_dim) if block_idx != total_blocks: out_features_dim = int(out_features_dim * reduction) transition_id = 'T_%d_middle' % block_idx conv_layer = ConvLayer('conv_0', out_features_dim, kernel_size=1, keep_prob=keep_prob, pre_activation=pre_activation) avg_pool_layer = PoolLayer('pool_0', 'avg', kernel_size=2, strides=2) transition = TransitionBlock(transition_id, [conv_layer, avg_pool_layer]) self.blocks.append(transition) image_size = image_size // 2 in_features_dim = out_features_dim # Transition to classes if pre_activation: global_avg_pool = PoolLayer('pool_0', 'avg', kernel_size=image_size, strides=image_size, activation='relu', use_bn=True) else: global_avg_pool = PoolLayer('pool_0', 'avg', kernel_size=image_size, strides=image_size, pre_activation=False) final_fc_layer = FCLayer('fc_0', data_provider.n_classes, use_bn=False, use_bias=True, activation=None) transition_to_classes = TransitionBlock('T_to_classes', [global_avg_pool, final_fc_layer]) self.blocks.append(transition_to_classes) # print information about the network if print_info: print('Set Standard %s' % model_type) if not bc_mode: print('Build %s model with %d blocks, ' '%d composite layers each.' % (model_type, total_blocks, layers_per_block)) if bc_mode: print('Build %s model with %d blocks, ' '%d bottleneck layers and %d composite layers each.' % ( model_type, total_blocks, layers_per_block, layers_per_block)) print('Reduction at transition layers: %.2f' % reduction) return self def set_net_from_config(self, net_config_json, init=None, print_info=True): # load config and init (if exist) for key in self.net_config.keys(): self.net_config[key] = net_config_json[key] self.blocks = [] for _i, block_config in enumerate(net_config_json['blocks']): block_init = init['blocks'][_i] if init is not None else None block = get_block_by_name(block_config['name']) self.blocks.append(block.set_from_config(block_config, block_init)) if print_info: print('Set DenseNet from config:') for k, v in self.net_config.items(): print('\t%s: %s' % (k, v)) print('\t%s: %d' % ('depth', self.depth)) return self def widen(self, loc, new_width, widen_type='output_dim', noise=None, image_channel=3): """ widen_type: "output_dim" or "kernel" """ block_idx = loc['block'] if block_idx == 0: input_dim = image_channel elif isinstance(self.blocks[block_idx - 1], TransitionBlock): input_dim = self.blocks[block_idx - 1].out_features_dim else: input_dim = self.blocks[block_idx - 1].out_features_dim(self.blocks[block_idx - 2].out_features_dim) change_out_dim, indices, magnifier = \ self.blocks[block_idx].widen(loc, new_width, widen_type, noise=noise, input_dim=input_dim) while change_out_dim: change_out_dim, indices, magnifier = self.blocks[block_idx + 1].prev_widen(indices, magnifier, noise=noise) block_idx += 1 def deepen(self, loc, new_layer_config, image_channel=3): new_layer_config['pre_activation'] = self.net_config['pre_activation'] block_idx = loc['block'] if block_idx == 0: input_dim = image_channel elif isinstance(self.blocks[block_idx - 1], TransitionBlock): input_dim = self.blocks[block_idx - 1].out_features_dim else: input_dim = self.blocks[block_idx - 1].out_features_dim(self.blocks[block_idx - 2].out_features_dim) return self.blocks[block_idx].deepen(loc, new_layer_config, input_dim) def set_identity4deepen(self, to_set_layers, data_provider, batch_size, batch_num=1, strict=True, noise=None): """ to_set_layers = [(new_layer, prev_layer), ...] """ task_list = {} for new_layer, prev_layer in to_set_layers: if new_layer.ready: continue if new_layer.use_bn and strict: task_id = id(prev_layer) if task_id in task_list: task_list[task_id][1].append(new_layer) else: task_list[task_id] = (prev_layer, [new_layer]) else: new_layer.set_identity_layer(strict=strict, noise=noise) if len(task_list) > 0: model = DenseNet(None, data_provider, None, net_config=self, only_forward=True) task_list = list(task_list.values()) fetches = [prev_layer.output_op for prev_layer, _ in task_list] statistics = [[0, 0] for _ in task_list] for _i in range(batch_num): input_images, _ = data_provider.train.next_batch(batch_size) outputs = model.sess.run(fetches, feed_dict={model.images: input_images, model.is_training: False}) for _j, out in enumerate(outputs): out = out.astype('float32') axis = tuple(range(len(out.shape) - 1)) mean = np.mean(out, axis=axis, keepdims=True) variance = np.mean(np.square(out - mean), axis=axis, keepdims=True) mean, variance = np.squeeze(mean), np.squeeze(variance) statistics[_j][0] += mean statistics[_j][1] += variance for _j, (prev_layer, new_layers) in enumerate(task_list): mean, variance = statistics[_j][0] / batch_num, statistics[_j][1] / batch_num for new_layer in new_layers: if new_layer.ready: continue param = { 'moving_mean': mean, 'moving_variance': variance, 'epsilon': self.bn_epsilon, } new_layer.set_identity_layer(strict=strict, param=param, noise=noise) def insert_miniblock(self, loc, miniblock_config, image_channel=3, noise=None): block_idx = loc['block'] if block_idx == 0: input_dim = image_channel elif isinstance(self.blocks[block_idx - 1], TransitionBlock): input_dim = self.blocks[block_idx - 1].out_features_dim else: input_dim = self.blocks[block_idx - 1].out_features_dim(self.blocks[block_idx - 2].out_features_dim) assert isinstance(self.blocks[block_idx], DenseBlock), 'Invalid' indices, magnifier = \ self.blocks[block_idx].insert_miniblock(loc['miniblock'], miniblock_config, input_dim, noise=noise) self.blocks[block_idx + 1].prev_widen(indices, magnifier, noise=noise) class DenseNet(BasicModel): def _build_graph(self, only_forward=False): _input = self.images output = _input # building blocks (transition and dense) for block in self.net_config.blocks: output = block.build(output, self, store_output_op=only_forward) if not only_forward: logits = output with tf.variable_scope('L2_Loss'): l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()]) prediction = tf.nn.softmax(logits) # losses cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=self.labels)) self.cross_entropy = cross_entropy # optimizer and train step optimizer = self.build_optimizer(self.learning_rate, self.run_config.opt_config[0], self.run_config.opt_config[1]) self.train_step = optimizer.minimize( cross_entropy + l2_loss * self.net_config.weight_decay) correct_prediction = tf.equal( tf.argmax(prediction, 1), tf.argmax(self.labels, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) ================================================ FILE: Examples/cai_eas/eas/models/layer_cascade.py ================================================ from models.layers import ConvLayer, FCLayer, PoolLayer, get_layer_by_name import tensorflow as tf class LayerCascade: def __init__(self, _id, layers): self._id = _id self.layers = layers self.output_op = None @property def id(self): return self._id @id.setter def id(self, value): self._id = value @property def out_features_dim(self): for layer in self.layers[::-1]: if isinstance(layer, ConvLayer): return layer.filter_num elif isinstance(layer, FCLayer): return layer.units return None @property def depth(self): depth = 0 for layer in self.layers: if isinstance(layer, ConvLayer) or isinstance(layer, FCLayer): depth += 1 return depth def get_str(self): layers_str = [layer.layer_str for layer in self.layers] return '-'.join(layers_str) def build(self, _input, densenet, store_output_op=False): output = _input with tf.variable_scope(self._id): for layer in self.layers: output = layer.build(output, densenet, store_output_op=store_output_op) if store_output_op: self.output_op = output return output def get_config(self): return { '_id': self._id, 'layers': [layer.get_config() for layer in self.layers] } def renew_init(self, densenet): return { '_id': self._id, 'layers': [layer.renew_init(densenet) for layer in self.layers] } def copy(self): return self.set_from_config(self.get_config(), init=self.renew_init(None)) @staticmethod def set_from_config(config_json, init=None, return_class=True): _id = config_json['_id'] layers = [] for _i, layer_config in enumerate(config_json['layers']): layer_init = init['layers'][_i] if init is not None else None layer = get_layer_by_name(layer_config['name']) layers.append(layer.set_from_config(layer_config, layer_init)) if return_class: return LayerCascade(_id, layers) else: return _id, layers """ Network Transformation Operations """ def prev_widen(self, indices, magnifier, noise=None): for layer in self.layers: if isinstance(layer, ConvLayer) or isinstance(layer, FCLayer): layer.prev_widen(indices, magnifier, noise=noise) break else: layer.prev_widen(indices, magnifier, noise=noise) def widen(self, idx, new_width, widen_type='output_dim', noise=None): assert idx < len(self.layers), 'Index out of range: %d' % idx if widen_type == 'output_dim': assert isinstance(self.layers[idx], ConvLayer) or \ isinstance(self.layers[idx], FCLayer), 'Operation not available' to_widen_layer = self.layers[idx] if isinstance(to_widen_layer, ConvLayer): indices, magnifier = to_widen_layer.widen_filters(new_filter_num=new_width, noise=noise) else: indices, magnifier = to_widen_layer.widen_units(new_units_num=new_width, noise=noise) after_widen_layer = None for _i in range(idx + 1, len(self.layers)): if isinstance(self.layers[_i], ConvLayer) or isinstance(self.layers[_i], FCLayer): self.layers[_i].prev_widen(indices, magnifier, noise=noise) after_widen_layer = self.layers[_i] break else: self.layers[_i].prev_widen(indices, magnifier, noise=noise) return after_widen_layer is None, indices, magnifier else: raise ValueError('%s is not supported' % widen_type) def deepen(self, idx, new_layer_config, input_dim): assert idx < len(self.layers), 'Index out of range: %d' % idx if new_layer_config['name'] == 'fc': assert idx == len(self.layers) - 1 or isinstance(self.layers[idx + 1], FCLayer), 'Invalid' assert isinstance(self.layers[idx], FCLayer) or isinstance(self.layers[idx], PoolLayer), 'Invalid' # prepare the new fc layer units = input_dim for _i in range(idx, -1, -1): if isinstance(self.layers[_i], FCLayer): units = self.layers[_i].units break elif isinstance(self.layers[_i], ConvLayer): units = self.layers[_i].filter_num break fc_idx = 0 for _i in range(0, idx + 1): if isinstance(self.layers[_i], FCLayer): fc_idx += 1 _id = 'fc_%d' % fc_idx # change the id of following fc layers for _i in range(idx + 1, len(self.layers)): if isinstance(self.layers[_i], FCLayer): self.layers[_i].id = 'fc_%d' % (fc_idx + 1) fc_idx += 1 prev_layer = None for _i in range(idx, -1, -1): if self.layers[_i].ready: prev_layer = self.layers[_i] break assert prev_layer is not None, 'Invalid' new_fc_layer = FCLayer(_id, units, ready=False, **new_layer_config) # insert the new layer into the cascade self.layers = self.layers[:idx + 1] + [new_fc_layer] + self.layers[idx + 1:] return new_fc_layer, prev_layer elif new_layer_config['name'] == 'conv': assert idx == len(self.layers) - 1 or not isinstance(self.layers[idx + 1], FCLayer), 'Invalid' assert isinstance(self.layers[idx], ConvLayer) or isinstance(self.layers[idx], FCLayer), 'Invalid' # prepare the new conv layer filter_num = input_dim for _i in range(idx, -1, -1): if isinstance(self.layers[_i], ConvLayer): filter_num = self.layers[_i].filter_num break conv_idx = 0 for _i in range(0, idx + 1): if isinstance(self.layers[_i], ConvLayer): conv_idx += 1 _id = 'conv_%d' % conv_idx # change the id of following conv layers for _i in range(idx + 1, len(self.layers)): if isinstance(self.layers[_i], ConvLayer): self.layers[_i].id = 'conv_%d' % (conv_idx + 1) conv_idx += 1 prev_layer = None for _i in range(idx, -1, -1): if self.layers[_i].ready: prev_layer = self.layers[_i] break assert prev_layer is not None, 'Invalid' new_conv_layer = ConvLayer(_id, filter_num, ready=False, **new_layer_config) self.layers = self.layers[:idx + 1] + [new_conv_layer] + self.layers[idx + 1:] return new_conv_layer, prev_layer else: raise ValueError('Not support to insert a %s layer' % new_layer_config['name']) ================================================ FILE: Examples/cai_eas/eas/models/layer_multi_branch.py ================================================ import tensorflow as tf import numpy as np from models.layer_cascade import LayerCascade class LayerMultiBranch: def __init__(self, _id, branches, merge=None, in_bottle=None, out_bottle=None): self._id = _id self.in_bottle = in_bottle self.branches = branches self.out_bottle = out_bottle self.merge = merge if self.merge == 'add': out_dim = [] for branch in self.branches: out_dim.append(branch.out_features_dim) assert np.std(out_dim) == 0, '<%s> require the output dim of all branches are the same' % self.merge elif self.merge is None: assert len(self.branches) == 1, 'Invalid' self.output_op = None @property def id(self): return self._id @id.setter def id(self, value): self._id = value @property def out_features_dim(self): if self.out_bottle: return self.out_bottle.out_features_dim out_dim = [] for branch in self.branches: out_dim.append(branch.out_features_dim) if self.merge == 'concat': return np.sum(out_dim) elif self.merge == 'add' or self.merge is None: return out_dim[0] else: pass @property def depth(self): depth = 0 if self.in_bottle: depth += self.in_bottle.depth if self.out_bottle: depth += self.out_bottle.depth branch_depth = [] for branch in self.branches: branch_depth.append(branch.depth) depth += np.max(branch_depth) return depth def get_str(self): in_bottle_str = 'N' if self.in_bottle is None else self.in_bottle.get_str() branches_str = [branch.get_str() for branch in self.branches] branches_str = '+'.join(branches_str) out_bottle_str = 'N' if self.out_bottle is None else self.out_bottle.get_str() return '%s~%s~%s' % (in_bottle_str, branches_str, out_bottle_str) def build(self, _input, densenet, store_output_op=False): with tf.variable_scope(self._id): output = _input # in bottle if self.in_bottle: output = self.in_bottle.build(output, densenet, store_output_op=store_output_op) # branches branch_out = [] for branch in self.branches: branch_out.append(branch.build(output, densenet, store_output_op=store_output_op)) if self.merge == 'concat': output = tf.concat(branch_out, axis=3) elif self.merge == 'add': output = tf.add_n(branch_out) elif self.merge is None: output = branch_out[0] else: raise ValueError('Do not support <%s>' % self.merge) # out bottle if self.out_bottle: output = self.out_bottle.build(output, densenet, store_output_op=store_output_op) if store_output_op: self.output_op = output return output def get_config(self): return { '_id': self._id, 'merge': self.merge, 'branches': [branch.get_config() for branch in self.branches], 'in_bottle': None if self.in_bottle is None else self.in_bottle.get_config(), 'out_bottle': None if self.out_bottle is None else self.out_bottle.get_config(), } def renew_init(self, densenet): return { '_id': self._id, 'branches': [branch.renew_init(densenet) for branch in self.branches], 'in_bottle': None if self.in_bottle is None else self.in_bottle.renew_init(densenet), 'out_bottle': None if self.out_bottle is None else self.out_bottle.renew_init(densenet), } @staticmethod def set_from_config(config_json, init=None): _id = config_json['_id'] merge = config_json['merge'] branches = [] for _i, branch_config in enumerate(config_json['branches']): branch_init = init['branches'][_i] if init is not None else None branch = LayerCascade.set_from_config(branch_config, branch_init) branches.append(branch) in_bottle = config_json['in_bottle'] if in_bottle: in_bottle_init = init['in_bottle'] if init is not None else None in_bottle = LayerCascade.set_from_config(in_bottle, in_bottle_init) out_bottle = config_json['out_bottle'] if out_bottle: out_bottle_init = init['out_bottle'] if init is not None else None out_bottle = LayerCascade.set_from_config(out_bottle, out_bottle_init) return LayerMultiBranch(_id, branches, merge, in_bottle=in_bottle, out_bottle=out_bottle) """ Network Transformation Operations """ def prev_widen(self, indices, magnifier, noise=None): if self.in_bottle: self.in_bottle.prev_widen(indices, magnifier, noise=noise) else: for branch in self.branches: branch.prev_widen(indices, magnifier, noise=noise) def widen(self, loc, new_width, widen_type='output_dim', noise=None): if loc['multi-branch'] == 'in_bottle': assert self.in_bottle is not None, 'Invalid' change_out_dim, indices, magnifier = self.in_bottle.widen(loc['layer'], new_width, widen_type, noise=noise) if change_out_dim: for branch in self.branches: branch.prev_widen(indices, magnifier, noise=noise) return False, None, None elif loc['multi-branch'] == 'out_bottle': assert self.out_bottle is not None, 'Invalid' change_out_dim, indices, magnifier = self.out_bottle.widen(loc['layer'], new_width, widen_type, noise=noise) return change_out_dim, indices, magnifier elif loc['multi-branch'] == 'branch': branch_idx = loc['branch'] branch = self.branches[branch_idx] old_branch_out_dim = branch.out_features_dim change_out_dim, indices, magnifier = branch.widen(loc['layer'], new_width, widen_type, noise=noise) if change_out_dim: assert self.merge != 'add', 'Invalid' prev_branch_out_dim = 0 for _i in range(0, branch_idx): prev_branch_out_dim += self.branches[_i].out_features_dim post_branch_out_dim = 0 for _i in range(branch_idx + 1, len(self.branches)): post_branch_out_dim += self.branches[_i].out_features_dim old_size = prev_branch_out_dim + old_branch_out_dim + post_branch_out_dim base = np.arange(old_size) indices = np.concatenate([ base[:prev_branch_out_dim], indices + prev_branch_out_dim, base[prev_branch_out_dim + old_branch_out_dim:] ]) magnifier = np.concatenate([ [1] * prev_branch_out_dim, magnifier, [1] * post_branch_out_dim, ]) if self.out_bottle is None: return True, indices, magnifier else: self.out_bottle.prev_widen(indices, magnifier, noise=noise) return False, None, None else: return False, None, None else: raise ValueError('Do not support %s' % loc['multi-branch']) def deepen(self, loc, new_layer_config, input_dim): if loc['multi-branch'] == 'in_bottle': assert self.in_bottle is not None, 'Invalid' return self.in_bottle.deepen(loc['layer'], new_layer_config, input_dim) elif loc['multi-branch'] == 'out_bottle': assert self.out_bottle is not None, 'Invalid' if self.merge == 'concat': input_dim = np.sum([branch.out_features_dim for branch in self.branches]) else: input_dim = self.branches[0].out_features_dim return self.out_bottle.deepen(loc['layer'], new_layer_config, input_dim) elif loc['multi-branch'] == 'branch': if self.in_bottle is not None: input_dim = self.in_bottle.out_features_dim return self.branches[loc['branch']].deepen(loc['layer'], new_layer_config, input_dim) else: raise ValueError('Do not support %s' % loc['multi-branch']) def remapped_branches(self, noise=None): if self.merge == 'add' or self.merge is None: size = self.out_features_dim indices = np.random.choice(np.arange(size), size) new_branches = [] for branch in self.branches: new_layers = [layer.copy() for layer in branch.layers[:-1]] last_layer = branch.layers[-1].copy().remap(indices, noise=noise) new_layers.append(last_layer) new_branch = LayerCascade(branch.id, new_layers) new_branches.append(new_branch) elif self.merge == 'concat': new_branches = [] offset = 0 indices = [] for branch in self.branches: size = branch.out_features_dim sub_indices = np.random.choice(np.arange(size), size) new_layers = [layer.copy() for layer in branch.layers[:-1]] last_layer = branch.layers[-1].copy().remap(sub_indices, noise=noise) new_layers.append(last_layer) new_branch = LayerCascade(branch.id, new_layers) new_branches.append(new_branch) indices.append(sub_indices + offset) offset += size indices = np.concatenate(indices) else: raise NotImplementedError return new_branches, indices ================================================ FILE: Examples/cai_eas/eas/models/layers.py ================================================ from models.basic_model import BasicModel import tensorflow as tf import numpy as np import copy def apply_noise(weights, noise_config): if noise_config is None: return weights noise_type = noise_config.get('type', 'normal') if noise_type == 'normal': ratio = noise_config.get('ratio', 1e-3) std = np.std(weights) noise = np.random.normal(0, std * ratio, size=weights.shape) elif noise_type == 'uniform': ratio = noise_config.get('ratio', 1e-3) mean, _max = np.mean(weights), np.max(weights) width = (_max - mean) * ratio noise = np.random.uniform(-width, width, size=weights.shape) else: raise NotImplementedError return weights + noise def get_layer_by_name(name): if name == 'conv': return ConvLayer elif name == 'fc': return FCLayer elif name == 'pool': return PoolLayer else: raise ValueError('Unknown layer type: %s' % name) def get_magnifier(old_size, indices): _l = np.zeros(old_size) for x in indices: _l[x] += 1 magnifier = (1.0 / _l)[indices] return magnifier def get_random_remapping(old_size, new_size): base = np.arange(old_size) indices = np.concatenate([base, np.random.choice(base, new_size - old_size)]) magnifier = get_magnifier(old_size, indices) return indices, magnifier class BaseLayer: """ _id, batch normalization, activation, dropout, ready """ def __init__(self, _id, use_bn=True, activation='relu', keep_prob=1.0, ready=True, pre_activation=True): self._id = _id self.use_bn = use_bn self.activation = activation self.keep_prob = keep_prob self.ready = ready self.pre_activation = pre_activation self._scope = None self._init = None self.output_op = None @property def id(self): return self._id @id.setter def id(self, value): self._id = value @property def init(self): return self._init @property def param_initializer(self): if self._init is None: return None param_initializer = {} for key in self.variable_list.keys(): if self._init[key] is not None: param_initializer[key] = tf.constant_initializer(self._init[key]) if len(param_initializer) == 0: param_initializer = None return param_initializer def renew_init(self, net: BasicModel): if net is None: return copy.deepcopy(self._init) self._init = {} for key, var_name in self.variable_list.items(): var = net.graph.get_tensor_by_name('%s/%s' % (self._scope, var_name)) self._init[key] = net.sess.run(var) if len(self._init) == 0: self._init = None return copy.deepcopy(self._init) def copy(self): return self.set_from_config(self.get_config(), layer_init=copy.deepcopy(self._init)) def get_config(self): return { '_id': self.id, 'use_bn': self.use_bn, 'activation': self.activation, 'keep_prob': self.keep_prob, 'pre_activation': self.pre_activation, } @property def variable_list(self): """ beta: mean scale gamma: variance scale y = gamma * (x - moving_mean) / sqrt(epsilon + moving_variance) + beta """ if self.use_bn: return { 'moving_mean': 'BatchNorm/moving_mean:0', 'moving_variance': 'BatchNorm/moving_variance:0', 'beta': 'BatchNorm/beta:0', 'gamma': 'BatchNorm/gamma:0', } else: return {} @staticmethod def set_from_config(layer_config, layer_init): raise NotImplementedError def build(self, _input, net, store_output_op): raise NotImplementedError def prev_widen(self, indices, magnifier, noise=None): raise NotImplementedError def set_identity_layer(self, strict, param, noise): raise NotImplementedError def widen_bn(self, indices, magnifier, noise=None): if self.use_bn: self._init['beta'] = self._init['beta'][indices] self._init['gamma'] = self._init['gamma'][indices] self._init['moving_mean'] = self._init['moving_mean'][indices] self._init['moving_variance'] = self._init['moving_variance'][indices] def set_bn_identity(self, strict=True, param=None, noise=None): if self.use_bn: if strict: self._init['moving_mean'] = param['moving_mean'] self._init['moving_variance'] = param['moving_variance'] self._init['beta'] = self._init['moving_mean'] self._init['gamma'] = np.sqrt(self._init['moving_variance'] + param['epsilon']) else: # use default initialization for batch normalization layer self._init['moving_mean'], self._init['moving_variance'] = None, None self._init['beta'], self._init['gamma'] = None, None class ConvLayer(BaseLayer): def __init__(self, _id, filter_num, kernel_size=3, strides=1, use_bn=True, activation='relu', keep_prob=1.0, ready=True, pre_activation=True, **kwargs): BaseLayer.__init__(self, _id, use_bn, activation, keep_prob, ready, pre_activation) self.filter_num = filter_num self.kernel_size = kernel_size self.strides = strides @property def layer_str(self): return 'C%d,%d,%d' % (self.filter_num, self.kernel_size, self.strides) @property def variable_list(self): var_list = {'kernel': 'kernel:0'} var_list.update(super(ConvLayer, self).variable_list) return var_list def get_config(self): return { 'name': 'conv', 'filter_num': self.filter_num, 'kernel_size': self.kernel_size, 'strides': self.strides, **super(ConvLayer, self).get_config(), } @staticmethod def set_from_config(layer_config, layer_init=None): conv_layer = ConvLayer(**layer_config) conv_layer._init = layer_init return conv_layer def build(self, _input, net: BasicModel, store_output_op=False): output = _input if not self.ready: return output with tf.variable_scope(self._id): self._scope = tf.get_variable_scope().name param_initializer = self.param_initializer if self.pre_activation: # batch normalization if self.use_bn: output = BasicModel.batch_norm(output, net.is_training, net.net_config.bn_epsilon, net.net_config.bn_decay, param_initializer=param_initializer) # activation output = BasicModel.activation(output, self.activation) # convolutional output = BasicModel.conv2d(output, self.filter_num, self.kernel_size, self.strides, param_initializer=param_initializer) else: # convolutional output = BasicModel.conv2d(output, self.filter_num, self.kernel_size, self.strides, param_initializer=param_initializer) # batch normalization if self.use_bn: output = BasicModel.batch_norm(output, net.is_training, net.net_config.bn_epsilon, net.net_config.bn_decay, param_initializer=param_initializer) # activation output = BasicModel.activation(output, self.activation) # dropout output = BasicModel.dropout(output, self.keep_prob, net.is_training) if store_output_op: self.output_op = output return output def widen_filters(self, new_filter_num, noise=None): """ Increase the filter number of a conv layer while preserving the functionality Proposed in 'Net2Net': https://arxiv.org/abs/1511.05641 """ assert new_filter_num > self.filter_num, 'Invalid new filter number: %d' % new_filter_num assert self._init is not None, 'Uninitialized layer' old_size, new_size = self.filter_num, new_filter_num indices, magnifier = get_random_remapping(old_size, new_size) # more filters self.filter_num = new_filter_num new_kernel = self._init['kernel'][:, :, :, indices] new_kernel[:, :, :, old_size:] = apply_noise(new_kernel[:, :, :, old_size:], noise.get('wider')) self._init['kernel'] = new_kernel if not self.pre_activation: # widen batch norm variables if use batch norm self.widen_bn(indices, magnifier, noise=noise) return indices, magnifier def prev_widen(self, indices, magnifier, noise=None): assert self._init is not None, 'Uninitialized layer' # rescale kernel self._init['kernel'] = self._init['kernel'][:, :, indices, :] * magnifier.reshape([1, 1, -1, 1]) if self.pre_activation: self.widen_bn(indices, magnifier, noise=noise) def set_identity_layer(self, strict=True, param=None, noise=None): self._init = {} self.set_bn_identity(strict, param, noise=noise) mid = self.kernel_size // 2 self._init['kernel'] = np.zeros([self.kernel_size, self.kernel_size, self.filter_num, self.filter_num]) self._init['kernel'][mid, mid] = np.eye(self.filter_num) self._init['kernel'] = apply_noise(self._init['kernel'], noise.get('deeper')) self.ready = True def remap(self, indices, noise=None): self.filter_num = len(indices) self._init['kernel'] = self._init['kernel'][:, :, :, indices] self._init['kernel'] = apply_noise(self._init['kernel'], noise.get('wider')) if not self.pre_activation: self.widen_bn(indices, None, noise=noise) return self class FCLayer(BaseLayer): def __init__(self, _id, units, use_bn=True, use_bias=False, activation='relu', keep_prob=1.0, ready=True, pre_activation=False, **kwargs): BaseLayer.__init__(self, _id, use_bn, activation, keep_prob, ready, pre_activation) self.units = units self.use_bias = use_bias @property def layer_str(self): return 'FC%d' % self.units @property def variable_list(self): var_list = {'W': 'W:0'} if self.use_bias: var_list['bias'] = 'bias:0' var_list.update(super(FCLayer, self).variable_list) return var_list def get_config(self): return { 'name': 'fc', 'units': self.units, 'use_bias': self.use_bias, **super(FCLayer, self).get_config(), } @staticmethod def set_from_config(layer_config, layer_init=None): fc_layer = FCLayer(**layer_config) fc_layer._init = layer_init return fc_layer def build(self, _input, net: BasicModel, store_output_op=False): output = _input if not self.ready: return output with tf.variable_scope(self._id): self._scope = tf.get_variable_scope().name param_initializer = self.param_initializer # flatten if not output = BasicModel.flatten(output) if self.pre_activation: # batch normalization if self.use_bn: output = BasicModel.batch_norm(output, net.is_training, net.net_config.bn_epsilon, net.net_config.bn_decay, param_initializer=param_initializer) # activation output = BasicModel.activation(output, self.activation) # FC output = BasicModel.fc_layer(output, self.units, self.use_bias, param_initializer=param_initializer) else: # FC output = BasicModel.fc_layer(output, self.units, self.use_bias, param_initializer=param_initializer) # batch normalization if self.use_bn: output = BasicModel.batch_norm(output, net.is_training, net.net_config.bn_epsilon, net.net_config.bn_decay, param_initializer=param_initializer) # activation output = BasicModel.activation(output, self.activation) # dropout output = BasicModel.dropout(output, self.keep_prob, net.is_training) if store_output_op: self.output_op = output return output def widen_units(self, new_units_num, noise=None): """ Increase the units number of a fc layer while preserving the functionality Proposed in 'Net2Net': https://arxiv.org/abs/1511.05641 W: [in_dim, out_units] bias: [out_units] """ assert new_units_num > self.units, 'Invalid new units number: %d' % new_units_num assert self._init is not None, 'Uninitialized layer' old_size, new_size = self.units, new_units_num indices, magnifier = get_random_remapping(old_size, new_size) # more units self._init['W'] = self._init['W'][:, indices] self._init['W'][:, old_size:] = apply_noise(self._init['W'][:, old_size:], noise.get('wider')) self.units = new_units_num # widen bias variable if exist if self.use_bias: self._init['bias'] = self._init['bias'][indices] self._init['bias'][old_size:] = apply_noise(self._init['bias'][old_size:], noise.get('wider')) if not self.pre_activation: # widen batch norm variables if use batch norm self.widen_bn(indices, magnifier, noise=noise) return indices, magnifier def prev_widen(self, indices, magnifier, noise=None): assert self._init is not None, 'Uninitialized layer' # rescale W self._init['W'] = self._init['W'][indices] * magnifier.reshape([-1, 1]) if self.pre_activation: self.widen_bn(indices, magnifier, noise=noise) def set_identity_layer(self, strict=True, param=None, noise=None): self._init = {} self.set_bn_identity(strict, param, noise=noise) if self.use_bias: self._init['bias'] = [0.0] * self.units self._init['W'] = np.eye(self.units) self._init['W'] = apply_noise(self._init['W'], noise.get('deeper')) self.ready = True def remap(self, indices, noise=None): self.units = len(indices) self._init['W'] = self._init['W'][:, indices] self._init['W'] = apply_noise(self._init['W'], noise.get('wider')) if self.use_bias: self._init['bias'] = self._init['bias'][indices] if not self.pre_activation: self.widen_bn(indices, None, noise=noise) return self class PoolLayer(BaseLayer): def __init__(self, _id, _type, kernel_size=2, strides=2, use_bn=False, activation=None, keep_prob=1.0, ready=True, pre_activation=True, **kwargs): BaseLayer.__init__(self, _id, use_bn, activation, keep_prob, ready, pre_activation) self._type = _type self.kernel_size = kernel_size self.strides = strides @property def layer_str(self): return 'P%d,%d' % (self.kernel_size, self.strides) def get_config(self): return { 'name': 'pool', '_type': self._type, 'kernel_size': self.kernel_size, 'strides': self.strides, **super(PoolLayer, self).get_config(), } @staticmethod def set_from_config(layer_config, layer_init=None): pool_layer = PoolLayer(**layer_config) pool_layer._init = layer_init return pool_layer def build(self, _input, net: BasicModel, store_output_op=False): output = _input if not self.ready: return output with tf.variable_scope(self._id): self._scope = tf.get_variable_scope().name param_initializer = self.param_initializer if self.pre_activation: # batch normalization if self.use_bn: output = BasicModel.batch_norm(output, net.is_training, net.net_config.bn_epsilon, net.net_config.bn_decay, param_initializer=param_initializer) # activation output = BasicModel.activation(output, self.activation) # Pooling if self._type == 'avg': output = BasicModel.avg_pool(output, k=self.kernel_size, s=self.strides) elif self._type == 'max': output = BasicModel.max_pool(output, k=self.kernel_size, s=self.strides) else: raise ValueError('Do not support the pooling type: %s' % self._type) else: # Pooling if self._type == 'avg': output = BasicModel.avg_pool(output, k=self.kernel_size, s=self.strides) elif self._type == 'max': output = BasicModel.max_pool(output, k=self.kernel_size, s=self.strides) else: raise ValueError('Do not support the pooling type: %s' % self._type) # batch normalization if self.use_bn: output = BasicModel.batch_norm(output, net.is_training, net.net_config.bn_epsilon, net.net_config.bn_decay, param_initializer=param_initializer) # activation output = BasicModel.activation(output, self.activation) # dropout output = BasicModel.dropout(output, self.keep_prob, net.is_training) if store_output_op: self.output_op = output return output def set_identity_layer(self, strict=True, param=None, noise=None): raise ValueError('Pooling layer can never be an identity layer') def prev_widen(self, indices, magnifier, noise=None): self.widen_bn(indices, magnifier, noise=noise) ================================================ FILE: Examples/cai_eas/eas/models/utils.py ================================================ from models.dense_net import DenseNetConfig, DenseNet from models.convnet import SimpleConvnetConfig, SimpleConvnet import numpy as np def get_model_config_by_name(name): if name == 'DenseNet': return DenseNetConfig elif name == 'SimpleConvnet': return SimpleConvnetConfig else: raise ValueError('Unknown model type %s' % name) def get_model_by_name(name): if name == 'DenseNet': return DenseNet elif name == 'SimpleConvnet': return SimpleConvnet else: raise ValueError('Unknown model type %s' % name) class RunConfig: def __init__(self, batch_size, n_epochs, init_lr, reduce_lr_epochs, reduce_lr_factors, opt_config, dataset, validation_size, validation_frequency, shuffle, normalization, should_save_logs, should_save_model, renew_logs=False, other_lr_schedule=None, include_extra=True, **kwargs): self.batch_size = batch_size self.n_epochs = n_epochs self.init_lr = init_lr self.reduce_lr_epochs = reduce_lr_epochs self.reduce_lr_factors = reduce_lr_factors self.opt_config = opt_config self.dataset = dataset self.validation_size = validation_size self.validation_frequency = validation_frequency self.shuffle = shuffle self.normalization = normalization self.should_save_logs = should_save_logs self.should_save_model = should_save_model self.renew_logs = renew_logs self.other_lr_schedule = other_lr_schedule self.include_extra = include_extra def get_config(self): return self.__dict__ def update(self, new_config): self.__dict__.update(new_config) def copy(self): return RunConfig(**self.get_config()) def learning_rate(self, epoch): if self.other_lr_schedule is None or self.other_lr_schedule.get('type') is None: lr = self.init_lr for reduce_lr_epoch, reduce_factor in zip(self.reduce_lr_epochs, self.reduce_lr_factors): if epoch >= reduce_lr_epoch * self.n_epochs: lr /= reduce_factor else: if self.other_lr_schedule['type'] == 'cosine': lr_max = self.init_lr lr_min = self.other_lr_schedule.get('lr_min', 0) lr = lr_min + 0.5 * (lr_max - lr_min) * (1 + np.cos((epoch - 1) / self.n_epochs * np.pi)) else: raise ValueError('Do not support %s' % self.other_lr_schedule['type']) return lr @staticmethod def get_default_run_config(dataset='C10+'): if dataset in ['C10', 'C10+', 'C100', 'C100+']: run_config = { 'batch_size': 64, 'n_epochs': 30, 'init_lr': 0.1, 'reduce_lr_epochs': [0.5, 0.75], # epochs * 0.5, epochs * 0.75 'reduce_lr_factors': [10, 10], 'opt_config': ['momentum', {'momentum': 0.9, 'use_nesterov': True}], 'dataset': dataset, # choices = [C10, C10+, C100, C100+] 'validation_size': None, # None or int 'validation_frequency': 10, 'shuffle': 'every_epoch', # None, once_prior_train, every_epoch 'normalization': 'by_channels', # None, divide_256, divide_255, by_channels 'should_save_logs': True, 'should_save_model': True, 'renew_logs': True, 'other_lr_schedule': {'type': 'cosine'}, # None, or cosine } elif dataset in ['SVHN']: run_config = { 'batch_size': 64, 'n_epochs': 40, 'init_lr': 0.1, 'reduce_lr_epochs': [0.5, 0.75], # epochs * 0.5, epochs * 0.75 'reduce_lr_factors': [10, 10], 'opt_config': ['momentum', {'momentum': 0.9, 'use_nesterov': True}], 'dataset': dataset, # choices = [C10, C10+, C100, C100+] 'validation_size': None, # None or int 'validation_frequency': 1, 'shuffle': True, 'normalization': 'divide_255', # None, divide_256, divide_255, by_channels 'should_save_logs': True, 'should_save_model': True, 'renew_logs': True, 'other_lr_schedule': {'type': 'cosine'}, # None, or cosine 'include_extra': False, } else: raise ValueError return run_config ================================================ FILE: Examples/cai_eas/start_nets/start_net_convnet_small_C10+/net.config ================================================ { "name": "SimpleConvnet", "weight_decay": 0.0001, "bn_epsilon": 1e-05, "bn_decay": 0.9, "drop_scheme": { "type": "conv", "conv_drop": 1.0, "pool_drop": 0.7, "fc_drop": 0.5 }, "layer_cascade": { "_id": "SimpleConvNet", "layers": [ { "name": "conv", "filter_num": 4, "kernel_size": 3, "strides": 1, "_id": "conv_0", "use_bn": true, "activation": "relu", "keep_prob": 1.0, "pre_activation": false }, { "name": "pool", "_type": "max", "kernel_size": 2, "strides": 2, "_id": "pool_0", "use_bn": false, "activation": null, "keep_prob": 1.0, "pre_activation": false }, { "name": "conv", "filter_num": 4, "kernel_size": 3, "strides": 1, "_id": "conv_1", "use_bn": true, "activation": "relu", "keep_prob": 1.0, "pre_activation": false }, { "name": "pool", "_type": "max", "kernel_size": 2, "strides": 2, "_id": "pool_1", "use_bn": false, "activation": null, "keep_prob": 1.0, "pre_activation": false }, { "name": "conv", "filter_num": 4, "kernel_size": 3, "strides": 1, "_id": "conv_2", "use_bn": true, "activation": "relu", "keep_prob": 1.0, "pre_activation": false }, { "name": "pool", "_type": "max", "kernel_size": 2, "strides": 2, "_id": "pool_2", "use_bn": false, "activation": null, "keep_prob": 1.0, "pre_activation": false }, { "name": "conv", "filter_num": 4, "kernel_size": 3, "strides": 1, "_id": "conv_3", "use_bn": true, "activation": "relu", "keep_prob": 1.0, "pre_activation": false }, { "name": "pool", "_type": "avg", "kernel_size": 4, "strides": 4, "_id": "pool_4", "use_bn": false, "activation": null, "keep_prob": 1.0, "pre_activation": false }, { "name": "fc", "units": 8, "use_bias": false, "_id": "fc_0", "use_bn": true, "activation": "relu", "keep_prob": 1.0, "pre_activation": false }, { "name": "fc", "units": 10, "use_bias": true, "_id": "fc_1", "use_bn": false, "activation": null, "keep_prob": 1.0, "pre_activation": false } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/README.md ================================================ # Compression examples (PyTorch) ## Requirements The base requirements for Auptimizer apply here. Additionally, in order for the examples to work, torch >= 1.7.0 is needed. ## Example details We provide an example configuration file and a corresponding training script for each of the compressors supported. Due to the fact that some compressors require additional modification of the training script, we provide individual training scripts for those compressors for clarity. For pruners, we also provide the configuration files for both one-time compression and automatic compression experiments. For one-time compression, the configuration file is named as ``exp_.json``. For automatic compression, the configuration file is named as ``exp_auto.json``. For quantizers, the configuration file is named as ``exp_.json``. To further demonstrate the usage of automatic compression experiments using different Proposers and hyperparameter settings, we provide multiple examples for each of the Proposer. The corresponding configuration files are named ``exp_auto_fpgm_.json``. In addition, to showcase the two use cases where the user can assign the same or different hyperparameter values for a group of layers, the configuration files ``exp_auto_fpgm_random_no_expand.json`` and ``exp_auto_fpgm_random.json`` can be used as references comparatively. Finally, ``exp_auto_fpgm_aup_args.json`` and ``mnist_aup_args.py`` demonstrate how to use the decorator ``@aup_args`` to launch a compression experiment. ## Running the examples For one-time compression: ```sh python -m aup.compression exp_level.json ``` For automatic compression: ```sh python -m aup.compression exp_auto_level.json --automatic ``` The ``--launch_dashboard`` flag can be added to the command if the user wants to show the dashboard while running the experiment. ================================================ FILE: Examples/compression/mnist_pytorch/exp_activation_apoz_rank.json ================================================ { "name": "PyTorch MNIST Activation APoZ Rank Filter Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_apoz_rank_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_activation_apoz_rank_dependency_aware.json ================================================ { "name": "PyTorch MNIST Activation APoZ Rank Filter Pruner (dependency aware)", "script": "mnist_dependency_aware.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_apoz_rank_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_activation_mean_rank.json ================================================ { "name": "PyTorch MNIST Activation Mean Rank Filter Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_mean_rank_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_activation_mean_rank_dependency_aware.json ================================================ { "name": "PyTorch MNIST Activation Mean Rank Filter Pruner (dependency aware)", "script": "mnist_dependency_aware.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_mean_rank_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_admm.json ================================================ { "name": "PyTorch MNIST ADMM Pruner", "script": "mnist_admm.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "admm", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"], "op_names": ["conv1"] }, { "sparsity": 0.5, "op_types": ["Conv2d"], "op_names": ["conv2"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_agp.json ================================================ { "name": "PyTorch MNIST AGP Pruner", "script": "mnist_agp.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "agp", "config_list": [{ "initial_sparsity": 0.0, "final_sparsity": 0.8, "start_epoch": 0, "end_epoch": 2, "frequency": 1, "op_names": ["conv1", "conv2", "fc1", "fc2"], "op_types": ["default"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_amc.json ================================================ { "name": "PyTorch MNIST AMC Pruner", "script": "mnist_amc.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "amc", "config_list": [{ "op_types": ["Conv2d", "Linear"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_activation_apoz_rank.json ================================================ { "name": "PyTorch MNIST Activation APoZ Rank Filter Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_apoz_rank_filter", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_activation_mean_rank.json ================================================ { "name": "PyTorch MNIST Activation Mean Rank Filter Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_mean_rank_filter", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_admm.json ================================================ { "name": "PyTorch MNIST ADMM Pruner (automatic)", "script": "mnist_admm.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "admm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"], "op_types": ["Conv2d"] }] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_agp.json ================================================ { "name": "PyTorch MNIST AGP Pruner (automatic)", "script": "mnist_no_speedup.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "agp", "config_list": [{ "initial_sparsity": 0.0, "final_sparsity": { "range": [0.0, 0.9], "type": "float" }, "start_epoch": 0, "end_epoch": 2, "frequency": 1, "op_names": ["conv1", "conv2", "fc1", "fc2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_aup_args.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (@aup_args) (automatic)", "script": "mnist_aup_args.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_bohb.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "bohb", "n_parallel": 4, "target": "max", "n_samples": 5, "num_samples": 4 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_hyperband.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "hyperband", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_hyperopt.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "hyperopt", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_no_expand.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1"] }, { "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_random.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_random_no_expand.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "expand_op_names": false, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_sequence.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float", "interval": 0.2 }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "sequence", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_fpgm_spearmint.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "spearmint", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_l1filter.json ================================================ { "name": "PyTorch MNIST L1 Filter Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "l1_filter", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_l2filter.json ================================================ { "name": "PyTorch MNIST L2 Filter Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "l2_filter", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_level.json ================================================ { "name": "PyTorch MNIST Level Pruner (automatic)", "script": "mnist_no_speedup.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "level", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2", "fc1", "fc2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_lottery_ticket.json ================================================ { "name": "PyTorch MNIST Lottery Ticket Pruner (automatic)", "script": "mnist_lottery_ticket.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "lottery_ticket", "config_list": [{ "prune_iterations": { "range": [1, 10], "type": "int" }, "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_types": ["Conv2d"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_auto_taylor_fo.json ================================================ { "name": "PyTorch MNIST Taylor FO Weight Filter Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "torch", "type": "pruning", "compressor": "taylor_fo_weight_filter", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_pytorch/exp_autocompress.json ================================================ { "name": "PyTorch MNIST Auto Compress Pruner", "script": "mnist_autocompress.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "auto_compress", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_fpgm.json ================================================ { "name": "PyTorch MNIST FPGM Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_fpgm_aup_args.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (@aup_args)", "script": "mnist_aup_args.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_fpgm_dependency_aware.json ================================================ { "name": "PyTorch MNIST FPGM Pruner (dependency aware)", "script": "mnist_dependency_aware.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_l1filter.json ================================================ { "name": "PyTorch MNIST L1 Filter Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "l1_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_l1filter_dependency_aware.json ================================================ { "name": "PyTorch MNIST L1 Filter Pruner (dependency aware)", "script": "mnist_dependency_aware.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "l1_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_l2filter.json ================================================ { "name": "PyTorch MNIST L2 Filter Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "l2_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_l2filter_dependency_aware.json ================================================ { "name": "PyTorch MNIST L2 Filter Pruner (dependency aware)", "script": "mnist_dependency_aware.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "l2_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_level.json ================================================ { "name": "PyTorch MNIST Level Pruner", "script": "mnist_no_speedup.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "level", "config_list": [{ "sparsity": 0.8, "op_types": ["default"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_lottery_ticket.json ================================================ { "name": "PyTorch MNIST Lottery Ticket Pruner", "script": "mnist_lottery_ticket.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "lottery_ticket", "config_list": [{ "prune_iterations": 5, "sparsity": 0.8, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_net_adapt.json ================================================ { "name": "PyTorch MNIST NetAdapt Pruner", "script": "mnist_net_adapt.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "net_adapt", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_quantization_bnn.json ================================================ { "name": "PyTorch MNIST BNN Quantizer", "script": "mnist_no_speedup.py", "resource": "cpu", "compression": { "framework": "torch", "type": "quantization", "compressor": "bnn", "config_list": [{ "quant_bits": 1, "quant_types": ["weight"], "op_types": ["Conv2d", "Linear"], "op_names": ["conv1", "conv2", "fc1", "fc2"] }] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_quantization_dorefa.json ================================================ { "name": "PyTorch MNIST DoReFa Quantizer", "script": "mnist_no_speedup.py", "resource": "cpu", "compression": { "framework": "torch", "type": "quantization", "compressor": "dorefa", "config_list": [{ "quant_types": ["weight"], "quant_bits": 8, "op_types": ["default"] }] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_quantization_naive.json ================================================ { "name": "PyTorch MNIST Naive Quantizer", "script": "mnist_no_speedup.py", "resource": "cpu", "compression": { "framework": "torch", "type": "quantization", "compressor": "naive", "config_list": [] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_quantization_qat.json ================================================ { "name": "PyTorch MNIST QAT Quantizer", "script": "mnist_no_speedup.py", "resource": "cpu", "compression": { "framework": "torch", "type": "quantization", "compressor": "qat", "config_list": [{ "quant_types": ["weight"], "quant_bits": { "weight": 8 }, "op_types": ["Conv2d", "Linear"] }, { "quant_types": ["output"], "quant_bits": 8, "quant_start_step": 7000, "op_types":["ReLU6"] }] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_sensitivity.json ================================================ { "name": "PyTorch MNIST Sensitivity Pruner", "script": "mnist_sensitivity.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "sensitivity", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_simulated_annealing.json ================================================ { "name": "PyTorch MNIST Simulated Annealing Pruner", "script": "mnist_simulated_annealing.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "simulated_annealing", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_taylor_fo.json ================================================ { "name": "PyTorch MNIST Taylor FO Weight Filter Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "taylor_fo_weight_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/exp_taylor_fo_dependency_aware.json ================================================ { "name": "PyTorch MNIST Taylor FO Weight Filter Pruner (dependency aware)", "script": "mnist_dependency_aware.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "taylor_fo_weight_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } } ================================================ FILE: Examples/compression/mnist_pytorch/mnist.py ================================================ #!/usr/bin/env python3 """ MNIST convolutional network using pytorch ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) _, test_acc = test(model, device, test_loader) compressor = aup.compression.create_compressor(model, config, optimizer=optimizer) model = compressor.compress() scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) _, test_acc = test(model, device, test_loader) scheduler.step() model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) if "save_model" in config and config["save_model"]: compressor.export_model( model_path="mnist_compressed.pth", speedup=True, folder_name=config["folder_name"]) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_admm.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=2, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) def short_term_fine_tuner(model, criterion, optimizer, epoch, callback): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() if callback: callback() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break scheduler.step() def evaluator(model): _, test_acc = test(model, device, test_loader) return test_acc compressor = aup.compression.create_compressor(model, config, trainer=short_term_fine_tuner, num_iterations=30, training_epochs=1) model = compressor.compress() model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", save_best_only=True, speedup=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_agp.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=2, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) _, test_acc = test(model, device, test_loader) compressor = aup.compression.create_compressor(model, config, optimizer=optimizer) model = compressor.compress() scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) _, test_acc = test(model, device, test_loader) scheduler.step() compressor.update_epoch(epoch) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", mask_path="mnist_compressed_mask.pth", save_best_only=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_amc.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np from PIL import Image import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) def new_getitem(self, index): img, target = self.data[index], int(self.targets[index]) # doing this so that it is consistent with all other datasets # to return a PIL Image img = Image.fromarray(img.numpy(), mode='L') img = img.convert('RGB') if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target datasets.mnist.MNIST.__getitem__ = new_getitem transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = torch.hub.load('pytorch/vision:v{}'.format(torchvision.__version__), 'mobilenet_v2', pretrained=True).to(device) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) def evaluator(val_loader, model): _, test_acc = test(model, device, val_loader) return test_acc compressor = aup.compression.create_compressor( model, config, evaluator=evaluator, val_loader=test_loader, flops_ratio=0.5, model_type="mobilenetv2", n_calibration_batches=1, n_points_per_layer=1, warmup=1, hidden1=32, hidden2=32, train_episode=1, max_episode_length=1) compressor.compress() flops, _, _ = compressor.count_flops_params((1, 3, 28, 28)) print("test_acc={} flops={}".format(test_acc, flops)) if "save_model" in config and config["save_model"]: compressor.export_model( model_path="mnist_compressed.pth", mask_path="mnist_compressed_mask.pth", save_best_only=True, folder_name=config["folder_name"]) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_aup_args.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) @aup.aup_args def main(compression_type, compression_framework, compressor, config_list): config = locals() # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) _, test_acc = test(model, device, test_loader) compressor = aup.compression.create_compressor(model, config, optimizer=optimizer) model = compressor.compress() scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) _, test_acc = test(model, device, test_loader) scheduler.step() model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) aup.aup_save_model( compressor.export_model, model_path="mnist_compressed.pth", speedup=True) return (test_acc - 1.) / np.log(flops) if __name__ == '__main__': if len(sys.argv) < 2: print("config file required") exit(1) main(sys.argv[1]) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_autocompress.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=2, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) def short_term_fine_tuner(model, optimizer, criterion, epoch, callback): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() if callback: callback() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break scheduler.step() def evaluator(model): _, test_acc = test(model, device, test_loader) return test_acc compressor = aup.compression.create_compressor( model, config, trainer=short_term_fine_tuner, evaluator=evaluator, dummy_input=torch.randn(1, 1, 28, 28).to(device), optimize_mode="maximize", num_iterations=1, admm_num_iterations=1, admm_training_epochs=1, experiment_data_dir="./autocompress") model = compressor.compress() flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", mask_path="mnist_compressed_mask.pth", save_best_only=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_dependency_aware.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) _, test_acc = test(model, device, test_loader) compressor = aup.compression.create_compressor(model, config, optimizer=optimizer, dependency_aware=True, dummy_input=torch.randn(1, 1, 28, 28).to(device)) model = compressor.compress() scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) _, test_acc = test(model, device, test_loader) scheduler.step() model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) if "save_model" in config and config["save_model"]: compressor.export_model( model_path="mnist_compressed.pth", save_best_only=True, speedup=True, folder_name=config["folder_name"]) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_lottery_ticket.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) compressor = aup.compression.create_compressor(model, config, optimizer=optimizer, lr_scheduler=scheduler) model = compressor.compress() for _ in compressor.get_prune_iterations(): compressor.prune_iteration_start() for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) _, test_acc = test(model, device, test_loader) scheduler.step() flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", mask_path="mnist_compressed_mask.pth", save_best_only=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_net_adapt.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=2, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) def short_term_fine_tuner(model): train(args, model, device, train_loader, optimizer, 1) scheduler.step() def evaluator(model): _, test_acc = test(model, device, test_loader) return test_acc compressor = aup.compression.create_compressor(model, config, short_term_fine_tuner=short_term_fine_tuner, evaluator=evaluator, optimize_mode="maximize") model = compressor.compress() model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", save_best_only=True, speedup=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_no_speedup.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) _, test_acc = test(model, device, test_loader) compressor = aup.compression.create_compressor(model, config, optimizer=optimizer) model = compressor.compress() scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) _, test_acc = test(model, device, test_loader) scheduler.step() flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", mask_path="mnist_compressed_mask.pth", save_best_only=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_pretrained.py ================================================ from __future__ import print_function import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) def main(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=14, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--save-model', action='store_true', default=False, help='For Saving the current Model') parser.add_argument('--speedup-model', action='store_true', default=False, help='For speeding up the current Model') args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) test(model, device, test_loader) scheduler.step() torch.save(model.state_dict(), "mnist_pretrained.pth") if __name__ == '__main__': main() ================================================ FILE: Examples/compression/mnist_pytorch/mnist_sensitivity.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) def short_term_fine_tuner(model): train(args, model, device, train_loader, optimizer, 1) scheduler.step() def evaluator(model): _, test_acc = test(model, device, test_loader) return test_acc compressor = aup.compression.create_compressor(model, config, finetuner=short_term_fine_tuner, evaluator=evaluator) model = compressor.compress(eval_args=[model], finetune_args=[model]) model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", save_best_only=True, speedup=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_pytorch/mnist_simulated_annealing.py ================================================ #!/usr/bin/env python3 from __future__ import print_function import argparse import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(config): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) _, test_acc = test(model, device, test_loader) def evaluator(model): _, test_acc = test(model, device, test_loader) return test_acc compressor = aup.compression.create_compressor(model, config, evaluator=evaluator, optimize_mode="maximize") model = compressor.compress() flops, _, _ = compressor.count_flops_params((1, 1, 28, 28)) flops = int(flops) print("test_acc={} flops={}".format(test_acc, flops)) compressor.export_model( model_path="mnist_compressed.pth", mask_path="mnist_compressed_mask.pth", save_best_only=True, **config) aup.print_result((test_acc - 1.) / np.log(flops)) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_tensorflow/README.md ================================================ # Compression examples (Tensorflow) ## Requirements The basic requirements for Auptimizer apply here. Additionally, in order for the examples to work, the following packages are required: * tensorflow >= 2.0 ## Running the examples For non-automatic examples: ```sh python -m aup.compression exp_fpgm.json ``` For automatic examples: ```sh python -m aup.compression exp_auto_fpgm.json --automatic ``` The ``--launch_dashboard`` flag can be added to the command if the user wants to show the dashboard while running the experiment. ================================================ FILE: Examples/compression/mnist_tensorflow/exp_auto_level.json ================================================ { "name": "Tensorflow MNIST Level Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "resource_args": { "save_model": true }, "compression": { "framework": "tensorflow", "type": "pruning", "compressor": "level", "config_list": [{ "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2", "fc1", "fc2"] } ] }, "proposer": "hyperopt", "n_parallel": 4, "target": "max", "n_samples": 5 } ================================================ FILE: Examples/compression/mnist_tensorflow/exp_level.json ================================================ { "name": "Tensorflow MNIST Level Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "tensorflow", "type": "pruning", "compressor": "level", "config_list": [{ "sparsity": 0.8, "op_types": ["default"] } ] } } ================================================ FILE: Examples/compression/mnist_tensorflow/mnist.py ================================================ #!/usr/bin/env python3 """ MNIST convolutional network using pytorch ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ from aup import print_result, aup_args import tensorflow as tf import numpy as np from tensorflow import keras from tensorflow.keras import layers import tqdm from math import log import sys import aup num_epochs = 1 batch_size = 64 num_classes = 10 input_shape = (28, 28, 1) def get_model(**kwargs): model = keras.Sequential( [ keras.Input(shape=input_shape), layers.Conv2D(kwargs['conv1'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(kwargs['conv2'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dropout(kwargs['dropout']), layers.Dense(num_classes, activation="softmax"), ] ) return model def test_model(model, test_dataset): correct = [] for (batch, (images, labels)) in enumerate(test_dataset): logits = model(images) correct += list(labels.numpy() == np.argmax(logits.numpy(), axis=1)) return np.mean(correct) def main(config): (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() train_dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(train_images[...,tf.newaxis]/255, tf.float32), tf.cast(train_labels,tf.int64))) train_dataset = train_dataset.shuffle(1000).batch(32) test_dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(test_images[...,tf.newaxis]/255, tf.float32), tf.cast(test_labels,tf.int64))) test_dataset = test_dataset.shuffle(1000).batch(32) model = get_model(dropout=0.1, conv1=32, conv2=64) optimizer = tf.keras.optimizers.Adam() loss_object = tf.keras.losses.SparseCategoricalCrossentropy() model.load_weights("mnist_pretrained.h5") def train_step(images, labels): with tf.GradientTape() as tape: logits = model(images, training=True) tf.debugging.assert_equal(logits.shape, (32, 10)) loss_value = loss_object(labels, logits) grads = tape.gradient(loss_value, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) compressor = aup.compression.create_compressor(model, config) compressor.compress() for epoch in range(num_epochs): for (batch, (images, labels)) in enumerate(train_dataset): train_step(images, labels) acc = test_model(model, test_dataset) print ('Epoch {} finished - test_acc={}'.format(epoch, acc)) compressor.export_model("mnist_compressed.h5", **config) aup.print_result(acc - 1.) if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) ================================================ FILE: Examples/compression/mnist_tensorflow/mnist_pretrained.py ================================================ #!/usr/bin/env python3 from aup import print_result, aup_args import tensorflow as tf import numpy as np from tensorflow import keras from tensorflow.keras import layers import tqdm from math import log import sys num_epochs = 14 batch_size = 64 num_classes = 10 input_shape = (28, 28, 1) def get_model(**kwargs): model = keras.Sequential( [ keras.Input(shape=input_shape), layers.Conv2D(kwargs['conv1'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(kwargs['conv2'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dropout(kwargs['dropout']), layers.Dense(num_classes, activation="softmax"), ] ) return model def test_model(model, test_dataset): correct = [] for (batch, (images, labels)) in enumerate(test_dataset): logits = model(images) correct += list(labels.numpy() == np.argmax(logits.numpy(), axis=1)) return np.mean(correct) def main(): (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() train_dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(train_images[...,tf.newaxis]/255, tf.float32), tf.cast(train_labels,tf.int64))) train_dataset = train_dataset.shuffle(1000).batch(32) test_dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(test_images[...,tf.newaxis]/255, tf.float32), tf.cast(test_labels,tf.int64))) test_dataset = test_dataset.shuffle(1000).batch(32) model = get_model(dropout=0.1, conv1=32, conv2=64) optimizer = tf.keras.optimizers.Adam() loss_object = tf.keras.losses.SparseCategoricalCrossentropy() def train_step(images, labels): with tf.GradientTape() as tape: logits = model(images, training=True) tf.debugging.assert_equal(logits.shape, (32, 10)) loss_value = loss_object(labels, logits) grads = tape.gradient(loss_value, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) for epoch in range(num_epochs): for (batch, (images, labels)) in enumerate(tqdm.tqdm(train_dataset)): train_step(images, labels) acc = test_model(model, test_dataset) print ('Epoch {} finished - test_acc={}'.format(epoch, acc)) model.save("mnist_pretrained.h5") print("test_acc={}".format(acc)) if __name__ == '__main__': main() ================================================ FILE: Examples/compression/utility_functions/README.md ================================================ # Compression utility functions This example provides an example training script ``mnist.py``, where the compression utility functions sensitivity analysis and channel dependency analysis are applied. To just use these utility functions, there is no need to launch an Auptimizer experiment. The analysis outputs will be saved to folder ``output``. ================================================ FILE: Examples/compression/utility_functions/mnist.py ================================================ #!/usr/bin/env python3 """ MNIST convolutional network using pytorch ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ from __future__ import print_function import argparse import os import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR import aup OUT_DIR = "output" class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) return test_loss, correct / len(test_loader.dataset) def main(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args, _ = parser.parse_known_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, ) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('/tmp/torch_mnist_data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('/tmp/torch_mnist_data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = Net().to(device) model.load_state_dict(torch.load("../mnist_pytorch/mnist_pretrained.pth", map_location=lambda storage, location: storage)) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) os.makedirs(OUT_DIR, exist_ok=True) # Example of Sensitivity Analysis usage s_analyzer = aup.compression.sensitivity_analysis.SensitivityAnalysis(model=model, val_func=lambda model: test(model, device, test_loader)) sensitivity = s_analyzer.analysis(val_args=[model]) s_analyzer.export(os.path.join(OUT_DIR, "sensitivity_analysis.log")) # ChannelDependency data = torch.ones(1, 1, 28, 28).to(device) channel_depen = aup.compression.shape_dependency.ChannelDependency(model, data) channel_depen.export(os.path.join(OUT_DIR, "channel_dependency.csv")) if __name__ == '__main__': main() ================================================ FILE: Examples/converter_examples/Convert_Benchmark/.gitignore ================================================ .ipynb_checkpoints/ *.tflite *.h5 *.tgz pytorch/ ================================================ FILE: Examples/converter_examples/Convert_Benchmark/Benchmark.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "\"\"\"\n", " Copyright (c) 2018 LG Electronics Inc.\n", " SPDX-License-Identifier: GPL-3.0-or-later\n", "\"\"\" \n", "import keras\n", "import subprocess\n", "from os import path\n", "import pandas as pd\n", "import json\n", "\n", "# see models here - https://www.tensorflow.org/lite/guide/hosted_models\n", "STORAGE_LINK = \"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/\"\n", "ADB_LOCAL = path.expanduser(\"~/Library/Android/sdk/platform-tools/adb\") # for MacOS\n", "\n", "ALPHA = [\"1.0\",\"0.75\",\"0.5\",\"0.25\"]\n", "SIZE = [\"224\",\"192\",\"160\",\"128\"]\n", "REP_DATA = \"repdata.py\"\n", "\n", "OP_TYPE = [\"float\", \"float16\", \"uint8\", \"int8\"]\n", "\n", "KEYS = [[a,b] for a in ALPHA for b in SIZE]\n", "MOBILENET = \"mobilenet_v1\"\n", "df = pd.DataFrame({\"model\":[], \"alpha\":[], \"size\":[], \"gpu\":[], \"nnapi\":[], \"op\": [], \"size_init\":[],\n", " \"size_peak\":[], \"warmup\":[], \"init\":[], \"inference\":[]})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def preprocess(name):\n", " try:\n", " process = subprocess.Popen(\" wget \" + STORAGE_LINK + name + \".tgz \", shell=True).wait()\n", " except:\n", " print(\"Could not retrieve\")\n", " return 0\n", "\n", " try:\n", " process = subprocess.Popen(\" tar -xzf \" + name + \".tgz\", shell=True).wait()\n", " except:\n", " print(\"Could not unpack\")\n", " return 0\n", "\n", " return 1\n", "\n", "def push_to_device(name):\n", " try:\n", " process = subprocess.Popen(ADB_LOCAL + \" push \" + name + \".tflite /data/local/tmp/\",\n", " shell=True, stdout=subprocess.DEVNULL).wait()\n", " except:\n", " print(\"Could not push\")\n", " return 0\n", " return 1\n", "\n", "\n", "def run_benchmark(name):\n", " command_template = ADB_LOCAL + \" shell /data/local/tmp/benchmark_model --graph=/data/local/tmp/\"\n", " command = command_template + name + \".tflite --num_threads=4\"\n", " process2 = subprocess.Popen(command.split(), stdout=subprocess.PIPE)\n", " output, error = process2.communicate()\n", " output = output.decode('utf-8')\n", " output = output.split(\"\\n\")\n", " infer, memo = \"\",\"\"\n", " for p in output:\n", " if \"Inference timings\" in p:\n", " infer = p\n", " if \"Peak memory footprint\" in p:\n", " memo = p\n", " #output = output.split(\"\\n\")\n", " #infer = output\n", " return (infer, memo)\n", "\n", "def compile_results(infer, memo, df, of, op, alpha, size):\n", " data = []\n", " try:\n", " idx = memo.find(\"init=\")\n", " max_size = float(memo[idx+5:idx + memo[idx:].find(\" \")-1])\n", " except:\n", " max_size = float('nan')\n", " idx = memo.find(\"overall=\")\n", " try:\n", " malloc_size = float(memo[idx+8:])\n", " except:\n", " malloc_size = float('nan')\n", " idx = infer.find(\"Warmup (avg):\")\n", " try:\n", " warmup = float(infer[idx+14:idx + infer[idx:].find(\",\")])\n", " except:\n", " warmup = float('nan')\n", " idx = infer.find(\"Init:\")\n", " try:\n", " init = float(infer[idx+6:idx + infer[idx:].find(\",\")])\n", " except:\n", " init = float('nan')\n", " idx = infer.find(\"Inference (avg):\")\n", " try:\n", " no_stat = float(infer[idx+17:])\n", " except:\n", " no_stat = float('nan')\n", " df = df.append(pd.Series([of, alpha, size, op, max_size, malloc_size,\n", " warmup, init, no_stat], index=df.columns), ignore_index=True)\n", " return df\n", "\n", "def convert(name, size, op):\n", " json_dic = {}\n", " json_dic[\"convert_from\"] = name+\"_frozen.pb\"\n", " json_dic[\"convert_to\"] = name+\"_\"+op+\"_converted.tflite\"\n", " json_dic[\"input_nodes\"] = \"input\"\n", " json_dic[\"output_nodes\"] = \"MobilenetV1/Predictions/Reshape_1:0\"\n", " command = \"\"\n", " if op == 'float':\n", " command = json.dumps([json_dic])\n", " if op == 'float16':\n", " quant_dic = {}\n", " quant_dic[\"type\"]=\"float16\"\n", " quant_dic[\"opsset\"] = \"tf\"\n", " json_dic[\"quantization\"] = quant_dic\n", " command = json.dumps([json_dic])\n", " if op == 'int8':\n", " quant_dic = {}\n", " quant_dic[\"type\"]=\"int8\"\n", " quant_dic[\"opsset\"] = \"int8\"\n", " quant_dic[\"load\"] = REP_DATA + \" --custom_data '1,\" + size + \",\" + size + \",\" + \"3,1000' --undefok custom_data\"\n", " json_dic[\"quantization\"] = quant_dic\n", " command = json.dumps([json_dic])\n", " if op == 'uint8':\n", " quant_dic = {}\n", " quant_dic[\"type\"]=\"uint8\"\n", " quant_dic[\"opsset\"] = \"tf\"\n", " quant_dic[\"load\"] = REP_DATA + \" --custom_data '1,\" + size + \",\" + size + \",\" + \"3,1000' --undefok custom_data\"\n", " json_dic[\"quantization\"] = quant_dic\n", " command = json.dumps([json_dic])\n", " try:\n", " process = subprocess.Popen(\"python3 -m aup.dlconvert -d \" + \"'\" + str(command) + \"'\", shell=True).wait()\n", " except:\n", " print(\"Could not Convert= \" + str(command))\n", " return 0\n", " return 1\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key in KEYS:\n", "\n", " name = MOBILENET + \"_\" + key[0] + \"_\" + key[1]\n", "\n", " print(\"Starting \" + str(name))\n", "\n", " if(not preprocess(name)):\n", " continue\n", "\n", " if(not push_to_device(name)):\n", " continue\n", "\n", " infer,memo = run_benchmark(name)\n", "\n", " df = compile_results(infer, memo, df, \"official\", \"float\", key[0], key[1])\n", "\n", " new_name = name + \"_quant\"\n", "\n", " print(\"Starting \" + str(new_name))\n", "\n", " if(not preprocess(new_name)):\n", " continue\n", "\n", " if(not push_to_device(new_name)):\n", " continue\n", "\n", " infer,memo = run_benchmark(new_name)\n", "\n", " df = compile_results(infer, memo , df, \"official\", \"int8\", key[0], key[1])\n", " \n", " print(\"Starting converted tflite benchmarking\")\n", "\n", " for op in OP_TYPE:\n", "\n", " converted = convert(name, key[1], op)\n", "\n", " if not converted:\n", " print(\"Failed \" + str(name) + \" \" + str(op))\n", " continue\n", "\n", " print(\"testing= \" + str(name) + \" \" + str(op))\n", "\n", " new_name = name + \"_\" + op + \"_converted\"\n", "\n", " if(not push_to_device(new_name)):\n", " continue\n", "\n", " infer,memo = run_benchmark(new_name)\n", "\n", " df = compile_results(infer, memo, df, \"converted\", str(op), key[0], key[1])\n", " \n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " model alpha size op size_init size_peak warmup init inference\n", "0 official 1.0 224 float 0.78906 26.74220 33998.60 2196.0 33562.00\n", "1 official 1.0 224 int8 0.87890 7.19531 24491.30 2017.0 23836.70\n", "2 converted 1.0 224 float 0.79687 26.69530 34708.60 1929.0 33686.70\n", "3 converted 1.0 224 float16 0.79296 34.55470 35102.30 2033.0 32647.30\n", "4 converted 1.0 224 uint8 1.39060 8.44531 24231.40 3134.0 23624.50\n", "5 converted 1.0 224 int8 1.39060 8.47266 27206.70 2890.0 23835.60\n", "6 official 1.0 192 float 0.78906 25.14060 25100.70 1669.0 24425.90\n", "7 official 1.0 192 int8 0.76562 6.64844 18825.00 2148.0 18390.10\n", "8 converted 1.0 192 float 0.79687 25.37500 24657.60 1756.0 24129.00\n", "9 converted 1.0 192 float16 0.79296 33.19920 26244.60 2075.0 25446.30\n", "10 converted 1.0 192 uint8 1.39060 7.90234 18417.10 3117.0 18072.60\n", "11 converted 1.0 192 int8 1.39060 8.06641 18780.40 2770.0 18272.40\n", "12 official 1.0 160 float 0.78906 23.96480 18737.10 1639.0 18147.10\n", "13 official 1.0 160 int8 0.76562 6.42969 14405.30 2022.0 13266.50\n", "14 converted 1.0 160 float 0.79687 24.06640 18471.30 1768.0 19325.60\n", "15 converted 1.0 160 float16 0.79687 31.91020 19203.30 1973.0 17638.40\n", "16 converted 1.0 160 uint8 1.39060 7.23828 17993.90 2886.0 13290.70\n", "17 converted 1.0 160 int8 1.39060 7.44141 21174.00 2649.0 20724.60\n", "18 official 1.0 128 float 0.78515 22.92970 12444.20 1581.0 11945.00\n", "19 official 1.0 128 int8 0.76562 6.20703 13523.20 2059.0 13424.40\n", "20 converted 1.0 128 float 0.79296 22.69530 12496.10 1804.0 11959.30\n", "21 converted 1.0 128 float16 0.78906 30.79300 13551.10 1828.0 12529.10\n", "22 converted 1.0 128 uint8 1.28120 6.80469 8976.34 2915.0 8992.86\n", "23 converted 1.0 128 int8 1.28120 6.92188 13422.40 2487.0 10108.50\n", "24 official 0.75 224 float 0.76562 17.43360 21263.80 1878.0 21159.90\n", "25 official 0.75 224 int8 0.82812 4.98047 17237.40 1988.0 16508.00\n", "26 converted 0.75 224 float 0.78120 17.39450 22503.70 1607.0 21283.40\n", "27 converted 0.75 224 float16 0.78120 22.75780 22192.10 2129.0 21192.60\n", "28 converted 0.75 224 uint8 1.21870 6.03516 22660.10 2414.0 16021.80\n", "29 converted 0.75 224 int8 1.21870 6.30859 15966.20 2838.0 15849.00\n", "30 official 0.75 192 float 0.76562 16.13670 16253.70 1612.0 15627.30\n", "31 official 0.75 192 int8 0.76562 4.30469 17908.20 1741.0 16690.40\n", "32 converted 0.75 192 float 0.78120 16.42580 16129.60 1602.0 16212.00\n", "33 converted 0.75 192 float16 0.78515 21.24220 15744.60 1838.0 15032.60\n", "34 converted 0.75 192 uint8 1.21870 5.56250 12433.50 2599.0 12068.50\n", "35 converted 0.75 192 int8 1.21870 5.62109 12045.20 2587.0 11737.50\n", "36 official 0.75 160 float 0.76562 15.27340 11488.00 1807.0 11356.20\n", "37 official 0.75 160 int8 0.76562 4.49219 9148.42 1905.0 9103.98\n", "38 converted 0.75 160 float 0.78515 15.10160 11819.20 1928.0 11335.40\n", "39 converted 0.75 160 float16 0.78515 19.90620 11790.90 1945.0 11200.20\n", "40 converted 0.75 160 uint8 1.02340 5.09375 9288.87 2472.0 9114.61\n", "41 converted 0.75 160 int8 1.02340 5.11328 9300.00 2543.0 9019.50\n", "42 official 0.75 128 float 0.76171 14.07810 10167.30 1584.0 9786.01\n", "43 official 0.75 128 int8 0.76562 3.81250 6325.96 1982.0 6249.11\n", "44 converted 0.75 128 float 0.77734 14.44530 7544.64 1898.0 7422.35\n", "45 converted 0.75 128 float16 0.78120 19.16020 8644.24 2002.0 8227.14\n", "46 converted 0.75 128 uint8 1.02340 4.91016 6686.88 2458.0 5826.48\n", "47 converted 0.75 128 int8 1.02340 4.85547 5921.82 2732.0 5877.53\n", "48 official 0.5 224 float 0.75781 11.06250 12346.40 1864.0 11818.80\n", "49 official 0.5 224 int8 0.76562 3.47656 9217.15 1663.0 9174.97\n", "50 converted 0.5 224 float 0.75781 10.81250 12604.10 1658.0 11342.90\n", "51 converted 0.5 224 float16 0.77734 13.45700 12766.20 1729.0 11792.10\n", "52 converted 0.5 224 uint8 1.07810 4.51172 9011.44 2313.0 8938.94\n", "53 converted 0.5 224 int8 1.07810 4.55078 9348.15 2308.0 9034.15\n", "54 official 0.5 192 float 0.75781 9.57031 9739.80 1711.0 8417.13\n", "55 official 0.5 192 int8 0.76562 2.90625 6879.19 1806.0 7067.31\n", "56 converted 0.5 192 float 0.75781 9.56641 14427.00 1585.0 11264.40\n", "57 converted 0.5 192 float16 0.77734 12.27340 9537.10 2055.0 9057.24\n", "58 converted 0.5 192 uint8 1.07810 3.95703 6889.69 2352.0 6899.43\n", "59 converted 0.5 192 int8 1.07810 3.68359 6866.42 2086.0 6884.52\n", "60 official 0.5 160 float 0.75390 8.84375 6275.54 1826.0 6035.32\n", "61 official 0.5 160 int8 0.76562 2.87109 8104.90 1843.0 7316.42\n", "62 converted 0.5 160 float 0.75390 8.88281 6463.68 2811.0 6170.63\n", "63 converted 0.5 160 float16 0.77343 11.58980 6799.38 1878.0 6314.49\n", "64 converted 0.5 160 uint8 1.02340 3.66797 5131.54 2370.0 4970.17\n", "65 converted 0.5 160 int8 1.02340 3.50781 5028.12 2082.0 4984.45\n", "66 official 0.5 128 float 0.75390 7.90234 6759.62 1512.0 5552.03\n", "67 official 0.5 128 int8 0.76562 2.24609 3510.57 1807.0 3630.16\n", "68 converted 0.5 128 float 0.75390 7.99609 6622.07 1956.0 6976.16\n", "69 converted 0.5 128 float16 0.77343 10.48830 4392.34 1880.0 4282.03\n", "70 converted 0.5 128 uint8 1.02340 2.83984 3540.63 2307.0 3417.62\n", "71 converted 0.5 128 int8 1.02340 3.18750 3475.96 2033.0 3435.91\n", "72 official 0.25 224 float 0.73437 6.48438 6391.63 1487.0 6242.10\n", "73 official 0.25 224 int8 0.50781 2.35156 4271.29 1878.0 4192.14\n", "74 converted 0.25 224 float 0.74609 6.46484 4858.95 1478.0 4722.11\n", "75 converted 0.25 224 float16 0.70000 7.48438 5223.81 1822.0 4951.89\n", "76 converted 0.25 224 uint8 0.76562 3.60156 7467.73 2050.0 4353.84\n", "77 converted 0.25 224 int8 0.76562 3.61719 4379.18 1977.0 4262.76\n", "78 official 0.25 192 float 0.73046 5.38672 3590.72 1661.0 3483.75\n", "79 official 0.25 192 int8 0.50781 1.98047 5196.78 1905.0 4826.97\n", "80 converted 0.25 192 float 0.74218 5.38672 6775.37 1907.0 5215.93\n", "81 converted 0.25 192 float16 0.74609 6.32812 5244.89 1851.0 4777.14\n", "82 converted 0.25 192 uint8 0.76562 2.92969 4020.21 2084.0 3792.61\n", "83 converted 0.25 192 int8 0.76562 3.05078 3359.21 2063.0 3350.96\n", "84 official 0.25 160 float 0.73046 4.32812 2661.74 1759.0 2663.75\n", "85 official 0.25 160 int8 0.50781 1.82031 2533.19 1761.0 2412.00\n", "86 converted 0.25 160 float 0.74218 4.53125 3285.08 1705.0 2617.05\n", "87 converted 0.25 160 float16 0.74609 5.35156 2636.35 1803.0 2572.78\n", "88 converted 0.25 160 uint8 0.76562 2.35156 2531.91 2044.0 2494.01\n", "89 converted 0.25 160 int8 0.76562 2.44922 2601.15 1997.0 2512.17\n", "90 official 0.25 128 float 0.50781 3.79297 2639.18 1623.0 2469.44\n", "91 official 0.25 128 int8 0.50781 1.55078 1830.96 1647.0 1757.80\n", "92 converted 0.25 128 float 0.50781 3.69531 1923.96 1845.0 1857.89\n", "93 converted 0.25 128 float16 0.75390 4.66797 2439.37 1901.0 2373.20\n", "94 converted 0.25 128 uint8 0.76562 2.08984 1878.13 2049.0 1788.82\n", "95 converted 0.25 128 int8 0.76562 1.95312 2336.19 2063.0 1838.06\n" ] } ], "source": [ "df = pd.read_pickle(\"./data_mobilenet.pkl\")\n", "print(df.to_string())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import subprocess\n", "from os import path\n", "import pandas as pd\n", "import logging\n", "import matplotlib.pylab as plt\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def visualize(d, target, taget_label=None):\n", " d['size'] = d['size'].astype('int')\n", "\n", " alphas = d['alpha'].unique().tolist()\n", " labels = ['converted-float', 'converted-int8', 'official-float', 'official-int8'] # sorted((d['model'] + \"-\" + d['op']).unique().tolist())\n", " shapes = ['ob','og','+b','+g'] # ['or','oy','ok','oc','+r','+k']\n", "\n", "\n", " fig, axes = plt.subplots(figsize=(8,8), ncols=2, nrows=2, sharex=True, sharey=True)\n", " for i, alpha in enumerate(alphas):\n", " ax = axes[i%2][i//2]\n", " for label, shape in zip(labels,shapes):\n", " model, op = label.split(\"-\")\n", " t = d[(d['alpha']==alpha) & (d['model']==model) & (d['op']==op)]\n", " ax.plot(t['size'], t[target], shape, label=label)\n", " ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))\n", " if i==0:\n", " ax.set_ylabel(taget_label)\n", " if i==1:\n", " ax.set_ylabel(taget_label)\n", " ax.set_xlabel('input size')\n", " if i==3:\n", " ax.set_xlabel('input size')\n", " ax.legend(title=\"Model alpha=%.2f\"%float(alpha))\n", " plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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0EwDNmjXzur1p06YA/PzzzwVqs2DBAn7++WeiNfGFSLkSEFCy7URELgeK3RW755Q9di9MvJ0fjz32mNvP1atXZ/z48R531Vxxsbu1Vq/L9AWsi4iIsBeTlJRkk5KSLlqvsABbr149a6213333nQXsyJEjrbXWJicnWx8fH/voo49aa6198cUXLWATEhJc7ZcvX24B2759e6/779SpkwXskiVLrLXWpqen2ypVqtjq1avbY8eOedSPjY21gI2Pj3eVbdy40QK2d+/eXvuYM2eOBew//vEPV1lCQoLHWAurR48e1t/f36anp7vKdu7caQEbGxvrdfyjRo3y2M/27dutj4+PDQsLcysPDQ21gN22bZtHmwcffNAC9scff8zXWD/99FML2OnTp7uVx8fHW6BAr7w4j79jx475Gld2Y8eOtYB98cUXvW7/97//bQH7yCOPuMoGDhxoATtlyhSvbYYNG2YBO27cuAKPJz+K+3MociXbtMlaKPhr06bC9RcREWGBdbYMxAK2APGAiJR9JREvKHa/OMXuhYu38/Luu+/ajz/+2O7atcueOXPGbt++3b766qu2evXqFrD/+te/3OqXZuye389hUcYDuuNGStTNN9/M9ddfz9SpU3nppZd45513yMzMzPNWy/Xr1wNZWW9voqKiWL58ORs2bCAyMpKtW7dy+vRpbrnlFgIDAz3q33rrrR7Py65atQqAtLQ0r3eB/PrrrwBs2bIlX8eZm//+97+8/fbbrF27liNHjnjMwn/kyBFCQkLyta/OnTt7lDVq1IgGDRqQnJzMsWPH3GZkDwwMpEmTJh5tGjRoAOCxZN7u3buZOHEiixYtYvfu3Zw5c8Zt+759+9x+HjFiRLHdQSMicilatoTIyIJNUNy5syYmFhFR7K7YvaQMGDDA7edGjRrx9NNPc80119CjRw9efPFFHn744UueC7O8UuJGStzAgQN54oknmDdvHgkJCbRp04Ybbrgh1/ppaWkAuV4UneXHjh1zq5/bbZF169b1KEtJSQFg4cKFLFy4MNexnDx5MtdtFzN58mQGDx5MzZo1uf3222nYsCFVqlTBGMOcOXP4/vvvOXfuXL73l9fx7dq1i7S0NLeLv7dl9QDXKgEXLlxwle3YsYObbrqJo0ePcssttxATE0NgYCAVKlQgOTmZ6dOnF2isJc35pe/8XcjJWZ7zy7GgbUSk/Bg+HGJi8rckuI8PlPGpvERESoxid8Xu3pRU7Ny9e3fq1avHvn37SEpK4vrrry/R/ssKJW6kxD3wwAM899xzDBo0iH379jF8+PA86zs/lAcPHvS6/cCBA271nO+HDh3yWt/bfpxtJk+ezBNPPJGPoyiY8+fPM2LECOrWrcv69es9vsicfzUoiEOHDnHNNdd4lDuPz9tfLPLrtddeIyUlhYSEBOLi4ty2zZw50+sM/2VpjhvnecntmVrnagbZn4ktTBsRKT+io+Hf/4ZHHsk7eePjA1OmZNUXERHF7ordf5M9di/J2LlOnTrs27fPbYWoKy12V+JGSlyNGjXo3bu3a/m4fv365VnfmdHP7cLinFE8IiICgObNm1OlShU2btxIWlqax0XQ237atWsHwLJly4rl4n/kyBGOHTvGPffc43HhP3nypOuW0oJYsmQJkZGRbmU7duxgz549hIWFXVJ2+ZdffgGgV69eXvv1JjExkZEjRxaon+JK3LRr147KlSuzYsUKTpw44TapXGZmpmuJyC5durjKnf+9YMECMjMz3WanP3HiBCtWrKBKlSqu3xURKX8efhjCwmD0aPB2KevcOetOGyVtRER+o9hdsbtT9ti9MPF2YaSlpbF161aMMYSHh7vKr7TYXcuBS6kYM2YMn332GfPnz/eYqT2njh07cs0117B8+XJmz57ttm327NksW7aMZs2a0alTJwD8/Pzo378/J06c8EgMrF27lg8++MCjj7Zt23LLLbfwn//8h6lTp3odx48//sjhw4cLcJS/CQ4OpkqVKqxbt87tls2MjAyefPJJjhw5UuB9Tp48mV27drl+zszMZOjQoWRmZvLQQw8VapxOYWFhgOcX5fz58z2WhHQaMWJEYSbMvGRbt25l69atbmXVqlXjgQce4NSpUx6/A2+++SbJycl07dqVRo0aucobN25MTEwMycnJ/OMf/3BrEx8fz6lTp3jggQeoWrVqkYxbREpHdDQkJsKmTTB5clYSZ/LkrJ8TE5W0ERHxRrF7FsXuvylMvA2wfft2tm7dSkZGhqvs4MGD7N2712OMJ0+eJC4ujrNnz3Lbbbe5PW52pcXuuuNGSkXDhg1p2LBhvuoaY5g+fTq33347ffv2pWfPnjRv3pyffvqJOXPmUL16dd577z23LOu4ceNYtGgRf/vb31i7di2dOnXiwIEDfPzxx9xxxx18/vnnHv18+OGHREVF8fDDD/PGG29w8803U6NGDfbu3csPP/zApk2bWLVqFcHBwQU+Xh8fH5544gkmTJjA9ddfT8+ePUlPT2fx4sWkpqbSpUsX118f8qtjx460bt2avn37EhgYyPz58/n+++9p06YNzz77bIHHmN1jjz1GQkIC9957L7179+bqq69m06ZNfP311/Tp04ePP/74kvafm+XLl7u+XJxfktu2bXO75XPatGlubVq0aAHg8WUybtw4EhMTee2119i4cSM33XQTW7ZsYe7cuQQHB3tc4AHeeustOnTowBNPPMGiRYto0aIF//vf/1i8eDHNmjVj7NixRXi0IlKaWrbU5MMiIvml2F2xuzeFibejo6PZtWsXO3fudCWctm7dym233Ub79u1p1qwZwcHB7Nu3j4ULF3Lw4EEaNWrkNQF1RcXuRbE0lV5l80UZXA78YrwtKei0detW+8c//tHWrVvX+vr62rp169r+/fvbrVu3et3XgQMH7EMPPWSDgoJspUqVbKtWrWxCQoJdvHixx5KCTsePH7djx461ERERtmrVqrZSpUo2LCzM3nHHHfZf//qXPXnypKtuQZcUzMjIsJMmTbItWrSwlSpVsldddZX94x//aJOTk13LBO7cudNV/2JLCjqXyLvmmmusv7+/vfrqq+2TTz5p09LSPPoODQ21oaGhXsflXApw8eLFbuUrVqywXbp0sTVq1LDVqlWzHTt2tJ999lme5+9SOc9pXq+cciu31tqUlBT7xBNP2IYNG1o/Pz9bt25d+9BDD9k9e/bkOobdu3fbuLg4W7duXevn52cbNmxon3zySZuamlpkx+mNlgMXuXxoOXARKS4lvRz4xSh23+mqfyXG7tYWPN52LnWe/dzt3r3bPvLII/aGG26wQUFB1tfX1wYEBNgbb7zRjhkzxh4/fjzX/ksjdi+N5cCNtUXzuIKUPcaYdRERERHr1q3Ls55zmTznnQtStsXFxTF9+nS3LLWUf/ocilw+2rRpw/r169dba9uU9lgg//GAiJR9ihfKH8Xul5/8fg6LMh7QHDciIiIiIiIiImWUEjciIiIiIiIiImWUEjciIiIiIiIiImWUEjci5cy0adOw1uoZWRERERGRMk6xuxQFJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5EpEwwxnDrrbcWqE1GRgbx8fE0bdoUf39/jDHMmTOH5ORkjDHExcUVy1hFRERERK5kit1LlhI3UqI2b4Y33oAxY7LeN28u7RFJbkaMGIExhsTExNIeSq4mTZrEqFGjuPrqq3nmmWeIj4+nefPmpTqmwnyJiYiIiJQ1itvLF8XuhVNeYnff0h6AXBkWLYJRo2DpUs9tkZEwfDhER5f8uKR8+/LLL6lWrRoLFy6kYsWKrvLk5OTSG5SIiIhIOaa4XYqLYvfC0x03UuzefRdiYrxf/CGrPCYGpk4t2XFJ+bd//35q167tduEXERERkcJR3C7FSbF74SlxI8Vq0SJ45BHIzMy7XmYmDByYVb+0rV69mr59+1KvXj38/f0JCQkhJiaGTz75xK3eJ598QmRkJIGBgVSuXJnrr7+e8ePHc+7cOY99hoWFERYWxqlTpxg6dCgNGzbE39+fJk2aMHHiRKy1rrrfffcdxhjuvvvuXMfYokUL/P39SU1NdSufP38+d9xxB0FBQfj7+9O4cWOGDh3KsWPHch3T8ePHeeqppwgLC8PPz48RI0YQFhbGyJEjAejSpQvGGNcru9OnTzN+/Hhat25N1apVqVatGu3bt2fmzJlex52ens7o0aNp3Lgx/v7+hIeH89JLL3k9Z3mJi4vDGMPOnTvZtWuXa2xhYWEXbXvgwAH+/Oc/ExYWRsWKFalTpw733HMP69at86iblpbGX//6V6Kioqhfv76r/p133smqVavc6k6bNs11fpYsWeJ2zkaMGFGg4xMREREpaeUxbgfF7ordf3M5x+56VEqK1ahRF7/4O2VmwujRpXvr5ZQpU3j00UepUKECd955J02bNuXw4cOsXbuWt956iz59+gAwbNgwxo8fT1BQEPfffz/VqlVj3rx5DBs2jPnz57NgwQKPTHJGRgZdu3Zl//79dOvWDV9fX+bMmcPzzz/P2bNniY+PB6Bdu3Zcc801fPXVV6SkpFC7dm23/axevZqtW7fSq1cvatWq5SofOXIkI0aMoFatWnTv3p3g4GB++OEHXn31Vb766itWrVpFQECA277S09OJiooiNTWVmJgYAgICCA8PZ/DgwcyZM4clS5YQGxvr9aJ67NgxoqKi2LBhAxEREQwYMIDMzEzmz5/P/fffz+bNmxkzZoyrvrWWPn36MHfuXBo3bszjjz9Oeno6U6dO5ccffyzQv9Ndd91FWFgYf/vb3wAYPHgwADVq1Miz3c6dO+nUqRP79+8nKiqKfv36sWfPHmbNmsV///tfPv30U7p37+6qv2XLFl588UUiIyP5wx/+QM2aNdm9ezeff/458+bN44svvuD3v/89AK1btyY+Pp6RI0cSGhrqNrlaeXhuVkRERK5s5S1uB8Xuit2voNjdWqvXZfoC1kVERNiLSUpKsklJSRetV1CbNlkLBX9t2lTkQ8mXzZs3W19fX1uzZk27ycsg9uzZY621duXKlRawDRo0sAcOHHBtz8jIsN27d7eAHTt2rFvb0NBQC9hu3brZ06dPu8oPHTpkAwMDbWBgoE1PT3eVjxs3zgL273//u8c4HnvsMQvYzz//3FX27bffWsC2b9/eHj161K1+QkKCBezgwYO9jik6OtqePHnSo5/4+HgL2MWLF3s7XTY2NtYCduLEiW7lZ86csV27drXGGLthwwZX+QcffGAB265dO3vmzBlXeUpKim3UqJEFbOfOnb32lZvQ0FAbGhrqUb5z504L2NjYWLfymJgYC9gxY8a4la9YscJWqFDB1qpVy544ccJVfuzYMfvrr7967H/Pnj02JCTENm/e3GNbYY7D2uL7HIpIyYuIiLDAOlsGYgFbgHhARMo+xe2/UezuTrF7ycXu+f0cFmU8UOrBhF7F9yrtxM3kybZQXwCTJxf5UPLl8ccft4B97bXX8qz3pz/9yQL2X//6l8e2n376yfr4+Njw8HC3cueFdtu2bR5tHnzwQQvYH3/80VW2Z88e6+PjY9u2betW99y5c7ZWrVo2ODjYZmRkuMrvuusuC3j90rLW2tatW9s6dep4HdPGjRu9tsnr4n/kyBFboUIFj/E5bdy40QJ26NChrrLbbrvNAvbbb7/1qO/8girOi/+ePXssYBs2bOj2Rev0xz/+0QJ2+vTp+er7L3/5iwXsrl273MqVuBERJW5EpLgobv+NYnd3it3zVpSxe2kkbvSolBSb48dLtt2l+u677wDo1q1bnvXWr18PQFRUlMe2Zs2aUb9+fXbu3ElaWhqBgYGubYGBgTRp0sSjTYMGDQA4evSoq6x+/fpER0ezcOFCkpKSuPbaawH44osvSE1NZciQIfj6/vbxXbVqFX5+fsyaNYtZs2Z59JGens6vv/7qcftmpUqV+N3vfpfn8XqzZs0aLly4kOszoBkZGUDW7YpO69evx8fHh06dOnldyo7DAAAgAElEQVTU93Y74saNG5kzZ45bWY0aNVy3VhbUhg0bALjlllvw8/Pz2B4VFcWMGTPYsGEDDz74oKt8xYoVTJ48mVWrVnH48GHS09Pd2u3bt4+GDRsWakwiIiIiZUF5i9tBsXtBKHb/TXmN3ZW4kWKT45HMYm93qZyTgNWrVy/PemlpaQCEhIR43R4SEsLu3bs5duyY28U/t2c4nRfxCxcuuJXHxcWxcOFCpk+fzsSJEwGYPn06ALGxsW51U1JSOH/+vGtSstycPHnS7eIfHBzsMWlZfqSkpABZXwJr1qzJsz+ntLQ0atWq5fXCW7duXY+yjRs3ehxPaGhooS/++fl3A9wmg/vss8/o3bs3lSpV4vbbb6dx48ZUrVoVHx8fEhMTWbJkSYEnZxMREREpa8pb3A6K3QtCsXv5j92VuJFiU9jJykprkjPnxXnfvn00b94813rOC/rBgwdp3Lixx/YDBw641Susu+++m4CAAGbMmMG4ceNISUlh3rx5tGrVilatWnmMKTMz02Om+ospzIXf2R/AkCFDeO211/LdJjU1lYyMDI8vgIMHD3rUj4uLc5sk7FJl/3fzxtu/28svv0zFihVZu3YtLVq0cKv/f//3fyxZsqTIxiciIiJSWspb3A6K3QtCsXv5j921HLgUm5YtITKyYG06d85qVxratWsHwLx58/Ksd8MNNwCQmJjose2XX35h7969hIeHX3SW9IupXLkyffr0Yf/+/XzzzTd8+OGHnD9/3iNj7xz70aNH2bx58yX1mV2FChUAz78mANx00034+PiwbNmyfO8vIiKCzMxMli9f7rHN27ksas5/t+XLl3P+/HmP7YsXLwayxun0yy+/cO2113pc+HM7DgAfHx+v50xERESkrCpvcTsods9JsfvlHbsrcSPFavhw8Mnnb5mPD7z8cvGOJy+PPvoovr6+jB49mqSkJI/te/fuBWDAgAEAjBkzhl9//dW1/cKFCzzzzDNkZmby8MMPF8mYnFnr9957j/feew9fX1/69+/vUW/IkCEADBw4kP3793tsP3XqlOs54Pxy3pa5e/duj23BwcH079+ftWvXMnr0aK8Xu+3bt7Nz507Xzw899BAAL774ImfPnnWVp6amui09WFzq16/P7bffTnJysmspQqf//e9/fPjhh9SsWZO7777bVR4WFsa2bdvczqm1lhEjRnj9HYGs87Znz57iOQgRERGRYlKe4nZQ7J6TYvfLO3bXo1JSrKKj4d//hkcegczM3Ov5+MCUKaV7u+W1117LW2+9xaBBg7jhhhvo2bMnTZs2JSUlhTVr1hAQEMDixYvp0KEDzz77LK+88grXXXcdvXv3pmrVqsybN49NmzbRqVMnhg4dWiRj6tixI02aNGHWrFlkZGTQo0cPgoODPepFR0czYcIEXnjhBZo2bcodd9xBeHg4J0+eZNeuXSxZsoROnTrx9ddf57vvLl264OPjwwsvvMCmTZuoWbMmAC+99BIAb775Jtu2bWP48OG8//77dOrUiauuuor9+/ezZcsW1qxZw8yZMwkPDwegX79+fPzxx3z++edcd9119OzZk4yMDGbPns2NN97I9u3bi+CM5e3tt9+mY8eODB06lAULFtC2bVv27NnDrFmz8PHxISEhgerVq7vqDxkyxPX70KtXL/z8/FixYgVJSUn06NGDL774wqOP6OhoPvroI3r06EFERAR+fn5ERkYSWdA/Y4mIiIiUoPIUt4Ni95wUu1/msXtRLE2lV9l8UcrLgWf3zTfWdu5svS4j2Llz1vayYuXKlfaee+6xderUsX5+fjYkJMR27drVzpo1y63ezJkzbceOHW21atWsv7+/vfbaa+2YMWPsmTNnPPaZ29J31ua9dJ+11o4ePdoCFrCzZ8/Oc+zLli2z9957rw0JCbF+fn42KCjItmrVyg4ZMsSuWbMm32Nyev/9922rVq1spUqVXGPI7ty5c/bvf/+7bd++vQ0ICLAVK1a0DRo0sFFRUfb111+3R44c8ag/cuRIGx4ebitWrGhDQ0PtsGHD7NmzZ4t9SUGnvXv32kGDBtmGDRtaPz8/W7t2bduzZ0+7evVqr30kJCTYVq1a2SpVqtjatWvbu+66y/7www+5/rsdOnTI9uvXzwYHB1sfHx8L2Pj4+Isei5YDF7l8aDlwESkuits9KXb/jWL3kondS2M5cGOzvtDlMmSMWRcRERGxbt26POs5l33L+Sxgcdi8GRYtylo6MCAgK1Nfms/GipQVJfk5FJHi1aZNG9avX7/eWtumtMcC+Y8HRKTsU9wuUvry+zksynhAj0pJiWrZUhd8EREREZGyTnG7SNmhyYlFRERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMooJW5ERERERERERMqocpe4McZMNMYsMsbsMcacMcakGmM2GGPijTG1c2nTwRjzlaPuGWPMD8aYwcaYCnn0090Yk2iMSTPGnDTG/M8YE3uRscUaY1Y76qc52nfPo34FY8wQx3icx/KVMaZD/s+IiIiIiIiIiFyuyl3iBhgCVAUWApOBD4DzwAjgB2NMg+yVjTE9gaVAJPAZ8CZQEXgd+MhbB8aYx4EvgOuAGcAU4GpgmjHm1VzavApMA0Ic9WcA1wNfOPaXs75x9P+aYzxvOsYXCSx1jFtERERERERErmC+pT2AQgiw1p7NWWiMGQsMA14AHnOUBZCVRLkA3GqtXesofxn4FuhtjLnPWvtRtv2EAa8CqUBba22yo3wUsAZ42hjzqbV2VbY2HYCnge3Ajdbao47yvwLrgFeNMV869+VwH9AbWAlEO4/JGPM2sByYYoz51lp7ovCnSkRERERERETKs3J3x423pI3DJ473ptnKegN1gI+cSZts+3jJ8eOjOfYzAPAH3syeaHEkY8Y5fhyUo43z57HOpI2jTTLwD8f+HsrRxtnvS9mPyVq7BvjYMe7eXo5TRERERERERK4Q5S5xk4cejvcfspVFOd6/9lJ/KXAa6GCM8c9nm3k56hSqjTGmEtDB0f+yAvQjIiIiIiIiIleQ8vioFADGmGeAakAg0BboRFbSZkK2atc43n/O2d5ae94YsxNoCTQCtuSjzQFjzCmgvjGmirX2tDGmKlAPOGmtPeBlqNsc782ylTUGKgA7rLXn89kmV8aYdblsap6f9iIiIlL+KR4QERG5PJXbxA3wDHBVtp+/BuKstb9mKwt0vKflsg9neY0CtqnqqHe6GPvI2UZERERERERErjDl9lEpa21da60B6gL3kHXXzAZjTETpjqzkWWvbeHsBW0t7bCL5ZYzh1ltvzXf9xMREjDGMGDGi2MYkIlKeKB4QEZGSoti9ZJXbxI2TtfaQtfYzIAaoDbyXbbPzzpVAj4bu5ccK0SYtx3tx9HEsl+3l1ubDm3njf28wZukY3vjfG2w+vLm0hyS5GDFiBMYYEhMTS3soxS4/Xzz//e9/iYmJoX79+lSuXJlGjRpx7733smrVqjzbiYiIiJRHitvLF8Xu7i632L08Pyrlxlq7yxiTBLQ2xgRZa48AP5E1/00zspbldjHG+ALhwHlgR7ZNPwFBjjarcrQJIesxqb3W2tOOfk8ZY/YB9YwxIV7muXGucpV9zpztZC1R3sgY4+tlnhtvbcq1RTsWMWrpKJbuWuqxLTI0kuGRw4luFF0KI5Py6qabbmLLli0EBQUVe1/PPfccr7zyCrVr1+auu+4iKCiIX375hblz5/Lpp5/y3nvv8cc//rHYxyEiIiJS3BS3S3FQ7H5pyv0dNzlc7Xi/4Hj/1vH+ey91I4EqwEpr7bls5Xm16ZajTqHaOJb/Xuno/5YC9FMuvbv+XWJmxHi9+AMs3bWUmBkxTN0wtYRHJuVZlSpVaN68ebFf/A8ePMirr77KVVddRVJSEu+88w4TJkxg9uzZzJ8/H2stw4cPL9YxiIiIiJQExe1SXBS7X5pylbgxxjQzxng8XmSM8THGjAWCyUrEHHVsmg0cAe4zxrTNVr8SMMbx4z9z7C4BOAc8bowJy9amJjDM8ePbOdo4f37RUc/ZJgz4s2N/CTnaOPsd4xiPs82NQF/gV+DTnMda3izasYhHvnyETJuZZ71Mm8nALwayaMeiEhpZ7lavXk3fvn2pV68e/v7+hISEEBMTwyeffOJW75NPPiEyMpLAwEAqV67M9ddfz/jx4zl37pzHPsPCwggLC+PUqVMMHTqUhg0b4u/vT5MmTZg4cSLWWlfd7777DmMMd999d65jbNGiBf7+/qSmprqVz58/nzvuuIOgoCD8/f1p3LgxQ4cO5dgxz6funGM6fvw4Tz31FGFhYfj5+TFixAjCwsIYOXIkAF26dMEY43pld/r0acaPH0/r1q2pWrUq1apVo3379sycOdPruNPT0xk9ejSNGzfG39+f8PBwXnrpJa/n7GJye0721ltvxRjD+fPnGTduHE2bNsXf358GDRrw3HPPkZ6e7qo7bdo01zEtWbLE7Tid+921axeZmZncfPPNBAcHu/XVpUsXqlevzq+//oqIiIhIeVYe43ZQ7K7Y/cqI3cvbo1J3AOONMcuBnUAKWStLdSZrcuKDwEBnZWvtcWPMQLISOInGmI+AVOBOspb9ng18nL0Da+1OY8xQ4A1grTHmYyAd6A3UByZZa1flaLPSGPMa8BTwgzFmNlCRrARMLeAv1trkHMfyEVmTKvcma1LlL8iao6cvWUuFD7TWHi/siSorRi0dddGLv1OmzWT00tGleuvllClTePTRR6lQoQJ33nknTZs25fDhw6xdu5a33nqLPn36ADBs2DDGjx9PUFAQ999/P9WqVWPevHkMGzaM+fPns2DBAipWrOi274yMDLp27cr+/fvp1q0bvr6+zJkzh+eff56zZ88SHx8PQLt27bjmmmv46quvSElJoXbt2m77Wb16NVu3bqVXr17UqlXLVT5y5EhGjBhBrVq16N69O8HBwfzwww+8+uqrfPXVV6xatYqAgAC3faWnpxMVFUVqaioxMTEEBAQQHh7O4MGDmTNnDkuWLCE2NpawsDCPc3Xs2DGioqLYsGEDERERDBgwgMzMTObPn8/999/P5s2bGTNmjKu+tZY+ffowd+5cGjduzOOPP056ejpTp07lxx9/vKR/N2/uv/9+li1bRrdu3QgICOCrr77ilVde4fDhwyQkZOVRW7duTXx8PCNHjiQ0NJS4uDhXe+dzs02bNqVixYqsXr2aI0eOuP2VYOnSpZw4cYK77rqryMcvIiIiUpLKW9wOit0Vu8e52l/2sbu1tty8gOuAN4GNZN1Jc56siX7XACOAWrm06wh8BRwFzgA/AkOACnn01QNYApwATjn6iL3I+OIc9U452i0BuudR39cxjh8d4zrqGGeHIjpf6yIiIuzFJCUl2aSkpIvWK6hNhzZZRlDg16ZDm4p8LPmxefNm6+vra2vWrGk3bfIcw549e6y11q5cudICtkGDBvbAgQOu7RkZGbZ79+4WsGPHjnVrGxoaagHbrVs3e/r0aVf5oUOHbGBgoA0MDLTp6emu8nHjxlnA/v3vf/cYx2OPPWYB+/nnn7vKvv32WwvY9u3b26NHj7rVT0hIsIAdPHiw1zFFR0fbkydPevQTHx9vAbt48WJvp8vGxsZawE6cONGt/MyZM7Zr167WGGM3bNjgKv/ggw8sYNu1a2fPnDnjKk9JSbGNGjWygO3cubPXvrxZvHixBWx8fLxbeefOnS1gIyIibEpKiqv85MmTtnHjxtbHx8ft381ae9G+X3/9dWuMsXXq1LEDBw60zz//vL333nutv7+/vf322+2hQ4fyPe7cFNfnUERKXkREhAXW2TIQO9kCxAMiUvYpbv+NYnd3it1/U9yxe34/h0UZD5SrR6WstZustY9ba1tba4Ostb7W2kBr7Y3W2hHW2tRc2q2w1t5hra1pra1srb3eWvu6tfaCt/qONl9Yaztba6tba6s6+ph+kfFNc9Sr6mjX2Vr7ZR71zzvGcb1jXDUd41yZ/7NSdi3aWbjbJwvb7lL985//5Pz587z88su0bNnSY3v9+vUBmDo165nel156ibp167q2+/r6MmnSJHx8fHjnnXe89vHGG29QuXJl18/BwcH07NmTtLQ0fvrpJ1f5Aw88gI+PD9Onu//Kpaen89FHHxEcHEy3bt3c9gtZf3WoUaOGW5u4uDhat27NBx984HVMkyZNomrVql635SYlJYUZM2bQtm1bnn32WbdtlSpVct1C+uGHH7rKnZnycePGUamS6+lAatWqxcsvv1yg/vNj4sSJbn/VqFq1Kv379yczM5O1a9cWaF+DBw/mP//5D+fPn2fKlClMmDCBWbNm0aBBA+Li4jxuwxQREREpT8pb3A6K3QtCsXv5j93L26NSUo4cP1e4J70K2+5SfffddwBuF1Vv1q9fD0BUVJTHtmbNmlG/fn127txJWloagYG/TckUGBhIkyZNPNo0aNAAgKNHj7rK6tevT3R0NAsXLiQpKYlrr70WgC+++ILU1FSGDBmCr+9vH99Vq1bh5+fHrFmzmDVrlkcf6enp/Prrrx63b1aqVInf/e53eR6vN2vWrOHChQten1OFrFtLAbZs2eIqW79+PT4+PnTq1Mmjvrfl/DZu3MicOXPcymrUqMHgwYPzNca2bdt6lHk71/nxyiuvMGzYMJ544gkef/xx6taty9atW3nhhRfo378/Gzdu5JVXXinQPkVERETKivIWt4Ni94JQ7F7+Y3clbqTYBPgHXLxSEba7VM5JwOrVq5dnvbS0NABCQkK8bg8JCWH37t0cO3bM7eKfM5vu5LyIX7jgfgNYXFwcCxcuZPr06UycOBHAlcWPjY11q5uSksL58+ddk5Ll5uTJk24X/+DgYI9Jy/IjJSUFyPoSWLNmTZ79OaWlpVGrVi38/Pw86mX/64fTxo0bPY4nNDQ03xd/b+c7t3Odl8TERJ577jnuvvtuXnvtNVd5REQEn332Gc2aNWPSpEkMGjSIRo0a5Xu/IiIiImVFeYvbQbF7QSh2L/+xe7l6VErKl+jwwk1WVth2l8p5sdi3b1+e9ZwX9IMHD3rdfuDAAbd6hXX33XcTEBDAjBkzuHDhAocPH2bevHm0atWKVq1aeYypZs2aF302MjQ01K1dYS78zv4AhgwZkmd/ixcvdmuTmprqyuhn5+1cxsXFeewvOTm5UOO9FF9+mfW0Y5cuXTy2ValShZtuuonMzEw2bNhQ0kMTERERKRLlLW4Hxe4Fodg9S3mO3ZW4kWLTMrglkaGRBWrTObQzLYM9n1EtCe3atQNg3rx5eda74YYbgKxsbk6//PILe/fuJTw8PNcsfX5VrlyZPn36sH//fr755hs+/PBDzp8/75Gxd4796NGjbN68+ZL6zK5ChQqA9wz3TTfdhI+PD8uWLcv3/iIiIsjMzGT58uUe27ydy5Lk4+OTaybfudxhbssGOstzrkQgIiIiUl6Ut7gdFLvnpNg9y+UauytxI8VqeORwfEz+fs18jA8vRxb9RFf59eijj+Lr68vo0aNJSkry2L53714ABgwYAMCYMWPcLggXLlzgmWeeITMzk4cffrhIxuRc4u69997jvffew9fXl/79+3vUGzJkCAADBw5k//79HttPnTrleg44v5y3Ze7evdtjW3BwMP3792ft2rWMHj3a64Vz+/bt7Ny50/XzQw89BMCLL77I2bNnXeWpqaluSw+Whtq1a7Nnzx6v22655RYA/v3vf3v8RWfevHmsWLGCSpUq0aFDh2Ifp4iIiEhxKU9xOyh2z0mxe5bLNXbXHDdSrKIbRfPv7v/mkS8fIdNm5lrPx/gwpccUohuV3u2W1157LW+99RaDBg3ihhtuoGfPnjRt2pSUlBTWrFlDQEAAixcvpkOHDjz77LO88sorXHfddfTu3ZuqVasyb948Nm3aRKdOnRg6dGiRjKljx440adKEWbNmkZGRQY8ePbzOgh4dHc2ECRN44YUXaNq0KXfccQfh4eGcPHmSXbt2sWTJEjp16sTXX3+d7767dOmCj48PL7zwAps2baJmzZpA1oz8AG+++Sbbtm1j+PDhvP/++3Tq1ImrrrqK/fv3s2XLFtasWcPMmTMJDw8HoF+/fnz88cd8/vnnXHfddfTs2ZOMjAxmz57NjTfeyPbt24vgjBVOdHQ0H330ET169CAiIgI/Pz8iIyOJjIykd+/e3HbbbXzzzTe0aNGCu+++m7p167Jlyxa+/PJLrLVMmDDB7fljERERkfKmPMXtoNg9J8Xul3nsXhRriutVNl/AuoiICHsx+V2H/lJ8s/0b2zmhs2UEHq/OCZ3tN9u/Kdb+C2LlypX2nnvusXXq1LF+fn42JCTEdu3a1c6aNcut3syZM23Hjh1ttWrVrL+/v7322mvtmDFj7JkzZzz2GRoaakNDQ732Fx8fbwG7ePFir9tHjx5tAQvY2bNn5zn2ZcuW2XvvvdeGhIRYPz8/GxQUZFu1amWHDBli16xZk+8xOb3//vu2VatWtlKlSq4xZHfu3Dn797//3bZv394GBATYihUr2gYNGtioqCj7+uuv2yNHjnjUHzlypA0PD7cVK1a0oaGhdtiwYfbs2bMWsJ07d85zPNktXrzYAjY+Pt6tvHPnzh7jdEpISLCATUhIcCs/dOiQ7devnw0ODrY+Pj4e+01PT7evv/66vfnmm2316tVthQoVbJ06dewf/vAHO3/+/HyPOS8l8TkUkZIRERFhgXW2DMQCtgDxgIiUfYrbPSl2/41i9yzFHbvn93NYlPGAsVlf6HIZMsasi4iIiFi3bl2e9ZzLvrVo0aLYx7T58GYW7VzE8XPHCfAPIDo8ulSfjRUpK0rycygixatNmzasX79+vbW2TWmPBfIfD4hI2ae4XaT05fdzWJTxgB6VkhLVMrilLvgiIiIiImWc4naRskOTE4uIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IiIiIiIiIiIlFFK3IhcggULFtChQwdq1KiBMYa77rrLtW3t2rXcfvvtBAUFYYyhdevWAMTFxWGMITk5uVB9JicnY4whLi7uksaemJiIMYYRI0YUqN3BgweJjY2lfv36VKhQAWMMx44dY9q0aRhjmDZt2iWNS0RERESkOCh2V+xeXvmW9gBEyqvk5GR69uxJjRo1GDBgAAEBATRv3hyA48eP84c//IGzZ8/ywAMPEBQURN26dUt5xEUjLi6OBQsW0K9fP5o0aYIxhkqVKpXaeBITE+nSpQvx8fEF/iITERERkSuDYnfF7uWZEjdS4ozJere2dMdxqb755hvOnj3LpEmTuP/++922rV69msOHDzN27FiGDRvmtm38+PE8//zz1KtXr1D91qtXjy1bthAYGFjosRdWeno6Cxcu5LbbbuODDz4o8f5FREREpORcLnE7KHZX7F6+KXEjUkj79+8H4Oqrry7QtpCQEEJCQgrdr5+fn+uvAyXt4MGDZGZmej0uEREREZGySrG7lGea40Ykh08++YTIyEgCAwOpXLky119/PePHj+fcuXPAb8+XxsfHA9ClSxeMMa5nRI0xxMbGAvDQQw+5bYO8n5NdvXo1ffv2pV69evj7+xMSEkJMTAyffPKJq05uz8n+/PPPPP/887Rt25Y6derg7+9PaGgojzzyCHv37r3k8xIWFkZoaCgA06dPdx1Xfp7XXbduHb169SI4ONg1rscee4wDBw541C3IccTFxdGlSxcARo4c6RqTMYbExMRLPmYRERERKdsUu3un2P3yojtuRLIZNmwY48ePJygoiPvvv59q1aoxb948hg0bxvz581mwYAFhYWHEx8eTmJjIkiVLiI2NJSwsDIDWrVsTHx/Pxo0bmTt3Lj179nRNbOZ8z82UKVN49NFHqVChAnfeeSdNmzbl8OHDrF27lrfeeos+ffrk2f4///kPb7/9Nl26dKFDhw5UrFiRzZs388477/DFF1+wdu3aQt/iCTB48GCSk5OZPHkyrVq1ck3mdrHj+vLLL+nVqxfWWnr37k1oaCjr1q3jn//8J3PnzmX58uWEh4cX6jicY5g+fTqdO3fm1ltvde3H+W8iIiIiIpcnxe65U+x+mbHW6nWZvoB1ERER9mKSkpJsUlLSResVlaynZEusu3xbuXKlBWyDBg3sgQMHXOUZGRm2e/fuFrBjx451lcfHx1vALl682GNfCQkJFrAJCQke22JjYy1gd+7c6SrbvHmz9fX1tTVr1rSbNm3yaLNnzx7Xf+/cudMCNjY21q3O3r177dmzZz3azp8/3/r4+NhBgwa5lS9evNgCNj4+3qNNbnLr21rvx3zixAlbq1Yt6+PjY5cuXepWf8KECRawt99+e4kfR1lU0p9DESk+ERERFlhny0AsYAsQD4hI2ae4/TeK3S9OsXvxyO/nsCjjAT0qJcXKGM9XfraVhqlTpwLw0ksvuc0i7+vry6RJk/Dx8eGdd94plr7/+c9/cv78eV5++WVatmzpsb1+/foX3YfzFs2cYmJiaNmyJfPnzy+SsRbE3LlzSU1NpW/fvtxyyy1u255++mnCwsJYuHAhu3fvdpWXxeMQERERudyVp7gdFLsXB8XuZZcelRJxWL9+PQBRUVEe25o1a0b9+vXZuXMnaWlpRT4r/HfffQdAt27dCr0Pay0ffPAB06ZN4/vvv+fo0aNcuHDBtb1ixYoX3cecOXPYuHGjW1nr1q1dtzUWVF7n1NfXl8jISJKTk9mwYQMNGzYssuMQERERkcubYnfF7lcSJW6kWFkvSweW1WUF09LSAHKdNT4kJITdu3dz7Ik51xEAACAASURBVNixIr/4Hzt2DOCSnmN96qmn+Nvf/kZISAhdu3alXr16VK5cGYBp06axa9eui+5jzpw5TJ8+3a0sNja20Bf//JxT+O34i+o4RERERKRgylPcDordQbH7lUSJGxEH5wX94MGDNG7c2GO7cxb1or7wA9SoUQOAffv2FWq5wMOHD/PGG29w3XXXsXLlSqpXr+62febMmfnaz7Rp01wz6BeF7OfUm5zntKiOQ0REREQub4rdFbtfSTTHjYjDDTfcAOB1KbpffvmFvXv3Eh4e7rpQF6V27doBMG/evEK137FjB5mZmcTExHhcMPfu3cuOHTsueYyFkdc5PX/+PMuWLQMgIiICKNxxVKhQAcDtlkwRERERubwpdi96it3LLiVuRBwGDBgAwJgxY/j1119d5RcuXOCZZ54hMzOThx9+uFj6fvT/2bvz+DrLMvH/nysFyiJhKwylQqEMsgSZAjKjFRIg0tEvSP0qCC5YbG0HRhEFdRikLbTgirgBOpRCUeZrWRzwBwNDNdBGqcomOgTHrS1MHZiqQMtiKyXX74/nnJKmSZOmT/bP+/U6r+ec576v577PoUmec3EvZ5/NVlttxZw5c3j88cc3Kl+xYsUm46vb5/3oRz/a4JfgCy+8wLRp01i3bl2p/e2ud7zjHey666585zvfWT8XuOorX/kKy5Yt4y1vecv6ObI9eR+77bYbwAaLpEmSJGlo8969fN67D1xOlZIqJkyYwKc+9Sm+8IUvcOihh3LKKaewww47cPfdd/PYY49x9NFH88lPfrJX2j7kkEO4+uqrOeusszj88MOZNGkSBxxwAH/605948MEHqa2t5b777us0fs899+T0009nwYIFjB8/nokTJ7Jq1Sq+//3vs+222zJ+/PiNFi7rC695zWu47rrrOPXUU2loaODUU09ln3324eGHH2bhwoXsueee/Mu//MsWvY8DDzyQMWPGsGDBArbeemvGjh1LRHDGGWcwduzYvn7LkiRJ6gPeu5fPe/eBy8SN+txAXNys6vOf/zyHH344V155Jd/61rd4+eWX2X///bn00ks5//zze3VV9GnTpnHooYdy+eWXs2jRIm6//XZGjRrFYYcdxoc+9KEu4+fNm8e4ceO46aabuOqqq9h99905+eSTmT17Nu9617t6rd9dmTRpEvfffz+f+cxnuOeee1i1ahV77rknZ511FjNmzGCvvfbaoP7mvo8RI0Zw2223ccEFF3DLLbfw/PPPk5kcffTR/vKXJEnaAgP5vh28d+8N3rsPTJED/adRPRYRDx9xxBFHPPzww5us98tf/hKAgw8+uC+6JakD/hxKQ8eRRx7JI4888khmHtnffYHu3w9IGvi8X5D6X3d/Dsu8H3CNG0mSJEmSpAHKxI0kSZIkSdIAZeJGkiRJkiRpgDJxI0mSJEmSNECZuJEkSZIkSRqgTNxIkiRJkiQNUCZuJEmSJEmSBigTN5IkSZIkSQOUiRtJkiRJkqQBysSNJEmSJEnSAGXiRpIkSZIkaYAycSNJkiRJkjRAmbiRJEmSJEkaoEzcSFtg4cKFTJgwgZ133pmI4B3veMf6soceeogTTjiBUaNGERGMHz8egDPPPJOIYPny5T1qc/ny5UQEZ5555hb1fdGiRUQEF198cbdjLr74YiKCRYsWbVHbkiRJUl/z3l2D1Vb93QFpsFq+fDmTJk1i5513ZsqUKdTW1nLQQQcBsHr1ak488UTWrFnDGWecwahRo9hzzz37uccDx6JFizjuuOOYNWtWp3981q5dy7XXXssNN9zA0qVLWbNmDXvvvTcnnHAC559/PmPHju3bTkuSJGnQ8t6957x3738mbtTn4pIAIGdlP/dky/zgBz9gzZo1fOlLX+K9733vBmUPPPAAK1eu5LLLLuPCCy/coOyzn/0sF1xwAWPGjOlRu2PGjOGXv/wlO+20U4/73lMf+chHOP3009lnn316tZ1169bR2NjI/fffz0EHHcR73vMeRo4cyYMPPsjXv/51vvWtb7FkyRIOOeSQXu2HJEnScDZU7tvBe/fe5L177zNxI/XQ//zP/wCw1157bVbZ6NGjGT16dI/b3Xrrrdf/34G+NmrUKEaNGtXr7dx2223cf//9NDY2snDhQmpqXp3VOWvWLGbPns3ll1/Odddd1+t9kSRJ0uDnvXvv8d6997nGjdTOzTffTH19PTvttBPbbbcdr3/96/nsZz/L2rVrgVfnl86aNQuA4447joggIpg/fz4RweTJkwH44Ac/uEEZbHqe7AMPPMBpp53GmDFjGDlyJKNHj2bixIncfPPN6+t0Nk/217/+NRdccAFveMMb2H333Rk5ciRjx45l+vTprFixopTPprN5shHBscceyx//+EemT5/O6NGjGTlyJHV1dVx//fUb1D3zzDM57rjjALjkkkvWfz5tr7t06VIATjzxxA1+8QNMmjQJgD/84Q+lvCdJkiQNXt67d85796HDETdSGxdeeCGf/exnGTVqFO9973t5zWtew913382FF17IPffcw8KFC9l3332ZNWsWixYtYvHixUyePJl9990XgPHjxzNr1iweffRRvve97zFp0qT1C5tVj52ZO3cuZ599NiNGjODkk0/mgAMOYOXKlTz00ENcffXVvPvd795k/L/927/xzW9+k+OOO44JEyawzTbb0NLSwrXXXssdd9zBQw891OMhnt3x3HPP8eY3v5ltttmGU045hbVr13LLLbcwZcoUampq1v9BrC4Cd8MNN9DQ0MCxxx67/hrVz7Gurg6Au+++m3PPPXeDPwB33nknAG95y1t67b1IkiRp4PPevee8dx9kMnPQPIDdgA8BtwG/Bf4MrAJ+BEwFatrV3xfITTwWbKKtycADwAuVNhYBJ22i/gjg48AvKv16BrgLmLCJmO2AS4BfAWuAlcDNwMElfV4PH3HEEdmVxx9/PB9//PEu65WFi0kups/a664lS5YkkHvvvXc+9dRT68+//PLLedJJJyWQl1122frzs2bNSiDvu+++ja51/fXXJ5DXX3/9RmWTJ09OIJctW7b+XEtLS2611Va5yy675GOPPbZRzH//93+vf75s2bIEcvLkyRvUWbFiRa5Zs2aj2HvuuSdramryrLPO2uD8fffdl0DOmjVro5jOdPaeqz9TU6dOzXXr1m3wvkaMGJEHH3zwZrXd2tqa73znOxPIQw45JD/60Y/mJz7xiTzuuONy6623znPOOSdffvnlbvd7MOjrn0NJveeII45I4OEcAPdOuRn3A5IGPu/bX+W9e9e8d+8d3f05LPN+YLCNuDkV+AbwFHAf8CTwV8A7gWuBt0XEqZnZfvWsnwO3d3C9xzpqJCIuB84HVgBzgW2A04E7IuKczLyyXf0AFgCnUCRhrgR2BU4DmiPiXZn5vXYxI4HvA28GHgK+CuxdeY8nRsTxmfnTLj+RAa66oFl3y/pz4bPqnMuLLrpog1Xkt9pqK770pS9x1113ce211260YFkZvvGNb7Bu3TpmzJixPmPd1mtf+9our9FZRn7ixInU1dVxzz33bHE/N2X77bfniiuuYMSIEevPHXLIIbz5zW+mubmZF154gde85jXdulZEcOutt3LJJZdw6aWX8vjjj68va2xs5L3vfS9bbTXYfn1JkiQNXIPpvh28d99S3rsPLoPt0/s1cDLw75nZWj0ZERdSjI55F0US57vt4h7NzIu700BETKBI2vwOOCozn62c/yLwMHB5RNyZmcvbhJ1OkbRZAjRm5ppKzDcpRgPNjYh7M/P5NjHnUSRtbgVOq76fiLiJIsl0XUS8vu37VO965JFHADj++OM3Knvd617Ha1/7WpYtW8aqVatKXxX+Jz/5CQBve9vbenyNzORf//VfmT9/Pj//+c959tlneeWVV9aXb7PNNl1e4/bbb+fRRx/d4Nz48ePXD5HclAMOOIDa2tqNzu+9994APPvss93+5b9mzRo+8IEPcPfdd3PVVVcxadIktt9+e+6//34++tGPUl9fzy233LJ+zqwkSZKGF+/dvXcfTgZV4iYz7+3k/NOVJMllwLFsnLjZHGdVjpdVkzaVNpZHxFXADOCDwKw2MWdXjhdVkzaVmAcriZgzKBI718P6ETrVdj7VNjmTmd+LiB8CxwANFCOLBq2OMvEDdVvBVatWAXS6avzo0aN58sknee6550r/5f/cc88BnWfeu+O8887jK1/5CqNHj+bv//7vGTNmDNtttx0A8+fP54knnujyGrfffjs33HDDBucmT57crV/+O++8c4fnq9n1tn+IuvK5z32OW265ha9+9av8wz/8w/rzb3vb27j11lsZP3485557rr/8JUmSSjKY7tvBe3fw3n04GVSJmy68XDmu66Bsr4j4B4o1cv4E/Dgzf9HJdaop2//ooOxuisTN8VQSNxGxLTABeAn4YScxZ1Riqkt07w/sA/w6M5d1EnNMJWZQJ24Gk+ov9Keffpr9999/o/Knnnpqg3plqv7i/P3vf9+j7QJXrlzJ1772NQ499FCWLFnCjjvuuEH5d77znW5dZ/78+etX0O9P1UXMqivYt/U3f/M37LLLLjzxxBP86U9/Yrfdduvr7kmSJKmfee/uvftwMiS2A4+IrYAPVF52lHA5AaiOyPkm8POIuC8i9ml3nR2AMcALmflUB9f5TeX4ujbn9qdYmHhpZnaUNOoo5sDK8dcdv6MOY9TLDj/8cICNtssD+O1vf8uKFSvYb7/9Os1Ob4k3vvGNQLESe08sXbqU1tZWJk6cuNEv/hUrVqzfom+gqM6l7SyTX92+saNtA9euXcvzzxezDrszhFSSJElDj/fufcd79/43JBI3wOeAQ4G7MrPtKk4vAXOAI4FdKo/q9KNjgaZKsqaqmo5d1Uk71fNtf/r7KqZTEfFwRw9g89O/w9iUKVMAuPTSSzf4pfPKK6/wiU98gtbWVqZOndorbZ999tlstdVWzJkzZ4PFvKpWrFixyfjqVnw/+tGPNviF+sILLzBt2jTWresop9h/qpn2J598ssPyY445BoDPfOYz6/8QVF188cWsW7eOo446aqM/dJI0nHk/IGk48d6973jv3v8G/VSpiPgoxWLC/0UxJWm9zFwJzGwX0hwREykWDf47iu3Fv9oHXdUAN2HCBD71qU/xhS98gUMPPZRTTjmFHXbYgbvvvpvHHnuMo48+mk9+8pO90vYhhxzC1VdfzVlnncXhhx/OpEmTOOCAA/jTn/7Egw8+SG1tLffd1/msuT333JPTTz+dBQsWMH78eCZOnMiqVav4/ve/z7bbbsv48eM3WrisPx144IGMGTOGBQsWsPXWWzN27FgigjPOOIOxY8fy6U9/mjvuuIOmpiYOOugg3vrWt7Lddttx//3388ADD7Dddtvx1a/6YytJkjRcee/ed7x373+DOnETER+hSLo8TrGb0zPdicvMdRFxLUXipp5XEzfVkS6dTYSsnn+uzbm+iulUZh7Z0fnK/2U7ojvX6EsDcXGzqs9//vMcfvjhXHnllXzrW9/i5ZdfZv/99+fSSy/l/PPP79XhfdOmTePQQw/l8ssvZ9GiRdx+++2MGjWKww47jA996ENdxs+bN49x48Zx0003cdVVV7H77rtz8sknM3v2bN71rnf1Wr97YsSIEdx2221ccMEF3HLLLTz//PNkJkcffTRjx45lzJgxPPLII3z+85/n3//937n++utpbW1l9OjRnHnmmfzTP/1Tj+YTS9JQNtjuByQNfAP5vh28d+8r3rv3v8gc2D+MnYmIjwFfBh6jSNqs3Mz4SRTbbt+TmW9tc34FxTo3e7Vf5yYi3kSx5fePMvOYyrltgReAtcBO7de5iYj3AP8PuDEzz6ic+2uKdWx+nZkH0k5E/DPwGeDSzJyxOe+r3XUePuKII454+OGHN1nvl7/8JQAHH3xwT5uStIX8Odx8UWx0wSD9M6Yh7Mgjj+SRRx55pLNESl/r7v2ApIHP+wWp/3X357DM+4FBucZNRPwTRdLmUeC4zU3aVLyxcmy/8lN1y/G3srG3tatDZfvvJcD2FDtBdRkD/A54EnhdROzXzRhJkiRJkjTMDLrETUTMoFiM+GGKkTZ/3ETdIyJio/cYEY3Axysvb2xX/M3K8dMRsUubmH2BD1OMrLm+Xcw3KsdLKyNwqjFHAacBfwC+Wz2fxTCnajtfaNvHykigYyimfy3u7L1JkiRJkqShb1CtcRMRk4HZwCvAD4GPRnWs/KuWZ+b8yvMrgAMiYglQXdr7MOD4yvMZmbmkbXBmLomIK4DzgF9ExK3ANhQJmF2BczJzebs2FwDvBE4BfhYRdwC7VWJGANMyc3W7mCuAkyoxP42IJmAf4FSK3bCmZGZrdz4XSRpuWlpeff61r0FjI9TV9V9/JEmSpN4yqBI3QHVa0QjgY53UWQzMrzz/NvB/gaMoph9tDfwvcDNwZWb+sKMLZOb5EfGfFCNspgOtwCPAFzPzzg7qZ2UtmyXAFOAcYA3QTLFOzZIOYtZGxAnABcB7KEYAraZYd2dWZm68r5wkDXNNTTB7NjQ3v3ru3HOLY309zJxZJHEkSZKkoWJQJW4y82Lg4s2oPw+Y18O25vNqAqg79ddRrLvz5c2IeYliu/L2W5ZLktrYeHDlxpqb4S1vKZ67YLEkSZKGikG3xo0kSV1paurvHkiSJEnlGFQjbiRpKEqHh3Spvn7D6VFdmTPHKVPatJaVLTQta2L12tXUjqylcb9G6vZwoSRJktS5/rpvN3EjIoLMpLW1lZoaB2FJfa36B6CDxdZFsRDx5iRtABYvLuJcsFjtNS1tYnbzbJqf2PgfVf3YembWz6RxnFk/SQOT9+1S/+qv+3Z/2sXIkSMBePHFF/u5J9LwVP3Zq/4sakM9nfbkdCm1N++ReUy8cWKHSRuA5ieamXjjRK772XV93DNJ6h7v26X+1V/37SZuxI477gjA008/zfPPP09ra6tTN6ReVv2/Zc8//zxPP/008OrPoja0enXfxmloalraxPQ7p9OarZus15qtTLtjGk1LzfxJGni8b5f63kC4b3eqlNh111158cUXeemll1ixYkV/d0calrbffnt23XXX/u7GgFRb27dxGppmN8/uMmlT1ZqtzGme45QpSQOO9+1S/+uP+/YeJW4ior6Etpdn5pMlXEdbqKamhr333ptnnnmG559/nrVr15q5l/pARDBy5Eh23HFHdt11V+eqd6Kniwy7OLGqWla2dDo9qjOLn1hMy8oWFyyWNKB43y71j/6+b+/piJtFwJb+hrgEmL2F11BJampqGDVqFKNGjervrkjSBurqNn9XqYYGFybWq5qW9WzaU9OyJhM3kgYc79ul4WdLpkotrjw2VwAzt6BdSdIwM3MmTJwIrd2Y6VJTAzNm9H6fNHisXtuzBY96GidJklSmLUncLMrMHo2YiQgTN5KkbmtshGuugenTN528qamBuXOdJqUN1Y7s2YJHPY2TJEkqU08nZrUAK7eg3S2NlyQNM1OnwsKFxTSojjQ0FOVTpvRtvzTwNe7Xs0xeT+MkSZLK1KMRN5n5+i1pdEvjJUnDU2Nj8WhpgaamYsvv2trinGvaqDN1e9RRP7Z+sxYobhjb4Po2kiRpQHA7cEnSoFNXZ6JGm2dm/Uwm3jixW1uC10QNM+pdKEmSJA0MvbqHVURsHRGHR8SBvdmOJEnSpjSOa+Sak66hJjZ961MTNcx9+1waxzlNSpIkDQylJG4i4t0RcXNE7Nrm3P4Ua9k8BDweEf8WEY7wkSRJ/WLqEVNZ+P6FNIzteKGkhrENLHz/QqYc7kJJkiRp4CgrkTIF2Cszn2lz7kvAXwP3ArsBk4APAnNLalOSJGmzNI5rpHFcIy0rW2ha1sTqtaupHVlL436NrmkjSZIGpLISN4cA36++iIha4P8AN2fm6RGxNfAoJm4kSdIAULdHnYkaSZI0KJS1xs3uwFNtXr+JIim0ACAzX6ZI7OxfUnuSJEmSJElDXlmJm+eBndq8bgAS+FGbc2uAHUtqT5IkSZIkacgra6rUb4C3RcRIioTNu4FfZOYf29QZC6wsqT1JkiRJkqQhr6wRN9cA4ygSOL8E9gOub1fnSIpdpiRJkiRJktQNpSRuMvMG4HPA9hRTpq4Evl4tj4gJFDtM3VdGe5IkSZIkScNBWVOlyMwLgQs7KX4I2AV4saz2JEmSJEmShrrSEjebkpl/Af7SF21JkiRJkiQNFWWtcSNJkiRJkqSSlTLiJiJaKXaT6kpmZp+M8pEkSZIkSRrsykqiNNNx4mZn4HXAdsDPgedKak+SJEmSJGnIKyVxk5nHdlYWETsCXwYmAO8soz1JkiRJkqThoNfXuMnM54HpwDrgst5uT5IkSZIkaajok8WJM7MVuA94R1+0J0mSJEmSNBT05a5S2wK79GF7kiRJkiRJg1qfJG4i4iDgVOC3fdGeJEmSJEnSUFDWduDXbeL6ewNvBkYA55fRniRJkiRJ0nBQ1nbgZ3ZR/l/AFzPz+pLakyRJkiRJGvLKStzs18n5VuDZzHyhpHYkSZIkSZKGjVISN5n5RBnXkSRJkiRJ0qv6clcpSZIkSZIkbYYeJW4i4m8jYq+eNrql8ZIkSZIkScNBT0fc/Bj40Ba0u6XxkiRJkiRJQ15PEzexhe1uabwkSZIkSdKQtyWLE38sIs7sYWxuQbuSJEmSJEnDQk8TN09SJF96OnLmSeC5HsZKkiRJkiQNCz1K3GTmviX3Q5IkSZIkSe24HbgkSZIkSdIAZeJGkiRJkiRpgDJxI0mSJEmSNEBtya5SkrTZWla20LSsidVrV1M7spbG/Rqp26Ouv7slSZIkSQOSiRtJfaJpaROzm2fT/ETzRmX1Y+uZWT+TxnGN/dAzSZIkSRq4nColqdfNe2QeE2+c2GHSBqD5iWYm3jiR6352XR/3TJIkSZIGNhM3knpV09Impt85ndZs3WS91mxl2h3TaFra1Ec9kyRJkqSBz8SNpF41u3l2l0mbqtZsZU7znF7ukSRJkiQNHqUmbiLi7RGxICJ+HhG/bXP+4Ij4VESMKbM9SQNby8qWTqdHdWbxE4tpWdnSSz2SJEmSpMGllMRNFG4AbgdOBfYH9mtT5VngM8D7y2hP0uDQtKxn0556GidJkiRJQ01ZI27+ETgDuB7YFbi8bWFmPg3cD5xYUnuSBoHVa1f3aZwkSZIkDTVlJW6mAj8HpmXmKiA7qPMbNhyFI2mIqx1Z26dxkiRJkjTUlJW4ORC4LzM7SthUrQR2L6k9SYNA436NfRonSZIkSUNNWYmbdcC2XdQZA7xQUnuSBoG6PeqoH1u/WTENYxuo26Oul3okSZIkSYNLWYmbx4FjIyI6KoyIbYHjgZ9tSSMRsVtEfCgibouI30bEnyNiVUT8KCKmRkSH7yciJkTEXRHxTCXmFxHxsYgYsYm2ToqIRZXrvxARP42IyV30b3JEPFCpv6oSf9Im6o+IiI9X+vPnSv/uiogJ3f9UpIFtZv1Majr+0dxITdQwo35GL/dIkiRJkgaPshI33wYOAr7cPnlSSY5cAewFzN/Cdk4F5gJ/B/wU+ArwXeBQ4Frg5vbJo4iYBDQD9cBtwJXANsCXgQUdNRIRHwHuqFz3xkqbewHzI+LyTmIup3h/oyv1bwReD9xRuV77+lFp/4pKf66s9K8eaK70Wxr0Gsc1cs1J13SZvKmJGua+fS6N45wmJUmSJElVW5V0nX8BTgY+SpFceR4gIm4F3kiR9PheZv7rFrbz60o7/56ZrdWTEXEh8ADwLuCdFMkcIqKWIonyCnBsZj5UOT8DuBc4JSJOz8wFba61L8WuWM8Ab8jM5ZXzs4EHgfMj4ruZ+eM2MROA84HfAUdl5rOV818EHgYuj4g7q9eqOB04BVgCNGbmmkrMN4EfAXMj4t7MfH4LPzOp3009Yir77rwvc5rnsPiJxRuVN4xtYEb9DJM2kiRJktROKYmbzHylMiXoIuAjFKNOoEiiPAfMqTy2tJ17Ozn/dCXhcRlwLJXEDUViZHfgW9WkTaX+moi4CGgCzmbDkTdTgJHA59smWjLz2Yj4DDAPOAv4cZuYsyrHy6pJm0rM8oi4CpgBfBCY1Sbm7MrxomrSphLzYETcRLG9+ikUW6xLg17juEYaxzXSsrKFpmVNrF67mtqRtTTu1+iaNpIkSZLUibJG3JCZ64CLI+IS4HXAbsAq4L8y85Wy2tmElyvHdW3OHV85/kcH9ZuBl4AJETEyM9d2I+budnW6087dFImb46kkbipr/kyotP/DTmLOqMSYuNGQUrdHnYkaSZIkSeqm0hI3VZUtwX9V9nU3JSK2Aj5Qedk2eXJg5fjr9jGZuS4ilgF1wDjgl92IeSoiXgReGxHbZ+ZLEbEDlR2zMvOpDrr3m8rxdW3O7Q+MAJZWEl7dielURDzcSdFB3YmXJEmDn/cDkiQNTaUsThwR+0fEByJit07KR1XKx5XRXgc+R7GQ8F2ZeU+b8ztVjqs6iaue37kHMTu1O/ZGGzt3Ui5JkiRJkoaBskbcXAC8A/hOJ+WrKBb8/S6vru1Sioj4KMXCwP9FMb1o2MnMIzs6X/k/b0f0cXckSVI/8H5AkqShqaztwI8FfpCZL3dUWDn/fTZeG2aLVLbZ/irwOHBcZj7Trkr70THtVc8/14OYVe2OvdHGc52US5IkSZKkYaCsETdjgFu7qPMkxVbepYiIjwFfBh6j2E57ZQfVfgW8gWKtmA3mfVfWxdmPYjHjpe1iRlViftwuZjSwA7AiM18CyMwXI+L3wJiIGN3BOjcHVI5t18z5HcUW5eMiYqsO1rnpKEYaElpaoKkJVq+G2lpobIQ61yqWJEmSpA6VNeLmL0BtF3V2BLKMxiLinyiSNo9SjLTpKGkDUN0+/K0dlNUD2wNL2uwo1VXM29rV6VFMZfvvJZX2j9mMdqRBq6kJGhrg0EPh3HNhpKqZWAAAIABJREFUxozieOihxfmmpv7uoSRJkiQNPGUlbh4DToyIrTsqjIhtgJMopjRtkYiYQbEY8cMUI23+uInqtwJ/BE6PiDe0uca2wKWVl99oF3M9sBb4SETs2yZmF+DCystvtoupvv50pV41Zl/gw5Xrtd/Wu9rupZX+VGOOAk4D/kCxJpA06M2bBxMnQnNzx+XNzUX5ddf1bb8kSZIkaaAra6rUjcDVwM0RcXZmPl0tiIg9KRIbewNf2JJGImIyMJtimtEPgY9GRPtqyzNzPkBmro6IaRQJnEURsQB4hmLK1oGV8ze1Dc7MZRHxSeBrwEMRcRPFiKJTgNcCX8rMH7eLWRIRVwDnAb+IiFuBbSgSMLsC52Tm8nb9XAC8s3Ldn0XEHcBulZgRwLTMXL3ZH5I0wDQ1wfTp0Nq66XqtrTBtGowdW0yfkiRJkiSVl7i5hiIJMQk4ISJ+AfyeYu2bwyimBP2AjUeqbK79KscRwMc6qbMYmF99kZm3R0QD8GngXcC2wG8pkixfy8yNpm9l5tcjYjnwCeADFCOTHgcuyswbOmo0M8+PiP+kGGEzHWgFHgG+mJl3dlA/I+I9FFOmpgDnAGuAZuDSzFzS+ccgDR6zZ3edtKlqbYU5c0zcSJIkSVJVKYmbzGyNiBOBSyi2+35jm+LngK8Al2RmN7++ddrOxcDFPYi7H/g/mxlzB3DHZsbMp03SqBv111Gs1fPlzWlHGixaWjqfHtWZxYuLOBcsliRJkqTy1rghM1/OzAsppvscChxdOY7KzIs62ypc0tDV0wWHXahYkiRJkgplTZVarzKqZosXIZY0+K3u4SpNPY2TJEmSpKGmtBE3ktRebW3fxkmSJEnSUFPaiJuIOAA4F/hbYBeKBYTby8zcv6w2JQ1sPV1k2MWJJUmSJKlQyoibiHgT8Cjwj8B4ip2booOHI3ykYaSuDurrNy+mocGFiSVJkiSpqqwRN58FRgJnAddVdkuSJGbOhIkTu7cleE0NzJjR+32SJEmSpMGirBEwRwG3ZuY1Jm0ktdXYCNdcUyRlNqWmBubOdZqUJEmSJLVVVuLmL8CTJV1L0hAzdSosXFhMg+pIQ0NRPmVK3/ZLkiRJkga6sqZKLQEOL+lakoagxsbi0dICTU3Flt+1tcU517RRT8QlAUDOyn7uiSRJktR7ykrcXAgsiYgzMvPbJV1T0hBUV2eiRluuZWXL+udf++nXaNyvkbo9/IclSZKkoaesxM0k4F5gfkR8CHgYeK6DepmZc0pqU5I0zDQtbWJ282yan2hef+7c/zgXgPqx9cysn0njOBdKkiRJ0tBRVuLm4jbPj6k8OpKAiRtJ0mab98g8pt85ndbseIuy5ieamXjjROa+fS5TDnfBJEmSJA0NZSVujivpOpKGgSiWJiFdmkTd1LS0aZNJm6rWbGXaHdMYu9NYR95IkiRpSCglcZOZi8u4jiRJHXnLt9/S7bqt2cqc5jkmbiRJkjQklLUduCRJvaLtQsTdtfiJxT2KkyRJkgaaUhM3EXFYRHwuIr4XET9oc37fiHh3ROxSZnuSpKGvaVlTn8ZJkiRJA0lZa9wQEbMptgWvJoParl5RA3wH+Bjw9bLalCQNfavXru7TOEmSJGkgKWXETUScDlwEfB8YD3y2bXlmLgUeAk4uoz1Jg0fExo/ulElVtSNr+zROkiRJGkjKmir1UeC3wKTM/AXwlw7q/BI4oKT2JEnDRON+PVtkuKdxkiRJ0kBSVuLm9cA9mdlRwqbqf4C/Kqk9SYNE5saP7pRJVXV71FE/tn6zYhrGNlC3R10v9UiSJEnqO2UlbgJo7aLOXwFrSmpPkjSMzKyfSU10709WTdQwo35GL/dIkiRJ6htlJW5+A0zorDAiaoCjAfdmlSRttsZxjVxz0jVdJm9qooa5b59L4zinSUmSJGloKCtxczNwRESc30n5hcBfA/+vpPYkScPM1COmsvD9C2kY29BhecPYBha+fyFTDp/Sxz2TJEmSek9Z24F/BTgV+EJEvJvKVuARcTlwDPAG4CfANSW1J0kahhrHNdI4rpGWlS00LWti9drV1I6spXG/Rte0kSRJ0pBUSuImM/8cEccBXwXeB4yoFJ1HsfbNjcBHMnNdGe1JGtxchFhbqm6POhM1kiRJGhbKGnFDZq4CzoyI84CjgN2AVcADmfmHstqRJEmSJEkaLkpJ3ETEB4D/zcx7MvMZ4J4yritJkiRJkjSclbU48XXAW0u6liRJkiRJkigvcfN0ideSJEmSJEkS5SVb/gM4LiJM3kiSJEmSJJWkrETLp4EdgXkRMaqka0qSJEmSJA1rZe0q9R2KHaQ+AJweEcsppk+13/Q3M7OxpDYlSZIkSZKGtLISN8e2eT4SOLDyaK99IkeSJEmSJEmdKCVxk5mubSNJkiRJklQyEy6SJEmSJEkDlIkbSZIkSZKkAaq0xE1E1ETEORHxk4hYFRHr2pQdHhFXR8TrympPkiRJkiRpqCslcRMR2wDfB74C7A88D0SbKsuAKcD7ymhPkiRJkiRpOChrxM0ngeOAS4C/Aq5tW5iZzwHNwN+X1J4kSZIkSdKQV1bi5n3A/Zk5OzNb6Xjb72XAPiW1J0mSJEmSNOSVlbjZD/hJF3WeAXYtqT1JkiRJkqQhr6zEzRpg5y7q7AM8V1J7kiRJkiRJQ15ZiZtHgYmVRYo3EhE7Uaxv80BJ7UmSJEmSJA15ZSVurgH2Bv41ImrbFkTEzsB8YBfgmyW1J0mSJEmSNORtVcZFMvM7EXECcCZwMvAsQEQ8BNQBI4GrMvOuMtqTJEmSJEkaDsoacUNmTgGmAI8DuwMBHAH8FpiameeU1ZYkSZIkSdJw0KMRN5XpUGsy8y9tz2fmfGB+RGxHMTVqVWa+uMW9lCRJkiRJGoZ6OuLmWeCfqi8i4rqIOLn6OjP/nJn/Y9JGkiRJkiSp53qauMl2sWcC47e4N5IkSZIkSVqvp4mbp4C/LrMjkiRJkiRJ2lBPd5W6F3hfRIyiSOIAvCMi9u0iLjNzag/blCRJkiRJGlZ6mrj5FPBXwAkUo3aSYqpUV9OlEjBxI0mSJEmS1A09Stxk5v8Cb42IrYHRwHLgK8BXy+uaJEmSJEnS8NbTETcAZObLwJMR8QSwPDOfKKdbkiRJkiRJ2qLETVVm7lfGdSRJkiRJkvSqnu4qJUmSJEmSpF5WWuImIg6IiCsj4oGI+E1ELO3g8bsS2jklIr4eET+MiNURkRFxYyd1962Ud/ZYsIl2JlfeywsRsSoiFkXESZuoPyIiPh4Rv4iIP0fEMxFxV0RM2ETMdhFxSUT8KiLWRMTKiLg5Ig7evE9FkiRJkiQNRaVMlYqINwE/ALYD1gH/WzluVLWE5i4C/gZ4AVgBHNSNmJ8Dt3dw/rGOKkfE5cD5levPBbYBTgfuiIhzMvPKdvUDWACcAvwKuBLYFTgNaI6Id2Xm99rFjAS+D7wZeIhiYee9gVOBEyPi+Mz8aTfemyRJkiRJGqJKSdwAnwVGAmcB12VmR0mbsnycIqHyW6ABuK8bMY9m5sXduXhlhMz5wO+AozLz2cr5LwIPA5dHxJ2ZubxN2OkUSZslQGNmrqnEfBP4ETA3Iu7NzOfbxJxHkbS5FTgtM1srMTdRJJmui4jXV89LkiRJkqThp6ypUkcBt2bmNb2ctCEz78vM32Rm9lITZ1WOl1WTNpV2lwNXUSSoPtgu5uzK8aJq0qYS8yBwE7A7RWIHWD9Cp9rOp9omZyojc34IHEKRmJIkSZIkScNUWYmbvwBPlnSt3rBXRPxDRFxYOR62ibrHV47/0UHZ3e3qEBHbAhOAlygSLl3GAPsD+wC/zsxl3YyRJEmSJEnDTFlTpZYAh5d0rd5wQuWxXkQsAiZn5pNtzu0AjAFeyMynOrjObyrH17U5tz8wAljayWijjmIOrBx/3Ul/O4qRJEmSJEnDTFmJmwuBJRFxRmZ+u6RrluElYA7FmjFLK+cOAy4GjgOaImJ8Zr5YKdupclzVyfWq53duc66vYjoVEQ93UtSdhZslSdIQ4P2AJElDU1mJm0nAvcD8iPgQxSK+z3VQLzNzTkltdikzVwIz251ujoiJFIsG/x3wIYodnSRJkiRJkgaUshI3F7d5fkzl0ZGkGAHTrzJzXURcS5G4qefVxE11pMtOHQa+er5tUqqvYjqVmUd2dL7yf96O6M41JEnS4Ob9gCRJQ1NZiZvjSrpOX/pD5bhD9URmvhgRvwfGRMToDta5OaBybLs2ze+AV4BxEbFVB+vcdBTzq8qxszVsOoqRJEmSJEnDTCmJm8xcXMZ1+tgbK8el7c7fC5wBvBW4vl3Z29rUASAz10TEEl4daXRfVzEUyZ4ngddFxH4d7CzVUYwkSZIkSRpmytoOfECKiCMiYqP3GBGNwMcrL29sV/zNyvHTEbFLm5h9gQ8Da9k4ofONyvHSyvbg1ZijgNMoRvd8t3o+M7NNO19o28eImESRAHocGIwJMUmSJEmSVJKypkr1mYh4B/COyss9K8c3RcT8yvM/ZuYnKs+vAA6ojIhZUTl3GHB85fmMzFzS9vqZuSQirgDOA34REbcC21AkYHYFzsnM5e26tQB4J3AK8LOIuAPYrRIzApiWmavbxVwBnFSJ+WlENAH7AKdS7IY1JTNbu/epSJIkSZKkoahHiZuIaAVagUMy89eV19mN0MzMLU0WjQcmtzs3rvIAeAKoJm6+Dfxf4CiK6UdbA/8L3AxcmZk/7KST50fEf1KMsJlO8V4fAb6YmXd2UD8j4j3AEmAKcA6wBmgGLm2fHKrErI2IE4ALgPdQjABaTbF1+azMfLzrj0KSJEmSJA1lPU2iNFMkal5q97rXZebFbLiL1abqzgPm9bCd+cD8zai/Dvhy5dHdmJcotitvv2W5JEmSJElSzxI3mXnspl5LkiRJkiRpyw3pxYklSZIkSZIGMxM3kiRJkiRJA5SJG0mSJEmSpAHKxI0kSZIkSdIAZeJGkiRJkiRpgDJxI0mSJEmSNECZuJEkSZIkSRqgTNxos8QlQVwS/d0NSZIkSZKGha3KuEhE7NONaq3A6sxcXUabkiRJkiRJQ10piRtgOZDdqRgRTwP/BlySmX8sqX1JkiRJkqQhp6ypUt8CmoEAVgGLgZsrx1WV84uBu4CXgQ8DD0bE7iW1L2kQccqdJEmSJHVPWYmbzwJ/A3wO2Dszj8/M92Tm8cDewBcq5ecD44BLgLHAP5fUviRJkiRJ0pBT1lSpzwE/z8wL2xdk5ovABRHxd8DnMvOdwCURMQl4O3BeSX1QyTY1IqKjspzVrdlykiRJkiSpm8oacVMPLOmizhKgoc3rnwCvLal9SZIkSZKkIaesETcjgT27qDO6Uq/qBWBdSe2rF3Q0gqY60sbRNZIkSZIk9b6yEjc/B06LiC9n5mPtCyPiMODdwKNtTu8L/KGk9iUNUE65kyRJkqSeKytxM5tix6gHI+JG4H7gf4G/Ao4G3gdsDcwBiIjtgInAHSW1L0mSJEmSNOSUkrjJzHsi4n3AN4CpwJQ2xdUtwqdm5j2Vc9sApwG/KqN9SQOXU+4kSZIkqefKGnFDZi6IiDuBScDhwE7AauBnwPcy8/k2dVcB93R4IUmSJEmSJAElJm4AMvMF4F8rDw1BjpCQJEmSJKnvlLIdeET8Y0TsXMa1JEmSJEmSVCglcQNcCTwVETdHxIkRUdZ1JUmSJEmShq2yEiz/DCwDTgH+P+D3EXF5ZRtwSdpAzkqn3UmSJElSN5SSuMnMz2fmIcDfUuwstTVwHvCziHgkIj4aEaPKaEuSJEmSJGm4KHVKU2Y+lJkfAUZTjL65E6gDvkIxCuf2MtuTJEmSJEkaynplLZrMfDkz/y0zJwFjgJmVorf3RnuSJEmSJElDUanbgbcVEQGcAEwGJlFMn3qlt9qTJEmSJEkaakpP3ETEwRTJmvdTTJkK4DfAtyoPDVItLdDUBKtXQ20tNDZCXV1/90qSJEmSpKGrlMRNROwKvIciYXMkRbJmNTAPmJ+ZS8poR/2jqQlmz4bm5o3L6uth5swiiSNJkiRJkspV1oibpyrXSuAHwHzgtsxcU9L11U/mzYPp06G1tePy5maYOBHmzoUpU/q2b5IkSZIkDXVlJW6WUSRrvp2Zvy/pmupnTU2bTtpUtbbCtGkwdqwjbyRJkiRJg0tEcczs3350ppTETWYeVMZ1NLDMnt110qaqtRXmzDFxI0mSJElSmXplO3ANfi0tHa9psymLFxdxkiRJkiSpHKXuKhURo4FGYAwwsoMqmZlzymxTvaOpqedx7jQlSZIkSVI5SkvcRMQlwAXtrhkUCxa3fW7iZhBYvbpv4yRJkiRJ0sZKmSoVEe8DZgA/BE6hSNLcALwXmAu0AguA48toT72vtrZv4yRJkiRJ6m0RGz+6U9afyhpxczawAnhrZq6L4t0tz8wFwIKIuA34d+A7JbWnXtbTRYZdnFiSJEmSpPKUtTjx64G7MnNdm3Mjqk8y8x7gHuCTJbWnXlZXB/X1mxfT0OD6NpIkSZL6RksLfO1rcOmlxdGNUtQdmcXjBz/o/DtvfX1RPlC2By9rxM3WwJ/avP4zsFO7Oo8BZ5XUnvrAzJkwcWL3tgSvqYEZM3q/T5IkSZKGt6YmmD27411w6+uL7zHOBNCmzJsH06d3/l23ubn4Ljx3LkyZ0rd960hZI26eAka3ef0kcFi7OnsB69Cg0dgI11xTJGU2paam+AftL0dJkiRJvWnevOILdUdJG3j1C/d11/VtvzR4NDVtOmlT1doK06b1fMflMpWVuPkZcGib1/cCx0TEGRGxQ0ScSLFo8c9Kak99ZOpUWLiwmAbVkYaGonwgZCElSZIkDV2D8Qu3Bp7Zs7s3qwSKenMGwL7YZU2VuhO4OiL2y8xlwOeA04D5lQfAy8BFJbWnPtTYWDxaWopffqtXF7tHNTa6po0kSZKkvtGTL9zOClBbLS2dj9bqzOLFRVx/fvctJXGTmfN5NUFDZv53RBwFnA/sDyw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9FBIS4tXP6XTqueee05NPPqk2bdro1ltvVXh4uI4dO6a0tDQlJiaqa9eu+vTTTwt97m7dusnhcOjJJ5/Uxo0b1aBBA0nS008/LUl69dVXtXXrVo0dO1bvvPOOunbtqgsvvFC7du3S5s2btXr1ar333nsKDw+XJPXt21fvv/++5s+fr/bt26t37946c+aMZs+erauvvlrbtm0rgU8sf2+88Ya6dOmiUaNGacmSJbrqqqu0Y8cOffjhh3I4HIqPj1e9evWy+o8cOTLr38Ndd92lGjVqaOXKldq0aZN69eqlBQsWeJ3D6XTqv//9r3r16qXIyEjVqFFDUVFRiirq180AUIlUpHxAIifIjZygkucEJfFoKl7++RKP/wQqjdJ6/Gd2n39ubXS09fm4z+ho135/8eWXX9o777zTXnDBBbZGjRq2adOm9uabb7Yffvhhjn7vvfee7dKli61bt64NCAiw7dq1sxMnTrQnT570OmZej6i0Nv9HbFprbVxcnJVkJdnZs2fnO/bly5fbu+++2zZt2tTWqFHDNm7c2Hbs2NGOHDnSrl69utBj8njnnXdsx44dba1atbLGkN3p06ftK6+8Yq+//nobFBRka9asaVu0aGG7d+9uX3rpJbt//36v/uPHj7fh4eG2Zs2aNjQ01I4ePdqeOnWq1B/96bFz5047bNgw27JlS1ujRg3bqFEj27t3b/vNN9/4PEd8fLzt2LGjrVOnjm3UqJG9/fbb7XfffZfn39vevXtt3759bUhIiHU4HFaSHTduXIHXwuPAAZQ38gFv5AS/IScom5ygPPIBY12/0FEJGWPWRkZGRq5du7a8hwLgPHkez5j7nt3SkJwsJSS4HvEZFOT6Rq0872EH/EVhfw47deqkdevWrbPWdiqLcRWEfACoPMgHgPJXHvkAt0oBAHKIiCAxAwCgqiMfAPwHixMDAAAAAAD4KQo3AAAAAAAAforCDQAAAAAAgJ+icAMAAAAAAOCnKNwAAAAAAAD4qQpXuDHGTDbGJBhjdhhjThpj0o0x640x44wxjfKI6WyM+cTd96Qx5jtjzAhjTLV8ztPTGLPMGHPYGHPMGPO1MWZAAWMbYIz5xt3/sDu+Zz79qxljRrrH47mWT4wxnQv/iQAAAAAAgMqqwhVuJI2UFCjpM0kvS3pX0llJsZK+M8a0yN7ZGNNbUpKkKEkfSXpVUk1JL0n6r68TGGOGS1ogqb2kWZKmSbpI0gxjzPN5xDwvaYakpu7+syR1kLTAfbzc/Y37/C+6x/Oqe3xRkpLc4wYAAAAAAFVY9fIeQDEEWWtP5W40xjwjabSkJyU94m4LkquIck7SjdbaNe72MZK+kNTHGHOftfa/2Y4TJul5SemSrrLWprrbJ0haLenPxpg51tpV2WI6S/qzpG2SrrbWHnS3/0PSWknPG2M+9hzL7T5JfSR9KcnpuSZjzBuSVkiaZoz5wlp7tPgfFQAAAAAAqMgq3IwbX0Ubtw/c2zbZ2vpIukDSfz1Fm2zHeNr99uFcxxkkKUDSq9kLLe5izCT322G5Yjzvn/EUbdwxqZJecx/voVwxnvM+nf2arLWrJb3vHncfH9cJAAAAAACqiApXuMlHL/f2u2xt3d3bT330T5J0QlJnY0xAIWMW5epTrBhjTC1Jnd3nX16E8wAAAAAAgCqkIt4qJUkyxjwhqa6kYElXSeoqV9HmuWzdLnVvf8wdb609a4xJkRQhqZWkzYWI2W2MOS6puTGmjrX2hDEmUFIzScestbt9DHWre3tJtrbWkqpJ2m6tPVvImDwZY9bmsattYeIBAEDFRz4AAEDlVGELN5KekHRhtvefShporf01W1uwe3s4j2N42usXMSbQ3e9EKZ4jdwwAAAAAAKhiKuytUtbaJtZaI6mJpDvlmjWz3hgTWb4jK3vW2k6+XpJ+KO+xAQAqD2OMbrzxxkL3X7ZsmYwxio2NLbUx4TfkAwCAskJOULYqbOHGw1q711r7kaQYSY0kvZ1tt2fmSrBXYM72Q8WIOZxrWxrnOJTHfgAoNcn7kjXl6ymamDRRU76eouR9yeU9JOQhNjZWxhgtW7asvIdS6gqTIC5cuFAxMTFq3ry5ateurVatWunuu+/WqlWr8o0DAHgjH6hYyAlyqmw5QUW+VSoHa22aMWaTpCuMMY2ttfslbZFr/ZtL5HosdxZjTHVJ4ZLOStqebdcWSY3dMatyxTSV6zapndbaE+7zHjfG/CKpmTGmqY91bjxPucq+Zs42uR5R3soYU93HOje+YgCgVCVsT9CEpAlKSkvy2hcVGqWxUWPlbOUsh5Ghorrmmmu0efNmNW7cuNTP9de//lV///vf1ahRI91+++1q3LixfvrpJ82bN09z5szR22+/rQcffLDUxwEAFR35AEoDOcH5qfAzbnK5yL09595+4d7e4qNvlKQ6kr601p7O1p5fTI9cfYoV437895fu899QhPMAQKmYvm66YmbF+EzSJCkpLUkxs2L01vq3ynhkqMjq1Kmjtm3blnqStmfPHj3//PO68MILtWnTJr355pt67rnnNHv2bC1evFjWWo0dO7ZUxwAAlQH5AEoLOcH5qVCFG2PMJcYYr9uLjDEOY8wzkkLkKsQcdO+aLWm/pPuMMVdl619L0kT329dzHS5e0mlJw40xYdliGkga7X77Rq4Yz/un3P08MWGS/ug+XnyuGM95J7rH44m5WtK9kn6VNCf3tQJASUvYnqChHw9Vps3Mt1+mzdSQBUOUsD2hjEaWt2+++Ub33nuvmjVrpoCAADVt2lQxMTH64IMPcvT74IMPFBUVpeDgYNWuXVsdOnTQs88+q9OnT3sdMywsTGFhYTp+/LhGjRqlli1bKiAgQBdffLEmT54sa21W36+++krGGN1xxx15jvGyyy5TQECA0tPTc7QvXrxYt956qxo3bqyAgAC1bt1ao0aN0qFD3nfHesZ05MgRPf744woLC1ONGjUUGxursLAwjR8/XpLUrVs3GWOyXtmdOHFCzz77rK644goFBgaqbt26uv766/Xee+/5HHdGRobi4uLUunVrBQQEKDw8XE8//bTPz6wged3PfuONN8oYo7Nnz2rSpElq06aNAgIC1KJFC/31r39VRkZGVt8ZM2ZkXVNiYmKO6/QcNy0tTZmZmbr22msVEhKS41zdunVTvXr19OuvvwoAkLeKmA9I5ATkBFUjJ6hot0rdKulZY8wKSSmSDsj1ZKlouRYn3iNpiKeztf/P3p1HWVWdift/djE7MAkEBCwQcQIVSk0bRBBLMbZGTMT5a0AMtuZnjFOMIWIxObbz3KKCRiOKHbG1JaDIoJK0DBIDmBgVJEQRZRJUxtq/P+69ZY1QVB2o6fmsddelzt777H2uqdS73ruH+FUIYSipBM6MEMIEYDVwOqljv18AnivcQYxxSQjhV8B9wNwQwnPAZmAg0AG4M8b4p2JtZocQ7gKuBt4LIbwANCSVgGkJ/CLGuLTYs0wgtanyQFKbKr9Mao+ec0gdFT40xvhVRT8oSSqvUbNG7TBIy8iP+YyeNbpKp0iPHTuWyy67jHr16nH66afTtWtXVq5cydy5c3nooYc4++yzARg2bBi33HILrVq14vzzz2evvfZi8uTJDBs2jClTpjB16lQaNmxY5N5btmzh5JNP5tNPP+WUU06hfv36TJo0ieuvv56NGzeSl5cHwDHHHMNBBx3Eq6++yqpVq9hnn32K3Oedd97hb3/7G2eeeSYtW7YsuD5y5EhGjBhBy5YtOe2002jTpg3vvfced9xxB6+++ip/+tOfaNq0aZF7bd68mRNOOIHVq1fTv39/mjZtSufOnbnyyiuZNGkSM2fOZNCgQXTq1KnEZ7V27VpOOOEE3n33XXJychgyZAj5+flMmTKF888/n0WLFjFmzJiC+jFGzj77bF566SW6dOnC5ZdfzubNm3niiSf461//Wqn/bqU5//zzefPNNznllFNo2rQpr776KrfffjsrV65k3LjU9x09evQgLy+PkSNHkp2dzeDBgwvaZ9a3d+3alYYNG/LOO+/w5ZdfFvk2b9asWaxfv54zzjgj8fFLUm1S0+IBMCYwJhhc0L7WxwQxxhrzAroDDwALSM2k2Upqo985wAigZRntjgVeBdYA3wJ/Ba4C6m2nrx8BM4H1wNfpPgbtYHyD0/W+TrebCZy2nfr10+P4a3pca9Lj7JXQ5zUvJycnSqr5Fi9eHBcvXpz4fRd+vjAygp1+Lfx8YeJjKY9FixbF+vXrxxYtWsSFC0uO4Z///GeMMcbZs2dHIHbs2DF+9tlnBeVbtmyJp512WgTiTTfdVKRtdnZ2BOIpp5wSv/nmm4Lrn3/+eWzWrFls1qxZ3Lx5c8H1m2++OQLx/vvvLzGOn//85xGI//M//1Nw7Y033ohA/MEPfhDXrFlTpP64ceMiEK+88spSx5Sbmxs3bNhQop+8vLwIxOnTp5f2ccVBgwZFIN52221Frn/77bfx5JNPjiGE+O677xZcf+aZZyIQjznmmPjtt98WXF+1alXcf//9IxD79u1bal+lmT59egRiXl5eket9+/aNQMzJyYmrVq0quL5hw4bYpUuXmJWVVeS/W4xxh33ffffdMYQQW7duHYcOHRqvv/76eNZZZ8VGjRrFk046KX7++eflHndZyvt7mJOTE4F5sRrETtF4QKpVjAe+Y0xQlDHBd3Z1TFAV8UCNWioVY1wYY7w8xtgjxtgqxlg/xtgsxnh0jHFEjHF1Ge3ejjH+e4yxRYyxSYzxsBjj3THGbaXVT7d5OcbYN8a4d4xxz3QfT+5gfOPT9fZMt+sbY3xlO/W3psdxWHpcLdLjnF3+T0WSKm7akopNc65ou8p6+OGH2bp1K8OHD6dbt24lyjt06ADAE0+k1t7fcMMNtG3btqC8fv363HnnnWRlZfHYY4+V2sd9991HkyZNCn5u06YNAwYMYN26dfz9738vuH7hhReSlZXFk08W/dOwefNmJkyYQJs2bTjllFOK3BdS3w42b968SJvBgwfTo0cPnnnmmVLHdOedd7LnnnuWWlaWVatW8fTTT3PUUUdx3XXXFSlr3LhxwVTv3//+9wXXM99o3XzzzTRuXLCKl5YtWzJ8+PCd6r88brvttiLfPu65555ccMEF5OfnM3fu3J2615VXXskf/vAHtm7dytixY7n11luZOHEiHTt2ZPDgwSWmS0uSvlPT4gEwJtgZxgQ1PyaoaUulJEkJ+mpTxVZkVrRdZf35z38GKBL8lGb+/PkAnHDCCSXKDjzwQDp06MCSJUtYt24dzZp9t3Vas2bNOOCAA0q06dixIwBr1qwpuNahQwdyc3N57bXXWLx4MYceeigAL7/8MqtXr+aqq66ifv3v/sz+6U9/okGDBkycOJGJEyeW6GPz5s188cUXJaZZN27cmMMPP3y7z1uaOXPmsG3btlLXk0NqCjjA+++/X3Bt/vz5ZGVl0bt37xL1Szt2c8GCBUyaNKnItebNm3PllVeWa4xHHXVUiWulfdblcfvttzNs2DCuuOIKLr/8ctq2bcvf/vY3fvOb33DBBRewYMECbr/99p26pyTVFTUtHgBjgp1hTFDzYwITN5JUhzVt1HTHlRJsV1mZzfrat2+/3Xrr1q0DoF27dqWWt2vXjmXLlrF27doiQVrxb70yMsHWtm1FJ2oOHjyY1157jSeffJLbbrsNoODbtkGDBhWpu2rVKrZu3VqweWBZNmzYUCRIa9OmTYnNBctj1apVQCpYmzNnznb7y1i3bh0tW7akQYMGJeoV/pYyY8GCBSWeJzs7u9xBWmmfd1mf9fbMmDGDX//61/z4dRgzNgAAIABJREFUxz/mrrvuKriek5PDiy++yIEHHsidd97JpZdeyv7771/u+0pSXVHT4gEwJtgZxgQ1PyaoUUulJEnJyu1csU0FK9qusjJ/1P/1r39tt14m8FqxYkWp5Z999lmRehX14x//mKZNm/L000+zbds2Vq5cyeTJkzniiCM44ogjSoypRYsWO1zDnJ2dXaRdRQK0TH8AV1111Xb7mz59epE2q1evLvjmrbDSPsvBgweXuN/SpUsrNN7KeOWV1Krkfv36lSjbY489+P73v09+fj7vvvvu7h6aJNUINS0eAGOCnWFMkFKTYwITN5JUh3Vr040+2X12qk3f7L50a1NyLfnucMwxxwAwefLk7dbr2bMnkPrWpbgPP/yQ5cuX07lz5zK/TSuvJk2acPbZZ/Ppp5/y+uuv8/vf/56tW7eW+GYtM/Y1a9awaNGiSvVZWL169YDSv4n6/ve/T1ZWFm+++Wa575eTk0N+fj5vvfVWibLSPsvdKSsrq8xv3DLHkpZ1vGfmevETQyRJKTUtHgBjguKMCVJqa0xg4kaS6rgb+9xIVijfn4OskMXwPslvSFdel112GfXr12f06NEsXry4RPny5csBGDJkCABjxowp8od727ZtXHvtteTn53PxxRcnMqbMUZRPPfUUTz31FPXr1+eCCy4oUe+qq64CYOjQoXz66aclyr/++uuC9frllZk+vWzZshJlbdq04YILLmDu3LmMHj261ADno48+YsmSJQU/X3TRRQD89re/ZePGjQXXV69eXeSI0Kqwzz778M9//rPUsuOOOw6ARx99tMQ3r5MnT+btt9+mcePG9OrVa5ePU5JqqpoUD4AxQXHGBCm1NSZwjxtJquNy98/l0dMe5ZJXLiE/5pdZLytkMfZHY8ndv+qmRR966KE89NBDXHrppfTs2ZMBAwbQtWtXVq1axZw5c2jatCnTp0+nV69eXHfdddx+++10796dgQMHsueeezJ58mQWLlxI7969+dWvfpXImI499lgOOOAAJk6cyJYtW/jRj35U6mkFubm53HrrrfzmN7+ha9eu/Pu//zudO3dmw4YNfPLJJ8ycOZPevXvzxz/+sdx99+vXj6ysLH7zm9+wcOFCWrRoAaROzgB44IEH+Mc//sGNN97I7373O3r37s33vvc9Pv30U95//33mzJnDs88+S+fOnQE477zzeO655/if//kfunfvzoABA9iyZQsvvPACRx99NB999FECn1jF5ObmMmHCBH70ox+Rk5NDgwYN6NOnD3369GHgwIGceOKJvP766xxyyCH8+Mc/pm3btrz//vu88sorxBi59dZbi+wTIEkqqibFA2BMUJwxQS2PCZI4U9xX9XwB83JycqKkmm/x4sVx8eLFu7SP1z96PfYd1zcyghKvvuP6xtc/en2X9r8zZs+eHX/yk5/E1q1bxwYNGsR27drFk08+OU6cOLFIvWeffTYee+yxca+99oqNGjWKhx56aBwzZkz89ttvS9wzOzs7Zmdnl9pfXl5eBOL06dNLLR89enQEIhBfeOGF7Y79zTffjGeddVZs165dbNCgQWzVqlU84ogj4lVXXRXnzJlT7jFl/O53v4tHHHFEbNy4ccEYCtu0aVO8//774w9+8IPYtGnT2LBhw9ixY8d4wgknxLvvvjt++eWXJeqPHDkydu7cOTZs2DBmZ2fHYcOGxY0bN0Yg9u3bd7vjKWz69OkRiHl5eUWu9+3bt8Q4M8aNGxeBOG7cuCLXP//883jeeefFNm3axKysrBL33bx5c7z77rvjv/3bv8W999471qtXL7Zu3TqeeuqpccqUKeUe8/aU9/cwJycnAvNiNYgFovGAVKsYD5RkTPAdY4KUXR0TVEU8EGLqD7pqoRDCvJycnJx58+ZV9VAkVVLmeMZDDjlkl/e1aOUipi2ZxlebvqJpo6bkds6t0jXsUnVR3t/DI488kvnz58+PMR65O8a1I8YDUu1hPCBVvaqIB1wqJUkqolubbgZmkiTVccYDUvXh5sSSJEmSJEnVlIkbSZIkSZKkasrEjSRJkiRJUjVl4kaSJEmSJKmaMnEjSZIkSZJUTZm4kSRJkiRJqqZM3EiSJEmSJFVTJm4kSZIkSZKqKRM3kiRJkiRJ1ZSJG0mSJEmSpGrKxI0kSZIkSVI1ZeJGkiRJkiSpmjJxI0mq1aZOnUqvXr1o3rw5IQTOOOOMgrK5c+dy0kkn0apVK0II9OjRA4DBgwcTQmDp0qUV6nPp0qWEEBg8eHClxj5jxgxCCIwYMWKn2q1YsYJBgwbRoUMH6tWrRwiBtWvXMn78eEIIjB8/vlLjkiSpJjImMCaoqepX9QAkSdpVli5dyoABA2jevDlDhgyhadOmHHzwwQB89dVXnHrqqWzcuJELL7yQVq1a0bZt2yoecTIGDx7M1KlTOe+88zjggAMIIdC4ceMqG8+MGTPo168feXl5Ox1wSpKUBGMCY4KazMSNJKmEEFLvMVbtOCrr9ddfZ+PGjdx5552cf/75RcreeecdVq5cyU033cSwYcOKlN1yyy1cf/31tG/fvkL9tm/fnvfff59mzZpVeOwVtXnzZl577TVOPPFEnnnmmd3evySp9qgt8QAYExgT1GwmbiRJtdann34KwL777rtTZe3ataNdu3YV7rdBgwYF3+LtbitWrCA/P7/U55Ikqa4yJlBN5h43kqQa5/nnn6dPnz40a9aMJk2acNhhh3HLLbewadMm4Lt14Hl5eQD069ePEELBWu4QAoMGDQLgoosuKlIG21/P/s4773DOOefQvn17GjVqRLt27ejfvz/PP/98QZ2y1rN/8MEHXH/99Rx11FG0bt2aRo0akZ2dzSWXXMLy5csr/bl06tSJ7OxsAJ588smC5yrPuvp58+Zx5pln0qZNm4Jx/fznP+ezzz4rUXdnnmPw4MH069cPgJEjRxaMKYTAjBkzKv3MkqS6zZigdMYEtYszbiRJNcqwYcO45ZZbaNWqFeeffz577bUXkydPZtiwYUyZMoWpU6fSqVMn8vLymDFjBjNnzmTQoEF06tQJgB49epCXl8eCBQt46aWXGDBgQMEGhJn3sowdO5bLLruMevXqcfrpp9O1a1dWrlzJ3Llzeeihhzj77LO32/4Pf/gDjzzyCP369aNXr140bNiQRYsW8dhjj/Hyyy8zd+7cCk/FBrjyyitZunQp9957L0cccUTBpos7eq5XXnmFM888kxgjAwcOJDs7m3nz5vHwww/z0ksv8dZbb9G5c+cKPUdmDE8++SR9+/bl+OOPL7hP5r+JJEkVYUxQNmOCWibG6KuWvoB5OTk5UVLNt3jx4rh48eLd1l9qNftu667cZs+eHYHYsWPH+NlnnxVc37JlSzzttNMiEG+66aaC63l5eRGI06dPL3GvcePGRSCOGzeuRNmgQYMiEJcsWVJwbdGiRbF+/fqxRYsWceHChSXa/POf/yz495IlSyIQBw0aVKTO8uXL48aNG0u0nTJlSszKyoqXXnppkevTp0+PQMzLyyvRpixl9R1j6c+8fv362LJly5iVlRVnzZpVpP6tt94agXjSSSft9ueojsr7e5iTkxOBebEaxALReECqVYwHvmNMsGPGBLtGVcQDLpWSpDouhJKv8pRVhSeeeAKAG264ochpD/Xr1+fOO+8kKyuLxx57bJf0/fDDD7N161aGDx9Ot27dSpR36NBhh/fITKUurn///nTr1o0pU6YkMtad8dJLL7F69WrOOeccjjvuuCJl11xzDZ06deK1115j2bJlBder43NIkiqnJsUDYEywKxgTVF8ulZIk1Rjz588H4IQTTihRduCBB9KhQweWLFnCunXrEj+94c9//jMAp5xySoXvEWPkmWeeYfz48fzlL39hzZo1bNu2raC8YcOGO7zHpEmTWLBgQZFrPXr0KJh+vLO295nWr1+fPn36sHTpUt59913222+/xJ5DkqTKMCYwJqhLTNxIUh0XSznis7oe/7lu3TqAMk93aNeuHcuWLWPt2rWJB2lr164FqNR686uvvpp77rmHdu3acfLJJ9O+fXuaNGkCwPjx4/nkk092eI9Jkybx5JNPFrk2aNCgCgdp5flM4bvnT+o5JEnVS02KB8CYAIwJ6hITN5KkGiMTeK1YsYIuXbqUKM+cdpB0gAbQvHlzAP71r39V6FjPlStXct9999G9e3dmz57N3nvvXaT82WefLdd9xo8fX3DSRRIKf6alKf6ZJvUckiRVhjGBMUFd4h43kqQao2fPngClHhn54Ycfsnz5cjp37lwQUCXpmGOOAWDy5MkVav/xxx+Tn59P//79SwQ2y5cv5+OPP670GCtie5/p1q1befPNNwHIyckBKvYc9erVAygydVqSpMowJkieMUH1ZeJGklRjDBkyBIAxY8bwxRdfFFzftm0b1157Lfn5+Vx88cW7pO/LLruM+vXrM3r0aBYvXlyifPny5dttnznm8q233ioSrGzYsIGhQ4eydevWRMdbXmeccQYtW7bk2WefLVizn3HPPfewZMkSTjzxxIK17BV5jn322QegyGaGkiRVhjFB8owJqi+XSkmSaoxevXpx3XXXcfvtt9O9e3cGDhzInnvuyeTJk1m4cCG9e/fmV7/61S7p+9BDD+Whhx7i0ksvpWfPngwYMICuXbuyatUq5syZQ9OmTZk+fXqZ7du2bcu5557LhAkT6NGjB/3792fdunW89tprNG7cmB49epTYYHB32GuvvXjiiSc466yz6Nu3L2eddRb77bcf8+bNY+rUqbRt25b/+q//qtRzHHTQQbRv354JEybQoEEDsrOzCSFw4YUXkp2dvbsfWZJUCxgTJM+YoPoycSNJKqE6bkKYcdttt9GzZ08eeOABnnrqKbZs2UKXLl0YM2YM11xzzS49vWDo0KF0796dO+64gxkzZjBp0iRatWrF4Ycfzs9+9rMdtn/88cfZf//9ee6553jwwQdp3bo1p59+OqNGjeLMM8/cZePekQEDBvD2229z8803M2XKFNatW0fbtm259NJLGT58OPvuu2+R+jv7HPXq1ePFF1/k+uuvZ+LEiaxfv54YI7179zZIk6RqrDrHA2BMsCsYE1RPIVb330ZVWAhhXk5OTs68efOqeiiSKun9998H4JBDDqnikUh1V3l/D4888kjmz58/P8Z45O4Y144YD0i1h/GAVPWqIh5wjxtJkiRJkqRqysSNJEmSJElSNWXiRpIkSZIkqZoycSNJkiRJklRNmbiRJEmSJEmqpkzcSJIkSZIkVVMmbiRJkiRJkqopEzeSJEmSJEnVlIkbSZIkSZKkasrEjSRJkiRJUjVl4kaSJEmSJKmaMnEjSZIkSZJUTZm4kSRJkiRJqqZM3EiSarWpU6fSq1cvmjdvTgiBM844o6Bs7ty5nHTSSbRq1YoQAj169ABg8ODBhBBYunRphfpcunQpIQQGDx5cqbHPmDGDEAIjRowod5sRI0YQQmDGjBmV6luSpNrGmEA1Vf2qHoAkSbvK0qVLGTBgAM2bN2fIkCE0bdqUgw8+GICvvvqKU089lY0bN3LhhRfSqlUr2rZtW8Ujrj5mzJhBv379yMvLKzNI3LRpE4899hhPPvkkH3/8MRs3bqRjx46cdNJJXHPNNWRnZ+/eQUuSVAZjgoozJqh6Jm4kSSWEkQGAmBereCSV8/rrr7Nx40buvPNOzj///CJl77zzDitXruSmm25i2LBhRcpuueUWrr/+etq3b1+hftu3b8/7779Ps2bNKjz2irr88ss599xz2W+//XZpP1u3biU3N5e3336bgw8+mPPOO49GjRoxZ84c7r//fp566ilmz57NoYceukvHIUnadWpLPADGBLuSMcGuZ+JGklRrffrppwDsu+++O1XWrl072rVrV+F+GzRoUPAt3u7WqlUrWrVqtcv7efHFF3n77bfJzc1l6tSpZGV9t/o6Ly+PUaNGcccdd/DEE0/s8rFIkrQjxgS7jjHBruceN5KkGuf555+nT58+NGvWjCZNmnDYYYdxyy23sGnTJuC7deB5eXkA9OvXjxACIQTGjx9PCIFBgwYBcNFFFxUpg+2vZ3/nnXc455xzaN++PY0aNaJdu3b079+f559/vqBOWevZP/jgA66//nqOOuooWrduTaNGjcjOzuaSSy5h+fLliXw2Za1nDyFw/PHH8+WXX3LJJZfQrl07GjVqRLdu3Rg3blyRuoMHD6Zfv34AjBw5suDzKXzfjz/+GIBTTz21SIAGMGDAAAC++OKLRJ5JkqSyGBOUzZig9nDGjSSpRhk2bBi33HILrVq14vzzz2evvfZi8uTJDBs2jClTpjB16lQ6depEXl4eM2bMYObMmQwaNIhOnToB0KNHD/Ly8liwYAEvvfQSAwYMKNiAMPNelrFjx3LZZZdRr149Tj/9dLp27crKlSuZO3cuDz30EGefffZ22//hD3/gkUceoV+/fvTq1YuGDRuyaNEiHnvsMV5++WXmzp1b4anY5bF27VqOPfZYGjZsyMCBA9m0aRMTJ05kyJAhZGVlFQSumc0an3zySfr27cvxxx9fcI/M59itWzcAJk+ezC9/+csigdorr7wCwIknnrjLnkWSJGOCijMmqGFijDXmBewD/Ax4EfgQ+BZYB7wFXAxkFavfCYjbeU3YTl+DgHeADek+ZgCnbad+PeAq4L30uFYDrwK9ttOmCTAS+DuwEVgJPA8cktDnNS8nJydKqvkWL14cFy9evNv6YwSREey2/spr9uzZEYgdO3aMn332WcH1LVu2xNNOOy0C8aabbiq4npeXF4E4ffr0EvcaN25cBOK4ceNKlA0aNCgCccmSJQXXFi1aFOvXrx9btGgRFy5cWKLNP//5z4J/L1myJAJx0KBBReosX748bty4sUTbKVOmxKysrHjppZcWuT59+vQIxLy8vBJtylLWM2f+9l188cVx69atRZ6rXr168ZBDDtmpvvPz8+NPfvKTCMRDDz00XnHFFfHaa6+N/fr1iw0aNIi/+MUv4pYtW8o97pqgvL+HOTk5EZgXq0HsFI0HpFrFeOA7xgQ7Zkywa1RFPFDTZtycBTwMfAZMB5YB3wN+AjwGnBJCOCvGWHz3rL8Ak0q538LSOgkh3AFcAywHxgINgXOBl0MIv4gxPlCsfgAmAANJJWEeAFoC5wCzQghnxhhfKtamEfAacCwwF7gX6Jh+xlNDCCfEGP9vh5+IJFVSZuPB8pZV5QaFmbXRN9xwQ5HTHurXr8+dd97Jq6++ymOPPVZiY8EkPPzww2zdupXhw4cXfLNUWIcOHXZ4j7K+Oevfvz/dunVjypQplR7n9uyxxx7cdddd1KtXr+DaoYceyrHHHsusWbPYsGEDe+21V7nuFULghRdeYOTIkYwZM4bFixcXlOXm5nL++edTv35NCzMkqe6qSfEAGBNUljFBzVLTPr0PgNOB/40x5mcuhhCGkZodcyapJM5/F2u3IMY4ojwdhBB6kUrafAQcHWNck77+n8A84I4QwisxxqWFmp1LKmkzG8iNMW5Mt3mE1GygsSGEN2KM6wu1uZpU0uYF4JzM84QQniOVZHoihHBY4eeUpLpu/vz5AJxwwgklyg488EA6dOjAkiVLWLduXeKnN/z5z38G4JRTTqnwPWKMPPPMM4wfP56//OUvrFmzhm3bthWUN2zYcIf3mDRpEgsWLChyrUePHgVTmbena9euNG3atMT1jh07ArBmzZpyB2kbN27kpz/9KZMnT+bBBx9kwIAB7LHHHrz99ttcccUV9OnTh4kTJxasbZckKUnGBMYEdUmNStzEGN8o4/qKdJLkJuB4SiZudsal6febMkmbdB9LQwgPAsOBi4C8Qm0uS7/fkEnapNvMSSdiLiSV2BkHBTN0Mv1cVzg5E2N8KYTwJnAc0JfUzCJJ2mVK+8asuh7/uW7dOoAyT3do164dy5YtY+3atYkHaWvXrgXK/oasPK6++mruuece2rVrx8knn0z79u1p0qQJAOPHj+eTTz7Z4T0mTZrEk08+WeTaoEGDyhWkNW/evNTrmW/BCgeMO3LrrbcyceJE7r33Xv7jP/6j4Popp5zCCy+8QI8ePfjlL39pkCZJNURNigfAmACMCeqSGpW42YEt6fetpZTtG0L4D1J75KwC/hRjfK+M+2RStn8spWwyqcTNCaQTNyGExkAv4BvgzTLaXJhuk9miuwuwH/BBjHFJGW2OS7cxcSNJaZnAa8WKFXTp0qVE+WeffVakXpIyAc6//vWvCh3ruXLlSu677z66d+/O7Nmz2XvvvYuUP/vss+W6z/jx4wtOuqhKmc0GMydNFHbEEUfQokULPvnkE1atWsU+++yzu4cnSarljAmMCeqSWnEceAihPvDT9I+lJVxOAjIzch4B/hJCmB5C2K/YffYE2gMbYoyflXKff6TfDyx0rQupjYk/jjGWljQqrc1B6fcPSn+iUttIUp3Xs2dPgBLHWgJ8+OGHLF++nM6dO5f5LVJlHHPMMUDqxISK+Pjjj8nPz6d///4lArTly5cXHKVZXWTWvJf1jVvmmNXSjvfctGkT69enVgeXZ6q3JEk7y5hg9zEmqHq1InED3Ap0B16NMRbexekbYDRwJNAi/cosPzoemJZO1mRk0rHryugnc73wb//ualOmEMK80l7Azqd/JakaGzJkCABjxowpEhxs27aNa6+9lvz8fC6++OJd0vdll11G/fr1GT16dJFN9zKWL1++3faZIzPfeuutIoHPhg0bGDp0KFu3lpb7rzqZb8SWLVtWavlxxx0HwM0331wQsGWMGDGCrVu3cvTRR5cISLXrGA9IqkuMCXYfY4KqV+OXSoUQriC1mfDfSC1JKhBjXAncWKzJrBBCf1KbBv8bqePF790NQ5UkVVKvXr247rrruP322+nevTsDBw5kzz33ZPLkySxcuJDevXvzq1/9apf0feihh/LQQw9x6aWX0rNnTwYMGEDXrl1ZtWoVc+bMoWnTpkyfXvbq1rZt23LuuecyYcIEevToQf/+/Vm3bh2vvfYajRs3pkePHiU2GKxKBx10EO3bt2fChAk0aNCA7OxsQghceOGFZGdn89vf/paXX36ZadOmcfDBB/PDH/6QJk2a8Pbbb/POO+/QpEkT7r3XP6+SpF3DmGD3MSaoejU6cRNCuJxU0mUxqdOcVpenXYxxawjhMVKJmz58l7jJzHQpayFk5vraQtd2V5syxRiPLO16+lu2nPLcQ5IKq46bEGbcdttt9OzZkwceeICnnnqKLVu20KVLF8aMGcM111yzS6fhDh06lO7du3PHHXcwY8YMJk2aRKtWrTj88MP52c9+tsP2jz/+OPvvvz/PPfccDz74IK1bt+b0009n1KhRnHnmmbts3BVRr149XnzxRa6//nomTpzI+vXriTHSu3dvsrOzad++PfPnz+e2227jf//3fxk3bhz5+fm0a9eOwYMH8+tf/7pC6/5VccYDkpJWneMBMCbYXYwJql6IsXr/MpYlhHAlcDewkFTSZuVOth9A6tjtKTHGHxa6vpzUPjf7Ft/nJoTwA1JHfr8VYzwufa0xsAHYBDQrvs9NCOE84PfA0zHGC9PXDiC1j80HMcaDKCaE8BvgZmBMjHH4zjxXsfvMy8nJyZk3b15FbyGpmnj//fcBOOSQQ6p4JFLdVd7fwyOPPJL58+fPLyuRsrsZD0i1h/GAVPWqIh6okXvchBB+TSppswDot7NJm7Rj0u/Fd37KHDn+Q0o6pVgd0sd/zwb2IHUS1A7bAB8By4ADQwidy9lGkiRJkiTVMTUucRNCGE5qM+J5pGbafLmdujkhhBLPGELIBa5K//h0seJH0u+/DSG0KNSmE/D/kZpZM65Ym4fT72PSM3AybY4GzgG+AP47cz2mpjll+rm98BjTM4GOI7X8a2ZZzyZJkiRJkmq/GrXHTQhhEDAK2Aa8CVwRQihebWmMcXz633cBXUMIs4HM1t6HAyek/z08xji7cOMY4+wQwl3A1cB7IYQXgIakEjAtgV/EGJcW63MC8BNgIPBuCOFlYJ90m3rA0BjjV8Xa3AWclm7zfyGEacB+wFmkTsMaEmPML8/nIkmSJEmSaqcalbgBMsuK6gFXllFnJjA+/e/fAT8Gjia1/KgB8DnwPPBAjPHN0m4QY7wmhPBXUjNsLgHygfnAf8YYXymlfkzvZTMbGAL8AtgIzCK1T83sUtpsCiGcBFwPnEdqBtBXpPbdyYsxljxXTpIkSZIk1Sk1KnETYxwBjNiJ+o8Dj1ewr/F8lwAqT/2tpPbduXsn2nxD6rjy4keWS5IkSZIk1bw9biRJkiRJkuoKEzeSJEk7kDpXQJIk1WVVFQ+YuJGkGiCzEXt+vnuWS1UhE6iVciiCJO02xgNS1aqqeMDEjSTVAI0aNQLg66+/ruKRSHVT5ncv87soSVXBeECqWlUVD5i4kaQaYO+99wZgxYoVrF+/nvz8fJduSLtYjJH8/HzWr1/PihUrgO9+FyWpKhgPSLtfdYgHatSpUpJUV7Vs2ZKvv/6ab775huXLl1f1cKQ6aY899qBly5ZVPQxJdZjxgFT1qiIeqFDiJoTQJ4G+l8YYlyVwH0mq9bKysujYsSOrV69m/fr1bNq0yW/YpN0ghECjRo3Ye++9admyJVlZTlaWVHWMB6SqUdXxQEVn3MwAKvv/ECOBUZW8hyTVGVlZWbRq1YpWrVpV9VAkSVIVMR6Q6p7KLJWamX7trADcWIl+JUmSJEmS6oTKJG5mxBgrNGMmhGDiRpIkSZIkaQcqujBrEbCyEv1Wtr0kSZIkSVKtV6EZNzHGwyrTaWXbS5IkSZIk1QUejSBJkiRJklRN7dLETQihQQihZwjhoF3ZjyRJkiRJUm2USOImhHB2COH5EELLQte6kNrLZi6wOITwhxBCZTZDliRJkiRJqlOSmnEzBDg4xri60LU7gQOA6cB7wADgooT6kyT4+fjdAAAgAElEQVRJkiRJqvWSStwcCszJ/BBCaAr8O/B8jPFE4PvA3zBxI0mSJEmSVG5JJW5aA58V+vkHpE6smgAQY9wCvAZ0Sag/SZIkSZKkWi+pxM16oFmhn/sCEXir0LWNwN4J9SdJkiRJklTrJbVZ8D+AU0IIjUglbM4G3osxflmoTjawMqH+JEmSJEmSar2kZtw8CuxPKoHzPtAZGFeszpGkTpmSJEmSJElSOSSSuIkxPgncCuxBasnUA8D9mfIQQi++O2FKkiRJkiRJ5ZDUUilijMOAYWUUzwVaAF8n1Z8kSZIkSVJtl1jiZntijJuBzbujL0mSJEmSpNoiqT1uJEmSJEmSlLBEZtyEEPJJnSa1IzHGuFtm+UiSJEmSJNV0SSVRZlF64qY5cCDQBPgLsDah/iRJkiRJkmq9RBI3McbjyyoLIewN3A30An6SRH+SJEmSJEl1wS7f4ybGuB64BNgK3LSr+5MkSZIkSaotdsvmxDHGfGA6cMbu6E+SJEmSJKk22J2nSjUGWuzG/iRJkiRJkmq03ZK4CSEcDJwFfLg7+pMkSZIkSaoNkjoO/Int3L8jcCxQD7gmif4kSZIkSZLqgqSOAx+8g/K/Af8ZYxyXUH+SJEmSJEm1XlKJm85lXM8H1sQYNyTUjyRJkiRJUp2RSOImxvhJEveRJEmSJEnSd3bnqVKSJEmSJEnaCRVK3IQQvh9C2LeinVa2vSRJkiRJUl1Q0aVSfwJGAqOqqL0kSVKFLVoE06bBV19B06aQmwvdulX1qCRJkkqqaOImVLLfyraXJEnaadOmwahRMGtWybI+feDGG1NJHEmSpOqiMpsTXxlCGFzBtrES/UqSJO20xx+HSy6B/PzSy2fNgv79YexYGDJk945NkiSpLBXdnHgZsI7UzJmKvJYBayszcEmSpPKaNm37SZuM/HwYOjRVX5IkqTqo0IybGGOnhMchSZK0y4wateOkTUZ+Powe7ZIpSZJUPXgcuCRJqtUWLSp9T5vtmTkz1U6SJKmqmbiRJEm1WkWXPblcSpIkVQcmbiRJUq321Ve7t50kSVKSTNxIkqRarWnT3dtOkiQpSSZuJElSrVbRTYbdnFiSJFUHJm4kSVKt1q0b9Omzc2369k21kyRJqmombiRJUq13442QVc6oJysLhg/fteORJEkqLxM3kiSp1svNhUcf3XHyJisLxo51mZQkSao+Ek3chBB+FEKYEEL4Swjhw0LXDwkhXBdCaJ9kf5IkSeV18cUwdWpqGVRp+vZNlQ8ZsnvHJUmStD31k7hJCCEA44H/l770LdCkUJU1wM1AAG5Lok9JkqSdlZubei1aBNOmpY78bto0dc09bSRJUnWUSOIG+DlwIfAEcA1wFVCwOjzGuCKE8DZwKiZuJElSFevWzUSNJEmqGZJaKnUx8BdgaIxxHRBLqfMPoHNC/UmSJEmSJNV6SSVuDgKmxxhLS9hkrARaJ9SfJEmSJElSrZdU4mYr0HgHddoDGxLqT5IkSZIkqdZLKnGzGDg+vUlxCSGExsAJwLuV6SSEsE8I4WchhBdDCB+GEL4NIawLIbwVQrg4hFDq84QQeoUQXg0hrE63eS+EcGUIod52+jothDAjff8NIYT/CyEM2sH4BoUQ3knXX5duf9p26tcLIVyVHs+36fG9GkLoVf5PRZIkSZIk1VZJJW5+BxwM3F08eZJOjtwF7Evq5KnKOAsYC/wb8H/APcB/A92Bx4DniyePQggDgFlAH+BF4AGgIXA3MKG0TkIIlwMvp+/7dLrPfYHxIYQ7ymhzB6nna5eu/zRwGPBy+n7F64d0/3elx/NAenx9gFnpcUuSJEmSpDosqVOl/gs4HbiCVHJlPUAI4QXgGFJJj5dijM9Usp8P0v38b4wxP3MxhDAMeAc4E/gJqWQOIYSmpJIo24DjY4xz09eHA28AA0MI58YYJxS6VyfgDmA1cFSMcWn6+ihgDnBNCOG/Y4x/KtSmF6nTtD4Cjo4xrklf/09gHnBHCOGVzL3SzgUGArOB3BjjxnSbR4C3gLEhhDdijOsr+ZlJkiRJkqQaKpEZNzHGbcBpwCigEXAgEEglUfYARpNK6FS2nzdijC8XTtqkr68AHkn/eHyhooGkNkSekEnapOtvBG5I/3hZsW6GpJ/hgcKJlnQy5ub0j5cWa5P5+aZM0ibdZinwYPp+FxVrk+n3hkzSJt1mDvBcetwDkSRJkiRJdVZSS6WIMW6NMY4glXA4BOhNaqlQ6xhjXoxxa1J9lWFL+r1wPyek3/9YSv1ZwDdArxBCo3K2mVysToXapPf86ZXu/82d6EeSJEmSJNUhSS2VKpA+EvzvSd93e0II9YGfpn8snDw5KP3+QfE2McatIYQlQDdgf+D9crT5LITwNdAhhLBHjPGbEMKepE/MijF+Vsrw/pF+P7DQtS5APeDjMhJapbUpUwhhXhlFB5envSRJqvmMByRJqp0SmXETQugSQvhpCGGfMspbpcv3T6K/UtxKaiPhV2OMUwpdb5Z+X1dGu8z15hVo06zY+67oo3kZ5ZIkSZIkqQ5IasbN9cAZwLNllK8jteHvf1NyT5lKCSFcQWpj4L8BFyZ575oixnhkadfT37zl7ObhSJKkKmA8IElS7ZTUHjfHA6/HGLeUVpi+/hoJ79mSPmb7XmAx0C/GuLpYleKzY4rLXF9bgTbrir3vij7WllEuSZIkSZLqgKQSN+2BpTuos4zUseCJCCFcCdwPLCSVtFlRSrXMXjsl9opJ74vTmdRmxh+Xs007YE9geYzxG4AY49fAv4C90uXFdU2/F94z5yNSR5Tvnx5HedpIkiRJkqQ6JqnEzWag6Q7q7A3EJDoLIfwauBtYQCpps7KMqm+k339YSlkfUkeVz44xbipnm1OK1alQm/Tx37PT/R+3E/1IkiRJkqQ6JKnEzULg1BBCg9IKQwgNgdNILWmqlBDCcFKbEc8DcmOMX26n+gvAl8C5IYSjCt2jMTAm/ePDxdqMAzYBl4cQOhVq0wIYlv7xkWJtMj//Nl0v06YT8P+l7zeuWJtMv2PS48m0ORo4B/iC1J5AkiRJkiSpjkpqc+KngYeA50MIlxVethRCaEsqsdERuL0ynYQQBgGjSC0zehO4IoRQvNrSGON4gBjjVyGEoaQSODNCCBOA1cDppI79fgF4rnDjGOOSEMKvgPuAuSGE50jNKBoIdADujDH+qVib2SGEu4CrgfdCCC8ADUklYFoCv4gxLi02zgnAT9L3fTeE8DKwT7pNPWBojPGrnf6QJEmSJElSrZFU4uZRUkmIAcBJIYT3SO370h44nNSSoNcpOVNlZ3VOv9cDriyjzkxgfOaHGOOkEEJf4LfAmUBj4ENSSZb7Yowllm/FGO8PISwFrgV+Smpm0mLghhjjk6V1GmO8JoTwV1IzbC4B8oH5wH/GGF8ppX4MIZxHasnUEOAXwEZgFjAmxji77I9BkiRJkiTVBYkkbmKM+SGEU4GRpI77PqZQ8VrgHmBkjDG/kv2MAEZUoN3bwL/vZJuXgZd3ss14CiWNylF/K6m9eu7emX4kSZIkSVLdkNSMm8yR38NCCDcABwPNSSVt/lbZhI0kSZIkSVJdlFjiJiOdpKn0JsSSJEmSJEl1XVKnSkmSJEmSJClhic24CSF0BX4JfB9oQWoD4eJijLFLUn1KkiRJkiTVZokkbkIIPyB1alQTYCvwefq9RNUk+pMkSZIkSaoLkppxcwvQCLgUeCJ9WpIkSZIkSZIqIanEzdHACzHGRxO6nyRJkiRJUp2X1ObEm4FlCd1LkiRJkiRJJJe4mQ30TOhekiRJkiRJIrnEzTCgVwjhwoTuJ0mSJEmSVOcltcfNAOANYHwI4WfAPGBtKfVijHF0Qn1KkiRJkiTVakklbkYU+vdx6VdpImDiRpIkSZIkqRySStz0S+g+kiRJkiRJSkskcRNjnJnEfSRJkiRJkvSdpDYnliRJkiRJUsKSWioFQAjhcOB84BBgzxjjienrnYDvA6/FGNck2ackSZIkSVJtlVjiJoQwitSx4JlZPLFQcRbwLHAlcH9SfUqSJEmSJNVmiSyVCiGcC9wAvAb0AG4pXB5j/BiYC5yeRH+SJEmSJEl1QVJ73FwBfAgMiDG+B2wupc77QNeE+pMkSZIkSar1kkrcHAZMiTGWlrDJ+BT4XkL9SZIkSZIk1XpJJW4CkL+DOt8DNibUnyRJkiRJUq2XVOLmH0CvsgpDCFlAb2BRQv1JkiRJkiTVekklbp4HckII15RRPgw4APh9Qv1JkiRJkiTVekkdB34PcBZwewjhbNJHgYcQ7gCOA44C/gw8mlB/kiRJkiRJtV4iiZsY47chhH7AvcAFQL100dWk9r55Grg8xrg1if4kSZIkSZLqgqRm3BBjXAcMDiFcDRwN7AOsA96JMX6RVD+SJEmSJEl1RSKJmxDCT4HPY4xTYoyrgSlJ3FeSJEmSJKkuS2pz4ieAHyZ0L0mSJEmSJJFc4mZFgveSJEmSJEkSySVb/gj0CyGYvJEkSZIkSUpIUomW3wJ7A4+HEFoldE9JkiRJkqQ6LalTpZ4ldYLUT4FzQwhLSS2fisXqxRhjbkJ9SpIkSZIk1WpJJW6OL/TvRsBB6VdxxRM5kiRVSBgZAIh5/mmRJElS7ZVI4ibG6N42kiRJkiRJCTPhIkmSJEmSVE2ZuJEkSXVSGBkKltxJkiRVV4klbkIIWSGEX4QQ/hxCWBdC2FqorGcI4aEQwoFJ9SdJkiRJklTbJbLHTQihITCZ1CbFq4H1wF6FqiwBhgBfAHlJ9ClJqju2NyuitDI3LJYkSVJtkdSMm18B/YCRwPeAxwoXxhjXArOAkxPqT5IkSZIkqdZLKnFzAfB2jHFUjDGf0o/9XgLsl1B/kqQ6JOZFYl7k9d6RPm9EGFHoz8yI1M993kiVO9tGkiRJtUkiS6WAzsD/7qDOaqBlQv1JkuqYxx+HSy6B/PzSy2fNgv79YexYGDJk945N1Z/L7SRJUk2V1IybjUDzHdTZD1ibUH+SpDpk2rTtJ20y8vNh6NBUfUmSJKk2SGrGzQKgfwihYYxxc/HCEEIzUvvbzE6oP0lSHTJq1I6TNhn5+TB6NOTm7toxqWYpbQZNZqaNs2skSVJ1ltSMm0eBjsAzIYSmhQtCCM2B8UAL4JGE+pMk1RGLFqWWQe2MmTNT7aSyLFr53f9A7vu/+4r8LEmSVJ0kMuMmxvhsCOEkYDBwOrAGIIQwF+gGNAIejDG+mkR/kqS6o8xlTyO2P0ti2jTo1i358ahmm/bxNEbNGsWsT77LBv7yj78EoE92H27scyO5+ztdS5IkVR9JzbghxjgEGAIsBloDAcgBPgQujjH+Iqm+JEl1x1df7d52qr0en/84/Z/uXyRpU9isT2bR/+n+PPHuE7t5ZJIkSWWr0Iyb9HKojcX3s4kxjgfGhxCakFoatS7G+HWlRylJqrOaNt1xnSTbqXaa9vE0LnnlEvLj9jdLyo/5DH15KNnNsp15I0mSqoWKzrhZA/w680MI4YkQwumZn2OM38YYPzVpI0mqrIpuMuzmxCps1KxRO0zaZOTHfEbPGr2LRyRJklQ+FU3cxGJtBwM9Kj0aSZKK6dYN+vTZuTZ9+7q/jb6zaOWiMpdHlWXmJzPdsFiSJFULFU3cfAYckORAJEkqy403QlY5/2JlZcHw4bt2PKpZpi0pa4frXdNOkiQpSRU9VeoN4IIQQitSSRyAM0IInXbQLsYYL65gn5KkOio3Fx59FC65BPK3s9olKwvGjnWZlIr6alPFdqquaDtJkqQkVTRxcx3wPeAkUrN2IqmlUjtaLhUBEzeSpJ128cXQqROMHg0zZ5Ys79s3NdPGpI2Ka9qoYjtVV7SdJElSkiqUuIkxfg78MITQAGgHLAXuAe5NbmiSJKWEsOM6M2d+l9CJcdeORzVLbueKZfMq2k6SJClJFZ1xA0CMcQuwLITwCbA0xvhJMsOSJElKRrc23eiT3WenNijum92Xbm3c4VqSJFW9im5OXESMsXOM8b4k7iVJUnExlnyVp0zKuLHPjWSF8oU9WSGL4X3c4VqSJFUPiSRuJEmSqrPc/XN59LRHd5i8yQpZjP3RWHL3d5mUJEmqHhJL3IQQuoYQHgghvBNC+EcI4eNSXh8l0M/AEML9IYQ3QwhfhRBiCOHpMup2SpeX9ZqwnX4GpZ9lQwhhXQhhRgjhtO3UrxdCuCqE8F4I4dsQwuoQwqshhF7badMkhDAyhPD3EMLGEMLKEMLzIYRDdu5TkSRJO3JxzsVM/X9T6Zvdt9Tyvtl9mfr/pjKk55DdPDJJkqSyVWqPm4wQwg+A14EmwFbg8/R7iaoJdHcDcASwAVgOHFyONn8BJpVyfWFplUMIdwDXpO8/FmgInAu8HEL4RYzxgWL1AzABGAj8HXgAaAmcA8wKIZwZY3ypWJtGwGvAscBcUhs7dwTOAk4NIZwQY/y/cjybJEkqp9z9c8ndP5dFKxcxbck0vtr0FU0bNSW3c6572kiSpGopkcQNcAvQCLgUeCLGWFrSJilXkUqofAj0BaaXo82CGOOI8tw8PUPmGuAj4OgY45r09f8E5gF3hBBeiTEuLdTsXFJJm9lAboxxY7rNI8BbwNgQwhsxxvWF2lxNKmnzAnBOjDE/3eY5UkmmJ0IIh2WuS5Kk5HRr081EjSRJqhGSWip1NPBCjPHRXZy0IcY4Pcb4jxh32faTl6bfb8okbdL9LgUeJJWguqhYm8vS7zdkkjbpNnOA54DWpBI7QMEMnUw/1xVOzqRn5rwJHEoqMSVJKoUbEUuSJKkuSCpxsxlYltC9doV9Qwj/EUIYln4/fDt1T0i//7GUssnF6hBCaAz0Ar4hlXDZYRugC7Af8EGMcUk520iSJEmSpDomqaVSs4GeCd1rVzgp/SoQQpgBDIoxLit0bU+gPbAhxvhZKff5R/r9wELXugD1gI/LmG1UWpuD0u8flDHe0tqUKYQwr4yi8uz/I0mSagHjAUmSaqekZtwMA3qFEC5M6H5J+QYYDRwJtEi/MvviHA9MSydrMpql39eVcb/M9eZV0EaSJEmSJNUxSc24GQC8AYwPIfyM1Ca+a0upF2OMoxPqc4dijCuBG4tdnhVC6E9q0+B/A35G6kSnGivGeGRp19PfvOXs5uFIkqQqYDwgSVLtlFTiZkShfx+XfpUmkpoBU6VijFtDCI+RStz04bvETWamS7NSG353vXBSane1kSRJkiRJdUxSiZt+Cd1nd/oi/V6wVCrG+HUI4V9A+xBCu1L2uemafi+8N81HwDZg/xBC/VL2uSmtzd/T72XtYVNaG0mSJEmSVMckkriJMc5M4j672THp94+LXX8DuBD4ITCuWNn/3979x8lV1Qcf/3wXJAh2AZU8Uq2bbP29iAXFp9J2F5maqgWxNa2oWDB5En+AoqKv2moiJrb6VMQKWDVpIFas0GLVhqLmcZCsFX9LC65oVRJURCO/svIrivt9/rh3ZZid2V/Mzs7Oft6v133dvfecc++Zk8ns2e/cc85zavIAkJn3RMRV3Pek0eemKkMR7PkB8LiIWN5gZalGZSRJkiRJ0iLTqsmJO1JEHBURE15jRFSA15WHF9Ulf6DcvzkiDqkpsww4DdjLxIDO+8v928vlwcfLHA28kOLpno+Nn8/MrLnP39XWMSJOpAgAfQtYiAExSZIkSZLUIq0aKtU2EfF84Pnl4SPK/TMiYmv5882Z+Yby53OAx5ZPxPyoPHcEcFz587rMvKr2+pl5VUScA7weuCYiLgX2owjAPBR4dWbuqqvWxcCfAiuBqyNiG/Cwssw+wJrMHK0rcw5wfFnmyxFRBR4N/BnFalirMnNseq0iSZIkSZK60awCNxExBowBT8rM/ymPcxpFMzMfaLDod4BT6s71lxvADcB44ObDwJ8AR1MMP3oQ8FPgX4DzM/PzTSp5ZkRcS/GEzVqK1/oN4F2ZeVmD/BkRLwKuAlYBrwbuAYaBt9cHh8oyeyPiWcCbgBdRPAE0CnwCeGtmfmvqppAkSZIkSd1stkGUYYpAzV11x3MuM8/i/qtYTZZ3C7BllvfZCmydQf57gfeU23TL3EWxXHn9kuWSJEmSJEmzC9xk5rGTHUuSJEmSJOmB6+rJiSVJkiRJkhYyAzeSJEmSJEkdysCNJEmSJElShzJwI0mSJEmS1KEM3EiSJEmSJHUoAzeSJEmSJEkdysCNJEmSJElShzJwI0mSJEmS1KH2bcVFIuLR08g2Boxm5mgr7ilJkiRJktTtWhK4AXYBOZ2MEfET4N+At2XmzS26vyRJkiRJUtdp1VCpfwKGgQD2ADuAfyn3e8rzO4DLgV8CpwFfjYhDW3R/SZIkSZKkrtOqwM07gKcA7wR+KzOPy8wXZeZxwG8Bf1emnwn0A28D+oC/atH9JUmSJEmSuk6rAjfvBP47M/86M++sTcjMOzPzTcA1wDszcywz3wb8F3BCi+4vSZIkSZLUdVoVuBkErpoiz1XAUM3xl4BHtej+kiRJkiRJXadVgZslwCOmyHNYmW/cHcC9Lbq/JEmSJElS12lV4Oa/gRdGxOGNEiPiCODPKYZHjVsG/KxF95ckSZIkSeo6rVoOfAPFilFfjYiLgC8APwX+F/D7wEuABwEbASLiwcAKYFuL7i9JkiRJktR1WhK4yczPRMRLgPcDq4FVNcnjS4SvzszPlOf2A14IfKcV95ckSZIkSepGrXrihsy8OCIuA04EjgQOAkaBq4FPZubPa/LuAT7T8EKSJEmSJEkCWhi4AcjMO4CPlJskSZIkSZIegJZMThwRr4qIg1txLUmSJEmSJBVatarU+cBNEfEvEfHHEdGq60qSJEmSJC1arQqw/BWwE1gJ/DtwY0ScXS4DLkmSJEmSpFloSeAmM/9vZj4JeDrFylIPAl4PXB0R34iI10TEw1txL0mSJEmSpMWipUOaMvNrmXk6cBjF0zeXAQPA31M8hfOJVt5PkiRJkiSpm83JXDSZ+cvM/LfMPBF4JLC+TDphLu4nSZIkSZLUjVq6HHitiAjgWcApwIkUw6d+NVf3kyRJkiRJ6jYtD9xExBMpgjUnUwyZCuC7wD+VmyRJkiRJkqahJYGbiHgo8CKKgM1TKYI1o8AWYGtmXtWK+0iSJEmSJC0mrXri5qbyWgl8FtgKfDwz72nR9SVJkiRJkhadVgVudlIEaz6cmTe26JqSJDU0snuE6s4qo3tH6V3SS2V5hYGlA/NdLUmSJKnlWhK4ycwntOI6kiRNpnp9lQ3DGxi+YXhC2mDfIOsH11Ppr8xDzSRJkqS5MSfLgUuS1GpbvrGFFRetaBi0ARi+YZgVF63ggqsvaHPNJEmSpLnT0lWlIuIwoAI8EljSIEtm5sZW3lOS1P2q11dZe9laxnJs0nxjOcaabWvoO6jPJ28kSZLUFVoWuImItwFvqrtmUExYXPuzgRtJ0oxsGN4wZdBm3FiOsXF4o4EbSZIkdYWWDJWKiJcA64DPAyspgjQfAl4MbAbGgIuB41pxP0nS4jGye6Tp8Khmdtywg5HdI3NUI0mSJKl9WvXEzSuBHwHPzsx7IwJgV2ZeDFwcER8H/gP4aIvuJ2mBGhmBahVGR6G3FyoVGHAxIE2iurM663KuNCVJkqSFrlWBmycDH83Me2vO7TP+Q2Z+JiI+A7wR2Naie0paQKpV2LABhhs8ODE4COvXF0Ecqd7o3tG2lpMkSZI6SatWlXoQcEvN8d3AQXV5vgk8pUX3k7SAbNkCK1Y0DtpAcX7FCrjAxYDUQO+S3raWkyRJkjpJqwI3NwGH1Rz/ADiiLs9vAvciaVGpVmHtWhibYl7ZsTFYs6bIL9WqLJ/do1izLSdJkiR1klYFbq4GDq85vgL4g4h4aUQcGBF/TDFp8dUtup+kBWLDhqmDNuPGxmCj686pzsDSAQb7BmdUZqhvyPltJEmS1BVaFbi5DDg8IpaXx+8E9gBbgVHg3ylWmnpLi+4naQEYGWk+PKqZHTuKclKt9YPr6Ynp/crqiR7WDa6b4xpJkiRJ7dGSwE1mbs3MAzJzZ3n8Q+Bo4P3AdmATcHRmfqkV95O0MMx22JPDpVSv0l9h0/Gbpgze9EQPm0/YTKXfYVKSJEnqDq1aVWqCMohz+lxdX1LnG53loj6zLafutvqo1Sw7eBkbhzey44YdE9KH+oZYN7jOoI0kSZK6ypwFbiSpd5aL+sy2nLpfpb9Cpb/CyO4RqjurjO4dpXdJL5XlFee0kSRJUlcycCNpzlRm+eDDbMtp8RhYOmCgRpIkSYtCqyYnlqQJBgZgcGaLATE0VJSTJEmSJBm4kTTH1q+Hnml+0vT0wDoXA5IkSZKkXzNwI2lOVSqwadPUwZueHti82WFSkiRJklTLwI2kObd6NWzfXgyDamRoqEhftaq99ZIkSZKkTufkxJLaolIptpERqFaLJb97e4tzzmkjSZIkSY0ZuJHUVgMDBmokSZIkabocKiVJkiRJktShDNxIkiRJkiR1KAM3kiRJkiRJHcrAjSRJkiRJUodacIGbiFgZEedFxOcjYjQiMiIumqLMMRFxeUTcGhF3R8Q1EfHaiNhnkjLHR8SVEbEnIu6IiC9HxClT3OeUiPhKmX9PWf74SfLvExGvK+tzd1m/yyPimKlbQpIkSZIkdbsFF7gB3gKcDvwOcONUmSPiRGAYGAQ+DpwP7Ae8B7i4SZnTgW3A4cBFwGbgN4GtEXF2kzJnA1uBw8r8FwFPBraV16vPH+X9zynrc35Zv0FguKy3JEmSJElaxBZi4OZ1wOOAXuCVk2WMiF6KIMqvgGMzc3VmvpEi6PNFYGVEnFRXZhlwNnAr8LTMPC0zXwccAXwfODMinlFX5hjgzDL9iMx8XWaeBjy1vM7Z5XVrnQSsBK4Cficz35iZq4FnlvXdHBG/Md1GkSRJkiRJ3WfBBW4y83OZ+d3MzGlkXwkcClycmV+ruSjXAG0AACAASURBVMY9FE/uwMTgzypgCXB+Zu6qKXMb8Lfl4Svqyowf/02Zb7zMLuB95fVeVldm/L5vKeszXuarwCVlvVdO+QrbLKLYJEmSJEnS3FtwgZsZOq7cf7pB2jBwF3BMRCyZZplP1eWZVZmI2B84prz/52dwH0mSJEmStIjsO98VmGOPL/f/U5+QmfdGxE5gAOgHrptGmZsi4k7gURFxQGbeFREHAo8E7sjMmxrU4bvl/nE1534b2Ae4PjPvnWaZpiLi602SnjCd8pIkaeGzPyBJUnfq9iduDir3e5qkj58/eBZlDqrbz8U9Dm6SLkmSJEmSFoFuf+JmUcjMpzY6X37zdlSbqyNJkuaB/QFJkrpTtz9xU/90TL3x87fPosyeuv1c3OP2JultMT4Rce02nTRJkiRJktQa3R64+U65nzBXTETsCywH7gWun2aZw4ADgR9l5l0AmXkncCPwkDK93mPLfe2cOd+nWPK7v6zHdMpIkiRJkqRFptsDN1eU+2c3SBsEDgCuysy90yzznLo8sypTLv99VXn/P5jBfdoqc+I2nTRJkiRJktQa3R64uRS4GTgpIp42frJcjvvt5eH768pcCOwFTo+IZTVlDgH+ujz8QF2Z8eM3l/nGyywDTiuvd2FdmfH7vr2sz3iZo4EXAj8DPjbF65MkSZIkSV1swU1OHBHPB55fHj6i3D8jIraWP9+cmW8AyMzRiFhDEcC5MiIuBm4Fnkex7PelwCW118/MnRHxRuBc4GsRcQnwC2Al8Cjg3Zn5xboyV0XEOcDrgWsi4lJgP4oAzEOBV2fmrrqXcjHwp+V1r46IbcDDyjL7AGsyc3QWTSRJkiRJkrrEggvcAL8DnFJ3rr/cAG4A3jCekJmfiIgh4M3AC4D9ge9RBFnOzZw4yCczz4uIXeV1/oLiyaRvAW/JzA81qlRmnhkR11I8YbMWGAO+AbwrMy9rkD8j4kUUQ6ZWAa8G7gGGgbdn5lVTN4W08IzsHuHw9x8OwHuf/V4qyysMLB2Y51pJkiRJUmdacIGbzDwLOGuGZb4APHeGZbYB22ZYZiuwdQb57wXeU25SV6teX2XD8AaGbxj+9bkzPn0GAIN9g6wfXE+lvzJf1ZMkSZKkjtTtc9yoxZyIWLOx5RtbWHHRivsFbWoN3zDMiotWcMHVF7S5ZpIkSZLU2QzcSJpT1eurrL1sLWM5Nmm+sRxjzbY1VK+vtqlmkiRJktT5DNxImlMbhjdMGbQZN5ZjbBzeOMc1kiRJkqSFw8CNpDkzsnuk6fCoZnbcsIOR3SNzVCNJkiRJWlgM3EiaM9Wdsxv2NNtykiRJktRtDNxImjOje0fbWk6SJEmSuo2BG0lzpndJb1vLSZIkSVK3MXAjac5UllfaWk6SJEmSuo2BG0lzZmDpAIN9gzMqM9Q3xMDSgTmqkSRJkiQtLAZuJM2p9YPr6YnpfdT0RA/rBtfNcY0kSZIkaeEwcCNpTlX6K2w6ftOUwZue6GHzCZup9DtMSpIkSZLGGbiRNOdWH7Wa7SdvZ6hvqGH6UN8Q20/ezqojV7W5ZpIkSZLU2fad7wpIWhwq/RUq/RVGdo9Q3VlldO8ovUt6qSyvOKeNJEmSJDVh4EZSWw0sHTBQI0mSJEnT5FApSZIkSZKkDmXgRpIkSZIkqUMZuJEkSZIkSepQBm4kSZIkSZI6lIEbSZIkSZKkDmXgRpIkSZIkqUMZuJEkSZIkSepQ+853BbRwjIxAtQqjo9DbC5UKDAzMd60kSZIkSepeBm40pWoVNmyA4eGJaYODsH59EcSRJEmSJEmt5VApTWrLFlixonHQBorzK1bABRe0t16SJEmSJC0GBm7UVLUKa9fC2Njk+cbGYM2aIr8kSZIkSWodAzdqasOGqYM248bGYOPGua2PJEmSJEmLjYEbNTQy0nx4VDM7dhTlJEmSJElSaxi4UUOzHfbkcClJkiRJklrHwI0aGh1tbzlJkiRJkjSRgRs11Nvb3nKSJEmSJGkiAzdqqFJpbzlJkiRJkjSRgRs1NDAAg4MzKzM0VJSTJEmSJEmtYeBGTa1fDz3TfIf09MC6dXNbH0mSJEmSFhsDN2qqUoFNm6YO3vT0wObNDpOSJEmSJKnVDNxoUqtXw/btxTCoRoaGivRVq9pbL0mSJEmSFoN957sC6nyVSrGNjEC1Wiz53dtbnHNOG0mSJEmS5o6BG03bwICBGkmSJEmS2smhUpIkSZIkSR3KwI0kSZIkSVKHMnCjGYm3BfG2mO9qSJIkSZLUEhHF1qkM3EiSJEmSJHUoAzeSJEmSJEkdysCNJEmSJElShzJwo2kb2T3y65/P/fK59zuWJEmSJEmtt+98V0Cda7JJiM/49BkTzuVbcy6rI0mSJEnSAzLZJMSN0rID/sz1iRtJkiRJkqQOZeBGTX32pZ+lJ6b3FumJHqrXV+e4RpIkSZIkzV7mxG06afPJwI2a2jC8gbEcm1besRxj4/DGOa6RJEmSJEmLi4EbNTSye4ThG4ZnVGbHDTucsFiSJEmSpBYycKOGqjtnN+xptuUkSZIkSdJEBm7U0Oje0baWkyRJkiRJE7kcuBrqXdLb1nKSJEmSJM2HTpmEuBkDN2qosrzS1nKSJEmSNBMjI1Ctwugo9PZCpQIDA/NdK6n1DNyooYGlAwz2Dc5oguKhviEGlvpJKUmSJGnuVKuwYQMMN/hTZXAQ1q8vgjhSt3COGzW1fnA9PTG9t0hP9LBucN0c10iSJEnSYrZlC6xY0ThoA8X5FSvgggvaWy9pLhm4UVOV/gqbjt80ZfCmJ3rYfMJmKv2GtSVJkiTNjWoV1q6FsbHJ842NwZo1RX6pGyyKwE1E7IqIbLL9pEmZYyLi8oi4NSLujohrIuK1EbHPJPc5PiKujIg9EXFHRHw5Ik6Zom6nRMRXyvx7yvLHP9DX3Cqrj1rN9pO3M9Q31DB9qG+I7SdvZ9WRq9pcM0mSJEmLyYYNUwdtxo2NwcaNc1sfqV0W0xw3e4C/b3D+jvoTEXEi8DHgHuAS4FbgBOA9wO8Bf9agzOnAecAtwEXAL4CVwNaIeHJmvqFBmbOBM4EfAZuB/YCTgG0R8erMPH/mL7P1Kv0VKv0VRnaPUN1ZZXTvKL1LeqksrzinjSRJkqQ5NzLSfHhUMzt2FOWcsFgL3WIK3NyemWdNlSkieimCKL8Cjs3Mr5Xn1wFXACsj4qTMvLimzDLgbIoAz9Myc1d5fgPwVeDMiPhYZn6xpswxFEGb7wNHZ+Zt5fl3AV8Hzo6Iy8av1QkGlg4YqJEkSZLUdrMd9lStGrjRwrcohkrN0ErgUODi8aANQGbeA7ylPHxlXZlVwBLg/NpASxmM+dvy8BV1ZcaP/2Y8aFOW2QW8r7zeyx7IC5EkSZKkbjA62iThrCi2mZaTSiMjcO658Pa3F/uRkfmu0USL6YmbJRFxMvBo4E7gGmA4M39Vl++4cv/pBtcYBu4CjomIJZm5dxplPlWXZzr3+RSwrszz1gbpkiRJkrRo9Pa2t5y630JaVn4xBW4eAXy47tzOiHhZZu6oOff4cv8/9RfIzHsjYicwAPQD102jzE0RcSfwqIg4IDPviogDgUcCd2TmTQ3q+t1y/7jpvLCI+HqTpCdMp7wkSVr47A9I6maz/QO6U/7wVmfZsmXyFcrGl5XfvBlWdcA6PItlqNSFQIUieHMg8GTgg8Ay4FMR8ZSavAeV+z1NrjV+/uBZlDmobj+Te0iSJEnSojQwUDwFMRNDQ85vo4kW4rLyi+KJm8x8W92pbwKviIg7KCYIPgv4k3bXq1Uy86mNzpffvB3V5upIkqR5YH9AUrdbv754CmI6S4L39MC6dXNfJy08s1lWfr6f3FoUgZtJfIAicFMbu61/Oqbe+Pnb68o8vEy7ZZIye+r2M7mHJEmSJC1af/ifAeubJNZNUDwGVCo553XSwrJQl5VfLEOlmvlZuT+w5tx3yv2E+WUiYl9gOXAvcP00yxxWXv9HmXkXQGbeCdwIPKRMr/fYcj9hzhxJkiRJkjRzD2RZ+fm02J+4+d1yXxuEuQJ4CfBs4KN1+QeBAyhWo9pbV+b3yjJfrCvznJo8ta4AXlqWuXCaZSRJkiRpUcq33v8JmpEROPzS4kmb9x6SVCrOaaPJNVwe/tAR6K/CklHY2wvXV+BnA1OXa6Ouf+ImIp5YruJUf34ZcH55eFFN0qXAzcBJEfG0mvz7A28vD99fd7kLgb3A6eV1x8scAvx1efiBujLjx28u89XW67TyevUBHUmSJEkS9w/SvOY1Bm00tfstD7+8CqcOwWmHw3POgOPWFfvTDi/OL682LjcPFsMTNy8EzoyIYeAG4OfAbwN/DOwPXA6cPZ45M0cjYg1FAOfKiLgYuBV4HsWy35cCl9TeIDN3RsQbgXOBr0XEJcAvgJXAo4B3Z+YX68pcFRHnAK8HromIS4H9yvo+FHh1Zu5qZUNIkiRJkrRY/XqS4SO3wAlroWcMEqidIimBZcPw6BWwbTNcvcrJidvgcxQBlyMphjMdSDHp738CHwY+nJn3e+YuMz8REUPAm4EXUAR4vkcRZDm3Pn9Z5ryI2AW8AfgLiqeZvgW8JTM/1KhimXlmRFxL8YTNWoo5tL4BvCszL3uAr1uSJEmSJJUGBuDJJ1a59ill0AbuH7SpPe4ZgxPWcERfHwMD8xu56frATWbuAHbMotwXgOfOsMw2YNsMy2wFts6kjCRJkiRJmrkY2gCj01wPvGcMBjcCBm4kSZIkSZqx+gmLpcmM7B7hmtGZrQd+zegORnaPMLB0/iZR6vrJiSVJkiRJkqo7Z7eu92zLtYqBG0mSJEmS1PVG985uXe/ZlmsVAzeSJEmSJKnr9S6Z3bresy3XKgZuJEmSJElS16ssn90kw7Mt1yoGbiRJkiRJUtcbWDrAYN/gjMoM9Q3N68TEYOBGkiRJkiQtEusH19MT0wuF9EQP6wbXzXGNplGP+a6AJEmSJEmzEVFs0nRV+itsOn7TlMGbnuhh8wmbqfTP7zApMHAjSZIkSVqARnaP/Prnc7987v2OpcmsPmo120/ezlDfUMP0ob4htp+8nVVHrmpzzRrbd74rIEmSJEnSdFWvr7JheAPDNwwDCcAZnz4DgMG+QdYPru+IpyTU2Sr9FSr9FUZ2j1DdWWV07yi9S3qpLK/M+5w29QzcSJIkSZIWhC3f2MLay9YylmMN04dvGGbFRSvYfMLmjnlaQp1tYOlAxwVq6jlUSpIkSZLU8arXVycN2owbyzHWbFtD9fpqm2omzS2fuJEkSZIkdbw//O0K8KvGiWfl/Q7HgI2PPtYhU+oKPnEjSZIkSepos5l4eMcNO5ywWF3BJ24kSZIkSR2turMKZx0+MWH8SZuzGq8JXt1Z7fj5S6Sp+MSNJEmSJKmjje4dbWs5qZMYuJEkSZIkdbTeJb1tLSd1EgM3kiRJkqSOVlk+u0mGZ1tO6iTOcSNJkiSprUZGoFqF0VHo7YVKBQachkSTGFg6wGDfIMM3DE+7zFDfkPPbqCsYuJEkSZLUFtUqbNgAww3+9h4chPXriyCO1Mj6wfWsuGgFYzl238kmkxL3RA/rBte1qWbS3HKolCRJkqQ5t2ULrFhRBm0OrVmi+X+fC4eOMDxcpF9wwbxVUR2u0l9h0/Gb6InJ/4ztiR42n7CZSr9RQHUHAzeSJEmS5lS1CmvXwlhfFU4dgtNqlnV+zhnF8alDjPVVWbOmyC81svqo1Ww/eTtDfUMN04f6hth+8nZWHbmqzTWT5o5DpSRJkjRjUY5OyJzfemhh2LABxp6yBU5YCz1jkNx/iEsCy4bh0SsY27aZjRtXOWRKTVX6K1T6K4zsHqG6s8ro3lF6l/RSWV5xTht1JQM3kiRJkubMyAgM/7AKLy2DNgD105KMH/eMwQlr2PHhPkZGKk5YrEkNLB0wUKNFwcCNJEmSpDlTrQI7K7DhV/dPGH/i5qwGj22deizVqoEbSQLnuJEkSZI0h767Z2TqTPWW7ZhdOUnqQj5xI0mSJGnO/HhJFc46vHmGJss5/3hJFfCRG0nyiRtJkiRNKmLiNp00CeCR/aNtLSdJ3cbAjSRJkqQ585jf6m1rOUnqNgZuJEmSNKnM+2/f/OZ9ae99b3Fcmy7Vqiwv1/We7nsj68pJ0iJn4EaSJEnTUq3C0BAc/sz7Jo0945/P5fBnjjA0VK4eJNUZWDrAYN/gxCXAmwkY6htymWdJKhm4kSRJ0pS2bIFnvbzKcP8QnFYz0exzzoDTDme4f4hnvbzKBRfMXx3VudYPrqcnpvenR0/0sG5w3RzXSJIWDgM3kiRJmlS1Cmv+YQv5khWwbLgYynJW3LcaUALLhsmXrOD/vO8Cn7zRBJX+CpuO3zRl8KYneth8wmYq/Q6TkqRxBm4kSZI0qdedVyWPXws9Y8WJ+iEv48c9Y+Txa3j9+UZuNNHqo1az/eTtDPUNNUwf6hti+8nbWXXkqjbXTJI6277zXQFJkiR1rpERuPaQDfcFbabSM8Y1B29kZKTCgFOUqE6lv0Klv8LI7hGqO6uM7h2ld0kvleUV57SRpCYM3EiSJKmpi7aP3Dc8ajqTyyawbAcXbR/hHUZu1MTA0gEDNZI0TQ6VkiRJUlPX/Lwc9jSDFYHuV06SJD0gBm4kSZLU3JLR9paTJEn3Y+BGkiRJTR3x+N62lpMkSfdn4EaSJElNnXxMuSxzTrNA1pWTJEkPiIEbSZIkNTWwdIAjegdnNMfNEb1DTjwrSVKLGLiRJEnSpM45cT0xzW5j0MM5J66b4xpJkrR4GLiRJEnSpCr9FTafsOm+4E39sKnyOOjhH5+3mUq/w6QkSWoVAzeSJEma0uqjVvP/Xrqdob6hicOmAob6hvh/L93OqiNXzUv9JEnqVvvOdwUkSZK0MFT6K1T6K4zsHqG6s8ro3lF6l/RSWV5xThtJkuaIgRtJkiTNyMDSAQM1kiS1iUOlJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJEmSJEmSOpSBG0mSJEmSpA4VmTnfddAciYhbHvzgBz/0iU984nxXRZKkReO6667j7rvvvjUzHzbfdQH7A5IkzYdW9gcM3HSxiNgJ9AK75rkqC8UTyv2357UWi4Nt3T62dfvY1u3T6W29DBjNzOXzXRGwPzALnf7+6ia2dfvY1u1jW7dPp7f1MlrUHzBwI5Ui4usAmfnU+a5Lt7Ot28e2bh/bun1sa80l31/tY1u3j23dPrZ1+yymtnaOG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJEmSJEmSOpSBG0mSJEmSpA7lqlKSJEmSJEkdyiduJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbhR14iIlRFxXkR8PiJGIyIj4qIZlP/HskxGxGOa5NknIl4XEddExN0RcWtEXB4Rx7TulXS+2bR12Xb/JyKGI+K2sv2uj4hLIuJxTcqcEhFfiYg7ImJPRFwZEcfPzavqTDNt64hYEhGnle12c9l210XEuRHRN0m5Rd3WEfGw8v358Yj4Xvn+3BMR/xkRqyOi4e/LiDim/Ay4tSxzTUS8NiL2meRex5ftu6ds7y9HxClz9+o6z0zbOyIeGxF/GRFXRMQPI+IXEfHTiPhkRDxzinst6vf2YmR/oH3sD7SP/YH2sU/QPvYHZiAz3dy6YgP+C0jg58B15c8XTbPsCTVlE3hMgzwB/GuZ/m3gXcAW4A7gXuDE+W6DTm1r4CFAtcx3NfD3wDuBDwO7gOMblDm7zP9D4D3A+4BbynOnz3cbdGJbA/sC/1nmuQ44r2zHHeW524En2dYN2+4V5ev9MfAR4B3ABWWbJXApEHVlTiz/799Rfha8q/xsSOBfm9zn9DL95rKd31O2ewJnz3c7dGp7AxeX50eAD5b5/61s/wRe0+Q+i/69vRi3mf6Oqitrf2AO2xr7A21pa+wPPNC2tk/QoW3NIu4PzHsF3NxatQHPBB5bdqiOnarzUFPuUOAn5QfBlTTvqL2oTPsCsH/N+aOBvcBu4Dfmux06sa3LD+IEXt4k/UF1x8eU+b8HHFJzfln5IXsPsGy+26HT2hr4szL9s0BPXdrbyrQLbOuGbXccxR9s9e32COAHZRu9oOZ8b/l/fi/wtJrz+wNXlflPqrvWsrI9b6ltU+CQsv0TeMZ8t0WHtvepwJENrjME/KL8dzisLs339iLdZvo7qqac/YE5bmvsD7SlrbE/8EDb2j5B57b1qSzS/oBDpdQ1MvNzmfndLP8nzsCmcn/aFPleWe7fkpn31Nz3q8AlFB2+lTO894I0k7aOiKOAFwOXZOYHm1zvl3WnXlHu/yYzb6vJt4siQr4EeNls6r7QzPB93V/u/yMzx+rSPlnuD607b1sDmXlFZm6rb7fM/AnwgfLw2JqklRRteXFmfq0m/z3AW8rDV3J/qyja8/yyfcfL3Ab8bXn4ChaBmbZ3Zm7NzKsbXGcHxR/Y+1F0zGr53l6k7A+0j/2B9rE/0D72CdrH/sD0GbjRohYRpwLPp/jm55ZJ8u1P8SFwF/D5Blk+Ve6Pa3Udu8CLy/1HI+KgiDg5Iv4qItY2mzuA+9rx0w3SbOvmRsr9cxqMvx4fw/vZuvO29dTG/5C4t+bcZO02TPFZcUxELJlmGdv6Po3aezb5bW9Nm/2BtrA/0D72B+aOfYL2sT9QY9/5roA0X8qJ2d5L8ZjpJ6fI/tvAPsD1mdnow+O75b7hpHqL3NHlvg/4PvCwmrSMiPdTjEf9FUBEHAg8ErgjM29qcD3burn/oBjn+6fAtRHxWYrHRp8K/D7FGPf3jWe2racWEfsCf1Ee1v7Cf3y5/5/6Mpl5b0TsBAYovvW8bhplboqIO4FHRcQBmXlXK+q/0EzS3s3y9wEVik7xcM1539uaNvsDbWN/oH3sD8wB+wTtY39gIp+40aJUfvvwIYoJxF4zjSIHlfs9TdLHzx/8AKvWjZaW+3MoHmF8IvAbwB9SdNxeBayryW9bz1L5+PRKivHrj6d4b7+BYlz8MPDPdX9o2NZTeydwOHB5Zn6m5vxs2m66ZQ5qkr4YNGvvCcpvLj9C8YjzWbWPP+N7W9Nkf6Ct7A+0if2BOWOfoH3sD9QxcKPF6nUUk1itqfvPrdYb/5z5NvDCzPx2Zt6RmVWKTsUY8PqI2G/eatglykf4LwHOpJij4TCKX1jPpfiGczgiTpy/Gi4sEfEairb8NvDSea5O15tJe5dLq34Y+D2K9/zZc15BdSv7A+1jf6BN7A+0nn2C9rE/0JiBGy06EfE44G+ACzPz8mkWmyrqPX7+9gdSty413ibbxh9/HpeZ/w3spPjG7Ynladt69t5EsZLEmzPzg5n5k8wczcxPUXSKH0QxHGCcbd1ERJxO0VbfAp6ZmbfWZZlN2023TLNvhbrWNNq7Nu8+wEUU7/V/AU5uMFmn721Nyf5A29kfaB/7Ay1kn6B97A80Z+BGi9GTKGcPj4is3Si+dQP4bnnu+eXx94FfAf3lmMt6jy33E8apiu+U+2YfiOPfcD4YIDPvBG4EHhIRhzXIb1s3Nz7h4OfqE8pO8W1AX0Q8rDxnWzcQEa+lGP//TYpOw08aZBt/X08YE11+RiynmBzv+mmWOQw4EPjRYhvLPs32Hs/7IOCjwEnAPwMvbjTPiO9tTZP9gfayP9A+9gdaxD5B+9gfmJyBGy1Gu4AtTbbxD4h/LY93wa+X87sKOAD4gwbXfE65v2KO6ryQja9acHh9QjkmdfwDc1dN0ng7PrvB9Wzr5sZXK6hf4nO8rX+jPPxFTZJtXSMi/hJ4D/BfFJ2G3U2yTtZugxSfFVdl5t5plll0bQ0zam/K4RP/SvHN2j8BL63/1r6O7a2p7ML+QDvZH2gf+wMtYJ+gfewPTENmurl13QYcCyTFChEzKXdlWe4xDdJeVKZ9Adi/5vzRwF5gN9A736+909qa4huDGyk6B0+vS3t7WfaKuvPHlOe/BxxSc34ZcAtwD7Bsvl97B7b1P5TpnwWW1KW9o0z7im3dtH3XlW3xNeChU+TtBX5W/t9/Ws35/Sn+qEvgpLoyy8v2vKW2TYFDyvZP4Bnz3Q4d2t5LKFZJSeAfgZ5pXN/3tpv9gQ5qa/sDbW1r+wMPvI3tE3RmWy/a/kCUlZYWvPIx5vFHmR8B/BHFI4mfL8/dnJlvmOIaV1I8Hv3YzPxeXVpQjJ9cSTFZ1jaKpSxfSPHB/IKcehnRrjDTto6IZwGXlYf/RtFx+98US1LuBn4/M8eX4xsv827g9cCPgEuB/Sja+mHAqzPz/Na/ss4zk7aOiEcCXwIeRfGN5aeBuykmbHt6+XMlM79Yd49F39YRcQqwlWIIxHk0HlO+KzO31pR5PkV73QNcDNwKPI9iBY9LgT/Pul+yEfFq4FyKjsIlFH/ArKT4N3v3VJ9R3WKm7R0RFwKnAjdz3x8k9a7MzCvr7rPo39uLkf2B9rE/0D72B9rHPkH72B+YgfmOHLm5tWoDzqL4z9ts2zWNa1xJk2/YyvR9KVaguJbil95twOXAMfP9+ju9rYGnUHxQ/oziF9MPgPcDvznJfU4FvgrcCfwc2AEcP9+vv5PbmuKx6LOB6yg6D78AbgAuBJ5gW8+6nZOiI1Bf7vfKz4Dbys+Ea8vPiH0mudcJZfv+vGzvrwKnzHcbdHJ713w2T7ad1eRei/q9vRi32fyOanCN8fec/YEWtzX2B9rS1tgfmMu2tk8wT23NIu4P+MSNJEmSJElSh3JyYkmSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuApRZrwAABkBJREFUJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJEmSJEmSOpSBG0mSJEmSpA5l4EaSJEmSJKlDGbiRJEmSJEnqUAZuJHW1iFgWERkRW+e7Lp0iIq6MiJzvekiS1C72ByayPyAtHAZuJKmDRMSpZcfy1PmuiyRJmh/2ByTV2ne+KyBJc+xG4InAnvmuSAf5C+CA+a6EJEltZH9gIvsD0gJh4EZSV8vMXwLfnu96dJLM/MF810GSpHayPzCR/QFp4XColKSu1mxMe0RsLc8vi4iXR8S1EXFPRPw0IjZFxEENrrWr3A6KiPMj4sayzLci4jUREXX5jy3vcVaTuu2KiF01x1cCF5aHF5Zlx7dl03itz4uIakTcFBF7I+LHEbEjIl5Vl2/CmPa6ezXazqrL/9CIeEdEXBcRd0fEnvLeK6aqpyRJ7WZ/wP6AtJD5xI2kxe7vgD8CtgHbgWcCa4DHAMc1yL8f8FngYODi8vgFwHuBxwOnPYC6bAVuB04EPgn8V03a7ZMVjIi1wAeBn1C8lpuBpcARwMuAf5ji3m9rcv6lQD9wV829+oArgWXA54FPAwcCxwOfjoiXZ+bmKe4nSVInsT9QsD8gdSADN5IWu98Fnjz+uHBE7AtcATwzIp6emV+py38YcD1weGbuLcu8Ffgq8KqIuCQzh2dTkczcWn5JdyLwiczcOoPiLwd+ATwlM3fXJkTEw6dx77Pqz0XEyyg6aV8Ezq1J+hDQB7woMy+uyX8wRQfu3Ij498z86QzqL0nSfLI/gP0BqVM5VErSYrehdox3Zt7LfY8nP71Jmb8a76SVZW4FNpaHL5uTWk7PvcAv609m5s0zvVBEVCi+sbseODEz7ynPPwUYAj5W20kr73M78FZgf4pvHSVJWijsDzRgf0DqDD5xI2mx+1qDcz8s94c0SLsXuKrB+SvL/ZEtqNNsfAR4N/CtiLgY2AF8ITN/NtMLRcSTgI8BdwDPrbvGM8r9QU3G6h9a7p840/tKkjSP7A/UsT8gdQ4DN5IWu0Zjxe8t9/s0SLs5M3/V4PxPyv2ESQzbITPPiYibgVcBrwFeC2RE7ADemJmNOqQTRMQjgMuBBwPPyszv1GV5WLl/Vrk185CZ1F+SpHlmf6CG/QGpszhUSpJm5uER0agD94hyv6fm3Fi5bxYkP7hltQIy858y83cpOlN/DGwBBoHPRMShkxYGIuIAiokM+4BVTcbmj7++MzIzJtnm8xFxSZLmmv2Bgv0BqQ0M3EjSzOwLHNPg/LHl/uqac7eV+9+qzxwRj6Hxt3Hj39416gxOS2benpmXZ+YaipUpHkrRYWsqInqAfwaeBqzPzI80yfqlcv8Hs62fJEldwP5Awf6A1AYGbiRp5t4REUvGDyLiocBbysMLa/J9GxgFToyIpTX5H8z9V2WodUu5f/RMKhQRz4xyCYo64/e9q0FarXMoVq/4UGZubJapfMT688CfRsSqJnV5cu3rlSSpS9kfsD8gtYVz3EjSzNwELAG+GRH/DjwIWEmxLOg/1D5OnJm/jIj3AuuAqyPi4xSfu88Cflxu9b5I0al6bUQ8jPvGyp+XmXsa5B/3ceCOiPgSsAsIim/Bjga+Dny2WcGIeDpwBnAPcGOTSQavzMwry59fTLFE6paIeA3wZYq5AR4FHAEcTjFp4e4G15EkqRvYH7A/ILWNgRtJmplfAH8I/C1wEvBwiiUy3wmc1yD/Wyk6XmuAtRQdr4uBs4Bv1WfOzNsi4gVluVOBA8uki7j/ePl6bwL+CDgKeC5Fp+sG4C+B92fmhGVBaxxQ7vcH/nqSfFeWdfxRRDwVeDXFMp8voXiU+yflazoPuHaS60iStNDZH7A/ILVNZOZ810GSFoSI2AWQmcvmtyaSJGm+2B+Q1G7OcSNJkiRJktShDNxIkiRJkiR1KAM3kiRJkiRJHco5biRJkiRJkjqUT9xIkiRJkiR1KAM3kiRJkiRJHcrAjSRJkiRJUocycCNJkiRJktShDNxIkiRJkiR1KAM3kiRJkiRJHcrAjSRJkiRJUocycCNJkiRJktShDNxIkiRJkiR1KAM3kiRJkiRJHcrAjSRJkiRJUocycCNJkiRJktShDNxIkiRJkiR1qP8PcpzXfo0voUMAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "image/png": { "height": 566, "width": 566 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "visualize(df, 'size_peak', 'Max resident set size [MB]')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.0.0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tensorflow as tf\n", "tf.__version__" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.2.4-tf'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tensorflow.python import keras\n", "keras.__version__" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Examples/converter_examples/Convert_Benchmark/README.md ================================================ # Quantization Benchmarking on Android Phone In this example, we use Converter to quantize and convert models into TensorFlow Lite format. We ran the experiment for image classification task using different configurations of MobileNet and obtained benchmarking results by deploying models on the LG G6 smartphone. The results reaffirm the benefits of model quantization for edge deployments in terms of inference speed and peak memory usage. The experiments are presented in the [Benchmark](Benchmark.ipynb) file along with figures showing model performance. To obtain the data pickle file, either run `Benchmark.ipynb` or `script_mobilenet.py`. The script first downloads different MobileNets in the Protobuf format and converts them to TensorFlow Lite using different quantization configurations. The script also downloads the official TensorFlow Lite versions (int8 and float32) of MobileNet to compare the performance of the converted models. To set up the mobile benchmarking pipeline please follow these steps. ### Setup 1. Android Studio / [adb](https://developer.android.com/studio/command-line/adb) ```bash export PATH=$PATH:~/Library/Android/sdk/platform-tools/ ``` 2. Compile model_benchmark See [step 1](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#on-android): Needs Ubuntu system / Android Studio. ```bash git clone git@github.com:tensorflow/tensorflow.git cd tensorflow bazel clean --expunge bazel build -c opt --config=android_arm --cxxopt='--std=c++11' tensorflow/lite/tools/benchmark:benchmark_model ``` 3. Prepare data on the phone See [step 2-4](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#on-android) ``` Android/Sdk/platform-tools/adb push bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model /data/local/tmp ``` If the device is not connected, follow instructions [here](https://askubuntu.com/questions/863587/adb-device-list-doesnt-show-phone) Copy model ```bash adb push model.tflite /data/local/tmp ``` 4. Measure performance See [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#parameters) for more control arguments. ```bash adb shell /data/local/tmp/benchmark_model \ --num_threads=4 \ --graph=/data/local/tmp/model.tflite \ --warmup_runs=1 \ --num_runs=50 ``` ================================================ FILE: Examples/converter_examples/Convert_Benchmark/repdata.py ================================================ """ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later Create a synthetic tf.dataset for inference speed measure """ import numpy as np from absl import flags import sys def parse_arg(): try: idx = sys.argv.index("--custom_data") custom_data = str(sys.argv[idx+1:][0]) except ValueError: custom_data = "1,224,224,3,1000" return custom_data def get_dataset(): custom_data = parse_arg() print(custom_data) batch_size, img_len, img_wid, channel_size, label_size = [int(x) for x in custom_data.split(",")] labels = np.random.randint(0, label_size, size=batch_size).astype(np.float) imgs = np.random.random(size=(batch_size, img_len, img_wid, channel_size)) class Dataset: def __init__(self, data): self.data = data def numpy(self): return self.data[np.newaxis,:] for i in range(batch_size): yield Dataset(imgs[i]), Dataset(labels[i]) ================================================ FILE: Examples/converter_examples/Convert_Benchmark/script_mobilenet.py ================================================ """ Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import keras import subprocess from os import path import pandas as pd import json # see models here - https://www.tensorflow.org/lite/guide/hosted_models STORAGE_LINK = "https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/" ADB_LOCAL = path.expanduser("~/Library/Android/sdk/platform-tools/adb") # for MacOS ALPHA = ["1.0","0.75","0.5","0.25"] SIZE = ["224","192","160","128"] REP_DATA = "repdata.py" OP_TYPE = ["float", "float16", "uint8", "int8"] KEYS = [[a,b] for a in ALPHA for b in SIZE] MOBILENET = "mobilenet_v1" df = pd.DataFrame({"model":[], "alpha":[], "size":[], "op": [], "size_init":[], "size_peak":[], "warmup":[], "init":[], "inference":[]}) def preprocess(name): try: process = subprocess.Popen(" wget " + STORAGE_LINK + name + ".tgz ", shell=True).wait() except: print("Could not retrieve") return 0 try: process = subprocess.Popen(" tar -xzf " + name + ".tgz", shell=True).wait() except: print("Could not unpack") return 0 return 1 def push_to_device(name): try: process = subprocess.Popen(ADB_LOCAL + " push " + name + ".tflite /data/local/tmp/", shell=True, stdout=subprocess.DEVNULL).wait() except: print("Could not push") return 0 return 1 def run_benchmark(name): command_template = ADB_LOCAL + " shell /data/local/tmp/benchmark_model --graph=/data/local/tmp/" command = command_template + name + ".tflite --num_threads=4" process2 = subprocess.Popen(command.split(), stdout=subprocess.PIPE) output, error = process2.communicate() output = output.decode('utf-8') output = output.split("\n") infer, memo = "","" for p in output: if "Inference timings" in p: infer = p if "Peak memory footprint" in p: memo = p #output = output.split("\n") #infer = output return (infer, memo) def compile_results(infer, memo, df, of, op, alpha, size): data = [] try: idx = memo.find("init=") max_size = float(memo[idx+5:idx + memo[idx:].find(" ")-1]) except: max_size = float('nan') idx = memo.find("overall=") try: malloc_size = float(memo[idx+8:]) except: malloc_size = float('nan') idx = infer.find("Warmup (avg):") try: warmup = float(infer[idx+14:idx + infer[idx:].find(",")]) except: warmup = float('nan') idx = infer.find("Init:") try: init = float(infer[idx+6:idx + infer[idx:].find(",")]) except: init = float('nan') idx = infer.find("Inference (avg):") try: no_stat = float(infer[idx+17:]) except: no_stat = float('nan') df = df.append(pd.Series([of, alpha, size, op, max_size, malloc_size, warmup, init, no_stat], index=df.columns), ignore_index=True) return df def convert(name, size, op): json_dic = {} json_dic["convert_from"] = name+"_frozen.pb" json_dic["convert_to"] = name+"_"+op+"_converted.tflite" json_dic["input_nodes"] = "input" json_dic["output_nodes"] = "MobilenetV1/Predictions/Reshape_1:0" command = "" if op == 'float': command = json.dumps([json_dic]) if op == 'float16': quant_dic = {} quant_dic["type"]="float16" quant_dic["opsset"] = "tf" json_dic["quantization"] = quant_dic command = json.dumps([json_dic]) if op == 'int8': quant_dic = {} quant_dic["type"]="int8" quant_dic["opsset"] = "int8" quant_dic["load"] = REP_DATA + " --custom_data '1," + size + "," + size + "," + "3,1000' --undefok custom_data" json_dic["quantization"] = quant_dic command = json.dumps([json_dic]) if op == 'uint8': quant_dic = {} quant_dic["type"]="uint8" quant_dic["opsset"] = "tf" quant_dic["load"] = REP_DATA + " --custom_data '1," + size + "," + size + "," + "3,1000' --undefok custom_data" json_dic["quantization"] = quant_dic command = json.dumps([json_dic]) try: process = subprocess.Popen("python3 -m aup.dlconvert -d " + "'" + str(command) + "'", shell=True).wait() except: print("Could not Convert= " + str(command)) return 0 return 1 for key in KEYS: name = MOBILENET + "_" + key[0] + "_" + key[1] print("Starting " + str(name)) if(not preprocess(name)): continue if(not push_to_device(name)): continue infer,memo = run_benchmark(name) df = compile_results(infer, memo, df, "official", "float", key[0], key[1]) new_name = name + "_quant" print("Starting " + str(new_name)) if(not preprocess(new_name)): continue if(not push_to_device(new_name)): continue infer,memo = run_benchmark(new_name) df = compile_results(infer, memo , df, "official", "int8", key[0], key[1]) print("Starting converted tflite benchmarking") for op in OP_TYPE: converted = convert(name, key[1], op) if not converted: print("Failed " + str(name) + " " + str(op)) continue print("testing= " + str(name) + " " + str(op)) new_name = name + "_" + op + "_converted" if(not push_to_device(new_name)): continue infer,memo = run_benchmark(new_name) df = compile_results(infer, memo, df, "converted", str(op), key[0], key[1]) print(df.to_string()) df.to_pickle("./data_mobilenet.pkl") ================================================ FILE: Examples/converter_examples/Convert_Profiler/.gitignore ================================================ mobilenet* input_models output_models *.tgz Dockerfile_* densenet* nasnet* squeezenet* ================================================ FILE: Examples/converter_examples/Convert_Profiler/README.md ================================================ # Profiling TensorFlow Lite/ONNX model performance for CPU In this example, we show how to use Converter to convert models into edge device-friendly formats (TensorFlow Lite and ONNX) and then profile their performance (i.e. runtime and memory usage) on CPU using Auptimizer's Profiler package. We use standard image classification models in Protobuf format and convert them to TensorFlow or ONNX. Once we have models in a common format, we can benchmark their performance using Profiler with different hardware settings. By pairing Auptimizer's Converter and Profiler capabilities, you can significantly reduce the effort needed to prepare an ML model for edge deployment. You can simply download models from various DL platforms and libraries, convert them to a common edge-friendly standard and estimate the script's runtime and memory usage for your target device constraints. ## How to run this example - *Step 1*: Download a few protobuf models for testing ``` python download_test_models.py ``` and create a new folder called `output\_models`. The `download_test_models.py` script should download the squeezenet, densenet, and nasnet-mobile models into `input_models` directory. - *Step 2*: Convert the models to TensorFlow Lite: ``` python -m aup.dlconvert -f convert_tflite.json ``` - *Step 3*: Profile the converted TensorFlow Lite models and the downloaded official TensorFlow Lite models released by TensorFlow for the same model architecture. Run the following to do profiling: ```bash python -m aup.profiler -e env_tflite.template -f model_names_tflite.txt ``` This creates an output text file with the details of the model performance. The Docker settings can be edited using the `env_tflite.template` file and the test script can be edited using `test_perf_tflite.py`. We show output from running the example on MacOS. From `tflite_output.txt`, we see that the performances of the converted models are very close to those of the official TensorFlow Lite models, which validates that the conversion from Protobuf to TensorFlow Lite works correctly. You can similarly convert the models to ONNX and profile the performances. The sample conversion configuration json file `convert_onnx.json` and the Profiling environment file `env_onnx.template` are provided. ================================================ FILE: Examples/converter_examples/Convert_Profiler/convert_onnx.json ================================================ [ { "convert_from":"input_models/densenet/densenet.pb", "convert_to":"output_models/densenet_converted.onnx", "input_nodes":"Placeholder:0", "output_nodes":"softmax_tensor:0,ArgMax:0" }, { "convert_from":"input_models/squeezenet.pb", "convert_to":"output_models/squeezenet_converted.onnx", "input_nodes":"Placeholder:0", "output_nodes":"softmax_tensor:0" }, { "convert_from":"input_models/nasnet_mobile.pb", "convert_to":"output_models/nasnet_mobile_converted.onnx", "input_nodes":"input:0", "output_nodes":"final_layer/predictions:0" } ] ================================================ FILE: Examples/converter_examples/Convert_Profiler/convert_tflite.json ================================================ [ { "convert_from":"input_models/squeezenet.pb", "convert_to":"output_models/squeezenet_converted.tflite", "input_nodes":"Placeholder:0", "output_nodes":"softmax_tensor:0" }, { "convert_from":"input_models/densenet/densenet.pb", "convert_to":"output_models/densenet_converted.tflite", "input_nodes":"Placeholder:0", "output_nodes":"softmax_tensor:0,ArgMax:0" }, { "convert_from":"input_models/nasnet_mobile.pb", "convert_to":"output_models/nasnet_mobile_converted.tflite", "input_nodes":"input:0", "output_nodes":"final_layer/predictions:0" } ] ================================================ FILE: Examples/converter_examples/Convert_Profiler/download_test_models.py ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later import os import json import argparse import logging import requests import shutil all_urls = {"densenet": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz", "squeezenet": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz", "nasnet_mobile": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz"} model_folder = "input_models" def download_all_url(all_urls, model_folder): for model, url in all_urls.items(): tarFilename = url.split('/')[-1] # check if the tar file already exists on disk # if not tarFilename[0].isdigit() and os.path.isfile(tarFilename): if os.path.isfile(tarFilename): print ("File already exists, skipping ", url) continue print("Downloading ", url) response = requests.get(url, stream=True) if response.status_code == 200: with open(tarFilename, 'wb') as f: f.write(response.raw.read()) save_folder = model_folder shutil.unpack_archive(tarFilename, save_folder) if __name__ == '__main__': download_all_url(all_urls, model_folder) ================================================ FILE: Examples/converter_examples/Convert_Profiler/env_onnx.template ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later #User data Variables IMAGEREPO=tensorflow/tensorflow:1.15.0-py3 APTREQUIREMENTS="wget" PRERUN="pip install onnxruntime" DIR=./ SCRIPT=test_perf_onnx.py COMMAND=python SAMPLETIME=3 OUTPUTFILE=onnx_output.txt DOCFILE=Dockerfile DOCKCPUS="4.0" DOCKMEMORY="2.0g" # Additional docker arguments could be passed as following: # To run Docker container with privilege capability change to 'true' # To use Volume instead of copying data with the current folder # use the format "-v /path/in/source:/path/in/destination" as string # DOCKER_ARGS="--privileged -v /data:/mnist_data" DOCKER_ARGS= ================================================ FILE: Examples/converter_examples/Convert_Profiler/env_tflite.template ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later #User data Variables IMAGEREPO=tensorflow/tensorflow:1.15.0-py3 APTREQUIREMENTS="wget" # PIPREQUIREMENTS="ipython numpy" PRERUN="wget https://dl.google.com/coral/python/tflite_runtime-1.14.0-cp36-cp36m-linux_x86_64.whl; pip install tflite_runtime-1.14.0-cp36-cp36m-linux_x86_64.whl" DIR=./ SCRIPT=test_perf_tflite.py COMMAND=python SAMPLETIME=3 OUTPUTFILE=tflite_output.txt DOCFILE=Dockerfile DOCKCPUS="4.0" DOCKMEMORY="2.0g" # Additional docker arguments could be passed as following: # To run Docker container with privilege capability change to 'true' # To use Volume instead of copying data with the current folder # use the format "-v /path/in/source:/path/in/destination" as string # DOCKER_ARGS="--privileged -v /data:/mnist_data" DOCKER_ARGS= ================================================ FILE: Examples/converter_examples/Convert_Profiler/model_names_tflite.txt ================================================ input_models/densenet/densenet.tflite output_models/densenet_converted.tflite input_models/squeezenet.tflite output_models/squeezenet_converted.tflite input_models/nasnet_mobile.tflite output_models/nasnet_mobile_converted.tflite ================================================ FILE: Examples/converter_examples/Convert_Profiler/test_perf_onnx.py ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later # Compute the overall inference time for a given tflite model import onnxruntime as rt import numpy as np import time WARMUP = 10 ITER = 600 CONSTANT = 0.5 def compute(model_path): now = time.monotonic() sess = rt.InferenceSession(model_path) input_det = sess.get_inputs()[0] label_det = sess.get_outputs()[0] input_name = input_det.name input_shape = input_det.shape if 'N' in input_shape: input_shape[input_shape.index('N')] = 1 input_shape[0] = 1 print("input_shape", input_shape) t1 = time.monotonic() - now now = time.monotonic() for i in range(WARMUP): X_test = np.random.random(size=input_shape).astype(np.float32) pred_onx = sess.run(None, {input_name: X_test})[0] t2 = time.monotonic() - now now = time.monotonic() for i in range(ITER): X_test = np.random.random(size=input_shape).astype(np.float32) pred_onx = sess.run(None, {input_name: X_test})[0] t3 = time.monotonic() - now return t1, t2/float(WARMUP), t3/float(ITER) if __name__ == "__main__": import sys if len(sys.argv) != 2: print("test_perf_onnx.py ") print(compute(sys.argv[1])) ================================================ FILE: Examples/converter_examples/Convert_Profiler/test_perf_tflite.py ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later # Compute the overall inference time for a given tflite model import tensorflow as tf from tflite_runtime.interpreter import Interpreter import tensorflow.random import numpy as np import time import os WARMUP = 10 ITER = 300 CONSTANT = 0.5 def compute(model_path): now = time.monotonic() intp = Interpreter(model_path) x = intp.tensor(intp.get_input_details()[0]['index']) iy = intp.get_output_details()[0]['index'] intp.allocate_tensors() t1 = time.monotonic() - now now = time.monotonic() for i in range(WARMUP): #x().fill(CONSTANT) x =np.random.rand() intp.invoke() y = intp.get_tensor(iy) t2 = time.monotonic() - now now = time.monotonic() for i in range(ITER): #x().fill(CONSTANT) x =np.random.rand() intp.invoke() y = intp.get_tensor(iy) t3 = time.monotonic() - now return t1, t2/float(WARMUP), t3/float(ITER) if __name__ == "__main__": import sys if len(sys.argv) != 2: print("test_perf_tflite.py ") print(compute(sys.argv[1])) ================================================ FILE: Examples/converter_examples/Convert_Profiler/tflite_output.txt ================================================ Usage stats for Experiment ran on : 2020-10-08 10:26:18.302189 NAME AVG CPU % PEAK CPU AVG MEM USAGE / LIMIT PEAK MEM NET I/O BLOCK I/O TOTAL TIME (ms) ----------------------- ----------- ---------- ----------------------- ---------- ---------------- ------------- ----------------- densenet 256.601% 260.97 162.9 MiB / 1.9 GiB 163.0 MiB 850.0 B / 42.0 B 0.0 B / 0.0 B 27640 densenet_converted 254.545% 259.71 162.9 MiB / 1.9 GiB 163.0 MiB 843.0 B / 42.0 B 0.0 B / 0.0 B 27635 squeezenet 272.655% 274.36 127.6 MiB / 1.9 GiB 127.7 MiB 773.0 B / 42.0 B 0.0 B / 0.0 B 3132 squeezenet_converted 274.83% 276.06 129.7 MiB / 1.9 GiB 129.8 MiB 731.0 B / 0.0 B 0.0 B / 0.0 B 3067 nasnet_mobile 155.766% 159.16 135.1 MiB / 1.9 GiB 135.3 MiB 839.0 B / 42.0 B 0.0 B / 0.0 B 24549 nasnet_mobile_converted 154.046% 159.38 123.0 MiB / 1.9 GiB 134.7 MiB 854.0 B / 42.0 B 0.0 B / 0.0 B 24515 ================================================ FILE: Examples/converter_examples/README.md ================================================ # Model Conversion Examples This folder contains examples that demonstrate Converter coverage and efficacy. ### - Evaluating supported model architectures [Tested_Models](/Examples/converter_examples/Tested_Models) - This example evaluates common model architecture coverage by the individual conversion functions. It also summarizes known issues. **We strongly recommend that users review this example first before running their conversion tasks.** ### - Benchmarking quantized TensorFlow Lite models on an Android phone [Convert_Benchmark](/Examples/converter_examples/Convert_Benchmark) - This example demonstrates how to benchmark quantized TensorFlow Lite model performance (i.e. running time and memory usage) on an Android phone. Specifically, we converted models from a TensorFlow frozen protobuf file to a quantized .tflite file, and benchmarked their performance on an LG G6 mobile phone. This example shows that models converted and quantized with Converter match the performance of the official quantized models provided in the [TensorFlow repo](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md). ### - Profiling performances of converted models using Auptimizer Profiler [Convert_Profiler](/Examples/converter_examples/Convert_Profiler) - This example demonstrates how to use Auptimizer-Profiler to profile TensorFlow or ONNX model performance on CPU. Performance benchmarking scripts are provided for TensorFlow and ONNX. ### How to run the examples If you have not done so, please install the packages in `dlconvert_requirements.txt` before running the examples included in this directory. ```bash pip install -r dlconvert_requirements.txt ``` We provide specific instructions for each example in the respective folder. ================================================ FILE: Examples/converter_examples/Tested_Models/.gitignore ================================================ test_models/ converted_models/ *.tgz *.gz *.pb *.h5 *_internal* ================================================ FILE: Examples/converter_examples/Tested_Models/README.md ================================================ # Model Coverage In this example, we have evaluated the capabilities and limitations of Converter conversion functions by applying the tool to select models in various supported formats. We tested the official models hosted/released by TensorFlow or PyTorch. Our goal was to cover as many models as possible. However, due to the limited availability of officially released models and the large number of model architectures, this evaluation does not aim to test all models exhaustively. Rather, its purpose is to identify and address common model conversion issues. **We strongly recommended reviewing model coverage and known issues for the specific conversions, before you start using Converter.** ## How to run the evaluation tests - *Step 0*: Make sure you have installed required packages listed in [dlconvert_requirement.txt](../dlconvert_requirements.txt). - *Step 1*: Prepare test models. There are two ways to get the test models: 1) Direct download via URLs and 2) Create the models using Python scripts. To download models, run `python download_models.py`. This will automatically download models from the URLs list in `download_urls.json` and save them into a `test_models` folder. The downloaded models are in Protobuf, Checkpoint or Savedmodel formats. To create models, run `python create_test_models.py`. This will create Keras and PyTorch models and save them into the `test_models` folder. - *Step 2*: Convert test models to TensorFlow Lite/ONNX. The configuration json files for each type of conversion (from one source format to either TensorFlow Lite or ONNX) are located in [conversion_jsons](/conversion_jsons). You can run model conversion with each json file individually to test a certain conversion; e.g., ```bash python3 -m aup.dlconvert -f conversion_jsons/convert_keras_to_onnx.json ``` In the json files, we set `skip:True` for the models that were tested but failed the conversion. ## Model support and known issues Here we share the findings from the evaluation test for each individual conversion function. The model names corresponds to their names in `download_models.py` or `create_test_models.py`. For the failed models, some failures were caused by non-supported operators in the conversion tool, while others by more model-specific issues that Converter cannot handle. Any model that is not listed below has not been tested. If not specified, the (un)supported models apply to both TensorFlow v2.3 and TensorFlow v1.15. Please make sure you are using the TensorFlow version no lower than v1.15 to run the tests. ### Savedmodel to TensorFlow Lite - Supported: resnet_50_TF2, efficientnet_b1_TF2, lstm, gru - Not supported: Non-supported operators: object detection models from [TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md), transformer models Model-specific issues: ssd_mobilenet_v1_coco, ssd_mobilenet_v2_coco - Known issues: - This conversion is only supported by Tensorflow v2.x. - For some models downloaded from [TensorFlow Hub](https://tfhub.dev/) in Savedmodel format, the signature is empty. Conversion from Savedmodel format requires non-empty signature and tag. ### Savedmodel to ONNX - Supported: resnet_50_TF2, efficientnet_b1_TF2, lstm, gru, ssd_mobilenet_v1_coco, ssd_mobilenet_v2_coco - Not supported: Non-supported operators: object detection models from [TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md), transformer models - Known issues: - This conversion is only supported by Tensorflow v2.x. - Conversion from Savedmodel format requires non-empty signature and tag. ### Protobuf to TensorFlow Lite - Supported: densenet, squeezenet, nasnet_mobile, inception_v3, mobilenet_v1_0.25_224 - Not supported: Non-supported input type uint 8: ssd_mobilenet_v1, ssd_mobilenet_v2 - Known issues: - Conversion requires correct input_nodes name and output_nodes name. When using the [summarize graph tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms#inspecting-graphs), you can get the names correctly. But you need to make them into format `input_name:0` or `output_name:0`; i.e., adding `:0` after the name you found from the summarize graph tool. - The model to be converted and the input should be in float32 format. - Issue with Tensorflow v1.15: None dimension is only allowed for the 1st dimension of the input. When saving model to protobuf, please specify input dimension. ### Protobuf to ONNX - Supported: densenet, squeezenet, nasnet_mobile, inception_v3, mobilenet_v1_0.25_224, ssd_mobilenet_v1, ssd_mobilenet_v2 - Known issues: - Conversion requires correct input_nodes name and output_nodes name. When using the [summarize graph tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms#inspecting-graphs), you can get the names correctly. But you need to make them into format `input_name:0` or `output_name:0`; i.e., adding `:0` after the name you found from the summarize graph tool. ### Keras to TensorFlow Lite - Supported: VGG16, ResNet50, InceptionV3, MobileNet, MobileNetV2, DenseNet121, NASNetMobile, gru (supported by TF v2.3 only) - Not supported: Model-specific conversion error: lstm - Known issues: - Need to set parameter `quantization: opsset` to `tf` to enable successful conversion. ### Keras to ONNX - Supported: VGG16, ResNet50, MobileNet, MobileNetV2, DenseNet121, NASNetMobile, lstm, gru (supported by TF v2.3 only), InceptionV3 (supported by TF v1.15 only) ### Checkpoint to TensorFlow Lite - Known issues: - This conversion is experimental. There is no direct conversion tool available. Therefore, the approach taken is to convert a checkpoint file to frozen protobuf first, then to TensorFlow Lite. Hence, this conversion can be fragile. ### Checkpoint to ONNX - Supported: mobilenet_v1_0.25_224, ssd_mobilenet_v1, ssd_mobilenet_v2, lstm ### PyTorch to TensorFlow Lite - Supported: all **image classification** models in [torchvision.models](https://pytorch.org/docs/stable/torchvision/models.html#) - Not supported: Non-supported operators: fcn_resnet50, deeplabv3_resnet50, rnn models, resnet_3d, resnet_MC, resnet(2+1)d (the last three models all have 3D convolution layers) - Known issues: - To use the converter properly, please make changes in your `~/.keras/keras.json`: ```bash "backend": "tensorflow", "image_data_format": "channels_first" ``` - Models with `BatchNorm2d`/`BatchNorm3d` layers are not supported in TF v1.15. ### PyTorch to ONNX - Supported: all **image classification**, **semantic segmentation**, and **video classification** models in [torchvision.models](https://pytorch.org/docs/stable/torchvision/models.html#) - Not supported: Non-supported operators: rnn models - Known issues: - For supported models, all models can be converted using `torch==1.5.1` and `torchvision==0.6.1`, while some models fail when using `torch==1.6.0` and `torchvision==0.7.0`. - Models with `BatchNorm2d`/`BatchNorm3d` layers are not supported in TF v1.15. ## More resources Auptimizer model conversion tools integrates [TFLite converter](https://www.tensorflow.org/lite/convert), [tf2onnx](https://github.com/onnx/tensorflow-onnx), [keras2onnx](https://github.com/onnx/keras-onnx), and [pytorch2keras](https://github.com/nerox8664/pytorch2keras) packages. Please check those resources for more info on model support. ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_checkpoint_to_onnx.json ================================================ [ { "convert_from":"test_models/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224.ckpt.meta", "convert_to":"converted_models/mobilenet_v1_0.25_224_ckpt_converted.onnx", "input_nodes":"batch:0", "output_nodes":"MobilenetV1/Predictions/Reshape_1:0", "input_shape":"1,224,224,3", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/model.ckpt.meta", "convert_to":"converted_models/ssd_mobilenet_v1_ckpt_converted.onnx", "input_nodes":"image_tensor:0", "output_nodes":"detection_boxes:0,detection_scores:0,num_detections:0,detection_classes:0", "input_shape":"1,224,224,3", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt.meta", "convert_to":"converted_models/ssd_mobilenet_v2_ckpt_converted.onnx", "input_nodes":"image_tensor:0", "output_nodes":"num_detections:0,detection_classes:0,detection_scores:0,detection_boxes:0", "skip": "False" }, { "convert_from":"test_models/lstm_ckpt/ckpt.meta", "convert_to":"converted_models/lstm_ckpt_converted.onnx", "input_nodes":"embedding_input:0", "output_nodes":"dense/BiasAdd:0", "skip": "False" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_checkpoint_to_tflite.json ================================================ [ { "convert_from":"test_models/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224.ckpt.meta", "convert_to":"converted_models/mobilenet_v1_0.25_224_ckpt_converted.tflite", "input_nodes":"batch:0", "output_nodes":"MobilenetV1/Predictions/Reshape_1:0", "skip": "True" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/model.ckpt.meta", "convert_to":"converted_models/ssd_mobilenet_v1_ckpt_converted.tflite", "input_nodes":"image_tensor:0", "output_nodes":"detection_boxes:0,detection_scores:0,num_detections:0,detection_classes:0", "input_shape": "1,224,224,3", "skip": "True" }, { "convert_from":"test_models/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt.meta", "convert_to":"converted_models/ssd_mobilenet_v2_ckpt_converted.tflite", "input_nodes":"image_tensor:0", "output_nodes":"num_detections:0,detection_classes:0,detection_scores:0,detection_boxes:0", "skip": "True" }, { "convert_from":"test_models/lstm_ckpt/ckpt.meta", "convert_to":"converted_models/lstm_ckpt_converted.tflite", "input_nodes":"embedding_input:0", "output_nodes":"dense/BiasAdd:0", "input_shape":"1,53", "skip": "True" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_keras_to_onnx.json ================================================ [ { "convert_from":"test_models/VGG16.h5", "convert_to":"converted_models/VGG16_keras.onnx", "skip":"False" }, { "convert_from":"test_models/ResNet50.h5", "convert_to":"converted_models/ResNet50_keras.onnx", "skip":"False" }, { "convert_from":"test_models/InceptionV3.h5", "convert_to":"converted_models/InceptionV3_keras.onnx", "skip":"True" }, { "convert_from":"test_models/MobileNet.h5", "convert_to":"converted_models/MobileNet_keras.onnx", "skip":"False" }, { "convert_from":"test_models/MobileNetV2.h5", "convert_to":"converted_models/MobileNetV2_keras.onnx", "skip":"False" }, { "convert_from":"test_models/DenseNet121.h5", "convert_to":"converted_models/DenseNet121_keras.onnx", "skip":"False" }, { "convert_from":"test_models/NASNetMobile.h5", "convert_to":"converted_models/NASNetMobile_keras.onnx", "skip":"False" }, { "convert_from":"test_models/lstm.h5", "convert_to":"converted_models/lstm_keras.onnx", "skip":"False" }, { "convert_from":"test_models/gru.h5", "convert_to":"converted_models/gru_keras.onnx", "skip":"False" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_keras_to_tflite.json ================================================ [ { "convert_from":"test_models/VGG16.h5", "convert_to":"converted_models/VGG16_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/ResNet50.h5", "convert_to":"converted_models/ResNet50_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/InceptionV3.h5", "convert_to":"converted_models/InceptionV3_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/MobileNet.h5", "convert_to":"converted_models/MobileNet_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/MobileNetV2.h5", "convert_to":"converted_models/MobileNetV2_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/DenseNet121.h5", "convert_to":"converted_models/DenseNet121_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/NASNetMobile.h5", "convert_to":"converted_models/NASNetMobile_keras.tflite", "quantization": { "opsset":"tf" }, "skip":"False" }, { "convert_from":"test_models/lstm.h5", "convert_to":"converted_models/lstm_keras.tflite", "skip":"True" }, { "convert_from":"test_models/gru.h5", "convert_to":"converted_models/gru_keras.tflite", "skip":"False" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_protobuf_to_onnx.json ================================================ [ { "convert_from":"test_models/densenet/densenet.pb", "convert_to":"converted_models/densenet_converted.onnx", "input_nodes":"Placeholder:0", "output_nodes":"softmax_tensor:0,ArgMax:0", "skip": "False" }, { "convert_from":"test_models/squeezenet.pb", "convert_to":"converted_models/squeezenet_converted.onnx", "input_nodes":"Placeholder:0", "output_nodes":"softmax_tensor:0", "skip": "False" }, { "convert_from":"test_models/nasnet_mobile.pb", "convert_to":"converted_models/nasnet_mobile_converted.onnx", "input_nodes":"input:0", "output_nodes":"final_layer/predictions:0", "skip": "False" }, { "convert_from":"test_models/inception_v3.pb", "convert_to":"converted_models/inception_v3_converted.onnx", "input_nodes":"input:0", "output_nodes":"InceptionV3/Predictions/Reshape_1:0", "skip": "False" }, { "convert_from":"test_models/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_frozen.pb", "convert_to":"converted_models/mobilenet_v1_0.25_224_converted.onnx", "input_nodes":"input:0", "output_nodes":"MobilenetV1/Predictions/Reshape_1:0", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb", "convert_to":"converted_models/ssd_mobilenet_v1_converted.onnx", "input_nodes":"image_tensor:0", "output_nodes":"detection_boxes:0,detection_scores:0,num_detections:0,detection_classes:0", "input_shape":"1,224,224,3", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb", "convert_to":"converted_models/ssd_mobilenet_v2_converted.onnx", "input_nodes":"image_tensor:0", "output_nodes":"num_detections:0,detection_classes:0,detection_scores:0,detection_boxes:0", "skip": "False" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_protobuf_to_tflite.json ================================================ [ { "convert_from":"test_models/densenet/densenet.pb", "convert_to":"converted_models/densenet_converted.tflite", "input_nodes":"Placeholder", "output_nodes":"softmax_tensor:0,ArgMax:0", "skip": "False" }, { "convert_from":"test_models/squeezenet.pb", "convert_to":"converted_models/squeezenet_converted.tflite", "input_nodes":"Placeholder", "output_nodes":"softmax_tensor:0", "skip": "False" }, { "convert_from":"test_models/nasnet_mobile.pb", "convert_to":"converted_models/nasnet_mobile_converted.tflite", "input_nodes":"input:0", "output_nodes":"final_layer/predictions:0", "skip": "False" }, { "convert_from":"test_models/inception_v3.pb", "convert_to":"converted_models/inception_v3_converted.tflite", "input_nodes":"input:0", "output_nodes":"InceptionV3/Predictions/Reshape_1:0", "skip": "False" }, { "convert_from":"test_models/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_frozen.pb", "convert_to":"converted_models/mobilenet_v1_0.25_224_converted.tflite", "input_nodes":"input:0", "output_nodes":"MobilenetV1/Predictions/Reshape_1:0", "input_shape": "1,224,224,3", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb", "convert_to":"converted_models/ssd_mobilenet_v1_converted.tflite", "input_nodes":"image_tensor:0", "output_nodes":"detection_boxes:0,detection_scores:0,num_detections:0,detection_classes:0", "input_shape": "1,224,224,3", "skip": "True" }, { "convert_from":"test_models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb", "convert_to":"converted_models/ssd_mobilenet_v2_converted.tflite", "input_nodes":"image_tensor:0", "output_nodes":"num_detections:0,detection_classes:0,detection_scores:0,detection_boxes:0", "input_shape": "1,224,224,3", "skip": "True" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_pytorch_to_onnx.json ================================================ [ { "convert_from":"test_models/resnet18.pt", "convert_to":"converted_models/resnet_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/alexnet.pt", "convert_to":"converted_models/alexnet_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/squeezenet1_0.pt", "convert_to":"converted_models/squeezenet1_0_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/densenet161.pt", "convert_to":"converted_models/densenet161_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/googlenet.pt", "convert_to":"converted_models/googlenet_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/shufflenet_v2_x1_0.pt", "convert_to":"converted_models/shufflenet_v2_x1_0_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/mobilenet_v2.pt", "convert_to":"converted_models/mobilenet_v2_x1_0_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/resnext50_32x4d.pt", "convert_to":"converted_models/resnext50_32x4d_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/wide_resnet50_2.pt", "convert_to":"converted_models/wide_resnet50_2_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/mnasnet1_0.pt", "convert_to":"converted_models/mnasnet1_0_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/inception_v3.pt", "convert_to":"converted_models/inception_v3_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,299,299", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/fcn_resnet50.pt", "convert_to":"converted_models/fcn_resnet50_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Segmentation", "skip":"False" }, { "convert_from":"test_models/deeplabv3_resnet50.pt", "convert_to":"converted_models/deeplabv3_resnet50_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Segmentation", "skip":"False" }, { "convert_from":"test_models/r3d_18.pt", "convert_to":"converted_models/r3d_18_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,16,112,112", "network_name":"Test_Model_Video", "skip":"False" }, { "convert_from":"test_models/mc3_18.pt", "convert_to":"converted_models/mc3_18_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,16,112,112", "network_name":"Test_Model_Video", "skip":"False" }, { "convert_from":"test_models/r2plus1d_18.pt", "convert_to":"converted_models/r2plus1d_18_torch.onnx", "network_script":"create_test_models.py", "input_shape":"1,3,16,112,112", "network_name":"Test_Model_Video", "skip":"False" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_pytorch_to_tflite.json ================================================ [ { "convert_from":"test_models/resnet18.pt", "convert_to":"converted_models/resnet_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/alexnet.pt", "convert_to":"converted_models/alexnet_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/squeezenet1_0.pt", "convert_to":"converted_models/squeezenet1_0_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/densenet161.pt", "convert_to":"converted_models/densenet161_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/googlenet.pt", "convert_to":"converted_models/googlenet_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/shufflenet_v2_x1_0.pt", "convert_to":"converted_models/shufflenet_v2_x1_0_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/mobilenet_v2.pt", "convert_to":"converted_models/mobilenet_v2_x1_0_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/resnext50_32x4d.pt", "convert_to":"converted_models/resnext50_32x4d_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/wide_resnet50_2.pt", "convert_to":"converted_models/wide_resnet50_2_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/mnasnet1_0.pt", "convert_to":"converted_models/mnasnet1_0_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/inception_v3.pt", "convert_to":"converted_models/inception_v3_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,299,299", "network_name":"Test_Model_Classification", "skip":"False" }, { "convert_from":"test_models/fcn_resnet50.pt", "convert_to":"converted_models/fcn_resnet50_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Segmentation", "skip":"True" }, { "convert_from":"test_models/deeplabv3_resnet50.pt", "convert_to":"converted_models/deeplabv3_resnet50_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,224,224", "network_name":"Test_Model_Segmentation", "skip":"True" }, { "convert_from":"test_models/r3d_18.pt", "convert_to":"converted_models/r3d_18_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,16,112,112", "network_name":"Test_Model_Video", "skip":"True" }, { "convert_from":"test_models/mc3_18.pt", "convert_to":"converted_models/mc3_18_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,16,112,112", "network_name":"Test_Model_Video", "skip":"True" }, { "convert_from":"test_models/r2plus1d_18.pt", "convert_to":"converted_models/r2plus1d_18_torch.tflite", "network_script":"create_test_models.py", "input_shape":"1,3,16,112,112", "network_name":"Test_Model_Video", "skip":"True" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_savedmodel_to_onnx.json ================================================ [ { "convert_from":"test_models/resnet_50_TF2", "convert_to":"converted_models/resnet_50_TF2_converted.onnx", "skip": "False" }, { "convert_from":"test_models/efficientnet_b1_TF2", "convert_to":"converted_models/efficientnet_b1_TF2_converted.onnx", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/saved_model", "convert_to":"converted_models/ssd_mobilenet_v1_converted.onnx", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v2_coco_2018_03_29/saved_model", "convert_to":"converted_models/ssd_mobilenet_v2_converted.onnx", "skip": "False" }, { "convert_from":"test_models/lstm_savedmodel", "convert_to":"converted_models/lstm_savedmodel_converted.onnx", "skip": "False" }, { "convert_from":"test_models/gru_savedmodel", "convert_to":"converted_models/gru_savedmodel_converted.onnx", "skip": "False" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/conversion_jsons/convert_savedmodel_to_tflite.json ================================================ [ { "convert_from":"test_models/resnet_50_TF2", "convert_to":"converted_models/resnet_50_TF2_converted.tflite", "skip": "False" }, { "convert_from":"test_models/efficientnet_b1_TF2", "convert_to":"converted_models/efficientnet_b1_TF2_converted.tflite", "skip": "False" }, { "convert_from":"test_models/lstm_savedmodel", "convert_to":"converted_models/lstm_savedmodel_converted.tflite", "skip": "False" }, { "convert_from":"test_models/gru_savedmodel", "convert_to":"converted_models/gru_savedmodel_converted.tflite", "skip": "False" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/saved_model", "convert_to":"converted_models/ssd_mobilenet_v1_converted.tflite", "skip": "True" }, { "convert_from":"test_models/ssd_mobilenet_v1_coco_2018_01_28/saved_model", "convert_to":"converted_models/ssd_mobilenet_v1_converted.tflite", "skip": "True" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/convert_pb_to_tflite.json ================================================ [ { "convert_from":"test_models/densenet/densenet.pb", "convert_to":"converted_models/densenet_converted.tflite", "input_nodes":"Placeholder", "output_nodes":"softmax_tensor:0,ArgMax:0" } ] ================================================ FILE: Examples/converter_examples/Tested_Models/create_test_models.py ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later import torchvision.models as models import torch import torch.nn as nn import tensorflow.keras.applications as applications import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.python.util import deprecation import string deprecation._PRINT_DEPRECATION_WARNINGS = False keras_model_list = [ "VGG16", "ResNet50", "InceptionV3", "MobileNet", "MobileNetV2", "DenseNet121", "NASNetMobile" ] classification_model_list = [ "resnet18", "alexnet", "squeezenet1_0", "densenet161", "inception_v3", "googlenet", "shufflenet_v2_x1_0", "mobilenet_v2", "resnext50_32x4d", "wide_resnet50_2", "mnasnet1_0" ] segmentation_model_list = [ "fcn_resnet50", "deeplabv3_resnet50" ] video_model_list = [ "r3d_18", "mc3_18", "r2plus1d_18" ] class Test_Model_Classification(object): def __init__(self, model_name): self.func = getattr(models, model_name) def create_model(self): model = self.func() return model class Test_Model_Segmentation(object): def __init__(self, model_name): self.func = getattr(models.segmentation, model_name) def create_model(self): model = self.func() return model class Test_Model_Video(object): def __init__(self, model_name): self.func = getattr(models.video, model_name) def create_model(self): model = self.func() return model def create_torch_cnn_models(): for model_name in classification_model_list: if model_name == "inception_v3": x = torch.randn(1, 3, 299, 299) else: x = torch.randn(1, 3, 224, 224) print("Creating model", model_name) model = Test_Model_Classification(model_name).create_model() model.eval() y = model(x) save_path = "test_models/" + model_name + ".pt" torch.save(model, save_path) print("Created Pytorch model saved to", save_path) for model_name in segmentation_model_list: x = torch.randn(1, 3, 224, 224) print("Creating model", model_name) model = Test_Model_Segmentation(model_name).create_model() model.eval() y = model(x) save_path = "test_models/" + model_name + ".pt" torch.save(model, save_path) print("Created Pytorch model saved to", save_path) for model_name in video_model_list: x = torch.randn(1, 3, 16, 112, 112) print("Creating model", model_name) model = Test_Model_Video(model_name).create_model() model.eval() y = model(x) save_path = "test_models/" + model_name + ".pt" torch.save(model, save_path) print("Created Pytorch model saved to", save_path) def create_keras_cnn_models(): for cls_name in keras_model_list: model_func = getattr(applications, cls_name) model = model_func(weights = None) save_path = "test_models/" + cls_name + ".h5" model.save(save_path) print("Created Keras model saved to", save_path) def create_tf_lstm_model(): tf.compat.v1.disable_eager_execution() # Create a test lstm network based on Pytorch doc model = keras.Sequential() # Add an Embedding layer expecting input vocab of size 1000, and # output embedding dimension of size 64. model.add(layers.Embedding(input_dim=1000, output_dim=64)) model.add(layers.LSTM(128)) model.add(layers.Dense(10)) # save checkpoint model.save_weights("test_models/lstm_ckpt/ckpt") ckpt_meta = "test_models/lstm_ckpt/ckpt.meta" tf.compat.v1.train.export_meta_graph(filename=ckpt_meta) # save keras model model.save("test_models/lstm.h5") # save TF savedmodel model.save("test_models/lstm_savedmodel", save_format='tf') # print model input/output names print("lstm model input name:", model.input) print("lstm model output name:",model.output) def create_tf_gru_model(): tf.compat.v1.disable_eager_execution() # Create a test rnn network based on Pytorch doc model = keras.Sequential() model.add(layers.Embedding(input_dim=1000, output_dim=64)) # The output of GRU will be a 3D tensor of shape (batch_size, timesteps, 256) model.add(layers.GRU(256, return_sequences=True)) # The output of SimpleRNN will be a 2D tensor of shape (batch_size, 128) model.add(layers.SimpleRNN(128)) model.add(layers.Dense(10)) # save keras model model.save("test_models/gru_TF1.h5") # save TF savedmodel model.save("test_models/gru_savedmodel",save_format='tf') # print model input/output names print("gru model input name:", model.input) print("gru model output name:", model.output) if __name__ == "__main__": create_torch_cnn_models() create_keras_cnn_models() create_tf_lstm_model() create_tf_gru_model() ================================================ FILE: Examples/converter_examples/Tested_Models/download_models.py ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later import os import json import argparse import logging import requests import shutil def download_all_url(url_file, model_folder): try: with open(url_file) as f: all_urls = json.load(f) except Exception as e: logging.fatal("The url json file could not be opened") raise e for model_type in all_urls: url_list = all_urls[model_type] for model_name in url_list: url = url_list[model_name] # print(url) tarFilename = url.split('/')[-1] # check if the tar file already exists on disk # if not tarFilename[0].isdigit() and os.path.isfile(tarFilename): if os.path.isfile(tarFilename): print ("File already exists, skipping ", url) continue print("Downloading ", url) response = requests.get(url, stream=True) if response.status_code == 200: with open(tarFilename, 'wb') as f: f.write(response.raw.read()) if tarFilename[0].isdigit(): save_folder = model_folder + "/" + model_name else: save_folder = model_folder shutil.unpack_archive(tarFilename, save_folder) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--url_file", required=True, help="Json file with urls for downloading models") parser.add_argument("--model_folder", default="test_models", help="folder path for saving downloaded models") args, unknownargs = parser.parse_known_args() download_all_url(args.url_file, "test_models") ================================================ FILE: Examples/converter_examples/Tested_Models/download_urls.json ================================================ { "protobuf_checkpoint":{ "densenet": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz", "squeezenet": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz", "nasnet_mobile": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz", "inception_v3": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz", "mobilenet_v1_0.25_224": "https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224.tgz", "ssd_mobilenet_v1": "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz", "ssd_mobilenet_v2": "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz" }, "savedmodel":{ "resnet_50_TF2": "https://storage.googleapis.com/tfhub-modules/tensorflow/resnet_50/classification/1.tar.gz", "efficientnet_b1_TF2": "https://storage.googleapis.com/tfhub-modules/tensorflow/efficientnet/b1/feature-vector/1.tar.gz" } } ================================================ FILE: Examples/converter_examples/dlconvert_requirements.txt ================================================ keras2onnx==1.7.0 tf2onnx pytest pytest-cov torch pytorch2keras ================================================ FILE: Examples/decorator_example/origin.py ================================================ #---------------------------------- def func(hyperparameter): return 'training results' #---------------------------------- import tensorflow as tf FLAGS = tf.app.flags.FLAGS # some arguments defined in flags def main(unused): return "training results" if __name__ == "__main__": tf.app.run() #---------------------------------- ================================================ FILE: Examples/decorator_example/use_decorator.py ================================================ #---------------------------------- #!/usr/bin/env python import sys from aup import aup_args @aup_args def func(hyperparameter): return 'training results' if __name__ == "__main__": func(sys.argv[1]) #---------------------------------- #!/usr/bin/env python import tensorflow as tf from aup import aup_flags FLAGS = tf.app.flags.FLAGS # some arguments defined in flags @aup_flags(FLAGS) def main(): return "training results" if __name__ == "__main__": tf.app.run() #---------------------------------- ================================================ FILE: Examples/demo/.gitignore ================================================ env_template.ini hpo.py .aup/ experiment.json ================================================ FILE: Examples/demo/demo.yml ================================================ # The configurations that used for the recording, feel free to edit them config: # Specify a command to be executed # like `/bin/bash -l`, `ls`, or any other commands # the default is bash for Linux # or powershell.exe for Windows command: bash -l # Specify the current working directory path # the default is the current working directory path cwd: ./Examples/demo # Export additional ENV variables env: recording: true # Explicitly set the number of columns # or use `auto` to take the current # number of columns of your shell cols: 105 # Explicitly set the number of rows # or use `auto` to take the current # number of rows of your shell rows: 24 # Amount of times to repeat GIF # If value is -1, play once # If value is 0, loop indefinitely # If value is a positive number, loop n times repeat: 0 # Quality # 1 - 100 quality: 80 # Delay between frames in ms # If the value is `auto` use the actual recording delays frameDelay: auto # Maximum delay between frames in ms # Ignored if the `frameDelay` isn't set to `auto` # Set to `auto` to prevent limiting the max idle time maxIdleTime: 2000 # The surrounding frame box # The `type` can be null, window, floating, or solid` # To hide the title use the value null # Don't forget to add a backgroundColor style with a null as type frameBox: type: solid title: LGE-ARC-AdvancedAI/auptimizer style: border: 0px black solid # boxShadow: none # margin: 0px # Add a watermark image to the rendered gif # You need to specify an absolute path for # the image on your machine or a URL, and you can also # add your own CSS styles watermark: imagePath: /Users/jason.liu/.terminalizer/SVL-aai.png style: position: absolute right: 15px bottom: 15px width: 100px opacity: 0.9 # Cursor style can be one of # `block`, `underline`, or `bar` cursorStyle: block # Font family # You can use any font that is installed on your machine # in CSS-like syntax fontFamily: "Monaco, Lucida Console, Ubuntu Mono, Monospace" # The size of the font fontSize: 24 # The height of lines lineHeight: 1 # The spacing between letters letterSpacing: 0 # Theme theme: background: "transparent" foreground: "#afafaf" cursor: "#c7c7c7" black: "#232628" red: "#fc4384" green: "#b3e33b" yellow: "#ffa727" blue: "#75dff2" magenta: "#ae89fe" cyan: "#708387" white: "#d5d5d0" brightBlack: "#626566" brightRed: "#ff7fac" brightGreen: "#c8ed71" brightYellow: "#ebdf86" brightBlue: "#75dff2" brightMagenta: "#ae89fe" brightCyan: "#b1c6ca" brightWhite: "#f9f9f4" # Records, feel free to edit them records: - delay: 476 content: "\e[H\e[2J" - delay: 10 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]1337;ShellIntegrationVersion=13;shell=bash\a" - delay: 12 content: "\e]133;C;\a\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 1093 content: l - delay: 96 content: s - delay: 232 content: "\r\n" - delay: 13 content: "\e]133;C;\a\e[34m.\e[m\e[m \e[34m..\e[m\e[m origin.py\r\n" - delay: 5 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 1112 content: p - delay: 152 content: 'y' - delay: 48 content: t - delay: 144 content: h - delay: 57 content: o - delay: 128 content: 'n' - delay: 135 content: ' ' - delay: 112 content: '-' - delay: 200 content: m - delay: 103 content: ' ' - delay: 162 content: a - delay: 87 content: u - delay: 89 content: p - delay: 304 content: . - delay: 191 content: s - delay: 65 content: e - delay: 111 content: t - delay: 137 content: u - delay: 64 content: p - delay: 439 content: "\r\n" - delay: 15 content: "\e]133;C;\a" - delay: 130 content: "\e[1;30mINFO\e[0m - Using default user jason.liu\r\nEnvironment file, or hit ENTER to fill in details:" - delay: 703 content: "\r\nAuptimizer Environment path - Auptimizer_PATH (.aup):" - delay: 296 content: "\r\nAuptimizer Temp folder - TMP_FOLDER (/tmp/auptmp):" - delay: 233 content: "\r\nNumber of CPUs (4):" - delay: 207 content: "\r\nGPU template file or comma-separated IDs (none):" - delay: 200 content: "\r\nNode template file (none) or comma-separated nodes (user@ip:[port] [keyfile]):" - delay: 216 content: "\r\nAWS template file (none) or comma-separated nodes (user@ip):[port] [keyfile])" - delay: 416 content: "\r\nUsername (jason.liu):" - delay: 431 content: "\r\nOverwrite (y/N)" - delay: 423 content: "\r\n\e[1;30mINFO\e[0m - Write env.ini to .aup/env.ini\r\n" - delay: 138 content: "\e[32m2019-07-25 14:33:46\e[0m - \e[34maup.setupdb.sqlite\e[0m - \e[1;30mINFO\e[0m - SQLite3 Database is created at .aup/sqlite3.db\r\n" - delay: 11 content: "\e[1;30mINFO\e[0m - Following commands have finished automatically\r\n/usr/local/opt/python/bin/python3.7 -m aup.setupdb.sqlite .aup/env.ini --user jason.liu --cpu 4 --log info\r\n" - delay: 20 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 1316 content: l - delay: 120 content: s - delay: 144 content: ' ' - delay: 472 content: . - delay: 207 content: a - delay: 104 content: u - delay: 104 content: p - delay: 256 content: "\r\n" - delay: 14 content: "\e]133;C;\a\e[34m.\e[m\e[m \e[34m..\e[m\e[m env.ini sqlite3.db\r\n" - delay: 5 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 248 content: m - delay: 88 content: o - delay: 79 content: r - delay: 65 content: e - delay: 81 content: ' ' - delay: 191 content: . - delay: 201 content: a - delay: 103 content: u - delay: 88 content: p - delay: 264 content: / - delay: 198 content: e - delay: 74 content: 'n' - delay: 217 content: 'v.ini ' - delay: 184 content: "\r\n" - delay: 17 content: "\e]133;C;\a\e[?1h\e=\r[Auptimizer]\r\nAuptimizer_PATH = .aup\r\nTMP_FOLDER = /tmp/auptmp\r\nSQL_ENGINE = sqlite\r\nSQLITE_FILE = .aup/sqlite3.db\r\n\r\n\e[7m.aup/env.ini (END)\e[m\e[K" - delay: 826 content: "\r\e[K\e[?1l\e>" - delay: 5 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 977 content: p - delay: 185 content: 'y' - delay: 71 content: t - delay: 136 content: h - delay: 48 content: o - delay: 144 content: 'n' - delay: 136 content: ' ' - delay: 200 content: '-' - delay: 457 content: m - delay: 126 content: ' ' - delay: 1089 content: a - delay: 120 content: u - delay: 89 content: p - delay: 304 content: . - delay: 350 content: i - delay: 177 content: 'n' - delay: 130 content: i - delay: 230 content: t - delay: 433 content: "\r\n" - delay: 13 content: "\e]133;C;\a" - delay: 122 content: "\e[34maup.utils\e[0m - \e[1;30mINFO\e[0m - Auptimizer environment at .aup\r\n" - delay: 6 content: "\e[34maup.utils\e[0m - \e[1;30mINFO\e[0m - Use default connector at .aup/sqlite3.db\r\nscript name []:" - delay: 626 content: h - delay: 96 content: p - delay: 88 content: o - delay: 264 content: . - delay: 192 content: p - delay: 144 content: 'y' - delay: 296 content: "\r\ncomputing resource for experiment [cpu]:" - delay: 1128 content: "\r\nNumber of parallel execution on cpu, up to 4 [1]:" - delay: 1159 content: '2' - delay: 191 content: "\r\nmax/min the score [max]:" - delay: 1361 content: m - delay: 120 content: i - delay: 193 content: 'n' - delay: 591 content: "\r\nworking path, [default:./Examples/demo]:" - delay: 823 content: "\r\nproposer [random]:" - delay: 1385 content: "\r\n" - delay: 113 content: 'number of jobs [1]:' - delay: 1862 content: '1' - delay: 224 content: '0' - delay: 208 content: "\r\nrandom seed [0]" - delay: 927 content: "\r\nstart adding hyperparameters, use 'stop' for variable name or ctrl+c to exit\r\nname:" - delay: 1022 content: x - delay: 273 content: "\r\nrange (separated by ,):" - delay: 1512 content: '0' - delay: 280 content: ',' - delay: 752 content: '5' - delay: 225 content: "\r\ntype: [choice]:" - delay: 1263 content: f - delay: 144 content: l - delay: 168 content: o - delay: 64 content: a - delay: 104 content: t - delay: 80 content: "\r\nname:" - delay: 358 content: s - delay: 113 content: t - delay: 112 content: o - delay: 584 content: p - delay: 288 content: "\r\n\e[34maup.init\e[0m - \e[1;30mINFO\e[0m - Write experiment config to experiment.json\r\n" - delay: 28 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 602 content: m - delay: 64 content: o - delay: 49 content: r - delay: 71 content: e - delay: 113 content: ' ' - delay: 2554 content: "\r\n.aup/ experiment.json origin.py \r\n\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\amore " - delay: 168 content: "\r\n.aup/ experiment.json origin.py \r\n\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\amore " - delay: 864 content: o - delay: 303 content: r - delay: 105 content: i - delay: 136 content: 'gin.py ' - delay: 199 content: "\r\n" - delay: 13 content: "\e]133;C;\a\e[?1h\e=\r# Demonstration code for 1D function, centered around a=1\r\nfrom time import sleep\r\n\r\n\r\ndef demo_func(x, a=1, b=0):\r\n sleep(1)\r\n return (x-a)*(x-a)+b\r\n\r\n\e[7morigin.py (END)\e[m\e[K" - delay: 1609 content: "\r\e[K\e[?1l\e>" - delay: 5 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 2168 content: p - delay: 241 content: 'y' - delay: 183 content: t - delay: 56 content: h - delay: 103 content: o - delay: 184 content: 'n' - delay: 688 content: ' ' - delay: 136 content: '-' - delay: 257 content: m - delay: 151 content: ' ' - delay: 760 content: a - delay: 128 content: u - delay: 104 content: p - delay: 329 content: . - delay: 193 content: c - delay: 55 content: o - delay: 113 content: 'n' - delay: 55 content: v - delay: 217 content: e - delay: 79 content: r - delay: 168 content: t - delay: 129 content: ' ' - delay: 656 content: o - delay: 208 content: r - delay: 111 content: i - delay: 136 content: g - delay: 88 content: i - delay: 47 content: 'n' - delay: 273 content: . - delay: 208 content: 'py ' - delay: 568 content: e - delay: 247 content: x - delay: 120 content: p - delay: 128 content: e - delay: 97 content: r - delay: 176 content: 'iment.json ' - delay: 663 content: d - delay: 329 content: e - delay: 415 content: m - delay: 96 content: o - delay: 255 content: _ - delay: 219 content: f - delay: 87 content: u - delay: 199 content: 'n' - delay: 112 content: c - delay: 457 content: "\r\n" - delay: 14 content: "\e]133;C;\a" - delay: 106 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 669 content: l - delay: 80 content: s - delay: 120 content: "\r\n" - delay: 14 content: "\e]133;C;\a\e[34m.\e[m\e[m \e[34m..\e[m\e[m \e[34m.aup\e[m\e[m experiment.json \e[31mhpo.py\e[m\e[m origin.py\r\n\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 497 content: m - delay: 79 content: o - delay: 32 content: r - delay: 89 content: e - delay: 95 content: ' ' - delay: 280 content: h - delay: 96 content: p - delay: 64 content: o - delay: 281 content: . - delay: 184 content: p - delay: 111 content: 'y' - delay: 248 content: "\r\n" - delay: 13 content: "\e]133;C;\a\e[?1h\e=\r#!/usr/bin/env python\r\n# Demonstration code for 1D function, centered around a=1\r\nfrom time import sleep\r\n\r\n\r\ndef demo_func(x, a=1, b=0):\r\n sleep(1)\r\n return (x-a)*(x-a)+b\r\n\r\n\r\ndef aup_wrapper(config):\r\n res = demo_func(x=config['x']) \r\n print_result(res)\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n from aup import BasicConfig, print_result\r\n if len(sys.argv) != 2:\r\n print(\"config file required\")\r\n exit(1)\r\n config = BasicConfig().load(sys.argv[1])\r\n aup_wrapper(config)\r\n\e[7mhpo.py (END)\e[m\e[K" - delay: 2905 content: "\r\e[K\e[?1l\e>" - delay: 5 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 808 content: p - delay: 160 content: 'y' - delay: 96 content: t - delay: 120 content: h - delay: 72 content: o - delay: 136 content: 'n' - delay: 127 content: ' ' - delay: 129 content: '-' - delay: 215 content: m - delay: 129 content: ' ' - delay: 327 content: a - delay: 113 content: u - delay: 103 content: p - delay: 265 content: . - delay: 1072 content: "\b\e[K" - delay: 96 content: ' ' - delay: 1023 content: e - delay: 216 content: x - delay: 105 content: p - delay: 127 content: e - delay: 120 content: r - delay: 152 content: i - delay: 129 content: 'ment.json ' - delay: 816 content: "\r\n" - delay: 21 content: "\e]133;C;\a" - delay: 120 content: "\e[32m2019-07-25 14:34:52\e[0m - \e[34mroot\e[0m - \e[1;30mINFO\e[0m - Using default user jason.liu\r\n\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.utils\e[0m - \e[1;30mINFO\e[0m - Auptimizer environment at .aup\r\n" - delay: 5 content: "\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.utils\e[0m - \e[1;30mINFO\e[0m - Use default connector at .aup/sqlite3.db\r\n" - delay: 75 content: "\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Experiment 1 is created\r\n\e[32m2019-07-25 14:34:52\e[0m - \e[34maup\e[0m - \e[1;30mINFO\e[0m - # Running Experiment\r\n\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.EE.Job\e[0m - \e[1;30mWARNING\e[0m - \e[33mUsing default local path for script as ./hpo.py\e[0m\r\n\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 1 with resource 2 in experiment 1\r\n\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.EE.Job\e[0m - \e[1;30mWARNING\e[0m - \e[33mCreate missing directory ./Examples/demo/jobs\e[0m\r\n" - delay: 5 content: "\e[32m2019-07-25 14:34:52\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 2 with resource 1 in experiment 1\r\n" - delay: 1090 content: "\e[32m2019-07-25 14:34:54\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 1 is finished with result 3.041771513051445\r\n\e[32m2019-07-25 14:34:54\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 3 with resource 3 in experiment 1\r\n\e[32m2019-07-25 14:34:54\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 2 is finished with result 6.635502080580377\r\n\e[32m2019-07-25 14:34:54\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 4 with resource 4 in experiment 1\r\n" - delay: 1100 content: "\e[32m2019-07-25 14:34:55\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 3 is finished with result 4.0554584276157115\r\n\e[32m2019-07-25 14:34:55\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 5 with resource 2 in experiment 1\r\n" - delay: 5 content: "\e[32m2019-07-25 14:34:55\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 4 is finished with result 2.973610247851776\r\n\e[32m2019-07-25 14:34:55\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 6 with resource 1 in experiment 1\r\n" - delay: 1100 content: "\e[32m2019-07-25 14:34:56\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 5 is finished with result 1.2505367316831435\r\n\e[32m2019-07-25 14:34:56\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 7 with resource 4 in experiment 1\r\n\e[32m2019-07-25 14:34:56\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 6 is finished with result 4.970539001687499\r\n\e[32m2019-07-25 14:34:56\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 8 with resource 3 in experiment 1\r\n" - delay: 1103 content: "\e[32m2019-07-25 14:34:57\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 7 is finished with result 1.4111920738895818\r\n\e[32m2019-07-25 14:34:57\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 9 with resource 1 in experiment 1\r\n\e[32m2019-07-25 14:34:57\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 8 is finished with result 11.963747115276082\r\n\e[32m2019-07-25 14:34:57\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Submitting job 10 with resource 4 in experiment 1\r\n" - delay: 1101 content: "\e[32m2019-07-25 14:34:58\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 9 is finished with result 14.579520294401313\r\n\e[32m2019-07-25 14:34:58\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Job 10 is finished with result 0.8412697707277041\r\n" - delay: 488 content: "\e[32m2019-07-25 14:34:58\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mINFO\e[0m - Finished\r\n\e[32m2019-07-25 14:34:58\e[0m - \e[34maup.EE.Experiment\e[0m - \e[1;30mCRITICAL\e[0m - \e[1;31mBest job (10) with score 0.841270 in experiment 1\e[0m\r\n" - delay: 37 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" - delay: 2077 content: l - delay: 88 content: s - delay: 145 content: ' ' - delay: 167 content: j - delay: 72 content: o - delay: 96 content: b - delay: 169 content: s - delay: 135 content: "\r\n" - delay: 13 content: "\e]133;C;\a\e[34m.\e[m\e[m 1.json 10.json 2.json 3.json 4.json 5.json 6.json 7.json 8.json 9.json\r\n\e[34m..\e[m\e[m 1.out 10.out 2.out 3.out 4.out 5.out 6.out 7.out 8.out 9.out\r\n" - delay: 5 content: "\e]1337;RemoteHost=local\a\e]1337;CurrentDir=./Examples/demo\a\e]133;D;0\a\e]133;A\a\e[0;35m>demo$> \e[0m\e]133;B\a" ================================================ FILE: Examples/demo/experiment_wrapper.json ================================================ { "name": "./demo/experiment_wrapper.json", "script": "hpo_wrapper.py", "resource": "cpu", "n_parallel": 2, "target": "min", "workingdir": "./", "proposer": "random", "n_samples": 10, "random_seed": 0, "parameter_config": [ { "name": "x", "range": [ 0, 5 ], "type": "float" } ] } ================================================ FILE: Examples/demo/hpo_wrapper.py ================================================ #!/usr/bin/env python # Demonstration code for 1D function, centered around a=1 from time import sleep from aup import aup_args import sys @aup_args def demo_func(x, a=1, b=0): sleep(1) return (x-a)*(x-a)+b if __name__ == "__main__": demo_func(sys.argv[1]) ================================================ FILE: Examples/demo/origin.py ================================================ # Demonstration code for 1D function, centered around a=1 from time import sleep def demo_func(x, a=1, b=0): sleep(1) return (x-a)*(x-a)+b ================================================ FILE: Examples/early_stopping/README.md ================================================ # Early stopping of jobs ## Usage In order to use early stopping to accelerate the HPO experiment, we need to add the following lines in the JSON configuration file: "resource_args": { "early_stop": { "aup_policy": "X", "aup_policy_steps": Y ... } } X can be bandit, median, truncation or curve_fitting. Y refers to the interval of epochs/iterations by which the ES policy is applied. In order to report intermediate results, "aup.print_result" should be called from the user script; e.g. at the end of an epoch, at the end of N iterations, etc. Examples can be found at Examples/early_stopping/quad_equation_min and Examples/early_stopping/mnist_keras. An additional parameter of "warmup" can also be used for all ES strategies (default is 0). "warmup" defines the number of initial iterations that should finish without an early stopping criterion. ## Strategies We provide 4 strategies for early stopping: bandit, truncation, median and curve fitting. Please checkout [early stopping document](https://lge-arc-advancedai.github.io/auptimizer/early_stop.html) for detailed usages. ================================================ FILE: Examples/early_stopping/mnist_keras/cpu.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/early_stopping/mnist_keras/exp_BOHB_bandit.json ================================================ { "name": "./early_stopping/mnist_keras/exp_BOHB_bandit.json", "proposer": "bohb", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_iterations": 50, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 10, "bandit_factor": 0.5 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_BOHB_curve_fitting.json ================================================ { "name": "./early_stopping/mnist_keras/exp_BOHB_curve_fitting.json", "proposer": "bohb", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_iterations": 50, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "curve_fitting", "aup_policy_steps": 10, "curve_fitting_max_iters": 235, "curve_fitting_threshold": 0.95, "curve_fitting_timeout": 60 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_BOHB_median.json ================================================ { "name": "./early_stopping/mnist_keras/exp_BOHB_median.json", "proposer": "bohb", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_iterations": 50, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 10 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_BOHB_trunc.json ================================================ { "name": "./early_stopping/mnist_keras/exp_BOHB_trunc.json", "proposer": "bohb", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_iterations": 50, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 10, "truncation_percentage": 0.3 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_random_bandit.json ================================================ { "name": "./early_stopping/mnist_keras/exp_random_bandit.json", "proposer": "random", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 10, "bandit_factor": 0.5 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_random_curve_fitting.json ================================================ { "name": "./early_stopping/mnist_keras/exp_random_curve_fitting.json", "proposer": "random", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "curve_fitting", "aup_policy_steps": 10, "curve_fitting_max_iters": 235, "curve_fitting_threshold": 0.95, "curve_fitting_timeout": 60 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_random_median.json ================================================ { "name": "./early_stopping/mnist_keras/exp_random_median.json", "proposer": "random", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 10 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_random_trunc.json ================================================ { "name": "./early_stopping/mnist_keras/exp_random_trunc.json", "proposer": "random", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 10, "truncation_percentage": 0.3 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_spearmint_bandit.json ================================================ { "name": "./early_stopping/mnist_keras/exp_spearmint_bandit.json", "proposer": "spearmint", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "engine":"GPEIChooser", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 10, "bandit_factor": 0.5 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_spearmint_curve_fitting.json ================================================ { "name": "./early_stopping/mnist_keras/exp_spearmint_curve_fitting.json", "proposer": "spearmint", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "engine":"GPEIChooser", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "curve_fitting", "aup_policy_steps": 10, "curve_fitting_max_iters": 235, "curve_fitting_threshold": 0.95, "curve_fitting_timeout": 60 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_spearmint_median.json ================================================ { "name": "./early_stopping/mnist_keras/exp_spearmint_median.json", "proposer": "spearmint", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "engine":"GPEIChooser", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 10 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/exp_spearmint_trunc.json ================================================ { "name": "./early_stopping/mnist_keras/exp_spearmint_trunc.json", "proposer": "spearmint", "script": "mnist.py", "resource": "cpu", "n_parallel": 4, "target":"max", "engine":"GPEIChooser", "n_samples": 50, "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 10, "truncation_percentage": 0.3 } }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/early_stopping/mnist_keras/mnist.py ================================================ #!/usr/bin/env python3 """ MNIST convolutional network using Keras ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import aup import tensorflow as tf import numpy as np from tensorflow import keras from tensorflow.keras import layers from math import log import sys num_epochs = 5 batch_size = 64 num_classes = 10 input_shape = (28, 28, 1) # Load example MNIST data and pre-process it # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Scale images to the [0, 1] range x_train = x_train.astype("float32") / 255 x_test = x_test.astype("float32") / 255 # Make sure images have shape (28, 28, 1) x_train = np.expand_dims(x_train, -1) x_test = np.expand_dims(x_test, -1) # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) def get_flops(): run_meta = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() # We use the Keras session graph in the call to the profiler. flops = tf.profiler.profile(graph=keras.backend.get_session().graph, run_meta=run_meta, cmd='op', options=opts) return flops.total_float_ops # Prints the "flops" of the model. def get_model(**kwargs): model = keras.Sequential( [ keras.Input(shape=input_shape), layers.Conv2D(kwargs['conv1'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(kwargs['conv2'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dropout(kwargs['dropout']), layers.Dense(num_classes, activation="softmax"), ] ) model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(learning_rate=kwargs['learning_rate']), metrics=["accuracy"]) return model class CustomCallback(keras.callbacks.Callback): flops = None def on_train_batch_end(self, batch, logs=None): if batch % 10 == 0: val_acc = logs["acc"] aup.print_result((val_acc-1.0) / (log(self.flops))) def on_train_begin(self, logs=None): self.flops = get_flops() def init(**kwargs): global model model = get_model(**kwargs) @aup.aup_args def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64): global model model.fit( x_train, y_train, batch_size=batch_size, epochs=num_epochs, verbose=1, validation_split=0.5, callbacks=[CustomCallback()], ) res = model.evaluate( x_test, y_test, batch_size=batch_size, verbose=1, ) flops = get_flops() return (res[1]-1.0) / log(flops) if __name__ == '__main__': if len(sys.argv) < 2: print("config file required") exit(1) do_train(sys.argv[1]) ================================================ FILE: Examples/early_stopping/quad_equation_min/cpu.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys import aup from time import sleep def rosenbrock(x, a=2, b=4, c=5): sleep(1) it = 1.0 for _ in range(10): res = x*x*a + x*b + c res += it it -= 1.0 / 10 aup.print_result(res) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = aup.BasicConfig().load(sys.argv[1]) rosenbrock(config['x']) ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_BOHB_bandit.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_BOHB_bandit.json", "proposer": "bohb", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_iterations": 100, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 1, "bandit_factor": 0.5 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_BOHB_median.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_BOHB_median.json", "proposer": "bohb", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_iterations": 100, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 1 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_BOHB_trunc.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_BOHB_trunc.json", "proposer": "bohb", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_iterations": 100, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 1, "truncation_percentage": 0.7 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_random_bandit.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_random_bandit.json", "proposer": "random", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 200, "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 1, "bandit_factor": 0.5 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_random_median.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_random_median.json", "proposer": "random", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 200, "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 1 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_random_trunc.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_random_trunc.json", "proposer": "random", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 200, "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 1, "truncation_percentage": 0.7 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_spearmint_bandit.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_spearmint_bandit.json", "proposer": "spearmint", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 50, "engine":"GPEIChooser", "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 1, "bandit_factor": 0.5 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_spearmint_median.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_spearmint_median.json", "proposer": "spearmint", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 50, "engine":"GPEIChooser", "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 1 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/early_stopping/quad_equation_min/quad_min_spearmint_trunc.json ================================================ { "name": "./early_stopping/quad_equation_min/quad_min_spearmint_trunc.json", "proposer": "spearmint", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 50, "engine":"GPEIChooser", "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 1, "truncation_percentage": 0.7 } }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/hpo_mnist/.gitignore ================================================ input_data/ .aup/ __pycache__/ logs/ ================================================ FILE: Examples/hpo_mnist/MNIST Hyperparameter Optimization Demo.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST Hyperparameter Optimization Demo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MNIST database of handwritten digits, available publicly, has a training set of 60,000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image.\n", "It is a good database for people who want to experiment on learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. For these reasons, MNIST is a very popular baseline dataset for AI researchers exploring effectiveness of their Neural Network architectures, hyperparameter tuning or optimization techniques.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we will show how to perform Hyperparameter Optimization on MNIST using LeNet (http://yann.lecun.com/exdb/lenet/), a basic and popular neural network. \n", "We modify a public implementation (https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py\n", "), to find the best hyperparameter configuration for LeNet. We also demonstrate how to effectively switch between various HPO proposers such as Spearmint and Hyperband." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running the Experiments " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We use the ‘mnist_hpo_demo.py’ file to accept hyperparameters from Auptimizer and run an Experiment. Since the values of the hyperparameters can widely swing the performance of the classification algorithm, finding an optimal configuration is very important. We define the range of hyperparameters for Auptimizer to consider in ‘exp_hpo_demo.json’. We choose the following hyperparameter ranges to optimize –\n", "\n", "1) Learning Rate: [ 0.001, 0.01 ]\n", "\n", "2) Dropout Probability: [ 0.0, 0.5 ]\n", "\n", "3) 1st layer Convolution filters: [ 20, 50 ]\n", "\n", "4) 2nd Layer Convolution filters: [ 40, 80 ]\n", "\n", "5) Fully Connected layer size: [ 700, 2000 ]\n", "\n", "\n", "While we iterate over different Proposer algorithms.\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Each instance of 'mnist_hpo_demo.py' would accept an experiment configuration of hyperparameters, train the defined neural network, evaluate the network, and return the final performance statistics. The ‘exp_hpo_demo.json’ file contains code to perform HPO using AWS resources and consider 100 samples for each proposer. We can easily switch between proposers and see how they choose different configurations to optimize the final performance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run the experiment use command- \n", "\n", "'python3 -m aup exp_hpo_demo.json' \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We next analyze the experiment results using the sqlite database for our experiments. The details for the experiment can be found inn the jobs profile, but here we aim to compare the proposers. Experiment 1 is performed using Grid Search, and Random, Spearmint, Hyperband, Hyperopt Proposers for Experiments 2,3,4 and 5 respectively." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "'''\n", "Connect to the Auptimizer database\n", "''' \n", "\n", "import sqlite3\n", "import time\n", "import datetime\n", "import random\n", "\n", "conn = sqlite3.connect('sqlite3.db')\n", "c = conn.cursor()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "'''\n", "Function to compile and return details from a Auptimizer experiment. \n", "Takes experiment id as input, returns score, jobs ran for the experiments, and end time for each job.\n", "''' \n", "\n", "def graph_data(ex):\n", " c.execute('SELECT score, jid, end_time FROM job WHERE eid = '+ str(ex))\n", " data = c.fetchall()\n", "\n", " jobs = []\n", " score = []\n", " times =[]\n", " \n", " for row in data:\n", " times.append(row[2])\n", " jobs.append(row[1])\n", " score.append(row[0])\n", "\n", " times = [a-min(times) for a in times] \n", " jobs = [a-min(jobs) for a in jobs]\n", " \n", " print(score)\n", " print(jobs)\n", " \n", " return (score, times, jobs)\n", " " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.9895, 0.9893, 0.9677, 0.9602, 0.9852, 0.9886, 0.9685, 0.9698, 0.9899, 0.9908, 0.9738, 0.9701, 0.9911, 0.9899, 0.9699, 0.9699, 0.9875, 0.9907, 0.9672, 0.9697, 0.9892, 0.9901, 0.9597, 0.9636, 0.9898, 0.9914, 0.9755, 0.9628, 0.9914, 0.9892, 0.9693, 0.9737, 0.9904, 0.992, 0.9691, 0.9591, 0.9898, 0.9873, 0.9662, 0.9525, 0.9815, 0.9892, 0.9613, 0.951, 0.9905, 0.9892, 0.9722, 0.9591, 0.9876, 0.9922, 0.9446, 0.9528, 0.9883, 0.9898, 0.9641, 0.9619, 0.9905, 0.9893, 0.9697, 0.9629, 0.9891, 0.9908, 0.976, 0.954, 0.9911, 0.9892, 0.9598, 0.9606, 0.9878, 0.9883, 0.9621, 0.9584, 0.9871, 0.9901, 0.9663, 0.956, 0.9896, 0.9869, 0.9628, 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98, 99]\n" ] } ], "source": [ "'''\n", "Compile results for grid search, random, spearmint, hyperband, hyperopt Proposers\n", "''' \n", "\n", "s_gri, t_gri, j_gri = graph_data(1)\n", "s_ran, t_ran, j_ran = graph_data(2)\n", "s_spe, t_spe, j_spe = graph_data(3)\n", "s_hpb, t_hpb, j_hpb = graph_data(4)\n", "s_opt, t_opt, j_opt = graph_data(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily visualize the performance of the hyperparameter optimization algorithms for our current experiment and how configurations can affect the final performance. In the plots below we compare job performance and time with accuracy achieved. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "'''\n", "Plots for the experiment results\n", "'''\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(20,15))\n", "\n", "\n", "plt.plot(j_gri[:100], s_gri[:100])\n", "plt.plot(j_ran, s_ran)\n", "plt.plot(j_spe, s_spe)\n", "plt.plot(j_hpb[:100], s_hpb[:100])\n", "plt.plot(j_opt, s_opt)\n", "\n", "plt.legend(['y = grid','y = random', 'y = spearmint', 'y = hyperband', 'y = hyperopt'], loc='upper left')\n", "\n", "plt.title('Hyperparameter Optimization using various proposers on MNIST')\n", "plt.xlabel('Jobs')\n", "plt.ylabel('Accuracy')\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "'''\n", "Plots for the experiment results\n", "'''\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(20,15))\n", "\n", "\n", "plt.plot(t_gri[:100], s_gri[:100])\n", "plt.plot(t_ran, s_ran)\n", "plt.plot(t_spe, s_spe)\n", "plt.plot(t_hpb[:100], s_hpb[:100])\n", "plt.plot(t_opt, s_opt)\n", "\n", "plt.legend(['y = grid','y = random', 'y = spearmint', 'y = hyperband', 'y = hyperopt'], loc='upper left')\n", "\n", "plt.title('Hyperparameter Optimization using various proposers on MNIST')\n", "plt.xlabel('Time')\n", "plt.ylabel('Accuracy')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "'''\n", "Plots for the experiment results\n", "'''\n", "\n", "def max_so_far(arr):\n", " mmax=-1\n", " new_arr=[]\n", " for _,x in enumerate(arr):\n", " if x>mmax:\n", " mmax=x\n", " new_arr.append(mmax)\n", " return new_arr\n", "\n", "plt.figure(figsize=(20,15))\n", "\n", "\n", "plt.plot(j_gri[:100], max_so_far(s_gri[:100]))\n", "plt.plot(j_ran, max_so_far(s_ran))\n", "plt.plot(j_spe, max_so_far(s_spe))\n", "plt.plot(j_hpb[:100], max_so_far(s_hpb[:100]))\n", "plt.plot(j_opt, max_so_far(s_opt))\n", "\n", "plt.legend(['y = grid','y = random', 'y = spearmint', 'y = hyperband', 'y = hyperopt'], loc='upper left')\n", "\n", "plt.title('Hyperparameter Optimization using various proposers on MNIST')\n", "plt.xlabel('Jobs')\n", "plt.ylabel('Max Accuracy')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Examples/hpo_mnist/README.md ================================================ # HPO on MNIST data using Auptimizer This folder contains demonstrating how to use Auptimizer for Hyperparameter Optimization on the MNIST dataset. The original source code is from [Tensorflow Tutorial](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/mnist). We modified `mnist_with_summaries.py` to use **Auptimizer** for model tuning. **Tensorflow** package is needed for this demo. Using the CPU version will slow down the demonstration to more than 1 minute per job, but it should not be a limiting factor. ## Changes We need to parse the input for hyperparameter values. So we add the following lines to parse the values original stored in `FLAGS`, which is commonly used in `Tensorflow` for parameter sharing. config = BasicConfig(**FLAGS.__dict__).load(sys.argv[1]) FLAGS.__dict__.update(config) # for hyperband only if "n_iterations" in FLAGS: FLAGS.max_steps = int(FLAGS.n_iterations) ## Runtime First, we could test the script with a testing configuration `demo.json`: python mnist_hpo.py demo.json Once the script runs smoothly, it will print #Auptimizer:0.9592 At the end of the run. Now it is the time to run **Auptimizer** for a large scale experiment. Set up the **Auptimizer**, if you haven't done that by `python -m aup.setup `. Then run as `python -m aup ` The following table lists the minor differences in the configuration of HPO algorithms. | Name | Comments | | ---- | -------- | | Random | `n_samples` specifies the total jobs to run | | Sequence | `n` or `interval` specifies the grid for each parameter | | Hyperband | `max_iter` specify the total iterations for training and `engine` specify the underneath proposer | | BOHB | Uses many extra parameters. Refer to the documentation for details. | | Spearmint | None | | Hyperopt | None | ## Analysis `MNIST Hyperparameter Optimization Demo.ipynb` presents detailed analysis of different HPO proposers under similar experimental settings for mnist.py. ## Additional Examples - Running with AWS and Job failure capabilities. We provide further examples to show the full array of Auptimizers capabilities to handle large scale HPO in an asynchronous cloud based distributed setting with job retry and experiment persistence capabilities. - `exp_aws_demo.json` : Demo experiment to run MNIST HPO using AWS instances. Ensure AWS instances are registered as resources and have the correct working directory. - `exp_aws_async.json` : Builds on the demo experiment, with a larger search space using asynchornous connections to AWS instances. Refer to the documentation for more details on async parameters. - `exp_aws_retry_job.json` : Builds on the demo experiment, with a larger search space on AWS instances using job failure and experiment persistence capabilities. Refer to the documentation for more details on job failure parameters. ================================================ FILE: Examples/hpo_mnist/demo.json ================================================ { "name": "./hpo_mnist/demo.json", "drop_out":0.9, "learning_rate":0.01 } ================================================ FILE: Examples/hpo_mnist/exp_aws_async.json ================================================ { "name": "./hpo_mnist/exp_hpo_demo.json", "workingdir": "/home/ubuntu/aup/mnist_try/", "proposer": "random", "script": "mnist_hpo_demo.py", "runtime_args": { "prescript": "source /home/ubuntu/.bashrc; source /home/ubuntu/.profile; source activate tensorflow_p36", "postscript": "source deactivate", "overwrite" : true }, "resource_args":{ "shutdown":false, "connection_retry": 5, "async_run":true, "async_reconnect": 60, "async_timeout":1000 }, "resource": "aws", "n_parallel": 5, "target":"max", "n_samples":100, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_aws_demo.json ================================================ { "name": "./hpo_mnist/exp_aws.json", "workingdir": "/home/ubuntu/aup/mnist_try/", "proposer": "random", "script": "mnist_hpo_demo.py", "runtime_args": { "prescript": "source /home/ubuntu/.bashrc; source /home/ubuntu/.profile; source activate tensorflow_p36", "postscript": "source deactivate" }, "resource_args":{ "shutdown":false, "connection_retry": 5 }, "resource": "aws", "n_parallel": 1, "target":"max", "n_samples":6, "parameter_config": [ { "name": "drop_out", "range": [0.5, 0.9], "type": "float" }, { "name": "learning_rate", "range": [ 0.01, 0.05, 0.001 ], "type": "choice" } ] } ================================================ FILE: Examples/hpo_mnist/exp_aws_retry_job.json ================================================ { "proposer": "random", "script": "mnist_hpo_fail.py", "workingdir": "/home/ubuntu/aup/mnist_try/", "resource": "aws", "n_parallel": 4, "target":"max", "n_samples": 100, "runtime_args": { "prescript": "source /home/ubuntu/.bashrc; source /home/ubuntu/.profile; source activate tensorflow_p36", "postscript": "source deactivate", "overwrite" : true }, "job_failure": { "ignore_fail": true, "job_retries": 3 }, "resource_args":{ "shutdown":false, "connection_retry": 5 }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_bohb.json ================================================ { "name": "./hpo_mnist/exp_bohb.json", "script": "mnist_hpo_demo.py", "resource": "cpu", "n_parallel": 4, "target": "max", "proposer": "bohb", "n_iterations": 16, "min_points_in_model": "", "top_n_percent": 15, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 10, "max_budget": 100, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_hyperband.json ================================================ { "name": "./hpo_mnist/exp_hyperband.json", "proposer": "hyperband", "script": "mnist_hpo_demo.py", "resource": "cpu", "n_parallel": 4, "target":"max", "max_iter": 243, "engine": "random", "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_hyperopt.json ================================================ { "name": "./hpo_mnist/exp_hyperopt.json", "proposer": "hyperopt", "script": "mnist_hpo_demo.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 100, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_random.json ================================================ { "name": "./hpo_mnist/exp_random.json", "proposer": "random", "script": "mnist_hpo_demo.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 100, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_random_async.json ================================================ { "name": "./hpo_mnist/exp_random_async.json", "proposer": "random", "script": "mnist_hpo_demo.py", "workingdir": "/home/ubuntu/", "resource": "aws", "resource_args": { "async_run": true, "async_reconnect": 60 }, "n_parallel": 4, "target":"max", "n_samples": 100, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/exp_sequence.json ================================================ { "name": "./hpo_mnist/exp_sequence.json", "proposer": "sequence", "script": "mnist_hpo_demo.py", "resource": "cpu", "n_parallel": 4, "target":"max", "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float", "n":3 }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float", "n":3 }, { "name": "conv1", "range": [ 20, 50 ], "type": "int", "n":3 }, { "name": "conv2", "range": [ 40, 80 ], "type": "int", "n":3 }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int", "n":3 } ] } ================================================ FILE: Examples/hpo_mnist/exp_spearmint.json ================================================ { "name": "./hpo_mnist/exp_spearmint.json", "proposer": "spearmint", "script": "mnist_hpo_demo.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 100, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/job_retries_random.json ================================================ { "name": "./hpo_mnist/job_retries_random.json", "proposer": "random", "script": "mnist_hpo_demo.py", "workingdir": "/home/ubuntu/", "resource": "aws", "n_parallel": 4, "target":"max", "n_samples": 100, "job_failure": { "ignore_fail": true, "job_retries": 3 }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" }, { "name": "fc1", "range": [ 700, 2000 ], "type": "int" } ] } ================================================ FILE: Examples/hpo_mnist/mnist_hpo_demo.py ================================================ #!/usr/bin/env python3 # Modified from https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py # The trained model is evaluated by a combination of accuracy and FLOP. # # Original License """ The MIT License (MIT) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. All contributions by Aymeric Damien: Copyright (c) 2015, Aymeric Damien. All rights reserved. All other contributions: Copyright (c) 2015, the respective contributors. All rights reserved. Each contributor holds copyright over their respective contributions. The project versioning (Git) records all such contribution source information. """ from __future__ import division, print_function, absolute_import import argparse import os import sys from datetime import datetime from math import log import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data from aup import BasicConfig, print_result # Training Parameters num_steps = 2000 batch_size = 128 # Network Parameters num_input = 784 # MNIST data input (img shape: 28*28) num_classes = 10 # MNIST total classes (0-9 digits) # Create the neural network def conv_net(x_dict, n_classes, reuse, is_training): # Define a scope for reusing the variables with tf.variable_scope('ConvNet', reuse=reuse): # TF Estimator input is a dict, in case of multiple inputs x = x_dict['images'] # MNIST data input is a 1-D vector of 784 features (28*28 pixels) # Reshape to match picture format [Height x Width x Channel] # Tensor input become 4-D: [Batch Size, Height, Width, Channel] x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer with 32 filters and a kernel size of 5 conv1 = tf.layers.conv2d(x, FLAGS.conv1, 5, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) # Convolution Layer with 64 filters and a kernel size of 3 conv2 = tf.layers.conv2d(conv1, FLAGS.conv2, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) # Flatten the data to a 1-D vector for the fully connected layer fc1 = tf.contrib.layers.flatten(conv2) # Fully connected layer (in tf contrib folder for now) fc1 = tf.layers.dense(fc1, FLAGS.fc1) # Apply Dropout (if is_training is False, dropout is not applied) fc1 = tf.layers.dropout(fc1, rate=FLAGS.dropout, training=is_training) # Output layer, class prediction out = tf.layers.dense(fc1, n_classes) return out # Define the model function (following TF Estimator Template) def model_fn(features, labels, mode): # Build the neural network # Because Dropout have different behavior at training and prediction time, we # need to create 2 distinct computation graphs that still share the same weights. logits_train = conv_net(features, num_classes, reuse=False, is_training=True) logits_test = conv_net(features, num_classes, reuse=True, is_training=False) # Predictions pred_classes = tf.argmax(logits_test, axis=1) pred_probas = tf.nn.softmax(logits_test) # If prediction mode, early return if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) # Evaluate the accuracy of the model acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) # TF Estimators requires to return a EstimatorSpec, that specify # the different ops for training, evaluating, ... estim_specs = tf.estimator.EstimatorSpec( mode=mode, predictions=pred_classes, loss=loss_op, train_op=train_op, eval_metric_ops={'accuracy': acc_op}) return estim_specs def train(): # Build the Estimator mnist = input_data.read_data_sets("./input_data/", one_hot=False) model = tf.estimator.Estimator(model_fn) # Define the input function for training input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.train.images}, y=mnist.train.labels, batch_size=batch_size, num_epochs=None, shuffle=True) # Train the Model model.train(input_fn, steps=num_steps) # Evaluate the Model # Define the input function for evaluating input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.test.images}, y=mnist.test.labels, batch_size=batch_size, shuffle=False) # Use the Estimator 'evaluate' method e = model.evaluate(input_fn) print("Testing Accuracy:", e['accuracy']) return e['accuracy'] def get_flop(): run_meta = tf.RunMetadata() with tf.Session(graph=tf.Graph()) as sess: x = tf.zeros([1,784]) out = conv_net({'images':x}, 10, False, False) opts = tf.profiler.ProfileOptionBuilder.float_operation() flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts) print("Model FLOPs is %d"%flops.total_float_ops) return flops.total_float_ops def main(_): log_dir = FLAGS.log_dir + "%s" % datetime.now() if tf.gfile.Exists(log_dir): tf.gfile.DeleteRecursively(log_dir) tf.gfile.MakeDirs(log_dir) acc = train() flop = get_flop() return (acc-1)/log(flop) # metric used in AMC if __name__ == '__main__': if len(sys.argv) < 2: print("config file required") exit(1) parser = argparse.ArgumentParser() parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate') parser.add_argument('--dropout', type=float, default=0.1, help='drop probability for training dropout.') parser.add_argument('--conv1', type=int, default=32, help='Conv1 layer size.') parser.add_argument('--conv2', type=int, default=64, help='Conv2 layer size.') parser.add_argument('--fc1', type=int, default=1024, help='FC1 layer size.') parser.add_argument( '--data_dir', type=str, default=os.path.join(os.getenv('TEST_TMPDIR', './'), 'input_data/'), help='Directory for storing input data') parser.add_argument( '--log_dir', type=str, default=os.path.join(os.getenv('TEST_TMPDIR', './'), 'logs/'), help='Summaries log directory') FLAGS, unparsed = parser.parse_known_args() config = BasicConfig(**FLAGS.__dict__).load(sys.argv[1]) FLAGS.__dict__.update(config) # for hyperband if "n_iterations" in FLAGS: FLAGS.max_steps = int(FLAGS.n_iterations * 100) # 100 times n_iteration units print("FLAGS => " + str(FLAGS)) val = main(config) print(str(val)) print_result(val) ================================================ FILE: Examples/hpo_mnist/mnist_hpo_fail.py ================================================ #!/usr/bin/env python3 # Modified from https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py # The trained model is evaluated by a combination of accuracy and FLOP. # # Original License """ The MIT License (MIT) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. All contributions by Aymeric Damien: Copyright (c) 2015, Aymeric Damien. All rights reserved. All other contributions: Copyright (c) 2015, the respective contributors. All rights reserved. Each contributor holds copyright over their respective contributions. The project versioning (Git) records all such contribution source information. """ from __future__ import division, print_function, absolute_import import argparse import os import sys from datetime import datetime from math import log import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data from aup import BasicConfig, print_result # Training Parameters num_steps = 2000 batch_size = 128 # Network Parameters num_input = 784 # MNIST data input (img shape: 28*28) num_classes = 10 # MNIST total classes (0-9 digits) # Create the neural network def conv_net(x_dict, n_classes, reuse, is_training): # Define a scope for reusing the variables with tf.variable_scope('ConvNet', reuse=reuse): # TF Estimator input is a dict, in case of multiple inputs x = x_dict['images'] # MNIST data input is a 1-D vector of 784 features (28*28 pixels) # Reshape to match picture format [Height x Width x Channel] # Tensor input become 4-D: [Batch Size, Height, Width, Channel] x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer with 32 filters and a kernel size of 5 conv1 = tf.layers.conv2d(x, FLAGS.conv1, 5, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) # Convolution Layer with 64 filters and a kernel size of 3 conv2 = tf.layers.conv2d(conv1, FLAGS.conv2, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) # Flatten the data to a 1-D vector for the fully connected layer fc1 = tf.contrib.layers.flatten(conv2) # Fully connected layer (in tf contrib folder for now) fc1 = tf.layers.dense(fc1, FLAGS.fc1) # Apply Dropout (if is_training is False, dropout is not applied) fc1 = tf.layers.dropout(fc1, rate=FLAGS.dropout, training=is_training) # Output layer, class prediction out = tf.layers.dense(fc1, n_classes) return out # Define the model function (following TF Estimator Template) def model_fn(features, labels, mode): # Build the neural network # Because Dropout have different behavior at training and prediction time, we # need to create 2 distinct computation graphs that still share the same weights. logits_train = conv_net(features, num_classes, reuse=False, is_training=True) logits_test = conv_net(features, num_classes, reuse=True, is_training=False) # Predictions pred_classes = tf.argmax(logits_test, axis=1) pred_probas = tf.nn.softmax(logits_test) # If prediction mode, early return if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) # Evaluate the accuracy of the model acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) # TF Estimators requires to return a EstimatorSpec, that specify # the different ops for training, evaluating, ... estim_specs = tf.estimator.EstimatorSpec( mode=mode, predictions=pred_classes, loss=loss_op, train_op=train_op, eval_metric_ops={'accuracy': acc_op}) return estim_specs def train(): # Build the Estimator mnist = input_data.read_data_sets("./input_data/", one_hot=False) model = tf.estimator.Estimator(model_fn) # Define the input function for training input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.train.images}, y=mnist.train.labels, batch_size=batch_size, num_epochs=None, shuffle=True) # Train the Model model.train(input_fn, steps=num_steps) # Evaluate the Model # Define the input function for evaluating input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.test.images}, y=mnist.test.labels, batch_size=batch_size, shuffle=False) # Use the Estimator 'evaluate' method e = model.evaluate(input_fn) print("Testing Accuracy:", e['accuracy']) return e['accuracy'] def get_flop(): run_meta = tf.RunMetadata() with tf.Session(graph=tf.Graph()) as sess: x = tf.zeros([1,784]) out = conv_net({'images':x}, 10, False, False) opts = tf.profiler.ProfileOptionBuilder.float_operation() flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts) print("Model FLOPs is %d"%flops.total_float_ops) return flops.total_float_ops def main(_): log_dir = FLAGS.log_dir + "%s" % datetime.now() if tf.gfile.Exists(log_dir): tf.gfile.DeleteRecursively(log_dir) tf.gfile.MakeDirs(log_dir) acc = train() flop = get_flop() return (acc-1)/log(flop) # metric used in AMC if __name__ == '__main__': if len(sys.argv) < 2: print("config file required") exit(1) import random if random.random()>0.75: raise Exception('failed', 'test123') parser = argparse.ArgumentParser() parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate') parser.add_argument('--dropout', type=float, default=0.1, help='drop probability for training dropout.') parser.add_argument('--conv1', type=int, default=32, help='Conv1 layer size.') parser.add_argument('--conv2', type=int, default=64, help='Conv2 layer size.') parser.add_argument('--fc1', type=int, default=1024, help='FC1 layer size.') parser.add_argument( '--data_dir', type=str, default=os.path.join(os.getenv('TEST_TMPDIR', './'), 'input_data/'), help='Directory for storing input data') parser.add_argument( '--log_dir', type=str, default=os.path.join(os.getenv('TEST_TMPDIR', './'), 'logs/'), help='Summaries log directory') FLAGS, unparsed = parser.parse_known_args() config = BasicConfig(**FLAGS.__dict__).load(sys.argv[1]) FLAGS.__dict__.update(config) # for hyperband if "n_iterations" in FLAGS: FLAGS.max_steps = int(FLAGS.n_iterations * 100) # 100 times n_iteration units print("FLAGS => " + str(FLAGS)) val = main(config) print(str(val)) print_result(val) ================================================ FILE: Examples/intermediate_results/README.md ================================================ # Intermediate Results ## Usage Intermediate results are non-final results for each job of an experiment used in order to better tell the evolution of a job through time. Each intermediate "result" can be one or multiple values. In order to let auptimizer know that intermediate results are to be tracked, the flag "track_intermediate_results" should be passed in the experiment config under "resource_args": "resource_args": { "track_intermediate_results": true } The script used in the experiment then has to call `aup.print_result` each time an intermediate result is to be saved. The last reported result will always be taken as the final result of a job. Please refer to `quad_euation_min/quad_min.py` for training script modification. The intermediate results will be shown on the dashboard if tracked. ## Examples Take the following main function in a user script: def main(x, a=2, b=4, c=5, n=10): it = 1.0 for _ in range(n): res = x*x*a + x*b + c res += it it -= 1.0 / 10 aup.print_result(res) 1. If "track_intermediate_results" is `false` or missing, all results except the last one will be discarded: ```json { "proposer": ..., "script": ..., "resource": ..., "n_parallel": ..., "target": "min", "n_samples": ..., "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ``` ``` sqlite> SELECT COUNT(*) FROM intermediate_result; COUNT(*) 0 ``` 2. If "track_intermediate_results" is present in the config and is set to `true`, then the values will be found in the database (200 jobs with 10 intermediate results each): ```json { "proposer": ..., "script": ..., "resource": ..., "n_parallel": ..., "target": "min", "n_samples": ..., "resource_args": { "track_intermediate_results": true }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ``` ``` sqlite> SELECT COUNT(*) FROM intermediate_result; 2000 ``` ================================================ FILE: Examples/intermediate_results/quad_equation_min/quad_min.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys import aup from time import sleep def rosenbrock(x, a=2, b=4, c=5): sleep(1) it = 1.0 for _ in range(10): res = x*x*a + x*b + c res += it it -= 1.0 / 10 aup.print_result(res) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = aup.BasicConfig().load(sys.argv[1]) rosenbrock(config['x']) ================================================ FILE: Examples/intermediate_results/quad_equation_min/quad_min_BOHB.json ================================================ { "name": "./intermediate_results/quad_equation_min/quad_min_BOHB_bandit.json", "proposer": "bohb", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_iterations": 100, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "resource_args": { "track_intermediate_results": true }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/intermediate_results/quad_equation_min/quad_min_random.json ================================================ { "name": "./intermediate_results/quad_equation_min/quad_min_random_bandit.json", "proposer": "random", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 200, "resource_args": { "track_intermediate_results": true }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/intermediate_results/quad_equation_min/quad_min_spearmint.json ================================================ { "name": "./intermediate_results/quad_equation_min/quad_min_spearmint_bandit.json", "proposer": "spearmint", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 50, "engine":"GPEIChooser", "resource_args": { "track_intermediate_results": true }, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/job_failure_control/README.md ================================================ # Job Failure control Examples This folder contains examples that demonstrate the different scenarios under job failure conditions and how Auptimizer handles them gracefully. The examples can be broadly divided into two categories - ## 1. Job failures based on incorrect or incomplete experiment configuration (For debugging purposes only) `experiment_no_*.json` files demonstrate an ill-configured experiment setup. They will result in standard error conditions and Auptimizer will report them, providing users with detailed description of the failure. - `experiment_no_nsample.json` : Auptimizer Error ( KeyError: "Specify number of samples to randomly draw") - `experiment_no_param_config.json` : Auptimizer Error (KeyError: "Specify the parameter configuration 'parameter_config' to be searched") - `experiment_no_proposer.json` : Auptimizer Error (KeyError: "Specify the optimization 'proposer'") - `experiment_no_resource.json` : Auptimizer Error (KeyError: "Missing required value for 'resource'") - `experiment_no_script.json` : Auptimizer Error (KeyError: "Missing required value for 'script'") - `experiment_extra_argument.json` : No Auptimizer Error (for extra variable c) ## 2. Experiment persistence and job retries upon job failure The following examples show how Auptimizer provides control for allowing job retries and experiment continuation upon job failures. - `experiment_job_retries.json` : Auptimizer retries failed job 3 times. - `experiment_job_ignore_fail.json` : Auptimizer ignores failed job, and continues the experiment - `experiment_job_retries_ignore_fail.json` : Auptimizer retries failed job 3 times. If all retries fail, Auptimizer ignores the failed job and continues the experiment. Auptimizer uses the `job_failure` variable to control this behavior, with the following parameters: - `job_retries` : number of times to retry a failed job, default is 0. Preference is given to a different resource, if multiple resources are available. - `ignore_fail` : whether to continue the experiment if a job fails, default is False. Currently [BOHB, EAS, Hyperband] proposers do not support experiment continuation upon job failure. The examples use `test.py` file, which will fail to return any result when `time=3`. When we run the base case `experiment_test.json` as: python3 -m aup experiment_test.json It will print something like: 2018-10-17 12:02:02 - aup.EE.Resource.CPUResourceManager - CRITICAL - Failed to run job: ******/Examples/job_failure_control/test.py ******/Examples/job_failure_control/jobs/79.json 2018-10-17 12:02:02 - aup.EE.Experiment - CRITICAL - Stop Experiment due to job failure Best job (80) with score 4.000000 in experiment 14 ## experiment ill-configuration Other `experiment_no_*.json` files demonstrate ill-configured experiment setup. ================================================ FILE: Examples/job_failure_control/experiment_extra_argument.json ================================================ { "name": "./job_failure_control/experiment_extra_argument.json", "script": "rosenbrock_hpo.py", "resource": "cpu", "n_parallel": 2, "target": "min", "proposer": "random", "n_samples": 20, "random_seed": 0, "parameter_config": [ { "name": "x", "range": [ 0, 10 ], "type": "float" }, { "name": "y", "range": [ 0, 10 ], "type": "float" }, { "name": "c", "range": [ 0, 1 ], "type": "float" } ] } ================================================ FILE: Examples/job_failure_control/experiment_job_ignore_fail.json ================================================ { "name": "./job_failure_control/experiment_job_ignore_fail.json", "proposer": "sequence", "n_samples": 10, "random_seed": 1, "script": "test.py", "job_failure": { "ignore_fail": true }, "parameter_config": [ { "name": "time", "range": [ 1, 10 ], "type": "int" } ], "resource": "cpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/job_failure_control/experiment_job_retries.json ================================================ { "name": "./job_failure_control/experiment_job_retries.json", "proposer": "sequence", "n_samples": 10, "random_seed": 1, "script": "test.py", "job_failure": { "job_retries": 3 }, "parameter_config": [ { "name": "time", "range": [ 1, 10 ], "type": "int" } ], "resource": "cpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/job_failure_control/experiment_job_retries_ignore_fail.json ================================================ { "name": "./job_failure_control/experiment_job_retries_ignore_fail.json", "proposer": "sequence", "n_samples": 10, "random_seed": 1, "script": "test.py", "job_failure": { "job_retries": 3, "ignore_fail": true }, "parameter_config": [ { "name": "time", "range": [ 1, 10 ], "type": "int" } ], "resource": "cpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/job_failure_control/experiment_no_nsample.json ================================================ { "name": "./job_failure_control/experiment_no_nsample.json", "proposer": "random", "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/job_failure_control/experiment_no_param_config.json ================================================ { "name": "./job_failure_control/experiment_no_param_config.json", "proposer": "random", "n_samples": 30, "random_seed": 1, "script": "rosenbrock_hpo.py", "resource": "cpu", "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/job_failure_control/experiment_no_proposer.json ================================================ { "name": "./job_failure_control/experiment_no_proposer.json", "n_samples": 30, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/job_failure_control/experiment_no_resource.json ================================================ { "name": "./job_failure_control/experiment_no_resource.json", "proposer": "random", "n_samples": 30, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/job_failure_control/experiment_no_script.json ================================================ { "name": "./job_failure_control/experiment_no_script.json", "proposer": "random", "n_samples": 30, "random_seed": 1, "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 4, "target":"min" } ================================================ FILE: Examples/job_failure_control/experiment_test.json ================================================ { "name": "./job_failure_control/experiment_test.json", "proposer": "sequence", "n_samples": 10, "random_seed": 1, "script": "test.py", "parameter_config": [ { "name": "time", "range": [ 1, 10 ], "type": "int" } ], "resource": "cpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/job_failure_control/rosenbrock_hpo.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys """ from aup import BasicConfig, print_result def rosenbrock(conf, a=1, b=100): x = conf.x y = conf.y return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) val = rosenbrock(config) print_result(val) """ from aup import aup_args @aup_args def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) rosenbrock(sys.argv[1]) ================================================ FILE: Examples/job_failure_control/test.py ================================================ #!/usr/bin/env python3 """ Job will fail when sleep time is 1 ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys from time import sleep from aup import BasicConfig, print_result if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) sleep(1+config.time/10.) if config.time == 3: exit(1) print_result(config.time) ================================================ FILE: Examples/mnist_keras_save_model/README.md ================================================ # Save best model feature This example showcases how to use saving the best model feature. This feature allows the user to save the best performing model after running the HPO experiment. This is achieved by running the training script again using the best hyperparamters obtained during HPO the experiment. The model, by default, will be saved to path ``aup_models/models_/``. ## Usage In order to use this feature, please add to the configuration file: "resource_args": { "save_model": true } Depending on whether ``@aup_args`` is used, the training script needs to be changed differently. Please check ``mnist.py`` and ``mnist_wo_decorator.py`` for examples of adapting the training script with and without using the decorator, respectively. ## Run experiment To run the experiment on the remote machine, make sure to change the ``node.txt`` file and "workingdir" in ``exp_random_node.json`` to the your own settings. Then run ```sh python -m aup.setup python -m aup exp_random_node.json ``` To run the experiment using the local cpu, please run ```sh python -m aup.setup cpu.ini python -m aup exp_random_cpu.json ``` ================================================ FILE: Examples/mnist_keras_save_model/cpu.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/mnist_keras_save_model/exp_random_cpu.json ================================================ { "name": "random_wo_decorator", "proposer": "random", "script": "mnist_wo_decorator.py", "resource": "cpu", "n_parallel": 4, "target":"max", "n_samples": 10, "resource_args": { "save_model": true }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/mnist_keras_save_model/exp_random_node.json ================================================ { "name": "random_w_decorator_remote", "workingdir": "/home/usr/aup_demo", "proposer": "random", "script": "mnist.py", "resource": "node", "n_parallel": 4, "target":"max", "n_samples": 10, "resource_args": { "save_model": true }, "runtime_args": { "overwrite": true }, "parameter_config": [ { "name": "dropout", "range": [0.0, 0.5], "type": "float" }, { "name": "learning_rate", "range": [ 0.001, 0.01 ], "type": "float" }, { "name": "conv1", "range": [ 20, 50 ], "type": "int" }, { "name": "conv2", "range": [ 40, 80 ], "type": "int" } ] } ================================================ FILE: Examples/mnist_keras_save_model/mnist.py ================================================ #!/usr/bin/env python3 """ MNIST convolutional network using Keras ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ from aup import print_result, aup_args, aup_save_model import tensorflow as tf import numpy as np from tensorflow import keras from tensorflow.keras import layers from math import log import sys num_epochs = 5 batch_size = 64 num_classes = 10 input_shape = (28, 28, 1) # Load example MNIST data and pre-process it # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Scale images to the [0, 1] range x_train = x_train.astype("float32") / 255 x_test = x_test.astype("float32") / 255 # Make sure images have shape (28, 28, 1) x_train = np.expand_dims(x_train, -1) x_test = np.expand_dims(x_test, -1) # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) def get_flops(): run_meta = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() # We use the Keras session graph in the call to the profiler. flops = tf.profiler.profile(graph=keras.backend.get_session().graph, run_meta=run_meta, cmd='op', options=opts) return flops.total_float_ops # Prints the "flops" of the model. def get_model(**kwargs): model = keras.Sequential( [ keras.Input(shape=input_shape), layers.Conv2D(kwargs['conv1'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(kwargs['conv2'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dropout(kwargs['dropout']), layers.Dense(num_classes, activation="softmax"), ] ) model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(learning_rate=kwargs['learning_rate']), metrics=["accuracy"]) return model @aup_args def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64): model = get_model(**locals()) model.fit( x_train, y_train, batch_size=batch_size, epochs=num_epochs, verbose=1, validation_split=0.5, ) res = model.evaluate( x_test, y_test, batch_size=batch_size, verbose=1, ) # register the saving model function # add model as argument aup_save_model(save_model, model) flops = get_flops() return (res[1]-1.0) / log(flops) def save_model(model): import os # dummy folders as example os.makedirs('f1/f2') os.makedirs('f1/f3') # actual model model.save('./f1/f2/mnist.h5') if __name__ == '__main__': if len(sys.argv) < 2: print("config file required") exit(1) do_train(sys.argv[1]) ================================================ FILE: Examples/mnist_keras_save_model/mnist_wo_decorator.py ================================================ #!/usr/bin/env python3 """ MNIST convolutional network using Keras ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ from aup import BasicConfig, print_result import tensorflow as tf import numpy as np from tensorflow import keras from tensorflow.keras import layers from math import log import sys num_epochs = 5 batch_size = 64 num_classes = 10 input_shape = (28, 28, 1) # Load example MNIST data and pre-process it # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Scale images to the [0, 1] range x_train = x_train.astype("float32") / 255 x_test = x_test.astype("float32") / 255 # Make sure images have shape (28, 28, 1) x_train = np.expand_dims(x_train, -1) x_test = np.expand_dims(x_test, -1) # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) def get_flops(): run_meta = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() # We use the Keras session graph in the call to the profiler. flops = tf.profiler.profile(graph=keras.backend.get_session().graph, run_meta=run_meta, cmd='op', options=opts) return flops.total_float_ops # Prints the "flops" of the model. def get_model(**kwargs): model = keras.Sequential( [ keras.Input(shape=input_shape), layers.Conv2D(kwargs['conv1'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(kwargs['conv2'], kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dropout(kwargs['dropout']), layers.Dense(num_classes, activation="softmax"), ] ) model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(learning_rate=kwargs['learning_rate']), metrics=["accuracy"]) return model def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64, save_model=False, folder_name=None): model = get_model(**locals()) model.fit( x_train, y_train, batch_size=batch_size, epochs=num_epochs, verbose=1, validation_split=0.5, ) res = model.evaluate( x_test, y_test, batch_size=batch_size, verbose=1, ) # this means we are in best job context # config also contains save_model flag and # folder_name path if save_model is True: import shutil import os path = os.path.join('aup_models', folder_name) if os.path.exists('aup_models') is False: os.makedirs('aup_models') if os.path.exists(path) is True: shutil.rmtree(path) os.makedirs(path) os.chdir(path) save_model_fun(model) flops = get_flops() return (res[1]-1.0) / log(flops) def save_model_fun(model): import os # dummy folders as example os.makedirs('f1/f2') os.makedirs('f1/f3') # actual model model.save('./f1/f2/mnist.h5') if __name__ == '__main__': if len(sys.argv) < 2: print("config file required") exit(1) config = BasicConfig() config.load(sys.argv[1]) val = do_train(**config) print(str(val)) print_result(val) ================================================ FILE: Examples/mnist_keras_save_model/node.txt ================================================ user@192.168.XX.XX user@192.168.XX.XX ================================================ FILE: Examples/profiler_examples/README.md ================================================ For more details about how to use Profiler and run the examples in this folder refer to [Profiler](https://github.com/LGE-ARC-AdvancedAI/auptimizer/tree/master/src/aup/profiler). We have also provided detailed experiments we performed using Profiler to estimate the performance of various edge-devices under [Experiments]( https://github.com/LGE-ARC-AdvancedAI/auptimizer/tree/master/Examples/profiler_examples/experiments). ================================================ FILE: Examples/profiler_examples/bench/download.sh ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later wget https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz wget https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224.tgz tar -xvzf mobilenet_v1_1.0_224.tgz tar -xvzf mobilenet_v1_0.75_224.tgz ================================================ FILE: Examples/profiler_examples/bench/test_perf.py ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later # Compute the overall inference time for a given tflite model import tensorflow as tf from tflite_runtime.interpreter import Interpreter import tensorflow.random import numpy as np import time import os WARMUP = 1 ITER = 500 CONSTANT = 0.5 def compute(model_path): now = time.monotonic() intp = Interpreter(model_path) x = intp.tensor(intp.get_input_details()[0]['index']) iy = intp.get_output_details()[0]['index'] intp.allocate_tensors() t1 = time.monotonic() - now now = time.monotonic() for i in range(WARMUP): #x().fill(CONSTANT) x =np.random.rand() intp.invoke() y = intp.get_tensor(iy) t2 = time.monotonic() - now now = time.monotonic() for i in range(ITER): #x().fill(CONSTANT) x =np.random.rand() intp.invoke() y = intp.get_tensor(iy) t3 = time.monotonic() - now return t1, t2/float(WARMUP), t3/float(ITER) if __name__ == "__main__": import sys if len(sys.argv) != 2: print("test_acc.py ") print(compute(sys.argv[1])) ================================================ FILE: Examples/profiler_examples/env_benchmark.template ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later #User data Variables IMAGEREPO=tensorflow/tensorflow:1.15.0-py3 APTREQUIREMENTS="wget" # PIPREQUIREMENTS="ipython numpy" PRERUN="wget https://dl.google.com/coral/python/tflite_runtime-1.14.0-cp36-cp36m-linux_x86_64.whl; pip install tflite_runtime-1.14.0-cp36-cp36m-linux_x86_64.whl" DIR=bench SCRIPT=test_perf.py COMMAND=python SAMPLETIME=3 OUTPUTFILE=out.txt DOCFILE=Dockerfile DOCKCPUS="4.0" DOCKMEMORY="2.0g" # Additional docker arguments could be passed as following: # To run Docker container with privilege capability change to 'true' # To use Volume instead of copying data with the current folder # use the format "-v /path/in/source:/path/in/destination" as string # DOCKER_ARGS="--privileged -v /data:/mnist_data" DOCKER_ARGS= ================================================ FILE: Examples/profiler_examples/env_mnist.template ================================================ # Copyright (c) 2020 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later #User data Variables IMAGEREPO=tensorflow/tensorflow:1.3.0 # APTREQUIREMENTS="curl vim" # PIPREQUIREMENTS="ipython numpy" DIR=mnist SCRIPT=mnist.py COMMAND=python SAMPLETIME=3 OUTPUTFILE=out.txt DOCFILE=Dockerfile DOCKCPUS="4.0" DOCKMEMORY="2.0g" # Additional docker arguments could be passed as following: # To run Docker container with privilege capability change to 'true' # To use Volume instead of copying data with the current folder # use the format "-v /path/in/source:/path/in/destination" as string # DOCKER_ARGS="--privileged -v /data:~/Documents/Data/mnist1" DOCKER_ARGS= ================================================ FILE: Examples/profiler_examples/experiments/Readme.md ================================================ # Use Profiler for On-Device Resource Footprint Estimation Profiler can be used to estimate model script performance and resource requirements for a target device on the development machine. Given a set of models and the resource constraints (i.e. memory and CPU) to reflect *target device specs* or *the desired resource budget*, Profiler can help with the following **two scenarios**: 1. The ranking of the runtimes and memory usages of the model scripts measured using Profiler on the development machine is indicative of their ranking on the target device. For instance, if `Model1` is faster than `Model2` when measured using Profiler on the development machine, `Model1` will be faster than `Model2` on the device. This ranking is valid only when the CPUs are running at **full utilization**. 2. The runtimes and memory usages of the model scripts measured using Profiler on the development machine is related by a simple **linear relationship** to the usage measured with a native profiling tool on the actual device. In other words, if a model runs in time `x` when measured using Profiler, it will run approximately in time `(a*x+b)` on the target device (where `a` and `b` can be discovered by profiling a few models on the device with a native profiling tool). The strength of this relationship depends on the architectural similarity between the models. In general, the models designed for the same task are architecturally similar as they are composed of the same set of layers, making Profiler a useful tool for model selection. To support these claims and showcase Profiler's capabilities, we have conducted extensive experiments on three devices: LG G6, Samsung S8, and NVIDIA Jetson Nano. For anyone interested in conducting similar experiments on their own devices, we have put together the following Experiment Guidelines. ## Guidelines for Experiment Setup The goal of the experiment is to explore the relationship of model runtime and resource footprint measured using Profiler on the development machine and model runtime and resource footprint measured with a native profiling tool on a target device. **For the first scenario**, to get a performance ordering of models, the user can acquire the CPU and memory constraints of the target device, and run Profiler on a desktop CPU with those constraints. Please make sure that the models used in Profiler match those to be deployed on-device (e.g., both are TFLite models). Our experiments show that the output from Profiler should provide the relative goodness of models for CPU-based devices. In some cases, when two models are very close in performance (e.g., with peak memory usage difference of ~10MB), a flipped ordering might be observed on-device compared to Profiler output. However, the performances should still be close to each other. **For the second scenario**, to acquire a linear relationship between Profiler estimates and on-device performance, the user needs to profile a set of models both using Profiler on the development machine and with a native profiling tool on the target device. Below, are the general steps to conduct this experiment. - ***Step 1***: Prepare the models and profiling scripts for both Profiler and the target device. The models running on Profiler and on-device should be in the same format / generated using the same framework. The scripts running on Profiler and on-device may be written in different programming languages, but should implement the same functionality (e.g., both running inference using batch size 1 for 500 iterations). - ***Step 2***: Acquire device resource constraints (i.e. number of CPU cores/threads and memory limit), and input these constraints in the Profiler user variables in `env.template`. - ***Step 3***: Run experiments. - Run the profiling script using Profiler for all models on a desktop CPU. - Run the profiling script using a native profiling tool on the target device. - ***Step 4***: Due to possible variances across experiments, we recommend running Step 3 multiple times and taking the averages of the measured metrics for further analysis. - ***Step 5 (optional)***: Use any regression analysis tool to establish the relationship between Profiler outputs and the on-device measurements for the profiled models. ## Experiment I - Profiler and TFLite Model Benchmark for LG G6 and Samsung S8 Deployments ### Experiment Configurations: - ***Models:*** InceptionV3, SqueezeNet, MobileNetV2-0.25x, -0.5x, -0.75x, -1.0x - ***Profiling script:*** Profile inference performance. Run inference using a selected model for 500 iterations with batchsize 1 on randomly generated input - ***Framework:*** TFLite - ***CPU:*** CPU = 1, 2, 4 for Profiler, use Thread = 1, 2, 4 on the phone - ***Memory:*** Max memory is 4 GB for both phones, but the memory variable is set to 2 GB in Profiler, which simulates a lower memory limit. - ***On-device profiling tool:*** [TFLite Model Benchmark Tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark) ### Results: The experiment was conducted 5 times. We measured the average inference time on one input sample across 500 iterations, and the peak memory usage. The averages of 5 experiments are shown in Figures 1 and 2. The formula and R-squared value for the best fitting line are also shown in the plots. Even though Profiler only outputs the script's total execution time, users can utilize the Profiler environment and measure the execution time of different functions in their profiling script like we did in this experiment. An example script for measuring the inference time is provided in [script](https://github.com/LGE-ARC-AdvancedAI/auptimizer/blob/master/Examples/profiler_examples/bench/test_perf.py). |![image](figures/G6_S8_time.png)| |:--:| | *Figure 1: Model inference time measured with Profiler on the desktop vs TFLite Model Benchmark on LG G6, and Profiler on the desktop vs TFLite Model Benchmark on Samsung S8.* | For both phones, a linear relationship can be established between the inference time measured in the Profiler environment and the inference time measured on the phones, for various numbers of CPUs/threads. |![image](figures/G6_S8_peakmemory.png)| |:--:| | *Figure 2: Peak memory usage measured on Profiler vs LG G6, and Profiler vs Samsung S8.* | The peak memory usage does not vary much across the number of CPUs/threads. So we only present one representative plot with `CPU/thread = 1` for both phones. Again, a linear relationship can be observed. The dot on the right-upper corner represents the InceptionV3 model, in both Figures 1 and 2, which has a significantly larger resource footprint than the rest of the models. ## Experiment II - Profiler and Jetson Stats for Nvidia Jetson Nano Deployment ### Experiment Configurations: - ***Models:*** *Image classification models*: ShuffleNetV2-0.5x, SqueezeNet, MobileNetV2-1.0x, ResNet18, InceptionV3 *Video classification models*: 3D-SqueezeNet, 3D-ShuffleNetV2-0.25x, -0.5x, -1.0x, -1.5x, -2.0x, 3D-MobileNetV2-0.25x, -0.5x, -0.75x, -1.0x, -2.0x (model credit to [Efficient-3DCNNs](https://github.com/okankop/Efficient-3DCNNs)) - ***Profiling script:*** Profile inference performance. Run inference using a selected model for 500 iterations with batchsize 1 on randomly generated input - ***Framework:*** Pytorch - ***CPU:*** CPU = 1 for Profiler, use 1 CPU core on NVIDIA Jetson Nano - ***Memory Limit:*** Max memory on NVIDIA Jetson Nano is 4 GB. The memory variable is set to 4 GB in Profiler. - ***On-device profiling tool:*** [jetson-stats](https://github.com/rbonghi/jetson_stats) ### Results: We ran the experiment 5 times. For this experiment, we measured the script's total execution time (direct output from Profiler, denoted as `Runtime` in figures), average memory usage, and peak memory usage. The averages of these measures across five experiments are shown in Figures 3 and 4. The formula and R-squared value for the best fitting line are also shown in the plots. |![image](figures/nano_image_models.png)| |:--:| | *Figure 3: Image model performances measured using Profiler on the desktop and Jetson Stats on NVIDIA Jetson Nano.* | |![image](figures/nano_video_models.png)| |:--:| | *Figure 4: Video model performances measured using Profiler on the desktop and Jetson Stats on NVIDIA Jetson Nano.* | Note that separate plots are made for image models and video models, respectively. This is due to the fact that video models profiled here are architecturally different from the image models. As we have discussed, Profiler estimation is only valid for models with similar architectures or layers. ## Conclusion From the experimental results shared above, we have shown that Profiler can help users build a good estimate of model runtime and memory usage on LG G6, Samsung S8, and NVIDIA Jetson Nano, for some popular image/video recognition models. We hope that Profiler's estimation capability can enable leaner and faster model development for resource-constrained devices. Profiler might have limitations for certain models/devices, resulting in inconsistencies between Profiler outputs and on-device measurements. Therefore, we welcome the community to run experiments on their device of choice and **report any inconsistencies or limitations via GitHub Issues**. ================================================ FILE: Examples/profiler_examples/internal/ImageNet Experiments.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Copyright (c) 2020 LG Electronics Inc.\n", "# SPDX-License-Identifier: GPL-3.0-or-later\n", "# ImageNet Experiments with MobileNet Inference using Profiler" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m We performed many ImageNet Experiments in the inference mode under different Compute constraints using Profiler. We used different MobileNet versions for the experiments detailed in https://www.tensorflow.org/lite/guide/hosted_models.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "We performed many ImageNet Experiments in the inference mode under different Compute constraints using Profiler. We used different MobileNet versions for the experiments detailed in https://www.tensorflow.org/lite/guide/hosted_models.\n", "\n", "The source code used for Inference is available at 'https://gitlab.lgsvl.net/jason.liu/compression/tree/compression_profiler/src/mobilenet' \n", "and specifically 'test_perf.py'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The experimental settings used 'Mobilenet_V1_0.25_224', 'Mobilenet_V1_0.50_224', 'Mobilenet_V1_0.75_224', 'Mobilenet_V1_1.0_224'. The CPU contraints were 4, 2, 1, 0.5 and Inference was performed with 500 and 5000 iterations over a single validation TFrecord." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We recorded the time taken for the inference process to finish per each iteration under these conditions and observed the following results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Iter = 5000\t\t\t\t\t\t\t\t\t\t\t\t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Exp | Mob 1.0 | Mob 0.75 |Mob 0.5 |Mob 0.25 |\n", "|----------|:-------------:|:-------------:|:-------------:|:-------------:|\n", "| CPU = 4 | 0.02016684028 | 0.01380343905 | 0.008244011551 | 0.004547368029 |\n", "| CPU = 2 | 0.02899219721 | 0.01863248097 | 0.01087483355 |0.005579292277 |\n", "| CPU = 1 | 0.05820066958 | 0.03714312666 | 0.02166576314 | 0.0111540261 |\n", "| CPU = 0.5 | 0.1165416765 | 0.07581892986 | 0.04407729727 | 0.02251805596 |" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='exp_img/5000_1.png') " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='exp_img/5000_2.png') " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='exp_img/5000_3.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Iter = 500" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Exp | Mob 1.0 | Mob 0.75 |Mob 0.5 |Mob 0.25 |\n", "|----------|:-------------:|:-------------:|:-------------:|:-------------:|\n", "| CPU = 4 | 0.02051069524 | 0.01426426142 | 0.008495391378 | 0.004515069188 |\n", "| CPU = 2 | 0.02922545242 | 0.01855015136 | 0.01072093597 | 0.005292796298 |\n", "| CPU = 1 | 0.05900840658 | 0.03781192797 | 0.02081295101 | 0.01101532969 |\n", "| CPU = 0.5 | 0.1174173313 | 0.07161711947 | 0.04177759899 | 0.02059194843 |" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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du7I/p06dKrAAEFgIrFOZO3duTJkypdp9bdu2jb17957y7wgsAAQWAqsWhYWFMWnSpPzt3BGssrKyMwqsnTt3xvr16y0Wi8ViOeOlMQPL9myeS+qGFhVYS5cujeHDh+dvb9y4MXr16lXr33EECwBHsHAEqxbpXKuuXbvGwoUL8+8iTJdhEFgACCwE1hlYs2ZNDBo0KDp37py9SzBduiGne/fu2TsNBRYAAguBBQACS2AJLABAYCGwAEBgIbAAQGAhsABAYAksBBYACCwEFgAILAQWAAgsgYXAAgCBhcACAIGFwAIAgSWwEFgAILAQWAAgsBBYACCwBJbAAgAEFgILAAQWAgsABBYCCwAElsBCYAGAwEJgAYDAQmABgMASWAgsABBYCCwAEFgILAAQWAJLYAEAAguBBQACC4EFAAJLYAksAEBgIbAAQGAhsABAYCGwAEBgCSwEFgAILAQWAAgsBBYACCyBhcACAIGFwAIAgYXAAgCBJbAEFgAgsBBYACCwEFgAILAElsBqgFWrVsWAAQOiU6dOMWHChDh69Gi91rvtttuiTZs2Jy0vv/xy9vFp06ZVu3/IkCGmCIDAouUG1pEjR6Jbt26xePHi2LdvX4wdOzZmz57d4PWSKVOmxOWXX56/PWrUqFixYoXJASCwaB2BVVxcHEOHDs3f3rRpU/Tu3bvB661ZsyZ69OgRr7/+ev6+dHvXrl0mB4DAonUEVlFRUUycODF/O73sl17GKy0tbdB6w4YNizvvvDN/u6SkJNq3bx+jR4+Ozp07x4gRI2LLli2mCIDAouUG1ty5c7OX9Kpq27Zt7N27t97rrVu3Lrp27Rrl5eX5+9auXRtdunTJ/jx06FBMnz49Bg4cGJWVlTV+Pzt37oz169dbLBaLxXLGS2MGlu3ZPJfUDc0ysAoLC2PSpEknHZkqKyur93rf//73Y+rUqbV+vYqKiujQoUNs375dKgPgCBYt8wjW0qVLY/jw4fnbGzdujF69ejVovQsvvDCWL19e69dLQdauXbvYvXu3SQIgsGiZgZXOoUov6y1cuDD/7sAZM2bUe70DBw5kR7ROPFS3aNGi6Nu3bxZkBw8ejMmTJ8fIkSNNEQCBRcsNrCS982/QoEHZSejjxo3LLsmQ07179+wdhKdb75lnnsnOtarJnDlzomfPntGxY8cYM2ZMo75eCgACi2YZWAAgsASWwAIABBYCCwAEFgILAARWtcB687kNUXLVd2PPqI81eDk4qyAqXtxqOAILAARWcuCyS88orqpGFgILAATWfzRGXOUWBBYACCyBJbAAAIGFwAIAgYXAAgCBJbAEFgAILIGFwAIAgYXAAgCBJbAEFgAILIGFwAIAgYXAAgCBJbAEFgAILIElsAAAgYXAAgCBhcACAIElsAQWAAgsgYXAAgCBhcACAIElsAQWAAgsgYXAAgCBhcACAIElsAQWAAgsgSWwbAKAM/fcS8ei4P7yM9rpznywPLbtOW5jCiyBJbAASC67u6xRdrwpshBYAktgAdAEO18ElsASWAB2vgLLjAUWAgtAYCGwEFgAAguBJbAEFoCdr8AyY4ElsAAQWAgsBBaAwEJgCSyBBWDnK7DMWGAJLAAEFgILgQUgsBBYCCwAO1+BJbAElsBqsFWrVsWAAQOiU6dOMWHChDh69Gi915s2bVq0adMmvwwZMsQUAYGFwKJ1BtaRI0eiW7dusXjx4ti3b1+MHTs2Zs+eXe/1Ro0aFStWrDA5QGAhsASWwCouLo6hQ4fmb2/atCl69+5d7/V69OgRu3btMjlAYCGwBJbAKioqiokTJ+Zvp5f90kt8paWldV6vpKQk2rdvH6NHj47OnTvHiBEjYsuWLaYICCwEFq0zsObOnRtTpkypdl/btm1j7969dV5v7dq10aVLl+zPQ4cOxfTp02PgwIFRWVlZ49fcuXNnrF+/3mKxWJp8acydr+3ZOmbcmIFlPo2zpG447wKrsLAwJk2adNKRqbKysgatl1RUVESHDh1i+/btUhlwBAtHsGh9R7CWLl0aw4cPz9/euHFj9OrVq8HrJSm62rVrF7t37zZJQGAhsGh9gZXOoeratWssXLgw/+7AGTNm1Gu9RYsWRd++fbPoOnjwYEyePDlGjhxpioDAQmDROgMrWbNmTQwaNCg7QX3cuHHZJRlyunfvnr2D8HTrzZkzJ3r27BkdO3aMMWPGNOrrpQACC4HFeRdYAHa+AsuMBZbAAkBgIbAQWAACC4GFwAKw8xVYAktgCSwABJYZCywEFoDAQmAhsAAEFgJLYAksADtfgWXGAguBBSCwEFgILACBhcASWAILwM5XYJmxwBJYAAgsBBYCC0BgIbAiKkvWxrGnR0XFI//V4OXYs1+OysN/N2yBBSCwEFjJsScvPqO4qhpZCCwAgYXA+o/GiKvcgsACEFgILIElsADsfAWWGQssgQWAwEJgIbAABBYCS2AJLAA7X4ElsASWwAKgKXe+bz63IUqu+u4Z7XQPziqIihe3Go7AElgCC8DONzlw2aWNsuNNkYXAElgCC8DON/waFTMWWAILAIFlxgILgQUgsBBYAktgAdj5CiwzFlgCC8DOV2CZscASWAAILASWwBJYAHa+AsuMBZbAArDzFVhmLLAEFgACC4GFwAKw8xVYAktgCSwAO1+BZcYCS2ABILDMWGAhsAAEFgJLYAksADtfgWXGAktgAbV57qVjUXB/+Rk9Ic98sDy27TluY9r5CiwzFlgCC0guu7usUZ6UU2Rh5yuwzFhgCSygCZ6YsfMVWGYssAQWeGIWWGYssMxYYAmsM7Fq1aoYMGBAdOrUKSZMmBBHjx6t93rz5s2LXr16xQUXXBAjR46MrVu35j82bdq0aNOmTX4ZMmSIKSKwsPMVWAJLYLXcwDpy5Eh069YtFi9eHPv27YuxY8fG7Nmz67Xe8uXLo3///llUlZaWRkFBQQwbNiz/d0eNGhUrVqwwOQQWdr4Cy4wFVusIrOLi4hg6dGj+9qZNm6J3794NXi/ZtWtXtG/fPiorK7PbPXr0yO4DgYWdr8AyY4HVKgKrqKgoJk6cmL+dXvZLL+OlI1ENWS9ZsmRJDB48OPvvkpKSLLZGjx4dnTt3jhEjRsSWLVtMEYGFna/AElgCq+UG1ty5c2PKlCnV7mvbtm3s3bu3Qevt2LEjOxdr5cqV2e21a9dGly5dsj8PHToU06dPj4EDB+aPbp1o586dsX79eovlnC+N+cRse7aOGTfmztd8WseMGzOwWuuMUzc0y8AqLCyMSZMmnXRkqqysrN7rbdu2LS666KJYsGDBKb9eRUVFdOjQIbZv3y6VcQQLRzccwXIEyxGslnkEa+nSpTF8+PD87Y0bN2ZHoOq73ubNm6Nfv36xbNmyWr9eCrJ27drF7t27TRKBhZ2vwBJYAqtlBlY6h6pr166xcOHC/LsDZ8yYUa/19u/fH3369InVq1ef9PcWLVoUffv2zYLs4MGDMXny5OwyDiCwsPMVWAJLYLXYwErWrFkTgwYNyk5CHzduXHZJhpzu3btn7yCsbb2ZM2dWu85Vbjl8+HD28Tlz5kTPnj2jY8eOMWbMmEZ9vRQEFgILgSWwmmVgAQLLjAWWGQssgQUILAQWAktgCSwQWNj5CiwzFlgCCzwxCywzFlhmLLAEFmfTcy8di4L7y8/owTrzwfLYtue4jSmwsPOtcedbWbI2jj096ox2usee/XJUHv67GbeSwNqwd3NMfvRH8dHf/XeDl4LVN8XWgzsEFufGZXeXNcoDNkUWAgs735p2vseevLhRdrwpssy4dQTW//xfwRnFVdXIEljY+WLG2Pl6+ciM/6Mx4iq3CCzsfDFj7HwFlhkLLIHlQWvn2xpn/OZzG6Lkqu+e0RPywVkFUfHiVsOx8xVYZiywBJYHrcAy4+TAZZc2ypNyiizsfAWWGQssgeVBK7DMOLyF34wFlhkLLIGFwEJgmbHAMmOBJbAQWAgs7HwFlsASWALLg1ZgeWIWWGYssMxYYAksBJYZCywzFlhmLLAEFgILgYWdr8ASWAJLYAksPDELLDMWWGYssASWB62LUJqxwDJjgWXGAktg0TwftC5C6YlZYJmxwDJjgSWwPDHb+ZqxGZuxwDJjgSWwsPPFjBFYZiywBJbA8qC18zVjM7bzFVhmLLAElgetna8Zm7EZCywzFlgCCztfzNiMW83Od8PezTH50R+d0U63YPVNsfXgDjMWWAILO19a5owrS9bGsadHndET8rFnvxyVh/9uxq1k5/s//1fQKDveFFlmLLAEFgKLFjnjY09e3ChPyimyzNjOt6XufM1YYAksT8wCy4ydn2PGdr5mbMYCy85XYJmxwDJjO18zNmOBRSt60Do/xxOzwDJjO18zFlgCyxOz83PMWGCZsZ2vGZuxwBJYdr5mbMZmbOdrxmYssPCgtfM1YzO28xVYZiywBJYHrZ2vGZuxGdv5mrEZCyxPzHa+ZmzGZmzna8ZmLLDwoLXzNWO/RsXOV2CZscASWB60AsuMm2TGfo2Kna/AMmOBhQetoxtm7InZjM3YjM1YYAksRzcc3fDEbMZmbMZmLLDwoPXEbMZmbMZmbMYCq/latWpVDBgwIDp16hQTJkyIo0ePCiwPWjM2YzM2YzM2Y4HVUEeOHIlu3brF4sWLY9++fTF27NiYPXu2wPKgNWMzNmMzNmMzFlgNVVxcHEOHDs3f3rRpU/Tu3VtgedCasRmbsRmbsRkLrIYqKiqKiRMn5m+nlwfbtGkTpaWlAsuD1ozN2IzN2IzNWGA1xNy5c2PKlCnV7mvbtm3s3btXYHnQmrEZm7EZm7EZC6yGKCwsjEmTJp10BKusrKzG9e+6664YMmSIxWKxWCwWy2mX1A2tMrCWLl0aw4cPz9/euHFj9OrVKwAAHMFqoHSuVdeuXWPhwoX5dxHOmDHDFAEAgXUm1qxZE4MGDYrOnTvHuHHjsks3AAAILAAAgQUAgMACABBYAAACi2bonnvuiYsvvjj7Zdjvfe974yc/+UlUVlZmH0sXZE3XDcstb3nLW6JPnz5x2223ZR8vKSnJ7j/xwq3p41/4whca5ft79NFH4x3veIdBtcAZz5s3L7tsygUXXBAjR46MrVu3GlYLfRynd2/Pnz/foM7zea5atSoGDBiQfV8TJkzIri1Zk2nTplX7HtP1ohBYrUq60n2/fv3iscceyy6+umHDhhg4cGDMnDmz2gM5PWCT48ePxzPPPBNvf/vbY+XKlU3+xJze8dm3b9/o0qWLYbWwGS9fvjz69++fRVW6nEpBQUEMGzbMwFrY47i8vDybbfr8Auv8nmd6Pu7WrVssXrw4f9mj2bNn17juqFGjYsWKFYYpsFqnAwcORIcOHbJfgF3VunXrsp9Manog54wePTr7PY9NHVhXXnllXHHFFQKrBc84Z9euXdG+ffv8T+m0jBmPHz8+uzxO+lNgnd/zLC4ujqFDh+Zvp++xd+/eNa7bo0eP7DGNwGqV/vjHP8b73//+Wtc58YH8xhtvZIeI3/a2t8Xjjz9e7wfyokWLqh02zi2//e1vT1r3iSeeiI9+9KPxwgsvCKwWOuOqlixZEoMHDza0Fjbj3E526tSpAus8n2eKt4kTJ+Zv5371XDoCXVX6+umHpRR86fqTI0aMiC1bthiuwGo97r333vjEJz5RpwdybmnXrl12PkB6oOUeSE3xk1J64H7oQx+KZ599Nl588UWB1QJnXNWOHTuyc7HSyxu0zBkLrPN/numlyylTplS7r23btid9nbVr12bP2enPQ4cOxfTp07OXOB2dFlitxrJly7IHZU1ee+21Gn9SOlE6PyB9/MRDwbfeemtccsklDf7eZs2alX9tX2C1zBnnbNu2LS666KJYsGCBgbXQGQusljHPwsLCmDRpUrUfhNPXSV+vNhUVFdnLntu3bzdggdU6HDx4MN761ree9Fr/3/72t+yw7uHDh0/7QE7SiZVPPfXUSU+m6V0kJ6rroeh08nNN63lNv+XMONm8eXN2Mm/aqdAyZyywWs48ly5dGsOHD8/f3rhxY3bk+XRSgKWjbLt37zZggdV6pKNE6S236VBuehCkEynT7dzRo7o8kC+77LLsLfbpSFN6l0l6d9g73/nO7B0wjcERrJY54/3792dvLV+9erUhtYLHscA6/+eZzrXq2rVrLFy4MP8uwnT5jZqCLb37OwVYCsbJkydn3wsCq1VJr4mnw8bpJZr0U1M6ufKWW2456XortT2Q009U6Z1+73rXu7LPkV5rf+CBBxrtexRYLXPG6S3nNf3UnL4WLe9xLLBaxjzXrFkTgwYNyo6mpXeHpnjL6d69e/ZOw2TOnDnRs2fP6NixY4wZMyZ27txpsAILAEBgAQAILAAABBYAgMACABBYAAACCwAAgQUAILAAAAQWAAACCwBAYAEACCwAAAQWAIDAAgAQWAAACCwAAIEFACCwAAAEFgAAAgsAQGABALR4/x91m/djAhdO2gAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='exp_img/500_1.png') " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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GMuuEmkzJDlgNeXvH2o7h2/tICVgNfXtH247B7Z2ammpXXHFF6LnPP//ctS7WRgFMrXHhrY4AAQs4RNRKpFvA1bWgD2WdSPSzbz2K5wQ0fPhwd1u/Wpp015/uSDv11FPdnVvJEN6CNXLkSDd1g2RnZ7vb6pctW8b2rsf2jrUdg9v7YAasplq/w7djcHtrrFXPnj0tJSUldBehpnWIFOx0t60CmIKlbgzROgMELOAw0RgNdXeoW0hX+xoU/MQTT9SYJyjWCUgtAfpA//73v+9eQ2M/XnnllaStY3jA0tgsDUYWdSVGajHQOrG94xdrOwa395EWsI6E+h2+HcO39wcffGB9+vRxrW6661Ahz/vBD37g7jSUiRMn2o9+9CM78cQTbeDAgdxRCAIWAAAAAQsAAAAELAAAAAIWAAAAAQsAAAAELAAAAAIWAAAAAQsAAAAELAAAAAIWAAAAAQsAAICABQAAAAIWAAAAAQsAAICABQAAAAIWAAAAAQsAAICAlZBx48ZZx44d7ZhjjrE+ffrY+vXrIy43dOhQa9asWaj06tWLvQMAAAhY4ebNm2fdunVzoSovL8+GDRtm5557bsRl+/bta6mpqewRAABAwEpEVlaWtWjRwsrLy2s81759e/c8AAAAASsBs2bNstNPP73G49nZ2S549evXz1q3bm29e/e2tWvXsncAAAABK5ZNmza5sVgLFiyo8VxaWpq1adPG/Zubm2vDhw+3Hj16RGzpAgAAIGBV2LBhg5188sk2derUuJYvKSmxli1bWkZGRsTnMzMzLT09nUKhUCgUCiVpRfniiAlYa9assa5du9qcOXPi/p2CggJr3ry5bdu2jQgMAABowQravXu3de7c2RYvXhxzuRkzZliXLl1sxYoVlpOTY0OGDHFTOgAAABCwwowaNara3Fa+7Nu3zz3frl07S0lJcf8fM2aMdejQwVq1amX9+/dPajMdAABAowlYAAAABCwAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAAACFgAAAAhYAAAABCwAAAACFgAAAAhYAAAABCwAAAACFgAAAAhYAAAABCwAAAACFgAAAAELAAAABCwAAAACFgAAAAELAAAABCwAAAACFgAAAAELAAA0HuXFxVZekM+GIGABAICYoWnfXivdutn2f5puRYvftsKUWZb//BTb+/A4yx0z0rLvuNF2//Yy23nJ/9iOvmdY4fw5bDQCFgAATU/Z7l1WsnG9C00KRAWzplnes0/a3ofGWM7I2yz71mtdaNrxk7NdaIq7VCxf+ObrbGACFgAAjUNpVqaVfPG5FX+41IWm/BeftbynH3GhSa1Me/5wle266uLEAlNFUcuUwpZeQy1WarlSC5ZastSipZCmFi61dKEJBKxx48ZZx44d7ZhjjrE+ffrY+vXr2Ts44rim+YoPzVrL5o3uQy6eog/eeEr+tOfdh6gvuirV48UfLw99oOq9NeYCwEE4/gvy3TG2/7NPrOi9he4Y1LG4b/J4yx33p8quud9daTsv75NwaNp1ZT/bPXiAe4294++xfU9MrDzOdYwvS7P9n3/G8U3AqmnevHnWrVs3F6ry8vJs2LBhdu6557J3DuUHQ8VBGVcwqOrXj6cUvTU3rmBQMP3FasEgWvFXdrWWig8ffQjFU/Rhpyu92oofq9CYyq4BF1Ve5d567YEr3Yrtp+2s7V3w2vQDH94V+7Nk/ReVH+Bc9aIJKcvJDl0Q6TNNx4Ubz6SuuT8Ns+z/d33lZ8TPzkv4GNTv7bnht5Zz5y2Vx94zj7vPQ39hpGOubMd2dgIBK3mysrKsRYsWVl5ezh6qhfrhYwaDOlwpUepefNN8raXqSjSeEleorPpwDgZS/7g+vINhMuExGtH+1oq65U8Qen2dbPR++x77W2VAqzpRFL2bWhm6q66wNZYEOKyhqSK0KLwoxPjWXx0//nhRnXbHSqLHREXIchctFaHLHw/+YsUfBwprCm0gYB0Ws2bNstNPP529EwcdvEk5WVZ9MMRT4g4G4++JLxhUtZjUVvwJu7aiq8x4W9l8t1ltpTHexqyg47ozKoKPuzqvOAEEuxoVlEJX6VXjQZLZmheqcxXBzwW0qqt4P15E41H8Pg3tr6r9gYCSbCsvzLDynGVWvmehlWc+b2Vbp1jZhrFW9sUdVrb691b6ycVW+lFfK13ao/Lfz35rZWtvq1zm62etfMfrVp6d5l7HSvcdsS3wqstqcXX12NfhqhZtXdSom61OFxJVdVTdfP4CQt1/6gZUd2Bj/YxAIwtYmzZtcmOxFixYEHWZzMxMS09Pp1SUzxc9a+tS77cvUyfY56lTbNWCf9uK1Nfsk9T5rny8ZDHbiXJQiuqW6tjKlNds1fSptvqFf9qapx+3LyY/ZOvG/8W+GjvKNv7xNsu4fYhtvv63tvWaX1rWgIss64q+SWsxdK9XUbb84TfuPTLuvNW957r77nHr8MXfJ7l1WjX1BbeOK+bOcev80bJlDW97frjYVixPsdXL/m1rl02xr94fZxvev9e2pN1omUuusR2LL7fdi/6vZS880/LfOcHy3+5gJW/910EpxW8dYwXvfNdyF/7Qvafee2va9Zbx3p0V6zTGrd+qD6bbig/nH9T69em8N91++/y5Z9y+1D7dcPfttum269z+1r7f3r93wvUmsyJofX315e41VF9UV7+ccH+orqx8bZarJxznTa8oXzTKgLVhwwY7+eSTberUqUTfeFshKq5Ao35QLmxrpWknWekHvSqvWj+93F3Nhq5cN010V7vl216uvPLVFbCuYIu2sGFxyFod/PgWfyeVHxjsb0EPjamrGutSl66bmAOH1c150+8ij0ObNa2yVVQtFYFxaDG7eEr3VbYo5aZXHldZ/3GtRO6Y+/KuyhalimPRHZPvdbOSJSfULxC9+63K43z5WZWvueoaK/v8hurH+M43Ktdl36oD67T50cpl/Pqk/497nZJ3/lfi67C4o5Uu+8GB91fLWcVrh967qnWsbNfXMaca0H6o61QDfjyhXsftw4rX1ev7Fm29L93TaJItWGvWrLGuXbvanDlMeJbQSarig7Ja878+ICs+cJNyJft2q8oPbhW9fkVxAU3lq3sOdC/oQ3TX/MoP7ry1R2w3A448vssw/MYKdS/6iRWrjUMbPCDuAck7f9rLdv3iB7b7Nz1tz7WnWM6tXSz39u/Z3r981/aNO97yJ/5vK3i6nRW+2M6KX/227Z9dz+OuItjEHZQK1h/cC6GywsqQqGCk91S3o9aj4uJM6+U/bxSsEv07989racUzj7HC59pY/hNt3XbUNs2960TLue0kt613Dfi+7f71ObVPNcBNFyBgxbZ7927r3LmzLV68mD2STPt3Vn5I7v208kNZ4yz0QbnliepX0yt/XfmB+eF5lQGtDh+aUcui/1O9FW3FL2hFwyFu5i2sfhyorlUFhtJVw6w0/Te2P61fRV09s+Ki4kQrWVC/rjcfIIqmHmuFz37L8h89LhQi9o7u5EJazo1dK4PEVT+0PTf2C43xCd7UEJpuI3wcWtW4QSstPTgXbX6qAQXWOKca2PXziiD669Pc36S/L3fkCbZvbMfKEDrlv63whTZum5Sktkh8my45IdQ65j479Lm18f4DF3b+M6O8hLoOAla4UaNGWbNmzWqUfftoBTm8zWMllSemiqtld2KqGkAbuppV0RW2QtrHP63eilaXboZEW9H8OvhWtN1vV29FqzixohHWR9/NtX1WZV2sONm6FtXgxYJOyKqHyah7/uJAA8N9vct4qPK9N79kpetnWckXqbb/45TQAGt/K7+fWsQHEz/btqbISFo351UXh+5c8wOxg3eVqsUnON3GQZlq4KbfxT/VgO9CrbroC3WfqnWsYhuHWscqLs7q3F3qL+Z8C+DWKQcu4tT6V3HxCTSJgIUm0Irmx6P4VjQ//uNQtqL5rhffilY1TiTUiqYTdrAVrTiL/ddQglJF2Kl3UPJjhCpeO9TN7VtC/L7XOh3C1pDQOLSq+eX8tAG+1Sg43UZo7rYkj0OLNdVA7j0jDvtUA6HWR11AqZ5UHK9l60cduDtSx3Ud60goQFdcIIZax6rCs2sd0+eW6gNAwEKjb0Wr+pCt1opW1aIVakV7r9vBaUXT69KKVvPkp781GJ4rTlChwdO+RUInsUYclA4ntQ752cMjjUNT117o++oa+1QDmrLCf1ZU1IXQdBVrbj4Q2nVjwcK2idc/3TTkPwNU9ypeM9Q65uudWsdKmOMKBCw0FcVZ1VvRfIuJb0XzcwX5D2B/RZzMVjTdZRVsRfPhwLeiVaxLtYDgr5wPUStawkGpnuHVbQs/J5NvUVSrhL/zzHfnqOWC7l4czHqvY02tVb7O+1Z1XbD5+l7XiwEd759cfKB++9ZyXYj5ug0CFtBk+UHSh7MVzd9h5q+gfZdGoBXNr1OoFc3f/aUularxcqE7wAhKQGI03MF/BlTU69DNQjquKuq+Owbqehe3hjHEmgRW78nd2QQsADFa0fxs274VzYcf34rmP6h9K1pdBvrWpehOLR/e/HxpupL3H/JZ/6nessYHPRCjaazkwPHuW8c0xtC3jlWNL6zTHGi+dUzjVf2xqm5y3wpe1UXOndgELADx8K1ovhvPD/j1d2AFW9GqxokRlIAj6ALMH9uaBNa3jvmLrvpOAutbxzS3WlXrWHASWMaNEbAAAOBiK9IksF/ccWAIgFrHEhhDqkAHAhYAAIhX0ZYDU6L4m3v8dCj6iqQPz3NDFkDAAgAAIGABAAAQsAAAAEDAAgAAIGABAAAQsAAAAEDAAgAAIGABAAAQsAAAAEDAAgAAIGABAAAQsAAAAAhYAAAAIGABAAAQsAAAAAhYAAAAIGABAAAQsAAAAAhYAAAAIGABAAAQsAAAAAhYAAAAIGABAAAQsAAAAAhYAAAABKyDb8SIETZ+/Piozw8dOtSaNWsWKr169WLvAAAAAlYkhYWFNmzYMBeaYgWsvn37WmpqKnsEAAAQsGozcOBAGzBggPs3VsBq3769ZWVlsUcAAAABqzY+NN1yyy1RA1Z2dra1aNHC+vXrZ61bt7bevXvb2rVr2TsAAICAFUusgJWWlmZt2rRx/+bm5trw4cOtR48eVl5ezh4CAAAErLoErHAlJSXWsmVLy8jIiPh8ZmampaenUygUCoVCoSStKF806oBVUFBgzZs3t23bthGBAQAALVh1CVgzZsywLl262IoVKywnJ8eGDBliffr0Ye8AAAACVqIBq127dpaSkuL+P2bMGOvQoYO1atXK+vfvn9RmOgAAgEYZsAAAAAhYOCKs21Zm764usU8zSm3DjjLLyuHOSwAACFiol+cX77c+9+XVKJc9nG9XP55vt71QaH+cVmgPphTZM+8U24tp+23+yhJL+6LUhbKte8osJ59QBgAAAQshCkt/fbXIhr1YaIOfKrBfP5ofMXDFU345Od8GPVHgXusvs4pcKFOAm76sMpR9tLHUVm+tbCUrKCaUAQBAwGpi9hWWuyCkVqql60pdQJr2/n57dmGxC06jXi50QUqtXD+fVLdQ1m98nvv9G/5RGcruf73IHp1f7EJZyscl9tZnlV2X6sak6xIAQMBCk7Rjb7lt2lXmQtGiNSX25qclLiz9fUFlKBsxtdBuerbAhaqLHqxbK9nFf6vsurz1uYJQ1+VTb1WGsvCuy915hDIAAAELTUxpmbmWqbWZlaFMAem19P0uLE2aWxTquvzDlMpQloyuyz/PKKzRdbn8q1L7bHMpXZcAAAIWmibfdalApGAU3nWpAFXfrksV/b6Cne+6VOBTKFMADO+6VFAEAICAhSZFXYXBrkuFMoUldS36rkt1OSpUqQuyvl2Xer3wrku9r95f60HXJQCAgIUmx3ddqoVKoUgtVsGuS7VoJaPrUi1s+v1g16Va4tQiF951qZY7AAABC2hSNHYrvOtSY7wUyoJdlxoLpjFhyei61Bi1YNel3lOBUGPa6LoEAAIW0CSpq1B3OyoU6e7H8K5L3SVZ365L3a2p39fdm77rUnd16n10l2ew61J3gQIACFhAkxPedan5whSWNH+Y77rUvGIKVZpnrD5dl34mf71msKjlTEEtWPx4s2CZtbyyVS1YfKALFj/5bLAQ9gAQsAA0WL7rUiFGM+mHd11qxv1kdF0equKDX7AQAgEQsAA0ePpuSR8iNKYsPGzoy8HDQ4kPbcHiJ5gNFh/ogsV3gwZLfb6yiRBICAQIWACQgPAAQQg8tCFwwhtFoW2nMXz+btfgvHB8+TtAwAIAQuBBKv67RoPhzQc2f6OF/zYFP3mv/5orbbPiEuoSCFgAgCYQAv33i/oxfP5u1+C8cPX5BoVI5bKH86tNT+LvhvXzxvkvhw9+F6mfqoQuUhCwAACNip+wVyU8sPmWOj8lib8D1n/NVX2+ED5aUWue/7aF4OS+ugNX6/Ji2oGxbrohxE9fwkS/IGABABodf9PFhh2V05P4u2H9vHH+GxYUlka9XBnSNO+bwlSy74z1X4nlvzzef1ep3vuZd4qr3ZTgb0Tw38agAhCwAACNig85utsy+L2k/muwFJB816MPT4Ofqt9Ev9GKgp9eV3PW6X0UDMNvIvAhkpsICFgAADRqfl45/+XxKj4I6TtE/aS/wRsRgjcfHKybCMLv+ox1E4EPmtxEQMACAKBR0ddkKeRoML5Cz9J1lUHNfzNDQ7mJwK8LNxEQsAAAaBLUAhXvTQRq2TqYNxGomxUELAAAUCX8JgKN+Ur0JgItCwIWAAAAAQsAAICABQAAAAIWAAAAAQsAAICABQAAAAIWAAAAAQsAAICABQAAgCMhYI0YMcLGjx/PngEAAASs+iosLLRhw4ZZs2bNCFgAAICAlQwDBw60AQMGuH8JWAAAgICVBFlZWe7fW265hYAFAAAIWMkUT8DKzMy09PR0CoVCoVAolKQV5YsmHbAAAABowSJgAQAAAhYBCwAAgIAFAABAwAIAACBgAQAAgIAFAABAwAIAACBgAQAAgIAFAABAwAIAACBgAQAAELAAAABAwAIAACBgAQAAELAAAABAwAIAACBgAQAAELAAAABAwAIAACBgAQAAELAAAABAwAIAACBgAQAAELAAAAAIWAAAACBgAQAAELAAAAAIWAAAACBgAQAAELAAAAAIWAAAACBgAQAAELAAAAAIWAAAACBgAQAAELAAAAAIWLZw4ULr3r27HX300TZo0CArKiqKuNzQoUOtWbNmodKrVy/2DgAAIGCFy8/Pt+OOO85mzpxpu3btsksvvdRGjx4dcdm+fftaamoqewQAABCwYklJSbGzzz479PPKlSutU6dOEZdt3769ZWVlsUcAAAABK5aJEyfa4MGDQz+re1Ddf3l5edWWy87OthYtWli/fv2sdevW1rt3b1u7di17BwAAELDCjR071m6++eZqjx111FG2c+fOao+lpaVZmzZt3L+5ubk2fPhw69Gjh5WXl7OHAAAAAStowoQJdu2114Z+9i1YBQUFMX+vpKTEWrZsaRkZGRGfz8zMtPT0dAqFQqFQKJSkFeWLIyJgzZ49284///zQzytWrLCOHTvW+nsKYM2bN7dt27YRgQEAAC1YQRpr1bZtW5s+fXroLsIRI0bUWG7GjBnWpUsXF8BycnJsyJAh1qdPH/YOAAAgYEWyZMkS69mzpxu8PmDAADd1g9euXTt3p6GMGTPGOnToYK1atbL+/fsntZkOAACgUQUsAAAAAhYAAAAIWAAAAAQsAAAAAhYAAAAIWAAAAAQsAAAAAhYAAAAIWAAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAAAQsAAAAAhYAAAABCwAAgIAFAAAAAhYAAAABCwAAgIAFAAAAAhYAAAABCwAAgIAFAAAAAhYAAAABCwAAgICVBAsXLrTu3bvb0UcfbYMGDbKioiL2DgAAIGDVVX5+vh133HE2c+ZM27Vrl1166aU2evRo9g4AACBg1VVKSoqdffbZoZ9XrlxpnTp1Yu8AAAACVl1NnDjRBg8eHPpZ3YPNmjWzvLw89hAAACBg1cXYsWPt5ptvrvbYUUcdZTt37mQPAQAAAlZdTJgwwa699trQz74Fq6CgIOLyTz/9tPXq1YtCoVAoFAolaUX5olEFrNmzZ9v5558f+nnFihXWsWNH4i8AAKAFq6401qpt27Y2ffr00F2EI0aMYO8AAAACVn0sWbLEevbsaa1bt7YBAwa4qRsAAAAIWAAAACBgAQAAELAAAAAIWAAAAAQsHCRbtmxx83udc845NZ77/e9/757LysqK+RpLly61bt26JfS+uiNz/PjxUZ/ni7Ybdz2IZ/9OmjTJvW940brK0KFDqz2u+WLQtOoI9YC6EDRu3Dg3ndIxxxxjffr0sfXr11NPCFiH90Bp3ry5denSxTZv3hx6vLi42M4999ykHyiFhYU2bNgw97rRAhZftN2460Fd96++WeG6664L/dy3b19LTU1l5zXxOkI9oC7IvHnz3OspVGmaJZ1ntC7UEwLWYT9Q7rjjDvf9i54mWh05cmS1A+X111+3U045xY499li75JJLbOvWraED5aSTTrLrr7/evvWtb9mFF15omzZtivh+AwcOdFNe6N9oAYsv2m7c9aAu+1dTprRv39727t0bekw/1/YhjsZfR6gH1IVI9P4tWrSw8vJy6gkB6/AeKIsWLapWia+++mpbtmxZ6EBZt26dffvb33ZXCdnZ2XbTTTfZBRdcEDpQtNwDDzxgOTk57spBzbPRKr3ccsstUQMWX7TduOtBXfavrkSffPLJ0M96b3149uvXz81T17t3b1u7di07sonVEeoBdSGaWbNm2emnn049IWAd/gOltLTUNcOquVfft9i1a1eX/P2Bou9mVN+3p2VatmxpX3/9tTtQ1O/trxTUpKvX1EETTayAxRdtN+56kOj+/eCDD9y3Kqh72UtLS7M2bdq4f3Nzc2348OHWo0eP0HujadQR6gF1IRK1hOm1FyxYQD0hYB3+A0V0lfDwww+7rwe68cYbK3dG1YFy++231+j3Pv7442358uXuQAn2dYtOiLGuEGIFrES/aBtHVj1IdP/eeuutrr7EUlJS4j64MzIy2JlNsI5QD6gL3oYNG+zkk0+2qVOnUk8IWA3nQHnttdfcXSG/+MUvXJNu8EBRM27wSkRXG6qgGzdudAfKiSeeGHpO42T0mrGacGMFLL5ou3HXg0T3r8Zp+PWIRh+2eq9t27axM5tgHaEeUBdkzZo1rhVtzpw51BMCVsM6UFTp1D/drl07dzdI8ED58ssv3SDF+fPnuyZc9aX7/m3fl/7oo4+6vm5dxVxxxRUx3zdWwOKLtht3PUhk/+7Zs8e9ZmZmZrXHZ8yY4e5g0oet1mPIkCFRx/2h8dYR6gF1wdu9e7d17tzZFi9eTD0hYDW8A0Uuv/xyu+qqqw7sjMDdIK+88kpoLpKf/OQn7irEHyhnnHGGXXnlle5A69+/v23fvj3hgKUDVHeMCF+03bjrQaz9G6wHH3/8sRs7EcmYMWOsQ4cO1qpVK/de4SEMTaOOUA+oC6oLo0aNijhv3r59+6gnBCwAAAACFgAAAAELAACAgAUAAAACFgAAAAELAACAgAUAAAACFgAAAAELAACAgAUAAAACFgAAAAELAACAgAUAAAACFgAAAAELAACAgAUAAEDAAgAAAAELAACAgAUAAEDAAgAAAAELAACAgAUAAEDAAoBk279/v5WXl7MhABCwAKA+OnfubO+++66VlZXZqaeeaoWFhYfkfYcOHWrNmjULlV69eoWeW7hwoXXv3t2OPvpoGzRokBUVFcX1HAAQsAA0qIBVUlLigs6hClh9+/a11NTUGo/n5+fbcccdZzNnzrRdu3bZpZdeaqNHj671OQAgYAFocAHrrLPOCrUmZWVl2YIFC+y0006zb37zm/bLX/7S9uzZ45ZfunSpnXnmmXb22Wfb8ccfbwUFBXV63/bt27v3CZeSkuJe21u5cqV16tSp1ucAgIAFoMEFrGAL1tdff23HHnusvfrqq5aTk+O6866++upQwNJyc+bMse3bt1d7rRkzZlTr9vNl2rRp1ZbLzs62Fi1aWL9+/ax169bWu3dvW7t2rXtu4sSJNnjw4NCy6gLUa+Tl5cV8DgAIWAAadMB6/PHHXfebp645BaLi4mIXsL7zne/U6z3T0tKsTZs27t/c3FwbPny49ejRww2wHzt2rN18883Vlj/qqKNs586dMZ8DAAIWgAYdsEaOHBmxJWrdunUuYGkwfDLpvVu2bGkZGRk2YcIEu/baa0PP+VYqdUXGeg4ACFgAGnTAmjx5srtDLxIFLLU2RRJvF2E4BaTmzZvbtm3bbPbs2Xb++eeHnluxYoV17NjR/T/WcwBAwALQ4AKWfOMb37BNmzbZhg0bXBfe3LlzXRfeM88844KM5smKFbDipSDWpUsXF5A0xmvIkCHWp08f95zGU7Vt29amT58eulNwxIgRtT4HAAQsAA0yYGkgu7rqVq9ebW+++ab17NnTzTd1xhlnWHp6ulsmGQFLxowZYx06dLBWrVpZ//79LTMzM/TckiVL3HtrAPyAAQPcGLB4ngMAAhYAAAABCwAAgIAFAABAwAIAAAABCwAAgIAFAABAwAIAAAABCwAAgIAFAABAwAIAAAABCwAAgIAFAABAwAIAAAABCwAAgIAFAABAwAIAACBgAQAAgIAFAABAwAIAACBgAQAAgIAFAABAwAIAAGgc/j/6nduJQXFJCgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='exp_img/500_2.png') " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='exp_img/500_3.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Caveat - " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An important factor to note about these results is that the code used reaches a threshold at CPU=4 where despite availablability of further resources, The average CPU utilization is throttled at (270% , 300%) range. Hence the code cannot scale beyond more computation resources. This is reflected in the results as well, where CPU = 4 becomes an outlier/" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3-final" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Examples/profiler_examples/issues.md ================================================ # Known issues + Remove any dangling Dockerfiles in the profiler directory before executing Profiler as they might interfere. + Once Docker has started running, if you hit Ctrl+Z, you will only exit from the Profiler script. Docker will continue to run in the background until completion unless you stop the Docker process separately. ================================================ FILE: Examples/profiler_examples/mnist/mnist.py ================================================ """ Convolutional Neural Network. Build and train a convolutional neural network with TensorFlow. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) This example is using TensorFlow layers API, see 'convolutional_network_raw' example for a raw implementation with variables. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import division, print_function, absolute_import # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/mnist_data", one_hot=False) import tensorflow as tf # Training Parameters learning_rate = 0.001 num_steps = 2000 batch_size = 128 # Network Parameters num_input = 784 # MNIST data input (img shape: 28*28) num_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.25 # Dropout, probability to drop a unit # Create the neural network def conv_net(x_dict, n_classes, dropout, reuse, is_training): # Define a scope for reusing the variables with tf.variable_scope('ConvNet', reuse=reuse): # TF Estimator input is a dict, in case of multiple inputs x = x_dict['images'] # MNIST data input is a 1-D vector of 784 features (28*28 pixels) # Reshape to match picture format [Height x Width x Channel] # Tensor input become 4-D: [Batch Size, Height, Width, Channel] x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer with 32 filters and a kernel size of 5 conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) # Convolution Layer with 64 filters and a kernel size of 3 conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) # Flatten the data to a 1-D vector for the fully connected layer fc1 = tf.contrib.layers.flatten(conv2) # Fully connected layer (in tf contrib folder for now) fc1 = tf.layers.dense(fc1, 1024) # Apply Dropout (if is_training is False, dropout is not applied) fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) # Output layer, class prediction out = tf.layers.dense(fc1, n_classes) return out # Define the model function (following TF Estimator Template) def model_fn(features, labels, mode): # Build the neural network # Because Dropout have different behavior at training and prediction time, we # need to create 2 distinct computation graphs that still share the same weights. logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True) logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False) # Predictions pred_classes = tf.argmax(logits_test, axis=1) pred_probas = tf.nn.softmax(logits_test) # If prediction mode, early return if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) # Evaluate the accuracy of the model acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) # TF Estimators requires to return a EstimatorSpec, that specify # the different ops for training, evaluating, ... estim_specs = tf.estimator.EstimatorSpec( mode=mode, predictions=pred_classes, loss=loss_op, train_op=train_op, eval_metric_ops={'accuracy': acc_op}) return estim_specs # Build the Estimator model = tf.estimator.Estimator(model_fn) # Define the input function for training input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.train.images}, y=mnist.train.labels, batch_size=batch_size, num_epochs=None, shuffle=True) # Train the Model model.train(input_fn, steps=num_steps) # Evaluate the Model # Define the input function for evaluating input_fn = tf.estimator.inputs.numpy_input_fn( x={'images': mnist.test.images}, y=mnist.test.labels, batch_size=batch_size, shuffle=False) # Use the Estimator 'evaluate' method e = model.evaluate(input_fn) print("Testing Accuracy:", e['accuracy']) ================================================ FILE: Examples/profiler_examples/model_names.txt ================================================ mobilenet_v1_0.75_224.tflite mobilenet_v1_1.0_224.tflite ================================================ FILE: Examples/quad_equation_min/QUAD_MIN_Demo.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Minimum of quadratic function Demo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we will show how to find the minimum of a quadratic function using Auptimizer. We also demonstrate how to effectively switch between various HPO proposers such as Spearmint and BOHB." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running the Experiments " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this experiment we try to find the best X such that f(X) is minimal.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each instance of 'quad_min.py' would accept an X that calculates the value of the quadratic function. The ‘quad_min_[proposer].json’ file contains code to perform HPO using CPU resources and consider 50 samples for each proposer. We can easily switch between proposers and see how they choose different configurations to optimize the final performance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run the experiment use command- \n", "\n", "'python3 -m aup quad_min_[proposer].json' \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We next analyze the experiment results using the sqlite database for our experiments. The details for the experiment can be found inn the jobs profile, but here we aim to compare the proposers. Experiment 1 is performed using BOHB, then random and finally Spearmint." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "'''\n", "Connect to the Auptimizer database\n", "''' \n", "\n", "import sqlite3\n", "import time\n", "import datetime\n", "import random\n", "\n", "conn = sqlite3.connect('sqlite3.db')\n", "c = conn.cursor()\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "'''\n", "Function to compile and return details from a Auptimizer experiment. \n", "Takes experiment id as input, returns score, jobs ran for the experiments, and end time for each job.\n", "''' \n", "\n", "def graph_data(ex, cursor):\n", " cursor.execute('SELECT score, jid, end_time FROM job WHERE typeof(score)==\\'real\\' and eid = '+ str(ex))\n", " data = cursor.fetchall()\n", "\n", " jobs = []\n", " score = []\n", " times =[]\n", " \n", " for row in data:\n", " times.append(row[2])\n", " jobs.append(row[1])\n", " score.append(row[0])\n", "\n", " times = [a-min(times) for a in times] \n", " jobs = [a-min(jobs) for a in jobs]\n", " \n", " print(score)\n", " print(jobs)\n", " \n", " return (score, times, jobs)\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6148.106139221136, 10438.637919012073, 7415.6298730541, 6060.406530867955, 3664.920102436913, 8514.390146411502, 3909.7273413323915, 16227.975850616904, 18949.333977751823, 3002.7529813288706, 12791.65578998259, 5710.14811693708, 6586.3076663701, 17482.08784937461, 106.05097215471763, 157.98208630594334, 11.44000290631149, 14146.904978499952, 12357.080808454182, 15445.805059455917, 19541.970126709333, 13032.9272883446, 4347.975242825175, 12432.524670292085, 288.5003098979952, 8357.696833979042, 422.36446655402744, 18209.8318006542, 5559.088337974499, 3511.112188373727, 1431.0292860232905, 12232.83011505206, 4248.207908109023, 6595.336180527122, 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201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298]\n" ] } ], "source": [ "'''\n", "Compile results for grid search, random, spearmint, hyperband, hyperopt Proposers\n", "''' \n", "\n", "s_ran, t_ran, j_ran = graph_data(2, c)\n", "s_spe, t_spe, j_spe = graph_data(3, c)\n", "s_bohb, t_bohb, j_bohb = graph_data(1, c)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily visualize the performance of the hyperparameter optimization algorithms for our current experiment and how configurations can affect the final performance. In the plots below we compare job performance and time with accuracy achieved. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Plots for the experiment results\n", "'''\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(20,15))\n", "\n", "\n", "plt.plot(j_bohb, s_bohb)\n", "plt.plot(j_ran, s_ran)\n", "plt.plot(j_spe, s_spe)\n", "\n", "plt.legend(['y = bohb','y = random', 'y = spearmint'], loc='upper left')\n", "\n", "plt.title('Hyperparameter Optimization using various proposers on Quad min')\n", "plt.xlabel('Jobs')\n", "plt.ylabel('Accuracy')\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "'''\n", "Plots for the experiment results\n", "'''\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(20,15))\n", "\n", "\n", "plt.plot(t_bohb, s_bohb)\n", "plt.plot(t_ran, s_ran)\n", "plt.plot(t_spe, s_spe)\n", "\n", "plt.legend(['y = bohb','y = random', 'y = spearmint'], loc='upper left')\n", "\n", "plt.title('Hyperparameter Optimization using various proposers on Quad min')\n", "plt.xlabel('Time')\n", "plt.ylabel('AMC score (less is better)')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "'''\n", "Plots for the experiment results\n", "'''\n", "\n", "import sys\n", "\n", "def min_so_far(arr):\n", " mmin=float('inf')\n", " new_arr=[]\n", " for _,x in enumerate(arr):\n", " if x" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,15))\n", "\n", "plt.plot(t_bohb_median, min_so_far(s_bohb_median))\n", "plt.plot(t_bohb_bandit, min_so_far(s_bohb_bandit))\n", "plt.plot(t_bohb_trunc, min_so_far(s_bohb_trunc))\n", "plt.plot(t_bohb, min_so_far(s_bohb))\n", "\n", "plt.legend(['y = bohb_median','y = bohb_bandit', 'y = bohb_trunc', 'y = bohb'], loc='lower right')\n", "\n", "plt.title('Hyperparameter Optimization using ES with various policies on Quad min')\n", "plt.xlabel('Time')\n", "plt.ylabel('Min res')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6060.406530867955, 3664.920102436913, 3909.7273413323915, 3002.7529813288706, 106.05097215471763, 157.98208630594334, 11.44000290631149, 288.5003098979952, 422.36446655402744, 10.303060762137134, 77.10024584741284, 215.54895165905884, 251.65086979919593, 195.46347563258027, 191.5105114029914, 34.43100582618657, 87.0522160740486, 183.1439686948838, 11.34880226594397, 3.5498130271587858, 10.615665529087309, 100.70466265645877, 5.8995577127201795, 63.33929155979352, 10.099046262581181, 23.781863082338973, 71.8013571039346]\n", "[0, 1, 3, 6, 11, 12, 13, 21, 23, 31, 40, 50, 58, 64, 66, 72, 79, 84, 94, 96, 123, 156, 163, 168, 170, 176, 195]\n", "[6060.406530867955, 3664.920102436913, 3002.7529813288706, 106.05097215471763, 157.98208630594334, 11.44000290631149, 10.303060762137134, 77.10024584741284, 34.43100582618657, 11.34880226594397, 3.5498130271587858, 10.615665529087309, 5.8995577127201795, 10.099046262581181]\n", "[0, 1, 6, 11, 12, 13, 31, 40, 72, 94, 96, 123, 163, 170]\n", "[6148.106139221136, 7415.6298730541, 6060.406530867955, 3664.920102436913, 3909.7273413323915, 3002.7529813288706, 106.05097215471763, 157.98208630594334, 11.44000290631149, 10.303060762137134, 11.34880226594397, 3.5498130271587858]\n", "[0, 2, 3, 4, 6, 9, 14, 15, 16, 34, 97, 99]\n" ] } ], "source": [ "s_random_median, t_random_median, j_random_median = graph_data(2, c2)\n", "s_random_trunc, t_random_trunc, j_random_trunc = graph_data(6, c2)\n", "s_random_bandit, t_random_bandit, j_random_bandit = graph_data(1, c2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,15))\n", "\n", "plt.plot(t_random_median, min_so_far(s_random_median))\n", "plt.plot(t_random_bandit, min_so_far(s_random_bandit))\n", "plt.plot(t_random_trunc, min_so_far(s_random_trunc))\n", "plt.plot(t_ran, min_so_far(s_ran))\n", "\n", "plt.legend(['y = random_median','y = random_bandit', 'y = random_trunc', 'y = random'], loc='lower right')\n", "\n", "plt.title('Hyperparameter Optimization using ES with various policies on Quad min')\n", "plt.xlabel('Time')\n", "plt.ylabel('Min res')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3.1, 3.1]\n", "[0, 1]\n", "[6148.106139221136, 7415.6298730541, 6060.406530867955, 3664.920102436913, 3909.7273413323915, 3002.7529813288706, 106.05097215471763, 157.98208630594334, 11.44000290631149, 10.303060762137134, 11.34880226594397, 3.5498130271587858]\n", "[0, 2, 3, 4, 6, 9, 14, 15, 16, 34, 97, 99]\n", "[11588.561807897513, 10264.661252507878, 13259.660327218779, 9756.147263836081, 192.30551743308826, 54.01004445189671, 10.657509615206326, 3.542402417237454]\n", "[0, 1, 2, 3, 4, 13, 66, 81]\n" ] } ], "source": [ "s_spearmint_median, t_spearmint_median, j_spearmint_median = graph_data(9, c2)\n", "s_spearmint_trunc, t_spearmint_trunc, j_spearmint_trunc = graph_data(1, c2)\n", "s_spearmint_bandit, t_spearmint_bandit, j_spearmint_bandit = graph_data(8, c2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,15))\n", "\n", "plt.plot(t_spearmint_median, min_so_far(s_spearmint_median))\n", "plt.plot(t_spearmint_bandit, min_so_far(s_spearmint_bandit))\n", "plt.plot(t_spearmint_trunc, min_so_far(s_spearmint_trunc))\n", "plt.plot(t_ran, min_so_far(s_ran))\n", "\n", "plt.legend(['y = spearmint_median','y = spearmint_bandit', 'y = spearmint_trunc', 'y = spearmint'], loc='lower right')\n", "\n", "plt.title('Hyperparameter Optimization using ES with various policies on Quad min')\n", "plt.xlabel('Time')\n", "plt.ylabel('Min res')\n", "\n", "plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Examples/quad_equation_min/cpu.ini ================================================ [Auptimizer] # Auptimizer environment folder to be created, this file will be copied over Auptimizer_PATH=./.aup # Temp folder TMP_FOLDER=./aup_tmp # SQL engine SQL_ENGINE=sqlite ================================================ FILE: Examples/quad_equation_min/quad_min.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function for HPO and aup ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys """ # ver 1.0 - modify existing code from aup import BasicConfig, print_result def rosenbrock(conf, a=1, b=100): x = conf.x y = conf.y return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) val = rosenbrock(config) print_result(val) """ from aup import aup_args from time import sleep @aup_args def rosenbrock(x, a=2, b=4, c=5): global it it = 1.0 res = None for _ in range(0, 10): sleep(1) res = x*x*a + x*b + c res += it it -= 1.0 / 10 return res if __name__ == "__main__": if len(sys.argv) != 2: print("config file required") exit(1) rosenbrock(sys.argv[1]) ================================================ FILE: Examples/quad_equation_min/quad_min_BOHB.json ================================================ { "name": "./quad_equation_min/quad_min_BOHB.json", "proposer": "bohb", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_iterations": 100, "num_samples": 64, "random_fraction": 0.3333333333333333, "bandwidth_factor": 3, "min_bandwidth": 0.001, "eta": 3, "min_budget": 1, "max_budget": 5, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/quad_equation_min/quad_min_random.json ================================================ { "name": "./quad_equation_min/quad_min_random.json", "proposer": "random", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 200, "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/quad_equation_min/quad_min_spearmint.json ================================================ { "name": "./quad_equation_min/quad_min_spearmint.json", "proposer": "spearmint", "script": "quad_min.py", "resource": "cpu", "n_parallel": 4, "target":"min", "n_samples": 50, "engine":"GPEIChooser", "parameter_config": [ { "name": "x", "range": [-1.0, 100.0], "type": "float" } ] } ================================================ FILE: Examples/tf_flags/README.md ================================================ # Demo for tf.app.flags Tensorflow flags is widely used for tensorflow to parse input arguments. It is naturally supported by **Auptimizer** with minor modification on you existing code. Similar to adopting **Auptimizer** for plain python training code, there are two parts of changes. A working example is shown as `rosenbrock_hpo.py`, whereas the plain code is `rosenbrock_tf.py`. ## Modification on training code Because `tf.flags` already takes care of the input argument, we just insert `aup.BasicConfig` properly: ```python def main(unused_arguments): config = BasicConfig().load(unused[1]) config.to_flags(FLAGS) if __name__ == "__main__" tf.app.run() ``` `config = BasicConfig().load(unused[1])` parses the input file for the parameter values. And `config.to_flags(FLAGS)` assigns the value to `tensorflow.app.flags.FLAGS`, thus it can be used without changing how to access those values in the original code. Similar to other examples, you need to call `aup.print_result()` at the end of the program to return your target to optimize. ## Modification on experiment configuration The same configuration of experiment can be used. See other examples for reference. ## Run Same to other example, you now can run **Auptimizer** as ```bash python -m aup experiment.json ``` to initiate your continuous training experiment. ================================================ FILE: Examples/tf_flags/experiment.json ================================================ { "name": "./tf_flags/experiment.json", "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "rosenbrock_hpo.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" }, { "name": "y", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "n_parallel": 2, "target":"min" } ================================================ FILE: Examples/tf_flags/rosenbrock_hpo.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function with tf.app.flags ============================================== .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import tensorflow as tf """ from aup import print_result, BasicConfig flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_float("x", 0, "x value") flags.DEFINE_float("y", 0, "y value") def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) def main(unused): ## change config = BasicConfig().load(unused[1]) config.to_flags(FLAGS) ## val = rosenbrock(FLAGS.x, FLAGS.y) print_result(val) if __name__ == "__main__": tf.app.run() """ from aup import aup_flags flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_float("x", 0, "x value") flags.DEFINE_float("y", 0, "y value") def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) @aup_flags(FLAGS) def main(unused): return rosenbrock(FLAGS.x, FLAGS.y) if __name__ == "__main__": tf.app.run() ================================================ FILE: Examples/tf_flags/rosenbrock_tf.py ================================================ #!/usr/bin/env python """ Modified Rosenbrock function with tf.app.flags ============================================== .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ import sys import tensorflow as tf flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_float("x", 0, "x value") flags.DEFINE_float("y", 0, "y value") def rosenbrock(x, y, a=1, b=100): return (a-x)*(a-x) + b*(y-x*x)*(y-x*x) def main(unused): val = rosenbrock(FLAGS.x, FLAGS.y) print(val) if __name__ == "__main__": tf.app.run() ================================================ FILE: Examples/tf_iris_diff_opt/History.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import matplotlib.pylab as plt\n", "from aup.ET.Connector.SQLiteConnector import SQLiteConnector\n", "import json\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# connect to the result (The database is copied from ~/.aup/sqlite3.db)\n", "sql = SQLiteConnector(\"./sqlite3.db\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get all experiments (this run is for iris using hyperopt, sequence, spearmint, random)\n", "eids = sql.get_all_experiment()\n", "eids" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2,\n", " 1,\n", " 1534397382,\n", " 1534397474,\n", " '{\"script\": \"premade_estimator_hpo.py\", \"n_parallel\": 2, \"parameter_config\": [{\"type\": \"int\", \"name\": \"layer1\", \"range\": [2, 6]}, {\"type\": \"int\", \"name\": \"layer2\", \"range\": [2, 6]}], \"resource\": \"gpu\", \"proposer\": \"sequence\", \"target\": \"max\", \"workingdir\": \"Examples/tf_iris_diff_opt\"}')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show details in one experiment\n", "sql.cursor.execute(\"SELECT * FROM experiment where eid = ?\", (eids[1],))\n", "sql.cursor.fetchone()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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jidscoreeidridstart_timeend_timejob_config
010.9666671115343973091534397318{'layer2': 2, 'tid': 0, 'layer1': 2}
120.9333331215343973091534397318{'layer2': 5, 'tid': 1, 'layer1': 2}
230.9666671115343973181534397325{'layer2': 4, 'tid': 2, 'layer1': 3}
340.9666671215343973181534397325{'layer2': 4, 'tid': 3, 'layer1': 3}
450.9666671115343973251534397332{'layer2': 4, 'tid': 4, 'layer1': 4}
560.9666671215343973251534397332{'layer2': 3, 'tid': 5, 'layer1': 5}
671.0000001115343973321534397339{'layer2': 3, 'tid': 6, 'layer1': 5}
780.2666671215343973321534397340{'layer2': 2, 'tid': 7, 'layer1': 2}
890.9666671115343973391534397346{'layer2': 5, 'tid': 8, 'layer1': 5}
9100.9666671215343973401534397347{'layer2': 3, 'tid': 9, 'layer1': 2}
10110.9666671115343973471534397354{'layer2': 5, 'tid': 10, 'layer1': 5}
11120.9666671215343973471534397354{'layer2': 5, 'tid': 11, 'layer1': 4}
12130.2666671115343973541534397361{'layer2': 3, 'tid': 12, 'layer1': 5}
13140.9666671215343973541534397361{'layer2': 5, 'tid': 13, 'layer1': 3}
14150.9666671115343973611534397368{'layer2': 4, 'tid': 14, 'layer1': 5}
15160.9666671215343973611534397368{'layer2': 4, 'tid': 15, 'layer1': 4}
16170.9666671115343973681534397375{'layer2': 3, 'tid': 16, 'layer1': 4}
17181.0000001215343973681534397375{'layer2': 5, 'tid': 17, 'layer1': 2}
18191.0000001115343973751534397382{'layer2': 4, 'tid': 18, 'layer1': 3}
19201.0000001215343973751534397382{'layer2': 4, 'tid': 19, 'layer1': 2}
\n", "
" ], "text/plain": [ " jid score eid rid start_time end_time \\\n", "0 1 0.966667 1 1 1534397309 1534397318 \n", "1 2 0.933333 1 2 1534397309 1534397318 \n", "2 3 0.966667 1 1 1534397318 1534397325 \n", "3 4 0.966667 1 2 1534397318 1534397325 \n", "4 5 0.966667 1 1 1534397325 1534397332 \n", "5 6 0.966667 1 2 1534397325 1534397332 \n", "6 7 1.000000 1 1 1534397332 1534397339 \n", "7 8 0.266667 1 2 1534397332 1534397340 \n", "8 9 0.966667 1 1 1534397339 1534397346 \n", "9 10 0.966667 1 2 1534397340 1534397347 \n", "10 11 0.966667 1 1 1534397347 1534397354 \n", "11 12 0.966667 1 2 1534397347 1534397354 \n", "12 13 0.266667 1 1 1534397354 1534397361 \n", "13 14 0.966667 1 2 1534397354 1534397361 \n", "14 15 0.966667 1 1 1534397361 1534397368 \n", "15 16 0.966667 1 2 1534397361 1534397368 \n", "16 17 0.966667 1 1 1534397368 1534397375 \n", "17 18 1.000000 1 2 1534397368 1534397375 \n", "18 19 1.000000 1 1 1534397375 1534397382 \n", "19 20 1.000000 1 2 1534397375 1534397382 \n", "\n", " job_config \n", "0 {'layer2': 2, 'tid': 0, 'layer1': 2} \n", "1 {'layer2': 5, 'tid': 1, 'layer1': 2} \n", "2 {'layer2': 4, 'tid': 2, 'layer1': 3} \n", "3 {'layer2': 4, 'tid': 3, 'layer1': 3} \n", "4 {'layer2': 4, 'tid': 4, 'layer1': 4} \n", "5 {'layer2': 3, 'tid': 5, 'layer1': 5} \n", "6 {'layer2': 3, 'tid': 6, 'layer1': 5} \n", "7 {'layer2': 2, 'tid': 7, 'layer1': 2} \n", "8 {'layer2': 5, 'tid': 8, 'layer1': 5} \n", "9 {'layer2': 3, 'tid': 9, 'layer1': 2} \n", "10 {'layer2': 5, 'tid': 10, 'layer1': 5} \n", "11 {'layer2': 5, 'tid': 11, 'layer1': 4} \n", "12 {'layer2': 3, 'tid': 12, 'layer1': 5} \n", "13 {'layer2': 5, 'tid': 13, 'layer1': 3} \n", "14 {'layer2': 4, 'tid': 14, 'layer1': 5} \n", "15 {'layer2': 4, 'tid': 15, 'layer1': 4} \n", "16 {'layer2': 3, 'tid': 16, 'layer1': 4} \n", "17 {'layer2': 5, 'tid': 17, 'layer1': 2} \n", "18 {'layer2': 4, 'tid': 18, 'layer1': 3} \n", "19 {'layer2': 4, 'tid': 19, 'layer1': 2} " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show details in job history for one experiment\n", "history = sql.get_all_history(1)\n", "history = pd.DataFrame(history)\n", "history.columns = ['jid', 'score','eid','rid','start_time','end_time','job_config']\n", "history" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Best Accuracy so far')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,8))\n", "plt.subplot(121)\n", "for i in eids:\n", " history = sql.get_all_history(i)\n", " history = pd.DataFrame(history)\n", " history.columns = ['jid', 'score','eid','rid','start_time','end_time','job_config']\n", " sql.cursor.execute(\"SELECT * FROM experiment where eid = ?\", (i,))\n", " label = json.loads(sql.cursor.fetchone()[4])['proposer']\n", " plt.plot(history.score, label=label)\n", "plt.legend()\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Accuracy\")\n", "\n", "plt.subplot(122)\n", "for i in eids:\n", " history = sql.get_all_history(i)\n", " history = pd.DataFrame(history)\n", " history.columns = ['jid', 'score','eid','rid','start_time','end_time','job_config']\n", " sql.cursor.execute(\"SELECT * FROM experiment where eid = ?\", (i,))\n", " label = json.loads(sql.cursor.fetchone()[4])['proposer']\n", " plt.plot(history.score.cummax(), label=label)\n", "plt.legend()\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Best Accuracy so far\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sql.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Examples/tf_iris_diff_opt/README.md ================================================ # Tensorflow Iris Example Here we demonstrate how to adopt an existing [tensorflow code](https://raw.githubusercontent.com/tensorflow/models/master/samples/core/get_started/premade_estimator.py) for **Auptimizer** using the automatic code generation tool. ## Steps 1. The original `premade_estimator.py` use two hyperparameters, `batch_size` and `train_steps`. To make the code more relevant to our hyperparameter search, we drop them as input arguments and use fixed values instead. 2. We add two hyperparameters into the `main()` function to update the number of neurons in each layer, i.e. `layer1` and `layer2`. The model is constructed accordingly in line 51. 3. Add the return value `probability` in the `main()` function and also remove the "__main__" segments as we will create it automatically. See `premade_estimator_hyper.py` for the changed script. 4. Write an experiment configution JSON file, such as: ```JSON { "proposer": "random", "script": "premade_estimator_wrapper.py", "n_samples": 20, "random_seed": 1, "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int" }, { "name": "layer2", "range": [ 2, 6 ], "type": "int" } ], "resource": "cpu", "n_parallel": 2, "target":"max" } ``` 4. Run code conversion as: `python -m aup.convert premade_estimator_hyper.py experiment_random.json main`. The output file is saved as `premade_estimator_wrapper.py`. Now we have the code for **Auptimizer** to tune. ## Run 1. If you haven't setup **Auptimzier** yet, run `python -m aup.setup ../../FirstTime/env_local_template.ini` to setup a local environment for testing (with CPU only). Then following instruction on the screen to run `python -m aup.setupdb.sqlite ./.aup/env.ini`. 2. Run **Auptimizer** as: `python -m aup experiment_demo.json`. ================================================ FILE: Examples/tf_iris_diff_opt/experiment_demo.json ================================================ { "name": "./tf_iris_diff_opt/experiment_demo.json", "proposer": "random", "script": "premade_estimator_wrapper.py", "n_samples": 4, "random_seed": 1, "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int" }, { "name": "layer2", "range": [ 2, 6 ], "type": "int" } ], "resource": "cpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/tf_iris_diff_opt/experiment_hpo.json ================================================ { "name": "./tf_iris_diff_opt/experiment_hpo.json", "proposer": "hyperopt", "random_seed": 1, "engine":"tpe", "n_samples": 20, "script": "premade_estimator_hpo.py", "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int" }, { "name": "layer2", "range": [ 2, 6 ], "type": "int" } ], "resource": "gpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/tf_iris_diff_opt/experiment_hyperband.json ================================================ { "name": "./tf_iris_diff_opt/experiment_hyperband.json", "proposer": "hyperopt", "random_seed": 1, "engine":"tpe", "n_samples": 20, "script": "premade_estimator_hpo.py", "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int" }, { "name": "layer2", "range": [ 2, 6 ], "type": "int" } ], "resource": "gpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/tf_iris_diff_opt/experiment_random.json ================================================ { "name": "./tf_iris_diff_opt/experiment_random.json", "proposer": "random", "script": "premade_estimator_hpo.py", "n_samples": 20, "random_seed": 1, "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int" }, { "name": "layer2", "range": [ 2, 6 ], "type": "int" } ], "resource": "gpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/tf_iris_diff_opt/experiment_sequence.json ================================================ { "name": "./tf_iris_diff_opt/experiment_sequence.json", "proposer": "sequence", "script": "premade_estimator_hpo.py", "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int" }, { "name": "layer2", "range": [ 2, 6 ], "type": "int" } ], "resource": "gpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/tf_iris_diff_opt/experiment_spearmint.json ================================================ { "name": "./tf_iris_diff_opt/experiment_spearmint.json", "proposer": "spearmint", "n_samples": 20, "random_seed": 1, "script": "premade_estimator_hpo.py", "engine":"GPEIChooser", "parameter_config": [ { "name": "layer1", "range": [ 2, 6 ], "type": "int", "size": 1 }, { "name": "layer2", "range": [ 2, 6 ], "type": "int", "size": 1 } ], "resource": "gpu", "n_parallel": 2, "target":"max" } ================================================ FILE: Examples/tf_iris_diff_opt/iris_data.py ================================================ # Download from https://raw.githubusercontent.com/tensorflow/models/master/samples/core/get_started/iris_data.py # Date: Jun 11, 2018 import pandas as pd import tensorflow as tf TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv" TEST_URL = "http://download.tensorflow.org/data/iris_test.csv" CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species'] SPECIES = ['Setosa', 'Versicolor', 'Virginica'] def maybe_download(): train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL) test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL) return train_path, test_path def load_data(y_name='Species'): """Returns the iris dataset as (train_x, train_y), (test_x, test_y).""" train_path, test_path = maybe_download() train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0) train_x, train_y = train, train.pop(y_name) test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0) test_x, test_y = test, test.pop(y_name) return (train_x, train_y), (test_x, test_y) def train_input_fn(features, labels, batch_size): """An input function for training""" # Convert the inputs to a Dataset. dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) # Shuffle, repeat, and batch the examples. dataset = dataset.shuffle(1000).repeat().batch(batch_size) # Return the dataset. return dataset def eval_input_fn(features, labels, batch_size): """An input function for evaluation or prediction""" features=dict(features) if labels is None: # No labels, use only features. inputs = features else: inputs = (features, labels) # Convert the inputs to a Dataset. dataset = tf.data.Dataset.from_tensor_slices(inputs) # Batch the examples assert batch_size is not None, "batch_size must not be None" dataset = dataset.batch(batch_size) # Return the dataset. return dataset # The remainder of this file contains a simple example of a csv parser, # implemented using the `Dataset` class. # `tf.parse_csv` sets the types of the outputs to match the examples given in # the `record_defaults` argument. CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]] def _parse_line(line): # Decode the line into its fields fields = tf.decode_csv(line, record_defaults=CSV_TYPES) # Pack the result into a dictionary features = dict(zip(CSV_COLUMN_NAMES, fields)) # Separate the label from the features label = features.pop('Species') return features, label def csv_input_fn(csv_path, batch_size): # Create a dataset containing the text lines. dataset = tf.data.TextLineDataset(csv_path).skip(1) # Parse each line. dataset = dataset.map(_parse_line) # Shuffle, repeat, and batch the examples. dataset = dataset.shuffle(1000).repeat().batch(batch_size) # Return the dataset. return dataset ================================================ FILE: Examples/tf_iris_diff_opt/premade_estimator.py ================================================ # Download from https://raw.githubusercontent.com/tensorflow/models/master/samples/core/get_started/premade_estimator.py # Date: Jun 11, 2018 # This the original code. The converted version for Auptimizer is `premade_estimator_hpo.py` # ######################################################################################################### # Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """An Example of a DNNClassifier for the Iris dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import tensorflow as tf import iris_data parser = argparse.ArgumentParser() parser.add_argument('--batch_size', default=100, type=int, help='batch size') parser.add_argument('--train_steps', default=1000, type=int, help='number of training steps') def main(argv): args = parser.parse_args(argv[1:]) # Fetch the data (train_x, train_y), (test_x, test_y) = iris_data.load_data() # Feature columns describe how to use the input. my_feature_columns = [] for key in train_x.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key)) # Build 2 hidden layer DNN with 10, 10 units respectively. classifier = tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 10 nodes each. hidden_units=[10, 10], # The model must choose between 3 classes. n_classes=3) # Train the Model. classifier.train( input_fn=lambda:iris_data.train_input_fn(train_x, train_y, args.batch_size), steps=args.train_steps) # Evaluate the model. eval_result = classifier.evaluate( input_fn=lambda:iris_data.eval_input_fn(test_x, test_y, args.batch_size)) print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result)) # Generate predictions from the model expected = ['Setosa', 'Versicolor', 'Virginica'] predict_x = { 'SepalLength': [5.1, 5.9, 6.9], 'SepalWidth': [3.3, 3.0, 3.1], 'PetalLength': [1.7, 4.2, 5.4], 'PetalWidth': [0.5, 1.5, 2.1], } predictions = classifier.predict( input_fn=lambda:iris_data.eval_input_fn(predict_x, labels=None, batch_size=args.batch_size)) template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"') for pred_dict, expec in zip(predictions, expected): class_id = pred_dict['class_ids'][0] probability = pred_dict['probabilities'][class_id] print(template.format(iris_data.SPECIES[class_id], 100 * probability, expec)) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) tf.app.run(main) ================================================ FILE: Examples/tf_iris_diff_opt/premade_estimator_hpo.py ================================================ #!/usr/bin/env python3 # Download from https://raw.githubusercontent.com/tensorflow/models/master/samples/core/get_started/premade_estimator.py # Date: Jun 11, 2018 # Modified for auptimizer ######################################################################################################################## # Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """An Example of a DNNClassifier for the Iris dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import sys import iris_data from aup import BasicConfig, print_result # # ChangeLog: # + remove argparse and set default batch_size, and train_step # + add aup config and change __main__ # + change main() to get conf, which contains the number of nodes per layer # + change main() to return eval_result["accuracy"]. # + add shebang line and execute permission # + change train_steps to be consistent with hyperband batch_size = 100 config = tf.ConfigProto() config.gpu_options.allow_growth=True sess = tf.Session(config=config) def main(conf): # Fetch the data (train_x, train_y), (test_x, test_y) = iris_data.load_data() if "n_iterations" in config: train_steps = 100 * config.n_iterations else: train_steps = 1000 # Feature columns describe how to use the input. my_feature_columns = [] for key in train_x.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key)) # Build 2 hidden layer DNN with 10, 10 units respectively. classifier = tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 10 nodes each. hidden_units=[conf["layer1"], conf["layer2"]], # The model must choose between 3 classes. n_classes=3) # Train the Model. classifier.train(input_fn=lambda:iris_data.train_input_fn(train_x, train_y,batch_size), steps=train_steps) # Evaluate the model. eval_result = classifier.evaluate(input_fn=lambda:iris_data.eval_input_fn(test_x, test_y,batch_size)) print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result)) # Generate predictions from the model expected = ['Setosa', 'Versicolor', 'Virginica'] predict_x = { 'SepalLength': [5.1, 5.9, 6.9], 'SepalWidth': [3.3, 3.0, 3.1], 'PetalLength': [1.7, 4.2, 5.4], 'PetalWidth': [0.5, 1.5, 2.1], } predictions = classifier.predict(input_fn=lambda:iris_data.eval_input_fn(predict_x, labels=None, batch_size=batch_size)) template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"') for pred_dict, expec in zip(predictions, expected): class_id = pred_dict['class_ids'][0] probability = pred_dict['probabilities'][class_id] print(template.format(iris_data.SPECIES[class_id], 100 * probability, expec)) return eval_result["accuracy"] if __name__ == '__main__': if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) val = main(config) print_result(val) ================================================ FILE: Examples/tf_iris_diff_opt/premade_estimator_hyper.py ================================================ # Download from https://raw.githubusercontent.com/tensorflow/models/master/samples/core/get_started/premade_estimator.py # Date: Jun 11, 2018 # Modified to work with aup.convert ######################################################################################################################## # Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """An Example of a DNNClassifier for the Iris dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import tensorflow as tf import iris_data #parser = argparse.ArgumentParser() #parser.add_argument('--batch_size', default=100, type=int, help='batch size') #parser.add_argument('--train_steps', default=1000, type=int, # help='number of training steps') batch_size=100 train_steps=1000 def main(layer1, layer2): #args = parser.parse_args(argv[1:]) # Fetch the data (train_x, train_y), (test_x, test_y) = iris_data.load_data() # Feature columns describe how to use the input. my_feature_columns = [] for key in train_x.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key)) # Build 2 hidden layer DNN with 10, 10 units respectively. classifier = tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 10 nodes each. hidden_units=[layer1, layer2], # The model must choose between 3 classes. n_classes=3) # Train the Model. classifier.train( input_fn=lambda:iris_data.train_input_fn(train_x, train_y, batch_size), steps=train_steps) # Evaluate the model. eval_result = classifier.evaluate( input_fn=lambda:iris_data.eval_input_fn(test_x, test_y, batch_size)) print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result)) # Generate predictions from the model expected = ['Setosa', 'Versicolor', 'Virginica'] predict_x = { 'SepalLength': [5.1, 5.9, 6.9], 'SepalWidth': [3.3, 3.0, 3.1], 'PetalLength': [1.7, 4.2, 5.4], 'PetalWidth': [0.5, 1.5, 2.1], } predictions = classifier.predict( input_fn=lambda:iris_data.eval_input_fn(predict_x, labels=None, batch_size=batch_size)) template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"') for pred_dict, expec in zip(predictions, expected): class_id = pred_dict['class_ids'][0] probability = pred_dict['probabilities'][class_id] print(template.format(iris_data.SPECIES[class_id], 100 * probability, expec)) return probability #if __name__ == '__main__': # tf.logging.set_verbosity(tf.logging.INFO) # tf.app.run(main) ================================================ FILE: Examples/tf_iris_diff_opt/premade_estimator_wrapper.py ================================================ #!/usr/bin/env python # Download from https://raw.githubusercontent.com/tensorflow/models/master/samples/core/get_started/premade_estimator.py # Date: Jun 11, 2018 # This is created by aup.convert ######################################################################################################################## # Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """An Example of a DNNClassifier for the Iris dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import tensorflow as tf import iris_data #parser = argparse.ArgumentParser() #parser.add_argument('--batch_size', default=100, type=int, help='batch size') #parser.add_argument('--train_steps', default=1000, type=int, # help='number of training steps') batch_size=100 train_steps=1000 def main(layer1, layer2): #args = parser.parse_args(argv[1:]) # Fetch the data (train_x, train_y), (test_x, test_y) = iris_data.load_data() # Feature columns describe how to use the input. my_feature_columns = [] for key in train_x.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key)) # Build 2 hidden layer DNN with 10, 10 units respectively. classifier = tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 10 nodes each. hidden_units=[layer1, layer2], # The model must choose between 3 classes. n_classes=3) # Train the Model. classifier.train( input_fn=lambda:iris_data.train_input_fn(train_x, train_y, batch_size), steps=train_steps) # Evaluate the model. eval_result = classifier.evaluate( input_fn=lambda:iris_data.eval_input_fn(test_x, test_y, batch_size)) print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result)) # Generate predictions from the model expected = ['Setosa', 'Versicolor', 'Virginica'] predict_x = { 'SepalLength': [5.1, 5.9, 6.9], 'SepalWidth': [3.3, 3.0, 3.1], 'PetalLength': [1.7, 4.2, 5.4], 'PetalWidth': [0.5, 1.5, 2.1], } predictions = classifier.predict( input_fn=lambda:iris_data.eval_input_fn(predict_x, labels=None, batch_size=batch_size)) template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"') for pred_dict, expec in zip(predictions, expected): class_id = pred_dict['class_ids'][0] probability = pred_dict['probabilities'][class_id] print(template.format(iris_data.SPECIES[class_id], 100 * probability, expec)) return probability #if __name__ == '__main__': # tf.logging.set_verbosity(tf.logging.INFO) # tf.app.run(main) def aup_wrapper(config): res = main(layer1=config['layer1'],layer2=config['layer2']) print_result(res) if __name__ == "__main__": import sys from aup import BasicConfig, print_result if len(sys.argv) != 2: print("config file required") exit(1) config = BasicConfig().load(sys.argv[1]) aup_wrapper(config) ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . ================================================ FILE: MANIFEST.in ================================================ include LICENSE include README.md recursive-include src/aup/dashboard/frontend/febuild/auptimizer-dashboard/ * ================================================ FILE: R-src/README.md ================================================ # Copyright (c) 2018 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later # R Package for Auptimizer ## Installation *IMPORTANT:* Auptimizer is well tested on Unix or similar OS. Windows users may have to make some changes to run Aptimizer. 1. [Install Python Auptimizer](../README.md) 2. Install Auptimizer for R, (run R from `R-src` folder): ```R install.packages("devtools") devtools::install("Rpackage") ``` ## Usage The workflow for Auptimizer is the same as the Python version. The difference is in how to change existing R code to use Auptimizer. 1. Setup Python Auptimizer environment using `python -m aup.setup` 2. Change your R script: a. Make all hyperparameters global variables. b. Add `#!/usr/bin/env Rscript` as the first line. c. Add `source("auptimizer")`. d. Add `get_config()`, which will automatically update the hyperparameters (set globally in step 2a). e. Add `print_result(score)` to return the target score you want to optimize for your script. f. Change file permission using `chmod u+x `. g. Add them into an Auptimizer experiment using `python -m aup.init`. 3. Run Auptimizer using `python -m aup experiment.json`. ## Examples See examples in [example]. + `exp_ridge.R` for synthetic Ridge regression. Run as `python -m aup ridge.json`. + `exp_rosenbrock.R` for analytic Rosenbrock function. Run as `python -m aup rosenbrock.json`. ================================================ FILE: R-src/Rpackage/DESCRIPTION ================================================ Package: auptimizer Title: R Helper Functions for Auptimizer Version: 0.0.2 Authors@R: c( person(given = "Jiayi", family = "Liu", role = c("aut","cre"), email = "Jason.Liu@lge.com", comment = c(ORCID = "0000-0001-6007-5256")), person(given = "Sauptik", family = "Dhar", role = c("aut"), email = "Sauptik.Dhar@lge.com"), person(given = "", family = "LG Electronics Inc.", role = c("cph", "asn"), email = "auptimizer@lge.com") ) Description: Enable R users to use Auptimizer for hyperparameter optimization (HPO). Auptimizer helps data scientists to easily apply HPO in their work and to speed up model training jobs by executing jobs asynchronously when computing resources are available. License: GPL (>=3) Encoding: UTF-8 LazyData: true Imports: rjson URL: https://github.com/LGE-ARC-AdvancedAI/auptimizer BugReports: https://github.com/LGE-ARC-AdvancedAI/auptimizer/issues RoxygenNote: 6.1.1 Suggests: testthat, knitr, rmarkdown VignetteBuilder: knitr ================================================ FILE: R-src/Rpackage/NAMESPACE ================================================ # Generated by roxygen2: do not edit by hand export(get_config) export(print_result) ================================================ FILE: R-src/Rpackage/R/auptimizer.R ================================================ # Copyright (c) 2018 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later #' Get hyperparameter configuration #' #' This function loads a configuration file in JSON format. It assumes that the first argument of the running script is #' the configuraiton file. It will parse and inject the hyperparameter variables into the global environment and return #' them. #' #' #' Assuming the R script is used as follows: `./test.R config.json`, the script should look like below: #' #' \preformatted{ #' #!Rscript #' #' # hyperparameter definitions #' get_config() #' # use hyperparamter to train model, #' # after training, return optimization target (e.g. validation accuracy) as score #' print_result(score) #' } #' #' #' #' @param filename optional JSON filename containing hyperparameters as dict, default to first argument in command line. #' @param env optional environment containing hyperparameters, default to global env. #' @return a matrix of hyperparameters as name-value pairs #' @export get_config <- function(filename, env) { if (missing(filename)){ args <- commandArgs(TRUE) if (length(args) < 1) { warning("R script requires an input configuration file for Auptimzier experiment.") return() } else if (length(args) > 1) { warning("R script takes the first input as configuration file.") } filename = args[1] } data <- rjson::fromJSON(file=filename) if (missing(env)){ env = globalenv() } for (name in names(data)) { if (length(data[name]) > 1){ warning("Auptimizer only supports hyperparameter as a single value, not a list. Only first value is used") } # set to global variables assign(name, data[name][[1]], envir = env) } return(data) } #' Report result to Auptimizer #' #' This function reports the results to Auptimizer. A numerical score will be converted to string automatically. If #' you want to report multiple scores, use ',' to seperate them and only the first value will be used by Auptimzier for #' Optimization. #' #' #' Assume the R script is used as follows `./test.R config.json`, the script should look like below: #' #' \preformatted{ #' #!Rscript #' #' # hyperparameter definitions #' get_config() #' # use hyperparamter to train model, #' # after training, return optimization target (e.g. validation accuracy) as score #' print_result(score) #' } #' #' @param score Score to be reported back to Auptimizer #' @export print_result <- function(score) { message(paste("\n#Auptimizer:", score)) } ================================================ FILE: R-src/Rpackage/man/get_config.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/auptimizer.R \name{get_config} \alias{get_config} \title{Get hyperparameter configuration} \usage{ get_config(filename, env) } \arguments{ \item{filename}{optional JSON filename contains hyperparameters as dict, default to first argument in command line.} \item{env}{optional environment contains hyperparameters, de fault to global env.} } \value{ A matrix of the infile } \description{ This function loads a configuration file in JSON format. It assumes that the first argument of the running script is the configuraiton file. It will parse and inject the hyperparameter variables into the global environment and return them. } \details{ Assume an R script is used as `./test.R config.json`, the script should look like below: \preformatted{ #!Rscript # hyperparameter definitions get_config() # use hyperparamter to train model, # after training, return optimization target (e.g. validation accuracy) as score print_result(score) } } ================================================ FILE: R-src/Rpackage/man/print_result.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/auptimizer.R \name{print_result} \alias{print_result} \title{Report result to Auptimizer} \usage{ print_result(score) } \arguments{ \item{score}{Score to be reported back to Auptimizer} } \description{ This function reports the results to Auptimizer. A numerical score will be converted to string automatically. If you want to report multiple scores, use ',' to seperate them and only the first value will be used by Auptimzier for Optimization. } \details{ Assume an R script is used as `./test.R config.json`, the script should look like below: \preformatted{ #!Rscript # hyperparameter definitions get_config() # use hyperparamter to train model, # after training, return optimization target (e.g. validation accuracy) as score print_result(score) } } ================================================ FILE: R-src/Rpackage/tests/testthat/test_IO.R ================================================ # Copyright (c) 2018 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later library(auptimizer) test_that("auptimizer output format", { expect_message(print_result(1), '\n#Auptimizer: 1') }) test_that("auptimizer input format", { a <- 2 b <- TRUE expect_equal(a, 2) get_config("test_io.json", environment()) expect_equal(b, FALSE) expect_equal(a, 1) }) ================================================ FILE: R-src/Rpackage/tests/testthat/test_io.json ================================================ {"a":1,"b":false} ================================================ FILE: R-src/Rpackage/tests/testthat.R ================================================ library(testthat) library(auptimizer) test_check("auptimizer") ================================================ FILE: R-src/Rpackage/vignettes/.gitignore ================================================ *.html *.R ================================================ FILE: R-src/Rpackage/vignettes/auptimizer.Rmd ================================================ --- title: "Auptimizer" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{auptimizer} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # R Package for Auptimizer ## Installation *IMPORTANT:* Auptimizer is well tested on Unix or similar OS. Windows users may have to make some changes to run Aptimizer. 1. [Install Python Auptimizer](https://github.com/LGE-ARC-AdvancedAI/auptimizer) 2. Install R Auptimizer ```R install.packages("auptimizer") ``` ## Usage The workflow for Auptimizer is the same as the Python version. The difference is in how to change existing R code to use Auptimizer. 1. Setup Python Auptimizer environment using `python -m aup.setup` 2. Change your R script: a. Make all hyperparameters global variables. b. Add `#!/usr/bin/env Rscript` as the first line. c. Add `library("auptimizer")`. d. Add `get_config()`, which will automatically update the hyperparameters (set globally in step 2a). e. Add `print_result(score)` to return the target score you want to optimize for your script. f. Change file permission using `chmod u+x `. g. Add them into an Auptimizer experiment using `python -m aup.init`. 3. Run Auptimizer using `python -m aup experiment.json`. ## Examples There are more examples in the `R-src/example` folder on the [Auptimizer Github repo](https://github.com/LGE-ARC-AdvancedAI/auptimizer/). + `exp_ridge.R` for synthetic Ridge regression. Run as `python -m aup ridge.json`. + `exp_rosenbrock.R` for analytic Rosenbrock function. Run as `python -m aup rosenbrock.json`. ================================================ FILE: R-src/examples/exp_ridge.R ================================================ #!/usr/bin/env Rscript # Copyright (c) 2018 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later # # Example code to use Auptimzier. library("auptimizer") source("ridgeRegression.R") # Synthetic Data Nt = 90 D = 100 w<-rnorm(D) trainX<-matrix(rnorm(Nt*D,0,1),nrow=Nt,ncol=D) trainy<-trainX%*%w + rnorm(Nt,0,1) # Add Gaussian noise Nv = 40 valX<-matrix(rnorm(Nv*D,0,1),nrow=Nv,ncol=D) valy<-valX%*%w + rnorm(Nv,0,1) # Add Gaussian noise # Hyperparameters lambda = 1.0 hp <- get_config() score <- ridgeRegression(trainX,trainy,valX,valy,lambda) print_result(score) ================================================ FILE: R-src/examples/exp_rosenbrock.R ================================================ #!/usr/bin/env Rscript # Copyright (c) 2018 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later # # Example code to use Auptimzier. library("auptimizer") source("rosenbrock.R") # Hyperparameters x <- 10 y <- 20 get_config() score <- rosen(c(x, y)) print_result(score) ================================================ FILE: R-src/examples/ridge.json ================================================ { "script": "exp_ridge.R", "resource": "cpu", "n_parallel": 1, "target": "min", "workingdir": "./", "proposer": "random", "n_samples": 10, "random_seed": 0, "parameter_config": [ { "name": "lambda", "range": [ 0.001, 10 ], "type": "float" } ] } ================================================ FILE: R-src/examples/ridgeRegression.R ================================================ # Copyright (c) 2018 LG Electronics Inc. # SPDX-License-Identifier: GPL-3.0-or-later ridgeRegression<-function(trainX,trainy,valX,valy,lambda){ ####################################################### # A Typical Ridge Regression Problem ####################################################### # N = No. of Training samples, D = Dimension of Problem N<-nrow(trainX) D <-ncol(trainX) I <- diag(D) # Closed form solution for Ridge regression: min (1/2N)*sum_i (w'*x_i - y_i)^2 + (lambda/2)*||w||^2 w <- solve((t(trainX)%*%trainX)/N + lambda*I) %*% ((t(trainX)%*%trainy)/N) # Predict on the validation samples and return the error. valErr<-MSE(valX,valy,w) return(valErr) } MSE<-function(X,y,w){ ##################################################### # This is the mean squared error ##################################################### N <- nrow(X) return((0.5*N)*norm(X%*%w-y)) } ================================================ FILE: R-src/examples/rosenbrock.R ================================================ rosen <- function(xx) { ########################################################################## # # ROSENBROCK FUNCTION # # Authors: Sonja Surjanovic, Simon Fraser University # Derek Bingham, Simon Fraser University # Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca. # # Copyright 2013. Derek Bingham, Simon Fraser University. # # THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY # FOR THE USE OF THIS SOFTWARE. If software is modified to produce # derivative works, such modified software should be clearly marked. # Additionally, this program is free software; you can redistribute it # and/or modify it under the terms of the GNU General Public License as # published by the Free Software Foundation; version 2.0 of the License. # Accordingly, this program is distributed in the hope that it will be # useful, but WITHOUT ANY WARRANTY; without even the implied warranty # of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # For function details and reference information, see: # http://www.sfu.ca/~ssurjano/ # ########################################################################## # # INPUT: # # xx = c(x1, x2, ..., xd) # ########################################################################## d <- length(xx) xi <- xx[1:(d-1)] xnext <- xx[2:d] sum <- sum(100*(xnext-xi^2)^2 + (xi-1)^2) y <- sum return(y) } ================================================ FILE: R-src/examples/rosenbrock.json ================================================ { "script": "exp_rosenbrock.R", "resource": "cpu", "n_parallel": 1, "target": "min", "workingdir": "./", "proposer": "random", "n_samples": 10, "random_seed": 0, "parameter_config": [ { "name": "x", "range": [ 0, 10 ], "type": "float" }, { "name": "y", "range": [ 0, 10 ], "type": "float" } ] } ================================================ FILE: README.md ================================================ # ![Auptimizer Logo](AuptimizerBlackLong.png) [![Documentation](https://img.shields.io/badge/doc-reference-blue.svg)](https://LGE-ARC-AdvancedAI.github.io/auptimizer) [![GPL 3.0 License](https://img.shields.io/badge/License-GPL%203.0-blue.svg)](https://opensource.org/licenses/GPL-3.0) [![Build Status](https://travis-ci.com/LGE-ARC-AdvancedAI/auptimizer.svg?branch=master)](https://travis-ci.com/LGE-ARC-AdvancedAI/auptimizer) [![coverage report](https://codecov.io/gh/LGE-ARC-AdvancedAI/auptimizer/branch/master/graph/badge.svg)](https://codecov.io/gh/LGE-ARC-AdvancedAI/auptimizer) **Auptimizer** is an optimization tool for Machine Learning (ML) that automates many of the tedious parts of the model building and deployment process. Currently, **Auptimizer** helps with: + **Getting the best models in minimum time** - Generate optimal models and achieve better performance by employing state-of-the-art hyperparameter optimization (HPO) and model compression techniques. Auptimizer will run and record sophisticated HPO and model compression experiments on compute resources of your choice with effortless consistency and reproducibility. + **Making your models edge-ready** - Get model-device compatibility and enhanced on-device performance by converting models into the industry-standard ONNX and TensorFlow Lite formats. Auptimizer-Converter provides validated conversion techniques to ensure worry-free format transformations. + **Selecting the most suitable model for your edge deployment effortlessly** - Compare how different models will perform under specific compute and memory constraints on a CPU-based edge device. Auptimizer-Profiler will help you identify the most efficient models without the hustle of going through multiple physical deployment cycles. Best of all, **Auptimizer** offers a consistent interface that allows users to switch between different HPO and compression algorithms, conversion frameworks, and computing resources with minimal changes to the existing code. ## What's New in Auptimizer v2.0 Auptimizer v2.0 introduces two core capabilities - Dashboard and Compressor! ### Dashboard [**Auptimizer Dashboard**](https://lge-arc-advancedai.github.io/auptimizer/dashboard.html) is a powerful analytics tool that complements Auptimizer's core hyperparameter optimization (HPO) and model compression capabilities. It provides insightful visualizations to help you track experiment progress, analyze and contrast jobs, experiments, and optimization approaches. Additionally, it can be used to control an experiment or even set up a new Auptimizer environment. ### Compressor [**Compressor**](https://lge-arc-advancedai.github.io/auptimizer/compression_main.html) is a model compression tool that helps reduce memory complexity and inference time of neural networks. It is particularly useful for adapting ML models for deployment on resource-constrained edge devices. Similar to Auptimizer-Hyperparameter Optimization (HPO), Compressor aims to provide a unified interface to the existing state-of-the-art toolkits. Currently, Compressor leverages [NNI (version 2.0)](https://nni.readthedocs.io/en/latest/model_compression.html) model compression modules. NNI is an open-source toolkit that supports two types of compression, pruning and quantization, for TensorFlow, and PyTorch models. In the future, we will be integrating other off-the-shelf toolkits to expand the selection of model compression approaches. ## Capabilities ### Hyperparameter Optimization Auptimizer automates tedious experimentation by performing and recording hyperparameter optimization experiments. Auptimizer provides a single seamless access point to top-notch HPO algorithms, including Bayesian optimization and multi-armed bandit. You can even integrate your own proprietary solution. Moreover, with Auptimizer, you can make the best use of your compute-resources. Whether you are using a couple of GPUs or AWS, Auptimizer will help you orchestrate compute resources for faster hyperparameter tuning. The table below shows a full list of currently supported techniques and resources for hyperparameter optimization. | Supported HPO Algorithms | Supported Infrastructure | | ----------- | ----------- | | Random
Grid
[Hyperband](https://github.com/zygmuntz/hyperband)
[Hyperopt](https://github.com/hyperopt/hyperopt)
[Spearmint](https://github.com/JasperSnoek/spearmint)
[BOHB](https://github.com/automl/HpBandSter)
[EAS (experimental)](https://github.com/han-cai/EAS)
Passive | Multiple CPUs
Multiple GPUs
Multiple Machines (SSH)
AWS EC2 instances | ### Profiler [**Profiler**](https://lge-arc-advancedai.github.io/auptimizer/profiler.html) is a simulator for profiling performance of machine learning model scripts. Given compute- and memory resource constraints for a CPU-based Edge device, Profiler can provide estimates of compute and memory usage for model scripts on the device. These estimations can be used to choose the best performing models or, in certain cases, to predict how much compute and memory models will use on the target device. Because Profiler mimics the target device environment on the user's development machine, the user can gain insights into the performance and resource needs of a model script without having to deploy it on the target device. Profiler helps accelerate the model selection cycle and simplifies finding model-device fit. Please see [Profiler](https://github.com/LGE-ARC-AdvancedAI/auptimizer/tree/master/src/aup/profiler) for usages. ### Converter [**Converter**](https://lge-arc-advancedai.github.io/auptimizer/dlconvert.html) is a format conversion tool for machine learning models. It encapsulates best practices of individual machine learning model conversions under a single API. Converter makes ML models suitable for edge (on-device) deployments by transforming them into the industry-standard ONNX and TensorFlow Lite formats and reducing model size through quantization. ## Install **Auptimizer** currently is well tested on Linux systems, it may require some tweaks for Windows users. ``` pip install auptimizer ``` **Note** Dependencies are not included. Using `pip install` [requirements.txt](https://github.com/LGE-ARC-AdvancedAI/auptimizer/blob/master/requirements.txt) will install necessary libraries for all functionalities. Usage for the UI dashboard: ``` dashboard --path --port ``` ## Documentation See more in [documentation](https://lge-arc-advancedai.github.io/auptimizer/) ## Example ``` cd Examples/demo # Setup environment (Interactively create the environment file based on user input) python -m aup.setup # Setup experiment python -m aup.init # Create training script - auto.py python -m aup.convert origin.py experiment.json demo_func # Run aup for this experiment python -m aup experiment.json ``` Each job's hyperparameter configuration is saved separately under `jobs/*.json` and is also recorded in the SQLite file `.aup/sqlite3.db`. ![gif demo](docs/images/demo.gif) More examples are under [Examples](https://github.com/LGE-ARC-AdvancedAI/auptimizer/tree/master/Examples). ## License [GPL 3.0 License](./LICENSE) ## Cite If you have used this software for research, please cite the following paper (accepted at IEEE Big Data 2019): ``` @misc{liu2019auptimizer, title={Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning}, author={Jiayi Liu and Samarth Tripathi and Unmesh Kurup and Mohak Shah}, year={2019}, eprint={1911.02522}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ================================================ FILE: docs/Database/schema.dot ================================================ /* * Graphviz of '<_io.TextIOWrapper name='' mode='r' encoding='UTF-8'>', created 2018-08-14 18:52:17.684968 * Generated from https://github.com/rm-hull/sql_graphviz */ digraph g { graph [ rankdir = "LR" ]; "user" [ shape=none label=<
user
uid INTEGER PRIMARY KEY NOT NULL
name TEXT UNIQUE
permission BLOB
>]; "resource" [ shape=none label=<
resource
rid INTEGER PRIMARY KEY NOT NULL
name TEXT
type TEXT
status TEXT
>]; "experiment" [ shape=none label=<
experiment
eid INTEGER PRIMARY KEY NOT NULL
uid INTEGER
start_time INTEGER
end_time INTEGER
exp_config BLOB
>]; "job" [ shape=none label=<
job
jid INTEGER PRIMARY KEY NOT NULL
score REAL
eid INTEGER
rid INTEGER
start_time INTEGER
end_time INTEGER
job_config BLOB
>]; "job_attempt" [ shape=none label=<
job_attempt
jaid INTEGER PRIMARY KEY NOT NULL
jid INTEGER
num INTEGER
rid INTEGER
start_time INTEGER
end_time INTEGER
>]; "intermediate_result" [ shape=none label=<
intermediate_result
irid INTEGER PRIMARY KEY NOT NULL
num INTEGER
score REAL
jid INTEGER
receive_time INTEGER
>]; "multiple_result" [ shape=none label=<
multiple_result
mrid INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL
label_order INTEGER
value REAL
receive_time INTEGER
jid INTEGER
irid INTEGER
eid INTEGER
is_last_result INTEGER
>]; "experiment":uid -> "user":uid; "job":eid -> "experiment":eid; "job":rid -> "resource":rid; "job":jid -> "job_attempt":jid; "job":jid -> "intermediate_result":jid; "job":jid -> "multiple_result":jid; "intermediate_result":irid -> "multiple_result":irid } ================================================ FILE: docs/Database/schema.sql ================================================ CREATE TABLE user (uid INTEGER PRIMARY KEY NOT NULL, name TEXT UNIQUE, permission BLOB); CREATE TABLE resource (rid INTEGER PRIMARY KEY NOT NULL, name TEXT, type TEXT, status TEXT); CREATE TABLE experiment (eid INTEGER PRIMARY KEY NOT NULL, uid INTEGER, start_time INTEGER, end_time INTEGER, exp_config BLOB, FOREIGN KEY(uid) REFERENCES user(uid)); CREATE TABLE job (jid INTEGER PRIMARY KEY NOT NULL, score REAL, eid INTEGER, rid INTEGER, start_time INTEGER, end_time INTEGER, job_config BLOB, FOREIGN KEY(eid) REFERENCES experiment(eid), FOREIGN KEY(rid) REFERENCES resource(rid)); CREATE TABLE job_attempt (jaid INTEGER PRIMARY KEY NOT NULL, jid INTEGER, num INTEGER, rid INTEGER, start_time INTEGER, end_time INTEGER, FOREIGN KEY(jid) REFERENCES job(jid), FOREIGN KEY(rid) REFERENCES resource(rid)); CREATE TABLE intermediate_result (irid INTEGER PRIMARY KEY NOT NULL, num INTEGER, score REAL, jid INTEGER, receive_time INTEGER, FOREIGN KEY(jid) REFERENCES job(jid)); CREATE TABLE multiple_result (mrid INTEGER PRIMARY KEY NOT NULL, label_order INTEGER, value REAL, receive_time INTEGER, jid INTEGER, irid INTEGER, eid INTEGER, is_last_result INTERGER, FOREIGN KEY(jid) REFERENCES job(jid), FOREIGN KEY(irid) REFERENCES intermediate_result(irid), FOREIGN KEY(eid) REFERENCES experiment(eid)); ================================================ FILE: docs/Database/sql_graphviz.py ================================================ #!/usr/bin/env python # https://github.com/rm-hull/sql_graphviz # run dot -Tpdf schema.dot > schema.pdf import sys from datetime import datetime from pyparsing import alphas, alphanums, Literal, Word, Forward, OneOrMore, ZeroOrMore, CharsNotIn, Suppress, QuotedString, Optional def field_act(s, loc, tok): return '{0} {1}'.format(tok[0].replace('"', ''), ' '.join(tok[1::]).replace('"', '\\"')) def field_list_act(s, loc, tok): return "\n ".join(tok) def create_table_act(s, loc, tok): return ''' "{tableName}" [ shape=none label=< {fields}
{tableName}
>];'''.format(**tok) def add_fkey_act(s, loc, tok): return ' "{tableName}":{keyName} -> "{fkTable}":{fkCol}'.format(**tok) def other_statement_act(s, loc, tok): return "" def grammar(): parenthesis = Forward() parenthesis <<= "(" + ZeroOrMore(CharsNotIn("()") | parenthesis) + ")" field_def = OneOrMore(Word(alphanums + "_\"'`:-") | parenthesis) field_def.setParseAction(field_act) tablename_def = ( Word(alphas + "`_") | QuotedString("\"") ) field_list_def = field_def + ZeroOrMore(Suppress(",") + field_def) field_list_def.setParseAction(field_list_act) create_table_def = Literal("CREATE") + "TABLE" + tablename_def.setResultsName("tableName") + "(" + field_list_def.setResultsName("fields") + ")" + ";" create_table_def.setParseAction(create_table_act) add_fkey_def = Literal("FOREIGN") + "KEY" + "(" + Word(alphanums).setResultsName("keyName") + ")" + "REFERENCES" + Word(alphanums).setResultsName("fkTable") + "(" + Word(alphanums + "_").setResultsName("fkCol") + ")" + Optional(Literal("DEFERRABLE")) + ";" add_fkey_def.setParseAction(add_fkey_act) other_statement_def = OneOrMore(CharsNotIn(";")) + ";" other_statement_def.setParseAction(other_statement_act) comment_def = "--" + ZeroOrMore(CharsNotIn("\n")) comment_def.setParseAction(other_statement_act) return OneOrMore(comment_def | create_table_def | add_fkey_def | other_statement_def) def graphviz(filename): print("/*") print(" * Graphviz of '%s', created %s" % (filename, datetime.now())) print(" * Generated from https://github.com/rm-hull/sql_graphviz") print(" */") print("digraph g { graph [ rankdir = \"LR\" ];") for i in grammar().parseFile(filename): if i != "": print(i) print("}") if __name__ == '__main__': filename = sys.stdin if len(sys.argv) == 1 else sys.argv[1] graphviz(filename) ================================================ FILE: docs/Dockerfile ================================================ FROM nginx COPY _build/html /usr/share/nginx/html # fix version urls RUN mkdir /usr/share/nginx/html/archive RUN mv /usr/share/nginx/html/_static/*.whl /usr/share/nginx/html/archive RUN mv /usr/share/nginx/html/_static/aup.py /usr/share/nginx/html/archive/ ================================================ FILE: docs/LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = ContinuousTrainingEngine SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile # pandoc ../README.md -o tmp.rst;sed 's/docs\///g' tmp.rst > README.rst;rm tmp.rst @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/README.rst ================================================ Auptimizer Quickstart ===================== |GPL 3.0 License| |pipeline status| |coverage report| |repo url| **Auptimizer** is an optimization tool for Machine Learning (ML) that automates many of the tedious parts of the model building and deployment process. Currently, **Auptimizer** helps with: - **Getting the best models in minimum time** - Generate optimal models and achieve better performance by employing state-of-the-art hyperparameter optimization (HPO) and model compression techniques techniques. Auptimizer will run and record sophisticated HPO and model compression experiments on compute resources of your choice with effortless consistency and reproducibility. - **Making your models edge-ready** - Get model-device compatibility and enhanced on-device performance by converting models into the industry-standard ONNX and TensorFlow Lite formats. Auptimizer-Converter provides validated conversion techniques to ensure worry-free format transformations. - **Selecting the most suitable model for your edge deployment effortlessly** - Compare how different models will perform under specific compute and memory constraints on a CPU-based edge device. Auptimizer-Profiler will help you identify the most efficient models without the hustle of going through multiple physical deployment cycles. Best of all, **Auptimizer** offers a consistent interface that allows users to switch between different HPO and compression algorithms, conversion frameworks, and computing resources with minimal changes to the existing code. What's New in Auptimizer v2.0 ----------------------------- Auptimizer v2.0 introduces two core capabilites - Dashboard and Compressor! Dashboard ~~~~~~~~~ `Auptimizer Dashboard `__ is a powerful analytics tool that complements Auptimizer's core hyperparameter optimization (HPO) and model compression capabilities. It provides insightful visualizations to help you track experiment progress, analyze and contrast jobs, experiments, and optimization approaches. Additionally, it can be used to control an experiment or even set up a new Auptimizer environment. Compressor ~~~~~~~~~~ `Compressor `__ is a model compression tool that helps reduce memory complexity and inference time of neural networks. It is particularly useful for adapting ML models for deployment on resource-constrained edge devices. Similar to Auptimizer-Hyperparameter Optimization (HPO), Compressor aims to provide a unified interface to the existing state-of-the-art toolkits. Currently, Compressor leverages `NNI (version 2.0) `__ model compression modules. NNI is an open-source toolkit that supports two types of compression, pruning and quantization, for TensorFlow, and PyTorch models. In the future, we will be integrating other off-the-shelf toolkits to expand the selection of model compression approaches. Capabilities ------------ Hyperparameter Optimization ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Auptimizer automates tedious experimentation by performing and recording hyperparameter experiments. Auptimizer provides a single seamless access point to top-notch HPO algorithms, including Bayesian optimization and multi-armed bandit. You can even integrate your own proprietary solution. Moreover, with Auptimizer, you can make the best use of your compute-resources. Whether you are using a couple of GPUs or AWS, Auptimizer will help you orchestrate compute resources for faster hyperparameter tuning. The table below shows a full list of currently supported techniques and resources for hyperparameter optimization. +----------------------------------------------------------------+-----------------------------------+ | Supported HPO Algorithms | Supported Infrastructure | +================================================================+===================================+ | | Random | | Multiple CPUs | | | Grid | | Multiple GPUs | | | `Hyperband `__ | | Multiple Machines (SSH) | | | `Hyperopt `__ | | EC2 instances | | | `Spearmint `__ | | | | `BOHB `__ | | | | `EAS (experimental) `__ | | | | Passive | | +----------------------------------------------------------------+-----------------------------------+ Profiler ~~~~~~~~ `Profiler `__ is a simulator for profiling performance of machine learning model scripts. Given compute- and memory resource constraints for a CPU-based Edge device, Profiler can provide estimates of compute and memory usage for model scripts on the device. These estimations can be used to choose best performing models or, in certain cases, to predict how much compute and memory models will use on the target device. Because Profiler mimics the target device environment on the user's development machine, the user can gain insights about the performance and resource needs of a model script without having to deploy it on the target device. Profiler helps accelerate the model selection cycle and simplifies finding model-device fit. Please see :doc:`prof_readme` for usages. Converter ~~~~~~~~~ `Converter `__ is a format conversion tool for machine learning models. It encapsulates best practices of individual machine learning model conversions under a single API. Converter makes ML models suitable for edge (on-device) deployments by transforming them into the industry-standard ONNX and TensorFlow Lite formats and reducing model size through quantization. Please see :doc:`dlconvert_readme` for usages. Install ------- **Auptimizer** currently is well tested on Linux systems, it may require some tweaks for Windows users. :: pip install auptimizer **Note** Dependencies are not included. Using ``pip install -r`` `requirements.txt `_ will install necessary libraries for all functionalities. Example ------- :: cd Examples/demo # Setup environment (Interactively create the environment file based on user input) python -m aup.setup # Setup experiment python -m aup.init # Create training script - auto.py python -m aup.convert origin.py experiment.json demo_func # Run aup for this experiment python -m aup experiment.json Each job’s hyperparameter configuration is saved separately under ``jobs/*.json`` and is also recorded in the SQLite file ``.aup/sqlite3.db``. .. figure:: ./images/demo.gif :alt: demo License ------- `GPL 3.0 License <./LICENSE>`__ .. |GPL 3.0 License| image:: https://img.shields.io/badge/License-GPL%203.0-blue.svg :target: https://opensource.org/licenses/GPL-3.0 .. |pipeline status| image:: https://travis-ci.org/LGE-ARC-AdvancedAI/auptimizer.svg?branch=master :target: https://travis-ci.org/LGE-ARC-AdvancedAI/auptimizer .. |coverage report| image:: https://codecov.io/gh/LGE-ARC-AdvancedAI/auptimizer/branch/master/graph/badge.svg :target: https://codecov.io/gh/LGE-ARC-AdvancedAI/auptimizer .. |repo url| image:: https://img.shields.io/badge/github-repo-information.svg :target: https://github.com/LGE-ARC-AdvancedAI/auptimizer Cite ---- If you have used this software for research, please cite the following paper (accepted at IEEE Big Data 2019): .. code-block:: none @misc{liu2019auptimizer, title={Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning}, author={Jiayi Liu and Samarth Tripathi and Unmesh Kurup and Mohak Shah}, year={2019}, eprint={1911.02522}, archivePrefix={arXiv}, primaryClass={cs.LG} } ================================================ FILE: docs/_static/.gitkeep ================================================ ================================================ FILE: docs/algorithm.rst ================================================ Configure HPO Algorithm ======================= Supported algorithms -------------------- **Auptimizer** supports a number of different HPO algorithms. The names and descriptions are listed below: =========== ============================================================================================================================ Name Algorithm =========== ============================================================================================================================ passive Manually run job (for debug purpose) random Random search sequence Grid Search spearmint `Spearmint `_: Bayesian Optimization based on Gaussian Process bohb `HpBandSter `_: Bayesian Optimization and HyperBand hyperopt `Hyperopt `_: Bayesian Optimization with Tree of Parzen Estimators (TPE) hyperband `Hyperband `_: Multi-armed bandit approach eas `EAS `_: Efficient Architecture Search by Network Transformation (Illustration purpose) =========== ============================================================================================================================ Use ``python -m aup.init`` to set up the experiment configuration interactively. For finer control, advanced users can change the configuration manually by directly modifying the ``experiment.json`` file. Configuration details --------------------- Below we cover the most common pieces. For requirements related to specific algorithms, please refer to the respective documentation. The general structure of the configuration file is as follows:: { "proposer": "random", "n_samples": 10, "random_seed": 1, "script": "auto.py", "parameter_config": [ { "name": "x", "range": [ -5, 5 ], "type": "float" } ], "resource": "cpu", "resource_args": { "save_model": true }, "job_failure": { "job_retries": 3, "ignore_fail": true }, "n_parallel": 3, "target":"min", "workingdir:"./" } ================ ======== ============================================================================== Name Default Explanation ================ ======== ============================================================================== proposer random hpo method used to propose new hyperparameter values (see below for full list) n_samples 10 number of jobs to run script - script to run n_parallel 1 number of parallel jobs job_retries 0 number of retries for failed jobs. ignore_fail False whether to continue the experiment if a job fails. target max search for max or min resource - type of resource to run the experiment, [cpu, gpu, aws, node, passive] parameter_config {} hyperparameter specification (see below) workingdir "./" path to run the script, important for running jobs remotely (SSH/AWS) resource_args {} other parameters to enable features like tracking intermediate results, saving best model, etc (see below) ================ ======== ============================================================================== for ``parameter_config``: ================= ====================================================================================== Name Content ================= ====================================================================================== name name of the hyperparameter variable. Must be the same as used in the training script range [min, max] or a list of values type `float`, `int`, `choice` types are supported ================= ====================================================================================== Minor modifications or changes may be required for each algorithm. These options can be found at the corresponding API pages under :doc:`aup.Proposer` (see API links below). for ``resource_args``: ========================== ======== ============================================================================== Name Default Explanation ========================== ======== ============================================================================== save_model False whether to save the best performing model (see below) multi_res_labels None a list of additional results to be tracked, e.g. ["flops", "param"] track_intermediate_results False if true, intermediate results during training epoches will be tracked early_stop None parameters related to early stopping strategies ========================== ======== ============================================================================== For details of the ``early_stop`` parameter and how to apply early stopping strategies to HPO experiments, please refer to :doc:`Early Stopping `. ``resource_args`` can also include SSH/AWS specific parameters, please refer to :ref:`AWSRuntimeAnchor` for more details. **Note**: | If ``job_failure`` is not specified, the experiment will stop whenever a job fails. | For ``job_retries``, preferance is given to a different resource, if multiple resources are available. | For ``ignore_fail``, currently [BOHB, EAS, Hyperband] proposers do not support experiment continuation upon job failure. Additional functionalities -------------------------- Track intermediate results ~~~~~~~~~~~~~~~~~~~~~~~~~~ This feature allows the user to save and track multiple intermediate results at different points during the HPO experiment. Auptimizer still uses the final result as the main result for the HPO algorithm, but saves the intermediate records in the database under the table ``intermediate_results``. Usage @@@@@ The feature can be used by adding the following parameter to the experiment configuration file:: "resource_args": { "track_intermediate_results": true } Then in the training script, ``aup.print_result(res)`` should be placed where the user wants the results to be tracked:: def main(*args, **kwargs): # model and data preparation for epoch in range(n_epochs): # training for one epoch aup.print_result(res) In the above example, the intermediate results are returned every epoch. The result at last epoch is regarded as the main result for the user script and is then used by the HPO algorithm. The intermediate results will be shown on the dashboard if tracked. **Note**: It is possible to use multiple results feature in conjunction with intermediate results to track multiple intermediate results as well. Save the best model ~~~~~~~~~~~~~~~~~~~ This feature allows the user to save the best performing model after running the HPO experiment. This is achieved by running the training script again using the best hyperparamters obtained during HPO the experiment. The model, by default, will be saved to path ``aup_models/models_/``. Usage @@@@@ In order to use this feature, please add the following parameter to the experiment configuration file:: "resource_args": { "save_model": true } Depending on whether the ``@aup_args`` decorator is used, the training script needs the following additional modifications. If ``@aup_args`` is used, the user needs to define a funtion to save the model, and register this function with ``aup_save_model``. We suggest using this approach if running the experiment on remote machines (SSH/AWS) to be able to correctly locate and retrieve the model saved on the remote machine. Please see the example below:: # define a function "save_model(model)" to save the model to a user-defined path def save_model(model): os.makedirs('model_train') model.save('./model_train/mnist.h5') @aup_args def main(*args, **kwargs): # training code ... # register the model saving function with model as argument aup.aup_save_model(save_model, model) ... If ``@aup_args`` is not used, the user needs to manually check whether the ``save_model`` parameter is True in the job's configuration. The main function should also take ``save_model`` and ``folder_name`` as arguments. Please see the example below:: def main(*args, **kwargs, save_model=False, folder_name=None): # training code ... if save_model is True: # manually locate the path for saving the model # this is important if running on remote machines path = os.path.join('aup_models', folder_name) if os.path.exists('aup_models') is False: os.makedirs('aup_models') if os.path.exists(path) is True: shutil.rmtree(path) os.makedirs(path) os.chdir(path) model.save('./model_train/mnist.h5') ... Return multiple results ~~~~~~~~~~~~~~~~~~~~~~~ This feature allows the user to save and track multiple secondary results along with the primary result for the HPO experiment. Auptimizer still uses the main result for the HPO algorithm, but saves the secondary results in the database under the table ``multiple_results``. There is no upper limit on how many secondary results the user can track. Usage @@@@@ The feature can be used by adding the following parameter to the experiment configuration file:: "resource_args": { "multi_res_labels": ["x", "y"] } In the above configuration file, ``x`` and ``y`` are the secondary results the user wants to track and record. The user script would then return the results as a list including the primary result ``res`` along with the secondary parameters as follows:: @aup_args def HPO(): res = calculate_results() return [res, x, y] In the above example, ``res`` is the primary result which is always placed at the first index of the returned list, which will be used by the HPO algorithm. The remaining results are matched directly with the list provided in ``multi_res_labels``. Hence, the length of the returned list from user script is 1 + length of ``multi_res_labels`` parameter. **Note**: It is possible to use multiple results feature in conjunction with intermediate results to track multiple intermediate results as well. Pause and resume jobs ~~~~~~~~~~~~~~~~~~~~~ + Serial: optimize parameters by running jobs sequentially + Parallel: optimize parameters by running jobs in parallel + Pause: pause and save current HPO status + Resume: resume previously paused HPO process +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | Algorithm | Documentation | Serial | Parallel | Pause (save) | Resume | +===========+=================================================+========+==========+==============+========+ | Random | :class:`aup.Proposer.RandomProposer` | |Y| | |Y| | |Y| | |Y| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | Sequence | :class:`aup.Proposer.SequenceProposer` | |Y| | |Y| | |Y| | |Y| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | Passive | :class:`aup.EE.Resource.PassiveResourceManager` | |Y| | |Y| | |Y| | |Y| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | Spearmint | :class:`aup.Proposer.SpearmintProposer` | |Y| | |Y| | |N| | |N| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | Hyperopt | :class:`aup.Proposer.HyperoptProposer` | |Y| | |Y| | |N| | |N| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | Hyperband | :class:`aup.Proposer.HyperbandProposer` | |Y| | |Y| | |N| | |N| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | BOHB | :class:`aup.Proposer.BOHBProposer` | |Y| | |Y| | |N| | |N| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ | EAS | :class:`aup.Proposer.EASProposer` | |Y| | |N| | |N| | |N| | +-----------+-------------------------------------------------+--------+----------+--------------+--------+ .. |Y| unicode:: U+2713 .. checked .. |N| unicode:: U+274C .. no check .. |?| unicode:: U+274C .. check pending ================================================ FILE: docs/archive/aup.py ================================================ """ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later Auptimizer client side functions ================================ This file can be copied to a remote machine instead of installing the whole Auptimizer package for job execution. APIs ---- """ from __future__ import print_function import logging import json import pickle import sys import inspect import functools import os import shutil logger = logging.getLogger("aup-minimal") # supported data loading format _SUPPORT_FORMAT = ("pkl", "json") global user_callback_fn global user_args global user_kwargs user_callback_fn = None user_args = [] user_kwargs = {} def print_result(result): """Function to print the result for :func:`parse_result`. This function should be the last line of your training code :param result: result from training code :type result: str """ if result is list: result = ','.join([str(r) for r in result]) else: result = str(result).lstrip() # avoid line break # force flush to get intermediate results in real time print("\n#Auptimizer:%s" % result, file=sys.stderr, flush=True) class BasicConfig(dict): """ User-friendly :class:`dict` supports: * load and save for json/pickle format (.json/.pkl) * easy key/value access as config.key or config["key"] * compatible with :class:`dict` :param kwargs: key-value pairs to initialize the configuration :type kwargs: dict """ def load(self, filename): """Load config parameters from JSON/pickle file :param filename: file name ends with [.json|.pkl] :type filename: string :return: configuration parsed from file :rtype: aup.BasicConfig """ name = "_load_" + BasicConfig._get_format(filename) func = getattr(self, name) data = func(filename) if type(data) is not dict: raise TypeError("Config must be dict") self.update(data) logger.debug("Load config from %s: %s" % (filename, data.__str__())) return self def save(self, filename): """ Save configuration as dict in JSON/pickle :param filename: file name ends with [.json|.pkl] :type filename: string """ name = "_save_" + BasicConfig._get_format(filename) func = getattr(self, name) func(filename) logger.debug("Config saved to %s" % filename) @staticmethod def _get_format(filename): name = filename.split(".")[-1].lower() if name not in _SUPPORT_FORMAT: raise ValueError("Un-support file format, choose from %s." % ",".join(_SUPPORT_FORMAT)) return name @staticmethod def _load_json(filename): with open(filename, 'r') as f: return json.load(f) @staticmethod def _load_pkl(filename): with open(filename, 'rb') as f: return pickle.load(f) def _save_json(self, filename): with open(filename, 'w') as f: json.dump(self, f) def _save_pkl(self, filename): with open(filename, 'wb') as f: pickle.dump(dict(self), f) @staticmethod def save_flags(filename): """ Save tf flags for reuse - not used, not tested :param filename: output file """ from absl import flags logger.info("Write flags into %s") with open(filename, 'w') as f: f.write(flags.FLAGS.flags_into_string()) def to_flags(self, FLAGS): """ Update values in FLAGS from BasicConfig :param FLAGS: tensorflow/absl FLAGS """ for i in FLAGS: if i in self: logger.debug("set %s in FLAGS", i) setattr(FLAGS, i, self[i]) else: logger.debug("Use default %s", i) def __setattr__(self, key, value): self.__setitem__(key, value) def __getattr__(self, key): return self.__getitem__(key) def __delattr__(self, key): self.__delitem__(key) def __hash__(self): return super(BasicConfig, self).__hash__() def aup_args(func): """Decorator to wrap optimization target function `func`. Arguments: func {function} -- A function computes optimization target with specified hyperparameters """ @functools.wraps(func) def wrapper(filename, **kwargs): """wrapper function Arguments: filename {str} -- configuration file kwargs {dict} -- additional arguments will overwrite existing configuration value Raises: ValueError: if a parameter is not assigned in config """ # get current frame stack frm = inspect.stack()[1] # get module from stack mod = inspect.getmodule(frm[0]) # get functions that contain "init" in name and call them functions_list = inspect.getmembers(sys.modules[mod.__name__], inspect.isfunction) functions_list = sorted(list(filter(lambda x: "init" in x[0], functions_list))) config = BasicConfig().load(filename) if kwargs: logger.critical("Overwritting config values from script, be cautious!") config.update(kwargs) for f in functions_list: f[1](**config) parameters = inspect.signature(func).parameters for p in parameters.items(): if p[0] not in config: if p[1].default is inspect.Parameter.empty: raise ValueError("`%s` is required in `%s()` but is not assigned in config file %s" % (p[0], func.__name__, filename)) logger.info("Using default value for %s", p[0]) run_config = dict() for p in config: if p in parameters: run_config[p] = config[p] else: logger.warning("%s is not used in optimization"%p) val = func(**run_config) print_result(val) save_model = config.get('save_model', False) if save_model is True: # this means this is the "best job" found # the user wants to save the model try: dir = config.get('folder_name', None) previous_dir = os.getcwd() if os.path.exists(dir) is True: logger.warning('Deleting {}'.format(dir)) shutil.rmtree(dir) os.makedirs(dir) os.chdir(dir) assert user_callback_fn is not None, \ "Please use 'aup_save_model' to register the save model callback fn" user_callback_fn(*user_args, **user_kwargs) except Exception as e: raise e finally: os.chdir(previous_dir) return wrapper def aup_flags(flags): """wrapper function for absl flags (or tf.app). It will assign values to flags parameters using the given configuration file as the first argument when executed from the command line. Arguments: args {list} -- a list of unused arguments passed by app.run() """ def decorator_wrapper(func): @functools.wraps(func) def wrapper(args): config = BasicConfig(**flags.__dict__).load(args[1]) flags.__dict__.update() parameters = inspect.signature(func).parameters if parameters: logger.warning("TF FLAG main() should not accept arguments with Auptimizer, it has %s", parameters.keys()) val = func({p:None for p in parameters}) else: val = func() print_result(val) return wrapper return decorator_wrapper def aup_save_model(callback_fn, *args, **kwargs): global user_callback_fn global user_args global user_kwargs user_callback_fn = callback_fn user_args = args user_kwargs = kwargs ================================================ FILE: docs/aup.EE.Experiment.rst ================================================ .. automodule:: aup.EE.Experiment :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.EE.Job.rst ================================================ .. automodule:: aup.EE.Job :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.EE.Resource.rst ================================================ Resource Managers ================= .. automodule:: aup.EE.Resource.AbstractResourceManager :members: :undoc-members: :show-inheritance: .. automodule:: aup.EE.Resource.CPUResourceManager :members: :show-inheritance: .. automodule:: aup.EE.Resource.GPUResourceManager :members: :show-inheritance: .. automodule:: aup.EE.Resource.SSHResourceManager :members: :show-inheritance: .. automodule:: aup.EE.Resource.AWSResourceManager :members: :show-inheritance: .. automodule:: aup.EE.Resource.PassiveResourceManager :members: :show-inheritance: ================================================ FILE: docs/aup.EE.rst ================================================ aup.EE - Experiment execution ============================= Overview -------- :mod:`aup.EE` supports all experiment execution-related code. + :mod:`aup.EE.Experiment` controls the flow of an experiment. + :mod:`aup.EE.Job` takes care of job execution. + :mod:`aup.EE.Resource` provides the backbone where :mod:`aup.EE.Job` runs on. APIs ---- .. toctree:: :maxdepth: 1 aup.EE.Experiment aup.EE.Job aup.EE.Resource ================================================ FILE: docs/aup.ET.Connector.rst ================================================ aup.ET.Connector package ======================== .. automodule:: aup.ET.Connector.AbstractConnector :members: :undoc-members: :show-inheritance: .. automodule:: aup.ET.Connector.SQLiteConnector :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.ET.rst ================================================ aup.ET - Experiment tracking package ==================================== .. toctree:: :caption: Modules for experiment tracking aup.ET.Connector ================================================ FILE: docs/aup.Proposer.BOHBProposer.rst ================================================ .. automodule:: aup.Proposer.BOHBProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.EASProposer.rst ================================================ .. automodule:: aup.Proposer.EASProposer :members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.HyperbandProposer.rst ================================================ .. automodule:: aup.Proposer.HyperbandProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.HyperoptProposer.rst ================================================ .. automodule:: aup.Proposer.HyperoptProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.RandomProposer.rst ================================================ .. automodule:: aup.Proposer.RandomProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.SequenceProposer.rst ================================================ .. automodule:: aup.Proposer.SequenceProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.SpearmintProposer.rst ================================================ .. automodule:: aup.Proposer.SpearmintProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.Proposer.rst ================================================ aup.Proposer package ==================== The proposer is the core component to optimize hyperparameters for model training. Use :func:`aup.Proposer.AbstractProposer.get_proposer` to initialize proposer. All of them adopt the same interface as described below. Proposers --------- .. toctree:: :maxdepth: 1 aup.Proposer.HyperbandProposer aup.Proposer.HyperoptProposer aup.Proposer.RandomProposer aup.Proposer.SequenceProposer aup.Proposer.SpearmintProposer aup.Proposer.BOHBProposer aup.Proposer.EASProposer .. automodule:: aup.Proposer.AbstractProposer :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.__main__.rst ================================================ .. automodule:: aup.__main__ :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.compression.rst ================================================ Compression main entry ====================== .. automodule:: aup.compression.__main__ :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.convert.rst ================================================ .. automodule:: aup.convert :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.dlconvert.rst ================================================ DLconvert - Model conversion ============================ .. automodule:: aup.dlconvert.__main__ :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.dlconvert_API.rst ================================================ aup.dlconvert package ======================== .. automodule:: aup.dlconvert.checkpoint_to_onnx :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.checkpoint_to_pb :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.checkpoint_to_tflite :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.keras_to_onnx :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.keras_to_pb :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.keras_to_tflite :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.pb_to_onnx :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.pb_to_tflite :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.pytorch_to_keras :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.pytorch_to_onnx :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.pytorch_to_tflite :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.savedmodel_to_onnx :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.savedmodel_to_tflite :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.to_frozen_pb :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.to_onnx :members: :undoc-members: :show-inheritance: .. automodule:: aup.dlconvert.to_tflite :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.init.rst ================================================ .. automodule:: aup.init :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.profiler.rst ================================================ Profiler - Profiling model scripts ================================== .. automodule:: aup.profiler.__main__ :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.rst ================================================ **Auptimizer** APIs =================== Main Entry ---------- .. toctree:: :maxdepth: 1 aup.__main__ aup.compression aup.setup aup.init aup.convert aup.setupdb aup.profiler aup.dlconvert Internal modules and packages ----------------------------- .. toctree:: :maxdepth: 2 aup.EE aup.ET aup.Proposer aup.utils aup.setupdb_API aup.dlconvert_API ================================================ FILE: docs/aup.setup.rst ================================================ .. automodule:: aup.setup :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.setupdb.rst ================================================ Database setup and reset ======================== .. automodule:: aup.setupdb.__main__ :members: :undoc-members: :show-inheritance: .. automodule:: aup.setupdb.reset ================================================ FILE: docs/aup.setupdb.sqlite.rst ================================================ .. automodule:: aup.setupdb.sqlite :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/aup.setupdb_API.rst ================================================ aup.setupdb package =================== .. toctree:: :maxdepth: 1 aup.setupdb.sqlite ================================================ FILE: docs/aup.utils.rst ================================================ .. automodule:: aup.utils :members: :undoc-members: :show-inheritance: .. automodule:: aup.aup :members: :undoc-members: :show-inheritance: :imported-members: ================================================ FILE: docs/aup.visualize.rst ================================================ .. automodule:: aup.visualize :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/compression.rst ================================================ Compressor ========== **Compressor** is a model compression tool that helps reduce memory complexity and inference time of neural networks. With Compressor, you can: 1. **Make your ML models suitable for deployment on resource-constrained devices.** Use Compressor to optimize models for Edge device's limited memory, compute, or power and enable uncompromised on-device intelligence. 2. **Slash latency and enhance the user experience of your AI-powered application.** Tap Compressor's speed-up functionality to accelerate your model's inference time. 3. **Maximize the cost-effectiveness of your neural nets.** Cut down on cloud or on-prem model storage and compute costs by reducing their footprint. Similar to Auptimizer-Hyperparameter Optimization (HPO), Compressor aims to provide a unified interface to the existing state-of-the-art toolkits. Currently, Compressor leverages `NNI (version 2.0) `__ model compression modules. NNI is an open-source toolkit that supports two types of compression, pruning and quantization, for TensorFlow, and PyTorch models. You can find more detail on supported compression algorithms (compressors) in the `NNI Compression documentation `__. In the future, we will be integrating other off-the-shelf toolkits to expand the selection of model compression approaches. How to run compression experiments ---------------------------------- Running a compression experiment is similar to running an HPO experiment and requires just a few steps: 1. Install Auptimizer and set up Auptimizer environment 2. Prepare an experiment configuration file 3. Modify a few lines in the training script 4. Run the experiment Auptimizer also includes the NNI `compression utility functions `__ that will help you design compression experiments more efficiently. These utility functions enable layer sensitivity and channel dependency analysis, which can guide the selection of layers to be pruned and the target sparsity levels. We recommend running this analysis to have a better understanding of the model architecture. For more details, please check :doc:`Utility functions `. Step #1. Installation and environment setup ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Compressor is automatically installed as a part of Auptimizer. For PyTorch compression algorithms, Pytorch version >= 1.7.0 is required. For Tensorflow compression algorithms, TensorFlow version >= 2.0 is required. Compression experiments use the same Auptimizer environment as the HPO experiments. Please refer to the :doc:`Install and setup Auptimizer ` and :doc:`Set up environment ` sections for detail on how to set up your Auptimizer environment. Step #2. Prepare an experiment configuration file ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Compressor supports two compression paradigms: 1. one-time compression 2. automatic compression. A **one-time compression** experiment runs one compression job with a fixed set of parameters. Whereas an **automatic compression** experiment leverages Auptimizer's HPO module to find the best possible parameters of a compression algorithm that generates the best compressed model. One-time approach is a good option for performing a dry-run or an experiment with a specific set of parameters. Alternatively, use automatic compression if you are not certain about the compression parameters or would like to explore the relationship between compressed model performance and different parameter settings. Below, we explain the differences between one-time and automatic compression configuration files. One-time compression configuration @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ Here is an example of the configuration file for one-time compression using the `L1Filter` pruning method:: { "name": "MNIST L1 Filter Pruner", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "l1_filter", "config_list": [ { "sparsity": 0.8, "op_types": ["Conv2d"] }, { "sparsity": 0.6, "op_names": ["conv1", "conv2"] }, { "exclude": True, "op_names": ["conv3"] } ] } } One-time compression experiment configurations take the following parameters, where all parameters except for **compression** have the same meaning as in :doc:`Configure HPO Algorithm `: + **name**: name of the experiment + **script**: script to run + **resource**: type of resource to run the experiment, [cpu, gpu, passive, node] + **workingdir**: path to run the script, important for running jobs remotely (SSH/AWS) + **compression**: compression-specific parameters + **framework**: either "torch" or "tensorflow" + **type**: either "pruning" or "quantization" + **compressor**: string, one from the list of supported compression algorithms for the given framework and type (see below) + **config_list**: a list of parameters which define the specific requirements for the chosen NNI compression algorithm The ``config_list`` parameter is specific to individual NNI compression algorithms. However, there a few parameters common among all compressors: + **op_types**: list of strings, the names of the specific type of layers to be compressed. If not specified, will use ``default`` as the value which denotes the default layer types supported by the chosen compression algorithm. + **op_names**: list of strings, the names of the layers to be compressed. Will overwrite ``op_types`` if both are provided. The layer names can be found using ``model.state_dict().keys()``. + **exclude**: "True" or "False" (default is "False"). When set to "True", the layers defined in ``op_types`` or ``op_names`` will be excluded from compression. In the above example, "config_list" means pruning all layers of the type "Conv2d" to 0.8 sparsity, except for layers named "conv1" and "conv2" which should be pruned to 0.6 sparsity and layer "conv3" which should be excluded from the pruning. Please refer to the `NNI docs `__ for more description of the "config_list" parameter. Each compressor will have an example of its supported "config_list". Automatic compression configuration @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ Here is an example of the configuration file for automatic compression using the `L1Filter` pruning method:: { "name": "MNIST L1 Filter Pruner (automatic)", "script": "mnist.py", "resource": "cpu", "compression": { "framework": "torch", "type": "pruning", "compressor": "l1_filter", "config_list": [ { "sparsity": { "range": [0.6, 0.8], "type": "float" } "op_types": ['Conv2d'] }, { "sparsity": { "range": [0.1, 0.9], "type": "float" }, "op_names": ["conv1", "conv2"] }, { "exclude": True, "op_names": ["conv3"] } ] }, "proposer": "random", "n_parallel": 4, "target": "max", "n_samples": 4 } An automatic compression experiment uses HPO to find the best hyperparameters of a chosen compression algorithm. The experiment is launched as an HPO experiment, therefore, its configuration recognizes all parameters in an HPO experiment (see :doc:`Configure HPO Algorithm ` for parameter definitions). Some important parameters include: + **proposer**: HPO method used to propose new hyperparameter values + **target**: "min" or "max", minimizing or maximizing user-defined HPO metric + **n_samples**: total number of jobs to run + **n_parallel**: number of parallel jobs Another notable difference in automatic compression configuration is that for the values of the parameters in ``config_list``, a search space is defined via the following parameters: + **range**: [min, max] or a list of values + **type**: `float`, `int`, `choice` types are supported Additional parameters may be needed for specific Proposers (see :doc:`Configure HPO Algorithm `). There are two potential scenarios for identifying the best hyperparameters. We will use hyperparameter "sparsity" as an example. In the first scenario, the user may set the same search range for the sparsity for a group of layers defined in ``op_names`` or ``op_types``, however, the user allows the Proposer to choose a different value in the defined range for each layer in the group. While in the second scenario, the user would like to use the same sparsity value for all layers in the group due to the dependency among those layers. To handle these two scenarios, we introduce an additional parameter ``expand_op_names`` ("true" or "false", default is "true"). If set to "true", Auptimizer will propose a different hyperparameter value for each layer defined in the group; whereas when set to "false", the same hyperparameter value will apply to all layers defined in the group. For example, if the configuration is written as follows, in one job, the hyperparameter Proposer may assign sparsity value 0.2 and 0.4 to "conv1" and "conv2" layers, respectively. However, if the ``expand_op_names`` is set to "false" in the following example, the Proposer will always assign the same value (e.g., 0.2) to both "conv1" and "conv2" layers:: [ { 'sparsity': { 'range': [0.1, 0.9], 'type': 'float' }, 'op_names': ['conv1', 'conv2'], 'expand_op_names': true, 'op_types': ['default'] } ] Step #3. Modify the training script ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Only a few modifications to the training script are needed to run a compression experiment. The modifications are the same for both one-time and automatic compression experiments. We first present an example training script and then explain the changes below:: #!/usr/bin/env python #Step 1: add shebang line to make script executable import aup #Step 2: import auptimizer package def main(config): #Step 3a: the main function should take "config" as argument ... # code to generate model and load dataset ... #Step 4: create a compressor and call the compress method to compress the model compressor = aup.compression.create_compressor(model, config, optimizer=optimizer) model = compressor.compress() #Step 5 (optional): speed-up the model by removing zero weights model = compressor.apply_speedup(dummy_input=torch.randn(1, 1, 28, 28).to(device)) ... code for model fine-tuning after compression ... #Step 6 (optional): export the compressed model and the mask compressor.export_model( model_path="model_compressed.pth", mask_path="model_mask.pth", speedup=True, folder_name=".") #Step 7: return the metric for HPO or any metric for one-time compression aup.print_result(validation_acc) # Step 3b: parse the configuration file and call the main function as follows if __name__ == '__main__': config = aup.BasicConfig().load(sys.argv[1]) main(config) 1. Add Shebang line ``#!/usr/bin/env python`` on top of the script and make the script executable (``chmod u+x script.py``). 2. Import Auptimizer package by ``import aup`` 3. Parse the configuration file using ``aup.BasicConfig.load(sys.argv[1])``. 4. Create the compressor and apply compression + this can happen before the optimizer is created: ``compressor = aup.compression.create_compressor(model, config)`` + or after the optimizer is defined: ``compressor = aup.compression.create_compressor(model, config, optimizer=optimizer)`` Note: any additional arguments specifically required by the compression algorithms must be passed here. Apply compression by ``compressor.compress()``. 5. (Optional) Speed-up can be applied for pruned models: ``model = compressor.apply_speedup(dummy_input=torch.randn(*input_shape).to(device))`` This will modify the actual architecture of the model by removing zero parameters. ``dummy_input`` should be a ``pytorch tensor`` that conforms to the model input shape. We recommend fine-tuning the model after applying model speed-up as pruning zero parameters may affect the accuracy of the model. Note: Not all pruners support speed-up, please refer to the **Model Speed-Up** section below for more detail. 6. (Optional) Export the model:: compressor.export_model( model_path="model_compressed.pth", mask_path="model_mask.pth", speedup=True, folder_name=".") This saves the model to disk. Note that the speed-up is only applied if it has not been applied yet; otherwise, the model is saved as it is. + ``model_path``: the path where the compressed model will be saved + ``mask_path``: is a pruning-only argument, the path where the pruning mask will be saved. + ``speedup``: must be present and True if speed-up has been applied; can be True if speed-up has not been applied yet, and will apply speed-up before saving the model. + ``folder_name``: (optional) the directory relative to the working directory to save the model, the model and mask files will be saved to ``working_directory/folder_name/model(mask)_path``. + ``dummy_input``: (optional) a ``pytorch tensor`` that conforms to the model input shape required only for applying speedup when speedup has not been applied yet. 7. Return the final result or any intermediate result by using ``aup.print_result``: + For one-time compression, this result can be any metric the user would like to visualize on the dashboard + For automatic compression, this result should be the metric to use in HPO A few compression algorithms require additional changes in training procedures. Please refer to :doc:`Supported Compression Algorithms ` section for specific requirements of each compressor. Step #4. Run experiment ~~~~~~~~~~~~~~~~~~~~~~~ A one-shot compression experiment is run by issuing the ``aup.compression`` command:: python -m aup.compression experiment.json Automatic compression experiments require the ``--automatic`` flag:: python -m aup.compression experiment.json --automatic Advanced usages --------------- Use decorator to modify training script ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ An alternative way to pass the configuration file to the training script is to use the decorator ``aup_args`` with the following changes:: @aup.aup_args def main(compression_type, compression_framework, compressor, config_list, folder_name = None, save_model = False): config = locals() ... if __name__ == '__main__': main(sys.argv[1]) Save the best model ~~~~~~~~~~~~~~~~~~~ Automatic compression experiments can use the "save best model" feature in HPO. If this feature is enabled, only the compressed model and mask obtained using the best hyperparameter combination will be exported, instead of all the models and masks for every hyperparameter combinations explored. To use this feature, please make sure to define the following in the configuration file:: "resource_args": { "save_model": true } There are two ways to modify the code for exporting only the best model and its mask: + In case the ``@aup_args`` decorator is used, then the compressor's export_model method can be registered as a model saving function:: aup_save_model( compressor.export_model, model_path="model_compressed.pth", mask_path="model_mask.pth", speedup=False) + Alternatively, if the decorator is not used, apply the following code:: if "save_model" in config and config["save_model"]: compressor.export_model( model_path="model_compressed.pth", mask_path="model_mask.pth", speedup=False, folder_name=config["folder_name"]) Model Speed-up -------------- NNI compression provides a `model speedup module `__ which aims to export models with their architecture modified to reflect the effect of pruning methods. Normally, users would export the model with its structure unchanged and, for pruning, a mask of the pruned weights. However, with model speed-up, the mask is applied to the model before exporting. **Important:** Note that without applying model speed-up, compression will not result in model size reduction or inference acceleration. In order to use model speed-up, the script should call ``compressor.apply_speedup`` with the appropriate parameters. Model speed-up can also be used during ``compressor.export_model``. Please see `Modify the Training Script` step above for detailed usages. Not all compression algorithms support model speed-up. Compressors that support model speed-up include: + ActivationAPoZRankFilter Pruner + ActivationMeanRankFilter Pruner + ADMM Pruner + FPGM Pruner + L1Filter Pruner + L2Filter Pruner + NetAdapt Pruner + Sensitivity Pruner + TaylorFOWeightFilter Pruner ================================================ FILE: docs/compression_main.rst ================================================ Model Compression ----------------- .. toctree:: :maxdepth: 1 compression compressors compression_utilities ================================================ FILE: docs/compression_utilities.rst ================================================ Utility Functions ================= The utility functions of the NNI compression module are also integrated into Auptimizer. They are useful for analyzing the network topology, making informed decisions on the target sparsity levels, and measuring model parameters and FLOPS. The basic usages are presented below. Please refer to `NNI compression utilities `__ for more advanced applications. Sensitivity analysis -------------------- Sensitivity analysis prunes the layers one-by-one to measure the sentivity of each layer to different levels of target sparsity:: # Example of Sensitivity Analysis usage # Define a test function to measure the model performance after pruning each layer def test(model, device, test_loader): ... return accuracy # return a metric for evaluation s_analyzer = aup.compression.sensitivity_analysis.SensitivityAnalysis(model=model, val_func=lambda model: test(model, device, test_loader)) sensitivity = s_analyzer.analysis(val_args=[model]) s_analyzer.export(os.path.join(OUT_DIR, "sensitivity_analysis.log")) The sensitivity analysis result will be saved to "sensitivity_analysis.log". Channel dependency ------------------ We recommend that users run a channel dependency analysis if they want to manually assign target sparsity levels to selected layers, as the layers with dependency on each other need to be assigned the same sparsity. Channel dependency can be done as follows:: # Assume input has dimension (1, 1, 28, 28) data = torch.ones(1, 1, 28, 28).to(device) channel_depen = aup.compression.shape_dependency.ChannelDependency(model, data) channel_depen.export(os.path.join(OUT_DIR, "channel_dependency.csv")) The channel dependency result will be saved to "channel_dependency.csv". Parameters / FLOPs counter -------------------------- The FLOPs, number of parameters, and detailed information per layer ("flops", "params", "weight_size", "input_size", "output_size", etc) can be measured using:: flops, params, results = compressor.count_flops_params(input_shape_tuple) print(results) The per layer information is saved in "results". ================================================ FILE: docs/compressors.rst ================================================ Supported Compression Algorithms ================================ For a high-level introduction of all NNI pruners and quantizers, and the full list of parameters required for each compression algorithm in ``config_list``, please refer to `compressors `__. We maintain the same parameters for each compression algorithm as in the original NNI compression module. In this section, we provide examples for all of the supported compression algorithms that include: + An example configuration (for one-time compression) to present the required "framework", "type" and "compressor" parameters. + An example of a``aup.create_compressor`` call. If the compressor supports `"dependency-aware" mode `__, it will be included in the call. Pruners ------- Level Pruner ~~~~~~~~~~~~ Supports both TensorFlow and PyTorch. **Configuration**:: "compression": { "framework": "tensorflow" | "torch" "type": "pruning", "compressor": "level", "config_list": [{ "sparsity": 0.8, "op_types": ["default"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None) Slim Pruner ~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "slim", "config_list": [{ "sparsity": 0.8, "op_types": ["BatchNorm2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None) FPGM Pruner ~~~~~~~~~~~ This pruner supports a dependency-aware mode to get better speed-up from the pruning. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "fpgm", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, dependency_aware=False, dummy_input=None) L1Filter Pruner ~~~~~~~~~~~~~~~ This pruner supports the dependency-aware mode. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "l1_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, dependency_aware=False, dummy_input=None) L2Filter Pruner ~~~~~~~~~~~~~~~ This pruner supports the dependency-aware mode. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "l2_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, dependency_aware=False, dummy_input=None) ActivationAPoZRankFilter Pruner ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This pruner supports the dependency-aware mode. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_apoz_rank_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, activation='relu', statistics_batch_num=1, dependency_aware=False, dummy_input=None) ActivationMeanRankFilter Pruner ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This pruner supports the dependency-aware mode. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "activation_mean_rank_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, activation='relu', statistics_batch_num=1, dependency_aware=False, dummy_input=None) TaylorFOWeightFilter Pruner ~~~~~~~~~~~~~~~~~~~~~~~~~~~ This pruner supports the dependency-aware mode. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "taylor_fo_weight_filter", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, statistics_batch_num=1, dependency_aware=False, dummy_input=None) AGP Pruner ~~~~~~~~~~ **Special requirements for usage** (example):: compressor = aup.compression.create_compressor(model, config, optimizer=optimizer) model = compressor.compress() for epoch in range(1, args.epochs + 1): # ... train the model here for one epoch compressor.update_epoch(epoch) Use ``compressor.update_epoch(epoch)`` to update epoch number when you finish one epoch in your training code. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "agp", "config_list": [{ "initial_sparsity": 0.0, "final_sparsity": 0.8, "start_epoch": 0, "end_epoch": 10, "frequency": 1, "op_types": ["default"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer, pruning_algorithm='level') NetAdapt Pruner ~~~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "net_adapt", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, short_term_fine_tuner, evaluator, optimize_mode='maximize', base_algo='l1', sparsity_per_iteration=0.05, experiment_data_dir='./') SimulatedAnnealing Pruner ~~~~~~~~~~~~~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "simulated_annealing", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, evaluator, optimize_mode='maximize', base_algo='l1', start_temperature=100, stop_temperature=20, cool_down_rate=0.9, perturbation_magnitude=0.35, experiment_data_dir='./') AutoCompress Pruner ~~~~~~~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "auto_compress", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, trainer, evaluator, dummy_input, num_iterations=3, optimize_mode='maximize', base_algo='l1', start_temperature=100, stop_temperature=20, cool_down_rate=0.9, perturbation_magnitude=0.35, admm_num_iterations=30, admm_training_epochs=5, row=0.0001, experiment_data_dir='./') AMC Pruner ~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "amc", "config_list": [{ "op_types": ["Conv2d", "Linear"] } ] } **Example creation**:: aup.create_compressor(model, config, evaluator, val_loader, suffix=None, model_type='mobilenet', dataset='cifar10', flops_ratio=0.5, lbound=0.2, rbound=1.0, reward='acc_reward', n_calibration_batches=60, n_points_per_layer=10, channel_round=8, hidden1=300, hidden2=300, lr_c=0.001, lr_a=0.0001, warmup=100, discount=1.0, bsize=64, rmsize=100, window_length=1, tau=0.01, init_delta=0.5, delta_decay=0.99, max_episode_length=1000000000.0, output_dir='./logs', debug=False, train_episode=800, epsilon=50000, seed=None) ADMM Pruner ~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "admm", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"], "op_names": ["conv1"] }, { "sparsity": 0.5, "op_types": ["Conv2d"], "op_names": ["conv2"] } ] } **Example creation**:: aup.create_compressor(model, config, trainer, num_iterations=30, training_epochs=5, row=0.0001, base_algo='l1') Lottery Ticket Hypothesis Pruner ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **Special requirements for usage** (example):: compressor = aup.compression.create_compressor(model, config, optimizer=optimizer, lr_scheduler=scheduler) model = compressor.compress() for _ in compressor.get_prune_iterations(): compressor.prune_iteration_start() for epoch in range(1, args.epochs + 1): # ... train model here for one epoch **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "lottery_ticket", "config_list": [{ "prune_iterations": 5, "sparsity": 0.8, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config, optimizer=None, lr_scheduler=None, reset_weights=True) Sensitivity Pruner ~~~~~~~~~~~~~~~~~~ **Special requirements for usage** (example):: compressor = aup.compression.create_compressor(model, config, finetuner=short_term_fine_tuner, evaluator=evaluator) model = compressor.compress(eval_args=[model], finetune_args=[model]) Notice the arguments passed to ``compressor.compress``. **Configuration**:: "compression": { "framework": "torch", "type": "pruning", "compressor": "sensitivity", "config_list": [{ "sparsity": 0.5, "op_types": ["Conv2d"] } ] } **Example creation**:: aup.create_compressor(model, config_list, evaluator, finetuner=None, base_algo='l1', sparsity_proportion_calc=None, sparsity_per_iter=0.1, acc_drop_threshold=0.05, checkpoint_dir=None) Quantizers ---------- Naive Quantizer ~~~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "quantization", "compressor": "naive", "config_list": [] } **Example creation**:: aup.create_compressor(model, config) QAT Quantizer ~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "quantization", "compressor": "qat", "config_list": [{ "quant_types": ["weight"], "quant_bits": { "weight": 8 }, "op_types": ["Conv2d", "Linear"] }, { "quant_types": ["output"], "quant_bits": 8, "quant_start_step": 7000, "op_types":["ReLU6"] }] } **Example creation**:: aup.create_compressor(model, config) DoReFa Quantizer ~~~~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "quantization", "compressor": "dorefa", "config_list": [{ "quant_types": ["weight"], "quant_bits": 8, "op_types": ["default"] }] } **Example creation**:: aup.create_compressor(model, config) BNN Quantizer ~~~~~~~~~~~~~ **Configuration**:: "compression": { "framework": "torch", "type": "quantization", "compressor": "bnn", "config_list": [{ "quant_bits": 1, "quant_types": ["weight"], "op_types": ["Conv2d", "Linear"], "op_names": ["conv1", "conv2", "fc1", "fc2"] }, { "quant_bits": 1, "quant_types": ["output"], "op_types": ["relu"], "op_names": ["relu1", "relu2", "relu3"] }] } **Example creation**:: aup.create_compressor(model, config) ================================================ FILE: docs/conf.py ================================================ # -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/stable/config # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath('../src')) import aup # -- Project information ----------------------------------------------------- project = 'Auptimizer' copyright = '2018, LG Electronics Inc.' author = 'LG Electronics Inc.' # The short X.Y version version = aup.__version__ # The full version, including alpha/beta/rc tags release = '' # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.intersphinx', 'sphinx.ext.autosummary', 'sphinxcontrib.programoutput', 'sphinx.ext.autosectionlabel' ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # #source_parsers = { # '.md': 'recommonmark.parser.CommonMarkParser', #} #source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'wiki'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' html_logo = "images/AuptimizerWhiteLong.png" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static', 'archive'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = { '**': [ 'globaltoc.html','about.html','navigation.html','relations.html','searchbox.html',]} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'Auptimizerdoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'Auptimizer.tex', 'Auptimizer Documentation', 'LG Electronics Inc.', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'Auptimizer', 'Auptimizer Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'Auptimizer', 'Auptimizer Documentation', author, 'Auptimizer', 'One line description of project.', 'Miscellaneous'), ] # Additional add_module_names = False modindex_common_prefix = [".",".aup.","aup."] # -- Extension configuration ------------------------------------------------- autodoc_inherit_docstrings = True html_theme_options = { #'canonical_url': '', #'analytics_id': 'UA-XXXXXXX-1', # Provided by Google in your dashboard 'logo_only': True, 'display_version': True, 'prev_next_buttons_location': 'bottom', 'style_external_links': False, # 'vcs_pageview_mode': '', #'style_nav_header_background': 'black', # Toc options 'collapse_navigation': True, 'sticky_navigation': True, 'navigation_depth': 4, 'includehidden': True, 'titles_only': False } ================================================ FILE: docs/dashboard.rst ================================================ Auptimizer Dashboard ==================== **Dashboard** is a powerful analytics tool that complements Auptimizer's core hyperparameter optimization (HPO) and model compression capabilities. Use the Dashboard to: - **Supercharge the analysis of your HPO or model compression experiments.** The dashboard provides insightful visualizations to help you analyze and contrast jobs, experiments, and optimization approaches. Get to the root of your experiment results by exploring the interplay of hyperparameters, the progression of intermediate results, and the efficacies of different HPO algorithms. - **Get a snapshot of the information that matters.** Use the dashboard to track experiment progress and get clutter-free insights on the ongoing and completed experiments. - **Run your experiments with ease.** Create, launch or stop an experiment or even set up a new Auptimizer environment using the dashboard. Launch the dashboard --------------------- There are two ways to launch the dashboard: 1. In terminal, use the ``dashboard`` command to visualize an exisiting Auptimizer experiment that is currently running or has been completed:: dashboard --port --path The ``port`` is the port number to show the dashboard on the local machine. The ``path`` should point to the database (default is `sqlite3.db`) for the corresponding experiment. 2. Use the ``launch_dashboard`` flag and the ``dashboard_port`` flag (optional) when starting an HPO or compression experiment:: python3 -m aup exp_config.json --launch_dashboard --dashboard_port python3 -m aup.compression exp_compression.json --launch_dashboard --dashboard_port If ``dashboard_port`` is not provided, a random port will be assigned. The local host address will be shown in the console when the experiment starts. **Important:** With this second approach, some dashboard functionalities, like starting a new experiment or restarting a past experiment, will be disabled. For full functionalities of the dashboard, please, use the first approach. Track experiment status and visualize results --------------------------------------------- Main page ~~~~~~~~~ When you open the dashboard in a web browser, you will first land on the main page with a list of completed and active experiments. This page presents the meta info of each experiment. You can check the experiment configuration via the ``CONFIG`` button, and access detailed results via the ``RESULTS`` button. Here, you can also stop or start an experiment. The page also offers a *tile view* layout that provides the same information as the *list view*. .. figure:: images/dashboard/main_page_list.png :alt: main_page_list Main Page List View .. figure:: images/dashboard/main_page_tile.png :alt: main_page_tile Main Page Tile View Experiment overview ~~~~~~~~~~~~~~~~~~~ This page provides a summary of an experiment's status and the best result and corresponding best hyperparameters so far. You can also select other experiments to view without going back to the main page. .. figure:: images/dashboard/overview.png :alt: overview Experiment Overview Job status ~~~~~~~~~~ The Job Status page shows the status, result, and hyperparameters of each job. Job details are presented in a scatter plot and a table. By default, the user-defined score used for HPO will be plotted for each job, while the best result over finished jobs is shown with a line. You can zoom in/out, check out details of each data point, and change the range of axes on the plot. Please refer to ``INTERACTION GUIDE`` for exploring the plot. If you return multiple metrics in an experiment, the other metrics can be shown on this plot as well. .. figure:: images/dashboard/job_status_plot.png :alt: job_status_plot Job Status Plot The job status table presents more detail for each job, including status, score, start/end time, all hyperparameters, etc. The table can be sorted by each column and exported as xls/xlsx/csv/txt file. .. figure:: images/dashboard/job_status_table.png :alt: job_status_table Job Status Table Hyperparameter interaction graph ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The hyperparameter interaction graph (HIG) is a parallel coordinate graph that is commonly used to show the interplay among different hyperparameters. Each vertical axis represents the range of one hyperparameter. You can interact with this graph by selecting certain hyperparameters, moving the vertical axes, highlighting certain ranges of one or multiple hyperparameters, etc. This HIG can provide more insights into the impact of each hyperparameter on the evaluation metric. .. figure:: images/dashboard/hig.png :alt: hig Hyperparameter Interaction Graph Intermediate results ~~~~~~~~~~~~~~~~~~~~ If you enable tracking intermediate results or use early stopping strategies, this page will show the intermediate results plotted over time for the selected jobs within one experiment. .. figure:: images/dashboard/intermediate_results.png :alt: intermediate_results Intermediate Results Multi-experiment comparison ~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can view the HPO progress for multiple experiments on this page. The best result for each experiment can be plotted over jobs or time. This can be particularly useful if you want to compare different HPO strategies. .. figure:: images/dashboard/multi_experiment.png :alt: multi_experiment Multi-Experiment Comparison Create new experiment --------------------- In addition to visualizing the results of existing experiments, you can also use the dashboard to create and run new experiments. There are two ways to do so: 1. click on the ``CREATE NEW EXPERIMENT`` button on the main page, 2. click on the ``Create experiment from copy`` button located on each experiment entry on the main page. Both approaches will open the following page, where on the top you will need to specify your working directory for the new experiment, and in the left panel, you can input the experiment configuration in json format. If you choose ``Create experiment from copy``, the configuration json file of the selected experiment will be copied over to the left panel for further modification. The right panel is for validation purposes. You can check the argument values parsed from the json file in the right panel to make sure everything in the configuration json file is correct. .. figure:: images/dashboard/create_experiment.png :alt: create_experiment Create New Experiment Please review the ``INTERACTION GUIDE`` for a complete guide on setting up the experiment. After clicking on the ``CREATE EXPERIMENT`` button, this experiment will be shown on top of the experiment list on the main page. You can click on the ``START`` button to run the experiment. Reset Auptimizer environment ---------------------------- You can also reset the Auptimizer environment via the dashboard. After you click on the ``RESET AUPTIMIZER ENVIRONMENT`` button on the main page, a software wizard will lead you through the Auptimizer environment set-up process. **Important:** the existing Auptimizer environment in the same working directory will be overwritten, which will erase existing databases. We suggest the user back up their databases for finished experiments before resetting the Auptimizer environment in the same working directory. .. figure:: images/dashboard/setup_environment.png :alt: setup_environment Set Up Auptimizer Environment Compression experiments ----------------------- For **one-shot compression experiments**, as each experiment contains only one job and there are no hyperparameters to be tuned or visualized, the dashboard will have a few modifications: 1. The `Experiment Overview` page will only show the experiment configuration instead of hyperparameters. 2. The `Job status` page will only show one job. 3. The `Hyperparameter Interaction Graph` page will not show a graph. 4. The `Multi-Experiment Comparison` page will show one data point for the experiment. For **automatic compression experiments**, the dashboard will function in the same way as for the HPO experiments. Note that in compression experiments, the hyperparameter names may not be explicitly specified in the configuration file. For example, the target sparsity for multiple layers might be a single hyperparameter. However, on the dashboard, the hyperparameter names will be rephrased for better clarity. .. figure:: images/dashboard/overview_compression.png :alt: overview_compression Experiment Overview for Automatic Compression experiments .. figure:: images/dashboard/hig_compression.png :alt: hig_compression Hyperparameter Interaction Graph for Automatic Compression experiments Dark mode --------- All pages are also available in the *dark mode*. .. figure:: images/dashboard/dark_mode.png :alt: dark_mode Experiment Overview in Dark Mode ================================================ FILE: docs/demo.rst ================================================ Examples ======== An easy way to get started with **Auptimizer** is to modify the demo code in the ``Examples`` folder. +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | Example | Folder | Purpose | +=========================================+=========================+===============================================================================+ | Basic Demo | ``demo`` | Tutorial | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `2D function with different HPOs` | ``2dfunc_diff_opt`` | Show how to switch between different optimizers | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `2D function with different resources` | ``2dfunc_diff_res`` | Show how to switch between different resources | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `MNIST DNN` | ``hpo_mnist`` | Show HPO usage for DNN | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `Tensorflow flags` | ``tf_flags`` | Show how to integrate with Tensorflow Flags | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `Tensorflow Iris` | ``tf_iris_diff_opt`` | Show example on Iris data | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `NAS integration` | ``cai_nas`` | Show how to do NAS (uses a publicly available open-source NAS implementation) | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ | `Failure Control` | ``job_failure_control`` | Show job failure control cases | +-----------------------------------------+-------------------------+-------------------------------------------------------------------------------+ Auto convert ~~~~~~~~~~~~ In example ``2dfunc_diff_opt``, the ``experiment_auto.json`` shows how experiment configuration is managed. The user can use:: python -m aup.convert rosenbrock_origin.py experiment_auto.json rosenbrock to automatically convert the original file to the **Auptimizer** version. The output file name is defined in ``experiment_auto.json`` as ``script``. Manual modification ~~~~~~~~~~~~~~~~~~~ We also provide a modified training script in example ``2dfunc_diff_opt`` for users' reference. In ``rosenbrock_hpo.py``, we show how to manually convert the function for tuning with **Auptimizer**. For the end-user’s experiment, simply replacing the ``rosenbrock()`` function with their code is enough to use **Auptimizer** (**Need to return the score in that function**). Experiment configurations ~~~~~~~~~~~~~~~~~~~~~~~~~ Different ``experiment*.json`` files in multiple examples illustrate how to specify the configuration for different HPO algorithms. Most of them are identical. To set up a new experiment, please define the corresponding ``parameter_config`` in the ``JSON`` file. ================================================ FILE: docs/developer.rst ================================================ For Developers --------------------------- .. toctree:: :maxdepth: 1 developer_guide version errors ================================================ FILE: docs/developer_guide.rst ================================================ Developer Guide =============== For Machine Learning development -------------------------------- **Auptimizer** makes it a little easier to debug your experiment / job in the following ways. Set level of logging ~~~~~~~~~~~~~~~~~~~~ Logging can be activated using the :code:`--log` flag. (E.g. :code:`python -m aup --log `). The following logging levels are available: 1. **error** - everything stops the process 2. **warn** - using default values 3. **info** - progress update 4. **debug** - everything else Test in passive mode ~~~~~~~~~~~~~~~~~~~~ Change :code:`resource` in :code:`experiment.json` to :code:`"passive"` and then run:: python -m aup By doing so, **Auptimizer** will run in a passive mode where it interactively prints running script with its working path and asks for the returned value. You should run your script in a second terminal to see whether it finishes correctly. And then you can return that value back to **Auptimizer**\'s command line. For Auptimizer Software Development ----------------------------------- Environment ~~~~~~~~~~~ Either use :code:`virtualenv`:: virtualenv testenv source testenv/bin/activate pip install -r requirements.txt export PYTHONPATH=`pwd`:$PYTHONPATH or:: export PYTHONPATH=:$PYTHONPATH Unit testing ~~~~~~~~~~~~ If you make changes to the **Auptimizer** code, you can run the included unit tests to make sure that you didn't break anything. If it's the first time you are running these tests, do:: chmod u+x tests/EE/test_Job.py to set the correct permissions. You can then run the tests using:: python -m unittest ================================================ FILE: docs/dlconvert.rst ================================================ Converter --------- .. toctree:: :maxdepth: 1 dlconvert_readme dlconvert_example ================================================ FILE: docs/dlconvert_example.rst ================================================ Examples ======== We provide extensive examples to demonstrate Converter coverage and efficacy. Please check out `converter_examples `__ for detailed usages. We provide specific instructions for each example in the respective folder. Evaluating supported model architectures ---------------------------------------- `Tested_Models `__ - This example evaluates common model architecture coverage by the individual conversion functions. It also summarizes known issues. **We strongly recommend that users review this example first before running their conversion tasks.** Benchmarking quantized TensorFlow Lite models on an Android phone ----------------------------------------------------------------- `Convert_Benchmark `__ - This example demonstrates how to benchmark quantized TensorFlow Lite model performance (i.e. running time and memory usage) on an Android phone. Specifically, we converted models from a TensorFlow frozen protobuf file to a quantized .tflite file, and benchmarked their performance on an LG G6 mobile phone. This example shows that models converted and quantized with Converter match the performance of the official quantized models provided in the `TensorFlow repo `__. Profiling performances of converted models using Auptimizer Profiler -------------------------------------------------------------------- `Convert_Profiler `__ - This example demonstrates how to use Auptimizer-Profiler to profile TensorFlow or ONNX model performance on CPU. Performance benchmarking scripts are provided for TensorFlow and ONNX. ================================================ FILE: docs/dlconvert_readme.rst ================================================ How to use Converter ==================== **Converter** is a format conversion tool for machine learning models. It encapsulates best practices of individual Machine Learning model conversions under a single API. Converter allows you to: 1. **Make your models edge device-friendly.** Transform models in Checkpoint (.meta), Keras (.h5/.hdf5), SavedModel (directory name), Protobuf (.pb), and Pytorch (.pt) into edge-optimized ONNX (.onnx) and TensorFlow Lite (.tflite) formats. 2. **Enhance model interoperability through standardization.** Boost model compatibility with countless compilers, inference engines, and SoCs by converting it into the industry-standard ONNX format. 3. **Get a smaller and faster model.** Make your model more compact and efficient by leveraging Quantization_ built into the TensorFlow Lite converter. The following source model formats (and file extensions) can be converted to **TensorFlow Lite (.tflite)** and **ONNX (.onnx)**: - **Checkpoint (.meta)** - **Keras (.h5/.hdf5)** - **SavedModel (directory name)** - **Protobuf (.pb)** - **PyTorch (.pt)** Additionally, Converter supports conversions: - from Checkpoint to Protobuf - from Keras to Protobuf - from PyTorch to Keras TensorFlow 1.15, 2.1 - 2.3 and PyTorch 1.6.0 are tested. The conversion from SavedModel to TensorFlow Lite/ONNX requires TensorFlow version 2.x. Other conversions can be run using both TensorFlow 1.15 or 2.x. Install ------- *Note:* Converter leverages conversion libraries that have different version requirements (mainly for TensorFlow). It is highly recommended to use Docker or Python's virtualenv to isolate your environment. 1. Install Auptimizer 2. Install additional libraries for using Converter: If you would like to convert from Checkpoint/Keras/Protobuf/SavedModel model formats, please run: ``pip install keras2onnx tf2onnx``. If you would like to convert from PyTorch format, please run:``pip install pytorch2keras keras2onnx tf2onnx`` Usage ----- 1. **Recommended:** Check whether your model architecture is supported for the target conversion `here `__. 2. **Important:** Ensure that you can load and run your model, otherwise you will not be able to convert it successfully. 3. Specify conversion parameters. There are certain parameters to specify for each type of conversion. These parameters need to be written in a configuration *.json* file. You can list configurations for multiple model conversion tasks in a single .json file to execute model conversions sequentially. An example configuration for converting a VGG16 Keras model to ONNX is as follows:: { "convert_from":"test_models/VGG16.h5", "convert_to":"converted_models/VGG16_keras.onnx", } 4. After preparing the configuration .json file, run the following command to start the conversions:: python -m aup.dlconvert -f Alternatively, you can also write the configuration in a *json dictionary* format, and run :: python -m aup.dlconvert -d Parameters ---------- For **all** conversions, the two required parameters are **convert_from** and **convert_to**. For each specific conversion, there can be additional parameters needed. These parameters are usually dependent on the source- and target model formats, and are summarized in the following chart: +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | | From || To || quantization|| input_nodes|| output_nodes| | network_script|| input_shape|| onnx_opset|| frozen || savedmodel_tag | | | | | | | | network_name | | | || savedmodel_signature| +============+================+==============+=============+==============+=================+=============+============+============+======================+ | Keras |TensorFlow Lite | Optional | | | | | | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | SavedModel |TensorFlow Lite | Optional | | | | | | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Checkpoint |TensorFlow Lite | Optional | Required |Required | | Optional | | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Protobuf |TensorFlow Lite | Optional |Required | Required | | Optional | | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | PyTorch |TensorFlow Lite | Optional | | |Required |Required | | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Keras |ONNX | | | | | |Optional | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | SavedModel |ONNX | | | | | |Optional | | Optional | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Checkpoint |ONNX | |Required | Required | | Optional |Optional | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Protobuf |ONNX | |Required | Required | | Optional |Optional | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | PyTorch |ONNX | | | | Required |Required | Optional | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Keras |Protobuf | | | Required | | | |Optional | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | Checkpoint |Protobuf | | | Required | | | |Optional | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ | PyTorch |Keras | | | | Required | Required | | | | +------------+----------------+--------------+-------------+--------------+-----------------+-------------+------------+------------+----------------------+ ``convert_from`` Required for **all** conversions. Input model file with one of the supported extensions: `.meta`, `.h5/.hdf5`, `.pb`, `.pt`, or a directory path for SavedModel. ``convert_to`` Required for **all** conversions. Output model name with one of the supported extensions: `.tflite`, `.onnx`, `.pb`, or `.h5/.hdf5`. .. _Quantization: ``quantization`` Parameter *quantization* includes a group of parameters used for enabling quantization while converting to TensorFlow Lite format. Post-training quantization is a built-in functionality of the TensorFlow Lite Converter. The Converter API supports calling this functionality. To specify quantization parameters, write in the configuration .json file:: { "convert_from":"test_models/VGG16.h5", "convert_to":"converted_models/VGG16_keras.tflite", "quantization": { "optimization":"default", "type":"float16", "opsset":"tf", "load":"repdata.py" } } More detail on post-training quantization capabilities and parameter setting can be found in `Post-training quantization `__ ``optimization`` Enable/disable quantization for conversion. Choose from `none` or `default`. Default is `none`. When using `none`, no quantization will be performed and the converted TensorFlow Lite model will be in float32 format. When using `default`, best practices will be applied for quantization with the other given information via `--type`, `--opsset`, and `--load`. ``type`` Target data type for constant values of the converted TensorFlow Lite model. Choose from `float32`, `float16`, `int8`,and `uint8`. Default is `float32`. ``opsset`` Set of OpsSet options supported by the target device (experimental). Choose from 1. `tflite`, which refers to `[tensorflow.lite.OpsSet.TFLITE_BUILTINS]` 2. `tf`, which refers to `[tensorflow.lite.OpsSet.SELECT_TF_OPS, tensorflow.lite.OpsSet.TFLITE_BUILTINS]` 3. `int8`, which refers to `[tensorflow.lite.OpsSet.TFLITE_BUILTINS_INT8]` Default is `tflite`. ``load`` A python script that implements a data generation function that generates representative data for quantizing variable data, such as feature maps. The function should be named `get_dataset`, and it should be a generator function that yields large enough dataset to represent typical data values. Check `representative data `__ for example. ``input_nodes`` Model input names (separated by comma), which can be found with `summarize graph tool `__. Those names typically end with `:0`, for example `input:0`. ``output_nodes`` Model output names (separated by comma). which can be found with `summarize graph tool `__. Those names typically end with `:0`, for example `output/Softmax:0`. ``input_shape`` If the `input_nodes` in *protobuf* or *checkpoint* has unspecified shapes other than the 1st dimension, the `input_shape` needs to be specified by a comma separated string, for example `1,3,224,224`. For multiple `input_nodes`, use `;` to separate their corresponding `input_shape`. The shape of `input_nodes` can also be checked using the `summarize graph tool `__, where unspecified shape is usually represented by **-1**. ``network_script`` Path to a Python script that contains the model definition of the PyTorch model to be converted. ``network_name`` Class name of the model to be converted, defined in the script specified in `network_script` . ``onnx_opset`` Opset version to use for ONNX. Default is `10`. The ONNX opset version updates can be found in `ONNX release notes `__. ``frozen`` Flag to control whether to create a frozen Protobuf. Default is `True`. ``savedmodel_tag`` Tag to use for SavedModel. Default is `serve`. The SavedModel to be converted *cannot have an emtpy tag*. ``savedmodel_signature`` Signature to use for SavedModel within the specified `--tag` value. Default is `serving_default`. The SavedModel to be converted *cannot have an emtpy signature*. ``skip`` This parameter is for converting selected models when there are multiple conversion configurations in the json file. When set to `True`, the model will be skipped and not be converted. Default is `False`. Known Issues ------------ 1. Limited support on certain model architectures 2. Quantization for TensorFlow Lite conversion can lead to `significant accuracy loss `__ ================================================ FILE: docs/early_stop.rst ================================================ Early Stopping ============== Early stopping (ES) strategies provide increased efficiency for HPO algorithms by reducing the computational cost. This is done by detecting the poor performance of certain hyperparameter settings early in the training and stopping the corresponding jobs. As a result, better hyperparameter configurations can be found sooner with reduced compute resources. ES is especially useful in the context of deep learning where the search space grows exponentially over increasing hyperparameters. ES strategies provide users with advanced tools to aggressively explore larger search spaces over limited resources, with tradeoffs between HPO speed and final model performance. Auptimizer provides 4 popular ES strategies namely – Bandit, Median, Truncation and Curve-Fitting, which can be applied to all Proposers. The Bandit, Median and Curve-Fitting strategies are inspired by the following papers, while the Truncation strategy is provided to be used as a benchmark for other ES strategies: =============== ============================================================================================================================================================================================================== Strategy Algorithm =============== ============================================================================================================================================================================================================== Bandit `HYPERBAND: Bandit-Based Configuration Evaluation For Hyperparameter Optimization `__ Median `Google Vizier: A Service for Black-Box Optimization `__ Curve-fitting `Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves `__ =============== ============================================================================================================================================================================================================== Usage @@@@@ The feature can be used by adding the following parameter to the experiment configuration file:: "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 5 ... } } + ``aup_policy``: the early stopping strategy in ["bandit", "median", "truncation" or "curve_fitting"]. + ``aup_policy_steps``: integer, the interval of epochs, by which the intermediate results are compared among jobs and the ES policy is applied. ``aup_policy`` and ``aup_policy_steps`` are the two parameters required for all ES strategies. There are also strategy-specifc parameters, which will be introduced below. In order to track the intermediate results to be used in ES, ``aup.print_result`` should be called from the user script in the training loop, e.g.:: def main(*args, **kwargs): # model and data preparation code for epoch in range(10): # training code for one epoch aup.print_result(res) Examples can be found in Auptimizer Gibhub repository at ``Examples/early_stopping/quad_equation_min`` and ``Examples/early_stopping/mnist_keras``. Additionally, when using ES, the ``track_intermediate_results`` feature will be triggerd automatically. This means that the resulting intermediate results will be stored in the database in the ``intermediate_results`` table and can be visualized via dashboard. This also means that the presence of ``track_intermediate_results`` in the experiment configuration file with any value, even false, will be ignored. An optional parameter of ``warmup`` can also be used for all ES strategies (default is 0). ``warmup`` defines the number of initial epochs that should finish before the ES strategy starts to apply. Strategies @@@@@@@@@@ Bandit ~~~~~~~ The bandit policy stops jobs that have a result lower than a specified percentage of the global best result of all jobs. This percentage is defined by the parameter ``bandit_factor``. The result to be compared among jobs is the best result obtained by the job up to the same epoch. Example:: "resource_args": { "early_stop": { "aup_policy": "bandit", "aup_policy_steps": 10, "bandit_factor": 0.5 } } In this example, we stop jobs with the best result which is worse than 0.5 of the global best result. This comparison and job cut-off is carried out every 10 epochs. Default value for ``bandit_factor`` is 0.5, with higher values indicating more aggressive ES strategy. Truncation ~~~~~~~~~~ The truncation policy cuts a fraction of the worst performing jobs, based on the jobs' best result obtained up to the same epoch. The fraction is specified by the parameter ``truncation_percentage``. Example:: "resource_args": { "early_stop": { "aup_policy": "truncation", "aup_policy_steps": 10, "truncation_percentage": 0.6 } } This example stops 60% of the jobs every 10 epochs. Default value for ``truncation_percentage`` is 0.3, with higher values indicating more aggressive ES strategy. Median ~~~~~~~ The median policy stops the jobs that yield worse results than the median of the running average of results of all jobs up to the same epoch. Example:: "resource_args": { "early_stop": { "aup_policy": "median", "aup_policy_steps": 10 } } Curve Fitting ~~~~~~~~~~~~~~ The curve fitting policy attempts to fit each job's history to a weighted combination of multiple, pre-selected functions, in order to predict its final (best) value. It then stops jobs that fail to attain at least a threshold of the best overall result across all jobs. The implementation is adapted from `NNI `__. **Caveats:** the biggest downside to curve fitting is that it is usually time-consuming. This makes curve fitting less applicable for small datasets, or tasks that train quickly. In order to address this issue, users should always provide a timeout for the maximum time allowed for each curve fitting process (default: 30s). After the specified time has run out, the curve-fitting process will be stopped and the last result obtained will be used. The longer the timeout, the better the results. Example:: "resource_args": { "early_stop": { "aup_policy": "curve_fitting", "aup_policy_steps": 10, "curve_fitting_threshold": 0.95, "curve_fitting_timeout": 60 } } Example for scripts that use ``aup.print_result`` for result reporting instead:: "resource_args": { "early_stop": { "aup_policy": "curve_fitting", "aup_policy_steps": 10, "curve_fitting_threshold": 0.95, "curve_fitting_timeout": 60, "curve_fitting_max_iters": 100 } } Default values for ``curve_fitting_threshold``, ``curve_fitting_timeout`` are 0.95 and 60. ``curve_fitting_max_iters`` defaults to None. ================================================ FILE: docs/edge.rst ================================================ Edge Deployment Tools --------------------- .. toctree:: :maxdepth: 1 profiler.rst dlconvert.rst ================================================ FILE: docs/environment.rst ================================================ Set up environment ================== **Auptimizer** needs to be initialized properly before use, either through `python -m aup.setup` for interactive setup or `python -m aup.setup ` with the specified configuration file. \ **We recommend users use the interactive setup when using auptimizer for the first time**. The configuration file is used to set up **all** environment-related information for **Auptimizer**, such as number of CPUs, GPUs, or remote servers. All your experiments and jobs will use the configuration. Configuration Options --------------------- Two templates can be found at :code:`Examples/2dfunc_diff_res/*.ini`. The detailed options are as follows. Environment Template File (\*.ini) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For a normal workstation, an example template is at :code:`Examples/2dfunc_diff_res/env_user_template.ini` for direct use. The command below will create a user environment profile (at `~/.aup/env.ini`) for quick-start:: python -m aup.setup ./Examples/2dfunc_diff_res/env_user_template.ini or to create a local environment folder for a particular experiment (in working folder `./.aup/env.ini`), use:: python -m aup.setup ./Examples/2dfunc_diff_res/env_local_template.ini The content of the template is the following: +------------------+-----------------------------+--------------------------------------+ | Name | Purpose | Default value | +==================+=============================+======================================+ | Auptimizer_PATH | configuration folder for | ``.aup`` | | | Auptimizer, containing this | | | | ``env.ini`` file and | | | | database file | | +------------------+-----------------------------+--------------------------------------+ | SQL_ENGINE | database engine, currently | ``sqlite`` | | | only supports sqlite | | +------------------+-----------------------------+--------------------------------------+ | TMP_FOLDER | temp folder for logging / | ``/tmp/aup`` or ``./aup_tmp`` | | | scratch | | +------------------+-----------------------------+--------------------------------------+ Available resources for model training are specified by additional arguments, or via interactive questions. ========= ======== ===================================================================================================== Argument Default Details ========= ======== ===================================================================================================== --aws none a file containing ``user@instance-id``, or a comma-separated list --cpu 4 Number of processors to use on a single machine. --gpu none a file containing the GPU id number on a machine, or a comma-separated list. --node none a file containing ``user@IP`` for training, or a comma-separated list ========= ======== ===================================================================================================== AWS Mapping Configuration (--aws) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **Auptimizer** has the capability to start and stop AWS instances and assign jobs dynamically to the instances depending on availability. To allow fragmented resource utilization within the AWS instance, edit the resources available to **Auptimizer** when installing it on the remote instance. 1. To use the Auptimizer’s AWS option, install and configure `boto3 `_ and ensure your AWS instances can be accessed with boto3. Then proceed to `install configure AWS CLI `_. 2. To run jobs on multiple AWS instances, we need to specify an AWS mapping file with `--aws `. Similar to the above-mentioned node case, `` is a text file containing the username, AWS instance id and the SSH key file (.pem file). The format of the file must be one of the following: + `@ ` + `@` + `@:port` + `@:port ` with each instance on a separate line, or if you setup the configuration interactively, you can provide them as comma-separated values. The ``username`` is the one used to log into the instance. The ``instance_id`` can be the EC2 instance ID, IP, or public DNS. Refer to ``Examples/2dfunc_diff_res/aws.txt`` for an example. 3. Either you need to install **Auptimizer** on every node, or you need to copy ``/src/aup.py`` to your remote working directory. 4. Finally, the environment of the instances might be *sourced* correspondingly for environment variables, e.g. ``PATH``. For instance, if you want to activate virtualenv on the instance before job running, use `prescript` in experiment.json (See :ref:`AWSRuntimeAnchor` for more detail). CPU Configuration (``--cpu``) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ We can specify how many parallel jobs to be run on CPUs. The current implementation does **NOT** provide real isolation of hardware. GPU Mapping Configuration (``--gpu``) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To run jobs on multiple GPUs, we need to specify a GPU mapping file with ``--gpu ``. This file is a text file containing the IDs of GPU cards to use (in ``CUDA_VISIBLE_DEVICES``). Specifically, each line contains a GPU id as an integer. + For single GPU without parallel execution, use ``Examples/2dfunc_diff_res/plainGPU.txt``. + Assign multiple jobs to run on the same device by assigning multiple resource IDs to the same GPU id, i.e. ``0,0``). See ``Examples/2dfunc_diff_res/singleGPU.txt``. + Assign multiple jobs on different GPUs on a local machine, i.e.:: 0 1 See ``Examples/2dfunc_diff_res/twoGPUs.txt``. Node Mapping Configuration (``--node``) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Similar to GPU mapping, users can specify the computing nodes to be used with ``--node `` during setup A node configuration file contains each node per line (e.g., ``username@hostname``.) An SSH connection without password authentication is required (use ``ssh-keygen`` to create keyless access). The format of the file must be one of the followings: + `@ ` + `@` + `@:port` + `@:port ` with each instance on a separate line, or if you setup the configuration interactively, you can provide them as comma-separated values. For remote execution, **Auptimizer** will not copy all job-related files to the remote machine. User should make sure the job script can run on the remote machine first. Refer to ``Examples/2dfunc_diff_res/node.txt`` for an example. Either you need to install **Auptimizer** on every node, or you need to copy ``/src/aup.py`` to your remote working directory. Optional arguments ~~~~~~~~~~~~~~~~~~ + ``--overwrite`` - overwrite existing ``.aup`` folder. Otherwise, do nothing + ``--log`` - choose log level from ``[debug,info,warn,error]`` + ``--user`` - not used. It specifies the user ownership for experiments. Examples ~~~~~~~~ Please refer to ``Examples/2dfunc_diff_res/README.md`` for examples of how to use different resources with **Auptimizer**. Database Setup -------------- During the setup, **Auptimizer** creates a SQL database to track the jobs and experiments (currently only ``sqlite`` is supported). Typically, users do not need to manually access it. Here we provide a little more detail for users to retrieve additional records for their analyses. The database schema is described below: .. figure:: images/schema.png :alt: SQL Schema SQL Schema Refresh tables ~~~~~~~~~~~~~~ To (re)create the database, users just need to follow the command printed after ``python -m aup.setup``:: python -m aup.setupdb .aup/env.ini This will parse the ``.aup/env.ini`` file to create the new database. Refresh tables with additional modification ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The following argument can be customized by changing it in the headings or from the command line to overwrite the ``aup.setup`` configuration: +-----------------------+-----------------------+-----------------------+ | Name | Purpose | Default value | +=======================+=======================+=======================+ | --cpu | Number of CPUs to be | 4 | | | used. | | +-----------------------+-----------------------+-----------------------+ | --name | Name for resources | “localhost” , | | | | Currently not in use. | +-----------------------+-----------------------+-----------------------+ | --user | username for | Currently not in use. | | | experiment tracking | | | | and control | | +-----------------------+-----------------------+-----------------------+ For example, if user wants to set maximum 2 jobs to be run on CPU in parallel, under name ``test``:: python -m aup.setupdb --cpus 2 --user test Then you can see the allocated ``resource`` table as:: echo "select * from resource;" | sqlite3 .aup/sqlite3.db 1|test|gpu|free 2|test|cpu|free 3|test|cpu|free 4|test|passive|free Reset ----- To reset the history of **Auptimizer** experiments, there are two levels. Full reset ~~~~~~~~~~ Removing :code:`.aup` folder will completely remove all history saved by **Auptimizer** as well as any configurations. Using ``python -m aup.setup --overwrite `` will overwrite the existing folder and remove only the history. Database reset ~~~~~~~~~~~~~~ Currently, **Auptimizer** experiments and jobs history is saved in ``.aup/sqlite.db`` database. There are two levels to reset the database itself. Reset all @@@@@@@@@ We can also just refresh the database for the history by resetting the database file. Use (locally in working folder):: python -m aup.setupdb .aup/env.ini or (user account-wise):: python -m aup.setupdb ~/.aup/env.ini Reset job status @@@@@@@@@@@@@@@@ Sometimes, when **Auptimizer** accidentally exits, the resources are not marked as free in the database and will prevent you from using them within **Auptimizer**. In such scenarios, you might want to reset the status of resources by:: python -m aup.setupdb.reset env.ini ================================================ FILE: docs/errors.rst ================================================ Errors and Solutions ==================== Setup stage ----------- + ``Failed to load environment template %s using ConfigParser`` Check the input ``env.ini`` template for potential mistakes or typos. + ``SQL engine XXX is not implemented`` Currently only ``sqlite`` is supported. + ``Cant' find XXX file for resource XXX`` The resource file is not found for Auptimizer + ``Error while finding module specification for XXX`` XXX can be either ``aup.init`` or ``aup.setup``. Double check whether you put the `aup.py` file for remote execution in your PYTHONPATH instead of the `aup` package. Run Experiment -------------- + Sometimes, when **Auptimizer** accidentally exits, the resources are not marked as free in the database and will prevent you from using them within **Auptimizer**. In such scenarios, you might want to reset the status of resources by:: python -m aup.setupdb.reset env.ini ================================================ FILE: docs/experiment.rst ================================================ Create and run a new experiment =============================== **Auptimizer** only needs a modified training script and an experiment configuration file (.json) to run a new experiment. 1. Create the ``experiment.json`` file to specify the experiment configuration and hyperparameters. Using ``python -m aup.init`` will guide you interactively. This json file structure is generally the same for most algorithms with minor modifications. See :doc:`algorithm` for more details. 2. Modify your training script. We provide three approaches for modifying the training script: + `Manual conversion <#manual-modification-of-training-code>`_; + `Python decorator <#code-conversion-with-decorator>`_; + `Auto conversion for script (beta) <#auto-code-conversion>`_. 3. Your experiment is now ready to run via ``python -m aup experiment.json``. For more details, see `Run experiment`_. Terminology ----------- For data science applications, the **user** (AI scientist/engineer) solves a given data mining problem with a specified machine learning model. A script (**code**) is written and some hyperparameters are identified to be explored during model training. Typically, the user carries out an **experiment** to examine a range of hyperparameter combinations and measures the **performance** of the model on a hold-out dataset. For example, testing a deep learning model by exploring the learning rate between 0 and 1, and dropout between 0.4 and 0.6. the performance is measured by accuracy. Each individual training process for a given selection of hyperparameters (e.g., learning rate = 0.1, dropout = 0.5) is called a **job**. All jobs run on an assigned computational **resource**, e.g. CPU, GPU. And after all jobs are finished, the user retrieves the best model from the training history for further analysis or application. Manual modification of training code ------------------------------------ If you plan to change your training script manually, the general flow of the conversion process is as follows: a. parse the configuration file (first argument from command line, i.e. ``sys.argv[1]``) using ``aup.BasicConfig.load(sys.argv[1])``. And use the hyperparameters parsed from the ``BasicConfig`` in your code, such as ``config.param_name`` or ``config['param_name']``, where ``param_name`` need to be consistent with the one used in ``experiment.json``. b. to report the result by using ``aup.print_result``. c. Add Shebang line ``#!/usr/bin/env python`` and make the script executable (``chmod u+x script.py``). Code conversion with decorator ------------------------------ For better control, you can use `aup_args` or `aup_flags` to decorate your code. The examples are in below: .. figure:: images/comparison.png :alt: Code comparison Example of using decorator for code conversion. Auto code conversion -------------------- If your training function takes all hyperparameters as input, then **Auptimizer** converts code to run if the training script is well defined as follows:: python -m aup.convert ') return output # Read in the chooser from file. Returns True only on success def _read_only(self): if os.path.exists(self.state_pkl): fh = open(self.state_pkl, 'rb') state = pickle.load(fh) fh.close() self.D = state['dims'] self.ls = state['ls'] self.amp2 = state['amp2'] self.noise = state['noise'] self.mean = state['mean'] self.hyper_samples = state['hyper_samples'] self.needs_burnin = False return True return False def _real_init(self, dims, values): self.locker.lock_wait(self.state_pkl) self.randomstate = npr.get_state() if os.path.exists(self.state_pkl): fh = open(self.state_pkl, 'r') state = pickle.load(fh) fh.close() self.D = state['dims'] self.ls = state['ls'] self.amp2 = state['amp2'] self.noise = state['noise'] self.mean = state['mean'] self.hyper_samples = state['hyper_samples'] self.needs_burnin = False else: # Input dimensionality. self.D = dims # Initial length scales. self.ls = np.ones(self.D) # Initial amplitude. self.amp2 = np.std(values)+1e-4 # Initial observation noise. self.noise = 1e-3 # Initial mean. self.mean = np.mean(values) # Save hyperparameter samples self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls)) self.locker.unlock(self.state_pkl) def cov(self, x1, x2=None): if x2 is None: return self.amp2 * (self.cov_func(self.ls, x1, None) + 1e-6*np.eye(x1.shape[0])) else: return self.amp2 * self.cov_func(self.ls, x1, x2) # Given a set of completed 'experiments' in the unit hypercube with # corresponding objective 'values', pick from the next experiment to # run according to the acquisition function. def next(self, grid, values, durations, candidates, pending, complete): # Don't bother using fancy GP stuff at first. if complete.shape[0] < 2: return int(candidates[0]) # Perform the real initialization. if self.D == -1: self._real_init(grid.shape[1], values[complete]) # Grab out the relevant sets. comp = grid[complete,:] cand = grid[candidates,:] pend = grid[pending,:] vals = values[complete] numcand = cand.shape[0] # Spray a set of candidates around the min so far best_comp = np.argmin(vals) cand2 = np.vstack((np.random.randn(10,comp.shape[1])*0.001 + comp[best_comp,:], cand)) if self.mcmc_iters > 0: # Possibly burn in. if self.needs_burnin: for mcmc_iter in range(self.burnin): self.sample_hypers(comp, vals) log("BURN %d/%d] mean: %.2f amp: %.2f " "noise: %.4f min_ls: %.4f max_ls: %.4f" % (mcmc_iter+1, self.burnin, self.mean, np.sqrt(self.amp2), self.noise, np.min(self.ls), np.max(self.ls))) self.needs_burnin = False # Sample from hyperparameters. # Adjust the candidates to hit ei peaks self.hyper_samples = [] for mcmc_iter in range(self.mcmc_iters): self.sample_hypers(comp, vals) log("%d/%d] mean: %.2f amp: %.2f noise: %.4f " "min_ls: %.4f max_ls: %.4f" % (mcmc_iter+1, self.mcmc_iters, self.mean, np.sqrt(self.amp2), self.noise, np.min(self.ls), np.max(self.ls))) self.dump_hypers() b = []# optimization bounds for i in range(0, cand.shape[1]): b.append((0, 1)) overall_ei = self.ei_over_hypers(comp,pend,cand2,vals) inds = np.argsort(np.mean(overall_ei,axis=1))[-self.grid_subset:] cand2 = cand2[inds,:] # Optimize each point in parallel if self.use_multiprocessing: pool = multiprocessing.Pool(self.grid_subset) results = [pool.apply_async(optimize_pt,args=( c,b,comp,pend,vals,copy.copy(self))) for c in cand2] for res in results: cand = np.vstack((cand, res.get(1e8))) pool.close() else: # This is old code to optimize each point in parallel. for i in range(0, cand2.shape[0]): log("Optimizing candidate %d/%d" % (i+1, cand2.shape[0])) #self.check_grad_ei(cand2[i,:].flatten(), comp, pend, vals) ret = spo.fmin_l_bfgs_b(self.grad_optimize_ei_over_hypers, cand2[i,:].flatten(), args=(comp,pend,vals), bounds=b, disp=0) cand2[i,:] = ret[0] cand = np.vstack((cand, cand2)) overall_ei = self.ei_over_hypers(comp,pend,cand,vals) best_cand = np.argmax(np.mean(overall_ei, axis=1)) if (best_cand >= numcand): return (int(numcand), cand[best_cand,:]) return int(candidates[best_cand]) else: # Optimize hyperparameters self.optimize_hypers(comp, vals) log("mean: %.2f amp: %.2f noise: %.4f " "min_ls: %.4f max_ls: %.4f" % (self.mean, np.sqrt(self.amp2), self.noise, np.min(self.ls), np.max(self.ls))) # Optimize over EI b = []# optimization bounds for i in range(0, cand.shape[1]): b.append((0, 1)) for i in range(0, cand2.shape[0]): ret = spo.fmin_l_bfgs_b(self.grad_optimize_ei, cand2[i,:].flatten(), args=(comp,vals,True), bounds=b, disp=0) cand2[i,:] = ret[0] cand = np.vstack((cand, cand2)) ei = self.compute_ei(comp, pend, cand, vals) best_cand = np.argmax(ei) if (best_cand >= numcand): return (int(numcand), cand[best_cand,:]) return int(candidates[best_cand]) # Compute EI over hyperparameter samples def ei_over_hypers(self,comp,pend,cand,vals): overall_ei = np.zeros((cand.shape[0], self.mcmc_iters)) for mcmc_iter in range(self.mcmc_iters): hyper = self.hyper_samples[mcmc_iter] self.mean = hyper[0] self.noise = hyper[1] self.amp2 = hyper[2] self.ls = hyper[3] overall_ei[:,mcmc_iter] = self.compute_ei(comp, pend, cand, vals) return overall_ei def check_grad_ei(self, cand, comp, pend, vals): (ei,dx1) = self.grad_optimize_ei_over_hypers(cand, comp, pend, vals) dx2 = dx1*0 idx = np.zeros(cand.shape[0]) for i in range(0, cand.shape[0]): idx[i] = 1e-6 (ei1,tmp) = self.grad_optimize_ei_over_hypers(cand + idx, comp, pend, vals) (ei2,tmp) = self.grad_optimize_ei_over_hypers(cand - idx, comp, pend, vals) dx2[i] = (ei - ei2)/(2*1e-6) idx[i] = 0 print('computed grads', dx1) print('finite diffs', dx2) print(dx1/dx2) print(np.sum((dx1 - dx2)**2)) time.sleep(2) # Adjust points by optimizing EI over a set of hyperparameter samples def grad_optimize_ei_over_hypers(self, cand, comp, pend, vals, compute_grad=True): summed_ei = 0 summed_grad_ei = np.zeros(cand.shape).flatten() ls = self.ls.copy() amp2 = self.amp2 mean = self.mean noise = self.noise for hyper in self.hyper_samples: self.mean = hyper[0] self.noise = hyper[1] self.amp2 = hyper[2] self.ls = hyper[3] if compute_grad: (ei,g_ei) = self.grad_optimize_ei(cand,comp,pend,vals,compute_grad) summed_grad_ei = summed_grad_ei + g_ei else: ei = self.grad_optimize_ei(cand,comp,pend,vals,compute_grad) summed_ei += ei self.mean = mean self.amp2 = amp2 self.noise = noise self.ls = ls.copy() if compute_grad: return (summed_ei, summed_grad_ei) else: return summed_ei # Adjust points based on optimizing their ei def grad_optimize_ei(self, cand, comp, pend, vals, compute_grad=True): if pend.shape[0] == 0: best = np.min(vals) cand = np.reshape(cand, (-1, comp.shape[1])) # The primary covariances for prediction. comp_cov = self.cov(comp) cand_cross = self.cov(comp, cand) # Compute the required Cholesky. obsv_cov = comp_cov + self.noise*np.eye(comp.shape[0]) obsv_chol = spla.cholesky(obsv_cov, lower=True) cov_grad_func = getattr(gp, 'grad_' + self.cov_func.__name__) cand_cross_grad = cov_grad_func(self.ls, comp, cand) # Predictive things. # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.solve_triangular(obsv_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v) u = (best - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) if not compute_grad: return ei # Gradients of ei w.r.t. mean and variance g_ei_m = -ncdf g_ei_s2 = 0.5*npdf / func_s # Apply covariance function grad_cross = np.squeeze(cand_cross_grad) grad_xp_m = np.dot(alpha.transpose(),grad_cross) grad_xp_v = np.dot(-2*spla.cho_solve( (obsv_chol, True),cand_cross).transpose(), grad_cross) grad_xp = 0.5*self.amp2*(grad_xp_m*g_ei_m + grad_xp_v*g_ei_s2) ei = -np.sum(ei) return ei, grad_xp.flatten() else: # If there are pending experiments, fantasize their outcomes. cand = np.reshape(cand, (-1, comp.shape[1])) # Create a composite vector of complete and pending. comp_pend = np.concatenate((comp, pend)) # Compute the covariance and Cholesky decomposition. comp_pend_cov = (self.cov(comp_pend) + self.noise*np.eye(comp_pend.shape[0])) comp_pend_chol = spla.cholesky(comp_pend_cov, lower=True) # Compute submatrices. pend_cross = self.cov(comp, pend) pend_kappa = self.cov(pend) # Use the sub-Cholesky. obsv_chol = comp_pend_chol[:comp.shape[0],:comp.shape[0]] # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.cho_solve((obsv_chol, True), pend_cross) # Finding predictive means and variances. pend_m = np.dot(pend_cross.T, alpha) + self.mean pend_K = pend_kappa - np.dot(pend_cross.T, beta) # Take the Cholesky of the predictive covariance. pend_chol = spla.cholesky(pend_K, lower=True) # Make predictions. npr.set_state(self.randomstate) pend_fant = np.dot(pend_chol, npr.randn(pend.shape[0],self.pending_samples)) + pend_m[:,None] # Include the fantasies. fant_vals = np.concatenate( (np.tile(vals[:,np.newaxis], (1,self.pending_samples)), pend_fant)) # Compute bests over the fantasies. bests = np.min(fant_vals, axis=0) # Now generalize from these fantasies. cand_cross = self.cov(comp_pend, cand) cov_grad_func = getattr(gp, 'grad_' + self.cov_func.__name__) cand_cross_grad = cov_grad_func(self.ls, comp_pend, cand) # Solve the linear systems. alpha = spla.cho_solve((comp_pend_chol, True), fant_vals - self.mean) beta = spla.solve_triangular(comp_pend_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v[:,np.newaxis]) u = (bests[np.newaxis,:] - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) # Gradients of ei w.r.t. mean and variance g_ei_m = -ncdf g_ei_s2 = 0.5*npdf / func_s # Apply covariance function # Squeeze can break the 1D case be careful if pend.shape[1] == 1: grad_cross = np.squeeze(cand_cross_grad, axis=(2,)) else: grad_cross = np.squeeze(cand_cross_grad) grad_xp_m = np.dot(alpha.transpose(),grad_cross) grad_xp_v = np.dot(-2*spla.cho_solve( (comp_pend_chol, True),cand_cross).transpose(), grad_cross) grad_xp = 0.5*self.amp2*(grad_xp_m*np.tile(g_ei_m,(comp.shape[1],1)).T + (grad_xp_v.T*g_ei_s2).T) ei = -np.mean(ei, axis=1) grad_xp = np.mean(grad_xp,axis=0) return ei, grad_xp.flatten() def compute_ei(self, comp, pend, cand, vals): if pend.shape[0] == 0: # If there are no pending, don't do anything fancy. # Current best. best = np.min(vals) # The primary covariances for prediction. comp_cov = self.cov(comp) cand_cross = self.cov(comp, cand) # Compute the required Cholesky. obsv_cov = comp_cov + self.noise*np.eye(comp.shape[0]) obsv_chol = spla.cholesky( obsv_cov, lower=True ) # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.solve_triangular(obsv_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v) u = (best - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) return ei else: # If there are pending experiments, fantasize their outcomes. # Create a composite vector of complete and pending. comp_pend = np.concatenate((comp, pend)) # Compute the covariance and Cholesky decomposition. comp_pend_cov = (self.cov(comp_pend) + self.noise*np.eye(comp_pend.shape[0])) comp_pend_chol = spla.cholesky(comp_pend_cov, lower=True) # Compute submatrices. pend_cross = self.cov(comp, pend) pend_kappa = self.cov(pend) # Use the sub-Cholesky. obsv_chol = comp_pend_chol[:comp.shape[0],:comp.shape[0]] # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.cho_solve((obsv_chol, True), pend_cross) # Finding predictive means and variances. pend_m = np.dot(pend_cross.T, alpha) + self.mean pend_K = pend_kappa - np.dot(pend_cross.T, beta) # Take the Cholesky of the predictive covariance. pend_chol = spla.cholesky(pend_K, lower=True) # Make predictions. npr.set_state(self.randomstate) pend_fant = np.dot(pend_chol, npr.randn(pend.shape[0],self.pending_samples)) + pend_m[:,None] # Include the fantasies. fant_vals = np.concatenate( (np.tile(vals[:,np.newaxis], (1,self.pending_samples)), pend_fant)) # Compute bests over the fantasies. bests = np.min(fant_vals, axis=0) # Now generalize from these fantasies. cand_cross = self.cov(comp_pend, cand) # Solve the linear systems. alpha = spla.cho_solve((comp_pend_chol, True), fant_vals - self.mean) beta = spla.solve_triangular(comp_pend_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v[:,np.newaxis]) u = (bests[np.newaxis,:] - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) return np.mean(ei, axis=1) def sample_hypers(self, comp, vals): if self.noiseless: self.noise = 1e-3 self._sample_noiseless(comp, vals) else: self._sample_noisy(comp, vals) self._sample_ls(comp, vals) self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls)) def _sample_ls(self, comp, vals): def logprob(ls): if np.any(ls < 0) or np.any(ls > self.max_ls): return -np.inf cov = (self.amp2 * (self.cov_func(ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + self.noise*np.eye(comp.shape[0])) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - self.mean) lp = (-np.sum(np.log(np.diag(chol))) - 0.5*np.dot(vals-self.mean, solve)) return lp self.ls = util.slice_sample(self.ls, logprob, compwise=True) def _sample_noisy(self, comp, vals): def logprob(hypers): mean = hypers[0] amp2 = hypers[1] noise = hypers[2] # This is pretty hacky, but keeps things sane. if mean > np.max(vals) or mean < np.min(vals): return -np.inf if amp2 < 0 or noise < 0: return -np.inf cov = (amp2 * (self.cov_func(self.ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + noise*np.eye(comp.shape[0])) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-mean, solve) # Roll in noise horseshoe prior. lp += np.log(np.log(1 + (self.noise_scale/noise)**2)) # Roll in amplitude lognormal prior lp -= 0.5*(np.log(np.sqrt(amp2))/self.amp2_scale)**2 return lp hypers = util.slice_sample(np.array( [self.mean, self.amp2, self.noise]), logprob, compwise=False) self.mean = hypers[0] self.amp2 = hypers[1] self.noise = hypers[2] def _sample_noiseless(self, comp, vals): def logprob(hypers): mean = hypers[0] amp2 = hypers[1] noise = 1e-3 # This is pretty hacky, but keeps things sane. if mean > np.max(vals) or mean < np.min(vals): return -np.inf if amp2 < 0: return -np.inf cov = (amp2 * (self.cov_func(self.ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + noise*np.eye(comp.shape[0])) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-mean, solve) # Roll in amplitude lognormal prior lp -= 0.5*(np.log(np.sqrt(amp2))/self.amp2_scale)**2 return lp hypers = util.slice_sample(np.array( [self.mean, self.amp2, self.noise]), logprob, compwise=False) self.mean = hypers[0] self.amp2 = hypers[1] self.noise = 1e-3 def optimize_hypers(self, comp, vals): mygp = gp.GP(self.cov_func.__name__) mygp.real_init(comp.shape[1], vals) mygp.optimize_hypers(comp,vals) self.mean = mygp.mean self.ls = mygp.ls self.amp2 = mygp.amp2 self.noise = mygp.noise # Save hyperparameter samples self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls)) self.dump_hypers() return ================================================ FILE: src/aup/Proposer/spearmint/chooser/GPEIperSecChooser.py ================================================ ## # Copyright (C) 2012 Jasper Snoek, Hugo Larochelle and Ryan P. Adams # # This code is written for research and educational purposes only to # supplement the paper entitled # "Practical Bayesian Optimization of Machine Learning Algorithms" # by Snoek, Larochelle and Adams # Advances in Neural Information Processing Systems, 2012 # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . import os from .. import gp import sys from .. import util import tempfile import numpy as np import numpy.random as npr import scipy.linalg as spla import scipy.stats as sps import scipy.optimize as spo import pickle from ..helpers import * from ..Locker import * import logging logger = logging.getLogger(__name__) log = logger.debug def init(expt_dir, arg_string): args = util.unpack_args(arg_string) return GPEIperSecChooser(expt_dir, **args) """ Chooser module for the Gaussian process expected improvement per second (EI) acquisition function. Candidates are sampled densely in the unit hypercube and then a subset of the most promising points are optimized to maximize EI per second over hyperparameter samples. Slice sampling is used to sample Gaussian process hyperparameters for two GPs, one over the objective function and the other over the running time of the algorithm. """ class GPEIperSecChooser: def __init__(self, expt_dir, covar="Matern52", mcmc_iters=10, pending_samples=100, noiseless=False, burnin=100, grid_subset=20): self.cov_func = getattr(gp, covar) self.locker = Locker() self.state_pkl = os.path.join(expt_dir, self.__module__ + ".pkl") self.stats_file = os.path.join(expt_dir, self.__module__ + "_hyperparameters.txt") self.mcmc_iters = int(mcmc_iters) self.burnin = int(burnin) self.needs_burnin = True self.pending_samples = pending_samples self.D = -1 self.hyper_iters = 1 # Number of points to optimize EI over self.grid_subset = int(grid_subset) self.noiseless = bool(int(noiseless)) self.hyper_samples = [] self.time_hyper_samples = [] self.noise_scale = 0.1 # horseshoe prior self.amp2_scale = 1 # zero-mean log normal prior self.max_ls = 10 # top-hat prior on length scales self.time_noise_scale = 0.1 # horseshoe prior self.time_amp2_scale = 1 # zero-mean log normal prior self.time_max_ls = 10 # top-hat prior on length scales self.ls = None self.amp2 = None self.noise = None self.mean = None self.time_ls = None self.time_amp2 = None self.time_mean = None self.time_noise = None # A simple function to dump out hyperparameters to allow for a hot start # if the optimization is restarted. def dump_hypers(self): self.locker.lock_wait(self.state_pkl) # Write the hyperparameters out to a Pickle. fh = tempfile.NamedTemporaryFile(mode='wb', delete=False) pickle.dump({ 'dims' : self.D, 'ls' : self.ls, 'amp2' : self.amp2, 'noise' : self.noise, 'mean' : self.mean, 'time_ls' : self.time_ls, 'time_amp2' : self.time_amp2, 'time_noise' : self.time_noise, 'time_mean' : self.time_mean }, fh) fh.close() # Use an atomic move for better NFS happiness. cmd = 'mv "%s" "%s"' % (fh.name, self.state_pkl) os.system(cmd) # TODO: Should check system-dependent return status. self.locker.unlock(self.state_pkl) def _real_init(self, dims, values, durations): self.locker.lock_wait(self.state_pkl) if os.path.exists(self.state_pkl): fh = open(self.state_pkl, 'rb') state = pickle.load(fh) fh.close() self.D = state['dims'] self.ls = state['ls'] self.amp2 = state['amp2'] self.noise = state['noise'] self.mean = state['mean'] self.time_ls = state['time_ls'] self.time_amp2 = state['time_amp2'] self.time_noise = state['time_noise'] self.time_mean = state['time_mean'] else: # Input dimensionality. self.D = dims # Initial length scales. self.ls = np.ones(self.D) self.time_ls = np.ones(self.D) # Initial amplitude. self.amp2 = np.std(values)+1e-4 self.time_amp2 = np.std(durations)+1e-4 # Initial observation noise. self.noise = 1e-3 self.time_noise = 1e-3 # Initial mean. self.mean = np.mean(values) self.time_mean = np.mean(np.log(durations)) self.locker.unlock(self.state_pkl) def cov(self, amp2, ls, x1, x2=None): if x2 is None: return amp2 * (self.cov_func(ls, x1, None) + 1e-6*np.eye(x1.shape[0])) else: return amp2 * self.cov_func(ls, x1, x2) # Given a set of completed 'experiments' in the unit hypercube with # corresponding objective 'values', pick from the next experiment to # run according to the acquisition function. def next(self, grid, values, durations, candidates, pending, complete): # Don't bother using fancy GP stuff at first. if complete.shape[0] < 2: return int(candidates[0]) # Perform the real initialization. if self.D == -1: self._real_init(grid.shape[1], values[complete], durations[complete]) # Grab out the relevant sets. comp = grid[complete,:] cand = grid[candidates,:] pend = grid[pending,:] vals = values[complete] durs = durations[complete] # Bring time into the log domain before we do anything # to maintain strict positivity durs = np.log(durs) # Spray a set of candidates around the min so far numcand = cand.shape[0] best_comp = np.argmin(vals) cand2 = np.vstack((np.random.randn(10,comp.shape[1])*0.001 + comp[best_comp,:], cand)) if self.mcmc_iters > 0: # Possibly burn in. if self.needs_burnin: for mcmc_iter in range(self.burnin): self.sample_hypers(comp, vals, durs) log("BURN %d/%d] mean: %.2f amp: %.2f " "noise: %.4f min_ls: %.4f max_ls: %.4f" % (mcmc_iter+1, self.burnin, self.mean, np.sqrt(self.amp2), self.noise, np.min(self.ls), np.max(self.ls))) self.needs_burnin = False # Sample from hyperparameters. # Adjust the candidates to hit ei/sec peaks self.hyper_samples = [] for mcmc_iter in range(self.mcmc_iters): self.sample_hypers(comp, vals, durs) log("%d/%d] mean: %.2f amp: %.2f noise: %.4f " "min_ls: %.4f max_ls: %.4f" % (mcmc_iter+1, self.mcmc_iters, self.mean, np.sqrt(self.amp2), self.noise, np.min(self.ls), np.max(self.ls))) log("%d/%d] time_mean: %.2fs time_amp: %.2f time_noise: %.4f " "time_min_ls: %.4f time_max_ls: %.4f" % (mcmc_iter+1, self.mcmc_iters, np.exp(self.time_mean), np.sqrt(self.time_amp2), np.exp(self.time_noise), np.min(self.time_ls), np.max(self.time_ls))) self.dump_hypers() # Pick the top candidates to optimize over overall_ei = self.ei_over_hypers(comp,pend,cand2,vals,durs) inds = np.argsort(np.mean(overall_ei, axis=1))[-self.grid_subset:] cand2 = cand2[inds,:] # Adjust the candidates to hit ei peaks b = []# optimization bounds for i in range(0, cand.shape[1]): b.append((0, 1)) for i in range(0, cand2.shape[0]): log("Optimizing candidate %d/%d" % (i+1, cand2.shape[0])) ret = spo.fmin_l_bfgs_b(self.grad_optimize_ei_over_hypers, cand2[i,:].flatten(), args=(comp,vals,durs,True), bounds=b, disp=0) cand2[i,:] = ret[0] cand = np.vstack((cand, cand2)) overall_ei = self.ei_over_hypers(comp,pend,cand,vals,durs) best_cand = np.argmax(np.mean(overall_ei, axis=1)) self.dump_hypers() if (best_cand >= numcand): return (int(numcand), cand[best_cand,:]) return int(candidates[best_cand]) else: # Optimize hyperparameters self.optimize_hypers(comp, vals, durs) log("mean: %f amp: %f noise: %f " "min_ls: %f max_ls: %f" % (self.mean, np.sqrt(self.amp2), self.noise, np.min(self.ls), np.max(self.ls))) # Pick the top candidates to optimize over ei = self.compute_ei_per_s(comp, pend, cand2, vals, durs) inds = np.argsort(np.mean(overall_ei, axis=1))[-self.grid_subset:] cand2 = cand2[inds,:] # Adjust the candidates to hit ei peaks b = []# optimization bounds for i in range(0, cand.shape[1]): b.append((0, 1)) for i in range(0, cand2.shape[0]): log("Optimizing candidate %d/%d" % (i+1, cand2.shape[0])) ret = spo.fmin_l_bfgs_b(self.grad_optimize_ei, cand2[i,:].flatten(), args=(comp,vals,durs,True), bounds=b, disp=0) cand2[i,:] = ret[0] cand = np.vstack((cand, cand2)) ei = self.compute_ei_per_s(comp, pend, cand, vals, durs) best_cand = np.argmax(ei) self.dump_hypers() if (best_cand >= numcand): return (int(numcand), cand[best_cand,:]) return int(candidates[best_cand]) # Compute EI over hyperparameter samples def ei_over_hypers(self,comp,pend,cand,vals,durs): overall_ei = np.zeros((cand.shape[0], self.mcmc_iters)) for mcmc_iter in range(self.mcmc_iters): hyper = self.hyper_samples[mcmc_iter] time_hyper = self.time_hyper_samples[mcmc_iter] self.mean = hyper[0] self.noise = hyper[1] self.amp2 = hyper[2] self.ls = hyper[3] self.time_mean = time_hyper[0] self.time_noise = time_hyper[1] self.time_amp2 = time_hyper[2] self.time_ls = time_hyper[3] overall_ei[:,mcmc_iter] = self.compute_ei_per_s(comp, pend, cand, vals, durs.squeeze()) return overall_ei def check_grad_ei_per(self, cand, comp, vals, durs): (ei,dx1) = self.grad_optimize_ei_over_hypers(cand, comp, vals, durs) dx2 = dx1*0 idx = np.zeros(cand.shape[0]) for i in range(0, cand.shape[0]): idx[i] = 1e-6 (ei1,tmp) = self.grad_optimize_ei_over_hypers(cand + idx, comp, vals, durs) (ei2,tmp) = self.grad_optimize_ei_over_hypers(cand - idx, comp, vals, durs) dx2[i] = (ei - ei2)/(2*1e-6) idx[i] = 0 log('computed grads %s'%dx1) log('finite diffs %s'% dx2) log(dx1/dx2) log(np.sum((dx1 - dx2)**2)) time.sleep(2) # Adjust points by optimizing EI over a set of hyperparameter samples def grad_optimize_ei_over_hypers(self, cand, comp, vals, durs, compute_grad=True): summed_ei = 0 summed_grad_ei = np.zeros(cand.shape).flatten() for mcmc_iter in range(self.mcmc_iters): hyper = self.hyper_samples[mcmc_iter] time_hyper = self.time_hyper_samples[mcmc_iter] self.mean = hyper[0] self.noise = hyper[1] self.amp2 = hyper[2] self.ls = hyper[3] self.time_mean = time_hyper[0] self.time_noise = time_hyper[1] self.time_amp2 = time_hyper[2] self.time_ls = time_hyper[3] if compute_grad: (ei,g_ei) = self.grad_optimize_ei(cand,comp,vals,durs,compute_grad) summed_grad_ei = summed_grad_ei + g_ei else: ei = self.grad_optimize_ei(cand,comp,vals,durs,compute_grad) summed_ei += ei if compute_grad: return (summed_ei, summed_grad_ei) else: return summed_ei def grad_optimize_ei(self, cand, comp, vals, durs, compute_grad=True): # Here we have to compute the gradients for ei per second # This means deriving through the two kernels, the one for predicting # time and the one predicting ei best = np.min(vals) cand = np.reshape(cand, (-1, comp.shape[1])) # First we make predictions for the durations # Compute covariances comp_time_cov = self.cov(self.time_amp2, self.time_ls, comp) cand_time_cross = self.cov(self.time_amp2, self.time_ls,comp,cand) # Cholesky decompositions obsv_time_cov = comp_time_cov + self.time_noise*np.eye(comp.shape[0]) obsv_time_chol = spla.cholesky( obsv_time_cov, lower=True ) # Linear systems t_alpha = spla.cho_solve((obsv_time_chol, True), durs - self.time_mean) # Predict marginal mean times and (possibly) variances func_time_m = np.dot(cand_time_cross.T, t_alpha) + self.time_mean # We don't really need the time variances now #func_time_v = self.time_amp2*(1+1e-6) - np.sum(t_beta**2, axis=0) # Bring time out of the log domain func_time_m = np.exp(func_time_m) # Compute derivative of cross-distances. grad_cross_r = gp.grad_dist2(self.time_ls, comp, cand) # Apply covariance function cov_grad_func = getattr(gp, 'grad_' + self.cov_func.__name__) cand_cross_grad = cov_grad_func(self.time_ls, comp, cand) grad_cross_t = np.squeeze(cand_cross_grad) # Now compute the gradients w.r.t. ei # The primary covariances for prediction. comp_cov = self.cov(self.amp2, self.ls, comp) cand_cross = self.cov(self.amp2, self.ls, comp, cand) # Compute the required Cholesky. obsv_cov = comp_cov + self.noise*np.eye(comp.shape[0]) obsv_chol = spla.cholesky( obsv_cov, lower=True ) cand_cross_grad = cov_grad_func(self.ls, comp, cand) # Predictive things. # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.solve_triangular(obsv_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v) u = (best - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*(u*ncdf + npdf) ei_per_s = -np.sum(ei/func_time_m) if not compute_grad: return ei grad_time_xp_m = np.dot(t_alpha.transpose(),grad_cross_t) # Gradients of ei w.r.t. mean and variance g_ei_m = -ncdf g_ei_s2 = 0.5*npdf / func_s # Apply covariance function grad_cross = np.squeeze(cand_cross_grad) grad_xp_m = np.dot(alpha.transpose(),grad_cross) grad_xp_v = np.dot(-2*spla.cho_solve((obsv_chol, True), cand_cross).transpose(),grad_cross) grad_xp = 0.5*self.amp2*(grad_xp_m*g_ei_m + grad_xp_v*g_ei_s2) grad_time_xp_m = 0.5*self.time_amp2*grad_time_xp_m*func_time_m grad_xp = (func_time_m*grad_xp - ei*grad_time_xp_m)/(func_time_m**2) return ei_per_s, grad_xp.flatten() def compute_ei_per_s(self, comp, pend, cand, vals, durs): # First we make predictions for the durations as that # doesn't depend on pending experiments # Compute covariances comp_time_cov = self.cov(self.time_amp2, self.time_ls, comp) cand_time_cross = self.cov(self.time_amp2, self.time_ls,comp,cand) # Cholesky decompositions obsv_time_cov = comp_time_cov + self.time_noise*np.eye(comp.shape[0]) obsv_time_chol = spla.cholesky( obsv_time_cov, lower=True ) # Linear systems t_alpha = spla.cho_solve((obsv_time_chol, True), durs - self.time_mean) #t_beta = spla.solve_triangular(obsv_time_chol, cand_time_cross, lower=True) # Predict marginal mean times and (possibly) variances func_time_m = np.dot(cand_time_cross.T, t_alpha) + self.time_mean # We don't really need the time variances now #func_time_v = self.time_amp2*(1+1e-6) - np.sum(t_beta**2, axis=0) # Bring time out of the log domain func_time_m = np.exp(func_time_m) if pend.shape[0] == 0: # If there are no pending, don't do anything fancy. # Current best. best = np.min(vals) # The primary covariances for prediction. comp_cov = self.cov(self.amp2, self.ls, comp) cand_cross = self.cov(self.amp2, self.ls, comp, cand) # Compute the required Cholesky. obsv_cov = comp_cov + self.noise*np.eye(comp.shape[0]) obsv_chol = spla.cholesky( obsv_cov, lower=True ) # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.solve_triangular(obsv_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v) u = (best - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) ei_per_s = ei/func_time_m return ei_per_s else: # If there are pending experiments, fantasize their outcomes. # Create a composite vector of complete and pending. comp_pend = np.concatenate((comp, pend)) # Compute the covariance and Cholesky decomposition. comp_pend_cov = self.cov(self.amp2, self.ls, comp_pend) + self.noise*np.eye(comp_pend.shape[0]) comp_pend_chol = spla.cholesky(comp_pend_cov, lower=True) # Compute submatrices. pend_cross = self.cov(self.amp2, self.ls, comp, pend) pend_kappa = self.cov(self.amp2, self.ls, pend) # Use the sub-Cholesky. obsv_chol = comp_pend_chol[:comp.shape[0],:comp.shape[0]] # Solve the linear systems. alpha = spla.cho_solve((obsv_chol, True), vals - self.mean) beta = spla.cho_solve((obsv_chol, True), pend_cross) # Finding predictive means and variances. pend_m = np.dot(pend_cross.T, alpha) + self.mean pend_K = pend_kappa - np.dot(pend_cross.T, beta) # Take the Cholesky of the predictive covariance. pend_chol = spla.cholesky(pend_K, lower=True) # Make predictions. pend_fant = np.dot(pend_chol, npr.randn(pend.shape[0],self.pending_samples)) + pend_m[:,None] # Include the fantasies. fant_vals = np.concatenate((np.tile(vals[:,np.newaxis], (1,self.pending_samples)), pend_fant)) # Compute bests over the fantasies. bests = np.min(fant_vals, axis=0) # Now generalize from these fantasies. cand_cross = self.cov(self.amp2, self.ls, comp_pend, cand) # Solve the linear systems. alpha = spla.cho_solve((comp_pend_chol, True), fant_vals - self.mean) beta = spla.solve_triangular(comp_pend_chol, cand_cross, lower=True) # Predict the marginal means and variances at candidates. func_m = np.dot(cand_cross.T, alpha) + self.mean func_v = self.amp2*(1+1e-6) - np.sum(beta**2, axis=0) # Expected improvement func_s = np.sqrt(func_v[:,np.newaxis]) u = (bests[np.newaxis,:] - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) return np.divide(np.mean(ei, axis=1), func_time_m) def sample_hypers(self, comp, vals, durs): if self.noiseless: self.noise = 1e-3 self._sample_noiseless(comp, vals) else: self._sample_noisy(comp, vals) self._sample_ls(comp, vals) self._sample_time_noisy(comp, durs.squeeze()) self._sample_time_ls(comp, durs.squeeze()) self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls)) self.time_hyper_samples.append((self.time_mean, self.time_noise, self.time_amp2, self.time_ls)) def _sample_ls(self, comp, vals): def logprob(ls): if np.any(ls < 0) or np.any(ls > self.max_ls): return -np.inf cov = self.amp2 * (self.cov_func(ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + self.noise*np.eye(comp.shape[0]) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - self.mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-self.mean, solve) return lp self.ls = util.slice_sample(self.ls, logprob, compwise=True) def _sample_time_ls(self, comp, vals): def logprob(ls): if np.any(ls < 0) or np.any(ls > self.time_max_ls): return -np.inf cov = self.time_amp2 * (self.cov_func(ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + self.time_noise*np.eye(comp.shape[0]) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - self.time_mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-self.time_mean, solve) return lp self.time_ls = util.slice_sample(self.time_ls, logprob, compwise=True) def _sample_noisy(self, comp, vals): def logprob(hypers): mean = hypers[0] amp2 = hypers[1] noise = hypers[2] # This is pretty hacky, but keeps things sane. if mean > np.max(vals) or mean < np.min(vals): return -np.inf if amp2 < 0 or noise < 0: return -np.inf cov = amp2 * (self.cov_func(self.ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + noise*np.eye(comp.shape[0]) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-mean, solve) # Roll in noise horseshoe prior. lp += np.log(np.log(1 + (self.noise_scale/noise)**2)) #lp -= 0.5*(np.log(noise)/self.noise_scale)**2 # Roll in amplitude lognormal prior lp -= 0.5*(np.log(amp2)/self.amp2_scale)**2 return lp hypers = util.slice_sample(np.array([self.mean, self.amp2, self.noise]), logprob, compwise=False) self.mean = hypers[0] self.amp2 = hypers[1] self.noise = hypers[2] def _sample_time_noisy(self, comp, vals): def logprob(hypers): mean = hypers[0] amp2 = hypers[1] noise = hypers[2] # This is pretty hacky, but keeps things sane. if mean > np.max(vals) or mean < np.min(vals): return -np.inf if amp2 < 0 or noise < 0: return -np.inf cov = amp2 * (self.cov_func(self.time_ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + noise*np.eye(comp.shape[0]) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-mean, solve) # Roll in noise horseshoe prior. lp += np.log(np.log(1 + (self.time_noise_scale/noise)**2)) #lp -= 0.5*(np.log(noise)/self.time_noise_scale)**2 # Roll in amplitude lognormal prior lp -= 0.5*(np.log(np.sqrt(amp2))/self.time_amp2_scale)**2 return lp hypers = util.slice_sample(np.array([self.time_mean, self.time_amp2, self.time_noise]), logprob, compwise=False) self.time_mean = hypers[0] self.time_amp2 = hypers[1] self.time_noise = hypers[2] def _sample_noiseless(self, comp, vals): def logprob(hypers): mean = hypers[0] amp2 = hypers[1] noise = 1e-3 # This is pretty hacky, but keeps things sane. if mean > np.max(vals) or mean < np.min(vals): return -np.inf if amp2 < 0: return -np.inf cov = amp2 * (self.cov_func(self.ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + noise*np.eye(comp.shape[0]) chol = spla.cholesky(cov, lower=True) solve = spla.cho_solve((chol, True), vals - mean) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-mean, solve) # Roll in amplitude lognormal prior lp -= 0.5*(np.log(amp2)/self.amp2_scale)**2 return lp hypers = util.slice_sample(np.array([self.mean, self.amp2, self.noise]), logprob, compwise=False) self.mean = hypers[0] self.amp2 = hypers[1] self.noise = 1e-3 def optimize_hypers(self, comp, vals, durs): # First the GP to observations mygp = gp.GP(self.cov_func.__name__) mygp.real_init(comp.shape[1], vals) mygp.optimize_hypers(comp,vals) self.mean = mygp.mean self.ls = mygp.ls self.amp2 = mygp.amp2 self.noise = mygp.noise # Now the GP to times timegp = gp.GP(self.cov_func.__name__) timegp.real_init(comp.shape[1], durs) timegp.optimize_hypers(comp, durs) self.time_mean = timegp.mean self.time_amp2 = timegp.amp2 self.time_noise = timegp.noise self.time_ls = timegp.ls # Save hyperparameter samples self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls)) self.time_hyper_samples.append((self.time_mean, self.time_noise, self.time_amp2, self.time_ls)) self.dump_hypers() ================================================ FILE: src/aup/Proposer/spearmint/chooser/RandomChooser.py ================================================ ## # Copyright (C) 2012 Jasper Snoek, Hugo Larochelle and Ryan P. Adams # # This code is written for research and educational purposes only to # supplement the paper entitled # "Practical Bayesian Optimization of Machine Learning Algorithms" # by Snoek, Larochelle and Adams # Advances in Neural Information Processing Systems, 2012 # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . import numpy as np import numpy.random as npr def init(expt_dir, arg_string): return RandomChooser() class RandomChooser: def __init__(self): pass def next(self, grid, values, durations, candidates, pending, complete): return int(candidates[int(np.floor(candidates.shape[0]*npr.rand()))]) ================================================ FILE: src/aup/Proposer/spearmint/chooser/RandomForestEIChooser.py ================================================ import numpy as np import numpy.random as npr import scipy.stats as sps import sklearn.ensemble import sklearn.ensemble.forest from .. import util import logging logger = logging.getLogger(__name__) log = logger.debug from sklearn.externals.joblib import Parallel, delayed def init(expt_dir, arg_string): args = util.unpack_args(arg_string) return RandomForestEIChooser(**args) class RandomForestRegressorWithVariance(sklearn.ensemble.RandomForestRegressor): def predict(self,X): # Check data X = np.atleast_2d(X) all_y_hat = [ tree.predict(X) for tree in self.estimators_ ] # Reduce y_hat = sum(all_y_hat) / self.n_estimators y_var = np.var(all_y_hat,axis=0,ddof=1) return y_hat, y_var class RandomForestEIChooser: def __init__(self,n_trees=50, max_depth=None, min_samples_split=1, max_monkeys=7, max_features="auto", n_jobs=1, random_state=None): self.n_trees = float(n_trees) self.max_depth = max_depth self.min_samples_split = min_samples_split self.max_features = max_features self.n_jobs = float(n_jobs) self.random_state = random_state self.rf = RandomForestRegressorWithVariance(n_estimators=n_trees, max_depth=max_depth, min_samples_split=min_samples_split, max_features=max_features, n_jobs=n_jobs, random_state=random_state) def next(self, grid, values, durations, candidates, pending, complete): # Grab out the relevant sets. # Don't bother using fancy RF stuff at first. if complete.shape[0] < 2: return int(candidates[0]) # Grab out the relevant sets. comp = grid[complete,:] cand = grid[candidates,:] pend = grid[pending,:] vals = values[complete] self.rf.fit(comp,vals) if pend.shape[0] != 0: # Generate fantasies for pending func_m, func_v = self.rf.predict(pend) vals_pend = func_m + np.sqrt(func_v) + npr.randn(func_m.shape[0]) # Re-fit using fantasies self.rf.fit(np.vstack[comp,pend],np.hstack[vals,vals_pend]) # Predict the marginal means and variances at candidates. func_m, func_v = self.rf.predict(cand) # Current best. best = np.min(vals) # Expected improvement func_s = np.sqrt(func_v) + 0.0001 u = (best - func_m) / func_s ncdf = sps.norm.cdf(u) npdf = sps.norm.pdf(u) ei = func_s*( u*ncdf + npdf) best_cand = np.argmax(ei) ei.sort() return int(candidates[best_cand]) ================================================ FILE: src/aup/Proposer/spearmint/chooser/SequentialChooser.py ================================================ ## # Copyright (C) 2012 Jasper Snoek, Hugo Larochelle and Ryan P. Adams # # This code is written for research and educational purposes only to # supplement the paper entitled # "Practical Bayesian Optimization of Machine Learning Algorithms" # by Snoek, Larochelle and Adams # Advances in Neural Information Processing Systems, 2012 # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . import numpy as np def init(expt_dir, arg_string): return SequentialChooser() class SequentialChooser: def __init__(self): pass def next(self, grid, values, durations, candidates, pending, complete): return int(candidates[0]) ================================================ FILE: src/aup/Proposer/spearmint/chooser/__init__.py ================================================ ================================================ FILE: src/aup/Proposer/spearmint/chooser/cma.py ================================================ #!/usr/bin/env python """Module cma implements the CMA-ES, Covariance Matrix Adaptation Evolution Strategy, a stochastic optimizer for robust non-linear non-convex derivative-free function minimization for Python versions 2.6, 2.7, 3.x (for Python 2.5 class SolutionDict would need to be re-implemented, because it depends on collections.MutableMapping, since version 0.91.01). CMA-ES searches for a minimizer (a solution x in R**n) of an objective function f (cost function), such that f(x) is minimal. Regarding f, only function values for candidate solutions need to be available, gradients are not necessary. Even less restrictive, only a passably reliable ranking of the candidate solutions in each iteration is necessary, the function values itself do not matter. Some termination criteria however depend on actual f-values. Two interfaces are provided: - function `fmin(func, x0, sigma0,...)` runs a complete minimization of the objective function func with CMA-ES. - class `CMAEvolutionStrategy` allows for minimization such that the control of the iteration loop remains with the user. Used packages: - unavoidable: `numpy` (see `barecmaes2.py` if `numpy` is not available), - avoidable with small changes: `time`, `sys` - optional: `matplotlib.pylab` (for `plot` etc., highly recommended), `pprint` (pretty print), `pickle` (in class `Sections`), `doctest`, `inspect`, `pygsl` (never by default) Testing ------- The code can be tested on a given system. Typing:: python cma.py --test or in the Python shell ``ipython -pylab``:: run cma.py --test runs ``doctest.testmod(cma)`` showing only exceptions (and not the tests that fail due to small differences in the output) and should run without complaints in about under two minutes. On some systems, the pop up windows must be closed manually to continue and finish the test. Install ------- The code can be installed by:: python cma.py --install which solely calls the ``setup`` function from the ``distutils.core`` package for installation. Example ------- :: import cma help(cma) # "this" help message, use cma? in ipython help(cma.fmin) help(cma.CMAEvolutionStrategy) help(cma.Options) cma.Options('tol') # display 'tolerance' termination options cma.Options('verb') # display verbosity options res = cma.fmin(cma.Fcts.tablet, 15 * [1], 1) res[0] # best evaluated solution res[5] # mean solution, presumably better with noise :See: `fmin()`, `Options`, `CMAEvolutionStrategy` :Author: Nikolaus Hansen, 2008-2012 :License: GPL 2 and 3 """ from __future__ import division # future is >= 3.0, this code has mainly been used with 2.6 & 2.7 from __future__ import with_statement # only necessary for python 2.5 and not in heavy use # from __future__ import collections.MutableMapping # does not exist in future, otherwise 2.5 would work from __future__ import print_function # for cross-checking, available from python 2.6 import sys if sys.version.startswith('3'): # in python 3.x range = range raw_input = input __version__ = "0.92.04 $Revision: 3322 $ $Date: 2012-11-22 18:05:10 +0100 (Thu, 22 Nov 2012) $" # bash: svn propset svn:keywords 'Date Revision' cma.py # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, version 2 or 3. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # # for testing: # pyflakes cma.py # finds bugs by static analysis # pychecker --limit 60 cma.py # also executes, gives 60 warnings (all checked) # python cma.py -t -quiet # executes implemented tests based on doctest # to create a html documentation file: # pydoc -w cma # edit the header (remove local pointers) # epydoc cma.py # comes close to javadoc but does not find the # # links of function references etc # doxygen needs @package cma as first line in the module docstring # some things like class attributes are not interpreted correctly # sphinx: doc style of doc.python.org, could not make it work # TODO: make those options that are only used in fmin an error in init of CMA, but still Options() should # work as input to CMA. # TODO: add a default logger in CMAEvolutionStrategy, see fmin() and optimize() first # tell() should probably not add data, but optimize() should handle even an after_iteration_handler. # TODO: CMAEvolutionStrategy(ones(10), 1).optimize(cma.fcts.elli) # should work like fmin # one problem: the data logger is not default and seemingly cannot be attached in one line # TODO: check combination of boundary handling and transformation: penalty must be computed # on gp.pheno(x_geno, bounds=None), but without bounds, check/remove usage of .geno everywhere # TODO: check whether all new solutions are put into self.sent_solutions # TODO: separate initialize==reset_state from __init__ # TODO: introduce Zpos == diffC which makes the code more consistent and the active update "exact" # TODO: split tell into a variable transformation part and the "pure" functionality # usecase: es.tell_geno(X, [func(es.pheno(x)) for x in X]) # genotypic repair is not part of tell_geno # TODO: read settable "options" from a (properties) file, see myproperties.py # # typical parameters in scipy.optimize: disp, xtol, ftol, maxiter, maxfun, callback=None # maxfev, diag (A sequency of N positive entries that serve as # scale factors for the variables.) # full_output -- non-zero to return all optional outputs. # If xtol < 0.0, xtol is set to sqrt(machine_precision) # 'infot -- a dictionary of optional outputs with the keys: # 'nfev': the number of function calls... # # see eg fmin_powell # typical returns # x, f, dictionary d # (xopt, {fopt, gopt, Hopt, func_calls, grad_calls, warnflag}, ) # # TODO: keep best ten solutions # TODO: implement constraints handling # TODO: option full_output -- non-zero to return all optional outputs. # TODO: extend function unitdoctest, or use unittest? # TODO: implement equal-fitness termination, covered by stagnation? # TODO: apply style guide: no capitalizations!? # TODO: check and test dispdata() # TODO: eigh(): thorough testing would not hurt # # TODO (later): implement readSignals from a file like properties file (to be called after tell()) import time # not really essential import collections, numpy as np # arange, cos, size, eye, inf, dot, floor, outer, zeros, linalg.eigh, sort, argsort, random, ones,... from numpy import inf, array, dot, exp, log, sqrt, sum # to access the built-in sum fct: __builtins__.sum or del sum removes the imported sum and recovers the shadowed try: import matplotlib.pylab as pylab # also: use ipython -pylab show = pylab.show savefig = pylab.savefig # we would like to be able to use cma.savefig() etc closefig = pylab.close except: pylab = None print(' Could not import matplotlib.pylab, therefore ``cma.plot()`` etc. is not available') def show(): pass __docformat__ = "reStructuredText" # this hides some comments entirely? sys.py3kwarning = True # TODO: out-comment from version 2.6 # why not package math? # TODO: check scitools.easyviz and how big the adaptation would be # changes: # 12/10/25: removed useless check_points from fmin interface # 12/10/17: bug fix printing number of infeasible samples, moved not-in-use methods # timesCroot and divCroot to the right class # 12/10/16 (0.92.00): various changes commit: bug bound[0] -> bounds[0], more_to_write fixed, # sigma_vec introduced, restart from elitist, trace normalization, max(mu,popsize/2) # is used for weight calculation. # 12/07/23: (bug:) BoundPenalty.update respects now genotype-phenotype transformation # 12/07/21: convert value True for noisehandling into 1 making the output compatible # 12/01/30: class Solution and more old stuff removed r3101 # 12/01/29: class Solution is depreciated, GenoPheno and SolutionDict do the job (v0.91.00, r3100) # 12/01/06: CMA_eigenmethod option now takes a function (integer still works) # 11/09/30: flat fitness termination checks also history length # 11/09/30: elitist option (using method clip_or_fit_solutions) # 11/09/xx: method clip_or_fit_solutions for check_points option for all sorts of # injected or modified solutions and even reliable adaptive encoding # 11/08/19: fixed: scaling and typical_x type clashes 1 vs array(1) vs ones(dim) vs dim * [1] # 11/07/25: fixed: fmin wrote first and last line even with verb_log==0 # fixed: method settableOptionsList, also renamed to versatileOptions # default seed depends on time now # 11/07/xx (0.9.92): added: active CMA, selective mirrored sampling, noise/uncertainty handling # fixed: output argument ordering in fmin, print now only used as function # removed: parallel option in fmin # 11/07/01: another try to get rid of the memory leak by replacing self.unrepaired = self[:] # 11/07/01: major clean-up and reworking of abstract base classes and of the documentation, # also the return value of fmin changed and attribute stop is now a method. # 11/04/22: bug-fix: option fixed_variables in combination with scaling # 11/04/21: stopdict is not a copy anymore # 11/04/15: option fixed_variables implemented # 11/03/23: bug-fix boundary update was computed even without boundaries # 11/03/12: bug-fix of variable annotation in plots # 11/02/05: work around a memory leak in numpy # 11/02/05: plotting routines improved # 10/10/17: cleaning up, now version 0.9.30 # 10/10/17: bug-fix: return values of fmin now use phenotyp (relevant # if input scaling_of_variables is given) # 08/10/01: option evalparallel introduced, # bug-fix for scaling being a vector # 08/09/26: option CMAseparable becomes CMA_diagonal # 08/10/18: some names change, test functions go into a class # 08/10/24: more refactorizing # 10/03/09: upper bound exp(min(1,...)) for step-size control # TODO: this would define the visible interface # __all__ = ['fmin', 'CMAEvolutionStrategy', 'plot', ...] # # emptysets = ('', (), [], {}) # array([]) does not work but also np.size(.) == 0 # "x in emptysets" cannot be well replaced by "not x" # which is also True for array([]) and None, but also for 0 and False, and False for NaN use_sent_solutions = True # 5-30% CPU slower, particularly for large lambda, will be mandatory soon #____________________________________________________________ #____________________________________________________________ # def unitdoctest(): """is used to describe test cases and might in future become helpful as an experimental tutorial as well. The main testing feature at the moment is by doctest with ``cma._test()`` or conveniently by ``python cma.py --test``. With the ``--verbose`` option added, the results will always slightly differ and many "failed" test cases might be reported. A simple first overall test: >>> import cma >>> res = cma.fmin(cma.fcts.elli, 3*[1], 1, CMA_diagonal=2, seed=1, verb_time=0) (3_w,7)-CMA-ES (mu_w=2.3,w_1=58%) in dimension 3 (seed=1) Covariance matrix is diagonal for 2 iterations (1/ccov=7.0) Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec 1 7 1.453161670768570e+04 1.2e+00 1.08e+00 1e+00 1e+00 2 14 3.281197961927601e+04 1.3e+00 1.22e+00 1e+00 2e+00 3 21 1.082851071704020e+04 1.3e+00 1.24e+00 1e+00 2e+00 100 700 8.544042012075362e+00 1.4e+02 3.18e-01 1e-03 2e-01 200 1400 5.691152415221861e-12 1.0e+03 3.82e-05 1e-09 1e-06 220 1540 3.890107746209078e-15 9.5e+02 4.56e-06 8e-11 7e-08 termination on tolfun : 1e-11 final/bestever f-value = 3.89010774621e-15 2.52273602735e-15 mean solution: [ -4.63614606e-08 -3.42761465e-10 1.59957987e-11] std deviation: [ 6.96066282e-08 2.28704425e-09 7.63875911e-11] Test on the Rosenbrock function with 3 restarts. The first trial only finds the local optimum, which happens in about 20% of the cases. >>> import cma >>> res = cma.fmin(cma.fcts.rosen, 4*[-1],1, ftarget=1e-6, restarts=3, verb_time=0, verb_disp=500, seed=3) (4_w,8)-CMA-ES (mu_w=2.6,w_1=52%) in dimension 4 (seed=3) Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec 1 8 4.875315645656848e+01 1.0e+00 8.43e-01 8e-01 8e-01 2 16 1.662319948123120e+02 1.1e+00 7.67e-01 7e-01 8e-01 3 24 6.747063604799602e+01 1.2e+00 7.08e-01 6e-01 7e-01 184 1472 3.701428610430019e+00 4.3e+01 9.41e-07 3e-08 5e-08 termination on tolfun : 1e-11 final/bestever f-value = 3.70142861043 3.70142861043 mean solution: [-0.77565922 0.61309336 0.38206284 0.14597202] std deviation: [ 2.54211502e-08 3.88803698e-08 4.74481641e-08 3.64398108e-08] (8_w,16)-CMA-ES (mu_w=4.8,w_1=32%) in dimension 4 (seed=4) Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec 1 1489 2.011376859371495e+02 1.0e+00 8.90e-01 8e-01 9e-01 2 1505 4.157106647905128e+01 1.1e+00 8.02e-01 7e-01 7e-01 3 1521 3.548184889359060e+01 1.1e+00 1.02e+00 8e-01 1e+00 111 3249 6.831867555502181e-07 5.1e+01 2.62e-02 2e-04 2e-03 termination on ftarget : 1e-06 final/bestever f-value = 6.8318675555e-07 1.18576673231e-07 mean solution: [ 0.99997004 0.99993938 0.99984868 0.99969505] std deviation: [ 0.00018973 0.00038006 0.00076479 0.00151402] >>> assert res[1] <= 1e-6 Notice the different termination conditions. Termination on the target function value ftarget prevents further restarts. Test of scaling_of_variables option >>> import cma >>> opts = cma.Options() >>> opts['seed'] = 456 >>> opts['verb_disp'] = 0 >>> opts['CMA_active'] = 1 >>> # rescaling of third variable: for searching in roughly >>> # x0 plus/minus 1e3*sigma0 (instead of plus/minus sigma0) >>> opts.scaling_of_variables = [1, 1, 1e3, 1] >>> res = cma.fmin(cma.fcts.rosen, 4 * [0.1], 0.1, **opts) termination on tolfun : 1e-11 final/bestever f-value = 2.68096173031e-14 1.09714829146e-14 mean solution: [ 1.00000001 1.00000002 1.00000004 1.00000007] std deviation: [ 3.00466854e-08 5.88400826e-08 1.18482371e-07 2.34837383e-07] The printed std deviations reflect the actual true value (not the one in the internal representation which would be different). >>> import cma >>> r = cma.fmin(cma.fcts.diffpow, 15 * [1], 1, CMA_dampsvec_fac=0.5, ftarget=1e-9) >>> assert(r[1] < 1e-9) >>> assert(r[2] < 13000) # only passed with CMA_dampsvec_fac :See: cma.main(), cma._test() """ pass #____________________________________________________________ #____________________________________________________________ # class BlancClass(object): """blanc container class for having a collection of attributes""" #_____________________________________________________________________ #_____________________________________________________________________ # class DerivedDictBase(collections.MutableMapping): """for conveniently adding features to a dictionary. The actual dictionary is in ``self.data``. Copy-paste and modify setitem, getitem, and delitem, if necessary""" def __init__(self, *args, **kwargs): # collections.MutableMapping.__init__(self) super(DerivedDictBase, self).__init__() # super(SolutionDict, self).__init__() # the same self.data = dict(*args, **kwargs) def __len__(self): return len(self.data) def __contains__(self, value): return value in self.data def __iter__(self): return iter(self.data) def __setitem__(self, key, value): """defines self[key] = value""" self.data[key] = value def __getitem__(self, key): """defines self[key]""" return self.data[key] def __delitem__(self, key): del self.data[key] class SolutionDict(DerivedDictBase): """dictionary with computation of an hash key for the inserted solutions and a stack of previously inserted same solutions. Each entry is meant to store additional information related to the solution. >>> import cma, numpy as np >>> d = cma.SolutionDict() >>> x = np.array([1,2,4]) >>> d[x] = {'x': x, 'iteration': 1} >>> d.get(x) == (d[x] if d.key(x) in d.keys() else None) The last line is always true. TODO: data_with_same_key behaves like a stack (see setitem and delitem), but rather should behave like a queue?! A queue is less consistent with the operation self[key] = ..., if self.data_with_same_key[key] is not empty. """ def __init__(self, *args, **kwargs): DerivedDictBase.__init__(self, *args, **kwargs) self.data_with_same_key = {} def key(self, x): try: return tuple(x) except TypeError: return x def __setitem__(self, key, value): """defines self[key] = value""" key = self.key(key) if key in self.data_with_same_key: self.data_with_same_key[key] += [self.data[key]] elif key in self.data: self.data_with_same_key[key] = [self.data[key]] self.data[key] = value def __getitem__(self, key): """defines self[key]""" return self.data[self.key(key)] def __delitem__(self, key): """remove only most current key-entry""" key = self.key(key) if key in self.data_with_same_key: if len(self.data_with_same_key[key]) == 1: self.data[key] = self.data_with_same_key.pop(key)[0] else: self.data[key] = self.data_with_same_key[key].pop(-1) else: del self.data[key] def truncate(self, max_len, min_iter): if len(self) > max_len: for k in list(self.keys()): if self[k]['iteration'] < min_iter: del self[k] # only deletes one item with k as key, should delete all? class SolutionDictOld(dict): """depreciated, SolutionDict should do, to be removed after SolutionDict has been successfully applied. dictionary with computation of an hash key for the inserted solutions and stack of previously inserted same solutions. Each entry is meant to store additional information related to the solution. Methods ``pop`` and ``get`` are modified accordingly. d = SolutionDict() x = array([1,2,4]) d.insert(x, {'x': x, 'iteration': 1}) d.get(x) == d[d.key(x)] if d.key(x) in d.keys() else d.get(x) is None TODO: not yet tested TODO: behaves like a stack (see _pop_derived), but rather should behave like a queue?! A queue is less consistent with the operation self[key] = ..., if self.more[key] is not empty. """ def __init__(self): self.more = {} # previously inserted same solutions self._pop_base = self.pop self.pop = self._pop_derived self._get_base = self.get self.get = self._get_derived def key(self, x): """compute the hash key of ``x``""" return tuple(x) def insert(self, x, datadict): key = self.key(x) if key in self.more: self.more[key] += [self[key]] elif key in self: self.more[key] = [self[key]] self[key] = datadict def _get_derived(self, x, default=None): return self._get_base(self.key(x), default) def _pop_derived(self, x): key = self.key(x) res = self[key] if key in self.more: if len(self.more[key]) == 1: self[key] = self.more.pop(key)[0] else: self[key] = self.more[key].pop(-1) return res class BestSolution(object): """container to keep track of the best solution seen""" def __init__(self, x=None, f=np.inf, evals=None): """initialize the best solution with `x`, `f`, and `evals`. Better solutions have smaller `f`-values. """ self.x = x self.x_geno = None self.f = f if f is not None and f is not np.nan else np.inf self.evals = evals self.evalsall = evals self.last = BlancClass() self.last.x = x self.last.f = f def update(self, arx, xarchive=None, arf=None, evals=None): """checks for better solutions in list `arx`, based on the smallest corresponding value in `arf`, alternatively, `update` may be called with a `BestSolution` instance like ``update(another_best_solution)`` in which case the better solution becomes the current best. `xarchive` is used to retrieve the genotype of a solution. """ if arf is not None: # find failsave minimum minidx = np.nanargmin(arf) if minidx is np.nan: return minarf = arf[minidx] # minarf = reduce(lambda x, y: y if y and y is not np.nan and y < x else x, arf, np.inf) if type(arx) == BestSolution: if self.evalsall is None: self.evalsall = arx.evalsall elif arx.evalsall is not None: self.evalsall = max((self.evalsall, arx.evalsall)) if arx.f is not None and arx.f < np.inf: self.update([arx.x], xarchive, [arx.f], arx.evals) return self elif minarf < np.inf and (minarf < self.f or self.f is None): self.x, self.f = arx[minidx], arf[minidx] self.x_geno = xarchive[self.x]['geno'] if xarchive is not None else None self.evals = None if not evals else evals - len(arf) + minidx+1 self.evalsall = evals elif evals: self.evalsall = evals self.last.x = arx[minidx] self.last.f = minarf def get(self): """return ``(x, f, evals)`` """ return self.x, self.f, self.evals, self.x_geno #____________________________________________________________ #____________________________________________________________ # class BoundPenalty(object): """Computes the boundary penalty. Must be updated each iteration, using the `update` method. Details ------- The penalty computes like ``sum(w[i] * (x[i]-xfeas[i])**2)``, where `xfeas` is the closest feasible (in-bounds) solution from `x`. The weight `w[i]` should be updated during each iteration using the update method. This class uses `GenoPheno.into_bounds` in method `update` to access domain boundary values and repair. This inconsistency is going to be removed in future. """ def __init__(self, bounds=None): """Argument bounds can be `None` or ``bounds[0]`` and ``bounds[1]`` are lower and upper domain boundaries, each is either `None` or a scalar or a list or array of appropriate size. """ ## # bounds attribute reminds the domain boundary values self.bounds = bounds self.gamma = 1 # a very crude assumption self.weights_initialized = False # gamma becomes a vector after initialization self.hist = [] # delta-f history def has_bounds(self): """return True, if any variable is bounded""" bounds = self.bounds if bounds in (None, [None, None]): return False for i in range(bounds[0]): if bounds[0][i] is not None and bounds[0][i] > -np.inf: return True for i in range(bounds[1]): if bounds[1][i] is not None and bounds[1][i] < np.inf: return True return False def repair(self, x, bounds=None, copy=False, copy_always=False): """sets out-of-bounds components of ``x`` on the bounds. Arguments --------- `bounds` can be `None`, in which case the "default" bounds are used, or ``[lb, ub]``, where `lb` and `ub` represent lower and upper domain bounds respectively that can be `None` or a scalar or a list or array of length ``len(self)`` code is more or less copy-paste from Solution.repair, but never tested """ # TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound # TODO: test whether np.max([bounds[0], x], axis=0) etc is speed relevant if bounds is None: bounds = self.bounds if copy_always: x_out = array(x, copy=True) if bounds not in (None, [None, None], (None, None)): # solely for effiency x_out = array(x, copy=True) if copy and not copy_always else x if bounds[0] is not None: if np.isscalar(bounds[0]): for i in range(len(x)): x_out[i] = max([bounds[0], x[i]]) else: for i in range(len(x)): if bounds[0][i] is not None: x_out[i] = max([bounds[0][i], x[i]]) if bounds[1] is not None: if np.isscalar(bounds[1]): for i in range(len(x)): x_out[i] = min([bounds[1], x[i]]) else: for i in range(len(x)): if bounds[1][i] is not None: x_out[i] = min([bounds[1][i], x[i]]) return x_out # convenience return #____________________________________________________________ # def __call__(self, x, archive, gp): """returns the boundary violation penalty for `x` ,where `x` is a single solution or a list or array of solutions. If `bounds` is not `None`, the values in `bounds` are used, see `__init__`""" if x in (None, (), []): return x if gp.bounds in (None, [None, None], (None, None)): return 0.0 if np.isscalar(x[0]) else [0.0] * len(x) # no penalty x_is_single_vector = np.isscalar(x[0]) x = [x] if x_is_single_vector else x pen = [] for xi in x: # CAVE: this does not work with already repaired values!! # CPU(N,lam,iter=20,200,100)?: 3s of 10s, array(xi): 1s (check again) # remark: one deep copy can be prevented by xold = xi first xpheno = gp.pheno(archive[xi]['geno']) xinbounds = gp.into_bounds(xpheno) fac = 1 # exp(0.1 * (log(self.scal) - np.mean(self.scal))) pen.append(sum(self.gamma * ((xinbounds - xpheno) / fac)**2) / len(xi)) return pen[0] if x_is_single_vector else pen #____________________________________________________________ # def feasible_ratio(self, solutions): """counts for each coordinate the number of feasible values in ``solutions`` and returns an array of length ``len(solutions[0])`` with the ratios. `solutions` is a list or array of repaired `Solution` instances """ count = np.zeros(len(solutions[0])) for x in solutions: count += x.unrepaired == x return count / float(len(solutions)) #____________________________________________________________ # def update(self, function_values, es, bounds=None): """updates the weights for computing a boundary penalty. Arguments --------- `function_values` all function values of recent population of solutions `es` `CMAEvolutionStrategy` object instance, in particular the method `into_bounds` of the attribute `gp` of type `GenoPheno` is used. `bounds` not (yet) in use other than for ``bounds == [None, None]`` nothing is updated. Reference: Hansen et al 2009, A Method for Handling Uncertainty... IEEE TEC, with addendum at http://www.lri.fr/~hansen/TEC2009online.pdf """ if bounds is None: bounds = self.bounds if bounds is None or (bounds[0] is None and bounds[1] is None): # no bounds ==> no penalty return self # len(function_values) * [0.0] # case without voilations N = es.N ### prepare # compute varis = sigma**2 * C_ii varis = es.sigma**2 * array(N * [es.C] if np.isscalar(es.C) else ( # scalar case es.C if np.isscalar(es.C[0]) else # diagonal matrix case [es.C[i][i] for i in range(N)])) # full matrix case # dmean = (es.mean - es.gp.into_bounds(es.mean)) / varis**0.5 dmean = (es.mean - es.gp.geno(es.gp.into_bounds(es.gp.pheno(es.mean)))) / varis**0.5 ### Store/update a history of delta fitness value fvals = sorted(function_values) l = 1 + len(fvals) val = fvals[3*l // 4] - fvals[l // 4] # exact interquartile range apart interpolation val = val / np.mean(varis) # new: val is normalized with sigma of the same iteration # insert val in history if np.isfinite(val) and val > 0: self.hist.insert(0, val) elif val == inf and len(self.hist) > 1: self.hist.insert(0, max(self.hist)) else: pass # ignore 0 or nan values if len(self.hist) > 20 + (3*N) / es.popsize: self.hist.pop() ### prepare dfit = np.median(self.hist) # median interquartile range damp = min(1, es.sp.mueff/10./N) ### set/update weights # Throw initialization error if len(self.hist) == 0: raise _Error('wrongful initialization, no feasible solution sampled. ' + 'Reasons can be mistakenly set bounds (lower bound not smaller than upper bound) or a too large initial sigma0 or... ' + 'See description of argument func in help(cma.fmin) or an example handling infeasible solutions in help(cma.CMAEvolutionStrategy). ') # initialize weights if (dmean.any() and (not self.weights_initialized or es.countiter == 2)): # TODO self.gamma = array(N * [2*dfit]) self.weights_initialized = True # update weights gamma if self.weights_initialized: edist = array(abs(dmean) - 3 * max(1, N**0.5/es.sp.mueff)) if 1 < 3: # this is better, around a factor of two # increase single weights possibly with a faster rate than they can decrease # value unit of edst is std dev, 3==random walk of 9 steps self.gamma *= exp((edist>0) * np.tanh(edist/3) / 2.)**damp # decrease all weights up to the same level to avoid single extremely small weights # use a constant factor for pseudo-keeping invariance self.gamma[self.gamma > 5 * dfit] *= exp(-1./3)**damp # self.gamma[idx] *= exp(5*dfit/self.gamma[idx] - 1)**(damp/3) elif 1 < 3 and (edist>0).any(): # previous method # CAVE: min was max in TEC 2009 self.gamma[edist>0] *= 1.1**min(1, es.sp.mueff/10./N) # max fails on cigtab(N=12,bounds=[0.1,None]): # self.gamma[edist>0] *= 1.1**max(1, es.sp.mueff/10./N) # this was a bug!? # self.gamma *= exp((edist>0) * np.tanh(edist))**min(1, es.sp.mueff/10./N) else: # alternative version, but not better solutions = es.pop # this has not been checked r = self.feasible_ratio(solutions) # has to be the averaged over N iterations self.gamma *= exp(np.max([N*[0], 0.3 - r], axis=0))**min(1, es.sp.mueff/10/N) es.more_to_write += list(self.gamma) if self.weights_initialized else N * [1.0] ### return penalty # es.more_to_write = self.gamma if not np.isscalar(self.gamma) else N*[1] return self # bound penalty values #____________________________________________________________ #____________________________________________________________ # class GenoPhenoBase(object): """depreciated, abstract base class for genotyp-phenotype transformation, to be implemented. See (and rather use) option ``transformation`` of ``fmin`` or ``CMAEvolutionStrategy``. Example ------- :: import cma class Mygpt(cma.GenoPhenoBase): def pheno(self, x): return x # identity for the time being gpt = Mygpt() optim = cma.CMAEvolutionStrategy(...) while not optim.stop(): X = optim.ask() f = [func(gpt.pheno(x)) for x in X] optim.tell(X, f) In case of a repair, we might pass the repaired solution into `tell()` (with check_points being True). TODO: check usecases in `CMAEvolutionStrategy` and implement option GenoPhenoBase """ def pheno(self, x): raise NotImplementedError() return x #____________________________________________________________ #____________________________________________________________ # class GenoPheno(object): """Genotype-phenotype transformation. Method `pheno` provides the transformation from geno- to phenotype, that is from the internal representation to the representation used in the objective function. Method `geno` provides the "inverse" pheno- to genotype transformation. The geno-phenotype transformation comprises, in this order: - insert fixed variables (with the phenotypic and therefore quite possibly "wrong" values) - affine linear transformation (scaling and shift) - user-defined transformation - projection into feasible domain (boundaries) - assign fixed variables their original phenotypic value By default all transformations are the identity. The boundary transformation is only applied, if the boundaries are given as argument to the method `pheno` or `geno` respectively. ``geno`` is not really necessary and might disappear in future. """ def __init__(self, dim, scaling=None, typical_x=None, bounds=None, fixed_values=None, tf=None): """return `GenoPheno` instance with fixed dimension `dim`. Keyword Arguments ----------------- `scaling` the diagonal of a scaling transformation matrix, multipliers in the genotyp-phenotyp transformation, see `typical_x` `typical_x` ``pheno = scaling*geno + typical_x`` `bounds` (obsolete, might disappear) list with two elements, lower and upper bounds both can be a scalar or a "vector" of length dim or `None`. Without effect, as `bounds` must be given as argument to `pheno()`. `fixed_values` a dictionary of variable indices and values, like ``{0:2.0, 2:1.1}``, that are not subject to change, negative indices are ignored (they act like incommenting the index), values are phenotypic values. `tf` list of two user-defined transformation functions, or `None`. ``tf[0]`` is a function that transforms the internal representation as used by the optimizer into a solution as used by the objective function. ``tf[1]`` does the back-transformation. For example :: tf_0 = lambda x: [xi**2 for xi in x] tf_1 = lambda x: [abs(xi)**0.5 fox xi in x] or "equivalently" without the `lambda` construct :: def tf_0(x): return [xi**2 for xi in x] def tf_1(x): return [abs(xi)**0.5 fox xi in x] ``tf=[tf_0, tf_1]`` is a reasonable way to guaranty that only positive values are used in the objective function. Details ------- If ``tf_1`` is ommitted, the initial x-value must be given as genotype (as the phenotype-genotype transformation is unknown) and injection of solutions might lead to unexpected results. """ self.N = dim self.bounds = bounds self.fixed_values = fixed_values if tf is not None: self.tf_pheno = tf[0] self.tf_geno = tf[1] # TODO: should not necessarily be needed # r = np.random.randn(dim) # assert all(tf[0](tf[1](r)) - r < 1e-7) # r = np.random.randn(dim) # assert all(tf[0](tf[1](r)) - r > -1e-7) print("WARNING in class GenoPheno: user defined transformations have not been tested thoroughly") else: self.tf_geno = None self.tf_pheno = None if fixed_values: if type(fixed_values) is not dict: raise _Error("fixed_values must be a dictionary {index:value,...}") if max(fixed_values.keys()) >= dim: raise _Error("max(fixed_values.keys()) = " + str(max(fixed_values.keys())) + " >= dim=N=" + str(dim) + " is not a feasible index") # convenience commenting functionality: drop negative keys for k in list(fixed_values.keys()): if k < 0: fixed_values.pop(k) if bounds: if len(bounds) != 2: raise _Error('len(bounds) must be 2 for lower and upper bounds') for i in (0,1): if bounds[i] is not None: bounds[i] = array(dim * [bounds[i]] if np.isscalar(bounds[i]) else [b for b in bounds[i]]) def vec_is_default(vec, default_val=0): """return True if `vec` has the value `default_val`, None or [None] are also recognized as default""" try: if len(vec) == 1: vec = vec[0] # [None] becomes None and is always default else: return False except TypeError: pass # vec is a scalar if vec is None or vec == array(None) or vec == default_val: return True return False self.scales = array(scaling) if vec_is_default(self.scales, 1): self.scales = 1 # CAVE: 1 is not array(1) elif self.scales.shape is not () and len(self.scales) != self.N: raise _Error('len(scales) == ' + str(len(self.scales)) + ' does not match dimension N == ' + str(self.N)) self.typical_x = array(typical_x) if vec_is_default(self.typical_x, 0): self.typical_x = 0 elif self.typical_x.shape is not () and len(self.typical_x) != self.N: raise _Error('len(typical_x) == ' + str(len(self.typical_x)) + ' does not match dimension N == ' + str(self.N)) if (self.scales is 1 and self.typical_x is 0 and self.bounds in (None, [None, None]) and self.fixed_values is None and self.tf_pheno is None): self.isidentity = True else: self.isidentity = False def into_bounds(self, y, bounds=None, copy_never=False, copy_always=False): """Argument `y` is a phenotypic vector, return `y` put into boundaries, as a copy iff ``y != into_bounds(y)``. Note: this code is duplicated in `Solution.repair` and might disappear in future. """ bounds = bounds if bounds is not None else self.bounds if bounds in (None, [None, None]): return y if not copy_always else array(y, copy=True) if bounds[0] is not None: if len(bounds[0]) not in (1, len(y)): raise ValueError('len(bounds[0]) = ' + str(len(bounds[0])) + ' and len of initial solution (' + str(len(y)) + ') disagree') if copy_never: # is rather slower for i in range(len(y)): y[i] = max(bounds[0][i], y[i]) else: y = np.max([bounds[0], y], axis=0) if bounds[1] is not None: if len(bounds[1]) not in (1, len(y)): raise ValueError('len(bounds[1]) = ' + str(len(bounds[1])) + ' and initial solution (' + str(len(y)) + ') disagree') if copy_never: for i in range(len(y)): y[i] = min(bounds[1][i], y[i]) else: y = np.min([bounds[1], y], axis=0) return y def pheno(self, x, bounds=None, copy=True, copy_always=False): """maps the genotypic input argument into the phenotypic space, boundaries are only applied if argument ``bounds is not None``, see help for class `GenoPheno` """ if copy_always and not copy: raise ValueError('arguments copy_always=' + str(copy_always) + ' and copy=' + str(copy) + ' have inconsistent values') if self.isidentity and bounds in (None, [None, None], (None, None)): return x if not copy_always else array(x, copy=copy_always) if self.fixed_values is None: y = array(x, copy=copy) # make a copy, in case else: # expand with fixed values y = list(x) # is a copy for i in sorted(self.fixed_values.keys()): y.insert(i, self.fixed_values[i]) y = array(y, copy=False) if self.scales is not 1: # just for efficiency y *= self.scales if self.typical_x is not 0: y += self.typical_x if self.tf_pheno is not None: y = array(self.tf_pheno(y), copy=False) if bounds is not None: y = self.into_bounds(y, bounds) if self.fixed_values is not None: for i, k in list(self.fixed_values.items()): y[i] = k return y def geno(self, y, bounds=None, copy=True, copy_always=False, archive=None): """maps the phenotypic input argument into the genotypic space. If `bounds` are given, first `y` is projected into the feasible domain. In this case ``copy==False`` leads to a copy. by default a copy is made only to prevent to modify ``y`` method geno is only needed if external solutions are injected (geno(initial_solution) is depreciated and will disappear) TODO: arg copy=True should become copy_never=False """ if archive is not None and bounds is not None: try: return archive[y]['geno'] except: pass x = array(y, copy=(copy and not self.isidentity) or copy_always) # bounds = self.bounds if bounds is None else bounds if bounds is not None: # map phenotyp into bounds first x = self.into_bounds(x, bounds) if self.isidentity: return x # user-defined transformation if self.tf_geno is not None: x = array(self.tf_geno(x), copy=False) else: _Error('t1 of options transformation was not defined but is needed as being the inverse of t0') # affine-linear transformation: shift and scaling if self.typical_x is not 0: x -= self.typical_x if self.scales is not 1: # just for efficiency x /= self.scales # kick out fixed_values if self.fixed_values is not None: # keeping the transformed values does not help much # therefore it is omitted if 1 < 3: keys = sorted(self.fixed_values.keys()) x = array([x[i] for i in range(len(x)) if i not in keys], copy=False) else: # TODO: is this more efficient? x = list(x) for key in sorted(list(self.fixed_values.keys()), reverse=True): x.remove(key) x = array(x, copy=False) return x #____________________________________________________________ #____________________________________________________________ # check out built-in package abc: class ABCMeta, abstractmethod, abstractproperty... # see http://docs.python.org/whatsnew/2.6.html PEP 3119 abstract base classes # class OOOptimizer(object): """"abstract" base class for an OO optimizer interface with methods `__init__`, `ask`, `tell`, `stop`, `result`, and `optimize`. Only `optimize` is fully implemented in this base class. Examples -------- All examples minimize the function `elli`, the output is not shown. (A preferred environment to execute all examples is ``ipython -pylab``.) First we need :: from cma import CMAEvolutionStrategy, CMADataLogger # CMAEvolutionStrategy derives from the OOOptimizer class elli = lambda x: sum(1e3**((i-1.)/(len(x)-1.)*x[i])**2 for i in range(len(x))) The shortest example uses the inherited method `OOOptimizer.optimize()`:: res = CMAEvolutionStrategy(8 * [0.1], 0.5).optimize(elli) The input parameters to `CMAEvolutionStrategy` are specific to this inherited class. The remaining functionality is based on interface defined by `OOOptimizer`. We might have a look at the result:: print(res[0]) # best solution and print(res[1]) # its function value `res` is the return value from method `CMAEvolutionStrategy.result()` appended with `None` (no logger). In order to display more exciting output we rather do :: logger = CMADataLogger() # derives from the abstract BaseDataLogger class res = CMAEvolutionStrategy(9 * [0.5], 0.3).optimize(elli, logger) logger.plot() # if matplotlib is available, logger == res[-1] or even shorter :: res = CMAEvolutionStrategy(9 * [0.5], 0.3).optimize(elli, CMADataLogger()) res[-1].plot() # if matplotlib is available Virtually the same example can be written with an explicit loop instead of using `optimize()`. This gives the necessary insight into the `OOOptimizer` class interface and gives entire control over the iteration loop:: optim = CMAEvolutionStrategy(9 * [0.5], 0.3) # a new CMAEvolutionStrategy instance calling CMAEvolutionStrategy.__init__() logger = CMADataLogger(optim) # get a logger instance # this loop resembles optimize() while not optim.stop(): # iterate X = optim.ask() # get candidate solutions f = [elli(x) for x in X] # evaluate solutions # maybe do something else that needs to be done optim.tell(X, f) # do all the real work: prepare for next iteration optim.disp(20) # display info every 20th iteration logger.add() # log another "data line" # final output print('termination by', optim.stop()) print('best f-value =', optim.result()[1]) print('best solution =', optim.result()[0]) logger.plot() # if matplotlib is available raw_input('press enter to continue') # prevents exiting and closing figures Details ------- Most of the work is done in the method `tell(...)`. The method `result()` returns more useful output. """ def __init__(self, xstart, **more_args): """``xstart`` is a mandatory argument""" self.xstart = xstart self.more_args = more_args self.initialize() def initialize(self): """(re-)set to the initial state""" self.countiter = 0 self.xcurrent = self.xstart[:] raise NotImplementedError('method initialize() must be implemented in derived class') def ask(self): """abstract method, AKA "get" or "sample_distribution", deliver new candidate solution(s), a list of "vectors" """ raise NotImplementedError('method ask() must be implemented in derived class') def tell(self, solutions, function_values): """abstract method, AKA "update", prepare for next iteration""" self.countiter += 1 raise NotImplementedError('method tell() must be implemented in derived class') def stop(self): """abstract method, return satisfied termination conditions in a dictionary like ``{'termination reason': value, ...}``, for example ``{'tolfun': 1e-12}``, or the empty dictionary ``{}``. The implementation of `stop()` should prevent an infinite loop. """ raise NotImplementedError('method stop() is not implemented') def disp(self, modulo=None): """abstract method, display some iteration infos if ``self.iteration_counter % modulo == 0``""" raise NotImplementedError('method disp() is not implemented') def result(self): """abstract method, return ``(x, f(x), ...)``, that is, the minimizer, its function value, ...""" raise NotImplementedError('method result() is not implemented') def optimize(self, objectivefct, logger=None, verb_disp=20, iterations=None): """find minimizer of `objectivefct` by iterating over `OOOptimizer` `self` with verbosity `verb_disp`, using `BaseDataLogger` `logger` with at most `iterations` iterations. :: return self.result() + (self.stop(), self, logger) Example ------- >>> import cma >>> res = cma.CMAEvolutionStrategy(7 * [0.1], 0.5).optimize(cma.fcts.rosen, cma.CMADataLogger(), 100) (4_w,9)-CMA-ES (mu_w=2.8,w_1=49%) in dimension 7 (seed=630721393) Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec 1 9 3.163954777181882e+01 1.0e+00 4.12e-01 4e-01 4e-01 0:0.0 2 18 3.299006223906629e+01 1.0e+00 3.60e-01 3e-01 4e-01 0:0.0 3 27 1.389129389866704e+01 1.1e+00 3.18e-01 3e-01 3e-01 0:0.0 100 900 2.494847340045985e+00 8.6e+00 5.03e-02 2e-02 5e-02 0:0.3 200 1800 3.428234862999135e-01 1.7e+01 3.77e-02 6e-03 3e-02 0:0.5 300 2700 3.216640032470860e-04 5.6e+01 6.62e-03 4e-04 9e-03 0:0.8 400 3600 6.155215286199821e-12 6.6e+01 7.44e-06 1e-07 4e-06 0:1.1 438 3942 1.187372505161762e-14 6.0e+01 3.27e-07 4e-09 9e-08 0:1.2 438 3942 1.187372505161762e-14 6.0e+01 3.27e-07 4e-09 9e-08 0:1.2 ('termination by', {'tolfun': 1e-11}) ('best f-value =', 1.1189867885201275e-14) ('solution =', array([ 1. , 1. , 1. , 0.99999999, 0.99999998, 0.99999996, 0.99999992])) >>> print(res[0]) [ 1. 1. 1. 0.99999999 0.99999998 0.99999996 0.99999992] """ if logger is None: if hasattr(self, 'logger'): logger = self.logger citer = 0 while not self.stop(): if iterations is not None and citer >= iterations: return self.result() citer += 1 X = self.ask() # deliver candidate solutions fitvals = [objectivefct(x) for x in X] self.tell(X, fitvals) # all the work is done here self.disp(verb_disp) logger.add(self) if logger else None logger.add(self, modulo=bool(logger.modulo)) if logger else None if verb_disp: self.disp(1) if verb_disp in (1, True): print('termination by', self.stop()) print('best f-value =', self.result()[1]) print('solution =', self.result()[0]) return self.result() + (self.stop(), self, logger) #____________________________________________________________ #____________________________________________________________ # class CMAEvolutionStrategy(OOOptimizer): """CMA-ES stochastic optimizer class with ask-and-tell interface. See `fmin` for the one-line-call functional interface. Calling sequence ================ ``optim = CMAEvolutionStrategy(x0, sigma0, opts)`` returns a class instance. Arguments --------- `x0` initial solution, starting point (phenotype). `sigma0` initial standard deviation. The problem variables should have been scaled, such that a single standard deviation on all variables is useful and the optimum is expected to lie within about `x0` +- ``3*sigma0``. See also options `scaling_of_variables`. Often one wants to check for solutions close to the initial point. This allows for an easier check for consistency of the objective function and its interfacing with the optimizer. In this case, a much smaller `sigma0` is advisable. `opts` options, a dictionary with optional settings, see class `Options`. Main interface / usage ====================== The ask-and-tell interface is inherited from the generic `OOOptimizer` interface for iterative optimization algorithms (see there). With :: optim = CMAEvolutionStrategy(8 * [0.5], 0.2) an object instance is generated. In each iteration :: solutions = optim.ask() is used to ask for new candidate solutions (possibly several times) and :: optim.tell(solutions, func_values) passes the respective function values to `optim`. Instead of `ask()`, the class `CMAEvolutionStrategy` also provides :: (solutions, func_values) = optim.ask_and_eval(objective_func) Therefore, after initialization, an entire optimization can be written in two lines like :: while not optim.stop(): optim.tell(*optim.ask_and_eval(objective_func)) Without the freedom of executing additional lines within the iteration, the same reads in a single line as :: optim.optimize(objective_func) Besides for termination criteria, in CMA-ES only the ranks of the `func_values` are relevant. Attributes and Properties ========================= - `inputargs` -- passed input arguments - `inopts` -- passed options - `opts` -- actually used options, some of them can be changed any time, see class `Options` - `popsize` -- population size lambda, number of candidate solutions returned by `ask()` Details ======= The following two enhancements are turned off by default. **Active CMA** is implemented with option ``CMA_active`` and conducts an update of the covariance matrix with negative weights. The exponential update is implemented, where from a mathematical viewpoint positive definiteness is guarantied. The update is applied after the default update and only before the covariance matrix is decomposed, which limits the additional computational burden to be at most a factor of three (typically smaller). A typical speed up factor (number of f-evaluations) is between 1.1 and two. References: Jastrebski and Arnold, CEC 2006, Glasmachers et al, GECCO 2010. **Selective mirroring** is implemented with option ``CMA_mirrors`` in the method ``get_mirror()``. Only the method `ask_and_eval()` will then sample selectively mirrored vectors. In selective mirroring, only the worst solutions are mirrored. With the default small number of mirrors, *pairwise selection* (where at most one of the two mirrors contribute to the update of the distribution mean) is implicitely guarantied under selective mirroring and therefore not explicitly implemented. References: Brockhoff et al, PPSN 2010, Auger et al, GECCO 2011. Examples ======== Super-short example, with output shown: >>> import cma >>> # construct an object instance in 4-D, sigma0=1 >>> es = cma.CMAEvolutionStrategy(4 * [1], 1, {'seed':234}) (4_w,8)-CMA-ES (mu_w=2.6,w_1=52%) in dimension 4 (seed=234) >>> >>> # iterate until termination >>> while not es.stop(): ... X = es.ask() ... es.tell(X, [cma.fcts.elli(x) for x in X]) ... es.disp() # by default sparse, see option verb_disp Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec 1 8 2.093015112685775e+04 1.0e+00 9.27e-01 9e-01 9e-01 0:0.0 2 16 4.964814235917688e+04 1.1e+00 9.54e-01 9e-01 1e+00 0:0.0 3 24 2.876682459926845e+05 1.2e+00 1.02e+00 9e-01 1e+00 0:0.0 100 800 6.809045875281943e-01 1.3e+02 1.41e-02 1e-04 1e-02 0:0.2 200 1600 2.473662150861846e-10 8.0e+02 3.08e-05 1e-08 8e-06 0:0.5 233 1864 2.766344961865341e-14 8.6e+02 7.99e-07 8e-11 7e-08 0:0.6 >>> >>> cma.pprint(es.result()) (Solution([ -1.98546755e-09, -1.10214235e-09, 6.43822409e-11, -1.68621326e-11]), 4.5119610261406537e-16, 1666, 1672, 209, array([ -9.13545269e-09, -1.45520541e-09, -6.47755631e-11, -1.00643523e-11]), array([ 3.20258681e-08, 3.15614974e-09, 2.75282215e-10, 3.27482983e-11])) >>> >>> # help(es.result) shows result(self) method of cma.CMAEvolutionStrategy instance return ``(xbest, f(xbest), evaluations_xbest, evaluations, iterations, pheno(xmean), effective_stds)`` Using the multiprocessing module, we can evaluate the function in parallel with a simple modification of the example :: import multiprocessing # prepare es = ... pool = multiprocessing.Pool(es.popsize) while not es.stop(): X = es.ask() es.tell(X, pool.map_async(cma.felli, X).get()) # use chunksize parameter as popsize/len(pool)? Example with a data logger, lower bounds (at zero) and handling infeasible solutions: >>> import cma >>> import numpy as np >>> es = cma.CMAEvolutionStrategy(10 * [0.2], 0.5, {'bounds': [0, np.inf]}) >>> logger = cma.CMADataLogger().register(es) >>> while not es.stop(): ... fit, X = [], [] ... while len(X) < es.popsize: ... curr_fit = np.NaN ... while curr_fit is np.NaN: ... x = es.ask(1)[0] ... curr_fit = cma.fcts.somenan(x, cma.fcts.elli) # might return np.NaN ... X.append(x) ... fit.append(curr_fit) ... es.tell(X, fit) ... logger.add() ... es.disp() >>> >>> assert es.result()[1] < 1e-9 >>> assert es.result()[2] < 9000 # by internal termination >>> logger.plot() # plot data >>> cma.show() >>> print(' *** if execution stalls close the figure window to continue (and check out ipython --pylab) ***') Example implementing restarts with increasing popsize (IPOP), output is not displayed: >>> import cma, numpy as np >>> >>> # restart with increasing population size (IPOP) >>> bestever = cma.BestSolution() >>> for lam in 10 * 2**np.arange(7): # 10, 20, 40, 80, ..., 10 * 2**6 ... es = cma.CMAEvolutionStrategy('6 - 8 * np.random.rand(9)', # 9-D ... 5, # initial std sigma0 ... {'popsize': lam, ... 'verb_append': bestever.evalsall}) # pass options ... logger = cma.CMADataLogger().register(es, append=bestever.evalsall) ... while not es.stop(): ... X = es.ask() # get list of new solutions ... fit = [cma.fcts.rastrigin(x) for x in X] # evaluate each solution ... es.tell(X, fit) # besides for termination only the ranking in fit is used ... ... # display some output ... logger.add() # add a "data point" to the log, writing in files ... es.disp() # uses option verb_disp with default 100 ... ... print('termination:', es.stop()) ... cma.pprint(es.best.__dict__) ... ... bestever.update(es.best) ... ... # show a plot ... logger.plot(); ... if bestever.f < 1e-8: # global optimum was hit ... break >>> assert es.result()[1] < 1e-8 On the Rastrigin function, usually after five restarts the global optimum is located. The final example shows how to resume: >>> import cma, pickle >>> >>> es = cma.CMAEvolutionStrategy(12 * [0.1], # a new instance, 12-D ... 0.5) # initial std sigma0 >>> logger = cma.CMADataLogger().register(es) >>> es.optimize(cma.fcts.rosen, logger, iterations=100) >>> logger.plot() >>> pickle.dump(es, open('saved-cma-object.pkl', 'wb')) >>> print('saved') >>> del es, logger # let's start fresh >>> >>> es = pickle.load(open('saved-cma-object.pkl', 'rb')) >>> print('resumed') >>> logger = cma.CMADataLogger(es.opts['verb_filenameprefix'] # use same name ... ).register(es, True) # True: append to old log data >>> es.optimize(cma.fcts.rosen, logger, verb_disp=200) >>> assert es.result()[2] < 15000 >>> cma.pprint(es.result()) >>> logger.plot() Missing Features ================ Option ``randn`` to pass a random number generator. :See: `fmin()`, `Options`, `plot()`, `ask()`, `tell()`, `ask_and_eval()` """ # __all__ = () # TODO this would be the interface #____________________________________________________________ @property # read only attribute decorator for a method def popsize(self): """number of samples by default returned by` ask()` """ return self.sp.popsize # this is not compatible with python2.5: # @popsize.setter # def popsize(self, p): # """popsize cannot be set (this might change in future) # """ # raise _Error("popsize cannot be changed (this might change in future)") #____________________________________________________________ #____________________________________________________________ def stop(self, check=True): """return a dictionary with the termination status. With ``check==False``, the termination conditions are not checked and the status might not reflect the current situation. """ if (check and self.countiter > 0 and self.opts['termination_callback'] and self.opts['termination_callback'] != str(self.opts['termination_callback'])): self.callbackstop = self.opts['termination_callback'](self) return self.stopdict(self if check else None) # update the stopdict and return a Dict #____________________________________________________________ #____________________________________________________________ def __init__(self, x0, sigma0, inopts = {}): """see class `CMAEvolutionStrategy` """ self.inputargs = dict(locals()) # for the record del self.inputargs['self'] # otherwise the instance self has a cyclic reference self.inopts = inopts opts = Options(inopts).complement() # Options() == fmin([],[]) == defaultOptions() if opts['noise_handling'] and eval(opts['noise_handling']): raise ValueError('noise_handling not available with class CMAEvolutionStrategy, use function fmin') if opts['restarts'] and eval(opts['restarts']): raise ValueError('restarts not available with class CMAEvolutionStrategy, use function fmin') if x0 == str(x0): x0 = eval(x0) self.mean = array(x0) # should not have column or row, is just 1-D if self.mean.ndim == 2: print('WARNING: input x0 should be a list or 1-D array, trying to flatten ' + str(self.mean.shape) + '-array') if self.mean.shape[0] == 1: self.mean = self.mean[0] elif self.mean.shape[1] == 1: self.mean = array([x[0] for x in self.mean]) if self.mean.ndim != 1: raise _Error('x0 must be 1-D array') if len(self.mean) <= 1: raise _Error('optimization in 1-D is not supported (code was never tested)') self.N = self.mean.shape[0] N = self.N self.mean.resize(N) # 1-D array, not really necessary?! self.x0 = self.mean self.mean = self.x0.copy() # goes to initialize self.sigma0 = sigma0 if isinstance(sigma0, str): # TODO: no real need here (do rather in fmin) self.sigma0 = eval(sigma0) # like '1./N' or 'np.random.rand(1)[0]+1e-2' if np.size(self.sigma0) != 1 or np.shape(self.sigma0): raise _Error('input argument sigma0 must be (or evaluate to) a scalar') self.sigma = self.sigma0 # goes to inialize # extract/expand options opts.evalall(locals()) # using only N self.opts = opts self.randn = opts['randn'] self.gp = GenoPheno(N, opts['scaling_of_variables'], opts['typical_x'], opts['bounds'], opts['fixed_variables'], opts['transformation']) self.boundPenalty = BoundPenalty(self.gp.bounds) s = self.gp.geno(self.mean) self.mean = self.gp.geno(self.mean, bounds=self.gp.bounds) self.N = len(self.mean) N = self.N if (self.mean != s).any(): print('WARNING: initial solution is out of the domain boundaries:') print(' x0 = ' + str(self.inputargs['x0'])) print(' ldom = ' + str(self.gp.bounds[0])) print(' udom = ' + str(self.gp.bounds[1])) self.fmean = np.NaN # TODO name should change? prints nan (OK with matlab&octave) self.fmean_noise_free = 0. # for output only self.sp = CMAParameters(N, opts) self.sp0 = self.sp # looks useless, as it is not a copy # initialization of state variables self.countiter = 0 self.countevals = max((0, opts['verb_append'])) if type(opts['verb_append']) is not bool else 0 self.ps = np.zeros(N) self.pc = np.zeros(N) stds = np.ones(N) self.sigma_vec = np.ones(N) if np.isfinite(self.sp.dampsvec) else 1 if np.all(self.opts['CMA_teststds']): # also 0 would not make sense stds = self.opts['CMA_teststds'] if np.size(stds) != N: raise _Error('CMA_teststds option must have dimension = ' + str(N)) if self.opts['CMA_diagonal']: # is True or > 0 # linear time and space complexity self.B = array(1) # works fine with np.dot(self.B, anything) and self.B.T self.C = stds**2 # TODO: remove this!? self.dC = self.C else: self.B = np.eye(N) # identity(N), do not from matlib import *, as eye is a matrix there # prevent equal eigenvals, a hack for np.linalg: self.C = np.diag(stds**2 * exp(1e-6*(np.random.rand(N)-0.5))) self.dC = np.diag(self.C) self.Zneg = np.zeros((N, N)) self.D = stds self.flgtelldone = True self.itereigenupdated = self.countiter self.noiseS = 0 # noise "signal" self.hsiglist = [] if not opts['seed']: np.random.seed() six_decimals = (time.time() - 1e6 * (time.time() // 1e6)) opts['seed'] = 1e5 * np.random.rand() + six_decimals + 1e5 * (time.time() % 1) opts['seed'] = int(opts['seed']) np.random.seed(opts['seed']) self.sent_solutions = SolutionDict() self.best = BestSolution() out = {} # TODO: obsolete, replaced by method results()? out['best'] = self.best # out['hsigcount'] = 0 out['termination'] = {} self.out = out self.const = BlancClass() self.const.chiN = N**0.5*(1-1./(4.*N)+1./(21.*N**2)) # expectation of norm(randn(N,1)) # attribute for stopping criteria in function stop self.stopdict = CMAStopDict() self.callbackstop = 0 self.fit = BlancClass() self.fit.fit = [] # not really necessary self.fit.hist = [] # short history of best self.fit.histbest = [] # long history of best self.fit.histmedian = [] # long history of median self.more_to_write = [] #[1, 1, 1, 1] # N*[1] # needed when writing takes place before setting # say hello if opts['verb_disp'] > 0: sweighted = '_w' if self.sp.mu > 1 else '' smirr = 'mirr%d' % (self.sp.lam_mirr) if self.sp.lam_mirr else '' print('(%d' % (self.sp.mu) + sweighted + ',%d' % (self.sp.popsize) + smirr + ')-CMA-ES' + ' (mu_w=%2.1f,w_1=%d%%)' % (self.sp.mueff, int(100*self.sp.weights[0])) + ' in dimension %d (seed=%d, %s)' % (N, opts['seed'], time.asctime())) # + func.__name__ if opts['CMA_diagonal'] and self.sp.CMA_on: s = '' if opts['CMA_diagonal'] is not True: s = ' for ' if opts['CMA_diagonal'] < np.inf: s += str(int(opts['CMA_diagonal'])) else: s += str(np.floor(opts['CMA_diagonal'])) s += ' iterations' s += ' (1/ccov=' + str(round(1./(self.sp.c1+self.sp.cmu))) + ')' print(' Covariance matrix is diagonal' + s) #____________________________________________________________ #____________________________________________________________ def ask(self, number=None, xmean=None, sigma_fac=1): """get new candidate solutions, sampled from a multi-variate normal distribution and transformed to f-representation (phenotype) to be evaluated. Arguments --------- `number` number of returned solutions, by default the population size ``popsize`` (AKA ``lambda``). `xmean` distribution mean `sigma` multiplier for internal sample width (standard deviation) Return ------ A list of N-dimensional candidate solutions to be evaluated Example ------- >>> import cma >>> es = cma.CMAEvolutionStrategy([0,0,0,0], 0.3) >>> while not es.stop() and es.best.f > 1e-6: # my_desired_target_f_value ... X = es.ask() # get list of new solutions ... fit = [cma.fcts.rosen(x) for x in X] # call function rosen with each solution ... es.tell(X, fit) # feed values :See: `ask_and_eval`, `ask_geno`, `tell` """ pop_geno = self.ask_geno(number, xmean, sigma_fac) # N,lambda=20,200: overall CPU 7s vs 5s == 40% overhead, even without bounds! # new data: 11.5s vs 9.5s == 20% # TODO: check here, whether this is necessary? # return [self.gp.pheno(x, copy=False, bounds=self.gp.bounds) for x in pop] # probably fine # return [Solution(self.gp.pheno(x, copy=False), copy=False) for x in pop] # here comes the memory leak, now solved # pop_pheno = [Solution(self.gp.pheno(x, copy=False), copy=False).repair(self.gp.bounds) for x in pop_geno] pop_pheno = [self.gp.pheno(x, copy=True, bounds=self.gp.bounds) for x in pop_geno] if not self.gp.isidentity or use_sent_solutions: # costs 25% in CPU performance with N,lambda=20,200 # archive returned solutions, first clean up archive if self.countiter % 30/self.popsize**0.5 < 1: self.sent_solutions.truncate(0, self.countiter - 1 - 3 * self.N/self.popsize**0.5) # insert solutions for i in range(len(pop_geno)): self.sent_solutions[pop_pheno[i]] = {'geno': pop_geno[i], 'pheno': pop_pheno[i], 'iteration': self.countiter} return pop_pheno #____________________________________________________________ #____________________________________________________________ def ask_geno(self, number=None, xmean=None, sigma_fac=1): """get new candidate solutions in genotyp, sampled from a multi-variate normal distribution. Arguments are `number` number of returned solutions, by default the population size `popsize` (AKA lambda). `xmean` distribution mean `sigma_fac` multiplier for internal sample width (standard deviation) `ask_geno` returns a list of N-dimensional candidate solutions in genotyp representation and is called by `ask`. :See: `ask`, `ask_and_eval` """ if number is None or number < 1: number = self.sp.popsize if xmean is None: xmean = self.mean if self.countiter == 0: self.tic = time.clock() # backward compatible self.elapsed_time = ElapsedTime() if self.opts['CMA_AII']: if self.countiter == 0: self.aii = AII(self.x0, self.sigma0) self.flgtelldone = False pop = self.aii.ask(number) return pop sigma = sigma_fac * self.sigma # update parameters for sampling the distribution # fac 0 1 10 # 150-D cigar: # 50749 50464 50787 # 200-D elli: == 6.9 # 99900 101160 # 100995 103275 == 2% loss # 100-D elli: == 6.9 # 363052 369325 < 2% loss # 365075 365755 # update distribution if self.sp.CMA_on and ( (self.opts['updatecovwait'] is None and self.countiter >= self.itereigenupdated + 1./(self.sp.c1+self.sp.cmu)/self.N/10 ) or (self.opts['updatecovwait'] is not None and self.countiter > self.itereigenupdated + self.opts['updatecovwait'] )): self.updateBD() # sample distribution if self.flgtelldone: # could be done in tell()!? self.flgtelldone = False self.ary = [] # each row is a solution arz = self.randn((number, self.N)) if 11 < 3: # mutate along the principal axes only perm = np.random.permutation(self.N) # indices for mutated principal component for i in range(min((len(arz), self.N))): # perm = np.random.permutation(self.N) # random principal component, should be much worse l = sum(arz[i]**2)**0.5 arz[i] *= 0 if 11 < 3: # mirrored sampling arz[i][perm[int(i/2)]] = l * (2 * (i % 2) - 1) else: arz[i][perm[i % self.N]] = l * np.sign(np.random.rand(1) - 0.5) if number == self.sp.popsize: self.arz = arz # is never used else: pass if 11 < 3: # normalize the length to chiN for i in range(len(arz)): # arz[i] *= exp(self.randn(1)[0] / 8) ss = sum(arz[i]**2)**0.5 arz[i] *= self.const.chiN / ss # or to average # arz *= 1 * self.const.chiN / np.mean([sum(z**2)**0.5 for z in arz]) # fac = np.mean(sum(arz**2, 1)**0.5) # print fac # arz *= self.const.chiN / fac self.ary = self.sigma_vec * np.dot(self.B, (self.D * arz).T).T pop = xmean + sigma * self.ary self.evaluations_per_f_value = 1 return pop def get_mirror(self, x): """return ``pheno(self.mean - (geno(x) - self.mean))``. TODO: this implementation is yet experimental. Selectively mirrored sampling improves to a moderate extend but overadditively with active CMA for quite understandable reasons. Optimal number of mirrors are suprisingly small: 1,2,3 for maxlam=7,13,20 however note that 3,6,10 are the respective maximal possible mirrors that must be clearly suboptimal. """ try: # dx = x.geno - self.mean, repair or boundary handling is not taken into account dx = self.sent_solutions[x]['geno'] - self.mean except: print('WARNING: use of geno is depreciated') dx = self.gp.geno(x, copy=True) - self.mean dx *= sum(self.randn(self.N)**2)**0.5 / self.mahalanobisNorm(dx) x = self.mean - dx y = self.gp.pheno(x, bounds=self.gp.bounds) if not self.gp.isidentity or use_sent_solutions: # costs 25% in CPU performance with N,lambda=20,200 self.sent_solutions[y] = {'geno': x, 'pheno': y, 'iteration': self.countiter} return y def mirror_penalized(self, f_values, idx): """obsolete and subject to removal (TODO), return modified f-values such that for each mirror one becomes worst. This function is useless when selective mirroring is applied with no more than (lambda-mu)/2 solutions. Mirrors are leading and trailing values in ``f_values``. """ assert len(f_values) >= 2 * len(idx) m = np.max(np.abs(f_values)) for i in len(idx): if f_values[idx[i]] > f_values[-1-i]: f_values[idx[i]] += m else: f_values[-1-i] += m return f_values def mirror_idx_cov(self, f_values, idx1): # will most likely be removed """obsolete and subject to removal (TODO), return indices for negative ("active") update of the covariance matrix assuming that ``f_values[idx1[i]]`` and ``f_values[-1-i]`` are the corresponding mirrored values computes the index of the worse solution sorted by the f-value of the better solution. TODO: when the actual mirror was rejected, it is better to return idx1 instead of idx2. Remark: this function might not be necessary at all: if the worst solution is the best mirrored, the covariance matrix updates cancel (cave: weights and learning rates), which seems what is desirable. If the mirror is bad, as strong negative update is made, again what is desirable. And the fitness--step-length correlation is in part addressed by using flat weights. """ idx2 = np.arange(len(f_values) - 1, len(f_values) - 1 - len(idx1), -1) f = [] for i in range(len(idx1)): f.append(min((f_values[idx1[i]], f_values[idx2[i]]))) # idx.append(idx1[i] if f_values[idx1[i]] > f_values[idx2[i]] else idx2[i]) return idx2[np.argsort(f)][-1::-1] #____________________________________________________________ #____________________________________________________________ # def ask_and_eval(self, func, args=(), number=None, xmean=None, sigma_fac=1, evaluations=1, aggregation=np.median): """samples `number` solutions and evaluates them on `func`, where each solution `s` is resampled until ``func(s) not in (numpy.NaN, None)``. Arguments --------- `func` objective function `args` additional parameters for `func` `number` number of solutions to be sampled, by default population size ``popsize`` (AKA lambda) `xmean` mean for sampling the solutions, by default ``self.mean``. `sigma_fac` multiplier for sampling width, standard deviation, for example to get a small perturbation of solution `xmean` `evaluations` number of evaluations for each sampled solution `aggregation` function that aggregates `evaluations` values to as single value. Return ------ ``(X, fit)``, where X -- list of solutions fit -- list of respective function values Details ------- When ``func(x)`` returns `NaN` or `None` a new solution is sampled until ``func(x) not in (numpy.NaN, None)``. The argument to `func` can be freely modified within `func`. Depending on the ``CMA_mirrors`` option, some solutions are not sampled independently but as mirrors of other bad solutions. This is a simple derandomization that can save 10-30% of the evaluations in particular with small populations, for example on the cigar function. Example ------- >>> import cma >>> x0, sigma0 = 8*[10], 1 # 8-D >>> es = cma.CMAEvolutionStrategy(x0, sigma0) >>> while not es.stop(): ... X, fit = es.ask_and_eval(cma.fcts.elli) # handles NaN with resampling ... es.tell(X, fit) # pass on fitness values ... es.disp(20) # print every 20-th iteration >>> print('terminated on ' + str(es.stop())) A single iteration step can be expressed in one line, such that an entire optimization after initialization becomes :: while not es.stop(): es.tell(*es.ask_and_eval(cma.fcts.elli)) """ # initialize popsize = self.sp.popsize if number is not None: popsize = number selective_mirroring = True nmirrors = self.sp.lam_mirr if popsize != self.sp.popsize: nmirrors = Mh.sround(popsize * self.sp.lam_mirr / self.sp.popsize) # TODO: now selective mirroring might be impaired assert nmirrors <= popsize // 2 self.mirrors_idx = np.arange(nmirrors) # might never be used self.mirrors_rejected_idx = [] # might never be used if xmean is None: xmean = self.mean # do the work fit = [] # or np.NaN * np.empty(number) X_first = self.ask(popsize) X = [] for k in range(int(popsize)): nreject = -1 f = np.NaN while f in (np.NaN, None): # rejection sampling nreject += 1 if k < popsize - nmirrors or nreject: if nreject: x = self.ask(1, xmean, sigma_fac)[0] else: x = X_first.pop(0) else: # mirrored sample if k == popsize - nmirrors and selective_mirroring: self.mirrors_idx = np.argsort(fit)[-1:-1-nmirrors:-1] x = self.get_mirror(X[self.mirrors_idx[popsize - 1 - k]]) if nreject == 1 and k >= popsize - nmirrors: self.mirrors_rejected_idx.append(k) # contraints handling test hardwired ccccccccccc if 11 < 3 and self.opts['vv'] and nreject < 2: # trying out negative C-update as constraints handling if not hasattr(self, 'constraints_paths'): k = 1 self.constraints_paths = [np.zeros(self.N) for _i in range(k)] Izero = np.zeros([self.N, self.N]) for i in range(self.N): if x[i] < 0: Izero[i][i] = 1 self.C -= self.opts['vv'] * Izero Izero[i][i] = 0 if 1 < 3 and sum([ (9 + i + 1) * x[i] for i in range(self.N)]) > 50e3: self.constraints_paths[0] = 0.9 * self.constraints_paths[0] + 0.1 * (x - self.mean) / self.sigma self.C -= (self.opts['vv'] / self.N) * np.outer(self.constraints_paths[0], self.constraints_paths[0]) f = func(x, *args) if f not in (np.NaN, None) and evaluations > 1: f = aggregation([f] + [func(x, *args) for _i in range(int(evaluations-1))]) if nreject + 1 % 1000 == 0: print(' %d solutions rejected (f-value NaN or None) at iteration %d' % (nreject, self.countiter)) fit.append(f) X.append(x) self.evaluations_per_f_value = int(evaluations) return X, fit #____________________________________________________________ def tell(self, solutions, function_values, check_points=None, copy=False): """pass objective function values to prepare for next iteration. This core procedure of the CMA-ES algorithm updates all state variables, in particular the two evolution paths, the distribution mean, the covariance matrix and a step-size. Arguments --------- `solutions` list or array of candidate solution points (of type `numpy.ndarray`), most presumably before delivered by method `ask()` or `ask_and_eval()`. `function_values` list or array of objective function values corresponding to the respective points. Beside for termination decisions, only the ranking of values in `function_values` is used. `check_points` If ``check_points is None``, only solutions that are not generated by `ask()` are possibly clipped (recommended). ``False`` does not clip any solution (not recommended). If ``True``, clips solutions that realize long steps (i.e. also those that are unlikely to be generated with `ask()`). `check_points` can be a list of indices to be checked in solutions. `copy` ``solutions`` can be modified in this routine, if ``copy is False`` Details ------- `tell()` updates the parameters of the multivariate normal search distribution, namely covariance matrix and step-size and updates also the attributes `countiter` and `countevals`. To check the points for consistency is quadratic in the dimension (like sampling points). Bugs ---- The effect of changing the solutions delivered by `ask()` depends on whether boundary handling is applied. With boundary handling, modifications are disregarded. This is necessary to apply the default boundary handling that uses unrepaired solutions but might change in future. Example ------- :: import cma func = cma.fcts.elli # choose objective function es = cma.CMAEvolutionStrategy(cma.np.random.rand(10), 1) while not es.stop(): X = es.ask() es.tell(X, [func(x) for x in X]) es.result() # where the result can be found :See: class `CMAEvolutionStrategy`, `ask()`, `ask_and_eval()`, `fmin()` """ #____________________________________________________________ # TODO: consider an input argument that flags injected trust-worthy solutions (which means # that they can be treated "absolut" rather than "relative") if self.flgtelldone: raise _Error('tell should only be called once per iteration') lam = len(solutions) if lam != array(function_values).shape[0]: raise _Error('for each candidate solution ' + 'a function value must be provided') if lam + self.sp.lam_mirr < 3: raise _Error('population size ' + str(lam) + ' is too small when option CMA_mirrors * popsize < 0.5') if not np.isscalar(function_values[0]): if np.isscalar(function_values[0][0]): if self.countiter <= 1: print('WARNING: function values are not a list of scalars (further warnings are suppressed)') function_values = [val[0] for val in function_values] else: raise _Error('objective function values must be a list of scalars') ### prepare N = self.N sp = self.sp if 11 < 3 and lam != sp.popsize: # turned off, because mu should stay constant, still not desastrous print('WARNING: population size has changed, recomputing parameters') self.sp.set(self.opts, lam) # not really tested if lam < sp.mu: # rather decrease cmean instead of having mu > lambda//2 raise _Error('not enough solutions passed to function tell (mu>lambda)') self.countiter += 1 # >= 1 now self.countevals += sp.popsize * self.evaluations_per_f_value self.best.update(solutions, self.sent_solutions, function_values, self.countevals) flgseparable = self.opts['CMA_diagonal'] is True \ or self.countiter <= self.opts['CMA_diagonal'] if not flgseparable and len(self.C.shape) == 1: # C was diagonal ie 1-D # enter non-separable phase (no easy return from here) self.B = np.eye(N) # identity(N) self.C = np.diag(self.C) idx = np.argsort(self.D) self.D = self.D[idx] self.B = self.B[:,idx] self.Zneg = np.zeros((N, N)) ### manage fitness fit = self.fit # make short cut # CPU for N,lam=20,200: this takes 10s vs 7s fit.bndpen = self.boundPenalty.update(function_values, self)(solutions, self.sent_solutions, self.gp) # for testing: # fit.bndpen = self.boundPenalty.update(function_values, self)([s.unrepaired for s in solutions]) fit.idx = np.argsort(array(fit.bndpen) + array(function_values)) fit.fit = array(function_values, copy=False)[fit.idx] # update output data TODO: this is obsolete!? However: need communicate current best x-value? # old: out['recent_x'] = self.gp.pheno(pop[0]) self.out['recent_x'] = array(solutions[fit.idx[0]]) # TODO: change in a data structure(?) and use current as identify self.out['recent_f'] = fit.fit[0] # fitness histories fit.hist.insert(0, fit.fit[0]) # if len(self.fit.histbest) < 120+30*N/sp.popsize or # does not help, as tablet in the beginning is the critical counter-case if ((self.countiter % 5) == 0): # 20 percent of 1e5 gen. fit.histbest.insert(0, fit.fit[0]) fit.histmedian.insert(0, np.median(fit.fit) if len(fit.fit) < 21 else fit.fit[self.popsize // 2]) if len(fit.histbest) > 2e4: # 10 + 30*N/sp.popsize: fit.histbest.pop() fit.histmedian.pop() if len(fit.hist) > 10 + 30*N/sp.popsize: fit.hist.pop() if self.opts['CMA_AII']: self.aii.tell(solutions, function_values) self.flgtelldone = True # for output: self.mean = self.aii.mean self.dC = self.aii.sigmai**2 self.sigma = self.aii.sigma self.D = 1e-11 + (self.aii.r**2)**0.5 self.more_to_write += [self.aii.sigma_r] return # TODO: clean up inconsistency when an unrepaired solution is available and used pop = [] # create pop from input argument solutions for s in solutions: # use phenotype before Solution.repair() if use_sent_solutions: x = self.sent_solutions.pop(s, None) # 12.7s vs 11.3s with N,lambda=20,200 if x is not None: pop.append(x['geno']) # TODO: keep additional infos or don't pop s from sent_solutions in the first place else: # print 'WARNING: solution not found in ``self.sent_solutions`` (is expected for injected solutions)' pop.append(self.gp.geno(s, copy=copy)) # cannot recover the original genotype with boundary handling if check_points in (None, True, 1): self.repair_genotype(pop[-1]) # necessary if pop[-1] was changed or injected by the user. else: # TODO: to be removed? # print 'WARNING: ``geno`` mapping depreciated' pop.append(self.gp.geno(s, copy=copy)) if check_points in (None, True, 1): self.repair_genotype(pop[-1]) # necessary or not? # print 'repaired' mold = self.mean sigma_fac = 1 # check and normalize each x - m # check_points is a flag (None is default: check non-known solutions) or an index list # should also a number possible (first check_points points)? if check_points not in (None, False, 0, [], ()): # useful in case of injected solutions and/or adaptive encoding, however is automatic with use_sent_solutions try: if len(check_points): idx = check_points except: idx = range(sp.popsize) for k in idx: self.repair_genotype(pop[k]) # sort pop if type(pop) is not array: # only arrays can be multiple indexed pop = array(pop, copy=False) pop = pop[fit.idx] if self.opts['CMA_elitist'] and self.best.f < fit.fit[0]: if self.best.x_geno is not None: xp = [self.best.x_geno] # xp = [self.best.xdict['geno']] # xp = [self.gp.geno(self.best.x[:])] # TODO: remove # print self.mahalanobisNorm(xp[0]-self.mean) self.clip_or_fit_solutions(xp, [0]) pop = array([xp[0]] + list(pop)) else: print('genotype for elitist not found') # compute new mean self.mean = mold + self.sp.cmean * \ (sum(sp.weights * pop[0:sp.mu].T, 1) - mold) # check Delta m (this is not default, but could become at some point) # CAVE: upper_length=sqrt(2)+2 is too restrictive, test upper_length = sqrt(2*N) thoroughly. # simple test case injecting self.mean: # self.mean = 1e-4 * self.sigma * np.random.randn(N) if 11 < 3 and self.opts['vv'] and check_points: # TODO: check_points might be an index-list cmean = self.sp.cmean / min(1, (sqrt(self.opts['vv']*N)+2) / ( # abuse of cmean (sqrt(self.sp.mueff) / self.sp.cmean) * self.mahalanobisNorm(self.mean - mold))) else: cmean = self.sp.cmean if 11 < 3: # plot length of mean - mold self.more_to_write += [sqrt(sp.mueff) * sum(((1./self.D) * dot(self.B.T, self.mean - mold))**2)**0.5 / self.sigma / sqrt(N) / cmean] # get learning rate constants cc, c1, cmu = sp.cc, sp.c1, sp.cmu if flgseparable: cc, c1, cmu = sp.cc_sep, sp.c1_sep, sp.cmu_sep # now the real work can start # evolution paths self.ps = (1-sp.cs) * self.ps + \ (sqrt(sp.cs*(2-sp.cs)*sp.mueff) / self.sigma / cmean) * \ dot(self.B, (1./self.D) * dot(self.B.T, (self.mean - mold) / self.sigma_vec)) # "hsig", correction with self.countiter seems not necessary, also pc starts with zero hsig = sum(self.ps**2) / (1-(1-sp.cs)**(2*self.countiter)) / self.N < 2 + 4./(N+1) if 11 < 3: # hsig = 1 # sp.cc = 4 / (N + 4) # sp.cs = 4 / (N + 4) # sp.cc = 1 # sp.damps = 2 # # sp.CMA_on = False # c1 = 0 # 2 / ((N + 1.3)**2 + 0 * sp.mu) # 1 / N**2 # cmu = min([1 - c1, cmu]) if self.countiter == 1: print('parameters modified') # hsig = sum(self.ps**2) / self.N < 2 + 4./(N+1) # adjust missing variance due to hsig, in 4-D with damps=1e99 and sig0 small # hsig leads to premature convergence of C otherwise #hsiga = (1-hsig**2) * c1 * cc * (2-cc) # to be removed in future c1a = c1 - (1-hsig**2) * c1 * cc * (2-cc) # adjust for variance loss if 11 < 3: # diagnostic data self.out['hsigcount'] += 1 - hsig if not hsig: self.hsiglist.append(self.countiter) if 11 < 3: # diagnostic message if not hsig: print(str(self.countiter) + ': hsig-stall') if 11 < 3: # for testing purpose hsig = 1 # TODO: # put correction term, but how? if self.countiter == 1: print('hsig=1') self.pc = (1-cc) * self.pc + \ hsig * (sqrt(cc*(2-cc)*sp.mueff) / self.sigma / cmean) * \ (self.mean - mold) / self.sigma_vec # covariance matrix adaptation/udpate if sp.CMA_on: # assert sp.c1 + sp.cmu < sp.mueff / N # ?? assert c1 + cmu <= 1 # default full matrix case if not flgseparable: Z = (pop[0:sp.mu] - mold) / (self.sigma * self.sigma_vec) Z = dot((cmu * sp.weights) * Z.T, Z) # learning rate integrated if self.sp.neg.cmuexp: tmp = (pop[-sp.neg.mu:] - mold) / (self.sigma * self.sigma_vec) self.Zneg *= 1 - self.sp.neg.cmuexp # for some reason necessary? self.Zneg += dot(sp.neg.weights * tmp.T, tmp) - self.C # self.update_exponential(dot(sp.neg.weights * tmp.T, tmp) - 1 * self.C, -1*self.sp.neg.cmuexp) if 11 < 3: # ?3 to 5 times slower?? Z = np.zeros((N,N)) for k in range(sp.mu): z = (pop[k]-mold) Z += np.outer((cmu * sp.weights[k] / (self.sigma * self.sigma_vec)**2) * z, z) self.C *= 1 - c1a - cmu self.C += np.outer(c1 * self.pc, self.pc) + Z self.dC = np.diag(self.C) # for output and termination checking else: # separable/diagonal linear case assert(c1+cmu <= 1) Z = np.zeros(N) for k in range(sp.mu): z = (pop[k]-mold) / (self.sigma * self.sigma_vec) # TODO see above Z += sp.weights[k] * z * z # is 1-D self.C = (1-c1a-cmu) * self.C + c1 * self.pc * self.pc + cmu * Z # TODO: self.C *= exp(cmuneg * (N - dot(sp.neg.weights, **2) self.dC = self.C self.D = sqrt(self.C) # C is a 1-D array self.itereigenupdated = self.countiter # idx = self.mirror_idx_cov() # take half of mirrored vectors for negative update # qqqqqqqqqqq if 1 < 3 and np.isfinite(sp.dampsvec): if self.countiter == 1: print("WARNING: CMA_dampsvec option is experimental") sp.dampsvec *= np.exp(sp.dampsvec_fading/self.N) # TODO: rank-lambda update: *= (1 + sum(z[z>1]**2-1) * exp(sum(z[z<1]**2-1)) self.sigma_vec *= np.exp((sp.cs/sp.dampsvec/2) * (self.ps**2 - 1)) # self.sigma_vec *= np.exp((sp.cs/sp.dampsvec) * (abs(self.ps) - (2/np.pi)**0.5)) self.more_to_write += [exp(np.mean((self.ps**2 - 1)**2))] # TODO: rank-mu update # step-size adaptation, adapt sigma if 1 < 3: # self.sigma *= sigma_fac * \ np.exp((min((1, (sp.cs/sp.damps) * (sqrt(sum(self.ps**2))/self.const.chiN - 1))))) else: self.sigma *= sigma_fac * \ np.exp((min((1000, (sp.cs/sp.damps/2) * (sum(self.ps**2)/N - 1))))) if 11 < 3: # derandomized MSR = natural gradient descent using mean(z**2) instead of mu*mean(z)**2 lengths = array([sum(z**2)**0.5 for z in self.arz[fit.idx[:self.sp.mu]]]) # print lengths[0::int(self.sp.mu/5)] self.sigma *= np.exp(self.sp.mueff**0.5 * dot(self.sp.weights, lengths / self.const.chiN - 1))**(2/(N+1)) if 11 < 3 and self.opts['vv']: if self.countiter < 2: print('constant sigma applied') print(self.opts['vv']) # N=10,lam=10: 0.8 is optimal self.sigma = self.opts['vv'] * self.sp.mueff * sum(self.mean**2)**0.5 / N if self.sigma * min(self.dC)**0.5 < self.opts['minstd']: self.sigma = self.opts['minstd'] / min(self.dC)**0.5 # g = self.countiter # N = self.N mindx = eval(self.opts['mindx']) if type(self.opts['mindx']) == type('') else self.opts['mindx'] if self.sigma * min(self.D) < mindx: # TODO: sigma_vec is missing here self.sigma = mindx / min(self.D) if self.sigma > 1e9 * self.sigma0: alpha = self.sigma / max(self.D) self.multiplyC(alpha) self.sigma /= alpha**0.5 self.opts['tolupsigma'] /= alpha**0.5 # to be compared with sigma # TODO increase sigma in case of a plateau? # Uncertainty noise measurement is done on an upper level # output, has moved up, e.g. as part of fmin, TODO to be removed if 11 < 3 and self.opts['verb_log'] > 0 and (self.countiter < 4 or self.countiter % self.opts['verb_log'] == 0): # this assumes that two logger with the same name access the same data! CMADataLogger(self.opts['verb_filenameprefix']).register(self, append=True).add() # self.writeOutput(solutions[fit.idx[0]]) self.flgtelldone = True # end tell() def result(self): """return ``(xbest, f(xbest), evaluations_xbest, evaluations, iterations, pheno(xmean), effective_stds)``""" # TODO: how about xcurrent? return self.best.get() + ( self.countevals, self.countiter, self.gp.pheno(self.mean), self.gp.scales * self.sigma * self.sigma_vec * self.dC**0.5) def clip_or_fit_solutions(self, pop, idx): """make sure that solutions fit to sample distribution, this interface will probably change. In particular the frequency of long vectors appearing in pop[idx] - self.mean is limited. """ for k in idx: self.repair_genotype(pop[k]) def repair_genotype(self, x): """make sure that solutions fit to sample distribution, this interface will probably change. In particular the frequency of x - self.mean being long is limited. """ mold = self.mean if 1 < 3: # hard clip at upper_length upper_length = self.N**0.5 + 2 * self.N / (self.N+2) # should become an Option, but how? e.g. [0, 2, 2] fac = self.mahalanobisNorm(x - mold) / upper_length if fac > 1: x = (x - mold) / fac + mold # print self.countiter, k, fac, self.mahalanobisNorm(pop[k] - mold) # adapt also sigma: which are the trust-worthy/injected solutions? elif 11 < 3: return exp(np.tanh(((upper_length*fac)**2/self.N-1)/2) / 2) else: if 'checktail' not in self.__dict__: # hasattr(self, 'checktail') raise NotImplementedError # from check_tail_smooth import CheckTail # for the time being # self.checktail = CheckTail() # print('untested feature checktail is on') fac = self.checktail.addchin(self.mahalanobisNorm(x - mold)) if fac < 1: x = fac * (x - mold) + mold return 1.0 # sigma_fac, not in use #____________________________________________________________ #____________________________________________________________ # def updateBD(self): """update internal variables for sampling the distribution with the current covariance matrix C. This method is O(N^3), if C is not diagonal. """ # itereigenupdated is always up-to-date in the diagonal case # just double check here if self.itereigenupdated == self.countiter: return if self.sp.neg.cmuexp: # cave: self.update_exponential(self.Zneg, -self.sp.neg.cmuexp) # self.C += self.Zpos # pos update after Zneg would be the correct update, overall: # self.C = self.Zpos + Cs * Mh.expms(-self.sp.neg.cmuexp*Csi*self.Zneg*Csi) * Cs self.Zneg = np.zeros((self.N, self.N)) if self.sigma_vec is not 1 and not np.all(self.sigma_vec == 1): self.C = dot(dot(np.diag(self.sigma_vec), self.C), np.diag(self.sigma_vec)) self.sigma_vec[:] = 1 if self.opts['CMA_const_trace'] in (True, 1, 2): # normalize trace of C if self.opts['CMA_const_trace'] == 2: s = np.exp(np.mean(np.log(self.dC))) else: s = np.mean(self.dC) self.C /= s self.dC /= s self.C = (self.C + self.C.T) / 2 # self.C = np.triu(self.C) + np.triu(self.C,1).T # should work as well # self.D, self.B = eigh(self.C) # hermitian, ie symmetric C is assumed if type(self.opts['CMA_eigenmethod']) == type(1): print('WARNING: option CMA_eigenmethod should be a function, not an integer') if self.opts['CMA_eigenmethod'] == -1: # pygsl # easy to install (well, in Windows install gsl binaries first, # set system path to respective libgsl-0.dll (or cp the dll to # python\DLLS ?), in unzipped pygsl edit # gsl_dist/gsl_site_example.py into gsl_dist/gsl_site.py # and run "python setup.py build" and "python setup.py install" # in MINGW32) if 1 < 3: # import pygsl on the fly try: import pygsl.eigen.eigenvectors # TODO efficient enough? except ImportError: print('WARNING: could not find pygsl.eigen module, either install pygsl \n' + ' or set option CMA_eigenmethod=1 (is much slower), option set to 1') self.opts['CMA_eigenmethod'] = 0 # use 0 if 1 is too slow self.D, self.B = pygsl.eigen.eigenvectors(self.C) elif self.opts['CMA_eigenmethod'] == 0: # TODO: thoroughly test np.linalg.eigh # numpy.linalg.eig crashes in 200-D # and EVecs with same EVals are not orthogonal self.D, self.B = np.linalg.eigh(self.C) # self.B[i] is a row and not an eigenvector else: # is overall two;ten times slower in 10;20-D self.D, self.B = Misc.eig(self.C) # def eig, see below else: self.D, self.B = self.opts['CMA_eigenmethod'](self.C) # assert(sum(self.D-DD) < 1e-6) # assert(sum(sum(np.dot(BB, BB.T)-np.eye(self.N))) < 1e-6) # assert(sum(sum(np.dot(BB * DD, BB.T) - self.C)) < 1e-6) idx = np.argsort(self.D) self.D = self.D[idx] self.B = self.B[:,idx] # self.B[i] is a row, columns self.B[:,i] are eigenvectors # assert(all(self.B[self.countiter % self.N] == self.B[self.countiter % self.N,:])) # qqqqqqqqqq if 11 < 3: # limit condition number to 1e13 climit = 1e13 # cave: conditioncov termination is 1e14 if self.D[-1] / self.D[0] > climit: self.D += self.D[-1] / climit for i in range(self.N): self.C[i][i] += self.D[-1] / climit if 11 < 3 and any(abs(sum(self.B[:,0:self.N-1] * self.B[:,1:], 0)) > 1e-6): print('B is not orthogonal') print(self.D) print(sum(self.B[:,0:self.N-1] * self.B[:,1:], 0)) else: # is O(N^3) # assert(sum(abs(self.C - np.dot(self.D * self.B, self.B.T))) < N**2*1e-11) pass self.D **= 0.5 self.itereigenupdated = self.countiter def multiplyC(self, alpha): """multiply C with a scalar and update all related internal variables (dC, D,...)""" self.C *= alpha if self.dC is not self.C: self.dC *= alpha self.D *= alpha**0.5 def update_exponential(self, Z, eta, BDpair=None): """exponential update of C that guarantees positive definiteness, that is, instead of the assignment ``C = C + eta * Z``, C gets C**.5 * exp(eta * C**-.5 * Z * C**-.5) * C**.5. Parameter Z should have expectation zero, e.g. sum(w[i] * z[i] * z[i].T) - C if E z z.T = C. This function conducts two eigendecompositions, assuming that B and D are not up to date, unless `BDpair` is given. Given BDpair, B is the eigensystem and D is the vector of sqrt(eigenvalues), one eigendecomposition is omitted. Reference: Glasmachers et al 2010, Exponential Natural Evolution Strategies """ if eta == 0: return if BDpair: B, D = BDpair else: D, B = self.opts['CMA_eigenmethod'](self.C) D **= 0.5 Csi = dot(B, (B / D).T) Cs = dot(B, (B * D).T) self.C = dot(Cs, dot(Mh.expms(eta * dot(Csi, dot(Z, Csi)), self.opts['CMA_eigenmethod']), Cs)) #____________________________________________________________ #____________________________________________________________ # def _updateCholesky(self, A, Ainv, p, alpha, beta): """not yet implemented""" # BD is A, p is A*Normal(0,I) distributed # input is assumed to be numpy arrays # Ainv is needed to compute the evolution path # this is a stump and is not tested raise _Error("not yet implemented") # prepare alpha = float(alpha) beta = float(beta) y = np.dot(Ainv, p) y_sum = sum(y**2) # compute scalars tmp = sqrt(1 + beta * y_sum / alpha) fac = (sqrt(alpha) / sum(y**2)) * (tmp - 1) facinv = (1. / (sqrt(alpha) * sum(y**2))) * (1 - 1. / tmp) # update matrices A *= sqrt(alpha) A += np.outer(fac * p, y) Ainv /= sqrt(alpha) Ainv -= np.outer(facinv * y, np.dot(y.T, Ainv)) #____________________________________________________________ #____________________________________________________________ def feedForResume(self, X, function_values): """Given all "previous" candidate solutions and their respective function values, the state of a `CMAEvolutionStrategy` object can be reconstructed from this history. This is the purpose of function `feedForResume`. Arguments --------- `X` (all) solution points in chronological order, phenotypic representation. The number of points must be a multiple of popsize. `function_values` respective objective function values Details ------- `feedForResume` can be called repeatedly with only parts of the history. The part must have the length of a multiple of the population size. `feedForResume` feeds the history in popsize-chunks into `tell`. The state of the random number generator might not be reconstructed, but this would be only relevant for the future. Example ------- :: import cma # prepare (x0, sigma0) = ... # initial values from previous trial X = ... # list of generated solutions from a previous trial f = ... # respective list of f-values # resume es = cma.CMAEvolutionStrategy(x0, sigma0) es.feedForResume(X, f) # continue with func as objective function while not es.stop(): X = es.ask() es.tell(X, [func(x) for x in X]) Credits to Dirk Bueche and Fabrice Marchal for the feeding idea. :See: class `CMAEvolutionStrategy` for a simple dump/load to resume """ if self.countiter > 0: print('WARNING: feed should generally be used with a new object instance') if len(X) != len(function_values): raise _Error('number of solutions ' + str(len(X)) + ' and number function values ' + str(len(function_values))+' must not differ') popsize = self.sp.popsize if (len(X) % popsize) != 0: raise _Error('number of solutions ' + str(len(X)) + ' must be a multiple of popsize (lambda) ' + str(popsize)) for i in range(len(X) / popsize): # feed in chunks of size popsize self.ask() # a fake ask, mainly for a conditioned calling of updateBD # and secondary to get possibly the same random state self.tell(X[i*popsize:(i+1)*popsize], function_values[i*popsize:(i+1)*popsize]) #____________________________________________________________ #____________________________________________________________ def readProperties(self): """reads dynamic parameters from property file (not implemented) """ print('not yet implemented') #____________________________________________________________ #____________________________________________________________ def mahalanobisNorm(self, dx): """ compute the Mahalanobis norm that is induced by the adapted covariance matrix C times sigma**2. Argument -------- A *genotype* difference `dx`. Example ------- >>> import cma, numpy >>> es = cma.CMAEvolutionStrategy(numpy.ones(10), 1) >>> xx = numpy.random.randn(2, 10) >>> d = es.mahalanobisNorm(es.gp.geno(xx[0]-xx[1])) `d` is the distance "in" the true sample distribution, sampled points have a typical distance of ``sqrt(2*es.N)``, where `N` is the dimension. In the example, `d` is the Euclidean distance, because C = I and sigma = 1. """ return sqrt(sum((self.D**-1 * np.dot(self.B.T, dx))**2)) / self.sigma #____________________________________________________________ #____________________________________________________________ # def timesCroot(self, mat): """return C**0.5 times mat, where mat can be a vector or matrix. Not functional, because _Croot=C**0.5 is never computed (should be in updateBD) """ print("WARNING: timesCroot is not yet tested") if self.opts['CMA_diagonal'] is True \ or self.countiter <= self.opts['CMA_diagonal']: res = (self._Croot * mat.T).T else: res = np.dot(self._Croot, mat) return res def divCroot(self, mat): """return C**-1/2 times mat, where mat can be a vector or matrix""" print("WARNING: divCroot is not yet tested") if self.opts['CMA_diagonal'] is True \ or self.countiter <= self.opts['CMA_diagonal']: res = (self._Crootinv * mat.T).T else: res = np.dot(self._Crootinv, mat) return res #____________________________________________________________ #____________________________________________________________ def disp_annotation(self): """print annotation for `disp()`""" print('Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec') sys.stdout.flush() #____________________________________________________________ #____________________________________________________________ def disp(self, modulo=None): # TODO: rather assign opt['verb_disp'] as default? """prints some infos according to `disp_annotation()`, if ``iteration_counter % modulo == 0`` """ if modulo is None: modulo = self.opts['verb_disp'] # console display if modulo: if (self.countiter-1) % (10 * modulo) < 1: self.disp_annotation() if self.countiter > 0 and (self.stop() or self.countiter < 4 or self.countiter % modulo < 1): if self.opts['verb_time']: toc = self.elapsed_time() stime = str(int(toc//60))+':'+str(round(toc%60,1)) else: stime = '' print(' '.join((repr(self.countiter).rjust(5), repr(self.countevals).rjust(7), '%.15e' % (min(self.fit.fit)), '%4.1e' % (self.D.max()/self.D.min()), '%6.2e' % self.sigma, '%6.0e' % (self.sigma * sqrt(min(self.dC))), '%6.0e' % (self.sigma * sqrt(max(self.dC))), stime))) # if self.countiter < 4: sys.stdout.flush() class Options(dict): """``Options()`` returns a dictionary with the available options and their default values for function fmin and for class CMAEvolutionStrategy. ``Options(opts)`` returns the subset of recognized options in dict(opts). ``Options('pop')`` returns a subset of recognized options that contain 'pop' in there keyword name, value or description. Option values can be "written" in a string and, when passed to fmin or CMAEvolutionStrategy, are evaluated using "N" and "popsize" as known values for dimension and population size (sample size, number of new solutions per iteration). All default option values are such a string. Details ------- All Options are originally defined via the input arguments of `fmin()`. Options starting with ``tol`` are termination "tolerances". For `tolstagnation`, the median over the first and the second half of at least `tolstagnation` iterations are compared for both, the per-iteration best and per-iteration median function value. Some options are, as mentioned (`restarts`,...), only used with `fmin`. Example ------- :: import cma cma.Options('tol') is a shortcut for cma.Options().match('tol') that returns all options that contain 'tol' in their name or description. :See: `fmin`(), `CMAEvolutionStrategy`, `CMAParameters` """ # @classmethod # self is the class, not the instance # @property # def default(self): # """returns all options with defaults""" # return fmin([],[]) @staticmethod def defaults(): """return a dictionary with default option values and description, calls `fmin([], [])`""" return fmin([], []) @staticmethod def versatileOptions(): """return list of options that can be changed at any time (not only be initialized), however the list might not be entirely up to date. The string ' #v ' in the default value indicates a 'versatile' option that can be changed any time. """ return tuple(sorted(i[0] for i in list(Options.defaults().items()) if i[1].find(' #v ') > 0)) def __init__(self, s=None, unchecked=False): """return an `Options` instance, either with the default options, if ``s is None``, or with all options whose name or description contains `s`, if `s` is a string (case is disregarded), or with entries from dictionary `s` as options, not complemented with default options or settings Returns: see above. """ # if not Options.defaults: # this is different from self.defaults!!! # Options.defaults = fmin([],[]) if s is None: super(Options, self).__init__(Options.defaults()) # self = Options.defaults() elif type(s) is str: super(Options, self).__init__(Options().match(s)) # we could return here else: super(Options, self).__init__(s) if not unchecked: for key in list(self.keys()): if key not in Options.defaults(): print('Warning in cma.Options.__init__(): invalid key ``' + str(key) + '`` popped') self.pop(key) # self.evaluated = False # would become an option entry def init(self, dict_or_str, val=None, warn=True): """initialize one or several options. Arguments --------- `dict_or_str` a dictionary if ``val is None``, otherwise a key. If `val` is provided `dict_or_str` must be a valid key. `val` value for key Details ------- Only known keys are accepted. Known keys are in `Options.defaults()` """ #dic = dict_or_key if val is None else {dict_or_key:val} dic = dict_or_str if val is not None: dic = {dict_or_str:val} for key, val in list(dic.items()): if key not in Options.defaults(): # TODO: find a better solution? if warn: print('Warning in cma.Options.init(): key ' + str(key) + ' ignored') else: self[key] = val return self def set(self, dic, val=None, warn=True): """set can assign versatile options from `Options.versatileOptions()` with a new value, use `init()` for the others. Arguments --------- `dic` either a dictionary or a key. In the latter case, val must be provided `val` value for key `warn` bool, print a warning if the option cannot be changed and is therefore omitted This method will be most probably used with the ``opts`` attribute of a `CMAEvolutionStrategy` instance. """ if val is not None: # dic is a key in this case dic = {dic:val} # compose a dictionary for key, val in list(dic.items()): if key in Options.versatileOptions(): self[key] = val elif warn: print('Warning in cma.Options.set(): key ' + str(key) + ' ignored') return self # to allow o = Options(o).set(new) def complement(self): """add all missing options with their default values""" for key in Options.defaults(): if key not in self: self[key] = Options.defaults()[key] return self def settable(self): """return the subset of those options that are settable at any time. Settable options are in `versatileOptions()`, but the list might be incomlete. """ return Options([i for i in list(self.items()) if i[0] in Options.versatileOptions()]) def __call__(self, key, default=None, loc=None): """evaluate and return the value of option `key` on the fly, or returns those options whose name or description contains `key`, case disregarded. Details ------- Keys that contain `filename` are not evaluated. For ``loc==None``, `self` is used as environment but this does not define `N`. :See: `eval()`, `evalall()` """ try: val = self[key] except: return self.match(key) if loc is None: loc = self # TODO: this hack is not so useful: popsize could be there, but N is missing try: if type(val) is str: val = val.split('#')[0].strip() # remove comments if type(val) == type('') and key.find('filename') < 0 and key.find('mindx') < 0: val = eval(val, globals(), loc) # invoke default # TODO: val in ... fails with array type, because it is applied element wise! # elif val in (None,(),[],{}) and default is not None: elif val is None and default is not None: val = eval(str(default), globals(), loc) except: pass # slighly optimistic: the previous is bug-free return val def eval(self, key, default=None, loc=None): """Evaluates and sets the specified option value in environment `loc`. Many options need `N` to be defined in `loc`, some need `popsize`. Details ------- Keys that contain 'filename' are not evaluated. For `loc` is None, the self-dict is used as environment :See: `evalall()`, `__call__` """ self[key] = self(key, default, loc) return self[key] def evalall(self, loc=None): """Evaluates all option values in environment `loc`. :See: `eval()` """ # TODO: this needs rather the parameter N instead of loc if 'N' in list(loc.keys()): # TODO: __init__ of CMA can be simplified popsize = self('popsize', Options.defaults()['popsize'], loc) for k in list(self.keys()): self.eval(k, Options.defaults()[k], {'N':loc['N'], 'popsize':popsize}) return self def match(self, s=''): """return all options that match, in the name or the description, with string `s`, case is disregarded. Example: ``cma.Options().match('verb')`` returns the verbosity options. """ match = s.lower() res = {} for k in sorted(self): s = str(k) + '=\'' + str(self[k]) + '\'' if match in s.lower(): res[k] = self[k] return Options(res) def pp(self): pprint(self) def printme(self, linebreak=80): for i in sorted(Options.defaults().items()): s = str(i[0]) + "='" + str(i[1]) + "'" a = s.split(' ') # print s in chunks l = '' # start entire to the left while a: while a and len(l) + len(a[0]) < linebreak: l += ' ' + a.pop(0) print(l) l = ' ' # tab for subsequent lines #____________________________________________________________ #____________________________________________________________ class CMAParameters(object): """strategy parameters like population size and learning rates. Note: contrary to `Options`, `CMAParameters` is not (yet) part of the "user-interface" and subject to future changes (it might become a `collections.namedtuple`) Example ------- >>> import cma >>> es = cma.CMAEvolutionStrategy(20 * [0.1], 1) (6_w,12)-CMA-ES (mu_w=3.7,w_1=40%) in dimension 20 (seed=504519190) # the seed is "random" by default >>> >>> type(es.sp) # sp contains the strategy parameters >>> >>> es.sp.disp() {'CMA_on': True, 'N': 20, 'c1': 0.004181139918745593, 'c1_sep': 0.034327992810300939, 'cc': 0.17176721127681213, 'cc_sep': 0.25259494835857677, 'cmean': 1.0, 'cmu': 0.0085149624979034746, 'cmu_sep': 0.057796356229390715, 'cs': 0.21434997799189287, 'damps': 1.2143499779918929, 'mu': 6, 'mu_f': 6.0, 'mueff': 3.7294589343030671, 'popsize': 12, 'rankmualpha': 0.3, 'weights': array([ 0.40240294, 0.25338908, 0.16622156, 0.10437523, 0.05640348, 0.01720771])} >>> >> es.sp == cma.CMAParameters(20, 12, cma.Options().evalall({'N': 20})) True :See: `Options`, `CMAEvolutionStrategy` """ def __init__(self, N, opts, ccovfac=1, verbose=True): """Compute strategy parameters, mainly depending on dimension and population size, by calling `set` """ self.N = N if ccovfac == 1: ccovfac = opts['CMA_on'] # that's a hack self.set(opts, ccovfac=ccovfac, verbose=verbose) def set(self, opts, popsize=None, ccovfac=1, verbose=True): """Compute strategy parameters as a function of dimension and population size """ alpha_cc = 1.0 # cc-correction for mueff, was zero before def cone(df, mu, N, alphacov=2.0): """rank one update learning rate, ``df`` is disregarded and obsolete, reduce alphacov on noisy problems, say to 0.5""" return alphacov / ((N + 1.3)**2 + mu) def cmu(df, mu, alphamu=0.0, alphacov=2.0): """rank mu learning rate, disregarding the constrant cmu <= 1 - cone""" c = alphacov * (alphamu + mu - 2 + 1/mu) / ((N + 2)**2 + alphacov * mu / 2) # c = alphacov * (alphamu + mu - 2 + 1/mu) / (2 * (N + 2)**1.5 + alphacov * mu / 2) # print 'cmu =', c return c def conedf(df, mu, N): """used for computing separable learning rate""" return 1. / (df + 2.*sqrt(df) + float(mu)/N) def cmudf(df, mu, alphamu): """used for computing separable learning rate""" return (alphamu + mu - 2. + 1./mu) / (df + 4.*sqrt(df) + mu/2.) sp = self N = sp.N if popsize: opts.evalall({'N':N, 'popsize':popsize}) else: popsize = opts.evalall({'N':N})['popsize'] # the default popsize is computed in Options() sp.popsize = popsize if opts['CMA_mirrors'] < 0.5: sp.lam_mirr = int(0.5 + opts['CMA_mirrors'] * popsize) elif opts['CMA_mirrors'] > 1: sp.lam_mirr = int(0.5 + opts['CMA_mirrors']) else: sp.lam_mirr = int(0.5 + 0.16 * min((popsize, 2 * N + 2)) + 0.29) # 0.158650... * popsize is optimal # lam = arange(2,22) # mirr = 0.16 + 0.29/lam # print(lam); print([int(0.5 + l) for l in mirr*lam]) # [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21] # [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4] sp.mu_f = sp.popsize / 2.0 # float value of mu if opts['CMA_mu'] is not None: sp.mu_f = opts['CMA_mu'] sp.mu = int(sp.mu_f + 0.499999) # round down for x.5 # in principle we have mu_opt = popsize/2 + lam_mirr/2, # which means in particular weights should only be negative for q > 0.5+mirr_frac/2 if sp.mu > sp.popsize - 2 * sp.lam_mirr + 1: print("WARNING: pairwise selection is not implemented, therefore " + " mu = %d > %d = %d - 2*%d + 1 = popsize - 2*mirr + 1 can produce a bias" % ( sp.mu, sp.popsize - 2 * sp.lam_mirr + 1, sp.popsize, sp.lam_mirr)) if sp.lam_mirr > sp.popsize // 2: raise _Error("fraction of mirrors in the population as read from option CMA_mirrors cannot be larger 0.5, " + "theoretically optimal is 0.159") sp.weights = log(max([sp.mu, sp.popsize / 2.0]) + 0.5) - log(1 + np.arange(sp.mu)) if 11 < 3: # equal recombination weights sp.mu = sp.popsize // 4 sp.weights = np.ones(sp.mu) print(sp.weights[:10]) sp.weights /= sum(sp.weights) sp.mueff = 1 / sum(sp.weights**2) sp.cs = (sp.mueff + 2) / (N + sp.mueff + 3) # TODO: clean up (here the cumulation constant is shorter if sigma_vec is used) sp.dampsvec = opts['CMA_dampsvec_fac'] * (N + 2) if opts['CMA_dampsvec_fac'] else np.Inf sp.dampsvec_fading = opts['CMA_dampsvec_fade'] if np.isfinite(sp.dampsvec): sp.cs = ((sp.mueff + 2) / (N + sp.mueff + 3))**0.5 # sp.cs = (sp.mueff + 2) / (N + 1.5*sp.mueff + 1) sp.cc = (4 + alpha_cc * sp.mueff / N) / (N + 4 + alpha_cc * 2 * sp.mueff / N) sp.cc_sep = (1 + 1/N + alpha_cc * sp.mueff / N) / (N**0.5 + 1/N + alpha_cc * 2 * sp.mueff / N) # \not\gg\cc sp.rankmualpha = opts['CMA_rankmualpha'] # sp.rankmualpha = _evalOption(opts['CMA_rankmualpha'], 0.3) sp.c1 = ccovfac * min(1, sp.popsize/6) * cone((N**2 + N) / 2, sp.mueff, N) # 2. / ((N+1.3)**2 + sp.mucov) sp.c1_sep = ccovfac * conedf(N, sp.mueff, N) if 11 < 3: sp.c1 = 0. print('c1 is zero') if opts['CMA_rankmu'] != 0: # also empty sp.cmu = min(1 - sp.c1, ccovfac * cmu((N**2+N)/2, sp.mueff, sp.rankmualpha)) sp.cmu_sep = min(1 - sp.c1_sep, ccovfac * cmudf(N, sp.mueff, sp.rankmualpha)) else: sp.cmu = sp.cmu_sep = 0 sp.neg = BlancClass() if opts['CMA_active']: # in principle we have mu_opt = popsize/2 + lam_mirr/2, # which means in particular weights should only be negative for q > 0.5+mirr_frac/2 sp.neg.mu_f = popsize - (popsize + sp.lam_mirr) / 2 if popsize > 2 else 1 sp.neg.weights = log(sp.mu_f + 0.5) - log(1 + np.arange(sp.popsize - int(sp.neg.mu_f), sp.popsize)) sp.neg.mu = len(sp.neg.weights) # maybe never useful? sp.neg.weights /= sum(sp.neg.weights) sp.neg.mueff = 1 / sum(sp.neg.weights**2) sp.neg.cmuexp = opts['CMA_activefac'] * 0.25 * sp.neg.mueff / ((N+2)**1.5 + 2 * sp.neg.mueff) assert sp.neg.mu >= sp.lam_mirr # not really necessary # sp.neg.minresidualvariance = 0.66 # not it use, keep at least 0.66 in all directions, small popsize is most critical else: sp.neg.cmuexp = 0 sp.CMA_on = sp.c1 + sp.cmu > 0 # print(sp.c1_sep / sp.cc_sep) if not opts['CMA_on'] and opts['CMA_on'] not in (None,[],(),''): sp.CMA_on = False # sp.c1 = sp.cmu = sp.c1_sep = sp.cmu_sep = 0 sp.damps = opts['CMA_dampfac'] * (0.5 + 0.5 * min([1, (sp.lam_mirr/(0.159*sp.popsize) - 1)**2])**1 + 2 * max([0, ((sp.mueff-1) / (N+1))**0.5 - 1]) + sp.cs ) if 11 < 3: # this is worse than damps = 1 + sp.cs for the (1,10000)-ES on 40D parabolic ridge sp.damps = 0.3 + 2 * max([sp.mueff/sp.popsize, ((sp.mueff-1)/(N+1))**0.5 - 1]) + sp.cs if 11 < 3: # this does not work for lambda = 4*N^2 on the parabolic ridge sp.damps = opts['CMA_dampfac'] * (2 - 0*sp.lam_mirr/sp.popsize) * sp.mueff/sp.popsize + 0.3 + sp.cs # nicer future setting print('damps =', sp.damps) if 11 < 3: sp.damps = 10 * sp.damps # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs; # sp.damps = 20 # 1. + 20 * sp.cs**-1 # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs; print('damps is %f' % (sp.damps)) sp.cmean = float(opts['CMA_cmean']) # sp.kappa = 1 # 4-D, lam=16, rank1, kappa < 4 does not influence convergence rate # in larger dim it does, 15-D with defaults, kappa=8 factor 2 if sp.cmean != 1: print(' cmean = %f' % (sp.cmean)) if verbose: if not sp.CMA_on: print('covariance matrix adaptation turned off') if opts['CMA_mu'] != None: print('mu = %f' % (sp.mu_f)) # return self # the constructor returns itself def disp(self): pprint(self.__dict__) #____________________________________________________________ #____________________________________________________________ class CMAStopDict(dict): """keep and update a termination condition dictionary, which is "usually" empty and returned by `CMAEvolutionStrategy.stop()`. Details ------- This could be a nested class, but nested classes cannot be serialized. :See: `stop()` """ def __init__(self, d={}): update = (type(d) == CMAEvolutionStrategy) inherit = (type(d) == CMAStopDict) super(CMAStopDict, self).__init__({} if update else d) self._stoplist = d._stoplist if inherit else [] # multiple entries self.lastiter = d.lastiter if inherit else 0 # probably not necessary if update: self._update(d) def __call__(self, es): """update the dictionary""" return self._update(es) def _addstop(self, key, cond, val=None): if cond: self.stoplist.append(key) # can have the same key twice if key in list(self.opts.keys()): val = self.opts[key] self[key] = val def _update(self, es): """Test termination criteria and update dictionary. """ if es.countiter == self.lastiter: if es.countiter == 0: self.__init__() return self try: if es == self.es: return self except: # self.es not yet assigned pass self.lastiter = es.countiter self.es = es self.stoplist = [] N = es.N opts = es.opts self.opts = opts # a hack to get _addstop going # fitness: generic criterion, user defined w/o default self._addstop('ftarget', es.best.f < opts['ftarget']) # maxiter, maxfevals: generic criteria self._addstop('maxfevals', es.countevals - 1 >= opts['maxfevals']) self._addstop('maxiter', es.countiter >= opts['maxiter']) # tolx, tolfacupx: generic criteria # tolfun, tolfunhist (CEC:tolfun includes hist) self._addstop('tolx', all([es.sigma*xi < opts['tolx'] for xi in es.pc]) and \ all([es.sigma*xi < opts['tolx'] for xi in sqrt(es.dC)])) self._addstop('tolfacupx', any([es.sigma * sig > es.sigma0 * opts['tolfacupx'] for sig in sqrt(es.dC)])) self._addstop('tolfun', es.fit.fit[-1] - es.fit.fit[0] < opts['tolfun'] and \ max(es.fit.hist) - min(es.fit.hist) < opts['tolfun']) self._addstop('tolfunhist', len(es.fit.hist) > 9 and \ max(es.fit.hist) - min(es.fit.hist) < opts['tolfunhist']) # worst seen false positive: table N=80,lam=80, getting worse for fevals=35e3 \approx 50 * N**1.5 # but the median is not so much getting worse # / 5 reflects the sparsity of histbest/median # / 2 reflects the left and right part to be compared l = int(max(opts['tolstagnation'] / 5. / 2, len(es.fit.histbest) / 10)); # TODO: why max(..., len(histbest)/10) ??? # TODO: the problem in the beginning is only with best ==> ??? if 11 < 3: # print(es.countiter, (opts['tolstagnation'], es.countiter > N * (5 + 100 / es.popsize), len(es.fit.histbest) > 100, np.median(es.fit.histmedian[:l]) >= np.median(es.fit.histmedian[l:2*l]), np.median(es.fit.histbest[:l]) >= np.median(es.fit.histbest[l:2*l]))) # equality should handle flat fitness self._addstop('tolstagnation', # leads sometimes early stop on ftablet, fcigtab, N>=50? 1 < 3 and opts['tolstagnation'] and es.countiter > N * (5 + 100 / es.popsize) and len(es.fit.histbest) > 100 and 2*l < len(es.fit.histbest) and np.median(es.fit.histmedian[:l]) >= np.median(es.fit.histmedian[l:2*l]) and np.median(es.fit.histbest[:l]) >= np.median(es.fit.histbest[l:2*l])) # iiinteger: stagnation termination can prevent to find the optimum self._addstop('tolupsigma', opts['tolupsigma'] and es.sigma / es.sigma0 / np.max(es.D) > opts['tolupsigma']) if 11 < 3 and 2*l < len(es.fit.histbest): # TODO: this might go wrong, because the nb of written columns changes tmp = np.array((-np.median(es.fit.histmedian[:l]) + np.median(es.fit.histmedian[l:2*l]), -np.median(es.fit.histbest[:l]) + np.median(es.fit.histbest[l:2*l]))) es.more_to_write += [(10**t if t < 0 else t + 1) for t in tmp] # the latter to get monotonicy if 1 < 3: # non-user defined, method specific # noeffectaxis (CEC: 0.1sigma), noeffectcoord (CEC:0.2sigma), conditioncov self._addstop('noeffectcoord', any([es.mean[i] == es.mean[i] + 0.2*es.sigma*sqrt(es.dC[i]) for i in range(N)])) if opts['CMA_diagonal'] is not True and es.countiter > opts['CMA_diagonal']: i = es.countiter % N self._addstop('noeffectaxis', sum(es.mean == es.mean + 0.1 * es.sigma * es.D[i] * es.B[:, i]) == N) self._addstop('conditioncov', es.D[-1] > 1e7 * es.D[0], 1e14) # TODO self._addstop('callback', es.callbackstop) # termination_callback if len(self): self._addstop('flat fitness: please (re)consider how to compute the fitness more elaborate', len(es.fit.hist) > 9 and \ max(es.fit.hist) == min(es.fit.hist)) if 11 < 3 and opts['vv'] == 321: self._addstop('||xmean||^2 1: f.write(' '.join(map(str, es.gp.scales))) else: f.write(str(es.gp.scales)) f.write(', typical_x: ') if np.size(es.gp.typical_x) > 1: f.write(' '.join(map(str, es.gp.typical_x))) else: f.write(str(es.gp.typical_x)) f.write('\n') f.close() except (IOError, OSError): print('could not open/write file ' + fn) fn = self.name_prefix + 'xrecentbest.dat' try: with open(fn, 'w') as f: f.write('% # iter+eval+sigma+0+fitness+xbest, ' + strseedtime + '\n') except (IOError, OSError): print('could not open/write file ' + fn) return self # end def __init__ def load(self, filenameprefix=None): """loads data from files written and return a data dictionary, *not* a prerequisite for using `plot()` or `disp()`. Argument `filenameprefix` is the filename prefix of data to be loaded (five files), by default ``'outcmaes'``. Return data dictionary with keys `xrecent`, `xmean`, `f`, `D`, `std` """ if not filenameprefix: filenameprefix = self.name_prefix for i in range(len(self.file_names)): fn = filenameprefix + self.file_names[i] + '.dat' try: self.__dict__[self.key_names[i]] = _fileToMatrix(fn) except: print('WARNING: reading from file "' + fn + '" failed') if self.key_names[i] in self.key_names_with_annotation: self.__dict__[self.key_names[i]].append(self.__dict__[self.key_names[i]][-1]) # copy last row to later fill in annotation position for display self.__dict__[self.key_names[i]] = array(self.__dict__[self.key_names[i]], copy=False) return self def add(self, es=None, more_data=[], modulo=None): # TODO: find a different way to communicate current x and f """append some logging data from `CMAEvolutionStrategy` class instance `es`, if ``number_of_times_called % modulo`` equals to zero, never if ``modulo==0``. The sequence ``more_data`` must always have the same length. When used for a different optimizer class, this function can be (easily?) adapted by changing the assignments under INTERFACE in the implemention. """ self.counter += 1 mod = modulo if modulo is not None else self.modulo if mod == 0 or (self.counter > 3 and self.counter % mod): return if es is None: try: es = self.es # must have been registered except AttributeError : raise _Error('call `add` with argument `es` or ``register(es)`` before ``add()``') elif not self.registered: self.register(es) # calls initialize # --- INTERFACE, can be changed if necessary --- if type(es) is not CMAEvolutionStrategy: # not necessary print('WARNING: expected, found ' + str(type(es)) + ' in method CMADataLogger.add') evals = es.countevals iteration = es.countiter sigma = es.sigma axratio = es.D.max()/es.D.min() xmean = es.mean # TODO: should be optionally phenotype? fmean_noise_free = es.fmean_noise_free fmean = es.fmean try: besteverf = es.best.f bestf = es.fit.fit[0] medianf = es.fit.fit[es.sp.popsize//2] worstf = es.fit.fit[-1] except: if self.counter > 1: # first call without f-values is OK raise try: xrecent = es.best.last.x except: xrecent = None maxD = es.D.max() minD = es.D.min() diagD = es.D diagC = es.sigma*es.sigma_vec*sqrt(es.dC) more_to_write = es.more_to_write es.more_to_write = [] # --- end interface --- try: # fit if self.counter > 1: fn = self.name_prefix + 'fit.dat' with open(fn, 'a') as f: f.write(str(iteration) + ' ' + str(evals) + ' ' + str(sigma) + ' ' + str(axratio) + ' ' + str(besteverf) + ' ' + '%.16e' % bestf + ' ' + str(medianf) + ' ' + str(worstf) + ' ' # + str(es.sp.popsize) + ' ' # + str(10**es.noiseS) + ' ' # + str(es.sp.cmean) + ' ' + ' '.join(str(i) for i in more_to_write) + ' '.join(str(i) for i in more_data) + '\n') # axlen fn = self.name_prefix + 'axlen.dat' with open(fn, 'a') as f: # does not rely on reference counting f.write(str(iteration) + ' ' + str(evals) + ' ' + str(sigma) + ' ' + str(maxD) + ' ' + str(minD) + ' ' + ' '.join(map(str, diagD)) + '\n') # stddev fn = self.name_prefix + 'stddev.dat' with open(fn, 'a') as f: f.write(str(iteration) + ' ' + str(evals) + ' ' + str(sigma) + ' ' + '0 0 ' + ' '.join(map(str, diagC)) + '\n') # xmean fn = self.name_prefix + 'xmean.dat' with open(fn, 'a') as f: if iteration < 1: # before first iteration f.write('0 0 0 0 0 ' + ' '.join(map(str, xmean)) + '\n') else: f.write(str(iteration) + ' ' + str(evals) + ' ' # + str(sigma) + ' ' + '0 ' + str(fmean_noise_free) + ' ' + str(fmean) + ' ' # TODO: this does not make sense # TODO should be optional the phenotyp? + ' '.join(map(str, xmean)) + '\n') # xrecent fn = self.name_prefix + 'xrecentbest.dat' if iteration > 0 and xrecent is not None: with open(fn, 'a') as f: f.write(str(iteration) + ' ' + str(evals) + ' ' + str(sigma) + ' ' + '0 ' + str(bestf) + ' ' + ' '.join(map(str, xrecent)) + '\n') except (IOError, OSError): if iteration <= 1: print('could not open/write file') def closefig(self): pylab.close(self.fighandle) def save(self, nameprefix, switch=False): """saves logger data to a different set of files, for ``switch=True`` also the loggers name prefix is switched to the new value """ if not nameprefix or type(nameprefix) is not str: _Error('filename prefix must be a nonempty string') if nameprefix == self.default_prefix: _Error('cannot save to default name "' + nameprefix + '...", chose another name') if nameprefix == self.name_prefix: return for name in CMADataLogger.names: open(nameprefix+name+'.dat', 'w').write(open(self.name_prefix+name+'.dat').read()) if switch: self.name_prefix = nameprefix def plot(self, fig=None, iabscissa=1, iteridx=None, plot_mean=True, # TODO: plot_mean default should be False foffset=1e-19, x_opt = None, fontsize=10): """ plot data from a `CMADataLogger` (using the files written by the logger). Arguments --------- `fig` figure number, by default 325 `iabscissa` ``0==plot`` versus iteration count, ``1==plot`` versus function evaluation number `iteridx` iteration indices to plot Return `CMADataLogger` itself. Examples -------- :: import cma logger = cma.CMADataLogger() # with default name # try to plot the "default logging" data (e.g. from previous fmin calls) logger.plot() # to continue you might need to close the pop-up window # once and call plot() again. # This behavior seems to disappear in subsequent # calls of plot(). Also using ipython with -pylab # option might help. cma.savefig('fig325.png') # save current figure logger.closefig() Dependencies: matlabplotlib/pylab. """ dat = self.load(self.name_prefix) try: # pylab: prodedural interface for matplotlib from matplotlib.pylab import figure, ioff, ion, subplot, semilogy, hold, plot, grid, \ axis, title, text, xlabel, isinteractive, draw, gcf except ImportError: ImportError('could not find matplotlib.pylab module, function plot() is not available') return if fontsize and pylab.rcParams['font.size'] != fontsize: print('global variable pylab.rcParams[\'font.size\'] set (from ' + str(pylab.rcParams['font.size']) + ') to ' + str(fontsize)) pylab.rcParams['font.size'] = fontsize # subtracted in the end, but return can happen inbetween if fig: figure(fig) else: figure(325) # show() # should not be necessary self.fighandle = gcf() # fighandle.number if iabscissa not in (0,1): iabscissa = 1 interactive_status = isinteractive() ioff() # prevents immediate drawing dat.x = dat.xmean # this is the genotyp if not plot_mean: try: dat.x = dat.xrecent except: pass if len(dat.x) < 2: print('not enough data to plot') return {} if iteridx is not None: dat.f = dat.f[np.where([x in iteridx for x in dat.f[:,0]])[0],:] dat.D = dat.D[np.where([x in iteridx for x in dat.D[:,0]])[0],:] iteridx.append(dat.x[-1,1]) # last entry is artificial dat.x = dat.x[np.where([x in iteridx for x in dat.x[:,0]])[0],:] dat.std = dat.std[np.where([x in iteridx for x in dat.std[:,0]])[0],:] if iabscissa == 0: xlab = 'iterations' elif iabscissa == 1: xlab = 'function evaluations' # use fake last entry in x and std for line extension-annotation if dat.x.shape[1] < 100: minxend = int(1.06*dat.x[-2, iabscissa]) # write y-values for individual annotation into dat.x dat.x[-1, iabscissa] = minxend # TODO: should be ax[1] idx = np.argsort(dat.x[-2,5:]) idx2 = np.argsort(idx) if x_opt is None: dat.x[-1,5+idx] = np.linspace(np.min(dat.x[:,5:]), np.max(dat.x[:,5:]), dat.x.shape[1]-5) else: dat.x[-1,5+idx] = np.logspace(np.log10(np.min(abs(dat.x[:,5:]))), np.log10(np.max(abs(dat.x[:,5:]))), dat.x.shape[1]-5) else: minxend = 0 if len(dat.f) == 0: print('nothing to plot') return # not in use anymore, see formatter above # xticklocs = np.arange(5) * np.round(minxend/4., -int(np.log10(minxend/4.))) # dfit(dfit<1e-98) = NaN; ioff() # turns update off # TODO: if abscissa==0 plot in chunks, ie loop over subsets where dat.f[:,0]==countiter is monotonous subplot(2,2,1) self.plotdivers(dat, iabscissa, foffset) # TODO: modularize also the remaining subplots subplot(2,2,2) hold(False) if x_opt is not None: # TODO: differentate neg and pos? semilogy(dat.x[:, iabscissa], abs(dat.x[:,5:]) - x_opt, '-') else: plot(dat.x[:, iabscissa], dat.x[:,5:],'-') hold(True) grid(True) ax = array(axis()) # ax[1] = max(minxend, ax[1]) axis(ax) ax[1] -= 1e-6 if dat.x.shape[1] < 100: yy = np.linspace(ax[2]+1e-6, ax[3]-1e-6, dat.x.shape[1]-5) #yyl = np.sort(dat.x[-1,5:]) idx = np.argsort(dat.x[-1,5:]) idx2 = np.argsort(idx) if x_opt is not None: semilogy([dat.x[-1, iabscissa], ax[1]], [abs(dat.x[-1,5:]), yy[idx2]], 'k-') # line from last data point semilogy(np.dot(dat.x[-2, iabscissa],[1,1]), array([ax[2]+1e-6, ax[3]-1e-6]), 'k-') else: # plot([dat.x[-1, iabscissa], ax[1]], [dat.x[-1,5:], yy[idx2]], 'k-') # line from last data point plot(np.dot(dat.x[-2, iabscissa],[1,1]), array([ax[2]+1e-6, ax[3]-1e-6]), 'k-') # plot(array([dat.x[-1, iabscissa], ax[1]]), # reshape(array([dat.x[-1,5:], yy[idx2]]).flatten(), (2,4)), '-k') for i in range(len(idx)): # TODOqqq: annotate phenotypic value!? # text(ax[1], yy[i], 'x(' + str(idx[i]) + ')=' + str(dat.x[-2,5+idx[i]])) text(dat.x[-1,iabscissa], dat.x[-1,5+i], 'x(' + str(i) + ')=' + str(dat.x[-2,5+i])) i = 2 # find smallest i where iteration count differs (in case the same row appears twice) while i < len(dat.f) and dat.f[-i][0] == dat.f[-1][0]: i += 1 title('Object Variables (' + ('mean' if plot_mean else 'curr best') + ', ' + str(dat.x.shape[1]-5) + '-D, popsize~' + (str(int((dat.f[-1][1] - dat.f[-i][1]) / (dat.f[-1][0] - dat.f[-i][0]))) if len(dat.f.T[0]) > 1 and dat.f[-1][0] > dat.f[-i][0] else 'NA') + ')') # pylab.xticks(xticklocs) # Scaling subplot(2,2,3) hold(False) semilogy(dat.D[:, iabscissa], dat.D[:,5:], '-b') hold(True) grid(True) ax = array(axis()) # ax[1] = max(minxend, ax[1]) axis(ax) title('Scaling (All Main Axes)') # pylab.xticks(xticklocs) xlabel(xlab) # standard deviations subplot(2,2,4) hold(False) # remove sigma from stds (graphs become much better readible) dat.std[:,5:] = np.transpose(dat.std[:,5:].T / dat.std[:,2].T) # ax = array(axis()) # ax[1] = max(minxend, ax[1]) # axis(ax) if 1 < 2 and dat.std.shape[1] < 100: # use fake last entry in x and std for line extension-annotation minxend = int(1.06*dat.x[-2, iabscissa]) dat.std[-1, iabscissa] = minxend # TODO: should be ax[1] idx = np.argsort(dat.std[-2,5:]) idx2 = np.argsort(idx) dat.std[-1,5+idx] = np.logspace(np.log10(np.min(dat.std[:,5:])), np.log10(np.max(dat.std[:,5:])), dat.std.shape[1]-5) dat.std[-1, iabscissa] = minxend # TODO: should be ax[1] yy = np.logspace(np.log10(ax[2]), np.log10(ax[3]), dat.std.shape[1]-5) #yyl = np.sort(dat.std[-1,5:]) idx = np.argsort(dat.std[-1,5:]) idx2 = np.argsort(idx) # plot(np.dot(dat.std[-2, iabscissa],[1,1]), array([ax[2]+1e-6, ax[3]-1e-6]), 'k-') # vertical separator # vertical separator plot(np.dot(dat.std[-2, iabscissa],[1,1]), array([np.min(dat.std[-2,5:]), np.max(dat.std[-2,5:])]), 'k-') hold(True) # plot([dat.std[-1, iabscissa], ax[1]], [dat.std[-1,5:], yy[idx2]], 'k-') # line from last data point for i in range(len(idx)): # text(ax[1], yy[i], ' '+str(idx[i])) text(dat.std[-1, iabscissa], dat.std[-1, 5+i], ' '+str(i)) semilogy(dat.std[:, iabscissa], dat.std[:,5:], '-') grid(True) title('Standard Deviations in All Coordinates') # pylab.xticks(xticklocs) xlabel(xlab) draw() # does not suffice if interactive_status: ion() # turns interactive mode on (again) draw() show() return self #____________________________________________________________ #____________________________________________________________ # @staticmethod def plotdivers(dat, iabscissa, foffset): """helper function for `plot()` that plots all what is in the upper left subplot like fitness, sigma, etc. Arguments --------- `iabscissa` in ``(0,1)`` 0==versus fevals, 1==versus iteration `foffset` offset to fitness for log-plot :See: `plot()` """ from matplotlib.pylab import semilogy, hold, grid, \ axis, title, text fontsize = pylab.rcParams['font.size'] hold(False) dfit = dat.f[:,5]-min(dat.f[:,5]) dfit[dfit<1e-98] = np.NaN if dat.f.shape[1] > 7: # semilogy(dat.f[:, iabscissa], abs(dat.f[:,[6, 7, 10, 12]])+foffset,'-k') semilogy(dat.f[:, iabscissa], abs(dat.f[:,[6, 7]])+foffset,'-k') hold(True) # (larger indices): additional fitness data, for example constraints values if dat.f.shape[1] > 8: # dd = abs(dat.f[:,7:]) + 10*foffset # dd = np.where(dat.f[:,7:]==0, np.NaN, dd) # cannot be semilogy(dat.f[:, iabscissa], np.abs(dat.f[:,8:]) + 10*foffset, 'm') hold(True) idx = np.where(dat.f[:,5]>1e-98)[0] # positive values semilogy(dat.f[idx, iabscissa], dat.f[idx,5]+foffset, '.b') hold(True) grid(True) idx = np.where(dat.f[:,5] < -1e-98) # negative values semilogy(dat.f[idx, iabscissa], abs(dat.f[idx,5])+foffset,'.r') semilogy(dat.f[:, iabscissa],abs(dat.f[:,5])+foffset,'-b') semilogy(dat.f[:, iabscissa], dfit, '-c') if 11 < 3: # delta-fitness as points dfit = dat.f[1:, 5] - dat.f[:-1,5] # should be negative usually semilogy(dat.f[1:,iabscissa], # abs(fit(g) - fit(g-1)) np.abs(dfit)+foffset, '.c') i = dfit > 0 # print(np.sum(i) / float(len(dat.f[1:,iabscissa]))) semilogy(dat.f[1:,iabscissa][i], # abs(fit(g) - fit(g-1)) np.abs(dfit[i])+foffset, '.r') # overall minimum i = np.argmin(dat.f[:,5]) semilogy(dat.f[i, iabscissa]*np.ones(2), dat.f[i,5]*np.ones(2), 'rd') # semilogy(dat.f[-1, iabscissa]*np.ones(2), dat.f[-1,4]*np.ones(2), 'rd') # AR and sigma semilogy(dat.f[:, iabscissa], dat.f[:,3], '-r') # AR semilogy(dat.f[:, iabscissa], dat.f[:,2],'-g') # sigma semilogy(dat.std[:-1, iabscissa], np.vstack([list(map(max, dat.std[:-1,5:])), list(map(min, dat.std[:-1,5:]))]).T, '-m', linewidth=2) text(dat.std[-2, iabscissa], max(dat.std[-2, 5:]), 'max std', fontsize=fontsize) text(dat.std[-2, iabscissa], min(dat.std[-2, 5:]), 'min std', fontsize=fontsize) ax = array(axis()) # ax[1] = max(minxend, ax[1]) axis(ax) text(ax[0]+0.01, ax[2], # 10**(log10(ax[2])+0.05*(log10(ax[3])-log10(ax[2]))), '.f_recent=' + repr(dat.f[-1,5]) ) # title('abs(f) (blue), f-min(f) (cyan), Sigma (green), Axis Ratio (red)') title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio', fontsize=fontsize-1) # pylab.xticks(xticklocs) def downsampling(self, factor=10, first=3, switch=True): """ rude downsampling of a `CMADataLogger` data file by `factor`, keeping also the first `first` entries. This function is a stump and subject to future changes. Arguments --------- - `factor` -- downsampling factor - `first` -- keep first `first` entries - `switch` -- switch the new logger name to oldname+'down' Details ------- ``self.name_prefix+'down'`` files are written Example ------- :: import cma cma.downsampling() # takes outcmaes* files cma.plot('outcmaesdown') """ newprefix = self.name_prefix + 'down' for name in CMADataLogger.names: f = open(newprefix+name+'.dat','w') iline = 0 cwritten = 0 for line in open(self.name_prefix+name+'.dat'): if iline < first or iline % factor == 0: f.write(line) cwritten += 1 iline += 1 f.close() print('%d' % (cwritten) + ' lines written in ' + newprefix+name+'.dat') if switch: self.name_prefix += 'down' return self #____________________________________________________________ #____________________________________________________________ # def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]): """displays selected data from (files written by) the class `CMADataLogger`. Arguments --------- `idx` indices corresponding to rows in the data file; if idx is a scalar (int), the first two, then every idx-th, and the last three rows are displayed. Too large index values are removed. Example ------- >>> import cma, numpy as np >>> res = cma.fmin(cma.fcts.elli, 7 * [0.1], 1, verb_disp=1e9) # generate data >>> assert res[1] < 1e-9 >>> assert res[2] < 4400 >>> l = cma.CMADataLogger() # == res[-1], logger with default name, "points to" above data >>> l.disp([0,-1]) # first and last >>> l.disp(20) # some first/last and every 20-th line >>> l.disp(np.r_[0:999999:100, -1]) # every 100-th and last >>> l.disp(np.r_[0, -10:0]) # first and ten last >>> cma.disp(l.name_prefix, np.r_[0::100, -10:]) # the same as l.disp(...) Details ------- The data line with the best f-value is displayed as last line. :See: `disp()` """ filenameprefix=self.name_prefix def printdatarow(dat, iteration): """print data of iteration i""" i = np.where(dat.f[:, 0] == iteration)[0][0] j = np.where(dat.std[:, 0] == iteration)[0][0] print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) + ' %5.1e' % (dat.f[i,3]) + ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:])) dat = CMADataLogger(filenameprefix).load() ndata = dat.f.shape[0] # map index to iteration number, is difficult if not all iteration numbers exist # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long # otherwise: if idx is None: idx = 100 if np.isscalar(idx): # idx = np.arange(0, ndata, idx) if idx: idx = np.r_[0, 1, idx:ndata-3:idx, -3:0] else: idx = np.r_[0, 1, -3:0] idx = array(idx) idx = idx[idx 1: f.write(' '.join(map(str, es.gp.scales))) else: f.write(str(es.gp.scales)) f.write(', typical_x: ') if np.size(es.gp.typical_x) > 1: f.write(' '.join(map(str, es.gp.typical_x))) else: f.write(str(es.gp.typical_x)) f.write('\n') f.close() except (IOError, OSError): print('could not open/write file ' + fn) if 11 < 3: fn = self.name_prefix + 'xrecentbest.dat' try: with open(fn, 'w') as f: f.write('% # iter+eval+sigma+0+fitness+xbest, ' + strseedtime + '\n') except (IOError, OSError): print('could not open/write file ' + fn) return self # end def __init__ def load(self, filenameprefix=None): """loads data from files written and return a data dictionary, *not* a prerequisite for using `plot()` or `disp()`. Argument `filenameprefix` is the filename prefix of data to be loaded (five files), by default ``'outcmaes'``. Return data dictionary with keys `xrecent`, `xmean`, `f`, `D`, `std` """ if not filenameprefix: filenameprefix = self.name_prefix dat = self # historical # dat.xrecent = _fileToMatrix(filenameprefix + 'xrecentbest.dat') dat.xmean = _fileToMatrix(filenameprefix + 'xmean.dat') dat.std = _fileToMatrix(filenameprefix + 'stddev' + '.dat') # a hack to later write something into the last entry for key in ['xmean', 'std']: # 'xrecent', dat.__dict__[key].append(dat.__dict__[key][-1]) # copy last row to later fill in annotation position for display dat.__dict__[key] = array(dat.__dict__[key], copy=False) dat.f = array(_fileToMatrix(filenameprefix + 'fit.dat')) dat.D = array(_fileToMatrix(filenameprefix + 'axlen' + '.dat')) return dat def add(self, fitness_values, es=None, more_data=[], modulo=None): # TODO: find a different way to communicate current x and f """append some logging data from `CMAEvolutionStrategy` class instance `es`, if ``number_of_times_called % modulo`` equals to zero, never if ``modulo==0``. The sequence ``more_data`` must always have the same length. """ self.counter += 1 fitness_values = np.sort(fitness_values) if fitness_values[0] < self.best_fitness: self.best_fitness = fitness_values[0] mod = modulo if modulo is not None else self.modulo if mod == 0 or (self.counter > 3 and self.counter % mod): return if es is None: try: es = self.es # must have been registered except AttributeError : raise _Error('call register() before add() or add(es)') elif not self.registered: self.register(es) if 11 < 3: try: # TODO: find a more decent interface to store and pass recent_x xrecent = es.best.last.x except: if self.counter == 2: # by now a recent_x should be available print('WARNING: es.out[\'recent_x\'] not found in CMADataLogger.add, count=' + str(self.counter)) try: # fit if es.update_count > 0: # fit = es.fit.fit[0] # TODO: where do we get the fitness from? fn = self.name_prefix + 'fit.dat' with open(fn, 'a') as f: f.write(str(es.update_count) + ' ' + str(es.update_count * es.lambda_) + ' ' + str(es.sigma) + ' ' + str(es.diagD[-1]/es.diagD[0]) + ' ' + str(self.best_fitness) + ' ' + '%.16e' % fitness_values[0] + ' ' + str(fitness_values[es.lambda_//2]) + ' ' + str(fitness_values[-1]) + ' ' # + str(es.sp.popsize) + ' ' # + str(10**es.noiseS) + ' ' # + str(es.sp.cmean) + ' ' # + ' '.join(str(i) for i in es.more_to_write) + ' '.join(str(i) for i in more_data) + '\n') # es.more_to_write = [] # axlen fn = self.name_prefix + 'axlen.dat' with open(fn, 'a') as f: # does not rely on reference counting f.write(str(es.update_count) + ' ' + str(es.update_count * es.lambda_) + ' ' + str(es.sigma) + ' ' + str(es.diagD[-1]) + ' ' + str(es.diagD[0]) + ' ' + ' '.join(map(str, es.diagD)) + '\n') # stddev fn = self.name_prefix + 'stddev.dat' with open(fn, 'a') as f: f.write(str(es.update_count) + ' ' + str(es.update_count * es.lambda_) + ' ' + str(es.sigma) + ' ' + '0 0 ' + ' '.join(map(str, es.sigma*np.sqrt([es.C[i][i] for i in range(es.dim)]))) + '\n') # xmean fn = self.name_prefix + 'xmean.dat' with open(fn, 'a') as f: if es.update_count < 1: f.write('0 0 0 0 0 ' + ' '.join(map(str, # TODO should be optional the phenotyp? # es.gp.geno(es.x0) es.mean)) + '\n') else: f.write(str(es.update_count) + ' ' + str(es.update_count * es.lambda_) + ' ' # + str(es.sigma) + ' ' + '0 0 0 ' # + str(es.fmean_noise_free) + ' ' # + str(es.fmean) + ' ' # TODO: this does not make sense # TODO should be optional the phenotyp? + ' '.join(map(str, es.centroid)) + '\n') # xrecent if 11 < 3: fn = self.name_prefix + 'xrecentbest.dat' if es.countiter > 0 and xrecent is not None: with open(fn, 'a') as f: f.write(str(es.countiter) + ' ' + str(es.countevals) + ' ' + str(es.sigma) + ' ' + '0 ' + str(es.fit.fit[0]) + ' ' + ' '.join(map(str, xrecent)) + '\n') except (IOError, OSError): if es.countiter == 1: print('could not open/write file') def closefig(self): pylab.close(self.fighandle) def save(self, nameprefix, switch=False): """saves logger data to a different set of files, for ``switch=True`` also the loggers name prefix is switched to the new value """ if not nameprefix or type(nameprefix) is not str: _Error('filename prefix must be a nonempty string') if nameprefix == self.default_prefix: _Error('cannot save to default name "' + nameprefix + '...", chose another name') if nameprefix == self.name_prefix: return for name in CMADataLogger.names: open(nameprefix+name+'.dat', 'w').write(open(self.name_prefix+name+'.dat').read()) if switch: self.name_prefix = nameprefix def plot(self, fig=None, iabscissa=1, iteridx=None, plot_mean=True, # TODO: plot_mean default should be False foffset=1e-19, x_opt = None, fontsize=10): """ plot data from a `CMADataLogger` (using the files written by the logger). Arguments --------- `fig` figure number, by default 325 `iabscissa` ``0==plot`` versus iteration count, ``1==plot`` versus function evaluation number `iteridx` iteration indices to plot Return `CMADataLogger` itself. Examples -------- :: import cma logger = cma.CMADataLogger() # with default name # try to plot the "default logging" data (e.g. from previous fmin calls) logger.plot() # to continue you might need to close the pop-up window # once and call plot() again. # This behavior seems to disappear in subsequent # calls of plot(). Also using ipython with -pylab # option might help. cma.savefig('fig325.png') # save current figure logger.closefig() Dependencies: matlabplotlib/pylab. """ dat = self.load(self.name_prefix) try: # pylab: prodedural interface for matplotlib from matplotlib.pylab import figure, ioff, ion, subplot, semilogy, hold, plot, grid, \ axis, title, text, xlabel, isinteractive, draw, gcf except ImportError: ImportError('could not find matplotlib.pylab module, function plot() is not available') return if fontsize and pylab.rcParams['font.size'] != fontsize: print('global variable pylab.rcParams[\'font.size\'] set (from ' + str(pylab.rcParams['font.size']) + ') to ' + str(fontsize)) pylab.rcParams['font.size'] = fontsize # subtracted in the end, but return can happen inbetween if fig: figure(fig) else: figure(325) # show() # should not be necessary self.fighandle = gcf() # fighandle.number if iabscissa not in (0,1): iabscissa = 1 interactive_status = isinteractive() ioff() # prevents immediate drawing if 11 < 3: dat.x = dat.xrecent if len(dat.x) < 2: print('not enough data to plot') return {} # if plot_mean: dat.x = dat.xmean # this is the genotyp if iteridx is not None: dat.f = dat.f[np.where([x in iteridx for x in dat.f[:,0]])[0],:] dat.D = dat.D[np.where([x in iteridx for x in dat.D[:,0]])[0],:] iteridx.append(dat.x[-1,1]) # last entry is artificial dat.x = dat.x[np.where([x in iteridx for x in dat.x[:,0]])[0],:] dat.std = dat.std[np.where([x in iteridx for x in dat.std[:,0]])[0],:] if iabscissa == 0: xlab = 'iterations' elif iabscissa == 1: xlab = 'function evaluations' # use fake last entry in x and std for line extension-annotation if dat.x.shape[1] < 100: minxend = int(1.06*dat.x[-2, iabscissa]) # write y-values for individual annotation into dat.x dat.x[-1, iabscissa] = minxend # TODO: should be ax[1] idx = np.argsort(dat.x[-2,5:]) idx2 = np.argsort(idx) if x_opt is None: dat.x[-1,5+idx] = np.linspace(np.min(dat.x[:,5:]), np.max(dat.x[:,5:]), dat.x.shape[1]-5) else: dat.x[-1,5+idx] = np.logspace(np.log10(np.min(abs(dat.x[:,5:]))), np.log10(np.max(abs(dat.x[:,5:]))), dat.x.shape[1]-5) else: minxend = 0 if len(dat.f) == 0: print('nothing to plot') return # not in use anymore, see formatter above # xticklocs = np.arange(5) * np.round(minxend/4., -int(np.log10(minxend/4.))) # dfit(dfit<1e-98) = NaN; ioff() # turns update off # TODO: if abscissa==0 plot in chunks, ie loop over subsets where dat.f[:,0]==countiter is monotonous subplot(2,2,1) self.plotdivers(dat, iabscissa, foffset) # TODO: modularize also the remaining subplots subplot(2,2,2) hold(False) if x_opt is not None: # TODO: differentate neg and pos? semilogy(dat.x[:, iabscissa], abs(dat.x[:,5:]) - x_opt, '-') else: plot(dat.x[:, iabscissa], dat.x[:,5:],'-') hold(True) grid(True) ax = array(axis()) # ax[1] = max(minxend, ax[1]) axis(ax) ax[1] -= 1e-6 if dat.x.shape[1] < 100: yy = np.linspace(ax[2]+1e-6, ax[3]-1e-6, dat.x.shape[1]-5) #yyl = np.sort(dat.x[-1,5:]) idx = np.argsort(dat.x[-1,5:]) idx2 = np.argsort(idx) if x_opt is not None: semilogy([dat.x[-1, iabscissa], ax[1]], [abs(dat.x[-1,5:]), yy[idx2]], 'k-') # line from last data point semilogy(np.dot(dat.x[-2, iabscissa],[1,1]), array([ax[2]+1e-6, ax[3]-1e-6]), 'k-') else: # plot([dat.x[-1, iabscissa], ax[1]], [dat.x[-1,5:], yy[idx2]], 'k-') # line from last data point plot(np.dot(dat.x[-2, iabscissa],[1,1]), array([ax[2]+1e-6, ax[3]-1e-6]), 'k-') # plot(array([dat.x[-1, iabscissa], ax[1]]), # reshape(array([dat.x[-1,5:], yy[idx2]]).flatten(), (2,4)), '-k') for i in range(len(idx)): # TODOqqq: annotate phenotypic value!? # text(ax[1], yy[i], 'x(' + str(idx[i]) + ')=' + str(dat.x[-2,5+idx[i]])) text(dat.x[-1,iabscissa], dat.x[-1,5+i], 'x(' + str(i) + ')=' + str(dat.x[-2,5+i])) i = 2 # find smallest i where iteration count differs (in case the same row appears twice) while i < len(dat.f) and dat.f[-i][0] == dat.f[-1][0]: i += 1 title('Object Variables (' + ('mean' if plot_mean else 'curr best') + ', ' + str(dat.x.shape[1]-5) + '-D, popsize~' + (str(int((dat.f[-1][1] - dat.f[-i][1]) / (dat.f[-1][0] - dat.f[-i][0]))) if len(dat.f.T[0]) > 1 and dat.f[-1][0] > dat.f[-i][0] else 'NA') + ')') # pylab.xticks(xticklocs) # Scaling subplot(2,2,3) hold(False) semilogy(dat.D[:, iabscissa], dat.D[:,5:], '-b') hold(True) grid(True) ax = array(axis()) # ax[1] = max(minxend, ax[1]) axis(ax) title('Scaling (All Main Axes)') # pylab.xticks(xticklocs) xlabel(xlab) # standard deviations subplot(2,2,4) hold(False) # remove sigma from stds (graphs become much better readible) dat.std[:,5:] = np.transpose(dat.std[:,5:].T / dat.std[:,2].T) # ax = array(axis()) # ax[1] = max(minxend, ax[1]) # axis(ax) if 1 < 2 and dat.std.shape[1] < 100: # use fake last entry in x and std for line extension-annotation minxend = int(1.06*dat.x[-2, iabscissa]) dat.std[-1, iabscissa] = minxend # TODO: should be ax[1] idx = np.argsort(dat.std[-2,5:]) idx2 = np.argsort(idx) dat.std[-1,5+idx] = np.logspace(np.log10(np.min(dat.std[:,5:])), np.log10(np.max(dat.std[:,5:])), dat.std.shape[1]-5) dat.std[-1, iabscissa] = minxend # TODO: should be ax[1] yy = np.logspace(np.log10(ax[2]), np.log10(ax[3]), dat.std.shape[1]-5) #yyl = np.sort(dat.std[-1,5:]) idx = np.argsort(dat.std[-1,5:]) idx2 = np.argsort(idx) # plot(np.dot(dat.std[-2, iabscissa],[1,1]), array([ax[2]+1e-6, ax[3]-1e-6]), 'k-') # vertical separator # vertical separator plot(np.dot(dat.std[-2, iabscissa],[1,1]), array([np.min(dat.std[-2,5:]), np.max(dat.std[-2,5:])]), 'k-') hold(True) # plot([dat.std[-1, iabscissa], ax[1]], [dat.std[-1,5:], yy[idx2]], 'k-') # line from last data point for i in range(len(idx)): # text(ax[1], yy[i], ' '+str(idx[i])) text(dat.std[-1, iabscissa], dat.std[-1, 5+i], ' '+str(i)) semilogy(dat.std[:, iabscissa], dat.std[:,5:], '-') grid(True) title('Standard Deviations in All Coordinates') # pylab.xticks(xticklocs) xlabel(xlab) draw() # does not suffice if interactive_status: ion() # turns interactive mode on (again) draw() show() return self #____________________________________________________________ #____________________________________________________________ # @staticmethod def plotdivers(dat, iabscissa, foffset): """helper function for `plot()` that plots all what is in the upper left subplot like fitness, sigma, etc. Arguments --------- `iabscissa` in ``(0,1)`` 0==versus fevals, 1==versus iteration `foffset` offset to fitness for log-plot :See: `plot()` """ from matplotlib.pylab import semilogy, hold, grid, \ axis, title, text fontsize = pylab.rcParams['font.size'] hold(False) dfit = dat.f[:,5]-min(dat.f[:,5]) dfit[dfit<1e-98] = np.NaN if dat.f.shape[1] > 7: # semilogy(dat.f[:, iabscissa], abs(dat.f[:,[6, 7, 10, 12]])+foffset,'-k') semilogy(dat.f[:, iabscissa], abs(dat.f[:,[6, 7]])+foffset,'-k') hold(True) # (larger indices): additional fitness data, for example constraints values if dat.f.shape[1] > 8: # dd = abs(dat.f[:,7:]) + 10*foffset # dd = np.where(dat.f[:,7:]==0, np.NaN, dd) # cannot be semilogy(dat.f[:, iabscissa], np.abs(dat.f[:,8:]) + 10*foffset, 'm') hold(True) idx = np.where(dat.f[:,5]>1e-98)[0] # positive values semilogy(dat.f[idx, iabscissa], dat.f[idx,5]+foffset, '.b') hold(True) grid(True) idx = np.where(dat.f[:,5] < -1e-98) # negative values semilogy(dat.f[idx, iabscissa], abs(dat.f[idx,5])+foffset,'.r') semilogy(dat.f[:, iabscissa],abs(dat.f[:,5])+foffset,'-b') semilogy(dat.f[:, iabscissa], dfit, '-c') if 11 < 3: # delta-fitness as points dfit = dat.f[1:, 5] - dat.f[:-1,5] # should be negative usually semilogy(dat.f[1:,iabscissa], # abs(fit(g) - fit(g-1)) np.abs(dfit)+foffset, '.c') i = dfit > 0 # print(np.sum(i) / float(len(dat.f[1:,iabscissa]))) semilogy(dat.f[1:,iabscissa][i], # abs(fit(g) - fit(g-1)) np.abs(dfit[i])+foffset, '.r') # overall minimum i = np.argmin(dat.f[:,5]) semilogy(dat.f[i, iabscissa]*np.ones(2), dat.f[i,5]*np.ones(2), 'rd') # semilogy(dat.f[-1, iabscissa]*np.ones(2), dat.f[-1,4]*np.ones(2), 'rd') # AR and sigma semilogy(dat.f[:, iabscissa], dat.f[:,3], '-r') # AR semilogy(dat.f[:, iabscissa], dat.f[:,2],'-g') # sigma semilogy(dat.std[:-1, iabscissa], np.vstack([list(map(max, dat.std[:-1,5:])), list(map(min, dat.std[:-1,5:]))]).T, '-m', linewidth=2) text(dat.std[-2, iabscissa], max(dat.std[-2, 5:]), 'max std', fontsize=fontsize) text(dat.std[-2, iabscissa], min(dat.std[-2, 5:]), 'min std', fontsize=fontsize) ax = array(axis()) # ax[1] = max(minxend, ax[1]) axis(ax) text(ax[0]+0.01, ax[2], # 10**(log10(ax[2])+0.05*(log10(ax[3])-log10(ax[2]))), '.f_recent=' + repr(dat.f[-1,5]) ) # title('abs(f) (blue), f-min(f) (cyan), Sigma (green), Axis Ratio (red)') title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio', fontsize=fontsize-1) # pylab.xticks(xticklocs) def downsampling(self, factor=10, first=3, switch=True): """ rude downsampling of a `CMADataLogger` data file by `factor`, keeping also the first `first` entries. This function is a stump and subject to future changes. Arguments --------- - `factor` -- downsampling factor - `first` -- keep first `first` entries - `switch` -- switch the new logger name to oldname+'down' Details ------- ``self.name_prefix+'down'`` files are written Example ------- :: import cma cma.downsampling() # takes outcmaes* files cma.plot('outcmaesdown') """ newprefix = self.name_prefix + 'down' for name in CMADataLogger.names: f = open(newprefix+name+'.dat','w') iline = 0 cwritten = 0 for line in open(self.name_prefix+name+'.dat'): if iline < first or iline % factor == 0: f.write(line) cwritten += 1 iline += 1 f.close() print('%d' % (cwritten) + ' lines written in ' + newprefix+name+'.dat') if switch: self.name_prefix += 'down' return self #____________________________________________________________ #____________________________________________________________ # def disp_header(self): heading = 'Iterat Nfevals function value axis ratio maxstd minstd' print(heading) def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]): """displays selected data from (files written by) the class `CMADataLogger`. Arguments --------- `idx` indices corresponding to rows in the data file; if idx is a scalar (int), the first two, then every idx-th, and the last three rows are displayed. Too large index values are removed. If ``len(idx) == 1``, only a single row is displayed, e.g. the last entry when ``idx == [-1]``. Example ------- >>> import cma, numpy as np >>> res = cma.fmin(cma.fcts.elli, 7 * [0.1], 1, verb_disp=1e9) # generate data >>> assert res[1] < 1e-9 >>> assert res[2] < 4400 >>> l = cma.CMADataLogger() # == res[-1], logger with default name, "points to" above data >>> l.disp([0,-1]) # first and last >>> l.disp(20) # some first/last and every 20-th line >>> l.disp(np.r_[0:999999:100, -1]) # every 100-th and last >>> l.disp(np.r_[0, -10:0]) # first and ten last >>> cma.disp(l.name_prefix, np.r_[0::100, -10:]) # the same as l.disp(...) Details ------- The data line with the best f-value is displayed as last line. :See: `disp()` """ filenameprefix=self.name_prefix def printdatarow(dat, iteration): """print data of iteration i""" i = np.where(dat.f[:, 0] == iteration)[0][0] j = np.where(dat.std[:, 0] == iteration)[0][0] print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) + ' %5.1e' % (dat.f[i,3]) + ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:])) dat = CMADataLogger(filenameprefix).load() ndata = dat.f.shape[0] # map index to iteration number, is difficult if not all iteration numbers exist # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long # otherwise: if idx is None: idx = 100 if np.isscalar(idx): # idx = np.arange(0, ndata, idx) if idx: idx = np.r_[0, 1, idx:ndata-3:idx, -3:0] else: idx = np.r_[0, 1, -3:0] idx = array(idx) idx = idx[idx<=ndata] # TODO: shouldn't this be "<"? idx = idx[-idx<=ndata] iters = dat.f[idx, 0] idxbest = np.argmin(dat.f[:,5]) iterbest = dat.f[idxbest, 0] if len(iters) == 1: printdatarow(dat, iters[0]) else: self.disp_header() for i in iters: printdatarow(dat, i) self.disp_header() printdatarow(dat, iterbest) sys.stdout.flush() def irg(ar): return range(len(ar)) class AII(object): """unstable experimental code, updates ps, sigma, sigmai, pr, r, sigma_r, mean, all from self. Depends on that the ordering of solutions has not change upon calling update should become a OOOptimizer in far future? """ # Try: ps**2 - 1 instead of (ps**2)**0.5 / chi1 - 1: compare learning rate etc # and dito for psr def __init__(self, x0, sigma0, randn=np.random.randn): """TODO: check scaling of r-learing: seems worse than linear: 9e3 25e3 65e3 (10,20,40-D)""" self.N = len(x0) N = self.N # parameters to play with: # PROBLEM: smaller eta_r even fails on *axparallel* cigar!! Also dampi needs to be smaller then! self.dampi = 4 * N # two times smaller is self.eta_r = 0 / N / 3 # c_r learning rate for direction, cigar: 4/N/3 is optimal in 10-D, 10/N/3 still works (15 in 20-D) but not on the axparallel cigar with recombination self.mu = 1 self.use_abs_sigma = 1 # without it is a problem on 20=D axpar-cigar!!, but why?? Because dampi is just boarderline self.use_abs_sigma_r = 1 # self.randn = randn self.x0 = array(x0, copy=True) self.sigma0 = sigma0 self.cs = 1 / N**0.5 # evolution path for step-size(s) self.damps = 1 self.use_sign = 0 self.use_scalar_product = 0 # sometimes makes it somewhat worse on Rosenbrock, don't know why self.csr = 1 / N**0.5 # cumulation for sigma_r self.dampsr = (4 * N)**0.5 self.chi1 = (2/np.pi)**0.5 self.chiN = N**0.5*(1-1./(4.*N)+1./(21.*N**2)) # expectation of norm(randn(N,1)) self.initialize() def initialize(self): """alias ``reset``, set all state variables to initial values""" N = self.N self.mean = array(self.x0, copy=True) self.sigma = self.sigma0 self.sigmai = np.ones(N) self.ps = np.zeros(N) # path for individual and globalstep-size(s) self.r = np.zeros(N) self.pr = 0 # cumulation for zr = N(0,1) self.sigma_r = 0 def ask(self, popsize): if popsize == 1: raise NotImplementedError() self.Z = [self.randn(self.N) for _i in range(popsize)] self.zr = list(self.randn(popsize)) pop = [self.mean + self.sigma * (self.sigmai * self.Z[k]) + self.zr[k] * self.sigma_r * self.r for k in range(popsize)] if not np.isfinite(pop[0][0]): raise ValueError() return pop def tell(self, X, f): """update """ mu = 1 if self.mu else int(len(f) / 4) idx = np.argsort(f)[:mu] zr = [self.zr[i] for i in idx] Z = [self.Z[i] for i in idx] X = [X[i] for i in idx] xmean = np.mean(X, axis=0) self.ps *= 1 - self.cs self.ps += (self.cs*(2-self.cs))**0.5 * mu**0.5 * np.mean(Z, axis=0) self.sigma *= np.exp((self.cs/self.damps) * (sum(self.ps**2)**0.5 / self.chiN - 1)) if self.use_abs_sigma: self.sigmai *= np.exp((1/self.dampi) * (np.abs(self.ps) / self.chi1 - 1)) else: self.sigmai *= np.exp((1.3/self.dampi/2) * (self.ps**2 - 1)) self.pr *= 1 - self.csr self.pr += (self.csr*(2-self.csr))**0.5 * mu**0.5 * np.mean(zr) fac = 1 if self.use_sign: fac = np.sign(self.pr) # produces readaptations on the cigar else: self.pr = max([0, self.pr]) if self.use_scalar_product: if np.sign(sum(self.r * (xmean - self.mean))) < 0: # and self.pr > 1: # if np.sign(sum(self.r * self.ps)) < 0: self.r *= -1 if self.eta_r: self.r *= (1 - self.eta_r) * self.sigma_r self.r += fac * self.eta_r * mu**0.5 * (xmean - self.mean) self.r /= sum(self.r**2)**0.5 if self.use_abs_sigma_r: self.sigma_r *= np.exp((1/self.dampsr) * ((self.pr**2)**0.5 / self.chi1 - 1)) else: # this is worse on the cigar, where the direction vector(!) behaves strangely self.sigma_r *= np.exp((1/self.dampsr) * (self.pr**2 - 1) / 2) self.sigma_r = max([self.sigma * sum(self.sigmai**2)**0.5 / 3, self.sigma_r]) # self.sigma_r = 0 self.mean = xmean def fmin(func, x0, sigma0=None, args=() # the follow string arguments are evaluated, besides the verb_filenameprefix , CMA_active='False # exponential negative update, conducted after the original update' , CMA_activefac='1 # learning rate multiplier for active update' , CMA_cmean='1 # learning rate for the mean value' , CMA_const_trace='False # normalize trace, value CMA_const_trace=2 normalizes sum log eigenvalues to zero' , CMA_diagonal='0*100*N/sqrt(popsize) # nb of iterations with diagonal covariance matrix, True for always' # TODO 4/ccov_separable? , CMA_eigenmethod='np.linalg.eigh # 0=numpy-s eigh, -1=pygsl, otherwise cma.Misc.eig (slower)' , CMA_elitist='False # elitism likely impairs global search performance' , CMA_mirrors='popsize < 6 # values <0.5 are interpreted as fraction, values >1 as numbers (rounded), otherwise about 0.16 is used' , CMA_mu='None # parents selection parameter, default is popsize // 2' , CMA_on='True # False or 0 for no adaptation of the covariance matrix' , CMA_rankmu='True # False or 0 for omitting rank-mu update of covariance matrix' , CMA_rankmualpha='0.3 # factor of rank-mu update if mu=1, subject to removal, default might change to 0.0' , CMA_dampfac='1 #v positive multiplier for step-size damping, 0.3 is close to optimal on the sphere' , CMA_dampsvec_fac='np.Inf # tentative and subject to changes, 0.5 would be a "default" damping for sigma vector update' , CMA_dampsvec_fade='0.1 # tentative fading out parameter for sigma vector update' , CMA_teststds='None # factors for non-isotropic initial distr. mainly for test purpose, see scaling_...' , CMA_AII='False # not yet tested' , bounds='[None, None] # lower (=bounds[0]) and upper domain boundaries, each a scalar or a list/vector' , eval_parallel='False # when True, func might be called with more than one solution as first argument' , eval_initial_x='False # ' , fixed_variables='None # dictionary with index-value pairs like {0:1.1, 2:0.1} that are not optimized' , ftarget='-inf #v target function value, minimization' , incpopsize='2 # in fmin(): multiplier for increasing popsize before each restart' , maxfevals='inf #v maximum number of function evaluations' , maxiter='100 + 50 * (N+3)**2 // popsize**0.5 #v maximum number of iterations' , mindx='0 #v minimal std in any direction, cave interference with tol*' , minstd='0 #v minimal std in any coordinate direction, cave interference with tol*' , noise_handling='False # maximal number of evaluations for noise treatment, only fmin' , noise_reevals=' 1.5 + popsize/20 # number of solution to be reevaluated for noise measurement, only fmin' , noise_eps='1e-7 # perturbation factor for noise handling reevaluations, only fmin' , noise_change_sigma='True # exponent to default sigma increment' , popsize='4+int(3*log(N)) # population size, AKA lambda, number of new solution per iteration' , randn='np.random.standard_normal #v randn((lam, N)) must return an np.array of shape (lam, N)' , restarts='0 # in fmin(): number of restarts' , restart_from_best='False' , scaling_of_variables='None # scale for each variable, sigma0 is interpreted w.r.t. this scale, in that effective_sigma0 = sigma0*scaling. Internally the variables are divided by scaling_of_variables and sigma is unchanged, default is ones(N)' , seed='None # random number seed' , termination_callback='None #v a function returning True for termination, called after each iteration step and could be abused for side effects' , tolfacupx='1e3 #v termination when step-size increases by tolfacupx (diverges). That is, the initial step-size was chosen far too small and better solutions were found far away from the initial solution x0' , tolupsigma='1e20 #v sigma/sigma0 > tolupsigma * max(sqrt(eivenvals(C))) indicates "creeping behavior" with usually minor improvements' , tolfun='1e-11 #v termination criterion: tolerance in function value, quite useful' , tolfunhist='1e-12 #v termination criterion: tolerance in function value history' , tolstagnation='int(100 + 100 * N**1.5 / popsize) #v termination if no improvement over tolstagnation iterations' , tolx='1e-11 #v termination criterion: tolerance in x-changes' , transformation='None # [t0, t1] are two mappings, t0 transforms solutions from CMA-representation to f-representation (tf_pheno), t1 is the (optional) back transformation, see class GenoPheno' , typical_x='None # used with scaling_of_variables' , updatecovwait='None #v number of iterations without distribution update, name is subject to future changes' # TODO: rename: iterwaitupdatedistribution? , verb_append='0 # initial evaluation counter, if append, do not overwrite output files' , verb_disp='100 #v verbosity: display console output every verb_disp iteration' , verb_filenameprefix='outcmaes # output filenames prefix' , verb_log='1 #v verbosity: write data to files every verb_log iteration, writing can be time critical on fast to evaluate functions' , verb_plot='0 #v in fmin(): plot() is called every verb_plot iteration' , verb_time='True #v output timings on console' , vv='0 #? versatile variable for hacking purposes, value found in self.opts[\'vv\']' ): """functional interface to the stochastic optimizer CMA-ES for non-convex function minimization. Calling Sequences ================= ``fmin([],[])`` returns all optional arguments, that is, all keyword arguments to fmin with their default values in a dictionary. ``fmin(func, x0, sigma0)`` minimizes `func` starting at `x0` and with standard deviation `sigma0` (step-size) ``fmin(func, x0, sigma0, ftarget=1e-5)`` minimizes `func` up to target function value 1e-5 ``fmin(func, x0, sigma0, args=('f',), **options)`` minimizes `func` called with an additional argument ``'f'``. `options` is a dictionary with additional keyword arguments, e.g. delivered by `Options()`. ``fmin(func, x0, sigma0, **{'ftarget':1e-5, 'popsize':40})`` the same as ``fmin(func, x0, sigma0, ftarget=1e-5, popsize=40)`` ``fmin(func, esobj, **{'maxfevals': 1e5})`` uses the `CMAEvolutionStrategy` object instance `esobj` to optimize `func`, similar to `CMAEvolutionStrategy.optimize()`. Arguments ========= `func` function to be minimized. Called as ``func(x,*args)``. `x` is a one-dimensional `numpy.ndarray`. `func` can return `numpy.NaN`, which is interpreted as outright rejection of solution `x` and invokes an immediate resampling and (re-)evaluation of a new solution not counting as function evaluation. `x0` list or `numpy.ndarray`, initial guess of minimum solution or `cma.CMAEvolutionStrategy` object instance. In this case `sigma0` can be omitted. `sigma0` scalar, initial standard deviation in each coordinate. `sigma0` should be about 1/4 of the search domain width where the optimum is to be expected. The variables in `func` should be scaled such that they presumably have similar sensitivity. See also option `scaling_of_variables`. Keyword Arguments ================= All arguments besides `args` and `verb_filenameprefix` are evaluated if they are of type `str`, see class `Options` for details. The following list might not be fully up-to-date, use ``cma.Options()`` or ``cma.fmin([],[])`` to get the actual list. :: args=() -- additional arguments for func, not in `cma.Options()` CMA_active='False # exponential negative update, conducted after the original update' CMA_activefac='1 # learning rate multiplier for active update' CMA_cmean='1 # learning rate for the mean value' CMA_dampfac='1 #v positive multiplier for step-size damping, 0.3 is close to optimal on the sphere' CMA_diagonal='0*100*N/sqrt(popsize) # nb of iterations with diagonal covariance matrix, True for always' CMA_eigenmethod='np.linalg.eigh # 0=numpy-s eigh, -1=pygsl, alternative: Misc.eig (slower)' CMA_elitist='False # elitism likely impairs global search performance' CMA_mirrors='0 # values <0.5 are interpreted as fraction, values >1 as numbers (rounded), otherwise about 0.16 is used' CMA_mu='None # parents selection parameter, default is popsize // 2' CMA_on='True # False or 0 for no adaptation of the covariance matrix' CMA_rankmu='True # False or 0 for omitting rank-mu update of covariance matrix' CMA_rankmualpha='0.3 # factor of rank-mu update if mu=1, subject to removal, default might change to 0.0' CMA_teststds='None # factors for non-isotropic initial distr. mainly for test purpose, see scaling_...' bounds='[None, None] # lower (=bounds[0]) and upper domain boundaries, each a scalar or a list/vector' eval_initial_x='False # ' fixed_variables='None # dictionary with index-value pairs like {0:1.1, 2:0.1} that are not optimized' ftarget='-inf #v target function value, minimization' incpopsize='2 # in fmin(): multiplier for increasing popsize before each restart' maxfevals='inf #v maximum number of function evaluations' maxiter='long(1e3*N**2/sqrt(popsize)) #v maximum number of iterations' mindx='0 #v minimal std in any direction, cave interference with tol*' minstd='0 #v minimal std in any coordinate direction, cave interference with tol*' noise_eps='1e-7 # perturbation factor for noise handling reevaluations, only fmin' noise_handling='False # maximal number of evaluations for noise treatment, only fmin' noise_reevals=' 1.5 + popsize/20 # number of solution to be reevaluated for noise measurement, only fmin' popsize='4+int(3*log(N)) # population size, AKA lambda, number of new solution per iteration' randn='np.random.standard_normal #v randn((lam, N)) must return an np.array of shape (lam, N)' restarts='0 # in fmin(): number of restarts' scaling_of_variables='None # scale for each variable, sigma0 is interpreted w.r.t. this scale, in that effective_sigma0 = sigma0*scaling. Internally the variables are divided by scaling_of_variables and sigma is unchanged, default is ones(N)' seed='None # random number seed' termination_callback='None #v in fmin(): a function returning True for termination, called after each iteration step and could be abused for side effects' tolfacupx='1e3 #v termination when step-size increases by tolfacupx (diverges). That is, the initial step-size was chosen far too small and better solutions were found far away from the initial solution x0' tolupsigma='1e20 #v sigma/sigma0 > tolupsigma * max(sqrt(eivenvals(C))) indicates "creeping behavior" with usually minor improvements' tolfun='1e-11 #v termination criterion: tolerance in function value, quite useful' tolfunhist='1e-12 #v termination criterion: tolerance in function value history' tolstagnation='int(100 * N**1.5 / popsize) #v termination if no improvement over tolstagnation iterations' tolx='1e-11 #v termination criterion: tolerance in x-changes' transformation='None # [t0, t1] are two mappings, t0 transforms solutions from CMA-representation to f-representation, t1 is the back transformation, see class GenoPheno' typical_x='None # used with scaling_of_variables' updatecovwait='None #v number of iterations without distribution update, name is subject to future changes' verb_append='0 # initial evaluation counter, if append, do not overwrite output files' verb_disp='100 #v verbosity: display console output every verb_disp iteration' verb_filenameprefix='outcmaes # output filenames prefix' verb_log='1 #v verbosity: write data to files every verb_log iteration, writing can be time critical on fast to evaluate functions' verb_plot='0 #v in fmin(): plot() is called every verb_plot iteration' verb_time='True #v output timings on console' vv='0 #? versatile variable for hacking purposes, value found in self.opts['vv']' Subsets of options can be displayed, for example like ``cma.Options('tol')``, see also class `Options`. Return ====== Similar to `OOOptimizer.optimize()` and/or `CMAEvolutionStrategy.optimize()`, return the list provided by `CMAEvolutionStrategy.result()` appended with an `OOOptimizer` and an `BaseDataLogger`:: res = optim.result() + (optim.stop(), optim, logger) where - ``res[0]`` (``xopt``) -- best evaluated solution - ``res[1]`` (``fopt``) -- respective function value - ``res[2]`` (``evalsopt``) -- respective number of function evaluations - ``res[3]`` (``evals``) -- number of overall conducted objective function evaluations - ``res[4]`` (``iterations``) -- number of overall conducted iterations - ``res[5]`` (``xmean``) -- mean of the final sample distribution - ``res[6]`` (``stds``) -- effective stds of the final sample distribution - ``res[-3]`` (``stop``) -- termination condition(s) in a dictionary - ``res[-2]`` (``cmaes``) -- class `CMAEvolutionStrategy` instance - ``res[-1]`` (``logger``) -- class `CMADataLogger` instance Details ======= This function is an interface to the class `CMAEvolutionStrategy`. The class can be used when full control over the iteration loop of the optimizer is desired. The noise handling follows closely [Hansen et al 2009, A Method for Handling Uncertainty in Evolutionary Optimization...] in the measurement part, but the implemented treatment is slightly different: for ``noiseS > 0``, ``evaluations`` (time) and sigma are increased by ``alpha``. For ``noiseS < 0``, ``evaluations`` (time) is decreased by ``alpha**(1/4)``. The option ``noise_handling`` switches the uncertainty handling on/off, the given value defines the maximal number of evaluations for a single fitness computation. If ``noise_handling`` is a list, the smallest element defines the minimal number and if the list has three elements, the median value is the start value for ``evaluations``. See also class `NoiseHandler`. Examples ======== The following example calls `fmin` optimizing the Rosenbrock function in 10-D with initial solution 0.1 and initial step-size 0.5. The options are specified for the usage with the `doctest` module. >>> import cma >>> # cma.Options() # returns all possible options >>> options = {'CMA_diagonal':10, 'seed':1234, 'verb_time':0} >>> >>> res = cma.fmin(cma.fcts.rosen, [0.1] * 10, 0.5, **options) (5_w,10)-CMA-ES (mu_w=3.2,w_1=45%) in dimension 10 (seed=1234) Covariance matrix is diagonal for 10 iterations (1/ccov=29.0) Iterat #Fevals function value axis ratio sigma minstd maxstd min:sec 1 10 1.264232686260072e+02 1.1e+00 4.40e-01 4e-01 4e-01 2 20 1.023929748193649e+02 1.1e+00 4.00e-01 4e-01 4e-01 3 30 1.214724267489674e+02 1.2e+00 3.70e-01 3e-01 4e-01 100 1000 6.366683525319511e+00 6.2e+00 2.49e-02 9e-03 3e-02 200 2000 3.347312410388666e+00 1.2e+01 4.52e-02 8e-03 4e-02 300 3000 1.027509686232270e+00 1.3e+01 2.85e-02 5e-03 2e-02 400 4000 1.279649321170636e-01 2.3e+01 3.53e-02 3e-03 3e-02 500 5000 4.302636076186532e-04 4.6e+01 4.78e-03 3e-04 5e-03 600 6000 6.943669235595049e-11 5.1e+01 5.41e-06 1e-07 4e-06 650 6500 5.557961334063003e-14 5.4e+01 1.88e-07 4e-09 1e-07 termination on tolfun : 1e-11 final/bestever f-value = 5.55796133406e-14 2.62435631419e-14 mean solution: [ 1. 1.00000001 1. 1. 1. 1.00000001 1.00000002 1.00000003 ...] std deviation: [ 3.9193387e-09 3.7792732e-09 4.0062285e-09 4.6605925e-09 5.4966188e-09 7.4377745e-09 1.3797207e-08 2.6020765e-08 ...] >>> >>> print('best solutions fitness = %f' % (res[1])) best solutions fitness = 2.62435631419e-14 >>> assert res[1] < 1e-12 The method :: cma.plot(); (based on `matplotlib.pylab`) produces a plot of the run and, if necessary:: cma.show() shows the plot in a window. To continue you might need to close the pop-up window. This behavior seems to disappear in subsequent calls of `cma.plot()` and is avoided by using `ipython` with `-pylab` option. Finally :: cma.savefig('myfirstrun') # savefig from matplotlib.pylab will save the figure in a png. :See: `CMAEvolutionStrategy`, `OOOptimizer.optimize(), `plot()`, `Options`, `scipy.optimize.fmin()` """ # style guides say there should be the above empty line try: # pass on KeyboardInterrupt opts = locals() # collect all local variables (i.e. arguments) in a dictionary del opts['func'] # remove those without a default value del opts['args'] del opts['x0'] # is not optional, no default available del opts['sigma0'] # is not optional for the constructor CMAEvolutionStrategy if not func: # return available options in a dictionary return Options(opts, True) # these opts are by definition valid # TODO: this is very ugly: incpopsize = Options({'incpopsize':incpopsize}).eval('incpopsize') restarts = Options({'restarts':restarts}).eval('restarts') del opts['restarts'] noise_handling = Options({'noise_handling': noise_handling}).eval('noise_handling') del opts['noise_handling']# otherwise CMA throws an error irun = 0 best = BestSolution() while 1: # recover from a CMA object if irun == 0 and isinstance(x0, CMAEvolutionStrategy): es = x0 x0 = es.inputargs['x0'] # for the next restarts if sigma0 is None or not np.isscalar(array(sigma0)): sigma0 = es.inputargs['sigma0'] # for the next restarts # ignore further input args and keep original options else: # default case if irun and opts['restart_from_best']: print('CAVE: restart_from_best is typically not useful') es = CMAEvolutionStrategy(best.x, sigma0, opts) else: es = CMAEvolutionStrategy(x0, sigma0, opts) if opts['eval_initial_x']: x = es.gp.pheno(es.mean, bounds=es.gp.bounds) es.best.update([x], None, [func(x, *args)], 1) es.countevals += 1 opts = es.opts # processed options, unambiguous append = opts['verb_append'] or es.countiter > 0 or irun > 0 logger = CMADataLogger(opts['verb_filenameprefix'], opts['verb_log']) logger.register(es, append).add() # initial values, not fitness values # if es.countiter == 0 and es.opts['verb_log'] > 0 and not es.opts['verb_append']: # logger = CMADataLogger(es.opts['verb_filenameprefix']).register(es) # logger.add() # es.writeOutput() # initial values for sigma etc noisehandler = NoiseHandler(es.N, noise_handling, np.median, opts['noise_reevals'], opts['noise_eps'], opts['eval_parallel']) while not es.stop(): X, fit = es.ask_and_eval(func, args, evaluations=noisehandler.evaluations, aggregation=np.median) # treats NaN with resampling # TODO: check args and in case use args=(noisehandler.evaluations, ) if 11 < 3 and opts['vv']: # inject a solution # use option check_point = [0] if 0 * np.random.randn() >= 0: X[0] = 0 + opts['vv'] * es.sigma**0 * np.random.randn(es.N) fit[0] = func(X[0], *args) # print fit[0] es.tell(X, fit) # prepare for next iteration if noise_handling: es.sigma *= noisehandler(X, fit, func, es.ask, args)**opts['noise_change_sigma'] es.countevals += noisehandler.evaluations_just_done # TODO: this is a hack, not important though es.disp() logger.add(more_data=[noisehandler.evaluations, 10**noisehandler.noiseS] if noise_handling else [], modulo=1 if es.stop() and logger.modulo else None) if opts['verb_log'] and opts['verb_plot'] and \ (es.countiter % max(opts['verb_plot'], opts['verb_log']) == 0 or es.stop()): logger.plot(324, fontsize=10) # end while not es.stop mean_pheno = es.gp.pheno(es.mean, bounds=es.gp.bounds) fmean = func(mean_pheno, *args) es.countevals += 1 es.best.update([mean_pheno], None, [fmean], es.countevals) best.update(es.best) # in restarted case # final message if opts['verb_disp']: srestarts = (' after %i restart' + ('s' if irun > 1 else '')) % irun if irun else '' for k, v in list(es.stop().items()): print('termination on %s=%s%s (%s)' % (k, str(v), srestarts, time.asctime())) print('final/bestever f-value = %e %e' % (es.best.last.f, best.f)) if es.N < 9: print('mean solution: ' + str(es.gp.pheno(es.mean))) print('std deviation: ' + str(es.sigma * sqrt(es.dC) * es.gp.scales)) else: print('mean solution: %s ...]' % (str(es.gp.pheno(es.mean)[:8])[:-1])) print('std deviations: %s ...]' % (str((es.sigma * sqrt(es.dC) * es.gp.scales)[:8])[:-1])) irun += 1 if irun > restarts or 'ftarget' in es.stopdict or 'maxfunevals' in es.stopdict: break opts['verb_append'] = es.countevals opts['popsize'] = incpopsize * es.sp.popsize # TODO: use rather options? opts['seed'] += 1 # while irun es.out['best'] = best # TODO: this is a rather suboptimal type for inspection in the shell if 1 < 3: return es.result() + (es.stop(), es, logger) else: # previously: to be removed return (best.x.copy(), best.f, es.countevals, dict((('stopdict', CMAStopDict(es.stopdict)) ,('mean', es.gp.pheno(es.mean)) ,('std', es.sigma * sqrt(es.dC) * es.gp.scales) ,('out', es.out) ,('opts', es.opts) # last state of options ,('cma', es) ,('inputargs', es.inputargs) )) ) # TODO refine output, can #args be flexible? # is this well usable as it is now? except KeyboardInterrupt: # Exception, e: if opts['verb_disp'] > 0: print(' in/outcomment ``raise`` in last line of cma.fmin to prevent/restore KeyboardInterrupt exception') raise # cave: swallowing this exception can silently mess up experiments, if ctrl-C is hit def plot(name=None, fig=None, abscissa=1, iteridx=None, plot_mean=True, # TODO: plot_mean default should be False foffset=1e-19, x_opt=None, fontsize=10): """ plot data from files written by a `CMADataLogger`, the call ``cma.plot(name, **argsdict)`` is a shortcut for ``cma.CMADataLogger(name).plot(**argsdict)`` Arguments --------- `name` name of the logger, filename prefix, None evaluates to the default 'outcmaes' `fig` filename or figure number, or both as a tuple (any order) `abscissa` 0==plot versus iteration count, 1==plot versus function evaluation number `iteridx` iteration indices to plot Return `None` Examples -------- :: cma.plot(); # the optimization might be still # running in a different shell cma.show() # to continue you might need to close the pop-up window # once and call cma.plot() again. # This behavior seems to disappear in subsequent # calls of cma.plot(). Also using ipython with -pylab # option might help. cma.savefig('fig325.png') cma.close() cdl = cma.CMADataLogger().downsampling().plot() Details ------- Data from codes in other languages (C, Java, Matlab, Scilab) have the same format and can be plotted just the same. :See: `CMADataLogger`, `CMADataLogger.plot()` """ CMADataLogger(name).plot(fig, abscissa, iteridx, plot_mean, foffset, x_opt, fontsize) def disp(name=None, idx=None): """displays selected data from (files written by) the class `CMADataLogger`. The call ``cma.disp(name, idx)`` is a shortcut for ``cma.CMADataLogger(name).disp(idx)``. Arguments --------- `name` name of the logger, filename prefix, `None` evaluates to the default ``'outcmaes'`` `idx` indices corresponding to rows in the data file; by default the first five, then every 100-th, and the last 10 rows. Too large index values are removed. Examples -------- :: import cma, numpy # assume some data are available from previous runs cma.disp(None,numpy.r_[0,-1]) # first and last cma.disp(None,numpy.r_[0:1e9:100,-1]) # every 100-th and last cma.disp(idx=numpy.r_[0,-10:0]) # first and ten last cma.disp(idx=numpy.r_[0:1e9:1e3,-10:0]) :See: `CMADataLogger.disp()` """ return CMADataLogger(name if name else 'outcmaes' ).disp(idx) #____________________________________________________________ def _fileToMatrix(file_name): """rudimentary method to read in data from a file""" # TODO: np.loadtxt() might be an alternative # try: if 1 < 3: lres = [] for line in open(file_name, 'r').readlines(): if len(line) > 0 and line[0] not in ('%', '#'): lres.append(list(map(float, line.split()))) res = lres else: fil = open(file_name, 'r') fil.readline() # rudimentary, assume one comment line lineToRow = lambda line: list(map(float, line.split())) res = list(map(lineToRow, fil.readlines())) fil.close() # close file could be omitted, reference counting should do during garbage collection, but... while res != [] and res[0] == []: # remove further leading empty lines del res[0] return res # except: print('could not read file ' + file_name) #____________________________________________________________ #____________________________________________________________ class NoiseHandler(object): """Noise handling according to [Hansen et al 2009, A Method for Handling Uncertainty in Evolutionary Optimization...] The interface of this class is yet versatile and subject to changes. The attribute ``evaluations`` serves to control the noise via number of evaluations, for example with `ask_and_eval()`, see also parameter ``maxevals`` and compare the example. Example ------- >>> import cma, numpy as np >>> func = cma.Fcts.noisysphere >>> es = cma.CMAEvolutionStrategy(np.ones(10), 1) >>> logger = cma.CMADataLogger().register(es) >>> nh = cma.NoiseHandler(es.N, maxevals=[1, 30]) >>> while not es.stop(): ... X, fit = es.ask_and_eval(func, evaluations=nh.evaluations) ... es.tell(X, fit) # prepare for next iteration ... es.sigma *= nh(X, fit, func, es.ask) # see method __call__ ... es.countevals += nh.evaluations_just_done # this is a hack, not important though ... logger.add(more_data = [nh.evaluations, nh.noiseS]) # add a data point ... es.disp() ... # nh.maxevals = ... it might be useful to start with smaller values and then increase >>> print(es.stop()) >>> print(es.result()[-2]) # take mean value, the best solution is totally off >>> assert sum(es.result()[-2]**2) < 1e-9 >>> print(X[np.argmin(fit)]) # not bad, but probably worse than the mean >>> logger.plot() The noise options of `fmin()` control a `NoiseHandler` instance similar to this example. The command ``cma.Options('noise')`` lists in effect the parameters of `__init__` apart from ``aggregate``. Details ------- The parameters reevals, theta, c_s, and alpha_t are set differently than in the original publication, see method `__init__()`. For a very small population size, say popsize <= 5, the measurement technique based on rank changes is likely to fail. Missing Features ---------------- In case no noise is found, ``self.lam_reeval`` should be adaptive and get at least as low as 1 (however the possible savings from this are rather limited). Another option might be to decide during the first call by a quantitative analysis of fitness values whether ``lam_reeval`` is set to zero. More generally, an automatic noise mode detection might also set the covariance matrix learning rates to smaller values. :See: `fmin()`, `ask_and_eval()` """ def __init__(self, N, maxevals=10, aggregate=np.median, reevals=None, epsilon=1e-7, parallel=False): """parameters are `N` dimension `maxevals` maximal value for ``self.evaluations``, where ``self.evaluations`` function calls are aggregated for noise treatment. With ``maxevals == 0`` the noise handler is (temporarily) "switched off". If `maxevals` is a list, min value and (for >2 elements) median are used to define minimal and initial value of ``self.evaluations``. Choosing ``maxevals > 1`` is only reasonable, if also the original ``fit`` values (that are passed to `__call__`) are computed by aggregation of ``self.evaluations`` values (otherwise the values are not comparable), as it is done within `fmin()`. `aggregate` function to aggregate single f-values to a 'fitness', e.g. ``np.median``. `reevals` number of solutions to be reevaluated for noise measurement, can be a float, by default set to ``1.5 + popsize/20``, zero switches noise handling off. `epsilon` multiplier for perturbation of the reevaluated solutions `parallel` a single f-call with all resampled solutions :See: `fmin()`, `Options`, `CMAEvolutionStrategy.ask_and_eval()` """ self.lam_reeval = reevals # 2 + popsize/20, see method indices(), originally 2 + popsize/10 self.epsilon = epsilon self.parallel = parallel self.theta = 0.5 # originally 0.2 self.cum = 0.3 # originally 1, 0.3 allows one disagreement of current point with resulting noiseS self.alphasigma = 1 + 2 / (N+10) self.alphaevals = 1 + 2 / (N+10) # originally 1.5 self.alphaevalsdown = self.alphaevals**-0.25 # originally 1/1.5 self.evaluations = 1 # to aggregate for a single f-evaluation self.minevals = 1 self.maxevals = int(np.max(maxevals)) if hasattr(maxevals, '__contains__'): # i.e. can deal with ``in`` if len(maxevals) > 1: self.minevals = min(maxevals) self.evaluations = self.minevals if len(maxevals) > 2: self.evaluations = np.median(maxevals) self.f_aggregate = aggregate self.evaluations_just_done = 0 # actually conducted evals, only for documentation self.noiseS = 0 def __call__(self, X, fit, func, ask=None, args=()): """proceed with noise measurement, set anew attributes ``evaluations`` (proposed number of evaluations to "treat" noise) and ``evaluations_just_done`` and return a factor for increasing sigma. Parameters ---------- `X` a list/sequence/vector of solutions `fit` the respective list of function values `func` the objective function, ``fit[i]`` corresponds to ``func(X[i], *args)`` `ask` a method to generate a new, slightly disturbed solution. The argument is mandatory if ``epsilon`` is not zero, see `__init__()`. `args` optional additional arguments to `func` Details ------- Calls the methods ``reeval()``, ``update_measure()`` and ``treat()`` in this order. ``self.evaluations`` is adapted within the method `treat()`. """ self.evaluations_just_done = 0 if not self.maxevals or self.lam_reeval == 0: return 1.0 res = self.reeval(X, fit, func, ask, args) if not len(res): return 1.0 self.update_measure() return self.treat() def get_evaluations(self): """return ``self.evaluations``, the number of evalutions to get a single fitness measurement""" return self.evaluations def treat(self): """adapt self.evaluations depending on the current measurement value and return ``sigma_fac in (1.0, self.alphasigma)`` """ if self.noiseS > 0: self.evaluations = min((self.evaluations * self.alphaevals, self.maxevals)) return self.alphasigma else: self.evaluations = max((self.evaluations * self.alphaevalsdown, self.minevals)) return 1.0 def reeval(self, X, fit, func, ask, args=()): """store two fitness lists, `fit` and ``fitre`` reevaluating some solutions in `X`. ``self.evaluations`` evaluations are done for each reevaluated fitness value. See `__call__()`, where `reeval()` is called. """ self.fit = list(fit) self.fitre = list(fit) self.idx = self.indices(fit) if not len(self.idx): return self.idx evals = int(self.evaluations) if self.f_aggregate else 1 fagg = np.median if self.f_aggregate is None else self.f_aggregate for i in self.idx: if self.epsilon: if self.parallel: self.fitre[i] = fagg(func(ask(evals, X[i], self.epsilon), *args)) else: self.fitre[i] = fagg([func(ask(1, X[i], self.epsilon)[0], *args) for _k in range(evals)]) else: self.fitre[i] = fagg([func(X[i], *args) for _k in range(evals)]) self.evaluations_just_done = evals * len(self.idx) return self.fit, self.fitre, self.idx def update_measure(self): """updated noise level measure using two fitness lists ``self.fit`` and ``self.fitre``, return ``self.noiseS, all_individual_measures``. Assumes that `self.idx` contains the indices where the fitness lists differ """ lam = len(self.fit) idx = np.argsort(self.fit + self.fitre) ranks = np.argsort(idx).reshape((2, lam)) rankDelta = ranks[0] - ranks[1] - np.sign(ranks[0] - ranks[1]) # compute rank change limits using both ranks[0] and ranks[1] r = np.arange(1, 2 * lam) # 2 * lam - 2 elements limits = [0.5 * (Mh.prctile(np.abs(r - (ranks[0,i] + 1 - (ranks[0,i] > ranks[1,i]))), self.theta*50) + Mh.prctile(np.abs(r - (ranks[1,i] + 1 - (ranks[1,i] > ranks[0,i]))), self.theta*50)) for i in self.idx] # compute measurement # max: 1 rankchange in 2*lambda is always fine s = np.abs(rankDelta[self.idx]) - Mh.amax(limits, 1) # lives roughly in 0..2*lambda self.noiseS += self.cum * (np.mean(s) - self.noiseS) return self.noiseS, s def indices(self, fit): """return the set of indices to be reevaluted for noise measurement, taking the ``lam_reeval`` best from the first ``2 * lam_reeval + 2`` values. Given the first values are the earliest, this is a useful policy also with a time changing objective. """ lam = self.lam_reeval if self.lam_reeval else 2 + len(fit) / 20 reev = int(lam) + ((lam % 1) > np.random.rand()) return np.argsort(array(fit, copy=False)[:2 * (reev + 1)])[:reev] #____________________________________________________________ #____________________________________________________________ class Sections(object): """plot sections through an objective function. A first rational thing to do, when facing an (expensive) application. By default 6 points in each coordinate are evaluated. This class is still experimental. Examples -------- >>> import cma, numpy as np >>> s = cma.Sections(cma.Fcts.rosen, np.zeros(3)).do(plot=False) >>> s.do(plot=False) # evaluate the same points again, i.e. check for noise >>> try: ... s.plot() ... except: ... print('plotting failed: pylab package is missing?') Details ------- Data are saved after each function call during `do()`. The filename is attribute ``name`` and by default ``str(func)``, see `__init__()`. A random (orthogonal) basis can be generated with ``cma.Rotation()(np.eye(3))``. The default name is unique in the function name, but it should be unique in all parameters of `__init__()` but `plot_cmd` and `load`. ``self.res`` is a dictionary with an entry for each "coordinate" ``i`` and with an entry ``'x'``, the middle point. Each entry ``i`` is again a dictionary with keys being different dx values and the value being a sequence of f-values. For example ``self.res[2][0.1] == [0.01, 0.01]``, which is generated using the difference vector ``self.basis[2]`` like ``self.res[2][dx] += func(self.res['x'] + dx * self.basis[2])``. :See: `__init__()` """ def __init__(self, func, x, args=(), basis=None, name=None, plot_cmd=pylab.plot if pylab else None, load=True): """ Parameters ---------- `func` objective function `x` point in search space, middle point of the sections `args` arguments passed to `func` `basis` evaluated points are ``func(x + locations[j] * basis[i]) for i in len(basis) for j in len(locations)``, see `do()` `name` filename where to save the result `plot_cmd` command used to plot the data, typically matplotlib pylabs `plot` or `semilogy` `load` load previous data from file ``str(func) + '.pkl'`` """ self.func = func self.args = args self.x = x self.name = name if name else str(func).replace(' ', '_').replace('>', '').replace('<', '') self.plot_cmd = plot_cmd # or semilogy self.basis = np.eye(len(x)) if basis is None else basis try: self.load() if any(self.res['x'] != x): self.res = {} self.res['x'] = x # TODO: res['x'] does not look perfect else: print(self.name + ' loaded') except: self.res = {} self.res['x'] = x def do(self, repetitions=1, locations=np.arange(-0.5, 0.6, 0.2), plot=True): """generates, plots and saves function values ``func(y)``, where ``y`` is 'close' to `x` (see `__init__()`). The data are stored in the ``res`` attribute and the class instance is saved in a file with (the weired) name ``str(func)``. Parameters ---------- `repetitions` for each point, only for noisy functions is >1 useful. For ``repetitions==0`` only already generated data are plotted. `locations` coordinated wise deviations from the middle point given in `__init__` """ if not repetitions: self.plot() return res = self.res for i in range(len(self.basis)): # i-th coordinate if i not in res: res[i] = {} # xx = np.array(self.x) # TODO: store res[i]['dx'] = self.basis[i] here? for dx in locations: xx = self.x + dx * self.basis[i] xkey = dx # xx[i] if (self.basis == np.eye(len(self.basis))).all() else dx if xkey not in res[i]: res[i][xkey] = [] n = repetitions while n > 0: n -= 1 res[i][xkey].append(self.func(xx, *self.args)) if plot: self.plot() self.save() return self def plot(self, plot_cmd=None, tf=lambda y: y): """plot the data we have, return ``self``""" if not plot_cmd: plot_cmd = self.plot_cmd colors = 'bgrcmyk' pylab.hold(False) res = self.res flatx, flatf = self.flattened() minf = np.inf for i in flatf: minf = min((minf, min(flatf[i]))) addf = 1e-9 - minf if minf <= 0 else 0 for i in sorted(res.keys()): # we plot not all values here if type(i) is int: color = colors[i % len(colors)] arx = sorted(res[i].keys()) plot_cmd(arx, [tf(np.median(res[i][x]) + addf) for x in arx], color + '-') pylab.text(arx[-1], tf(np.median(res[i][arx[-1]])), i) pylab.hold(True) plot_cmd(flatx[i], tf(np.array(flatf[i]) + addf), color + 'o') pylab.ylabel('f + ' + str(addf)) pylab.draw() show() # raw_input('press return') return self def flattened(self): """return flattened data ``(x, f)`` such that for the sweep through coordinate ``i`` we have for data point ``j`` that ``f[i][j] == func(x[i][j])`` """ flatx = {} flatf = {} for i in self.res: if type(i) is int: flatx[i] = [] flatf[i] = [] for x in sorted(self.res[i]): for d in sorted(self.res[i][x]): flatx[i].append(x) flatf[i].append(d) return flatx, flatf def save(self, name=None): """save to file""" import pickle name = name if name else self.name fun = self.func del self.func # instance method produces error pickle.dump(self, open(name + '.pkl', "wb" )) self.func = fun return self def load(self, name=None): """load from file""" import pickle name = name if name else self.name s = pickle.load(open(name + '.pkl', 'rb')) self.res = s.res # disregard the class return self #____________________________________________________________ #____________________________________________________________ class _Error(Exception): """generic exception of cma module""" pass #____________________________________________________________ #____________________________________________________________ # class ElapsedTime(object): """32-bit C overflows after int(2**32/1e6) == 4294s about 72 min""" def __init__(self): self.tic0 = time.clock() self.tic = self.tic0 self.lasttoc = time.clock() self.lastdiff = time.clock() - self.lasttoc self.time_to_add = 0 self.messages = 0 def __call__(self): toc = time.clock() if toc - self.tic >= self.lasttoc - self.tic: self.lastdiff = toc - self.lasttoc self.lasttoc = toc else: # overflow, reset self.tic if self.messages < 3: self.messages += 1 print(' in cma.ElapsedTime: time measure overflow, last difference estimated from', self.tic0, self.tic, self.lasttoc, toc, toc - self.lasttoc, self.lastdiff) self.time_to_add += self.lastdiff + self.lasttoc - self.tic self.tic = toc # reset self.lasttoc = toc self.elapsedtime = toc - self.tic + self.time_to_add return self.elapsedtime #____________________________________________________________ #____________________________________________________________ # class TimeIt(object): def __init__(self, fct, args=(), seconds=1): pass class Misc(object): #____________________________________________________________ #____________________________________________________________ # class MathHelperFunctions(object): """static convenience math helper functions, if the function name is preceded with an "a", a numpy array is returned """ @staticmethod def aclamp(x, upper): return -Misc.MathHelperFunctions.apos(-x, -upper) @staticmethod def expms(A, eig=np.linalg.eigh): """matrix exponential for a symmetric matrix""" # TODO: check that this works reliably for low rank matrices # first: symmetrize A D, B = eig(A) return np.dot(B, (np.exp(D) * B).T) @staticmethod def amax(vec, vec_or_scalar): return array(Misc.MathHelperFunctions.max(vec, vec_or_scalar)) @staticmethod def max(vec, vec_or_scalar): b = vec_or_scalar if np.isscalar(b): m = [max(x, b) for x in vec] else: m = [max(vec[i], b[i]) for i in range(len(vec))] return m @staticmethod def amin(vec_or_scalar, vec_or_scalar2): return array(Misc.MathHelperFunctions.min(vec_or_scalar, vec_or_scalar2)) @staticmethod def min(a, b): iss = np.isscalar if iss(a) and iss(b): return min(a, b) if iss(a): a, b = b, a # now only b can be still a scalar if iss(b): return [min(x, b) for x in a] else: # two non-scalars must have the same length return [min(a[i], b[i]) for i in range(len(a))] @staticmethod def norm(vec, expo=2): return sum(vec**expo)**(1/expo) @staticmethod def apos(x, lower=0): """clips argument (scalar or array) from below at lower""" if lower == 0: return (x > 0) * x else: return lower + (x > lower) * (x - lower) @staticmethod def prctile(data, p_vals=[0, 25, 50, 75, 100], sorted_=False): """``prctile(data, 50)`` returns the median, but p_vals can also be a sequence. Provides for small samples better values than matplotlib.mlab.prctile, however also slower. """ ps = [p_vals] if np.isscalar(p_vals) else p_vals if not sorted_: data = sorted(data) n = len(data) d = [] for p in ps: fi = p * n / 100 - 0.5 if fi <= 0: # maybe extrapolate? d.append(data[0]) elif fi >= n - 1: d.append(data[-1]) else: i = int(fi) d.append((i+1 - fi) * data[i] + (fi - i) * data[i+1]) return d[0] if np.isscalar(p_vals) else d @staticmethod def sround(nb): # TODO: to be vectorized """return stochastic round: floor(nb) + (rand() 1000: n = np.random.randn() / np.random.randn() return n / 25 @staticmethod def standard_finite_cauchy(size=1): try: l = len(size) except TypeError: l = 0 if l == 0: return array([Mh.cauchy_with_variance_one() for _i in range(size)]) elif l == 1: return array([Mh.cauchy_with_variance_one() for _i in range(size[0])]) elif l == 2: return array([[Mh.cauchy_with_variance_one() for _i in range(size[1])] for _j in range(size[0])]) else: raise _Error('len(size) cannot be large than two') @staticmethod def likelihood(x, m=None, Cinv=None, sigma=1, detC=None): """return likelihood of x for the normal density N(m, sigma**2 * Cinv**-1)""" # testing: MC integrate must be one: mean(p(x_i)) * volume(where x_i are uniformely sampled) # for i in range(3): print mean([cma.likelihood(20*r-10, dim * [0], None, 3) for r in rand(10000,dim)]) * 20**dim if m is None: dx = x else: dx = x - m # array(x) - array(m) n = len(x) s2pi = (2*np.pi)**(n/2.) if Cinv is None: return exp(-sum(dx**2) / sigma**2 / 2) / s2pi / sigma**n if detC is None: detC = 1. / np.linalg.linalg.det(Cinv) return exp(-np.dot(dx, np.dot(Cinv, dx)) / sigma**2 / 2) / s2pi / abs(detC)**0.5 / sigma**n @staticmethod def loglikelihood(self, x, previous=False): """return log-likelihood of `x` regarding the current sample distribution""" # testing of original fct: MC integrate must be one: mean(p(x_i)) * volume(where x_i are uniformely sampled) # for i in range(3): print mean([cma.likelihood(20*r-10, dim * [0], None, 3) for r in rand(10000,dim)]) * 20**dim # TODO: test this!! # c=cma.fmin... # c[3]['cma'].loglikelihood(...) if previous and hasattr(self, 'lastiter'): sigma = self.lastiter.sigma Crootinv = self.lastiter._Crootinv xmean = self.lastiter.mean D = self.lastiter.D elif previous and self.countiter > 1: raise _Error('no previous distribution parameters stored, check options importance_mixing') else: sigma = self.sigma Crootinv = self._Crootinv xmean = self.mean D = self.D dx = array(x) - xmean # array(x) - array(m) n = self.N logs2pi = n * log(2*np.pi) / 2. logdetC = 2 * sum(log(D)) dx = np.dot(Crootinv, dx) res = -sum(dx**2) / sigma**2 / 2 - logs2pi - logdetC/2 - n*log(sigma) if 1 < 3: # testing s2pi = (2*np.pi)**(n/2.) detC = np.prod(D)**2 res2 = -sum(dx**2) / sigma**2 / 2 - log(s2pi * abs(detC)**0.5 * sigma**n) assert res2 < res + 1e-8 or res2 > res - 1e-8 return res #____________________________________________________________ #____________________________________________________________ # # C and B are arrays rather than matrices, because they are # addressed via B[i][j], matrices can only be addressed via B[i,j] # tred2(N, B, diagD, offdiag); # tql2(N, diagD, offdiag, B); # Symmetric Householder reduction to tridiagonal form, translated from JAMA package. @staticmethod def eig(C): """eigendecomposition of a symmetric matrix, much slower than `numpy.linalg.eigh`, return ``(EVals, Basis)``, the eigenvalues and an orthonormal basis of the corresponding eigenvectors, where ``Basis[i]`` the i-th row of ``Basis`` columns of ``Basis``, ``[Basis[j][i] for j in range(len(Basis))]`` the i-th eigenvector with eigenvalue ``EVals[i]`` """ # class eig(object): # def __call__(self, C): # Householder transformation of a symmetric matrix V into tridiagonal form. # -> n : dimension # -> V : symmetric nxn-matrix # <- V : orthogonal transformation matrix: # tridiag matrix == V * V_in * V^t # <- d : diagonal # <- e[0..n-1] : off diagonal (elements 1..n-1) # Symmetric tridiagonal QL algorithm, iterative # Computes the eigensystem from a tridiagonal matrix in roughtly 3N^3 operations # -> n : Dimension. # -> d : Diagonale of tridiagonal matrix. # -> e[1..n-1] : off-diagonal, output from Householder # -> V : matrix output von Householder # <- d : eigenvalues # <- e : garbage? # <- V : basis of eigenvectors, according to d # tred2(N, B, diagD, offdiag); B=C on input # tql2(N, diagD, offdiag, B); # private void tred2 (int n, double V[][], double d[], double e[]) { def tred2 (n, V, d, e): # This is derived from the Algol procedures tred2 by # Bowdler, Martin, Reinsch, and Wilkinson, Handbook for # Auto. Comp., Vol.ii-Linear Algebra, and the corresponding # Fortran subroutine in EISPACK. num_opt = False # factor 1.5 in 30-D for j in range(n): d[j] = V[n-1][j] # d is output argument # Householder reduction to tridiagonal form. for i in range(n-1,0,-1): # Scale to avoid under/overflow. h = 0.0 if not num_opt: scale = 0.0 for k in range(i): scale = scale + abs(d[k]) else: scale = sum(abs(d[0:i])) if scale == 0.0: e[i] = d[i-1] for j in range(i): d[j] = V[i-1][j] V[i][j] = 0.0 V[j][i] = 0.0 else: # Generate Householder vector. if not num_opt: for k in range(i): d[k] /= scale h += d[k] * d[k] else: d[:i] /= scale h = np.dot(d[:i],d[:i]) f = d[i-1] g = h**0.5 if f > 0: g = -g e[i] = scale * g h = h - f * g d[i-1] = f - g if not num_opt: for j in range(i): e[j] = 0.0 else: e[:i] = 0.0 # Apply similarity transformation to remaining columns. for j in range(i): f = d[j] V[j][i] = f g = e[j] + V[j][j] * f if not num_opt: for k in range(j+1, i): g += V[k][j] * d[k] e[k] += V[k][j] * f e[j] = g else: e[j+1:i] += V.T[j][j+1:i] * f e[j] = g + np.dot(V.T[j][j+1:i],d[j+1:i]) f = 0.0 if not num_opt: for j in range(i): e[j] /= h f += e[j] * d[j] else: e[:i] /= h f += np.dot(e[:i],d[:i]) hh = f / (h + h) if not num_opt: for j in range(i): e[j] -= hh * d[j] else: e[:i] -= hh * d[:i] for j in range(i): f = d[j] g = e[j] if not num_opt: for k in range(j, i): V[k][j] -= (f * e[k] + g * d[k]) else: V.T[j][j:i] -= (f * e[j:i] + g * d[j:i]) d[j] = V[i-1][j] V[i][j] = 0.0 d[i] = h # end for i-- # Accumulate transformations. for i in range(n-1): V[n-1][i] = V[i][i] V[i][i] = 1.0 h = d[i+1] if h != 0.0: if not num_opt: for k in range(i+1): d[k] = V[k][i+1] / h else: d[:i+1] = V.T[i+1][:i+1] / h for j in range(i+1): if not num_opt: g = 0.0 for k in range(i+1): g += V[k][i+1] * V[k][j] for k in range(i+1): V[k][j] -= g * d[k] else: g = np.dot(V.T[i+1][0:i+1], V.T[j][0:i+1]) V.T[j][:i+1] -= g * d[:i+1] if not num_opt: for k in range(i+1): V[k][i+1] = 0.0 else: V.T[i+1][:i+1] = 0.0 if not num_opt: for j in range(n): d[j] = V[n-1][j] V[n-1][j] = 0.0 else: d[:n] = V[n-1][:n] V[n-1][:n] = 0.0 V[n-1][n-1] = 1.0 e[0] = 0.0 # Symmetric tridiagonal QL algorithm, taken from JAMA package. # private void tql2 (int n, double d[], double e[], double V[][]) { # needs roughly 3N^3 operations def tql2 (n, d, e, V): # This is derived from the Algol procedures tql2, by # Bowdler, Martin, Reinsch, and Wilkinson, Handbook for # Auto. Comp., Vol.ii-Linear Algebra, and the corresponding # Fortran subroutine in EISPACK. num_opt = False # using vectors from numpy makes it faster if not num_opt: for i in range(1,n): # (int i = 1; i < n; i++): e[i-1] = e[i] else: e[0:n-1] = e[1:n] e[n-1] = 0.0 f = 0.0 tst1 = 0.0 eps = 2.0**-52.0 for l in range(n): # (int l = 0; l < n; l++) { # Find small subdiagonal element tst1 = max(tst1, abs(d[l]) + abs(e[l])) m = l while m < n: if abs(e[m]) <= eps*tst1: break m += 1 # If m == l, d[l] is an eigenvalue, # otherwise, iterate. if m > l: iiter = 0 while 1: # do { iiter += 1 # (Could check iteration count here.) # Compute implicit shift g = d[l] p = (d[l+1] - g) / (2.0 * e[l]) r = (p**2 + 1)**0.5 # hypot(p,1.0) if p < 0: r = -r d[l] = e[l] / (p + r) d[l+1] = e[l] * (p + r) dl1 = d[l+1] h = g - d[l] if not num_opt: for i in range(l+2, n): d[i] -= h else: d[l+2:n] -= h f = f + h # Implicit QL transformation. p = d[m] c = 1.0 c2 = c c3 = c el1 = e[l+1] s = 0.0 s2 = 0.0 # hh = V.T[0].copy() # only with num_opt for i in range(m-1, l-1, -1): # (int i = m-1; i >= l; i--) { c3 = c2 c2 = c s2 = s g = c * e[i] h = c * p r = (p**2 + e[i]**2)**0.5 # hypot(p,e[i]) e[i+1] = s * r s = e[i] / r c = p / r p = c * d[i] - s * g d[i+1] = h + s * (c * g + s * d[i]) # Accumulate transformation. if not num_opt: # overall factor 3 in 30-D for k in range(n): # (int k = 0; k < n; k++) { h = V[k][i+1] V[k][i+1] = s * V[k][i] + c * h V[k][i] = c * V[k][i] - s * h else: # about 20% faster in 10-D hh = V.T[i+1].copy() # hh[:] = V.T[i+1][:] V.T[i+1] = s * V.T[i] + c * hh V.T[i] = c * V.T[i] - s * hh # V.T[i] *= c # V.T[i] -= s * hh p = -s * s2 * c3 * el1 * e[l] / dl1 e[l] = s * p d[l] = c * p # Check for convergence. if abs(e[l]) <= eps*tst1: break # } while (Math.abs(e[l]) > eps*tst1); d[l] = d[l] + f e[l] = 0.0 # Sort eigenvalues and corresponding vectors. if 11 < 3: for i in range(n-1): # (int i = 0; i < n-1; i++) { k = i p = d[i] for j in range(i+1, n): # (int j = i+1; j < n; j++) { if d[j] < p: # NH find smallest k>i k = j p = d[j] if k != i: d[k] = d[i] # swap k and i d[i] = p for j in range(n): # (int j = 0; j < n; j++) { p = V[j][i] V[j][i] = V[j][k] V[j][k] = p # tql2 N = len(C[0]) if 11 < 3: V = np.array([x[:] for x in C]) # copy each "row" N = V[0].size d = np.zeros(N) e = np.zeros(N) else: V = [[x[i] for i in range(N)] for x in C] # copy each "row" d = N * [0.] e = N * [0.] tred2(N, V, d, e) tql2(N, d, e, V) return (array(d), array(V)) Mh = Misc.MathHelperFunctions def pprint(to_be_printed): """nicely formated print""" try: import pprint as pp # generate an instance PrettyPrinter # pp.PrettyPrinter().pprint(to_be_printed) pp.pprint(to_be_printed) except ImportError: print('could not use pprint module, will apply regular print') print(to_be_printed) class Rotation(object): """Rotation class that implements an orthogonal linear transformation, one for each dimension. Used to implement non-separable test functions. Example: >>> import cma, numpy as np >>> R = cma.Rotation() >>> R2 = cma.Rotation() # another rotation >>> x = np.array((1,2,3)) >>> print(R(R(x), inverse=1)) [ 1. 2. 3.] """ dicMatrices = {} # store matrix if necessary, for each dimension def __init__(self): self.dicMatrices = {} # otherwise there might be shared bases which is probably not what we want def __call__(self, x, inverse=False): # function when calling an object """Rotates the input array `x` with a fixed rotation matrix (``self.dicMatrices['str(len(x))']``) """ N = x.shape[0] # can be an array or matrix, TODO: accept also a list of arrays? if str(N) not in self.dicMatrices: # create new N-basis for once and all B = np.random.randn(N, N) for i in range(N): for j in range(0, i): B[i] -= np.dot(B[i], B[j]) * B[j] B[i] /= sum(B[i]**2)**0.5 self.dicMatrices[str(N)] = B if inverse: return np.dot(self.dicMatrices[str(N)].T, x) # compute rotation else: return np.dot(self.dicMatrices[str(N)], x) # compute rotation # Use rotate(x) to rotate x rotate = Rotation() #____________________________________________________________ #____________________________________________________________ # class FitnessFunctions(object): """ versatile container for test objective functions """ def __init__(self): self.counter = 0 # number of calls or any other practical use def rot(self, x, fun, rot=1, args=()): """returns ``fun(rotation(x), *args)``, ie. `fun` applied to a rotated argument""" if len(np.shape(array(x))) > 1: # parallelized res = [] for x in x: res.append(self.rot(x, fun, rot, args)) return res if rot: return fun(rotate(x, *args)) else: return fun(x) def somenan(self, x, fun, p=0.1): """returns sometimes np.NaN, otherwise fun(x)""" if np.random.rand(1) < p: return np.NaN else: return fun(x) def rand(self, x): """Random test objective function""" return np.random.random(1)[0] def linear(self, x): return -x[0] def lineard(self, x): if 1 < 3 and any(array(x) < 0): return np.nan if 1 < 3 and sum([ (10 + i) * x[i] for i in range(len(x))]) > 50e3: return np.nan return -sum(x) def sphere(self, x): """Sphere (squared norm) test objective function""" # return np.random.rand(1)[0]**0 * sum(x**2) + 1 * np.random.rand(1)[0] return sum((x+0)**2) def spherewithoneconstraint(self, x): return sum((x+0)**2) if x[0] > 1 else np.nan def elliwithoneconstraint(self, x, idx=[-1]): return self.ellirot(x) if all(array(x)[idx] > 1) else np.nan def spherewithnconstraints(self, x): return sum((x+0)**2) if all(array(x) > 1) else np.nan def noisysphere(self, x, noise=4.0, cond=1.0): """noise=10 does not work with default popsize, noise handling does not help """ return self.elli(x, cond=cond) * (1 + noise * np.random.randn() / len(x)) def spherew(self, x): """Sphere (squared norm) with sum x_i = 1 test objective function""" # return np.random.rand(1)[0]**0 * sum(x**2) + 1 * np.random.rand(1)[0] # s = sum(abs(x)) # return sum((x/s+0)**2) - 1/len(x) # return sum((x/s)**2) - 1/len(x) return -0.01*x[0] + abs(x[0])**-2 * sum(x[1:]**2) def partsphere(self, x): """Sphere (squared norm) test objective function""" self.counter += 1 # return np.random.rand(1)[0]**0 * sum(x**2) + 1 * np.random.rand(1)[0] dim = len(x) x = array([x[i % dim] for i in range(2*dim)]) N = 8 i = self.counter % dim #f = sum(x[i:i + N]**2) f = sum(x[np.random.randint(dim, size=N)]**2) return f def sectorsphere(self, x): """asymmetric Sphere (squared norm) test objective function""" return sum(x**2) + (1e6-1) * sum(x[x<0]**2) def cornersphere(self, x): """Sphere (squared norm) test objective function constraint to the corner""" nconstr = len(x) - 0 if any(x[:nconstr] < 1): return np.NaN return sum(x**2) - nconstr def cornerelli(self, x): """ """ if any(x < 1): return np.NaN return self.elli(x) - self.elli(np.ones(len(x))) def cornerellirot(self, x): """ """ if any(x < 1): return np.NaN return self.ellirot(x) def normalSkew(self, f): N = np.random.randn(1)[0]**2 if N < 1: N = f * N # diminish blow up lower part return N def noiseC(self, x, func=sphere, fac=10, expon=0.8): f = func(self, x) N = np.random.randn(1)[0]/np.random.randn(1)[0] return max(1e-19, f + (float(fac)/len(x)) * f**expon * N) def noise(self, x, func=sphere, fac=10, expon=1): f = func(self, x) #R = np.random.randn(1)[0] R = np.log10(f) + expon * abs(10-np.log10(f)) * np.random.rand(1)[0] # sig = float(fac)/float(len(x)) # R = log(f) + 0.5*log(f) * random.randn(1)[0] # return max(1e-19, f + sig * (f**np.log10(f)) * np.exp(R)) # return max(1e-19, f * np.exp(sig * N / f**expon)) # return max(1e-19, f * normalSkew(f**expon)**sig) return f + 10**R # == f + f**(1+0.5*RN) def cigar(self, x, rot=0, cond=1e6): """Cigar test objective function""" if rot: x = rotate(x) x = [x] if np.isscalar(x[0]) else x # scalar into list f = [x[0]**2 + cond * sum(x[1:]**2) for x in x] return f if len(f) > 1 else f[0] # 1-element-list into scalar def tablet(self, x, rot=0): """Tablet test objective function""" if rot: x = rotate(x) x = [x] if np.isscalar(x[0]) else x # scalar into list f = [1e6*x[0]**2 + sum(x[1:]**2) for x in x] return f if len(f) > 1 else f[0] # 1-element-list into scalar def cigtab(self, y): """Cigtab test objective function""" X = [y] if np.isscalar(y[0]) else y f = [1e-4 * x[0]**2 + 1e4 * x[1]**2 + sum(x[2:]**2) for x in X] return f if len(f) > 1 else f[0] def twoaxes(self, y): """Cigtab test objective function""" X = [y] if np.isscalar(y[0]) else y N2 = len(X[0]) // 2 f = [1e6 * sum(x[0:N2]**2) + sum(x[N2:]**2) for x in X] return f if len(f) > 1 else f[0] def ellirot(self, x): return fcts.elli(array(x), 1) def hyperelli(self, x): N = len(x) return sum((np.arange(1, N+1) * x)**2) def elli(self, x, rot=0, xoffset=0, cond=1e6, actuator_noise=0.0, both=False): """Ellipsoid test objective function""" if not np.isscalar(x[0]): # parallel evaluation return [self.elli(xi, rot) for xi in x] # could save 20% overall if rot: x = rotate(x) N = len(x) if actuator_noise: x = x + actuator_noise * np.random.randn(N) ftrue = sum(cond**(np.arange(N)/(N-1.))*(x+xoffset)**2) alpha = 0.49 + 1./N beta = 1 felli = np.random.rand(1)[0]**beta * ftrue * \ max(1, (10.**9 / (ftrue+1e-99))**(alpha*np.random.rand(1)[0])) # felli = ftrue + 1*np.random.randn(1)[0] / (1e-30 + # np.abs(np.random.randn(1)[0]))**0 if both: return (felli, ftrue) else: # return felli # possibly noisy value return ftrue # + np.random.randn() def elliconstraint(self, x, cfac = 1e8, tough=True, cond=1e6): """ellipsoid test objective function with "constraints" """ N = len(x) f = sum(cond**(np.arange(N)[-1::-1]/(N-1)) * x**2) cvals = (x[0] + 1, x[0] + 1 + 100*x[1], x[0] + 1 - 100*x[1]) if tough: f += cfac * sum(max(0,c) for c in cvals) else: f += cfac * sum(max(0,c+1e-3)**2 for c in cvals) return f def rosen(self, x, alpha=1e2): """Rosenbrock test objective function""" x = [x] if np.isscalar(x[0]) else x # scalar into list f = [sum(alpha*(x[:-1]**2-x[1:])**2 + (1.-x[:-1])**2) for x in x] return f if len(f) > 1 else f[0] # 1-element-list into scalar def diffpow(self, x, rot=0): """Diffpow test objective function""" N = len(x) if rot: x = rotate(x) return sum(np.abs(x)**(2.+4.*np.arange(N)/(N-1.)))**0.5 def rosenelli(self, x): N = len(x) return self.rosen(x[:N/2]) + self.elli(x[N/2:], cond=1) def ridge(self, x, expo=2): x = [x] if np.isscalar(x[0]) else x # scalar into list f = [x[0] + 100*np.sum(x[1:]**2)**(expo/2.) for x in x] return f if len(f) > 1 else f[0] # 1-element-list into scalar def ridgecircle(self, x, expo=0.5): """happy cat by HG Beyer""" a = len(x) s = sum(x**2) return ((s - a)**2)**(expo/2) + s/a + sum(x)/a def happycat(self, x, alpha=1./8): s = sum(x**2) return ((s - len(x))**2)**alpha + (s/2 + sum(x)) / len(x) + 0.5 def flat(self,x): return 1 return 1 if np.random.rand(1) < 0.9 else 1.1 return np.random.randint(1,30) def branin(self, x): # in [0,15]**2 y = x[1] x = x[0] + 5 return (y - 5.1*x**2 / 4 / np.pi**2 + 5 * x / np.pi - 6)**2 + 10 * (1 - 1/8/np.pi) * np.cos(x) + 10 - 0.397887357729738160000 def goldsteinprice(self, x): x1 = x[0] x2 = x[1] return (1 + (x1 +x2 + 1)**2 * (19 - 14 * x1 + 3 * x1**2 - 14 * x2 + 6 * x1 * x2 + 3 * x2**2)) * ( 30 + (2 * x1 - 3 * x2)**2 * (18 - 32 * x1 + 12 * x1**2 + 48 * x2 - 36 * x1 * x2 + 27 * x2**2)) - 3 def griewank(self, x): # was in [-600 600] x = (600./5) * x return 1 - np.prod(np.cos(x/sqrt(1.+np.arange(len(x))))) + sum(x**2)/4e3 def rastrigin(self, x): """Rastrigin test objective function""" if not np.isscalar(x[0]): N = len(x[0]) return [10*N + sum(xi**2 - 10*np.cos(2*np.pi*xi)) for xi in x] # return 10*N + sum(x**2 - 10*np.cos(2*np.pi*x), axis=1) N = len(x) return 10*N + sum(x**2 - 10*np.cos(2*np.pi*x)) def schaffer(self, x): """ Schaffer function x0 in [-100..100]""" N = len(x); s = x[0:N-1]**2 + x[1:N]**2; return sum(s**0.25 * (np.sin(50*s**0.1)**2 + 1)) def schwefelelli(self, x): s = 0 f = 0 for i in range(len(x)): s += x[i] f += s**2 return f def schwefelmult(self, x, pen_fac = 1e4): """multimodal Schwefel function with domain -500..500""" y = [x] if np.isscalar(x[0]) else x N = len(y[0]) f = array([418.9829*N - 1.27275661e-5*N - sum(x * np.sin(np.abs(x)**0.5)) + pen_fac * sum((abs(x) > 500) * (abs(x) - 500)**2) for x in y]) return f if len(f) > 1 else f[0] def optprob(self, x): n = np.arange(len(x)) + 1 f = n * x * (1-x)**(n-1) return sum(1-f) def lincon(self, x, theta=0.01): """ridge like linear function with one linear constraint""" if x[0] < 0: return np.NaN return theta * x[1] + x[0] def rosen_nesterov(self, x, rho=100): """needs exponential number of steps in a non-increasing f-sequence. x_0 = (-1,1,...,1) See Jarre (2011) "On Nesterov's Smooth Chebyshev-Rosenbrock Function" """ f = 0.25 * (x[0] - 1)**2 f += rho * sum((x[1:] - 2 * x[:-1]**2 + 1)**2) return f fcts = FitnessFunctions() Fcts = fcts # for cross compatibility, as if the functions were static members of class Fcts def felli(x): # unbound function, needed to test multiprocessor return sum(1e6**(np.arange(len(x))/(len(x)-1))*(x)**2) #____________________________________________ #____________________________________________________________ def _test(module=None): # None is fine when called from inside the module import doctest print(doctest.testmod(module)) # this is pretty coool! def process_test(stream=None): """ """ import fileinput s1 = "" s2 = "" s3 = "" state = 0 for line in fileinput.input(stream): # takes argv as file or stdin if 1 < 3: s3 += line if state < -1 and line.startswith('***'): print(s3) if line.startswith('***'): s3 = "" if state == -1: # found a failed example line s1 += '\n\n*** Failed Example:' + line s2 += '\n\n\n' # line # state = 0 # wait for 'Expected:' line if line.startswith('Expected:'): state = 1 continue elif line.startswith('Got:'): state = 2 continue elif line.startswith('***'): # marks end of failed example state = 0 elif line.startswith('Failed example:'): state = -1 elif line.startswith('Exception raised'): state = -2 # in effect more else: if state == 1: s1 += line + '' if state == 2: s2 += line + '' #____________________________________________________________ #____________________________________________________________ # def main(argv=None): """to install and/or test from the command line use:: python cma.py [options | func dim sig0 [optkey optval][optkey optval]...] --test (or -t) to run the doctest, ``--test -v`` to get (much) verbosity and ``--test -q`` to run it quietly with output only in case of errors. install to install cma.py (uses setup from distutils.core). --fcts and --doc for more infos or start ipython --pylab. Examples -------- First, testing with the local python distribution:: python cma.py --test If succeeded install (uses setup from distutils.core):: python cma.py install A single run on the ellipsoid function:: python cma.py elli 10 1 """ if argv is None: argv = sys.argv # should have better been sys.argv[1:] # uncomment for unit test # _test() # handle input arguments, getopt might be helpful ;-) if len(argv) >= 1: # function and help if len(argv) == 1 or argv[1].startswith('-h') or argv[1].startswith('--help'): print(main.__doc__) fun = None elif argv[1].startswith('-t') or argv[1].startswith('--test'): import doctest if len(argv) > 2 and (argv[2].startswith('--v') or argv[2].startswith('-v')): # verbose print('doctest for cma.py: due to different platforms and python versions') print('and in some cases due to a missing unique random seed') print('many examples will "fail". This is OK, if they give a similar') print('to the expected result and if no exception occurs. ') # if argv[1][2] == 'v': doctest.testmod(report=True) # this is quite cool! else: # was: if len(argv) > 2 and (argv[2].startswith('--qu') or argv[2].startswith('-q')): print('doctest for cma.py: launching (it might be necessary to close a few pop up windows to finish)') fn = '__cma_doctest__.txt' stdout = sys.stdout try: with open(fn, 'w') as f: sys.stdout = f doctest.testmod(report=True) # this is quite cool! finally: sys.stdout = stdout process_test(fn) print('doctest for cma.py: finished (no other output should be seen after launching)') return elif argv[1] == '--doc': print(__doc__) print(CMAEvolutionStrategy.__doc__) print(fmin.__doc__) fun = None elif argv[1] == '--fcts': print('List of valid function names:') print([d for d in dir(fcts) if not d.startswith('_')]) fun = None elif argv[1] in ('install', '--install'): from distutils.core import setup setup(name = "cma", version = __version__, author = "Nikolaus Hansen", # packages = ["cma"], py_modules = ["cma"], ) fun = None elif argv[1] in ('plot',): plot() raw_input('press return') fun = None elif len(argv) > 3: fun = eval('fcts.' + argv[1]) else: print('try -h option') fun = None if fun is not None: if len(argv) > 2: # dimension x0 = np.ones(eval(argv[2])) if len(argv) > 3: # sigma sig0 = eval(argv[3]) opts = {} for i in range(5, len(argv), 2): opts[argv[i-1]] = eval(argv[i]) # run fmin if fun is not None: tic = time.time() fmin(fun, x0, sig0, **opts) # ftarget=1e-9, tolfacupx=1e9, verb_log=10) # plot() # print ' best function value ', res[2]['es'].best[1] print('elapsed time [s]: + %.2f', round(time.time() - tic, 2)) elif not len(argv): fmin(fcts.elli, np.ones(6)*0.1, 0.1, ftarget=1e-9) #____________________________________________________________ #____________________________________________________________ # # mainly for testing purpose # executed when called from an OS shell if __name__ == "__main__": # for i in range(1000): # how to find the memory leak # main(["cma.py", "rastrigin", "10", "5", "popsize", "200", "maxfevals", "24999", "verb_log", "0"]) main() ================================================ FILE: src/aup/Proposer/spearmint/gp.py ================================================ ## # Copyright (C) 2012 Jasper Snoek, Hugo Larochelle and Ryan P. Adams # # This code is written for research and educational purposes only to # supplement the paper entitled # "Practical Bayesian Optimization of Machine Learning Algorithms" # by Snoek, Larochelle and Adams # Advances in Neural Information Processing Systems, 2012 # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . """ gp.py contains utility functions related to computation in Gaussian processes. """ import numpy as np import scipy.linalg as spla import scipy.optimize as spo import scipy.io as sio SQRT_3 = np.sqrt(3.0) SQRT_5 = np.sqrt(5.0) def dist2(ls, x1, x2=None): # Assumes NxD and MxD matrices. # Compute the squared distance matrix, given length scales. if x2 is None: # Find distance with self for x1. # Rescale. xx1 = x1 / ls xx2 = xx1 else: # Rescale. xx1 = x1 / ls xx2 = x2 / ls r2 = np.maximum(-(np.dot(xx1, 2*xx2.T) - np.sum(xx1*xx1, axis=1)[:,np.newaxis] - np.sum(xx2*xx2, axis=1)[:,np.newaxis].T), 0.0) return r2 def grad_dist2(ls, x1, x2=None): if x2 is None: x2 = x1 # Rescale. x1 = x1 / ls x2 = x2 / ls N = x1.shape[0] M = x2.shape[0] D = x1.shape[1] gX = np.zeros((x1.shape[0],x2.shape[0],x1.shape[1])) code = \ """ for (int i=0; i 100000): val = spla.cholesky(np.eye(covmat.shape[0])) break try: val = spla.cholesky(covmat + jitter*np.eye(covmat.shape[0]), lower=True) passed = True except ValueError: jitter = jitter*1.1 print("Covariance matrix not PSD, adding jitter:", jitter) passed = False return val def memoize(amp2, noise, ls): if ( 'corr' not in state or state['amp2'] != amp2 or state['noise'] != noise or np.any(state['ls'] != ls)): # Get the correlation matrix (corr, grad_corr) = self.cov_func(ls, comp, None, grad=True) # Scale and add noise & jitter. covmat = (amp2 * (corr + 1e-6*np.eye(comp.shape[0])) + noise * np.eye(comp.shape[0])) # Memoize state['corr'] = corr state['grad_corr'] = grad_corr state['chol'] = jitter_chol(covmat) state['amp2'] = amp2 state['noise'] = noise state['ls'] = ls return (state['chol'], state['corr'], state['grad_corr']) def nlogprob(hypers): amp2 = np.exp(hypers[0]) noise = np.exp(hypers[1]) ls = np.exp(hypers[2:]) chol = memoize(amp2, noise, ls)[0] solve = spla.cho_solve((chol, True), diffs) lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(diffs, solve) return -lp def grad_nlogprob(hypers): amp2 = np.exp(hypers[0]) noise = np.exp(hypers[1]) ls = np.exp(hypers[2:]) chol, corr, grad_corr = memoize(amp2, noise, ls) solve = spla.cho_solve((chol, True), diffs) inv_cov = spla.cho_solve((chol, True), np.eye(chol.shape[0])) jacobian = np.outer(solve, solve) - inv_cov grad = np.zeros(self.D + 2) # Log amplitude gradient. grad[0] = 0.5 * np.trace(np.dot( jacobian, corr + 1e-6*np.eye(chol.shape[0]))) * amp2 # Log noise gradient. grad[1] = 0.5 * np.trace(np.dot( jacobian, np.eye(chol.shape[0]))) * noise # Log length scale gradients. for dd in range(self.D): grad[dd+2] = 1 * np.trace(np.dot( jacobian, -amp2*grad_corr[:,:,dd]*comp[:,dd][:,np.newaxis]/(np.exp(ls[dd]))))*np.exp(ls[dd]) # Roll in the prior variance. #grad -= 2*hypers/self.hyper_prior return -grad # Initial length scales. self.ls = np.ones(self.D) # Initial amplitude. self.amp2 = np.std(vals) # Initial observation noise. self.noise = 1e-3 hypers = np.zeros(self.ls.shape[0]+2) hypers[0] = np.log(self.amp2) hypers[1] = np.log(self.noise) hypers[2:] = np.log(self.ls) # Use a bounded bfgs just to prevent the length-scales and noise from # getting into regions that are numerically unstable b = [(-10,10),(-10,10)] for i in range(comp.shape[1]): b.append((-10,5)) hypers = spo.fmin_l_bfgs_b(nlogprob, hypers, grad_nlogprob, args=(), bounds=b, disp=0) #hypers = spo.fmin_bfgs(nlogprob, hypers, grad_nlogprob, maxiter=100) hypers = hypers[0] #hypers = spo.fmin_bfgs(nlogprob, hypers, grad_nlogprob, maxiter=100) self.amp2 = np.exp(hypers[0]) self.noise = np.exp(hypers[1]) self.ls = np.exp(hypers[2:]) def main(): try: import matplotlib.pyplot as plt except: pass # Let's start with some random values x = np.linspace(0,1,10)[:,np.newaxis]*10#np.random.rand(100)[:,np.newaxis] y = np.random.randn(10) mygp = GP(covar='ARDSE') mygp.real_init(x.shape[1], y) # Sample some functions given these hyperparameters and plot them for i in range(0,5): x = np.linspace(0,1,100)[:,np.newaxis]*10 K = mygp.cov(x) y = np.random.randn(100) fsamp = mygp.mean + np.dot(spla.cholesky(K).transpose(), y) try: plt.plot(x, fsamp) except: pass print('Loglikelihood before optimizing: ', mygp.logprob(x,y)) mygp.optimize_hypers(x,y) print('Loglikelihood after optimizing: ', mygp.logprob(x,y)) try: plt.show() except: print('Install matplotlib to get figures') if __name__ == '__main__': main() ================================================ FILE: src/aup/Proposer/spearmint/helpers.py ================================================ import os import sys import subprocess import tempfile from google.protobuf import text_format from .spearmint_pb2 import * def log(*args): '''Write a msg to stderr.''' for v in args: sys.stderr.write(str(v)) sys.stderr.write("\n") def sh(cmd): '''Run a shell command (blocking until completion).''' subprocess.check_call(cmd, shell=True) def redirect_output(path): '''Redirect stdout and stderr to a file.''' outfile = open(path, 'a') sys.stdout = outfile sys.stderr = outfile def check_dir(path): '''Create a directory if it doesn't exist.''' if not os.path.exists(path): os.mkdir(path) def grid_for(job): return os.path.join(job.expt_dir, 'expt-grid.pkl') def file_write_safe(path, data): '''Write data to a temporary file, then move to the destination path.''' fh = tempfile.NamedTemporaryFile(mode='w', delete=False) fh.write(data) fh.close() cmd = 'mv "%s" "%s"' % (fh.name, path) sh(cmd) def save_experiment(filename, expt): file_write_safe(filename, text_format.MessageToString(expt)) def load_experiment(filename): fh = open(filename, 'rb') expt = Experiment() text_format.Merge(fh.read(), expt) fh.close() return expt def job_output_file(job): return os.path.join(job.expt_dir, 'output', '%08d.out' % (job.id)) def job_file_for(job): '''Get the path to the job file corresponding to a job object.''' return os.path.join(job.expt_dir, 'jobs', '%08d.pb' % (job.id)) def save_job(job): filename = job_file_for(job) file_write_safe(filename, job.SerializeToString()) def load_job(filename): fh = open(filename, 'rb') job = Job() job.ParseFromString(fh.read()) fh.close() return job ================================================ FILE: src/aup/Proposer/spearmint/runner.py ================================================ import sys import os import traceback from spearmint_pb2 import * from ExperimentGrid import * from helpers import * # System dependent modules DEFAULT_MODULES = [ 'packages/epd/7.1-2', 'packages/matlab/r2011b', 'mpi/openmpi/1.2.8/intel', 'libraries/mkl/10.0', 'packages/cuda/4.0', ] MCR_LOCATION = "/home/matlab/v715" # hack def job_runner(job): '''This fn runs in a new process. Now we are going to do a little bookkeeping and then spin off the actual job that does whatever it is we're trying to achieve.''' redirect_output(job_output_file(job)) log("Running in wrapper mode for '%s'\n" % (job.id)) ExperimentGrid.job_running(job.expt_dir, job.id) # Update metadata and save the job file, which will be read by the job wrappers. job.start_t = int(time.time()) job.status = 'running' save_job(job) success = False start_time = time.time() try: if job.language == MATLAB: run_matlab_job(job) elif job.language == PYTHON: run_python_job(job) elif job.language == SHELL: run_torch_job(job) elif job.language == MCR: run_mcr_job(job) else: raise Exception("That function type has not been implemented.") success = True except: log("-" * 40) log("Problem running the job:") log(sys.exc_info()) log(traceback.print_exc(limit=1000)) log("-" * 40) end_time = time.time() duration = end_time - start_time # The job output is written back to the job file, so we read it back in to # get the results. job_file = job_file_for(job) job = load_job(job_file) log("Job file reloaded.") if not job.HasField("value"): log("Could not find value in output file.") success = False if success: log("Completed successfully in %0.2f seconds. [%f]" % (duration, job.value)) # Update the status for this job. ExperimentGrid.job_complete(job.expt_dir, job.id, job.value, duration) job.status = 'complete' else: log("Job failed in %0.2f seconds." % (duration)) # Update the experiment status for this job. ExperimentGrid.job_broken(job.expt_dir, job.id) job.status = 'broken' job.end_t = int(time.time()) job.duration = duration save_job(job) def run_matlab_job(job): '''Run it as a Matlab function.''' log("Running matlab job.") job_file = job_file_for(job) function_call = "matlab_wrapper('%s'),quit;" % (job_file) matlab_cmd = ('matlab -nosplash -nodesktop -r "%s"' % (function_call)) log(matlab_cmd) sh(matlab_cmd) # TODO: change this function to be more flexible when running python jobs # regarding the python path, experiment directory, etc... def run_python_job(job): '''Run a Python function.''' log("Running python job.\n") # Add experiment directory to the system path. sys.path.append(os.path.realpath(job.expt_dir)) # Convert the PB object into useful parameters. params = {} for param in job.param: dbl_vals = param.dbl_val._values int_vals = param.int_val._values str_vals = param.str_val._values if len(dbl_vals) > 0: params[param.name] = np.array(dbl_vals) elif len(int_vals) > 0: params[param.name] = np.array(int_vals, dtype=int) elif len(str_vals) > 0: params[param.name] = str_vals else: raise Exception("Unknown parameter type.") # Load up this module and run module = __import__(job.name) result = module.main(job.id, params) log("Got result %f\n" % (result)) # Store the result. job.value = result save_job(job) def run_torch_job(job): '''Run a torch based job.''' params = {} for param in job.param: dbl_vals = param.dbl_val._values int_vals = param.int_val._values str_vals = param.str_val._values if len(dbl_vals) > 0: params[param.name] = dbl_vals elif len(int_vals) > 0: params[param.name] = int_vals elif len(str_vals) > 0: params[param.name] = str_vals else: raise Exception("Unknown parameter type.") #TODO: this passes args correctly for experiment utils, but we need to # figure out how to get the result back out when the experiment completes. param_str = "" for pname, pval in params.iteritems(): if len(pval) == 1: pval = str(pval[0]) else: pval = ','.join([str(v) for v in pval]) param_str += "-" + pname + " " + pval + " " cmd = "./%s %s" % (job.name, param_str) log("Executing command: %s\n" % (cmd)) sh(cmd) def run_shell_job(job): '''Run a shell based job.''' log("Running shell job.\n") # Change into the directory. os.chdir(job.expt_dir) cmd = './%s %s' % (job.name, job_file_for(job)) log("Executing command '%s'\n" % (cmd)) sh(cmd) def run_mcr_job(job): '''Run a compiled Matlab job.''' log("Running a compiled Matlab job.\n") # Change into the directory. os.chdir(job.expt_dir) if os.environ.has_key('MATLAB'): mcr_loc = os.environ['MATLAB'] else: mcr_loc = MCR_LOCATION cmd = './run_%s.sh %s %s' % (job.name, mcr_loc, job_file_for(job)) log("Executing command '%s'\n" % (cmd)) sh(cmd) ================================================ FILE: src/aup/Proposer/spearmint/sobol_lib.py ================================================ from __future__ import print_function import math from numpy import * def i4_bit_hi1 ( n ): #*****************************************************************************80 # ## I4_BIT_HI1 returns the position of the high 1 bit base 2 in an integer. # # Example: # # N Binary BIT # ---- -------- ---- # 0 0 0 # 1 1 1 # 2 10 2 # 3 11 2 # 4 100 3 # 5 101 3 # 6 110 3 # 7 111 3 # 8 1000 4 # 9 1001 4 # 10 1010 4 # 11 1011 4 # 12 1100 4 # 13 1101 4 # 14 1110 4 # 15 1111 4 # 16 10000 5 # 17 10001 5 # 1023 1111111111 10 # 1024 10000000000 11 # 1025 10000000001 11 # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 26 Nov 2011 # # Author: # # Original MATLAB version by John Burkardt. # PYTHON version by Corrado Chisari # Modified by Jasper Snoek to scale to 1111 dimensions # # Parameters: # # Input, integer N, the integer to be measured. # N should be nonnegative. If N is nonpositive, the value will always be 0. # # Output, integer BIT, the number of bits base 2. # i = math.floor ( n ) bit = 0 while ( 1 ): if ( i <= 0 ): break bit += 1 i = math.floor ( i / 2. ) return bit def i4_bit_lo0 ( n ): #*****************************************************************************80 # ## I4_BIT_LO0 returns the position of the low 0 bit base 2 in an integer. # # Example: # # N Binary BIT # ---- -------- ---- # 0 0 1 # 1 1 2 # 2 10 1 # 3 11 3 # 4 100 1 # 5 101 2 # 6 110 1 # 7 111 4 # 8 1000 1 # 9 1001 2 # 10 1010 1 # 11 1011 3 # 12 1100 1 # 13 1101 2 # 14 1110 1 # 15 1111 5 # 16 10000 1 # 17 10001 2 # 1023 1111111111 1 # 1024 10000000000 1 # 1025 10000000001 1 # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 22 February 2011 # # Author: # # Original MATLAB version by John Burkardt. # PYTHON version by Corrado Chisari # # Parameters: # # Input, integer N, the integer to be measured. # N should be nonnegative. # # Output, integer BIT, the position of the low 1 bit. # bit = 0 i = math.floor ( n ) while ( 1 ): bit = bit + 1 i2 = math.floor ( i / 2. ) if ( i == 2 * i2 ): break i = i2 return bit def i4_sobol_generate ( m, n, skip ): #*****************************************************************************80 # ## I4_SOBOL_GENERATE generates a Sobol dataset. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 22 February 2011 # # Author: # # Original MATLAB version by John Burkardt. # PYTHON version by Corrado Chisari # # Parameters: # # Input, integer M, the spatial dimension. # # Input, integer N, the number of points to generate. # # Input, integer SKIP, the number of initial points to skip. # # Output, real R(M,N), the points. # r=zeros((m,n)) for j in range (1, n+1): seed = skip + j - 2 [ r[0:m,j-1], seed ] = i4_sobol ( m, seed ) return r def i4_sobol ( dim_num, seed ): #*****************************************************************************80 # ## I4_SOBOL generates a new quasirandom Sobol vector with each call. # # Discussion: # # The routine adapts the ideas of Antonov and Saleev. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 26 February 2013 # # Author: # # Original FORTRAN77 version by Bennett Fox. # MATLAB version by John Burkardt. # PYTHON version by Corrado Chisari # PYTHON version modified by Jasper Snoek to scale (Joe & Kuo) # # Reference: # # Antonov, Saleev, # USSR Computational Mathematics and Mathematical Physics, # Volume 19, 1980, pages 252 - 256. # # Paul Bratley, Bennett Fox, # Algorithm 659: # Implementing Sobol's Quasirandom Sequence Generator, # ACM Transactions on Mathematical Software, # Volume 14, Number 1, pages 88-100, 1988. # # Bennett Fox, # Algorithm 647: # Implementation and Relative Efficiency of Quasirandom # Sequence Generators, # ACM Transactions on Mathematical Software, # Volume 12, Number 4, pages 362-376, 1986. # # Ilya Sobol, # USSR Computational Mathematics and Mathematical Physics, # Volume 16, pages 236-242, 1977. # # Ilya Sobol, Levitan, # The Production of Points Uniformly Distributed in a Multidimensional # Cube (in Russian), # Preprint IPM Akad. Nauk SSSR, # Number 40, Moscow 1976. # # Stephen Joe, Frances Kuo, # Remark on Algorithm 659: Implementing Sobol's Quasirandom Sequence Generator, # ACM Transactions on Mathematical Software, # Volume 29, Number 1, March 2003, pages 49-57. # # Parameters: # # Input, integer DIM_NUM, the number of spatial dimensions. # DIM_NUM must satisfy 1 <= DIM_NUM <= 1111. # # Input/output, integer SEED, the "seed" for the sequence. # This is essentially the index in the sequence of the quasirandom # value to be generated. On output, SEED has been set to the # appropriate next value, usually simply SEED+1. # If SEED is less than 0 on input, it is treated as though it were 0. # An input value of 0 requests the first (0-th) element of the sequence. # # Output, real QUASI(DIM_NUM), the next quasirandom vector. # global atmost global dim_max global dim_num_save global initialized global lastq global log_max global maxcol global poly global recipd global seed_save global v if ( not 'initialized' in globals().keys() ): initialized = 0 dim_num_save = -1 if ( not initialized or dim_num != dim_num_save ): initialized = 1 dim_max = 1111 dim_num_save = -1 log_max = 30 seed_save = -1 # # Initialize (part of) V. # v = zeros((dim_max,log_max)) v[0,0] = 1 v[1,0] = 1 v[2,0] = 1 v[3,0] = 1 v[4,0] = 1 v[5,0] = 1 v[6,0] = 1 v[7,0] = 1 v[8,0] = 1 v[9,0] = 1 v[10,0] = 1 v[11,0] = 1 v[12,0] = 1 v[13,0] = 1 v[14,0] = 1 v[15,0] = 1 v[16,0] = 1 v[17,0] = 1 v[18,0] = 1 v[19,0] = 1 v[20,0] = 1 v[21,0] = 1 v[22,0] = 1 v[23,0] = 1 v[24,0] = 1 v[25,0] = 1 v[26,0] = 1 v[27,0] = 1 v[28,0] = 1 v[29,0] = 1 v[30,0] = 1 v[31,0] = 1 v[32,0] = 1 v[33,0] = 1 v[34,0] = 1 v[35,0] = 1 v[36,0] = 1 v[37,0] = 1 v[38,0] = 1 v[39,0] = 1 v[40,0] = 1 v[41,0] = 1 v[42,0] = 1 v[43,0] = 1 v[44,0] = 1 v[45,0] = 1 v[46,0] = 1 v[47,0] = 1 v[48,0] = 1 v[49,0] = 1 v[50,0] = 1 v[51,0] = 1 v[52,0] = 1 v[53,0] = 1 v[54,0] = 1 v[55,0] = 1 v[56,0] = 1 v[57,0] = 1 v[58,0] = 1 v[59,0] = 1 v[60,0] = 1 v[61,0] = 1 v[62,0] = 1 v[63,0] = 1 v[64,0] = 1 v[65,0] = 1 v[66,0] = 1 v[67,0] = 1 v[68,0] = 1 v[69,0] = 1 v[70,0] = 1 v[71,0] = 1 v[72,0] = 1 v[73,0] = 1 v[74,0] = 1 v[75,0] = 1 v[76,0] = 1 v[77,0] = 1 v[78,0] = 1 v[79,0] = 1 v[80,0] = 1 v[81,0] = 1 v[82,0] = 1 v[83,0] = 1 v[84,0] = 1 v[85,0] = 1 v[86,0] = 1 v[87,0] = 1 v[88,0] = 1 v[89,0] = 1 v[90,0] = 1 v[91,0] = 1 v[92,0] = 1 v[93,0] = 1 v[94,0] = 1 v[95,0] = 1 v[96,0] = 1 v[97,0] = 1 v[98,0] = 1 v[99,0] = 1 v[100,0] = 1 v[101,0] = 1 v[102,0] = 1 v[103,0] = 1 v[104,0] = 1 v[105,0] = 1 v[106,0] = 1 v[107,0] = 1 v[108,0] = 1 v[109,0] = 1 v[110,0] = 1 v[111,0] = 1 v[112,0] = 1 v[113,0] = 1 v[114,0] = 1 v[115,0] = 1 v[116,0] = 1 v[117,0] = 1 v[118,0] = 1 v[119,0] = 1 v[120,0] = 1 v[121,0] = 1 v[122,0] = 1 v[123,0] = 1 v[124,0] = 1 v[125,0] = 1 v[126,0] = 1 v[127,0] = 1 v[128,0] = 1 v[129,0] = 1 v[130,0] = 1 v[131,0] = 1 v[132,0] = 1 v[133,0] = 1 v[134,0] = 1 v[135,0] = 1 v[136,0] = 1 v[137,0] = 1 v[138,0] = 1 v[139,0] = 1 v[140,0] = 1 v[141,0] = 1 v[142,0] = 1 v[143,0] = 1 v[144,0] = 1 v[145,0] = 1 v[146,0] = 1 v[147,0] = 1 v[148,0] = 1 v[149,0] = 1 v[150,0] = 1 v[151,0] = 1 v[152,0] = 1 v[153,0] = 1 v[154,0] = 1 v[155,0] = 1 v[156,0] = 1 v[157,0] = 1 v[158,0] = 1 v[159,0] = 1 v[160,0] = 1 v[161,0] = 1 v[162,0] = 1 v[163,0] = 1 v[164,0] = 1 v[165,0] = 1 v[166,0] = 1 v[167,0] = 1 v[168,0] = 1 v[169,0] = 1 v[170,0] = 1 v[171,0] = 1 v[172,0] = 1 v[173,0] = 1 v[174,0] = 1 v[175,0] = 1 v[176,0] = 1 v[177,0] = 1 v[178,0] = 1 v[179,0] = 1 v[180,0] = 1 v[181,0] = 1 v[182,0] = 1 v[183,0] = 1 v[184,0] = 1 v[185,0] = 1 v[186,0] = 1 v[187,0] = 1 v[188,0] = 1 v[189,0] = 1 v[190,0] = 1 v[191,0] = 1 v[192,0] = 1 v[193,0] = 1 v[194,0] = 1 v[195,0] = 1 v[196,0] = 1 v[197,0] = 1 v[198,0] = 1 v[199,0] = 1 v[200,0] = 1 v[201,0] = 1 v[202,0] = 1 v[203,0] = 1 v[204,0] = 1 v[205,0] = 1 v[206,0] = 1 v[207,0] = 1 v[208,0] = 1 v[209,0] = 1 v[210,0] = 1 v[211,0] = 1 v[212,0] = 1 v[213,0] = 1 v[214,0] = 1 v[215,0] = 1 v[216,0] = 1 v[217,0] = 1 v[218,0] = 1 v[219,0] = 1 v[220,0] = 1 v[221,0] = 1 v[222,0] = 1 v[223,0] = 1 v[224,0] = 1 v[225,0] = 1 v[226,0] = 1 v[227,0] = 1 v[228,0] = 1 v[229,0] = 1 v[230,0] = 1 v[231,0] = 1 v[232,0] = 1 v[233,0] = 1 v[234,0] = 1 v[235,0] = 1 v[236,0] = 1 v[237,0] = 1 v[238,0] = 1 v[239,0] = 1 v[240,0] = 1 v[241,0] = 1 v[242,0] = 1 v[243,0] = 1 v[244,0] = 1 v[245,0] = 1 v[246,0] = 1 v[247,0] = 1 v[248,0] = 1 v[249,0] = 1 v[250,0] = 1 v[251,0] = 1 v[252,0] = 1 v[253,0] = 1 v[254,0] = 1 v[255,0] = 1 v[256,0] = 1 v[257,0] = 1 v[258,0] = 1 v[259,0] = 1 v[260,0] = 1 v[261,0] = 1 v[262,0] = 1 v[263,0] = 1 v[264,0] = 1 v[265,0] = 1 v[266,0] = 1 v[267,0] = 1 v[268,0] = 1 v[269,0] = 1 v[270,0] = 1 v[271,0] = 1 v[272,0] = 1 v[273,0] = 1 v[274,0] = 1 v[275,0] = 1 v[276,0] = 1 v[277,0] = 1 v[278,0] = 1 v[279,0] = 1 v[280,0] = 1 v[281,0] = 1 v[282,0] = 1 v[283,0] = 1 v[284,0] = 1 v[285,0] = 1 v[286,0] = 1 v[287,0] = 1 v[288,0] = 1 v[289,0] = 1 v[290,0] = 1 v[291,0] = 1 v[292,0] = 1 v[293,0] = 1 v[294,0] = 1 v[295,0] = 1 v[296,0] = 1 v[297,0] = 1 v[298,0] = 1 v[299,0] = 1 v[300,0] = 1 v[301,0] = 1 v[302,0] = 1 v[303,0] = 1 v[304,0] = 1 v[305,0] = 1 v[306,0] = 1 v[307,0] = 1 v[308,0] = 1 v[309,0] = 1 v[310,0] = 1 v[311,0] = 1 v[312,0] = 1 v[313,0] = 1 v[314,0] = 1 v[315,0] = 1 v[316,0] = 1 v[317,0] = 1 v[318,0] = 1 v[319,0] = 1 v[320,0] = 1 v[321,0] = 1 v[322,0] = 1 v[323,0] = 1 v[324,0] = 1 v[325,0] = 1 v[326,0] = 1 v[327,0] = 1 v[328,0] = 1 v[329,0] = 1 v[330,0] = 1 v[331,0] = 1 v[332,0] = 1 v[333,0] = 1 v[334,0] = 1 v[335,0] = 1 v[336,0] = 1 v[337,0] = 1 v[338,0] = 1 v[339,0] = 1 v[340,0] = 1 v[341,0] = 1 v[342,0] = 1 v[343,0] = 1 v[344,0] = 1 v[345,0] = 1 v[346,0] = 1 v[347,0] = 1 v[348,0] = 1 v[349,0] = 1 v[350,0] = 1 v[351,0] = 1 v[352,0] = 1 v[353,0] = 1 v[354,0] = 1 v[355,0] = 1 v[356,0] = 1 v[357,0] = 1 v[358,0] = 1 v[359,0] = 1 v[360,0] = 1 v[361,0] = 1 v[362,0] = 1 v[363,0] = 1 v[364,0] = 1 v[365,0] = 1 v[366,0] = 1 v[367,0] = 1 v[368,0] = 1 v[369,0] = 1 v[370,0] = 1 v[371,0] = 1 v[372,0] = 1 v[373,0] = 1 v[374,0] = 1 v[375,0] = 1 v[376,0] = 1 v[377,0] = 1 v[378,0] = 1 v[379,0] = 1 v[380,0] = 1 v[381,0] = 1 v[382,0] = 1 v[383,0] = 1 v[384,0] = 1 v[385,0] = 1 v[386,0] = 1 v[387,0] = 1 v[388,0] = 1 v[389,0] = 1 v[390,0] = 1 v[391,0] = 1 v[392,0] = 1 v[393,0] = 1 v[394,0] = 1 v[395,0] = 1 v[396,0] = 1 v[397,0] = 1 v[398,0] = 1 v[399,0] = 1 v[400,0] = 1 v[401,0] = 1 v[402,0] = 1 v[403,0] = 1 v[404,0] = 1 v[405,0] = 1 v[406,0] = 1 v[407,0] = 1 v[408,0] = 1 v[409,0] = 1 v[410,0] = 1 v[411,0] = 1 v[412,0] = 1 v[413,0] = 1 v[414,0] = 1 v[415,0] = 1 v[416,0] = 1 v[417,0] = 1 v[418,0] = 1 v[419,0] = 1 v[420,0] = 1 v[421,0] = 1 v[422,0] = 1 v[423,0] = 1 v[424,0] = 1 v[425,0] = 1 v[426,0] = 1 v[427,0] = 1 v[428,0] = 1 v[429,0] = 1 v[430,0] = 1 v[431,0] = 1 v[432,0] = 1 v[433,0] = 1 v[434,0] = 1 v[435,0] = 1 v[436,0] = 1 v[437,0] = 1 v[438,0] = 1 v[439,0] = 1 v[440,0] = 1 v[441,0] = 1 v[442,0] = 1 v[443,0] = 1 v[444,0] = 1 v[445,0] = 1 v[446,0] = 1 v[447,0] = 1 v[448,0] = 1 v[449,0] = 1 v[450,0] = 1 v[451,0] = 1 v[452,0] = 1 v[453,0] = 1 v[454,0] = 1 v[455,0] = 1 v[456,0] = 1 v[457,0] = 1 v[458,0] = 1 v[459,0] = 1 v[460,0] = 1 v[461,0] = 1 v[462,0] = 1 v[463,0] = 1 v[464,0] = 1 v[465,0] = 1 v[466,0] = 1 v[467,0] = 1 v[468,0] = 1 v[469,0] = 1 v[470,0] = 1 v[471,0] = 1 v[472,0] = 1 v[473,0] = 1 v[474,0] = 1 v[475,0] = 1 v[476,0] = 1 v[477,0] = 1 v[478,0] = 1 v[479,0] = 1 v[480,0] = 1 v[481,0] = 1 v[482,0] = 1 v[483,0] = 1 v[484,0] = 1 v[485,0] = 1 v[486,0] = 1 v[487,0] = 1 v[488,0] = 1 v[489,0] = 1 v[490,0] = 1 v[491,0] = 1 v[492,0] = 1 v[493,0] = 1 v[494,0] = 1 v[495,0] = 1 v[496,0] = 1 v[497,0] = 1 v[498,0] = 1 v[499,0] = 1 v[500,0] = 1 v[501,0] = 1 v[502,0] = 1 v[503,0] = 1 v[504,0] = 1 v[505,0] = 1 v[506,0] = 1 v[507,0] = 1 v[508,0] = 1 v[509,0] = 1 v[510,0] = 1 v[511,0] = 1 v[512,0] = 1 v[513,0] = 1 v[514,0] = 1 v[515,0] = 1 v[516,0] = 1 v[517,0] = 1 v[518,0] = 1 v[519,0] = 1 v[520,0] = 1 v[521,0] = 1 v[522,0] = 1 v[523,0] = 1 v[524,0] = 1 v[525,0] = 1 v[526,0] = 1 v[527,0] = 1 v[528,0] = 1 v[529,0] = 1 v[530,0] = 1 v[531,0] = 1 v[532,0] = 1 v[533,0] = 1 v[534,0] = 1 v[535,0] = 1 v[536,0] = 1 v[537,0] = 1 v[538,0] = 1 v[539,0] = 1 v[540,0] = 1 v[541,0] = 1 v[542,0] = 1 v[543,0] = 1 v[544,0] = 1 v[545,0] = 1 v[546,0] = 1 v[547,0] = 1 v[548,0] = 1 v[549,0] = 1 v[550,0] = 1 v[551,0] = 1 v[552,0] = 1 v[553,0] = 1 v[554,0] = 1 v[555,0] = 1 v[556,0] = 1 v[557,0] = 1 v[558,0] = 1 v[559,0] = 1 v[560,0] = 1 v[561,0] = 1 v[562,0] = 1 v[563,0] = 1 v[564,0] = 1 v[565,0] = 1 v[566,0] = 1 v[567,0] = 1 v[568,0] = 1 v[569,0] = 1 v[570,0] = 1 v[571,0] = 1 v[572,0] = 1 v[573,0] = 1 v[574,0] = 1 v[575,0] = 1 v[576,0] = 1 v[577,0] = 1 v[578,0] = 1 v[579,0] = 1 v[580,0] = 1 v[581,0] = 1 v[582,0] = 1 v[583,0] = 1 v[584,0] = 1 v[585,0] = 1 v[586,0] = 1 v[587,0] = 1 v[588,0] = 1 v[589,0] = 1 v[590,0] = 1 v[591,0] = 1 v[592,0] = 1 v[593,0] = 1 v[594,0] = 1 v[595,0] = 1 v[596,0] = 1 v[597,0] = 1 v[598,0] = 1 v[599,0] = 1 v[600,0] = 1 v[601,0] = 1 v[602,0] = 1 v[603,0] = 1 v[604,0] = 1 v[605,0] = 1 v[606,0] = 1 v[607,0] = 1 v[608,0] = 1 v[609,0] = 1 v[610,0] = 1 v[611,0] = 1 v[612,0] = 1 v[613,0] = 1 v[614,0] = 1 v[615,0] = 1 v[616,0] = 1 v[617,0] = 1 v[618,0] = 1 v[619,0] = 1 v[620,0] = 1 v[621,0] = 1 v[622,0] = 1 v[623,0] = 1 v[624,0] = 1 v[625,0] = 1 v[626,0] = 1 v[627,0] = 1 v[628,0] = 1 v[629,0] = 1 v[630,0] = 1 v[631,0] = 1 v[632,0] = 1 v[633,0] = 1 v[634,0] = 1 v[635,0] = 1 v[636,0] = 1 v[637,0] = 1 v[638,0] = 1 v[639,0] = 1 v[640,0] = 1 v[641,0] = 1 v[642,0] = 1 v[643,0] = 1 v[644,0] = 1 v[645,0] = 1 v[646,0] = 1 v[647,0] = 1 v[648,0] = 1 v[649,0] = 1 v[650,0] = 1 v[651,0] = 1 v[652,0] = 1 v[653,0] = 1 v[654,0] = 1 v[655,0] = 1 v[656,0] = 1 v[657,0] = 1 v[658,0] = 1 v[659,0] = 1 v[660,0] = 1 v[661,0] = 1 v[662,0] = 1 v[663,0] = 1 v[664,0] = 1 v[665,0] = 1 v[666,0] = 1 v[667,0] = 1 v[668,0] = 1 v[669,0] = 1 v[670,0] = 1 v[671,0] = 1 v[672,0] = 1 v[673,0] = 1 v[674,0] = 1 v[675,0] = 1 v[676,0] = 1 v[677,0] = 1 v[678,0] = 1 v[679,0] = 1 v[680,0] = 1 v[681,0] = 1 v[682,0] = 1 v[683,0] = 1 v[684,0] = 1 v[685,0] = 1 v[686,0] = 1 v[687,0] = 1 v[688,0] = 1 v[689,0] = 1 v[690,0] = 1 v[691,0] = 1 v[692,0] = 1 v[693,0] = 1 v[694,0] = 1 v[695,0] = 1 v[696,0] = 1 v[697,0] = 1 v[698,0] = 1 v[699,0] = 1 v[700,0] = 1 v[701,0] = 1 v[702,0] = 1 v[703,0] = 1 v[704,0] = 1 v[705,0] = 1 v[706,0] = 1 v[707,0] = 1 v[708,0] = 1 v[709,0] = 1 v[710,0] = 1 v[711,0] = 1 v[712,0] = 1 v[713,0] = 1 v[714,0] = 1 v[715,0] = 1 v[716,0] = 1 v[717,0] = 1 v[718,0] = 1 v[719,0] = 1 v[720,0] = 1 v[721,0] = 1 v[722,0] = 1 v[723,0] = 1 v[724,0] = 1 v[725,0] = 1 v[726,0] = 1 v[727,0] = 1 v[728,0] = 1 v[729,0] = 1 v[730,0] = 1 v[731,0] = 1 v[732,0] = 1 v[733,0] = 1 v[734,0] = 1 v[735,0] = 1 v[736,0] = 1 v[737,0] = 1 v[738,0] = 1 v[739,0] = 1 v[740,0] = 1 v[741,0] = 1 v[742,0] = 1 v[743,0] = 1 v[744,0] = 1 v[745,0] = 1 v[746,0] = 1 v[747,0] = 1 v[748,0] = 1 v[749,0] = 1 v[750,0] = 1 v[751,0] = 1 v[752,0] = 1 v[753,0] = 1 v[754,0] = 1 v[755,0] = 1 v[756,0] = 1 v[757,0] = 1 v[758,0] = 1 v[759,0] = 1 v[760,0] = 1 v[761,0] = 1 v[762,0] = 1 v[763,0] = 1 v[764,0] = 1 v[765,0] = 1 v[766,0] = 1 v[767,0] = 1 v[768,0] = 1 v[769,0] = 1 v[770,0] = 1 v[771,0] = 1 v[772,0] = 1 v[773,0] = 1 v[774,0] = 1 v[775,0] = 1 v[776,0] = 1 v[777,0] = 1 v[778,0] = 1 v[779,0] = 1 v[780,0] = 1 v[781,0] = 1 v[782,0] = 1 v[783,0] = 1 v[784,0] = 1 v[785,0] = 1 v[786,0] = 1 v[787,0] = 1 v[788,0] = 1 v[789,0] = 1 v[790,0] = 1 v[791,0] = 1 v[792,0] = 1 v[793,0] = 1 v[794,0] = 1 v[795,0] = 1 v[796,0] = 1 v[797,0] = 1 v[798,0] = 1 v[799,0] = 1 v[800,0] = 1 v[801,0] = 1 v[802,0] = 1 v[803,0] = 1 v[804,0] = 1 v[805,0] = 1 v[806,0] = 1 v[807,0] = 1 v[808,0] = 1 v[809,0] = 1 v[810,0] = 1 v[811,0] = 1 v[812,0] = 1 v[813,0] = 1 v[814,0] = 1 v[815,0] = 1 v[816,0] = 1 v[817,0] = 1 v[818,0] = 1 v[819,0] = 1 v[820,0] = 1 v[821,0] = 1 v[822,0] = 1 v[823,0] = 1 v[824,0] = 1 v[825,0] = 1 v[826,0] = 1 v[827,0] = 1 v[828,0] = 1 v[829,0] = 1 v[830,0] = 1 v[831,0] = 1 v[832,0] = 1 v[833,0] = 1 v[834,0] = 1 v[835,0] = 1 v[836,0] = 1 v[837,0] = 1 v[838,0] = 1 v[839,0] = 1 v[840,0] = 1 v[841,0] = 1 v[842,0] = 1 v[843,0] = 1 v[844,0] = 1 v[845,0] = 1 v[846,0] = 1 v[847,0] = 1 v[848,0] = 1 v[849,0] = 1 v[850,0] = 1 v[851,0] = 1 v[852,0] = 1 v[853,0] = 1 v[854,0] = 1 v[855,0] = 1 v[856,0] = 1 v[857,0] = 1 v[858,0] = 1 v[859,0] = 1 v[860,0] = 1 v[861,0] = 1 v[862,0] = 1 v[863,0] = 1 v[864,0] = 1 v[865,0] = 1 v[866,0] = 1 v[867,0] = 1 v[868,0] = 1 v[869,0] = 1 v[870,0] = 1 v[871,0] = 1 v[872,0] = 1 v[873,0] = 1 v[874,0] = 1 v[875,0] = 1 v[876,0] = 1 v[877,0] = 1 v[878,0] = 1 v[879,0] = 1 v[880,0] = 1 v[881,0] = 1 v[882,0] = 1 v[883,0] = 1 v[884,0] = 1 v[885,0] = 1 v[886,0] = 1 v[887,0] = 1 v[888,0] = 1 v[889,0] = 1 v[890,0] = 1 v[891,0] = 1 v[892,0] = 1 v[893,0] = 1 v[894,0] = 1 v[895,0] = 1 v[896,0] = 1 v[897,0] = 1 v[898,0] = 1 v[899,0] = 1 v[900,0] = 1 v[901,0] = 1 v[902,0] = 1 v[903,0] = 1 v[904,0] = 1 v[905,0] = 1 v[906,0] = 1 v[907,0] = 1 v[908,0] = 1 v[909,0] = 1 v[910,0] = 1 v[911,0] = 1 v[912,0] = 1 v[913,0] = 1 v[914,0] = 1 v[915,0] = 1 v[916,0] = 1 v[917,0] = 1 v[918,0] = 1 v[919,0] = 1 v[920,0] = 1 v[921,0] = 1 v[922,0] = 1 v[923,0] = 1 v[924,0] = 1 v[925,0] = 1 v[926,0] = 1 v[927,0] = 1 v[928,0] = 1 v[929,0] = 1 v[930,0] = 1 v[931,0] = 1 v[932,0] = 1 v[933,0] = 1 v[934,0] = 1 v[935,0] = 1 v[936,0] = 1 v[937,0] = 1 v[938,0] = 1 v[939,0] = 1 v[940,0] = 1 v[941,0] = 1 v[942,0] = 1 v[943,0] = 1 v[944,0] = 1 v[945,0] = 1 v[946,0] = 1 v[947,0] = 1 v[948,0] = 1 v[949,0] = 1 v[950,0] = 1 v[951,0] = 1 v[952,0] = 1 v[953,0] = 1 v[954,0] = 1 v[955,0] = 1 v[956,0] = 1 v[957,0] = 1 v[958,0] = 1 v[959,0] = 1 v[960,0] = 1 v[961,0] = 1 v[962,0] = 1 v[963,0] = 1 v[964,0] = 1 v[965,0] = 1 v[966,0] = 1 v[967,0] = 1 v[968,0] = 1 v[969,0] = 1 v[970,0] = 1 v[971,0] = 1 v[972,0] = 1 v[973,0] = 1 v[974,0] = 1 v[975,0] = 1 v[976,0] = 1 v[977,0] = 1 v[978,0] = 1 v[979,0] = 1 v[980,0] = 1 v[981,0] = 1 v[982,0] = 1 v[983,0] = 1 v[984,0] = 1 v[985,0] = 1 v[986,0] = 1 v[987,0] = 1 v[988,0] = 1 v[989,0] = 1 v[990,0] = 1 v[991,0] = 1 v[992,0] = 1 v[993,0] = 1 v[994,0] = 1 v[995,0] = 1 v[996,0] = 1 v[997,0] = 1 v[998,0] = 1 v[999,0] = 1 v[1000,0] = 1 v[1001,0] = 1 v[1002,0] = 1 v[1003,0] = 1 v[1004,0] = 1 v[1005,0] = 1 v[1006,0] = 1 v[1007,0] = 1 v[1008,0] = 1 v[1009,0] = 1 v[1010,0] = 1 v[1011,0] = 1 v[1012,0] = 1 v[1013,0] = 1 v[1014,0] = 1 v[1015,0] = 1 v[1016,0] = 1 v[1017,0] = 1 v[1018,0] = 1 v[1019,0] = 1 v[1020,0] = 1 v[1021,0] = 1 v[1022,0] = 1 v[1023,0] = 1 v[1024,0] = 1 v[1025,0] = 1 v[1026,0] = 1 v[1027,0] = 1 v[1028,0] = 1 v[1029,0] = 1 v[1030,0] = 1 v[1031,0] = 1 v[1032,0] = 1 v[1033,0] = 1 v[1034,0] = 1 v[1035,0] = 1 v[1036,0] = 1 v[1037,0] = 1 v[1038,0] = 1 v[1039,0] = 1 v[1040,0] = 1 v[1041,0] = 1 v[1042,0] = 1 v[1043,0] = 1 v[1044,0] = 1 v[1045,0] = 1 v[1046,0] = 1 v[1047,0] = 1 v[1048,0] = 1 v[1049,0] = 1 v[1050,0] = 1 v[1051,0] = 1 v[1052,0] = 1 v[1053,0] = 1 v[1054,0] = 1 v[1055,0] = 1 v[1056,0] = 1 v[1057,0] = 1 v[1058,0] = 1 v[1059,0] = 1 v[1060,0] = 1 v[1061,0] = 1 v[1062,0] = 1 v[1063,0] = 1 v[1064,0] = 1 v[1065,0] = 1 v[1066,0] = 1 v[1067,0] = 1 v[1068,0] = 1 v[1069,0] = 1 v[1070,0] = 1 v[1071,0] = 1 v[1072,0] = 1 v[1073,0] = 1 v[1074,0] = 1 v[1075,0] = 1 v[1076,0] = 1 v[1077,0] = 1 v[1078,0] = 1 v[1079,0] = 1 v[1080,0] = 1 v[1081,0] = 1 v[1082,0] = 1 v[1083,0] = 1 v[1084,0] = 1 v[1085,0] = 1 v[1086,0] = 1 v[1087,0] = 1 v[1088,0] = 1 v[1089,0] = 1 v[1090,0] = 1 v[1091,0] = 1 v[1092,0] = 1 v[1093,0] = 1 v[1094,0] = 1 v[1095,0] = 1 v[1096,0] = 1 v[1097,0] = 1 v[1098,0] = 1 v[1099,0] = 1 v[1100,0] = 1 v[1101,0] = 1 v[1102,0] = 1 v[1103,0] = 1 v[1104,0] = 1 v[1105,0] = 1 v[1106,0] = 1 v[1107,0] = 1 v[1108,0] = 1 v[1109,0] = 1 v[1110,0] = 1 v[2,1] = 1 v[3,1] = 3 v[4,1] = 1 v[5,1] = 3 v[6,1] = 1 v[7,1] = 3 v[8,1] = 3 v[9,1] = 1 v[10,1] = 3 v[11,1] = 1 v[12,1] = 3 v[13,1] = 1 v[14,1] = 3 v[15,1] = 1 v[16,1] = 1 v[17,1] = 3 v[18,1] = 1 v[19,1] = 3 v[20,1] = 1 v[21,1] = 3 v[22,1] = 1 v[23,1] = 3 v[24,1] = 3 v[25,1] = 1 v[26,1] = 1 v[27,1] = 1 v[28,1] = 3 v[29,1] = 1 v[30,1] = 3 v[31,1] = 1 v[32,1] = 3 v[33,1] = 3 v[34,1] = 1 v[35,1] = 3 v[36,1] = 1 v[37,1] = 1 v[38,1] = 1 v[39,1] = 3 v[40,1] = 1 v[41,1] = 3 v[42,1] = 1 v[43,1] = 1 v[44,1] = 1 v[45,1] = 3 v[46,1] = 3 v[47,1] = 1 v[48,1] = 3 v[49,1] = 3 v[50,1] = 1 v[51,1] = 1 v[52,1] = 3 v[53,1] = 3 v[54,1] = 1 v[55,1] = 3 v[56,1] = 3 v[57,1] = 3 v[58,1] = 1 v[59,1] = 3 v[60,1] = 1 v[61,1] = 3 v[62,1] = 1 v[63,1] = 1 v[64,1] = 3 v[65,1] = 3 v[66,1] = 1 v[67,1] = 1 v[68,1] = 1 v[69,1] = 1 v[70,1] = 3 v[71,1] = 1 v[72,1] = 1 v[73,1] = 3 v[74,1] = 1 v[75,1] = 1 v[76,1] = 1 v[77,1] = 3 v[78,1] = 3 v[79,1] = 1 v[80,1] = 3 v[81,1] = 3 v[82,1] = 1 v[83,1] = 3 v[84,1] = 3 v[85,1] = 3 v[86,1] = 1 v[87,1] = 3 v[88,1] = 3 v[89,1] = 3 v[90,1] = 1 v[91,1] = 3 v[92,1] = 3 v[93,1] = 1 v[94,1] = 3 v[95,1] = 3 v[96,1] = 3 v[97,1] = 1 v[98,1] = 3 v[99,1] = 1 v[100,1] = 3 v[101,1] = 1 v[102,1] = 1 v[103,1] = 3 v[104,1] = 3 v[105,1] = 1 v[106,1] = 3 v[107,1] = 3 v[108,1] = 1 v[109,1] = 1 v[110,1] = 1 v[111,1] = 3 v[112,1] = 3 v[113,1] = 1 v[114,1] = 3 v[115,1] = 3 v[116,1] = 1 v[117,1] = 3 v[118,1] = 1 v[119,1] = 1 v[120,1] = 3 v[121,1] = 3 v[122,1] = 3 v[123,1] = 1 v[124,1] = 1 v[125,1] = 1 v[126,1] = 3 v[127,1] = 1 v[128,1] = 1 v[129,1] = 3 v[130,1] = 1 v[131,1] = 1 v[132,1] = 3 v[133,1] = 3 v[134,1] = 1 v[135,1] = 3 v[136,1] = 1 v[137,1] = 3 v[138,1] = 3 v[139,1] = 3 v[140,1] = 3 v[141,1] = 1 v[142,1] = 1 v[143,1] = 1 v[144,1] = 3 v[145,1] = 3 v[146,1] = 1 v[147,1] = 1 v[148,1] = 3 v[149,1] = 1 v[150,1] = 1 v[151,1] = 1 v[152,1] = 1 v[153,1] = 1 v[154,1] = 1 v[155,1] = 3 v[156,1] = 1 v[157,1] = 3 v[158,1] = 1 v[159,1] = 1 v[160,1] = 1 v[161,1] = 3 v[162,1] = 1 v[163,1] = 3 v[164,1] = 1 v[165,1] = 3 v[166,1] = 3 v[167,1] = 3 v[168,1] = 1 v[169,1] = 1 v[170,1] = 3 v[171,1] = 3 v[172,1] = 1 v[173,1] = 3 v[174,1] = 1 v[175,1] = 3 v[176,1] = 1 v[177,1] = 1 v[178,1] = 3 v[179,1] = 1 v[180,1] = 3 v[181,1] = 1 v[182,1] = 3 v[183,1] = 1 v[184,1] = 3 v[185,1] = 1 v[186,1] = 1 v[187,1] = 1 v[188,1] = 3 v[189,1] = 3 v[190,1] = 1 v[191,1] = 3 v[192,1] = 3 v[193,1] = 1 v[194,1] = 3 v[195,1] = 1 v[196,1] = 1 v[197,1] = 1 v[198,1] = 3 v[199,1] = 1 v[200,1] = 3 v[201,1] = 1 v[202,1] = 1 v[203,1] = 3 v[204,1] = 1 v[205,1] = 1 v[206,1] = 3 v[207,1] = 3 v[208,1] = 1 v[209,1] = 1 v[210,1] = 3 v[211,1] = 3 v[212,1] = 3 v[213,1] = 1 v[214,1] = 3 v[215,1] = 3 v[216,1] = 3 v[217,1] = 1 v[218,1] = 3 v[219,1] = 1 v[220,1] = 3 v[221,1] = 1 v[222,1] = 1 v[223,1] = 1 v[224,1] = 3 v[225,1] = 1 v[226,1] = 1 v[227,1] = 1 v[228,1] = 3 v[229,1] = 1 v[230,1] = 1 v[231,1] = 1 v[232,1] = 1 v[233,1] = 1 v[234,1] = 3 v[235,1] = 3 v[236,1] = 3 v[237,1] = 1 v[238,1] = 1 v[239,1] = 1 v[240,1] = 1 v[241,1] = 3 v[242,1] = 3 v[243,1] = 3 v[244,1] = 1 v[245,1] = 3 v[246,1] = 3 v[247,1] = 1 v[248,1] = 1 v[249,1] = 1 v[250,1] = 1 v[251,1] = 3 v[252,1] = 1 v[253,1] = 1 v[254,1] = 3 v[255,1] = 1 v[256,1] = 3 v[257,1] = 3 v[258,1] = 1 v[259,1] = 1 v[260,1] = 3 v[261,1] = 3 v[262,1] = 1 v[263,1] = 1 v[264,1] = 1 v[265,1] = 1 v[266,1] = 3 v[267,1] = 1 v[268,1] = 3 v[269,1] = 3 v[270,1] = 1 v[271,1] = 3 v[272,1] = 3 v[273,1] = 1 v[274,1] = 1 v[275,1] = 1 v[276,1] = 3 v[277,1] = 3 v[278,1] = 3 v[279,1] = 1 v[280,1] = 3 v[281,1] = 3 v[282,1] = 1 v[283,1] = 3 v[284,1] = 3 v[285,1] = 1 v[286,1] = 3 v[287,1] = 1 v[288,1] = 3 v[289,1] = 3 v[290,1] = 3 v[291,1] = 1 v[292,1] = 3 v[293,1] = 1 v[294,1] = 1 v[295,1] = 3 v[296,1] = 1 v[297,1] = 3 v[298,1] = 1 v[299,1] = 1 v[300,1] = 1 v[301,1] = 3 v[302,1] = 3 v[303,1] = 3 v[304,1] = 1 v[305,1] = 1 v[306,1] = 3 v[307,1] = 1 v[308,1] = 3 v[309,1] = 1 v[310,1] = 1 v[311,1] = 1 v[312,1] = 1 v[313,1] = 1 v[314,1] = 1 v[315,1] = 3 v[316,1] = 1 v[317,1] = 1 v[318,1] = 3 v[319,1] = 1 v[320,1] = 3 v[321,1] = 3 v[322,1] = 1 v[323,1] = 1 v[324,1] = 1 v[325,1] = 1 v[326,1] = 3 v[327,1] = 1 v[328,1] = 3 v[329,1] = 1 v[330,1] = 3 v[331,1] = 1 v[332,1] = 1 v[333,1] = 1 v[334,1] = 1 v[335,1] = 3 v[336,1] = 3 v[337,1] = 1 v[338,1] = 1 v[339,1] = 1 v[340,1] = 1 v[341,1] = 1 v[342,1] = 3 v[343,1] = 3 v[344,1] = 3 v[345,1] = 1 v[346,1] = 1 v[347,1] = 3 v[348,1] = 3 v[349,1] = 3 v[350,1] = 3 v[351,1] = 3 v[352,1] = 1 v[353,1] = 3 v[354,1] = 3 v[355,1] = 1 v[356,1] = 3 v[357,1] = 3 v[358,1] = 3 v[359,1] = 3 v[360,1] = 1 v[361,1] = 1 v[362,1] = 1 v[363,1] = 1 v[364,1] = 1 v[365,1] = 1 v[366,1] = 3 v[367,1] = 1 v[368,1] = 1 v[369,1] = 3 v[370,1] = 1 v[371,1] = 1 v[372,1] = 1 v[373,1] = 3 v[374,1] = 1 v[375,1] = 1 v[376,1] = 1 v[377,1] = 3 v[378,1] = 3 v[379,1] = 3 v[380,1] = 1 v[381,1] = 3 v[382,1] = 1 v[383,1] = 1 v[384,1] = 3 v[385,1] = 3 v[386,1] = 3 v[387,1] = 1 v[388,1] = 3 v[389,1] = 3 v[390,1] = 1 v[391,1] = 3 v[392,1] = 1 v[393,1] = 3 v[394,1] = 3 v[395,1] = 1 v[396,1] = 3 v[397,1] = 3 v[398,1] = 3 v[399,1] = 1 v[400,1] = 1 v[401,1] = 3 v[402,1] = 3 v[403,1] = 1 v[404,1] = 3 v[405,1] = 1 v[406,1] = 3 v[407,1] = 1 v[408,1] = 1 v[409,1] = 1 v[410,1] = 3 v[411,1] = 3 v[412,1] = 3 v[413,1] = 3 v[414,1] = 1 v[415,1] = 3 v[416,1] = 1 v[417,1] = 1 v[418,1] = 3 v[419,1] = 1 v[420,1] = 3 v[421,1] = 1 v[422,1] = 1 v[423,1] = 1 v[424,1] = 3 v[425,1] = 1 v[426,1] = 3 v[427,1] = 1 v[428,1] = 3 v[429,1] = 1 v[430,1] = 3 v[431,1] = 3 v[432,1] = 3 v[433,1] = 3 v[434,1] = 3 v[435,1] = 3 v[436,1] = 3 v[437,1] = 3 v[438,1] = 1 v[439,1] = 3 v[440,1] = 3 v[441,1] = 3 v[442,1] = 3 v[443,1] = 3 v[444,1] = 1 v[445,1] = 3 v[446,1] = 1 v[447,1] = 3 v[448,1] = 3 v[449,1] = 3 v[450,1] = 1 v[451,1] = 3 v[452,1] = 1 v[453,1] = 3 v[454,1] = 1 v[455,1] = 3 v[456,1] = 3 v[457,1] = 1 v[458,1] = 3 v[459,1] = 3 v[460,1] = 3 v[461,1] = 3 v[462,1] = 3 v[463,1] = 3 v[464,1] = 3 v[465,1] = 3 v[466,1] = 3 v[467,1] = 1 v[468,1] = 1 v[469,1] = 1 v[470,1] = 1 v[471,1] = 1 v[472,1] = 1 v[473,1] = 3 v[474,1] = 3 v[475,1] = 1 v[476,1] = 1 v[477,1] = 3 v[478,1] = 3 v[479,1] = 1 v[480,1] = 1 v[481,1] = 1 v[482,1] = 3 v[483,1] = 3 v[484,1] = 1 v[485,1] = 1 v[486,1] = 3 v[487,1] = 3 v[488,1] = 3 v[489,1] = 3 v[490,1] = 1 v[491,1] = 1 v[492,1] = 3 v[493,1] = 1 v[494,1] = 3 v[495,1] = 3 v[496,1] = 1 v[497,1] = 3 v[498,1] = 3 v[499,1] = 1 v[500,1] = 1 v[501,1] = 1 v[502,1] = 3 v[503,1] = 3 v[504,1] = 3 v[505,1] = 1 v[506,1] = 1 v[507,1] = 3 v[508,1] = 3 v[509,1] = 3 v[510,1] = 3 v[511,1] = 3 v[512,1] = 1 v[513,1] = 1 v[514,1] = 1 v[515,1] = 3 v[516,1] = 1 v[517,1] = 3 v[518,1] = 3 v[519,1] = 1 v[520,1] = 3 v[521,1] = 3 v[522,1] = 3 v[523,1] = 3 v[524,1] = 1 v[525,1] = 1 v[526,1] = 3 v[527,1] = 1 v[528,1] = 1 v[529,1] = 3 v[530,1] = 1 v[531,1] = 3 v[532,1] = 1 v[533,1] = 3 v[534,1] = 1 v[535,1] = 3 v[536,1] = 3 v[537,1] = 1 v[538,1] = 1 v[539,1] = 3 v[540,1] = 3 v[541,1] = 1 v[542,1] = 3 v[543,1] = 3 v[544,1] = 1 v[545,1] = 3 v[546,1] = 3 v[547,1] = 1 v[548,1] = 1 v[549,1] = 3 v[550,1] = 1 v[551,1] = 3 v[552,1] = 3 v[553,1] = 1 v[554,1] = 1 v[555,1] = 3 v[556,1] = 1 v[557,1] = 3 v[558,1] = 1 v[559,1] = 3 v[560,1] = 1 v[561,1] = 1 v[562,1] = 3 v[563,1] = 3 v[564,1] = 1 v[565,1] = 1 v[566,1] = 1 v[567,1] = 3 v[568,1] = 3 v[569,1] = 1 v[570,1] = 3 v[571,1] = 1 v[572,1] = 1 v[573,1] = 3 v[574,1] = 3 v[575,1] = 1 v[576,1] = 1 v[577,1] = 3 v[578,1] = 1 v[579,1] = 3 v[580,1] = 1 v[581,1] = 1 v[582,1] = 1 v[583,1] = 1 v[584,1] = 1 v[585,1] = 3 v[586,1] = 1 v[587,1] = 1 v[588,1] = 1 v[589,1] = 1 v[590,1] = 3 v[591,1] = 1 v[592,1] = 3 v[593,1] = 1 v[594,1] = 1 v[595,1] = 3 v[596,1] = 3 v[597,1] = 1 v[598,1] = 1 v[599,1] = 3 v[600,1] = 1 v[601,1] = 3 v[602,1] = 1 v[603,1] = 3 v[604,1] = 3 v[605,1] = 3 v[606,1] = 1 v[607,1] = 3 v[608,1] = 3 v[609,1] = 3 v[610,1] = 1 v[611,1] = 1 v[612,1] = 3 v[613,1] = 3 v[614,1] = 3 v[615,1] = 1 v[616,1] = 1 v[617,1] = 1 v[618,1] = 1 v[619,1] = 3 v[620,1] = 1 v[621,1] = 3 v[622,1] = 1 v[623,1] = 3 v[624,1] = 1 v[625,1] = 1 v[626,1] = 3 v[627,1] = 3 v[628,1] = 1 v[629,1] = 1 v[630,1] = 1 v[631,1] = 3 v[632,1] = 3 v[633,1] = 1 v[634,1] = 3 v[635,1] = 1 v[636,1] = 3 v[637,1] = 1 v[638,1] = 1 v[639,1] = 1 v[640,1] = 1 v[641,1] = 1 v[642,1] = 1 v[643,1] = 3 v[644,1] = 1 v[645,1] = 3 v[646,1] = 3 v[647,1] = 1 v[648,1] = 3 v[649,1] = 3 v[650,1] = 3 v[651,1] = 1 v[652,1] = 3 v[653,1] = 1 v[654,1] = 1 v[655,1] = 3 v[656,1] = 3 v[657,1] = 1 v[658,1] = 1 v[659,1] = 3 v[660,1] = 3 v[661,1] = 1 v[662,1] = 1 v[663,1] = 1 v[664,1] = 3 v[665,1] = 1 v[666,1] = 3 v[667,1] = 3 v[668,1] = 1 v[669,1] = 1 v[670,1] = 3 v[671,1] = 1 v[672,1] = 1 v[673,1] = 3 v[674,1] = 1 v[675,1] = 3 v[676,1] = 1 v[677,1] = 1 v[678,1] = 1 v[679,1] = 3 v[680,1] = 3 v[681,1] = 3 v[682,1] = 3 v[683,1] = 1 v[684,1] = 1 v[685,1] = 3 v[686,1] = 3 v[687,1] = 1 v[688,1] = 1 v[689,1] = 1 v[690,1] = 1 v[691,1] = 3 v[692,1] = 1 v[693,1] = 1 v[694,1] = 3 v[695,1] = 3 v[696,1] = 3 v[697,1] = 1 v[698,1] = 1 v[699,1] = 3 v[700,1] = 3 v[701,1] = 1 v[702,1] = 3 v[703,1] = 3 v[704,1] = 1 v[705,1] = 1 v[706,1] = 3 v[707,1] = 3 v[708,1] = 3 v[709,1] = 3 v[710,1] = 3 v[711,1] = 3 v[712,1] = 3 v[713,1] = 1 v[714,1] = 3 v[715,1] = 3 v[716,1] = 1 v[717,1] = 3 v[718,1] = 1 v[719,1] = 3 v[720,1] = 1 v[721,1] = 1 v[722,1] = 3 v[723,1] = 3 v[724,1] = 1 v[725,1] = 1 v[726,1] = 1 v[727,1] = 3 v[728,1] = 1 v[729,1] = 3 v[730,1] = 3 v[731,1] = 1 v[732,1] = 3 v[733,1] = 3 v[734,1] = 1 v[735,1] = 3 v[736,1] = 1 v[737,1] = 1 v[738,1] = 3 v[739,1] = 3 v[740,1] = 3 v[741,1] = 1 v[742,1] = 1 v[743,1] = 1 v[744,1] = 3 v[745,1] = 1 v[746,1] = 1 v[747,1] = 1 v[748,1] = 3 v[749,1] = 3 v[750,1] = 3 v[751,1] = 1 v[752,1] = 3 v[753,1] = 3 v[754,1] = 1 v[755,1] = 3 v[756,1] = 1 v[757,1] = 1 v[758,1] = 3 v[759,1] = 3 v[760,1] = 3 v[761,1] = 1 v[762,1] = 3 v[763,1] = 3 v[764,1] = 1 v[765,1] = 1 v[766,1] = 1 v[767,1] = 3 v[768,1] = 1 v[769,1] = 3 v[770,1] = 3 v[771,1] = 3 v[772,1] = 3 v[773,1] = 3 v[774,1] = 3 v[775,1] = 3 v[776,1] = 3 v[777,1] = 1 v[778,1] = 3 v[779,1] = 3 v[780,1] = 1 v[781,1] = 3 v[782,1] = 1 v[783,1] = 1 v[784,1] = 3 v[785,1] = 3 v[786,1] = 3 v[787,1] = 1 v[788,1] = 3 v[789,1] = 3 v[790,1] = 3 v[791,1] = 3 v[792,1] = 3 v[793,1] = 1 v[794,1] = 3 v[795,1] = 3 v[796,1] = 3 v[797,1] = 1 v[798,1] = 1 v[799,1] = 1 v[800,1] = 3 v[801,1] = 3 v[802,1] = 1 v[803,1] = 3 v[804,1] = 3 v[805,1] = 1 v[806,1] = 3 v[807,1] = 1 v[808,1] = 3 v[809,1] = 1 v[810,1] = 3 v[811,1] = 1 v[812,1] = 3 v[813,1] = 3 v[814,1] = 3 v[815,1] = 3 v[816,1] = 3 v[817,1] = 3 v[818,1] = 1 v[819,1] = 1 v[820,1] = 3 v[821,1] = 1 v[822,1] = 3 v[823,1] = 1 v[824,1] = 1 v[825,1] = 1 v[826,1] = 1 v[827,1] = 1 v[828,1] = 3 v[829,1] = 1 v[830,1] = 1 v[831,1] = 1 v[832,1] = 3 v[833,1] = 1 v[834,1] = 3 v[835,1] = 1 v[836,1] = 1 v[837,1] = 3 v[838,1] = 3 v[839,1] = 3 v[840,1] = 1 v[841,1] = 3 v[842,1] = 1 v[843,1] = 3 v[844,1] = 1 v[845,1] = 1 v[846,1] = 3 v[847,1] = 1 v[848,1] = 3 v[849,1] = 3 v[850,1] = 1 v[851,1] = 3 v[852,1] = 1 v[853,1] = 3 v[854,1] = 3 v[855,1] = 1 v[856,1] = 3 v[857,1] = 3 v[858,1] = 1 v[859,1] = 3 v[860,1] = 3 v[861,1] = 3 v[862,1] = 3 v[863,1] = 3 v[864,1] = 3 v[865,1] = 1 v[866,1] = 3 v[867,1] = 1 v[868,1] = 1 v[869,1] = 3 v[870,1] = 3 v[871,1] = 3 v[872,1] = 1 v[873,1] = 1 v[874,1] = 3 v[875,1] = 3 v[876,1] = 3 v[877,1] = 3 v[878,1] = 3 v[879,1] = 3 v[880,1] = 3 v[881,1] = 1 v[882,1] = 3 v[883,1] = 3 v[884,1] = 3 v[885,1] = 3 v[886,1] = 1 v[887,1] = 3 v[888,1] = 1 v[889,1] = 3 v[890,1] = 3 v[891,1] = 3 v[892,1] = 1 v[893,1] = 3 v[894,1] = 1 v[895,1] = 3 v[896,1] = 1 v[897,1] = 1 v[898,1] = 1 v[899,1] = 3 v[900,1] = 3 v[901,1] = 1 v[902,1] = 3 v[903,1] = 1 v[904,1] = 1 v[905,1] = 3 v[906,1] = 3 v[907,1] = 1 v[908,1] = 3 v[909,1] = 1 v[910,1] = 1 v[911,1] = 1 v[912,1] = 1 v[913,1] = 3 v[914,1] = 1 v[915,1] = 3 v[916,1] = 1 v[917,1] = 1 v[918,1] = 3 v[919,1] = 1 v[920,1] = 3 v[921,1] = 1 v[922,1] = 3 v[923,1] = 3 v[924,1] = 3 v[925,1] = 3 v[926,1] = 3 v[927,1] = 3 v[928,1] = 1 v[929,1] = 3 v[930,1] = 3 v[931,1] = 3 v[932,1] = 3 v[933,1] = 1 v[934,1] = 3 v[935,1] = 3 v[936,1] = 1 v[937,1] = 3 v[938,1] = 3 v[939,1] = 3 v[940,1] = 3 v[941,1] = 3 v[942,1] = 1 v[943,1] = 1 v[944,1] = 1 v[945,1] = 1 v[946,1] = 3 v[947,1] = 3 v[948,1] = 3 v[949,1] = 1 v[950,1] = 3 v[951,1] = 3 v[952,1] = 1 v[953,1] = 1 v[954,1] = 3 v[955,1] = 3 v[956,1] = 1 v[957,1] = 1 v[958,1] = 3 v[959,1] = 3 v[960,1] = 1 v[961,1] = 3 v[962,1] = 1 v[963,1] = 1 v[964,1] = 3 v[965,1] = 1 v[966,1] = 3 v[967,1] = 3 v[968,1] = 3 v[969,1] = 3 v[970,1] = 3 v[971,1] = 1 v[972,1] = 3 v[973,1] = 1 v[974,1] = 1 v[975,1] = 3 v[976,1] = 3 v[977,1] = 3 v[978,1] = 3 v[979,1] = 1 v[980,1] = 3 v[981,1] = 1 v[982,1] = 1 v[983,1] = 3 v[984,1] = 3 v[985,1] = 3 v[986,1] = 3 v[987,1] = 3 v[988,1] = 3 v[989,1] = 1 v[990,1] = 1 v[991,1] = 3 v[992,1] = 1 v[993,1] = 3 v[994,1] = 1 v[995,1] = 1 v[996,1] = 3 v[997,1] = 1 v[998,1] = 1 v[999,1] = 1 v[1000,1] = 1 v[1001,1] = 3 v[1002,1] = 3 v[1003,1] = 1 v[1004,1] = 1 v[1005,1] = 3 v[1006,1] = 1 v[1007,1] = 1 v[1008,1] = 1 v[1009,1] = 3 v[1010,1] = 1 v[1011,1] = 3 v[1012,1] = 1 v[1013,1] = 1 v[1014,1] = 3 v[1015,1] = 3 v[1016,1] = 1 v[1017,1] = 3 v[1018,1] = 1 v[1019,1] = 1 v[1020,1] = 3 v[1021,1] = 3 v[1022,1] = 3 v[1023,1] = 3 v[1024,1] = 3 v[1025,1] = 1 v[1026,1] = 3 v[1027,1] = 1 v[1028,1] = 1 v[1029,1] = 1 v[1030,1] = 3 v[1031,1] = 1 v[1032,1] = 1 v[1033,1] = 1 v[1034,1] = 3 v[1035,1] = 1 v[1036,1] = 1 v[1037,1] = 3 v[1038,1] = 1 v[1039,1] = 3 v[1040,1] = 3 v[1041,1] = 3 v[1042,1] = 3 v[1043,1] = 3 v[1044,1] = 1 v[1045,1] = 1 v[1046,1] = 1 v[1047,1] = 3 v[1048,1] = 3 v[1049,1] = 3 v[1050,1] = 3 v[1051,1] = 1 v[1052,1] = 3 v[1053,1] = 3 v[1054,1] = 3 v[1055,1] = 3 v[1056,1] = 1 v[1057,1] = 1 v[1058,1] = 3 v[1059,1] = 3 v[1060,1] = 3 v[1061,1] = 1 v[1062,1] = 3 v[1063,1] = 1 v[1064,1] = 1 v[1065,1] = 3 v[1066,1] = 3 v[1067,1] = 1 v[1068,1] = 3 v[1069,1] = 3 v[1070,1] = 1 v[1071,1] = 1 v[1072,1] = 1 v[1073,1] = 1 v[1074,1] = 1 v[1075,1] = 3 v[1076,1] = 1 v[1077,1] = 1 v[1078,1] = 3 v[1079,1] = 3 v[1080,1] = 1 v[1081,1] = 1 v[1082,1] = 1 v[1083,1] = 3 v[1084,1] = 1 v[1085,1] = 1 v[1086,1] = 3 v[1087,1] = 3 v[1088,1] = 1 v[1089,1] = 3 v[1090,1] = 3 v[1091,1] = 3 v[1092,1] = 3 v[1093,1] = 3 v[1094,1] = 3 v[1095,1] = 3 v[1096,1] = 3 v[1097,1] = 1 v[1098,1] = 1 v[1099,1] = 3 v[1100,1] = 3 v[1101,1] = 1 v[1102,1] = 1 v[1103,1] = 3 v[1104,1] = 1 v[1105,1] = 3 v[1106,1] = 3 v[1107,1] = 3 v[1108,1] = 3 v[1109,1] = 3 v[1110,1] = 1 v[3,2] = 7 v[4,2] = 5 v[5,2] = 1 v[6,2] = 3 v[7,2] = 3 v[8,2] = 7 v[9,2] = 5 v[10,2] = 5 v[11,2] = 7 v[12,2] = 7 v[13,2] = 1 v[14,2] = 3 v[15,2] = 3 v[16,2] = 7 v[17,2] = 5 v[18,2] = 1 v[19,2] = 1 v[20,2] = 5 v[21,2] = 3 v[22,2] = 7 v[23,2] = 1 v[24,2] = 7 v[25,2] = 5 v[26,2] = 1 v[27,2] = 3 v[28,2] = 7 v[29,2] = 7 v[30,2] = 1 v[31,2] = 1 v[32,2] = 1 v[33,2] = 5 v[34,2] = 7 v[35,2] = 7 v[36,2] = 5 v[37,2] = 1 v[38,2] = 3 v[39,2] = 3 v[40,2] = 7 v[41,2] = 5 v[42,2] = 5 v[43,2] = 5 v[44,2] = 3 v[45,2] = 3 v[46,2] = 3 v[47,2] = 1 v[48,2] = 1 v[49,2] = 5 v[50,2] = 1 v[51,2] = 1 v[52,2] = 5 v[53,2] = 3 v[54,2] = 3 v[55,2] = 3 v[56,2] = 3 v[57,2] = 1 v[58,2] = 3 v[59,2] = 7 v[60,2] = 5 v[61,2] = 7 v[62,2] = 3 v[63,2] = 7 v[64,2] = 1 v[65,2] = 3 v[66,2] = 3 v[67,2] = 5 v[68,2] = 1 v[69,2] = 3 v[70,2] = 5 v[71,2] = 5 v[72,2] = 7 v[73,2] = 7 v[74,2] = 7 v[75,2] = 1 v[76,2] = 1 v[77,2] = 3 v[78,2] = 3 v[79,2] = 1 v[80,2] = 1 v[81,2] = 5 v[82,2] = 1 v[83,2] = 5 v[84,2] = 7 v[85,2] = 5 v[86,2] = 1 v[87,2] = 7 v[88,2] = 5 v[89,2] = 3 v[90,2] = 3 v[91,2] = 1 v[92,2] = 5 v[93,2] = 7 v[94,2] = 1 v[95,2] = 7 v[96,2] = 5 v[97,2] = 1 v[98,2] = 7 v[99,2] = 3 v[100,2] = 1 v[101,2] = 7 v[102,2] = 1 v[103,2] = 7 v[104,2] = 3 v[105,2] = 3 v[106,2] = 5 v[107,2] = 7 v[108,2] = 3 v[109,2] = 3 v[110,2] = 5 v[111,2] = 1 v[112,2] = 3 v[113,2] = 3 v[114,2] = 1 v[115,2] = 3 v[116,2] = 5 v[117,2] = 1 v[118,2] = 3 v[119,2] = 3 v[120,2] = 3 v[121,2] = 7 v[122,2] = 1 v[123,2] = 1 v[124,2] = 7 v[125,2] = 3 v[126,2] = 1 v[127,2] = 3 v[128,2] = 7 v[129,2] = 5 v[130,2] = 5 v[131,2] = 7 v[132,2] = 5 v[133,2] = 5 v[134,2] = 3 v[135,2] = 1 v[136,2] = 3 v[137,2] = 3 v[138,2] = 3 v[139,2] = 1 v[140,2] = 3 v[141,2] = 3 v[142,2] = 7 v[143,2] = 3 v[144,2] = 3 v[145,2] = 1 v[146,2] = 7 v[147,2] = 5 v[148,2] = 1 v[149,2] = 7 v[150,2] = 7 v[151,2] = 5 v[152,2] = 7 v[153,2] = 5 v[154,2] = 1 v[155,2] = 3 v[156,2] = 1 v[157,2] = 7 v[158,2] = 3 v[159,2] = 7 v[160,2] = 3 v[161,2] = 5 v[162,2] = 7 v[163,2] = 3 v[164,2] = 1 v[165,2] = 3 v[166,2] = 3 v[167,2] = 3 v[168,2] = 1 v[169,2] = 5 v[170,2] = 7 v[171,2] = 3 v[172,2] = 3 v[173,2] = 7 v[174,2] = 7 v[175,2] = 7 v[176,2] = 5 v[177,2] = 3 v[178,2] = 1 v[179,2] = 7 v[180,2] = 1 v[181,2] = 3 v[182,2] = 7 v[183,2] = 5 v[184,2] = 3 v[185,2] = 3 v[186,2] = 3 v[187,2] = 7 v[188,2] = 1 v[189,2] = 1 v[190,2] = 3 v[191,2] = 1 v[192,2] = 5 v[193,2] = 7 v[194,2] = 1 v[195,2] = 3 v[196,2] = 5 v[197,2] = 3 v[198,2] = 5 v[199,2] = 3 v[200,2] = 3 v[201,2] = 7 v[202,2] = 5 v[203,2] = 5 v[204,2] = 3 v[205,2] = 3 v[206,2] = 1 v[207,2] = 3 v[208,2] = 7 v[209,2] = 7 v[210,2] = 7 v[211,2] = 1 v[212,2] = 5 v[213,2] = 7 v[214,2] = 1 v[215,2] = 3 v[216,2] = 1 v[217,2] = 1 v[218,2] = 7 v[219,2] = 1 v[220,2] = 3 v[221,2] = 1 v[222,2] = 7 v[223,2] = 1 v[224,2] = 5 v[225,2] = 3 v[226,2] = 5 v[227,2] = 3 v[228,2] = 1 v[229,2] = 1 v[230,2] = 5 v[231,2] = 5 v[232,2] = 3 v[233,2] = 3 v[234,2] = 5 v[235,2] = 7 v[236,2] = 1 v[237,2] = 5 v[238,2] = 3 v[239,2] = 7 v[240,2] = 7 v[241,2] = 3 v[242,2] = 5 v[243,2] = 3 v[244,2] = 3 v[245,2] = 1 v[246,2] = 7 v[247,2] = 3 v[248,2] = 1 v[249,2] = 3 v[250,2] = 5 v[251,2] = 7 v[252,2] = 1 v[253,2] = 3 v[254,2] = 7 v[255,2] = 1 v[256,2] = 5 v[257,2] = 1 v[258,2] = 3 v[259,2] = 1 v[260,2] = 5 v[261,2] = 3 v[262,2] = 1 v[263,2] = 7 v[264,2] = 1 v[265,2] = 5 v[266,2] = 5 v[267,2] = 5 v[268,2] = 3 v[269,2] = 7 v[270,2] = 1 v[271,2] = 1 v[272,2] = 7 v[273,2] = 3 v[274,2] = 1 v[275,2] = 1 v[276,2] = 7 v[277,2] = 5 v[278,2] = 7 v[279,2] = 5 v[280,2] = 7 v[281,2] = 7 v[282,2] = 3 v[283,2] = 7 v[284,2] = 1 v[285,2] = 3 v[286,2] = 7 v[287,2] = 7 v[288,2] = 3 v[289,2] = 5 v[290,2] = 1 v[291,2] = 1 v[292,2] = 7 v[293,2] = 1 v[294,2] = 5 v[295,2] = 5 v[296,2] = 5 v[297,2] = 1 v[298,2] = 5 v[299,2] = 1 v[300,2] = 7 v[301,2] = 5 v[302,2] = 5 v[303,2] = 7 v[304,2] = 1 v[305,2] = 1 v[306,2] = 7 v[307,2] = 1 v[308,2] = 7 v[309,2] = 7 v[310,2] = 1 v[311,2] = 1 v[312,2] = 3 v[313,2] = 3 v[314,2] = 3 v[315,2] = 7 v[316,2] = 7 v[317,2] = 5 v[318,2] = 3 v[319,2] = 7 v[320,2] = 3 v[321,2] = 1 v[322,2] = 3 v[323,2] = 7 v[324,2] = 5 v[325,2] = 3 v[326,2] = 3 v[327,2] = 5 v[328,2] = 7 v[329,2] = 1 v[330,2] = 1 v[331,2] = 5 v[332,2] = 5 v[333,2] = 7 v[334,2] = 7 v[335,2] = 1 v[336,2] = 1 v[337,2] = 1 v[338,2] = 1 v[339,2] = 5 v[340,2] = 5 v[341,2] = 5 v[342,2] = 7 v[343,2] = 5 v[344,2] = 7 v[345,2] = 1 v[346,2] = 1 v[347,2] = 3 v[348,2] = 5 v[349,2] = 1 v[350,2] = 3 v[351,2] = 3 v[352,2] = 7 v[353,2] = 3 v[354,2] = 7 v[355,2] = 5 v[356,2] = 3 v[357,2] = 5 v[358,2] = 3 v[359,2] = 1 v[360,2] = 7 v[361,2] = 1 v[362,2] = 7 v[363,2] = 7 v[364,2] = 1 v[365,2] = 1 v[366,2] = 7 v[367,2] = 7 v[368,2] = 7 v[369,2] = 5 v[370,2] = 5 v[371,2] = 1 v[372,2] = 1 v[373,2] = 7 v[374,2] = 5 v[375,2] = 5 v[376,2] = 7 v[377,2] = 5 v[378,2] = 1 v[379,2] = 1 v[380,2] = 5 v[381,2] = 5 v[382,2] = 5 v[383,2] = 5 v[384,2] = 5 v[385,2] = 5 v[386,2] = 1 v[387,2] = 3 v[388,2] = 1 v[389,2] = 5 v[390,2] = 7 v[391,2] = 3 v[392,2] = 3 v[393,2] = 5 v[394,2] = 7 v[395,2] = 3 v[396,2] = 7 v[397,2] = 1 v[398,2] = 7 v[399,2] = 7 v[400,2] = 1 v[401,2] = 3 v[402,2] = 5 v[403,2] = 1 v[404,2] = 5 v[405,2] = 5 v[406,2] = 3 v[407,2] = 7 v[408,2] = 3 v[409,2] = 7 v[410,2] = 7 v[411,2] = 5 v[412,2] = 7 v[413,2] = 5 v[414,2] = 7 v[415,2] = 1 v[416,2] = 1 v[417,2] = 5 v[418,2] = 3 v[419,2] = 5 v[420,2] = 1 v[421,2] = 5 v[422,2] = 3 v[423,2] = 7 v[424,2] = 1 v[425,2] = 5 v[426,2] = 7 v[427,2] = 7 v[428,2] = 3 v[429,2] = 5 v[430,2] = 1 v[431,2] = 3 v[432,2] = 5 v[433,2] = 1 v[434,2] = 5 v[435,2] = 3 v[436,2] = 3 v[437,2] = 3 v[438,2] = 7 v[439,2] = 3 v[440,2] = 5 v[441,2] = 1 v[442,2] = 3 v[443,2] = 7 v[444,2] = 7 v[445,2] = 3 v[446,2] = 7 v[447,2] = 5 v[448,2] = 3 v[449,2] = 3 v[450,2] = 1 v[451,2] = 7 v[452,2] = 5 v[453,2] = 1 v[454,2] = 1 v[455,2] = 3 v[456,2] = 7 v[457,2] = 1 v[458,2] = 7 v[459,2] = 1 v[460,2] = 7 v[461,2] = 3 v[462,2] = 7 v[463,2] = 3 v[464,2] = 5 v[465,2] = 7 v[466,2] = 3 v[467,2] = 5 v[468,2] = 3 v[469,2] = 1 v[470,2] = 1 v[471,2] = 1 v[472,2] = 5 v[473,2] = 7 v[474,2] = 7 v[475,2] = 3 v[476,2] = 3 v[477,2] = 1 v[478,2] = 1 v[479,2] = 1 v[480,2] = 5 v[481,2] = 5 v[482,2] = 7 v[483,2] = 3 v[484,2] = 1 v[485,2] = 1 v[486,2] = 3 v[487,2] = 3 v[488,2] = 7 v[489,2] = 3 v[490,2] = 3 v[491,2] = 5 v[492,2] = 1 v[493,2] = 3 v[494,2] = 7 v[495,2] = 3 v[496,2] = 3 v[497,2] = 7 v[498,2] = 3 v[499,2] = 5 v[500,2] = 7 v[501,2] = 5 v[502,2] = 7 v[503,2] = 7 v[504,2] = 3 v[505,2] = 3 v[506,2] = 5 v[507,2] = 1 v[508,2] = 3 v[509,2] = 5 v[510,2] = 3 v[511,2] = 1 v[512,2] = 3 v[513,2] = 5 v[514,2] = 1 v[515,2] = 1 v[516,2] = 3 v[517,2] = 7 v[518,2] = 7 v[519,2] = 1 v[520,2] = 5 v[521,2] = 1 v[522,2] = 3 v[523,2] = 7 v[524,2] = 3 v[525,2] = 7 v[526,2] = 3 v[527,2] = 5 v[528,2] = 1 v[529,2] = 7 v[530,2] = 1 v[531,2] = 1 v[532,2] = 3 v[533,2] = 5 v[534,2] = 3 v[535,2] = 7 v[536,2] = 1 v[537,2] = 5 v[538,2] = 5 v[539,2] = 1 v[540,2] = 1 v[541,2] = 3 v[542,2] = 1 v[543,2] = 3 v[544,2] = 3 v[545,2] = 7 v[546,2] = 1 v[547,2] = 7 v[548,2] = 3 v[549,2] = 1 v[550,2] = 7 v[551,2] = 3 v[552,2] = 1 v[553,2] = 7 v[554,2] = 3 v[555,2] = 5 v[556,2] = 3 v[557,2] = 5 v[558,2] = 7 v[559,2] = 3 v[560,2] = 3 v[561,2] = 3 v[562,2] = 5 v[563,2] = 1 v[564,2] = 7 v[565,2] = 7 v[566,2] = 1 v[567,2] = 3 v[568,2] = 1 v[569,2] = 3 v[570,2] = 7 v[571,2] = 7 v[572,2] = 1 v[573,2] = 3 v[574,2] = 7 v[575,2] = 3 v[576,2] = 1 v[577,2] = 5 v[578,2] = 3 v[579,2] = 1 v[580,2] = 1 v[581,2] = 1 v[582,2] = 5 v[583,2] = 3 v[584,2] = 3 v[585,2] = 7 v[586,2] = 1 v[587,2] = 5 v[588,2] = 3 v[589,2] = 5 v[590,2] = 1 v[591,2] = 3 v[592,2] = 1 v[593,2] = 3 v[594,2] = 1 v[595,2] = 5 v[596,2] = 7 v[597,2] = 7 v[598,2] = 1 v[599,2] = 1 v[600,2] = 5 v[601,2] = 3 v[602,2] = 1 v[603,2] = 5 v[604,2] = 1 v[605,2] = 1 v[606,2] = 7 v[607,2] = 7 v[608,2] = 3 v[609,2] = 5 v[610,2] = 5 v[611,2] = 1 v[612,2] = 7 v[613,2] = 1 v[614,2] = 5 v[615,2] = 1 v[616,2] = 1 v[617,2] = 3 v[618,2] = 1 v[619,2] = 5 v[620,2] = 7 v[621,2] = 5 v[622,2] = 7 v[623,2] = 7 v[624,2] = 1 v[625,2] = 5 v[626,2] = 1 v[627,2] = 1 v[628,2] = 3 v[629,2] = 5 v[630,2] = 1 v[631,2] = 5 v[632,2] = 5 v[633,2] = 3 v[634,2] = 1 v[635,2] = 3 v[636,2] = 1 v[637,2] = 5 v[638,2] = 5 v[639,2] = 3 v[640,2] = 3 v[641,2] = 3 v[642,2] = 3 v[643,2] = 1 v[644,2] = 1 v[645,2] = 3 v[646,2] = 1 v[647,2] = 3 v[648,2] = 5 v[649,2] = 5 v[650,2] = 7 v[651,2] = 5 v[652,2] = 5 v[653,2] = 7 v[654,2] = 5 v[655,2] = 7 v[656,2] = 1 v[657,2] = 3 v[658,2] = 7 v[659,2] = 7 v[660,2] = 3 v[661,2] = 5 v[662,2] = 5 v[663,2] = 7 v[664,2] = 5 v[665,2] = 5 v[666,2] = 3 v[667,2] = 3 v[668,2] = 3 v[669,2] = 1 v[670,2] = 7 v[671,2] = 1 v[672,2] = 5 v[673,2] = 5 v[674,2] = 5 v[675,2] = 3 v[676,2] = 3 v[677,2] = 5 v[678,2] = 1 v[679,2] = 3 v[680,2] = 1 v[681,2] = 3 v[682,2] = 3 v[683,2] = 3 v[684,2] = 7 v[685,2] = 1 v[686,2] = 7 v[687,2] = 7 v[688,2] = 3 v[689,2] = 7 v[690,2] = 1 v[691,2] = 1 v[692,2] = 5 v[693,2] = 7 v[694,2] = 1 v[695,2] = 7 v[696,2] = 1 v[697,2] = 7 v[698,2] = 7 v[699,2] = 1 v[700,2] = 3 v[701,2] = 7 v[702,2] = 5 v[703,2] = 1 v[704,2] = 3 v[705,2] = 5 v[706,2] = 5 v[707,2] = 5 v[708,2] = 1 v[709,2] = 1 v[710,2] = 7 v[711,2] = 1 v[712,2] = 7 v[713,2] = 1 v[714,2] = 7 v[715,2] = 7 v[716,2] = 3 v[717,2] = 1 v[718,2] = 1 v[719,2] = 5 v[720,2] = 1 v[721,2] = 5 v[722,2] = 1 v[723,2] = 5 v[724,2] = 3 v[725,2] = 5 v[726,2] = 5 v[727,2] = 5 v[728,2] = 5 v[729,2] = 5 v[730,2] = 3 v[731,2] = 3 v[732,2] = 7 v[733,2] = 3 v[734,2] = 3 v[735,2] = 5 v[736,2] = 5 v[737,2] = 3 v[738,2] = 7 v[739,2] = 1 v[740,2] = 5 v[741,2] = 7 v[742,2] = 5 v[743,2] = 1 v[744,2] = 5 v[745,2] = 5 v[746,2] = 3 v[747,2] = 5 v[748,2] = 5 v[749,2] = 7 v[750,2] = 5 v[751,2] = 3 v[752,2] = 5 v[753,2] = 5 v[754,2] = 5 v[755,2] = 1 v[756,2] = 5 v[757,2] = 5 v[758,2] = 5 v[759,2] = 5 v[760,2] = 1 v[761,2] = 3 v[762,2] = 5 v[763,2] = 3 v[764,2] = 1 v[765,2] = 7 v[766,2] = 5 v[767,2] = 5 v[768,2] = 7 v[769,2] = 1 v[770,2] = 5 v[771,2] = 3 v[772,2] = 3 v[773,2] = 1 v[774,2] = 5 v[775,2] = 3 v[776,2] = 7 v[777,2] = 1 v[778,2] = 7 v[779,2] = 5 v[780,2] = 1 v[781,2] = 1 v[782,2] = 3 v[783,2] = 1 v[784,2] = 1 v[785,2] = 7 v[786,2] = 1 v[787,2] = 5 v[788,2] = 5 v[789,2] = 3 v[790,2] = 7 v[791,2] = 3 v[792,2] = 7 v[793,2] = 5 v[794,2] = 3 v[795,2] = 1 v[796,2] = 1 v[797,2] = 3 v[798,2] = 1 v[799,2] = 3 v[800,2] = 5 v[801,2] = 5 v[802,2] = 7 v[803,2] = 5 v[804,2] = 3 v[805,2] = 7 v[806,2] = 7 v[807,2] = 7 v[808,2] = 3 v[809,2] = 7 v[810,2] = 3 v[811,2] = 7 v[812,2] = 1 v[813,2] = 3 v[814,2] = 1 v[815,2] = 7 v[816,2] = 7 v[817,2] = 1 v[818,2] = 7 v[819,2] = 3 v[820,2] = 7 v[821,2] = 3 v[822,2] = 7 v[823,2] = 3 v[824,2] = 7 v[825,2] = 3 v[826,2] = 5 v[827,2] = 1 v[828,2] = 1 v[829,2] = 7 v[830,2] = 3 v[831,2] = 1 v[832,2] = 5 v[833,2] = 5 v[834,2] = 7 v[835,2] = 1 v[836,2] = 5 v[837,2] = 5 v[838,2] = 5 v[839,2] = 7 v[840,2] = 1 v[841,2] = 5 v[842,2] = 5 v[843,2] = 1 v[844,2] = 5 v[845,2] = 5 v[846,2] = 3 v[847,2] = 1 v[848,2] = 3 v[849,2] = 1 v[850,2] = 7 v[851,2] = 3 v[852,2] = 1 v[853,2] = 3 v[854,2] = 5 v[855,2] = 7 v[856,2] = 7 v[857,2] = 7 v[858,2] = 1 v[859,2] = 1 v[860,2] = 7 v[861,2] = 3 v[862,2] = 1 v[863,2] = 5 v[864,2] = 5 v[865,2] = 5 v[866,2] = 1 v[867,2] = 1 v[868,2] = 1 v[869,2] = 1 v[870,2] = 1 v[871,2] = 5 v[872,2] = 3 v[873,2] = 5 v[874,2] = 1 v[875,2] = 3 v[876,2] = 5 v[877,2] = 3 v[878,2] = 1 v[879,2] = 1 v[880,2] = 1 v[881,2] = 1 v[882,2] = 3 v[883,2] = 7 v[884,2] = 3 v[885,2] = 7 v[886,2] = 5 v[887,2] = 7 v[888,2] = 1 v[889,2] = 5 v[890,2] = 5 v[891,2] = 7 v[892,2] = 5 v[893,2] = 3 v[894,2] = 3 v[895,2] = 7 v[896,2] = 5 v[897,2] = 3 v[898,2] = 1 v[899,2] = 1 v[900,2] = 3 v[901,2] = 1 v[902,2] = 3 v[903,2] = 1 v[904,2] = 1 v[905,2] = 3 v[906,2] = 7 v[907,2] = 1 v[908,2] = 7 v[909,2] = 1 v[910,2] = 1 v[911,2] = 5 v[912,2] = 1 v[913,2] = 7 v[914,2] = 5 v[915,2] = 3 v[916,2] = 7 v[917,2] = 3 v[918,2] = 5 v[919,2] = 3 v[920,2] = 1 v[921,2] = 1 v[922,2] = 5 v[923,2] = 5 v[924,2] = 1 v[925,2] = 7 v[926,2] = 7 v[927,2] = 3 v[928,2] = 7 v[929,2] = 3 v[930,2] = 7 v[931,2] = 1 v[932,2] = 5 v[933,2] = 1 v[934,2] = 5 v[935,2] = 3 v[936,2] = 7 v[937,2] = 3 v[938,2] = 5 v[939,2] = 7 v[940,2] = 7 v[941,2] = 7 v[942,2] = 3 v[943,2] = 3 v[944,2] = 1 v[945,2] = 1 v[946,2] = 5 v[947,2] = 5 v[948,2] = 3 v[949,2] = 7 v[950,2] = 1 v[951,2] = 1 v[952,2] = 1 v[953,2] = 3 v[954,2] = 5 v[955,2] = 3 v[956,2] = 1 v[957,2] = 1 v[958,2] = 3 v[959,2] = 3 v[960,2] = 7 v[961,2] = 5 v[962,2] = 1 v[963,2] = 1 v[964,2] = 3 v[965,2] = 7 v[966,2] = 1 v[967,2] = 5 v[968,2] = 7 v[969,2] = 3 v[970,2] = 7 v[971,2] = 5 v[972,2] = 5 v[973,2] = 7 v[974,2] = 3 v[975,2] = 5 v[976,2] = 3 v[977,2] = 1 v[978,2] = 5 v[979,2] = 3 v[980,2] = 1 v[981,2] = 1 v[982,2] = 7 v[983,2] = 5 v[984,2] = 1 v[985,2] = 7 v[986,2] = 3 v[987,2] = 7 v[988,2] = 5 v[989,2] = 1 v[990,2] = 7 v[991,2] = 1 v[992,2] = 7 v[993,2] = 7 v[994,2] = 1 v[995,2] = 1 v[996,2] = 7 v[997,2] = 1 v[998,2] = 5 v[999,2] = 5 v[1000,2] = 1 v[1001,2] = 1 v[1002,2] = 7 v[1003,2] = 5 v[1004,2] = 7 v[1005,2] = 1 v[1006,2] = 5 v[1007,2] = 3 v[1008,2] = 5 v[1009,2] = 3 v[1010,2] = 3 v[1011,2] = 7 v[1012,2] = 1 v[1013,2] = 5 v[1014,2] = 1 v[1015,2] = 1 v[1016,2] = 5 v[1017,2] = 5 v[1018,2] = 3 v[1019,2] = 3 v[1020,2] = 7 v[1021,2] = 5 v[1022,2] = 5 v[1023,2] = 1 v[1024,2] = 1 v[1025,2] = 1 v[1026,2] = 3 v[1027,2] = 1 v[1028,2] = 5 v[1029,2] = 7 v[1030,2] = 7 v[1031,2] = 1 v[1032,2] = 7 v[1033,2] = 5 v[1034,2] = 7 v[1035,2] = 3 v[1036,2] = 7 v[1037,2] = 3 v[1038,2] = 1 v[1039,2] = 3 v[1040,2] = 7 v[1041,2] = 3 v[1042,2] = 1 v[1043,2] = 5 v[1044,2] = 5 v[1045,2] = 3 v[1046,2] = 5 v[1047,2] = 1 v[1048,2] = 3 v[1049,2] = 5 v[1050,2] = 5 v[1051,2] = 5 v[1052,2] = 1 v[1053,2] = 1 v[1054,2] = 7 v[1055,2] = 7 v[1056,2] = 1 v[1057,2] = 5 v[1058,2] = 5 v[1059,2] = 1 v[1060,2] = 3 v[1061,2] = 5 v[1062,2] = 1 v[1063,2] = 5 v[1064,2] = 3 v[1065,2] = 5 v[1066,2] = 3 v[1067,2] = 3 v[1068,2] = 7 v[1069,2] = 5 v[1070,2] = 7 v[1071,2] = 3 v[1072,2] = 7 v[1073,2] = 3 v[1074,2] = 1 v[1075,2] = 3 v[1076,2] = 7 v[1077,2] = 7 v[1078,2] = 3 v[1079,2] = 3 v[1080,2] = 1 v[1081,2] = 1 v[1082,2] = 3 v[1083,2] = 3 v[1084,2] = 3 v[1085,2] = 3 v[1086,2] = 3 v[1087,2] = 5 v[1088,2] = 5 v[1089,2] = 3 v[1090,2] = 3 v[1091,2] = 3 v[1092,2] = 1 v[1093,2] = 3 v[1094,2] = 5 v[1095,2] = 7 v[1096,2] = 7 v[1097,2] = 1 v[1098,2] = 5 v[1099,2] = 7 v[1100,2] = 3 v[1101,2] = 7 v[1102,2] = 1 v[1103,2] = 1 v[1104,2] = 3 v[1105,2] = 5 v[1106,2] = 7 v[1107,2] = 5 v[1108,2] = 3 v[1109,2] = 3 v[1110,2] = 3 v[5,3] = 1 v[6,3] = 7 v[7,3] = 9 v[8,3] = 13 v[9,3] = 11 v[10,3] = 1 v[11,3] = 3 v[12,3] = 7 v[13,3] = 9 v[14,3] = 5 v[15,3] = 13 v[16,3] = 13 v[17,3] = 11 v[18,3] = 3 v[19,3] = 15 v[20,3] = 5 v[21,3] = 3 v[22,3] = 15 v[23,3] = 7 v[24,3] = 9 v[25,3] = 13 v[26,3] = 9 v[27,3] = 1 v[28,3] = 11 v[29,3] = 7 v[30,3] = 5 v[31,3] = 15 v[32,3] = 1 v[33,3] = 15 v[34,3] = 11 v[35,3] = 5 v[36,3] = 11 v[37,3] = 1 v[38,3] = 7 v[39,3] = 9 v[40,3] = 7 v[41,3] = 7 v[42,3] = 1 v[43,3] = 15 v[44,3] = 15 v[45,3] = 15 v[46,3] = 13 v[47,3] = 3 v[48,3] = 3 v[49,3] = 15 v[50,3] = 5 v[51,3] = 9 v[52,3] = 7 v[53,3] = 13 v[54,3] = 3 v[55,3] = 7 v[56,3] = 5 v[57,3] = 11 v[58,3] = 9 v[59,3] = 1 v[60,3] = 9 v[61,3] = 1 v[62,3] = 5 v[63,3] = 7 v[64,3] = 13 v[65,3] = 9 v[66,3] = 9 v[67,3] = 1 v[68,3] = 7 v[69,3] = 3 v[70,3] = 5 v[71,3] = 1 v[72,3] = 11 v[73,3] = 11 v[74,3] = 13 v[75,3] = 7 v[76,3] = 7 v[77,3] = 9 v[78,3] = 9 v[79,3] = 1 v[80,3] = 1 v[81,3] = 3 v[82,3] = 9 v[83,3] = 15 v[84,3] = 1 v[85,3] = 5 v[86,3] = 13 v[87,3] = 1 v[88,3] = 9 v[89,3] = 9 v[90,3] = 9 v[91,3] = 9 v[92,3] = 9 v[93,3] = 13 v[94,3] = 11 v[95,3] = 3 v[96,3] = 5 v[97,3] = 11 v[98,3] = 11 v[99,3] = 13 v[100,3] = 5 v[101,3] = 3 v[102,3] = 15 v[103,3] = 1 v[104,3] = 11 v[105,3] = 11 v[106,3] = 7 v[107,3] = 13 v[108,3] = 15 v[109,3] = 11 v[110,3] = 13 v[111,3] = 9 v[112,3] = 11 v[113,3] = 15 v[114,3] = 15 v[115,3] = 13 v[116,3] = 3 v[117,3] = 15 v[118,3] = 7 v[119,3] = 9 v[120,3] = 11 v[121,3] = 13 v[122,3] = 11 v[123,3] = 9 v[124,3] = 9 v[125,3] = 5 v[126,3] = 13 v[127,3] = 9 v[128,3] = 1 v[129,3] = 13 v[130,3] = 7 v[131,3] = 7 v[132,3] = 7 v[133,3] = 7 v[134,3] = 7 v[135,3] = 5 v[136,3] = 9 v[137,3] = 7 v[138,3] = 13 v[139,3] = 11 v[140,3] = 9 v[141,3] = 11 v[142,3] = 15 v[143,3] = 3 v[144,3] = 13 v[145,3] = 11 v[146,3] = 1 v[147,3] = 11 v[148,3] = 3 v[149,3] = 3 v[150,3] = 9 v[151,3] = 11 v[152,3] = 1 v[153,3] = 7 v[154,3] = 1 v[155,3] = 15 v[156,3] = 15 v[157,3] = 3 v[158,3] = 1 v[159,3] = 9 v[160,3] = 1 v[161,3] = 7 v[162,3] = 13 v[163,3] = 11 v[164,3] = 3 v[165,3] = 13 v[166,3] = 11 v[167,3] = 7 v[168,3] = 3 v[169,3] = 3 v[170,3] = 5 v[171,3] = 13 v[172,3] = 11 v[173,3] = 5 v[174,3] = 11 v[175,3] = 1 v[176,3] = 3 v[177,3] = 9 v[178,3] = 7 v[179,3] = 15 v[180,3] = 7 v[181,3] = 5 v[182,3] = 13 v[183,3] = 7 v[184,3] = 9 v[185,3] = 13 v[186,3] = 15 v[187,3] = 13 v[188,3] = 9 v[189,3] = 7 v[190,3] = 15 v[191,3] = 7 v[192,3] = 9 v[193,3] = 5 v[194,3] = 11 v[195,3] = 11 v[196,3] = 13 v[197,3] = 13 v[198,3] = 9 v[199,3] = 3 v[200,3] = 5 v[201,3] = 13 v[202,3] = 9 v[203,3] = 11 v[204,3] = 15 v[205,3] = 11 v[206,3] = 7 v[207,3] = 1 v[208,3] = 7 v[209,3] = 13 v[210,3] = 3 v[211,3] = 13 v[212,3] = 3 v[213,3] = 13 v[214,3] = 9 v[215,3] = 15 v[216,3] = 7 v[217,3] = 13 v[218,3] = 13 v[219,3] = 3 v[220,3] = 13 v[221,3] = 15 v[222,3] = 15 v[223,3] = 11 v[224,3] = 9 v[225,3] = 13 v[226,3] = 9 v[227,3] = 15 v[228,3] = 1 v[229,3] = 1 v[230,3] = 15 v[231,3] = 11 v[232,3] = 11 v[233,3] = 7 v[234,3] = 1 v[235,3] = 11 v[236,3] = 13 v[237,3] = 9 v[238,3] = 13 v[239,3] = 3 v[240,3] = 5 v[241,3] = 11 v[242,3] = 13 v[243,3] = 9 v[244,3] = 9 v[245,3] = 13 v[246,3] = 1 v[247,3] = 11 v[248,3] = 15 v[249,3] = 13 v[250,3] = 3 v[251,3] = 13 v[252,3] = 7 v[253,3] = 15 v[254,3] = 1 v[255,3] = 15 v[256,3] = 3 v[257,3] = 3 v[258,3] = 11 v[259,3] = 7 v[260,3] = 13 v[261,3] = 7 v[262,3] = 7 v[263,3] = 9 v[264,3] = 7 v[265,3] = 5 v[266,3] = 15 v[267,3] = 9 v[268,3] = 5 v[269,3] = 5 v[270,3] = 7 v[271,3] = 15 v[272,3] = 13 v[273,3] = 15 v[274,3] = 5 v[275,3] = 15 v[276,3] = 5 v[277,3] = 3 v[278,3] = 1 v[279,3] = 11 v[280,3] = 7 v[281,3] = 1 v[282,3] = 5 v[283,3] = 7 v[284,3] = 9 v[285,3] = 3 v[286,3] = 11 v[287,3] = 1 v[288,3] = 15 v[289,3] = 1 v[290,3] = 3 v[291,3] = 15 v[292,3] = 11 v[293,3] = 13 v[294,3] = 5 v[295,3] = 13 v[296,3] = 1 v[297,3] = 7 v[298,3] = 1 v[299,3] = 15 v[300,3] = 7 v[301,3] = 5 v[302,3] = 1 v[303,3] = 1 v[304,3] = 15 v[305,3] = 13 v[306,3] = 11 v[307,3] = 11 v[308,3] = 13 v[309,3] = 5 v[310,3] = 11 v[311,3] = 7 v[312,3] = 9 v[313,3] = 7 v[314,3] = 1 v[315,3] = 5 v[316,3] = 3 v[317,3] = 9 v[318,3] = 5 v[319,3] = 5 v[320,3] = 11 v[321,3] = 5 v[322,3] = 1 v[323,3] = 7 v[324,3] = 1 v[325,3] = 11 v[326,3] = 7 v[327,3] = 9 v[328,3] = 13 v[329,3] = 15 v[330,3] = 13 v[331,3] = 3 v[332,3] = 1 v[333,3] = 11 v[334,3] = 13 v[335,3] = 15 v[336,3] = 1 v[337,3] = 1 v[338,3] = 11 v[339,3] = 9 v[340,3] = 13 v[341,3] = 3 v[342,3] = 13 v[343,3] = 11 v[344,3] = 15 v[345,3] = 13 v[346,3] = 9 v[347,3] = 9 v[348,3] = 9 v[349,3] = 5 v[350,3] = 5 v[351,3] = 5 v[352,3] = 5 v[353,3] = 1 v[354,3] = 15 v[355,3] = 5 v[356,3] = 9 v[357,3] = 11 v[358,3] = 7 v[359,3] = 15 v[360,3] = 5 v[361,3] = 3 v[362,3] = 13 v[363,3] = 5 v[364,3] = 3 v[365,3] = 11 v[366,3] = 5 v[367,3] = 1 v[368,3] = 11 v[369,3] = 13 v[370,3] = 9 v[371,3] = 11 v[372,3] = 3 v[373,3] = 7 v[374,3] = 13 v[375,3] = 15 v[376,3] = 1 v[377,3] = 7 v[378,3] = 11 v[379,3] = 1 v[380,3] = 13 v[381,3] = 1 v[382,3] = 15 v[383,3] = 1 v[384,3] = 9 v[385,3] = 7 v[386,3] = 3 v[387,3] = 9 v[388,3] = 11 v[389,3] = 1 v[390,3] = 9 v[391,3] = 13 v[392,3] = 13 v[393,3] = 3 v[394,3] = 11 v[395,3] = 7 v[396,3] = 9 v[397,3] = 1 v[398,3] = 7 v[399,3] = 15 v[400,3] = 9 v[401,3] = 1 v[402,3] = 5 v[403,3] = 13 v[404,3] = 5 v[405,3] = 11 v[406,3] = 3 v[407,3] = 9 v[408,3] = 15 v[409,3] = 11 v[410,3] = 13 v[411,3] = 5 v[412,3] = 1 v[413,3] = 7 v[414,3] = 7 v[415,3] = 5 v[416,3] = 13 v[417,3] = 7 v[418,3] = 7 v[419,3] = 9 v[420,3] = 5 v[421,3] = 11 v[422,3] = 11 v[423,3] = 1 v[424,3] = 1 v[425,3] = 15 v[426,3] = 3 v[427,3] = 13 v[428,3] = 9 v[429,3] = 13 v[430,3] = 9 v[431,3] = 9 v[432,3] = 11 v[433,3] = 5 v[434,3] = 5 v[435,3] = 13 v[436,3] = 15 v[437,3] = 3 v[438,3] = 9 v[439,3] = 15 v[440,3] = 3 v[441,3] = 11 v[442,3] = 11 v[443,3] = 15 v[444,3] = 15 v[445,3] = 3 v[446,3] = 11 v[447,3] = 15 v[448,3] = 15 v[449,3] = 3 v[450,3] = 1 v[451,3] = 3 v[452,3] = 1 v[453,3] = 3 v[454,3] = 3 v[455,3] = 1 v[456,3] = 3 v[457,3] = 13 v[458,3] = 1 v[459,3] = 11 v[460,3] = 5 v[461,3] = 15 v[462,3] = 7 v[463,3] = 15 v[464,3] = 9 v[465,3] = 1 v[466,3] = 7 v[467,3] = 1 v[468,3] = 9 v[469,3] = 11 v[470,3] = 15 v[471,3] = 1 v[472,3] = 13 v[473,3] = 9 v[474,3] = 13 v[475,3] = 11 v[476,3] = 7 v[477,3] = 3 v[478,3] = 7 v[479,3] = 3 v[480,3] = 13 v[481,3] = 7 v[482,3] = 9 v[483,3] = 7 v[484,3] = 7 v[485,3] = 3 v[486,3] = 3 v[487,3] = 9 v[488,3] = 9 v[489,3] = 7 v[490,3] = 5 v[491,3] = 11 v[492,3] = 13 v[493,3] = 13 v[494,3] = 7 v[495,3] = 7 v[496,3] = 15 v[497,3] = 9 v[498,3] = 5 v[499,3] = 5 v[500,3] = 3 v[501,3] = 3 v[502,3] = 13 v[503,3] = 3 v[504,3] = 9 v[505,3] = 3 v[506,3] = 1 v[507,3] = 11 v[508,3] = 1 v[509,3] = 3 v[510,3] = 11 v[511,3] = 15 v[512,3] = 11 v[513,3] = 11 v[514,3] = 11 v[515,3] = 9 v[516,3] = 13 v[517,3] = 7 v[518,3] = 9 v[519,3] = 15 v[520,3] = 9 v[521,3] = 11 v[522,3] = 1 v[523,3] = 3 v[524,3] = 3 v[525,3] = 9 v[526,3] = 7 v[527,3] = 15 v[528,3] = 13 v[529,3] = 13 v[530,3] = 7 v[531,3] = 15 v[532,3] = 9 v[533,3] = 13 v[534,3] = 9 v[535,3] = 15 v[536,3] = 13 v[537,3] = 15 v[538,3] = 9 v[539,3] = 13 v[540,3] = 1 v[541,3] = 11 v[542,3] = 7 v[543,3] = 11 v[544,3] = 3 v[545,3] = 13 v[546,3] = 5 v[547,3] = 1 v[548,3] = 7 v[549,3] = 15 v[550,3] = 3 v[551,3] = 13 v[552,3] = 7 v[553,3] = 13 v[554,3] = 13 v[555,3] = 11 v[556,3] = 3 v[557,3] = 5 v[558,3] = 3 v[559,3] = 13 v[560,3] = 11 v[561,3] = 9 v[562,3] = 9 v[563,3] = 3 v[564,3] = 11 v[565,3] = 11 v[566,3] = 7 v[567,3] = 9 v[568,3] = 13 v[569,3] = 11 v[570,3] = 7 v[571,3] = 15 v[572,3] = 13 v[573,3] = 7 v[574,3] = 5 v[575,3] = 3 v[576,3] = 1 v[577,3] = 5 v[578,3] = 15 v[579,3] = 15 v[580,3] = 3 v[581,3] = 11 v[582,3] = 1 v[583,3] = 7 v[584,3] = 3 v[585,3] = 15 v[586,3] = 11 v[587,3] = 5 v[588,3] = 5 v[589,3] = 3 v[590,3] = 5 v[591,3] = 5 v[592,3] = 1 v[593,3] = 15 v[594,3] = 5 v[595,3] = 1 v[596,3] = 5 v[597,3] = 3 v[598,3] = 7 v[599,3] = 5 v[600,3] = 11 v[601,3] = 3 v[602,3] = 13 v[603,3] = 9 v[604,3] = 13 v[605,3] = 15 v[606,3] = 5 v[607,3] = 3 v[608,3] = 5 v[609,3] = 9 v[610,3] = 5 v[611,3] = 3 v[612,3] = 11 v[613,3] = 1 v[614,3] = 13 v[615,3] = 9 v[616,3] = 15 v[617,3] = 3 v[618,3] = 5 v[619,3] = 11 v[620,3] = 9 v[621,3] = 1 v[622,3] = 3 v[623,3] = 15 v[624,3] = 9 v[625,3] = 9 v[626,3] = 9 v[627,3] = 11 v[628,3] = 7 v[629,3] = 5 v[630,3] = 13 v[631,3] = 1 v[632,3] = 15 v[633,3] = 3 v[634,3] = 13 v[635,3] = 9 v[636,3] = 13 v[637,3] = 5 v[638,3] = 1 v[639,3] = 5 v[640,3] = 1 v[641,3] = 13 v[642,3] = 13 v[643,3] = 7 v[644,3] = 7 v[645,3] = 1 v[646,3] = 9 v[647,3] = 5 v[648,3] = 11 v[649,3] = 9 v[650,3] = 11 v[651,3] = 13 v[652,3] = 3 v[653,3] = 15 v[654,3] = 15 v[655,3] = 13 v[656,3] = 15 v[657,3] = 7 v[658,3] = 5 v[659,3] = 7 v[660,3] = 9 v[661,3] = 7 v[662,3] = 9 v[663,3] = 9 v[664,3] = 9 v[665,3] = 11 v[666,3] = 9 v[667,3] = 3 v[668,3] = 11 v[669,3] = 15 v[670,3] = 13 v[671,3] = 13 v[672,3] = 5 v[673,3] = 9 v[674,3] = 15 v[675,3] = 1 v[676,3] = 1 v[677,3] = 9 v[678,3] = 5 v[679,3] = 13 v[680,3] = 3 v[681,3] = 13 v[682,3] = 15 v[683,3] = 3 v[684,3] = 1 v[685,3] = 3 v[686,3] = 11 v[687,3] = 13 v[688,3] = 1 v[689,3] = 15 v[690,3] = 9 v[691,3] = 9 v[692,3] = 3 v[693,3] = 1 v[694,3] = 9 v[695,3] = 1 v[696,3] = 9 v[697,3] = 1 v[698,3] = 13 v[699,3] = 11 v[700,3] = 15 v[701,3] = 7 v[702,3] = 11 v[703,3] = 15 v[704,3] = 13 v[705,3] = 15 v[706,3] = 1 v[707,3] = 9 v[708,3] = 9 v[709,3] = 7 v[710,3] = 3 v[711,3] = 5 v[712,3] = 11 v[713,3] = 7 v[714,3] = 3 v[715,3] = 9 v[716,3] = 5 v[717,3] = 15 v[718,3] = 7 v[719,3] = 5 v[720,3] = 3 v[721,3] = 13 v[722,3] = 7 v[723,3] = 1 v[724,3] = 1 v[725,3] = 9 v[726,3] = 15 v[727,3] = 15 v[728,3] = 15 v[729,3] = 11 v[730,3] = 3 v[731,3] = 5 v[732,3] = 15 v[733,3] = 13 v[734,3] = 7 v[735,3] = 15 v[736,3] = 15 v[737,3] = 11 v[738,3] = 11 v[739,3] = 9 v[740,3] = 5 v[741,3] = 15 v[742,3] = 9 v[743,3] = 7 v[744,3] = 3 v[745,3] = 13 v[746,3] = 1 v[747,3] = 1 v[748,3] = 5 v[749,3] = 1 v[750,3] = 3 v[751,3] = 1 v[752,3] = 7 v[753,3] = 1 v[754,3] = 1 v[755,3] = 5 v[756,3] = 1 v[757,3] = 11 v[758,3] = 11 v[759,3] = 9 v[760,3] = 9 v[761,3] = 5 v[762,3] = 13 v[763,3] = 7 v[764,3] = 7 v[765,3] = 7 v[766,3] = 1 v[767,3] = 1 v[768,3] = 9 v[769,3] = 9 v[770,3] = 11 v[771,3] = 11 v[772,3] = 15 v[773,3] = 7 v[774,3] = 5 v[775,3] = 5 v[776,3] = 3 v[777,3] = 11 v[778,3] = 1 v[779,3] = 3 v[780,3] = 7 v[781,3] = 13 v[782,3] = 7 v[783,3] = 7 v[784,3] = 7 v[785,3] = 3 v[786,3] = 15 v[787,3] = 15 v[788,3] = 11 v[789,3] = 9 v[790,3] = 3 v[791,3] = 9 v[792,3] = 3 v[793,3] = 15 v[794,3] = 13 v[795,3] = 5 v[796,3] = 3 v[797,3] = 3 v[798,3] = 3 v[799,3] = 5 v[800,3] = 9 v[801,3] = 15 v[802,3] = 9 v[803,3] = 9 v[804,3] = 1 v[805,3] = 5 v[806,3] = 9 v[807,3] = 9 v[808,3] = 15 v[809,3] = 5 v[810,3] = 15 v[811,3] = 7 v[812,3] = 9 v[813,3] = 1 v[814,3] = 9 v[815,3] = 9 v[816,3] = 5 v[817,3] = 11 v[818,3] = 5 v[819,3] = 15 v[820,3] = 15 v[821,3] = 11 v[822,3] = 7 v[823,3] = 7 v[824,3] = 7 v[825,3] = 1 v[826,3] = 1 v[827,3] = 11 v[828,3] = 11 v[829,3] = 13 v[830,3] = 15 v[831,3] = 3 v[832,3] = 13 v[833,3] = 5 v[834,3] = 1 v[835,3] = 7 v[836,3] = 1 v[837,3] = 11 v[838,3] = 3 v[839,3] = 13 v[840,3] = 15 v[841,3] = 3 v[842,3] = 5 v[843,3] = 3 v[844,3] = 5 v[845,3] = 7 v[846,3] = 3 v[847,3] = 9 v[848,3] = 9 v[849,3] = 5 v[850,3] = 1 v[851,3] = 7 v[852,3] = 11 v[853,3] = 9 v[854,3] = 3 v[855,3] = 5 v[856,3] = 11 v[857,3] = 13 v[858,3] = 13 v[859,3] = 13 v[860,3] = 9 v[861,3] = 15 v[862,3] = 5 v[863,3] = 7 v[864,3] = 1 v[865,3] = 15 v[866,3] = 11 v[867,3] = 9 v[868,3] = 15 v[869,3] = 15 v[870,3] = 13 v[871,3] = 13 v[872,3] = 13 v[873,3] = 1 v[874,3] = 11 v[875,3] = 9 v[876,3] = 15 v[877,3] = 9 v[878,3] = 5 v[879,3] = 15 v[880,3] = 5 v[881,3] = 7 v[882,3] = 3 v[883,3] = 11 v[884,3] = 3 v[885,3] = 15 v[886,3] = 7 v[887,3] = 13 v[888,3] = 11 v[889,3] = 7 v[890,3] = 3 v[891,3] = 7 v[892,3] = 13 v[893,3] = 5 v[894,3] = 13 v[895,3] = 15 v[896,3] = 5 v[897,3] = 13 v[898,3] = 9 v[899,3] = 1 v[900,3] = 15 v[901,3] = 11 v[902,3] = 5 v[903,3] = 5 v[904,3] = 1 v[905,3] = 11 v[906,3] = 3 v[907,3] = 3 v[908,3] = 7 v[909,3] = 1 v[910,3] = 9 v[911,3] = 7 v[912,3] = 15 v[913,3] = 9 v[914,3] = 9 v[915,3] = 3 v[916,3] = 11 v[917,3] = 15 v[918,3] = 7 v[919,3] = 1 v[920,3] = 3 v[921,3] = 1 v[922,3] = 1 v[923,3] = 1 v[924,3] = 9 v[925,3] = 1 v[926,3] = 5 v[927,3] = 15 v[928,3] = 15 v[929,3] = 7 v[930,3] = 5 v[931,3] = 5 v[932,3] = 7 v[933,3] = 9 v[934,3] = 7 v[935,3] = 15 v[936,3] = 13 v[937,3] = 13 v[938,3] = 11 v[939,3] = 1 v[940,3] = 9 v[941,3] = 11 v[942,3] = 1 v[943,3] = 13 v[944,3] = 1 v[945,3] = 7 v[946,3] = 15 v[947,3] = 15 v[948,3] = 5 v[949,3] = 5 v[950,3] = 1 v[951,3] = 11 v[952,3] = 3 v[953,3] = 9 v[954,3] = 11 v[955,3] = 9 v[956,3] = 9 v[957,3] = 9 v[958,3] = 1 v[959,3] = 9 v[960,3] = 3 v[961,3] = 5 v[962,3] = 15 v[963,3] = 1 v[964,3] = 1 v[965,3] = 9 v[966,3] = 7 v[967,3] = 3 v[968,3] = 3 v[969,3] = 1 v[970,3] = 9 v[971,3] = 9 v[972,3] = 11 v[973,3] = 9 v[974,3] = 9 v[975,3] = 13 v[976,3] = 13 v[977,3] = 3 v[978,3] = 13 v[979,3] = 11 v[980,3] = 13 v[981,3] = 5 v[982,3] = 1 v[983,3] = 5 v[984,3] = 5 v[985,3] = 9 v[986,3] = 9 v[987,3] = 3 v[988,3] = 13 v[989,3] = 13 v[990,3] = 9 v[991,3] = 15 v[992,3] = 9 v[993,3] = 11 v[994,3] = 7 v[995,3] = 11 v[996,3] = 9 v[997,3] = 13 v[998,3] = 9 v[999,3] = 1 v[1000,3] = 15 v[1001,3] = 9 v[1002,3] = 7 v[1003,3] = 7 v[1004,3] = 1 v[1005,3] = 7 v[1006,3] = 9 v[1007,3] = 9 v[1008,3] = 15 v[1009,3] = 1 v[1010,3] = 11 v[1011,3] = 1 v[1012,3] = 13 v[1013,3] = 13 v[1014,3] = 15 v[1015,3] = 9 v[1016,3] = 13 v[1017,3] = 7 v[1018,3] = 15 v[1019,3] = 3 v[1020,3] = 9 v[1021,3] = 3 v[1022,3] = 1 v[1023,3] = 13 v[1024,3] = 7 v[1025,3] = 5 v[1026,3] = 9 v[1027,3] = 3 v[1028,3] = 1 v[1029,3] = 7 v[1030,3] = 1 v[1031,3] = 1 v[1032,3] = 13 v[1033,3] = 3 v[1034,3] = 3 v[1035,3] = 11 v[1036,3] = 1 v[1037,3] = 7 v[1038,3] = 13 v[1039,3] = 15 v[1040,3] = 15 v[1041,3] = 5 v[1042,3] = 7 v[1043,3] = 13 v[1044,3] = 13 v[1045,3] = 15 v[1046,3] = 11 v[1047,3] = 13 v[1048,3] = 1 v[1049,3] = 13 v[1050,3] = 13 v[1051,3] = 3 v[1052,3] = 9 v[1053,3] = 15 v[1054,3] = 15 v[1055,3] = 11 v[1056,3] = 15 v[1057,3] = 9 v[1058,3] = 15 v[1059,3] = 1 v[1060,3] = 13 v[1061,3] = 15 v[1062,3] = 1 v[1063,3] = 1 v[1064,3] = 5 v[1065,3] = 11 v[1066,3] = 5 v[1067,3] = 1 v[1068,3] = 11 v[1069,3] = 11 v[1070,3] = 5 v[1071,3] = 3 v[1072,3] = 9 v[1073,3] = 1 v[1074,3] = 3 v[1075,3] = 5 v[1076,3] = 13 v[1077,3] = 9 v[1078,3] = 7 v[1079,3] = 7 v[1080,3] = 1 v[1081,3] = 9 v[1082,3] = 9 v[1083,3] = 15 v[1084,3] = 7 v[1085,3] = 5 v[1086,3] = 5 v[1087,3] = 15 v[1088,3] = 13 v[1089,3] = 9 v[1090,3] = 7 v[1091,3] = 13 v[1092,3] = 3 v[1093,3] = 13 v[1094,3] = 11 v[1095,3] = 13 v[1096,3] = 7 v[1097,3] = 9 v[1098,3] = 13 v[1099,3] = 13 v[1100,3] = 13 v[1101,3] = 15 v[1102,3] = 9 v[1103,3] = 5 v[1104,3] = 5 v[1105,3] = 3 v[1106,3] = 3 v[1107,3] = 3 v[1108,3] = 1 v[1109,3] = 3 v[1110,3] = 15 v[7,4] = 9 v[8,4] = 3 v[9,4] = 27 v[10,4] = 15 v[11,4] = 29 v[12,4] = 21 v[13,4] = 23 v[14,4] = 19 v[15,4] = 11 v[16,4] = 25 v[17,4] = 7 v[18,4] = 13 v[19,4] = 17 v[20,4] = 1 v[21,4] = 25 v[22,4] = 29 v[23,4] = 3 v[24,4] = 31 v[25,4] = 11 v[26,4] = 5 v[27,4] = 23 v[28,4] = 27 v[29,4] = 19 v[30,4] = 21 v[31,4] = 5 v[32,4] = 1 v[33,4] = 17 v[34,4] = 13 v[35,4] = 7 v[36,4] = 15 v[37,4] = 9 v[38,4] = 31 v[39,4] = 25 v[40,4] = 3 v[41,4] = 5 v[42,4] = 23 v[43,4] = 7 v[44,4] = 3 v[45,4] = 17 v[46,4] = 23 v[47,4] = 3 v[48,4] = 3 v[49,4] = 21 v[50,4] = 25 v[51,4] = 25 v[52,4] = 23 v[53,4] = 11 v[54,4] = 19 v[55,4] = 3 v[56,4] = 11 v[57,4] = 31 v[58,4] = 7 v[59,4] = 9 v[60,4] = 5 v[61,4] = 17 v[62,4] = 23 v[63,4] = 17 v[64,4] = 17 v[65,4] = 25 v[66,4] = 13 v[67,4] = 11 v[68,4] = 31 v[69,4] = 27 v[70,4] = 19 v[71,4] = 17 v[72,4] = 23 v[73,4] = 7 v[74,4] = 5 v[75,4] = 11 v[76,4] = 19 v[77,4] = 19 v[78,4] = 7 v[79,4] = 13 v[80,4] = 21 v[81,4] = 21 v[82,4] = 7 v[83,4] = 9 v[84,4] = 11 v[85,4] = 1 v[86,4] = 5 v[87,4] = 21 v[88,4] = 11 v[89,4] = 13 v[90,4] = 25 v[91,4] = 9 v[92,4] = 7 v[93,4] = 7 v[94,4] = 27 v[95,4] = 15 v[96,4] = 25 v[97,4] = 15 v[98,4] = 21 v[99,4] = 17 v[100,4] = 19 v[101,4] = 19 v[102,4] = 21 v[103,4] = 5 v[104,4] = 11 v[105,4] = 3 v[106,4] = 5 v[107,4] = 29 v[108,4] = 31 v[109,4] = 29 v[110,4] = 5 v[111,4] = 5 v[112,4] = 1 v[113,4] = 31 v[114,4] = 27 v[115,4] = 11 v[116,4] = 13 v[117,4] = 1 v[118,4] = 3 v[119,4] = 7 v[120,4] = 11 v[121,4] = 7 v[122,4] = 3 v[123,4] = 23 v[124,4] = 13 v[125,4] = 31 v[126,4] = 17 v[127,4] = 1 v[128,4] = 27 v[129,4] = 11 v[130,4] = 25 v[131,4] = 1 v[132,4] = 23 v[133,4] = 29 v[134,4] = 17 v[135,4] = 25 v[136,4] = 7 v[137,4] = 25 v[138,4] = 27 v[139,4] = 17 v[140,4] = 13 v[141,4] = 17 v[142,4] = 23 v[143,4] = 5 v[144,4] = 17 v[145,4] = 5 v[146,4] = 13 v[147,4] = 11 v[148,4] = 21 v[149,4] = 5 v[150,4] = 11 v[151,4] = 5 v[152,4] = 9 v[153,4] = 31 v[154,4] = 19 v[155,4] = 17 v[156,4] = 9 v[157,4] = 9 v[158,4] = 27 v[159,4] = 21 v[160,4] = 15 v[161,4] = 15 v[162,4] = 1 v[163,4] = 1 v[164,4] = 29 v[165,4] = 5 v[166,4] = 31 v[167,4] = 11 v[168,4] = 17 v[169,4] = 23 v[170,4] = 19 v[171,4] = 21 v[172,4] = 25 v[173,4] = 15 v[174,4] = 11 v[175,4] = 5 v[176,4] = 5 v[177,4] = 1 v[178,4] = 19 v[179,4] = 19 v[180,4] = 19 v[181,4] = 7 v[182,4] = 13 v[183,4] = 21 v[184,4] = 17 v[185,4] = 17 v[186,4] = 25 v[187,4] = 23 v[188,4] = 19 v[189,4] = 23 v[190,4] = 15 v[191,4] = 13 v[192,4] = 5 v[193,4] = 19 v[194,4] = 25 v[195,4] = 9 v[196,4] = 7 v[197,4] = 3 v[198,4] = 21 v[199,4] = 17 v[200,4] = 25 v[201,4] = 1 v[202,4] = 27 v[203,4] = 25 v[204,4] = 27 v[205,4] = 25 v[206,4] = 9 v[207,4] = 13 v[208,4] = 3 v[209,4] = 17 v[210,4] = 25 v[211,4] = 23 v[212,4] = 9 v[213,4] = 25 v[214,4] = 9 v[215,4] = 13 v[216,4] = 17 v[217,4] = 17 v[218,4] = 3 v[219,4] = 15 v[220,4] = 7 v[221,4] = 7 v[222,4] = 29 v[223,4] = 3 v[224,4] = 19 v[225,4] = 29 v[226,4] = 29 v[227,4] = 19 v[228,4] = 29 v[229,4] = 13 v[230,4] = 15 v[231,4] = 25 v[232,4] = 27 v[233,4] = 1 v[234,4] = 3 v[235,4] = 9 v[236,4] = 9 v[237,4] = 13 v[238,4] = 31 v[239,4] = 29 v[240,4] = 31 v[241,4] = 5 v[242,4] = 15 v[243,4] = 29 v[244,4] = 1 v[245,4] = 19 v[246,4] = 5 v[247,4] = 9 v[248,4] = 19 v[249,4] = 5 v[250,4] = 15 v[251,4] = 3 v[252,4] = 5 v[253,4] = 7 v[254,4] = 15 v[255,4] = 17 v[256,4] = 17 v[257,4] = 23 v[258,4] = 11 v[259,4] = 9 v[260,4] = 23 v[261,4] = 19 v[262,4] = 3 v[263,4] = 17 v[264,4] = 1 v[265,4] = 27 v[266,4] = 9 v[267,4] = 9 v[268,4] = 17 v[269,4] = 13 v[270,4] = 25 v[271,4] = 29 v[272,4] = 23 v[273,4] = 29 v[274,4] = 11 v[275,4] = 31 v[276,4] = 25 v[277,4] = 21 v[278,4] = 29 v[279,4] = 19 v[280,4] = 27 v[281,4] = 31 v[282,4] = 3 v[283,4] = 5 v[284,4] = 3 v[285,4] = 3 v[286,4] = 13 v[287,4] = 21 v[288,4] = 9 v[289,4] = 29 v[290,4] = 3 v[291,4] = 17 v[292,4] = 11 v[293,4] = 11 v[294,4] = 9 v[295,4] = 21 v[296,4] = 19 v[297,4] = 7 v[298,4] = 17 v[299,4] = 31 v[300,4] = 25 v[301,4] = 1 v[302,4] = 27 v[303,4] = 5 v[304,4] = 15 v[305,4] = 27 v[306,4] = 29 v[307,4] = 29 v[308,4] = 29 v[309,4] = 25 v[310,4] = 27 v[311,4] = 25 v[312,4] = 3 v[313,4] = 21 v[314,4] = 17 v[315,4] = 25 v[316,4] = 13 v[317,4] = 15 v[318,4] = 17 v[319,4] = 13 v[320,4] = 23 v[321,4] = 9 v[322,4] = 3 v[323,4] = 11 v[324,4] = 7 v[325,4] = 9 v[326,4] = 9 v[327,4] = 7 v[328,4] = 17 v[329,4] = 7 v[330,4] = 1 v[331,4] = 27 v[332,4] = 1 v[333,4] = 9 v[334,4] = 5 v[335,4] = 31 v[336,4] = 21 v[337,4] = 25 v[338,4] = 25 v[339,4] = 21 v[340,4] = 11 v[341,4] = 1 v[342,4] = 23 v[343,4] = 19 v[344,4] = 27 v[345,4] = 15 v[346,4] = 3 v[347,4] = 5 v[348,4] = 23 v[349,4] = 9 v[350,4] = 25 v[351,4] = 7 v[352,4] = 29 v[353,4] = 11 v[354,4] = 9 v[355,4] = 13 v[356,4] = 5 v[357,4] = 11 v[358,4] = 1 v[359,4] = 3 v[360,4] = 31 v[361,4] = 27 v[362,4] = 3 v[363,4] = 17 v[364,4] = 27 v[365,4] = 11 v[366,4] = 13 v[367,4] = 15 v[368,4] = 29 v[369,4] = 15 v[370,4] = 1 v[371,4] = 15 v[372,4] = 23 v[373,4] = 25 v[374,4] = 13 v[375,4] = 21 v[376,4] = 15 v[377,4] = 3 v[378,4] = 29 v[379,4] = 29 v[380,4] = 5 v[381,4] = 25 v[382,4] = 17 v[383,4] = 11 v[384,4] = 7 v[385,4] = 15 v[386,4] = 5 v[387,4] = 21 v[388,4] = 7 v[389,4] = 31 v[390,4] = 13 v[391,4] = 11 v[392,4] = 23 v[393,4] = 5 v[394,4] = 7 v[395,4] = 23 v[396,4] = 27 v[397,4] = 21 v[398,4] = 29 v[399,4] = 15 v[400,4] = 7 v[401,4] = 27 v[402,4] = 27 v[403,4] = 19 v[404,4] = 7 v[405,4] = 15 v[406,4] = 27 v[407,4] = 27 v[408,4] = 19 v[409,4] = 19 v[410,4] = 9 v[411,4] = 15 v[412,4] = 1 v[413,4] = 3 v[414,4] = 29 v[415,4] = 29 v[416,4] = 5 v[417,4] = 27 v[418,4] = 31 v[419,4] = 9 v[420,4] = 1 v[421,4] = 7 v[422,4] = 3 v[423,4] = 19 v[424,4] = 19 v[425,4] = 29 v[426,4] = 9 v[427,4] = 3 v[428,4] = 21 v[429,4] = 31 v[430,4] = 29 v[431,4] = 25 v[432,4] = 1 v[433,4] = 3 v[434,4] = 9 v[435,4] = 27 v[436,4] = 5 v[437,4] = 27 v[438,4] = 25 v[439,4] = 21 v[440,4] = 11 v[441,4] = 29 v[442,4] = 31 v[443,4] = 27 v[444,4] = 21 v[445,4] = 29 v[446,4] = 17 v[447,4] = 9 v[448,4] = 17 v[449,4] = 13 v[450,4] = 11 v[451,4] = 25 v[452,4] = 15 v[453,4] = 21 v[454,4] = 11 v[455,4] = 19 v[456,4] = 31 v[457,4] = 3 v[458,4] = 19 v[459,4] = 5 v[460,4] = 3 v[461,4] = 3 v[462,4] = 9 v[463,4] = 13 v[464,4] = 13 v[465,4] = 3 v[466,4] = 29 v[467,4] = 7 v[468,4] = 5 v[469,4] = 9 v[470,4] = 23 v[471,4] = 13 v[472,4] = 21 v[473,4] = 23 v[474,4] = 21 v[475,4] = 31 v[476,4] = 11 v[477,4] = 7 v[478,4] = 7 v[479,4] = 3 v[480,4] = 23 v[481,4] = 1 v[482,4] = 23 v[483,4] = 5 v[484,4] = 9 v[485,4] = 17 v[486,4] = 21 v[487,4] = 1 v[488,4] = 17 v[489,4] = 29 v[490,4] = 7 v[491,4] = 5 v[492,4] = 17 v[493,4] = 13 v[494,4] = 25 v[495,4] = 17 v[496,4] = 9 v[497,4] = 19 v[498,4] = 9 v[499,4] = 5 v[500,4] = 7 v[501,4] = 21 v[502,4] = 19 v[503,4] = 13 v[504,4] = 9 v[505,4] = 7 v[506,4] = 3 v[507,4] = 9 v[508,4] = 3 v[509,4] = 15 v[510,4] = 31 v[511,4] = 29 v[512,4] = 29 v[513,4] = 25 v[514,4] = 13 v[515,4] = 9 v[516,4] = 21 v[517,4] = 9 v[518,4] = 31 v[519,4] = 7 v[520,4] = 15 v[521,4] = 5 v[522,4] = 31 v[523,4] = 7 v[524,4] = 15 v[525,4] = 27 v[526,4] = 25 v[527,4] = 19 v[528,4] = 9 v[529,4] = 9 v[530,4] = 25 v[531,4] = 25 v[532,4] = 23 v[533,4] = 1 v[534,4] = 9 v[535,4] = 7 v[536,4] = 11 v[537,4] = 15 v[538,4] = 19 v[539,4] = 15 v[540,4] = 27 v[541,4] = 17 v[542,4] = 11 v[543,4] = 11 v[544,4] = 31 v[545,4] = 13 v[546,4] = 25 v[547,4] = 25 v[548,4] = 9 v[549,4] = 7 v[550,4] = 13 v[551,4] = 29 v[552,4] = 19 v[553,4] = 5 v[554,4] = 19 v[555,4] = 31 v[556,4] = 25 v[557,4] = 13 v[558,4] = 25 v[559,4] = 15 v[560,4] = 5 v[561,4] = 9 v[562,4] = 29 v[563,4] = 31 v[564,4] = 9 v[565,4] = 29 v[566,4] = 27 v[567,4] = 25 v[568,4] = 27 v[569,4] = 11 v[570,4] = 17 v[571,4] = 5 v[572,4] = 17 v[573,4] = 3 v[574,4] = 23 v[575,4] = 15 v[576,4] = 9 v[577,4] = 9 v[578,4] = 17 v[579,4] = 17 v[580,4] = 31 v[581,4] = 11 v[582,4] = 19 v[583,4] = 25 v[584,4] = 13 v[585,4] = 23 v[586,4] = 15 v[587,4] = 25 v[588,4] = 21 v[589,4] = 31 v[590,4] = 19 v[591,4] = 3 v[592,4] = 11 v[593,4] = 25 v[594,4] = 7 v[595,4] = 15 v[596,4] = 19 v[597,4] = 7 v[598,4] = 5 v[599,4] = 3 v[600,4] = 13 v[601,4] = 13 v[602,4] = 1 v[603,4] = 23 v[604,4] = 5 v[605,4] = 25 v[606,4] = 11 v[607,4] = 25 v[608,4] = 15 v[609,4] = 13 v[610,4] = 21 v[611,4] = 11 v[612,4] = 23 v[613,4] = 29 v[614,4] = 5 v[615,4] = 17 v[616,4] = 27 v[617,4] = 9 v[618,4] = 19 v[619,4] = 15 v[620,4] = 5 v[621,4] = 29 v[622,4] = 23 v[623,4] = 19 v[624,4] = 1 v[625,4] = 27 v[626,4] = 3 v[627,4] = 23 v[628,4] = 21 v[629,4] = 19 v[630,4] = 27 v[631,4] = 11 v[632,4] = 17 v[633,4] = 13 v[634,4] = 27 v[635,4] = 11 v[636,4] = 31 v[637,4] = 23 v[638,4] = 5 v[639,4] = 9 v[640,4] = 21 v[641,4] = 31 v[642,4] = 29 v[643,4] = 11 v[644,4] = 21 v[645,4] = 17 v[646,4] = 15 v[647,4] = 7 v[648,4] = 15 v[649,4] = 7 v[650,4] = 9 v[651,4] = 21 v[652,4] = 27 v[653,4] = 25 v[654,4] = 29 v[655,4] = 11 v[656,4] = 3 v[657,4] = 21 v[658,4] = 13 v[659,4] = 23 v[660,4] = 19 v[661,4] = 27 v[662,4] = 17 v[663,4] = 29 v[664,4] = 25 v[665,4] = 17 v[666,4] = 9 v[667,4] = 1 v[668,4] = 19 v[669,4] = 23 v[670,4] = 5 v[671,4] = 23 v[672,4] = 1 v[673,4] = 17 v[674,4] = 17 v[675,4] = 13 v[676,4] = 27 v[677,4] = 23 v[678,4] = 7 v[679,4] = 7 v[680,4] = 11 v[681,4] = 13 v[682,4] = 17 v[683,4] = 13 v[684,4] = 11 v[685,4] = 21 v[686,4] = 13 v[687,4] = 23 v[688,4] = 1 v[689,4] = 27 v[690,4] = 13 v[691,4] = 9 v[692,4] = 7 v[693,4] = 1 v[694,4] = 27 v[695,4] = 29 v[696,4] = 5 v[697,4] = 13 v[698,4] = 25 v[699,4] = 21 v[700,4] = 3 v[701,4] = 31 v[702,4] = 15 v[703,4] = 13 v[704,4] = 3 v[705,4] = 19 v[706,4] = 13 v[707,4] = 1 v[708,4] = 27 v[709,4] = 15 v[710,4] = 17 v[711,4] = 1 v[712,4] = 3 v[713,4] = 13 v[714,4] = 13 v[715,4] = 13 v[716,4] = 31 v[717,4] = 29 v[718,4] = 27 v[719,4] = 7 v[720,4] = 7 v[721,4] = 21 v[722,4] = 29 v[723,4] = 15 v[724,4] = 17 v[725,4] = 17 v[726,4] = 21 v[727,4] = 19 v[728,4] = 17 v[729,4] = 3 v[730,4] = 15 v[731,4] = 5 v[732,4] = 27 v[733,4] = 27 v[734,4] = 3 v[735,4] = 31 v[736,4] = 31 v[737,4] = 7 v[738,4] = 21 v[739,4] = 3 v[740,4] = 13 v[741,4] = 11 v[742,4] = 17 v[743,4] = 27 v[744,4] = 25 v[745,4] = 1 v[746,4] = 9 v[747,4] = 7 v[748,4] = 29 v[749,4] = 27 v[750,4] = 21 v[751,4] = 23 v[752,4] = 13 v[753,4] = 25 v[754,4] = 29 v[755,4] = 15 v[756,4] = 17 v[757,4] = 29 v[758,4] = 9 v[759,4] = 15 v[760,4] = 3 v[761,4] = 21 v[762,4] = 15 v[763,4] = 17 v[764,4] = 17 v[765,4] = 31 v[766,4] = 9 v[767,4] = 9 v[768,4] = 23 v[769,4] = 19 v[770,4] = 25 v[771,4] = 3 v[772,4] = 1 v[773,4] = 11 v[774,4] = 27 v[775,4] = 29 v[776,4] = 1 v[777,4] = 31 v[778,4] = 29 v[779,4] = 25 v[780,4] = 29 v[781,4] = 1 v[782,4] = 23 v[783,4] = 29 v[784,4] = 25 v[785,4] = 13 v[786,4] = 3 v[787,4] = 31 v[788,4] = 25 v[789,4] = 5 v[790,4] = 5 v[791,4] = 11 v[792,4] = 3 v[793,4] = 21 v[794,4] = 9 v[795,4] = 23 v[796,4] = 7 v[797,4] = 11 v[798,4] = 23 v[799,4] = 11 v[800,4] = 1 v[801,4] = 1 v[802,4] = 3 v[803,4] = 23 v[804,4] = 25 v[805,4] = 23 v[806,4] = 1 v[807,4] = 23 v[808,4] = 3 v[809,4] = 27 v[810,4] = 9 v[811,4] = 27 v[812,4] = 3 v[813,4] = 23 v[814,4] = 25 v[815,4] = 19 v[816,4] = 29 v[817,4] = 29 v[818,4] = 13 v[819,4] = 27 v[820,4] = 5 v[821,4] = 9 v[822,4] = 29 v[823,4] = 29 v[824,4] = 13 v[825,4] = 17 v[826,4] = 3 v[827,4] = 23 v[828,4] = 19 v[829,4] = 7 v[830,4] = 13 v[831,4] = 3 v[832,4] = 19 v[833,4] = 23 v[834,4] = 5 v[835,4] = 29 v[836,4] = 29 v[837,4] = 13 v[838,4] = 13 v[839,4] = 5 v[840,4] = 19 v[841,4] = 5 v[842,4] = 17 v[843,4] = 9 v[844,4] = 11 v[845,4] = 11 v[846,4] = 29 v[847,4] = 27 v[848,4] = 23 v[849,4] = 19 v[850,4] = 17 v[851,4] = 25 v[852,4] = 13 v[853,4] = 1 v[854,4] = 13 v[855,4] = 3 v[856,4] = 11 v[857,4] = 1 v[858,4] = 17 v[859,4] = 29 v[860,4] = 1 v[861,4] = 13 v[862,4] = 17 v[863,4] = 9 v[864,4] = 17 v[865,4] = 21 v[866,4] = 1 v[867,4] = 11 v[868,4] = 1 v[869,4] = 1 v[870,4] = 25 v[871,4] = 5 v[872,4] = 7 v[873,4] = 29 v[874,4] = 29 v[875,4] = 19 v[876,4] = 19 v[877,4] = 1 v[878,4] = 29 v[879,4] = 13 v[880,4] = 3 v[881,4] = 1 v[882,4] = 31 v[883,4] = 15 v[884,4] = 13 v[885,4] = 3 v[886,4] = 1 v[887,4] = 11 v[888,4] = 19 v[889,4] = 5 v[890,4] = 29 v[891,4] = 13 v[892,4] = 29 v[893,4] = 23 v[894,4] = 3 v[895,4] = 1 v[896,4] = 31 v[897,4] = 13 v[898,4] = 19 v[899,4] = 17 v[900,4] = 5 v[901,4] = 5 v[902,4] = 1 v[903,4] = 29 v[904,4] = 23 v[905,4] = 3 v[906,4] = 19 v[907,4] = 25 v[908,4] = 19 v[909,4] = 27 v[910,4] = 9 v[911,4] = 27 v[912,4] = 13 v[913,4] = 15 v[914,4] = 29 v[915,4] = 23 v[916,4] = 13 v[917,4] = 25 v[918,4] = 25 v[919,4] = 17 v[920,4] = 19 v[921,4] = 17 v[922,4] = 15 v[923,4] = 27 v[924,4] = 3 v[925,4] = 25 v[926,4] = 17 v[927,4] = 27 v[928,4] = 3 v[929,4] = 27 v[930,4] = 31 v[931,4] = 23 v[932,4] = 13 v[933,4] = 31 v[934,4] = 11 v[935,4] = 15 v[936,4] = 7 v[937,4] = 21 v[938,4] = 19 v[939,4] = 27 v[940,4] = 19 v[941,4] = 21 v[942,4] = 29 v[943,4] = 7 v[944,4] = 31 v[945,4] = 13 v[946,4] = 9 v[947,4] = 9 v[948,4] = 7 v[949,4] = 21 v[950,4] = 13 v[951,4] = 11 v[952,4] = 9 v[953,4] = 11 v[954,4] = 29 v[955,4] = 19 v[956,4] = 11 v[957,4] = 19 v[958,4] = 21 v[959,4] = 5 v[960,4] = 29 v[961,4] = 13 v[962,4] = 7 v[963,4] = 19 v[964,4] = 19 v[965,4] = 27 v[966,4] = 23 v[967,4] = 31 v[968,4] = 1 v[969,4] = 27 v[970,4] = 21 v[971,4] = 7 v[972,4] = 3 v[973,4] = 7 v[974,4] = 11 v[975,4] = 23 v[976,4] = 13 v[977,4] = 29 v[978,4] = 11 v[979,4] = 31 v[980,4] = 19 v[981,4] = 1 v[982,4] = 5 v[983,4] = 5 v[984,4] = 11 v[985,4] = 5 v[986,4] = 3 v[987,4] = 27 v[988,4] = 5 v[989,4] = 7 v[990,4] = 11 v[991,4] = 31 v[992,4] = 1 v[993,4] = 27 v[994,4] = 31 v[995,4] = 31 v[996,4] = 23 v[997,4] = 5 v[998,4] = 21 v[999,4] = 27 v[1000,4] = 9 v[1001,4] = 25 v[1002,4] = 3 v[1003,4] = 15 v[1004,4] = 19 v[1005,4] = 1 v[1006,4] = 19 v[1007,4] = 9 v[1008,4] = 5 v[1009,4] = 25 v[1010,4] = 21 v[1011,4] = 15 v[1012,4] = 25 v[1013,4] = 29 v[1014,4] = 15 v[1015,4] = 21 v[1016,4] = 11 v[1017,4] = 19 v[1018,4] = 15 v[1019,4] = 3 v[1020,4] = 7 v[1021,4] = 13 v[1022,4] = 11 v[1023,4] = 25 v[1024,4] = 17 v[1025,4] = 1 v[1026,4] = 5 v[1027,4] = 31 v[1028,4] = 13 v[1029,4] = 29 v[1030,4] = 23 v[1031,4] = 9 v[1032,4] = 5 v[1033,4] = 29 v[1034,4] = 7 v[1035,4] = 17 v[1036,4] = 27 v[1037,4] = 7 v[1038,4] = 17 v[1039,4] = 31 v[1040,4] = 9 v[1041,4] = 31 v[1042,4] = 9 v[1043,4] = 9 v[1044,4] = 7 v[1045,4] = 21 v[1046,4] = 3 v[1047,4] = 3 v[1048,4] = 3 v[1049,4] = 9 v[1050,4] = 11 v[1051,4] = 21 v[1052,4] = 11 v[1053,4] = 31 v[1054,4] = 9 v[1055,4] = 25 v[1056,4] = 5 v[1057,4] = 1 v[1058,4] = 31 v[1059,4] = 13 v[1060,4] = 29 v[1061,4] = 9 v[1062,4] = 29 v[1063,4] = 1 v[1064,4] = 11 v[1065,4] = 19 v[1066,4] = 7 v[1067,4] = 27 v[1068,4] = 13 v[1069,4] = 31 v[1070,4] = 7 v[1071,4] = 31 v[1072,4] = 7 v[1073,4] = 25 v[1074,4] = 23 v[1075,4] = 21 v[1076,4] = 29 v[1077,4] = 11 v[1078,4] = 11 v[1079,4] = 13 v[1080,4] = 11 v[1081,4] = 27 v[1082,4] = 1 v[1083,4] = 23 v[1084,4] = 31 v[1085,4] = 21 v[1086,4] = 23 v[1087,4] = 21 v[1088,4] = 19 v[1089,4] = 31 v[1090,4] = 5 v[1091,4] = 31 v[1092,4] = 25 v[1093,4] = 25 v[1094,4] = 19 v[1095,4] = 17 v[1096,4] = 11 v[1097,4] = 25 v[1098,4] = 7 v[1099,4] = 13 v[1100,4] = 1 v[1101,4] = 29 v[1102,4] = 17 v[1103,4] = 23 v[1104,4] = 15 v[1105,4] = 7 v[1106,4] = 29 v[1107,4] = 17 v[1108,4] = 13 v[1109,4] = 3 v[1110,4] = 17 v[13,5] = 37 v[14,5] = 33 v[15,5] = 7 v[16,5] = 5 v[17,5] = 11 v[18,5] = 39 v[19,5] = 63 v[20,5] = 59 v[21,5] = 17 v[22,5] = 15 v[23,5] = 23 v[24,5] = 29 v[25,5] = 3 v[26,5] = 21 v[27,5] = 13 v[28,5] = 31 v[29,5] = 25 v[30,5] = 9 v[31,5] = 49 v[32,5] = 33 v[33,5] = 19 v[34,5] = 29 v[35,5] = 11 v[36,5] = 19 v[37,5] = 27 v[38,5] = 15 v[39,5] = 25 v[40,5] = 63 v[41,5] = 55 v[42,5] = 17 v[43,5] = 63 v[44,5] = 49 v[45,5] = 19 v[46,5] = 41 v[47,5] = 59 v[48,5] = 3 v[49,5] = 57 v[50,5] = 33 v[51,5] = 49 v[52,5] = 53 v[53,5] = 57 v[54,5] = 57 v[55,5] = 39 v[56,5] = 21 v[57,5] = 7 v[58,5] = 53 v[59,5] = 9 v[60,5] = 55 v[61,5] = 15 v[62,5] = 59 v[63,5] = 19 v[64,5] = 49 v[65,5] = 31 v[66,5] = 3 v[67,5] = 39 v[68,5] = 5 v[69,5] = 5 v[70,5] = 41 v[71,5] = 9 v[72,5] = 19 v[73,5] = 9 v[74,5] = 57 v[75,5] = 25 v[76,5] = 1 v[77,5] = 15 v[78,5] = 51 v[79,5] = 11 v[80,5] = 19 v[81,5] = 61 v[82,5] = 53 v[83,5] = 29 v[84,5] = 19 v[85,5] = 11 v[86,5] = 9 v[87,5] = 21 v[88,5] = 19 v[89,5] = 43 v[90,5] = 13 v[91,5] = 13 v[92,5] = 41 v[93,5] = 25 v[94,5] = 31 v[95,5] = 9 v[96,5] = 11 v[97,5] = 19 v[98,5] = 5 v[99,5] = 53 v[100,5] = 37 v[101,5] = 7 v[102,5] = 51 v[103,5] = 45 v[104,5] = 7 v[105,5] = 7 v[106,5] = 61 v[107,5] = 23 v[108,5] = 45 v[109,5] = 7 v[110,5] = 59 v[111,5] = 41 v[112,5] = 1 v[113,5] = 29 v[114,5] = 61 v[115,5] = 37 v[116,5] = 27 v[117,5] = 47 v[118,5] = 15 v[119,5] = 31 v[120,5] = 35 v[121,5] = 31 v[122,5] = 17 v[123,5] = 51 v[124,5] = 13 v[125,5] = 25 v[126,5] = 45 v[127,5] = 5 v[128,5] = 5 v[129,5] = 33 v[130,5] = 39 v[131,5] = 5 v[132,5] = 47 v[133,5] = 29 v[134,5] = 35 v[135,5] = 47 v[136,5] = 63 v[137,5] = 45 v[138,5] = 37 v[139,5] = 47 v[140,5] = 59 v[141,5] = 21 v[142,5] = 59 v[143,5] = 33 v[144,5] = 51 v[145,5] = 9 v[146,5] = 27 v[147,5] = 13 v[148,5] = 25 v[149,5] = 43 v[150,5] = 3 v[151,5] = 17 v[152,5] = 21 v[153,5] = 59 v[154,5] = 61 v[155,5] = 27 v[156,5] = 47 v[157,5] = 57 v[158,5] = 11 v[159,5] = 17 v[160,5] = 39 v[161,5] = 1 v[162,5] = 63 v[163,5] = 21 v[164,5] = 59 v[165,5] = 17 v[166,5] = 13 v[167,5] = 31 v[168,5] = 3 v[169,5] = 31 v[170,5] = 7 v[171,5] = 9 v[172,5] = 27 v[173,5] = 37 v[174,5] = 23 v[175,5] = 31 v[176,5] = 9 v[177,5] = 45 v[178,5] = 43 v[179,5] = 31 v[180,5] = 63 v[181,5] = 21 v[182,5] = 39 v[183,5] = 51 v[184,5] = 27 v[185,5] = 7 v[186,5] = 53 v[187,5] = 11 v[188,5] = 1 v[189,5] = 59 v[190,5] = 39 v[191,5] = 23 v[192,5] = 49 v[193,5] = 23 v[194,5] = 7 v[195,5] = 55 v[196,5] = 59 v[197,5] = 3 v[198,5] = 19 v[199,5] = 35 v[200,5] = 13 v[201,5] = 9 v[202,5] = 13 v[203,5] = 15 v[204,5] = 23 v[205,5] = 9 v[206,5] = 7 v[207,5] = 43 v[208,5] = 55 v[209,5] = 3 v[210,5] = 19 v[211,5] = 9 v[212,5] = 27 v[213,5] = 33 v[214,5] = 27 v[215,5] = 49 v[216,5] = 23 v[217,5] = 47 v[218,5] = 19 v[219,5] = 7 v[220,5] = 11 v[221,5] = 55 v[222,5] = 27 v[223,5] = 35 v[224,5] = 5 v[225,5] = 5 v[226,5] = 55 v[227,5] = 35 v[228,5] = 37 v[229,5] = 9 v[230,5] = 33 v[231,5] = 29 v[232,5] = 47 v[233,5] = 25 v[234,5] = 11 v[235,5] = 47 v[236,5] = 53 v[237,5] = 61 v[238,5] = 59 v[239,5] = 3 v[240,5] = 53 v[241,5] = 47 v[242,5] = 5 v[243,5] = 19 v[244,5] = 59 v[245,5] = 5 v[246,5] = 47 v[247,5] = 23 v[248,5] = 45 v[249,5] = 53 v[250,5] = 3 v[251,5] = 49 v[252,5] = 61 v[253,5] = 47 v[254,5] = 39 v[255,5] = 29 v[256,5] = 17 v[257,5] = 57 v[258,5] = 5 v[259,5] = 17 v[260,5] = 31 v[261,5] = 23 v[262,5] = 41 v[263,5] = 39 v[264,5] = 5 v[265,5] = 27 v[266,5] = 7 v[267,5] = 29 v[268,5] = 29 v[269,5] = 33 v[270,5] = 31 v[271,5] = 41 v[272,5] = 31 v[273,5] = 29 v[274,5] = 17 v[275,5] = 29 v[276,5] = 29 v[277,5] = 9 v[278,5] = 9 v[279,5] = 31 v[280,5] = 27 v[281,5] = 53 v[282,5] = 35 v[283,5] = 5 v[284,5] = 61 v[285,5] = 1 v[286,5] = 49 v[287,5] = 13 v[288,5] = 57 v[289,5] = 29 v[290,5] = 5 v[291,5] = 21 v[292,5] = 43 v[293,5] = 25 v[294,5] = 57 v[295,5] = 49 v[296,5] = 37 v[297,5] = 27 v[298,5] = 11 v[299,5] = 61 v[300,5] = 37 v[301,5] = 49 v[302,5] = 5 v[303,5] = 63 v[304,5] = 63 v[305,5] = 3 v[306,5] = 45 v[307,5] = 37 v[308,5] = 63 v[309,5] = 21 v[310,5] = 21 v[311,5] = 19 v[312,5] = 27 v[313,5] = 59 v[314,5] = 21 v[315,5] = 45 v[316,5] = 23 v[317,5] = 13 v[318,5] = 15 v[319,5] = 3 v[320,5] = 43 v[321,5] = 63 v[322,5] = 39 v[323,5] = 19 v[324,5] = 63 v[325,5] = 31 v[326,5] = 41 v[327,5] = 41 v[328,5] = 15 v[329,5] = 43 v[330,5] = 63 v[331,5] = 53 v[332,5] = 1 v[333,5] = 63 v[334,5] = 31 v[335,5] = 7 v[336,5] = 17 v[337,5] = 11 v[338,5] = 61 v[339,5] = 31 v[340,5] = 51 v[341,5] = 37 v[342,5] = 29 v[343,5] = 59 v[344,5] = 25 v[345,5] = 63 v[346,5] = 59 v[347,5] = 47 v[348,5] = 15 v[349,5] = 27 v[350,5] = 19 v[351,5] = 29 v[352,5] = 45 v[353,5] = 35 v[354,5] = 55 v[355,5] = 39 v[356,5] = 19 v[357,5] = 43 v[358,5] = 21 v[359,5] = 19 v[360,5] = 13 v[361,5] = 17 v[362,5] = 51 v[363,5] = 37 v[364,5] = 5 v[365,5] = 33 v[366,5] = 35 v[367,5] = 49 v[368,5] = 25 v[369,5] = 45 v[370,5] = 1 v[371,5] = 63 v[372,5] = 47 v[373,5] = 9 v[374,5] = 63 v[375,5] = 15 v[376,5] = 25 v[377,5] = 25 v[378,5] = 15 v[379,5] = 41 v[380,5] = 13 v[381,5] = 3 v[382,5] = 19 v[383,5] = 51 v[384,5] = 49 v[385,5] = 37 v[386,5] = 25 v[387,5] = 49 v[388,5] = 13 v[389,5] = 53 v[390,5] = 47 v[391,5] = 23 v[392,5] = 35 v[393,5] = 29 v[394,5] = 33 v[395,5] = 21 v[396,5] = 35 v[397,5] = 23 v[398,5] = 3 v[399,5] = 43 v[400,5] = 31 v[401,5] = 63 v[402,5] = 9 v[403,5] = 1 v[404,5] = 61 v[405,5] = 43 v[406,5] = 3 v[407,5] = 11 v[408,5] = 55 v[409,5] = 11 v[410,5] = 35 v[411,5] = 1 v[412,5] = 63 v[413,5] = 35 v[414,5] = 49 v[415,5] = 19 v[416,5] = 45 v[417,5] = 9 v[418,5] = 57 v[419,5] = 51 v[420,5] = 1 v[421,5] = 47 v[422,5] = 41 v[423,5] = 9 v[424,5] = 11 v[425,5] = 37 v[426,5] = 19 v[427,5] = 55 v[428,5] = 23 v[429,5] = 55 v[430,5] = 55 v[431,5] = 13 v[432,5] = 7 v[433,5] = 47 v[434,5] = 37 v[435,5] = 11 v[436,5] = 43 v[437,5] = 17 v[438,5] = 3 v[439,5] = 25 v[440,5] = 19 v[441,5] = 55 v[442,5] = 59 v[443,5] = 37 v[444,5] = 33 v[445,5] = 43 v[446,5] = 1 v[447,5] = 5 v[448,5] = 21 v[449,5] = 5 v[450,5] = 63 v[451,5] = 49 v[452,5] = 61 v[453,5] = 21 v[454,5] = 51 v[455,5] = 15 v[456,5] = 19 v[457,5] = 43 v[458,5] = 47 v[459,5] = 17 v[460,5] = 9 v[461,5] = 53 v[462,5] = 45 v[463,5] = 11 v[464,5] = 51 v[465,5] = 25 v[466,5] = 11 v[467,5] = 25 v[468,5] = 47 v[469,5] = 47 v[470,5] = 1 v[471,5] = 43 v[472,5] = 29 v[473,5] = 17 v[474,5] = 31 v[475,5] = 15 v[476,5] = 59 v[477,5] = 27 v[478,5] = 63 v[479,5] = 11 v[480,5] = 41 v[481,5] = 51 v[482,5] = 29 v[483,5] = 7 v[484,5] = 27 v[485,5] = 63 v[486,5] = 31 v[487,5] = 43 v[488,5] = 3 v[489,5] = 29 v[490,5] = 39 v[491,5] = 3 v[492,5] = 59 v[493,5] = 59 v[494,5] = 1 v[495,5] = 53 v[496,5] = 63 v[497,5] = 23 v[498,5] = 63 v[499,5] = 47 v[500,5] = 51 v[501,5] = 23 v[502,5] = 61 v[503,5] = 39 v[504,5] = 47 v[505,5] = 21 v[506,5] = 39 v[507,5] = 15 v[508,5] = 3 v[509,5] = 9 v[510,5] = 57 v[511,5] = 61 v[512,5] = 39 v[513,5] = 37 v[514,5] = 21 v[515,5] = 51 v[516,5] = 1 v[517,5] = 23 v[518,5] = 43 v[519,5] = 27 v[520,5] = 25 v[521,5] = 11 v[522,5] = 13 v[523,5] = 21 v[524,5] = 43 v[525,5] = 7 v[526,5] = 11 v[527,5] = 33 v[528,5] = 55 v[529,5] = 1 v[530,5] = 37 v[531,5] = 35 v[532,5] = 27 v[533,5] = 61 v[534,5] = 39 v[535,5] = 5 v[536,5] = 19 v[537,5] = 61 v[538,5] = 61 v[539,5] = 57 v[540,5] = 59 v[541,5] = 21 v[542,5] = 59 v[543,5] = 61 v[544,5] = 57 v[545,5] = 25 v[546,5] = 55 v[547,5] = 27 v[548,5] = 31 v[549,5] = 41 v[550,5] = 33 v[551,5] = 63 v[552,5] = 19 v[553,5] = 57 v[554,5] = 35 v[555,5] = 13 v[556,5] = 63 v[557,5] = 35 v[558,5] = 17 v[559,5] = 11 v[560,5] = 11 v[561,5] = 49 v[562,5] = 41 v[563,5] = 55 v[564,5] = 5 v[565,5] = 45 v[566,5] = 17 v[567,5] = 35 v[568,5] = 5 v[569,5] = 31 v[570,5] = 31 v[571,5] = 37 v[572,5] = 17 v[573,5] = 45 v[574,5] = 51 v[575,5] = 1 v[576,5] = 39 v[577,5] = 49 v[578,5] = 55 v[579,5] = 19 v[580,5] = 41 v[581,5] = 13 v[582,5] = 5 v[583,5] = 51 v[584,5] = 5 v[585,5] = 49 v[586,5] = 1 v[587,5] = 21 v[588,5] = 13 v[589,5] = 17 v[590,5] = 59 v[591,5] = 51 v[592,5] = 11 v[593,5] = 3 v[594,5] = 61 v[595,5] = 1 v[596,5] = 33 v[597,5] = 37 v[598,5] = 33 v[599,5] = 61 v[600,5] = 25 v[601,5] = 27 v[602,5] = 59 v[603,5] = 7 v[604,5] = 49 v[605,5] = 13 v[606,5] = 63 v[607,5] = 3 v[608,5] = 33 v[609,5] = 3 v[610,5] = 15 v[611,5] = 9 v[612,5] = 13 v[613,5] = 35 v[614,5] = 39 v[615,5] = 11 v[616,5] = 59 v[617,5] = 59 v[618,5] = 1 v[619,5] = 57 v[620,5] = 11 v[621,5] = 5 v[622,5] = 57 v[623,5] = 13 v[624,5] = 31 v[625,5] = 13 v[626,5] = 11 v[627,5] = 55 v[628,5] = 45 v[629,5] = 9 v[630,5] = 55 v[631,5] = 55 v[632,5] = 19 v[633,5] = 25 v[634,5] = 41 v[635,5] = 23 v[636,5] = 45 v[637,5] = 29 v[638,5] = 63 v[639,5] = 59 v[640,5] = 27 v[641,5] = 39 v[642,5] = 21 v[643,5] = 37 v[644,5] = 7 v[645,5] = 61 v[646,5] = 49 v[647,5] = 35 v[648,5] = 39 v[649,5] = 9 v[650,5] = 29 v[651,5] = 7 v[652,5] = 25 v[653,5] = 23 v[654,5] = 57 v[655,5] = 5 v[656,5] = 19 v[657,5] = 15 v[658,5] = 33 v[659,5] = 49 v[660,5] = 37 v[661,5] = 25 v[662,5] = 17 v[663,5] = 45 v[664,5] = 29 v[665,5] = 15 v[666,5] = 25 v[667,5] = 3 v[668,5] = 3 v[669,5] = 49 v[670,5] = 11 v[671,5] = 39 v[672,5] = 15 v[673,5] = 19 v[674,5] = 57 v[675,5] = 39 v[676,5] = 15 v[677,5] = 11 v[678,5] = 3 v[679,5] = 57 v[680,5] = 31 v[681,5] = 55 v[682,5] = 61 v[683,5] = 19 v[684,5] = 5 v[685,5] = 41 v[686,5] = 35 v[687,5] = 59 v[688,5] = 61 v[689,5] = 39 v[690,5] = 41 v[691,5] = 53 v[692,5] = 53 v[693,5] = 63 v[694,5] = 31 v[695,5] = 9 v[696,5] = 59 v[697,5] = 13 v[698,5] = 35 v[699,5] = 55 v[700,5] = 41 v[701,5] = 49 v[702,5] = 5 v[703,5] = 41 v[704,5] = 25 v[705,5] = 27 v[706,5] = 43 v[707,5] = 5 v[708,5] = 5 v[709,5] = 43 v[710,5] = 5 v[711,5] = 5 v[712,5] = 17 v[713,5] = 5 v[714,5] = 15 v[715,5] = 27 v[716,5] = 29 v[717,5] = 17 v[718,5] = 9 v[719,5] = 3 v[720,5] = 55 v[721,5] = 31 v[722,5] = 1 v[723,5] = 45 v[724,5] = 45 v[725,5] = 13 v[726,5] = 57 v[727,5] = 17 v[728,5] = 3 v[729,5] = 61 v[730,5] = 15 v[731,5] = 49 v[732,5] = 15 v[733,5] = 47 v[734,5] = 9 v[735,5] = 37 v[736,5] = 45 v[737,5] = 9 v[738,5] = 51 v[739,5] = 61 v[740,5] = 21 v[741,5] = 33 v[742,5] = 11 v[743,5] = 21 v[744,5] = 63 v[745,5] = 63 v[746,5] = 47 v[747,5] = 57 v[748,5] = 61 v[749,5] = 49 v[750,5] = 9 v[751,5] = 59 v[752,5] = 19 v[753,5] = 29 v[754,5] = 21 v[755,5] = 23 v[756,5] = 55 v[757,5] = 23 v[758,5] = 43 v[759,5] = 41 v[760,5] = 57 v[761,5] = 9 v[762,5] = 39 v[763,5] = 27 v[764,5] = 41 v[765,5] = 35 v[766,5] = 61 v[767,5] = 29 v[768,5] = 57 v[769,5] = 63 v[770,5] = 21 v[771,5] = 31 v[772,5] = 59 v[773,5] = 35 v[774,5] = 49 v[775,5] = 3 v[776,5] = 49 v[777,5] = 47 v[778,5] = 49 v[779,5] = 33 v[780,5] = 21 v[781,5] = 19 v[782,5] = 21 v[783,5] = 35 v[784,5] = 11 v[785,5] = 17 v[786,5] = 37 v[787,5] = 23 v[788,5] = 59 v[789,5] = 13 v[790,5] = 37 v[791,5] = 35 v[792,5] = 55 v[793,5] = 57 v[794,5] = 1 v[795,5] = 29 v[796,5] = 45 v[797,5] = 11 v[798,5] = 1 v[799,5] = 15 v[800,5] = 9 v[801,5] = 33 v[802,5] = 19 v[803,5] = 53 v[804,5] = 43 v[805,5] = 39 v[806,5] = 23 v[807,5] = 7 v[808,5] = 13 v[809,5] = 13 v[810,5] = 1 v[811,5] = 19 v[812,5] = 41 v[813,5] = 55 v[814,5] = 1 v[815,5] = 13 v[816,5] = 15 v[817,5] = 59 v[818,5] = 55 v[819,5] = 15 v[820,5] = 3 v[821,5] = 57 v[822,5] = 37 v[823,5] = 31 v[824,5] = 17 v[825,5] = 1 v[826,5] = 3 v[827,5] = 21 v[828,5] = 29 v[829,5] = 25 v[830,5] = 55 v[831,5] = 9 v[832,5] = 37 v[833,5] = 33 v[834,5] = 53 v[835,5] = 41 v[836,5] = 51 v[837,5] = 19 v[838,5] = 57 v[839,5] = 13 v[840,5] = 63 v[841,5] = 43 v[842,5] = 19 v[843,5] = 7 v[844,5] = 13 v[845,5] = 37 v[846,5] = 33 v[847,5] = 19 v[848,5] = 15 v[849,5] = 63 v[850,5] = 51 v[851,5] = 11 v[852,5] = 49 v[853,5] = 23 v[854,5] = 57 v[855,5] = 47 v[856,5] = 51 v[857,5] = 15 v[858,5] = 53 v[859,5] = 41 v[860,5] = 1 v[861,5] = 15 v[862,5] = 37 v[863,5] = 61 v[864,5] = 11 v[865,5] = 35 v[866,5] = 29 v[867,5] = 33 v[868,5] = 23 v[869,5] = 55 v[870,5] = 11 v[871,5] = 59 v[872,5] = 19 v[873,5] = 61 v[874,5] = 61 v[875,5] = 45 v[876,5] = 13 v[877,5] = 49 v[878,5] = 13 v[879,5] = 63 v[880,5] = 5 v[881,5] = 61 v[882,5] = 5 v[883,5] = 31 v[884,5] = 17 v[885,5] = 61 v[886,5] = 63 v[887,5] = 13 v[888,5] = 27 v[889,5] = 57 v[890,5] = 1 v[891,5] = 21 v[892,5] = 5 v[893,5] = 11 v[894,5] = 39 v[895,5] = 57 v[896,5] = 51 v[897,5] = 53 v[898,5] = 39 v[899,5] = 25 v[900,5] = 41 v[901,5] = 39 v[902,5] = 37 v[903,5] = 23 v[904,5] = 31 v[905,5] = 25 v[906,5] = 33 v[907,5] = 17 v[908,5] = 57 v[909,5] = 29 v[910,5] = 27 v[911,5] = 23 v[912,5] = 47 v[913,5] = 41 v[914,5] = 29 v[915,5] = 19 v[916,5] = 47 v[917,5] = 41 v[918,5] = 25 v[919,5] = 5 v[920,5] = 51 v[921,5] = 43 v[922,5] = 39 v[923,5] = 29 v[924,5] = 7 v[925,5] = 31 v[926,5] = 45 v[927,5] = 51 v[928,5] = 49 v[929,5] = 55 v[930,5] = 17 v[931,5] = 43 v[932,5] = 49 v[933,5] = 45 v[934,5] = 9 v[935,5] = 29 v[936,5] = 3 v[937,5] = 5 v[938,5] = 47 v[939,5] = 9 v[940,5] = 15 v[941,5] = 19 v[942,5] = 51 v[943,5] = 45 v[944,5] = 57 v[945,5] = 63 v[946,5] = 9 v[947,5] = 21 v[948,5] = 59 v[949,5] = 3 v[950,5] = 9 v[951,5] = 13 v[952,5] = 45 v[953,5] = 23 v[954,5] = 15 v[955,5] = 31 v[956,5] = 21 v[957,5] = 15 v[958,5] = 51 v[959,5] = 35 v[960,5] = 9 v[961,5] = 11 v[962,5] = 61 v[963,5] = 23 v[964,5] = 53 v[965,5] = 29 v[966,5] = 51 v[967,5] = 45 v[968,5] = 31 v[969,5] = 29 v[970,5] = 5 v[971,5] = 35 v[972,5] = 29 v[973,5] = 53 v[974,5] = 35 v[975,5] = 17 v[976,5] = 59 v[977,5] = 55 v[978,5] = 27 v[979,5] = 51 v[980,5] = 59 v[981,5] = 27 v[982,5] = 47 v[983,5] = 15 v[984,5] = 29 v[985,5] = 37 v[986,5] = 7 v[987,5] = 49 v[988,5] = 55 v[989,5] = 5 v[990,5] = 19 v[991,5] = 45 v[992,5] = 29 v[993,5] = 19 v[994,5] = 57 v[995,5] = 33 v[996,5] = 53 v[997,5] = 45 v[998,5] = 21 v[999,5] = 9 v[1000,5] = 3 v[1001,5] = 35 v[1002,5] = 29 v[1003,5] = 43 v[1004,5] = 31 v[1005,5] = 39 v[1006,5] = 3 v[1007,5] = 45 v[1008,5] = 1 v[1009,5] = 41 v[1010,5] = 29 v[1011,5] = 5 v[1012,5] = 59 v[1013,5] = 41 v[1014,5] = 33 v[1015,5] = 35 v[1016,5] = 27 v[1017,5] = 19 v[1018,5] = 13 v[1019,5] = 25 v[1020,5] = 27 v[1021,5] = 43 v[1022,5] = 33 v[1023,5] = 35 v[1024,5] = 17 v[1025,5] = 17 v[1026,5] = 23 v[1027,5] = 7 v[1028,5] = 35 v[1029,5] = 15 v[1030,5] = 61 v[1031,5] = 61 v[1032,5] = 53 v[1033,5] = 5 v[1034,5] = 15 v[1035,5] = 23 v[1036,5] = 11 v[1037,5] = 13 v[1038,5] = 43 v[1039,5] = 55 v[1040,5] = 47 v[1041,5] = 25 v[1042,5] = 43 v[1043,5] = 15 v[1044,5] = 57 v[1045,5] = 45 v[1046,5] = 1 v[1047,5] = 49 v[1048,5] = 63 v[1049,5] = 57 v[1050,5] = 15 v[1051,5] = 31 v[1052,5] = 31 v[1053,5] = 7 v[1054,5] = 53 v[1055,5] = 27 v[1056,5] = 15 v[1057,5] = 47 v[1058,5] = 23 v[1059,5] = 7 v[1060,5] = 29 v[1061,5] = 53 v[1062,5] = 47 v[1063,5] = 9 v[1064,5] = 53 v[1065,5] = 3 v[1066,5] = 25 v[1067,5] = 55 v[1068,5] = 45 v[1069,5] = 63 v[1070,5] = 21 v[1071,5] = 17 v[1072,5] = 23 v[1073,5] = 31 v[1074,5] = 27 v[1075,5] = 27 v[1076,5] = 43 v[1077,5] = 63 v[1078,5] = 55 v[1079,5] = 63 v[1080,5] = 45 v[1081,5] = 51 v[1082,5] = 15 v[1083,5] = 27 v[1084,5] = 5 v[1085,5] = 37 v[1086,5] = 43 v[1087,5] = 11 v[1088,5] = 27 v[1089,5] = 5 v[1090,5] = 27 v[1091,5] = 59 v[1092,5] = 21 v[1093,5] = 7 v[1094,5] = 39 v[1095,5] = 27 v[1096,5] = 63 v[1097,5] = 35 v[1098,5] = 47 v[1099,5] = 55 v[1100,5] = 17 v[1101,5] = 17 v[1102,5] = 17 v[1103,5] = 3 v[1104,5] = 19 v[1105,5] = 21 v[1106,5] = 13 v[1107,5] = 49 v[1108,5] = 61 v[1109,5] = 39 v[1110,5] = 15 v[19,6] = 13 v[20,6] = 33 v[21,6] = 115 v[22,6] = 41 v[23,6] = 79 v[24,6] = 17 v[25,6] = 29 v[26,6] = 119 v[27,6] = 75 v[28,6] = 73 v[29,6] = 105 v[30,6] = 7 v[31,6] = 59 v[32,6] = 65 v[33,6] = 21 v[34,6] = 3 v[35,6] = 113 v[36,6] = 61 v[37,6] = 89 v[38,6] = 45 v[39,6] = 107 v[40,6] = 21 v[41,6] = 71 v[42,6] = 79 v[43,6] = 19 v[44,6] = 71 v[45,6] = 61 v[46,6] = 41 v[47,6] = 57 v[48,6] = 121 v[49,6] = 87 v[50,6] = 119 v[51,6] = 55 v[52,6] = 85 v[53,6] = 121 v[54,6] = 119 v[55,6] = 11 v[56,6] = 23 v[57,6] = 61 v[58,6] = 11 v[59,6] = 35 v[60,6] = 33 v[61,6] = 43 v[62,6] = 107 v[63,6] = 113 v[64,6] = 101 v[65,6] = 29 v[66,6] = 87 v[67,6] = 119 v[68,6] = 97 v[69,6] = 29 v[70,6] = 17 v[71,6] = 89 v[72,6] = 5 v[73,6] = 127 v[74,6] = 89 v[75,6] = 119 v[76,6] = 117 v[77,6] = 103 v[78,6] = 105 v[79,6] = 41 v[80,6] = 83 v[81,6] = 25 v[82,6] = 41 v[83,6] = 55 v[84,6] = 69 v[85,6] = 117 v[86,6] = 49 v[87,6] = 127 v[88,6] = 29 v[89,6] = 1 v[90,6] = 99 v[91,6] = 53 v[92,6] = 83 v[93,6] = 15 v[94,6] = 31 v[95,6] = 73 v[96,6] = 115 v[97,6] = 35 v[98,6] = 21 v[99,6] = 89 v[100,6] = 5 v[101,6] = 1 v[102,6] = 91 v[103,6] = 53 v[104,6] = 35 v[105,6] = 95 v[106,6] = 83 v[107,6] = 19 v[108,6] = 85 v[109,6] = 55 v[110,6] = 51 v[111,6] = 101 v[112,6] = 33 v[113,6] = 41 v[114,6] = 55 v[115,6] = 45 v[116,6] = 95 v[117,6] = 61 v[118,6] = 27 v[119,6] = 37 v[120,6] = 89 v[121,6] = 75 v[122,6] = 57 v[123,6] = 61 v[124,6] = 15 v[125,6] = 117 v[126,6] = 15 v[127,6] = 21 v[128,6] = 27 v[129,6] = 25 v[130,6] = 27 v[131,6] = 123 v[132,6] = 39 v[133,6] = 109 v[134,6] = 93 v[135,6] = 51 v[136,6] = 21 v[137,6] = 91 v[138,6] = 109 v[139,6] = 107 v[140,6] = 45 v[141,6] = 15 v[142,6] = 93 v[143,6] = 127 v[144,6] = 3 v[145,6] = 53 v[146,6] = 81 v[147,6] = 79 v[148,6] = 107 v[149,6] = 79 v[150,6] = 87 v[151,6] = 35 v[152,6] = 109 v[153,6] = 73 v[154,6] = 35 v[155,6] = 83 v[156,6] = 107 v[157,6] = 1 v[158,6] = 51 v[159,6] = 7 v[160,6] = 59 v[161,6] = 33 v[162,6] = 115 v[163,6] = 43 v[164,6] = 111 v[165,6] = 45 v[166,6] = 121 v[167,6] = 105 v[168,6] = 125 v[169,6] = 87 v[170,6] = 101 v[171,6] = 41 v[172,6] = 95 v[173,6] = 75 v[174,6] = 1 v[175,6] = 57 v[176,6] = 117 v[177,6] = 21 v[178,6] = 27 v[179,6] = 67 v[180,6] = 29 v[181,6] = 53 v[182,6] = 117 v[183,6] = 63 v[184,6] = 1 v[185,6] = 77 v[186,6] = 89 v[187,6] = 115 v[188,6] = 49 v[189,6] = 127 v[190,6] = 15 v[191,6] = 79 v[192,6] = 81 v[193,6] = 29 v[194,6] = 65 v[195,6] = 103 v[196,6] = 33 v[197,6] = 73 v[198,6] = 79 v[199,6] = 29 v[200,6] = 21 v[201,6] = 113 v[202,6] = 31 v[203,6] = 33 v[204,6] = 107 v[205,6] = 95 v[206,6] = 111 v[207,6] = 59 v[208,6] = 99 v[209,6] = 117 v[210,6] = 63 v[211,6] = 63 v[212,6] = 99 v[213,6] = 39 v[214,6] = 9 v[215,6] = 35 v[216,6] = 63 v[217,6] = 125 v[218,6] = 99 v[219,6] = 45 v[220,6] = 93 v[221,6] = 33 v[222,6] = 93 v[223,6] = 9 v[224,6] = 105 v[225,6] = 75 v[226,6] = 51 v[227,6] = 115 v[228,6] = 11 v[229,6] = 37 v[230,6] = 17 v[231,6] = 41 v[232,6] = 21 v[233,6] = 43 v[234,6] = 73 v[235,6] = 19 v[236,6] = 93 v[237,6] = 7 v[238,6] = 95 v[239,6] = 81 v[240,6] = 93 v[241,6] = 79 v[242,6] = 81 v[243,6] = 55 v[244,6] = 9 v[245,6] = 51 v[246,6] = 63 v[247,6] = 45 v[248,6] = 89 v[249,6] = 73 v[250,6] = 19 v[251,6] = 115 v[252,6] = 39 v[253,6] = 47 v[254,6] = 81 v[255,6] = 39 v[256,6] = 5 v[257,6] = 5 v[258,6] = 45 v[259,6] = 53 v[260,6] = 65 v[261,6] = 49 v[262,6] = 17 v[263,6] = 105 v[264,6] = 13 v[265,6] = 107 v[266,6] = 5 v[267,6] = 5 v[268,6] = 19 v[269,6] = 73 v[270,6] = 59 v[271,6] = 43 v[272,6] = 83 v[273,6] = 97 v[274,6] = 115 v[275,6] = 27 v[276,6] = 1 v[277,6] = 69 v[278,6] = 103 v[279,6] = 3 v[280,6] = 99 v[281,6] = 103 v[282,6] = 63 v[283,6] = 67 v[284,6] = 25 v[285,6] = 121 v[286,6] = 97 v[287,6] = 77 v[288,6] = 13 v[289,6] = 83 v[290,6] = 103 v[291,6] = 41 v[292,6] = 11 v[293,6] = 27 v[294,6] = 81 v[295,6] = 37 v[296,6] = 33 v[297,6] = 125 v[298,6] = 71 v[299,6] = 41 v[300,6] = 41 v[301,6] = 59 v[302,6] = 41 v[303,6] = 87 v[304,6] = 123 v[305,6] = 43 v[306,6] = 101 v[307,6] = 63 v[308,6] = 45 v[309,6] = 39 v[310,6] = 21 v[311,6] = 97 v[312,6] = 15 v[313,6] = 97 v[314,6] = 111 v[315,6] = 21 v[316,6] = 49 v[317,6] = 13 v[318,6] = 17 v[319,6] = 79 v[320,6] = 91 v[321,6] = 65 v[322,6] = 105 v[323,6] = 75 v[324,6] = 1 v[325,6] = 45 v[326,6] = 67 v[327,6] = 83 v[328,6] = 107 v[329,6] = 125 v[330,6] = 87 v[331,6] = 15 v[332,6] = 81 v[333,6] = 95 v[334,6] = 105 v[335,6] = 65 v[336,6] = 45 v[337,6] = 59 v[338,6] = 103 v[339,6] = 23 v[340,6] = 103 v[341,6] = 99 v[342,6] = 67 v[343,6] = 99 v[344,6] = 47 v[345,6] = 117 v[346,6] = 71 v[347,6] = 89 v[348,6] = 35 v[349,6] = 53 v[350,6] = 73 v[351,6] = 9 v[352,6] = 115 v[353,6] = 49 v[354,6] = 37 v[355,6] = 1 v[356,6] = 35 v[357,6] = 9 v[358,6] = 45 v[359,6] = 81 v[360,6] = 19 v[361,6] = 127 v[362,6] = 17 v[363,6] = 17 v[364,6] = 105 v[365,6] = 89 v[366,6] = 49 v[367,6] = 101 v[368,6] = 7 v[369,6] = 37 v[370,6] = 33 v[371,6] = 11 v[372,6] = 95 v[373,6] = 95 v[374,6] = 17 v[375,6] = 111 v[376,6] = 105 v[377,6] = 41 v[378,6] = 115 v[379,6] = 5 v[380,6] = 69 v[381,6] = 101 v[382,6] = 27 v[383,6] = 27 v[384,6] = 101 v[385,6] = 103 v[386,6] = 53 v[387,6] = 9 v[388,6] = 21 v[389,6] = 43 v[390,6] = 79 v[391,6] = 91 v[392,6] = 65 v[393,6] = 117 v[394,6] = 87 v[395,6] = 125 v[396,6] = 55 v[397,6] = 45 v[398,6] = 63 v[399,6] = 85 v[400,6] = 83 v[401,6] = 97 v[402,6] = 45 v[403,6] = 83 v[404,6] = 87 v[405,6] = 113 v[406,6] = 93 v[407,6] = 95 v[408,6] = 5 v[409,6] = 17 v[410,6] = 77 v[411,6] = 77 v[412,6] = 127 v[413,6] = 123 v[414,6] = 45 v[415,6] = 81 v[416,6] = 85 v[417,6] = 121 v[418,6] = 119 v[419,6] = 27 v[420,6] = 85 v[421,6] = 41 v[422,6] = 49 v[423,6] = 15 v[424,6] = 107 v[425,6] = 21 v[426,6] = 51 v[427,6] = 119 v[428,6] = 11 v[429,6] = 87 v[430,6] = 101 v[431,6] = 115 v[432,6] = 63 v[433,6] = 63 v[434,6] = 37 v[435,6] = 121 v[436,6] = 109 v[437,6] = 7 v[438,6] = 43 v[439,6] = 69 v[440,6] = 19 v[441,6] = 77 v[442,6] = 49 v[443,6] = 71 v[444,6] = 59 v[445,6] = 35 v[446,6] = 7 v[447,6] = 13 v[448,6] = 55 v[449,6] = 101 v[450,6] = 127 v[451,6] = 103 v[452,6] = 85 v[453,6] = 109 v[454,6] = 29 v[455,6] = 61 v[456,6] = 67 v[457,6] = 21 v[458,6] = 111 v[459,6] = 67 v[460,6] = 23 v[461,6] = 57 v[462,6] = 75 v[463,6] = 71 v[464,6] = 101 v[465,6] = 123 v[466,6] = 41 v[467,6] = 107 v[468,6] = 101 v[469,6] = 107 v[470,6] = 125 v[471,6] = 27 v[472,6] = 47 v[473,6] = 119 v[474,6] = 41 v[475,6] = 19 v[476,6] = 127 v[477,6] = 33 v[478,6] = 31 v[479,6] = 109 v[480,6] = 7 v[481,6] = 91 v[482,6] = 91 v[483,6] = 39 v[484,6] = 125 v[485,6] = 105 v[486,6] = 47 v[487,6] = 125 v[488,6] = 123 v[489,6] = 91 v[490,6] = 9 v[491,6] = 103 v[492,6] = 45 v[493,6] = 23 v[494,6] = 117 v[495,6] = 9 v[496,6] = 125 v[497,6] = 73 v[498,6] = 11 v[499,6] = 37 v[500,6] = 61 v[501,6] = 79 v[502,6] = 21 v[503,6] = 5 v[504,6] = 47 v[505,6] = 117 v[506,6] = 67 v[507,6] = 53 v[508,6] = 85 v[509,6] = 33 v[510,6] = 81 v[511,6] = 121 v[512,6] = 47 v[513,6] = 61 v[514,6] = 51 v[515,6] = 127 v[516,6] = 29 v[517,6] = 65 v[518,6] = 45 v[519,6] = 41 v[520,6] = 95 v[521,6] = 57 v[522,6] = 73 v[523,6] = 33 v[524,6] = 117 v[525,6] = 61 v[526,6] = 111 v[527,6] = 59 v[528,6] = 123 v[529,6] = 65 v[530,6] = 47 v[531,6] = 105 v[532,6] = 23 v[533,6] = 29 v[534,6] = 107 v[535,6] = 37 v[536,6] = 81 v[537,6] = 67 v[538,6] = 29 v[539,6] = 115 v[540,6] = 119 v[541,6] = 75 v[542,6] = 73 v[543,6] = 99 v[544,6] = 103 v[545,6] = 7 v[546,6] = 57 v[547,6] = 45 v[548,6] = 61 v[549,6] = 95 v[550,6] = 49 v[551,6] = 101 v[552,6] = 101 v[553,6] = 35 v[554,6] = 47 v[555,6] = 119 v[556,6] = 39 v[557,6] = 67 v[558,6] = 31 v[559,6] = 103 v[560,6] = 7 v[561,6] = 61 v[562,6] = 127 v[563,6] = 87 v[564,6] = 3 v[565,6] = 35 v[566,6] = 29 v[567,6] = 73 v[568,6] = 95 v[569,6] = 103 v[570,6] = 71 v[571,6] = 75 v[572,6] = 51 v[573,6] = 87 v[574,6] = 57 v[575,6] = 97 v[576,6] = 11 v[577,6] = 105 v[578,6] = 87 v[579,6] = 41 v[580,6] = 73 v[581,6] = 109 v[582,6] = 69 v[583,6] = 35 v[584,6] = 121 v[585,6] = 39 v[586,6] = 111 v[587,6] = 1 v[588,6] = 77 v[589,6] = 39 v[590,6] = 47 v[591,6] = 53 v[592,6] = 91 v[593,6] = 3 v[594,6] = 17 v[595,6] = 51 v[596,6] = 83 v[597,6] = 39 v[598,6] = 125 v[599,6] = 85 v[600,6] = 111 v[601,6] = 21 v[602,6] = 69 v[603,6] = 85 v[604,6] = 29 v[605,6] = 55 v[606,6] = 11 v[607,6] = 117 v[608,6] = 1 v[609,6] = 47 v[610,6] = 17 v[611,6] = 65 v[612,6] = 63 v[613,6] = 47 v[614,6] = 117 v[615,6] = 17 v[616,6] = 115 v[617,6] = 51 v[618,6] = 25 v[619,6] = 33 v[620,6] = 123 v[621,6] = 123 v[622,6] = 83 v[623,6] = 51 v[624,6] = 113 v[625,6] = 95 v[626,6] = 121 v[627,6] = 51 v[628,6] = 91 v[629,6] = 109 v[630,6] = 43 v[631,6] = 55 v[632,6] = 35 v[633,6] = 55 v[634,6] = 87 v[635,6] = 33 v[636,6] = 37 v[637,6] = 5 v[638,6] = 3 v[639,6] = 45 v[640,6] = 21 v[641,6] = 105 v[642,6] = 127 v[643,6] = 35 v[644,6] = 17 v[645,6] = 35 v[646,6] = 37 v[647,6] = 97 v[648,6] = 97 v[649,6] = 21 v[650,6] = 77 v[651,6] = 123 v[652,6] = 17 v[653,6] = 89 v[654,6] = 53 v[655,6] = 105 v[656,6] = 75 v[657,6] = 25 v[658,6] = 125 v[659,6] = 13 v[660,6] = 47 v[661,6] = 21 v[662,6] = 125 v[663,6] = 23 v[664,6] = 55 v[665,6] = 63 v[666,6] = 61 v[667,6] = 5 v[668,6] = 17 v[669,6] = 93 v[670,6] = 57 v[671,6] = 121 v[672,6] = 69 v[673,6] = 73 v[674,6] = 93 v[675,6] = 121 v[676,6] = 105 v[677,6] = 75 v[678,6] = 91 v[679,6] = 67 v[680,6] = 95 v[681,6] = 75 v[682,6] = 9 v[683,6] = 69 v[684,6] = 97 v[685,6] = 99 v[686,6] = 93 v[687,6] = 11 v[688,6] = 53 v[689,6] = 19 v[690,6] = 73 v[691,6] = 5 v[692,6] = 33 v[693,6] = 79 v[694,6] = 107 v[695,6] = 65 v[696,6] = 69 v[697,6] = 79 v[698,6] = 125 v[699,6] = 25 v[700,6] = 93 v[701,6] = 55 v[702,6] = 61 v[703,6] = 17 v[704,6] = 117 v[705,6] = 69 v[706,6] = 97 v[707,6] = 87 v[708,6] = 111 v[709,6] = 37 v[710,6] = 93 v[711,6] = 59 v[712,6] = 79 v[713,6] = 95 v[714,6] = 53 v[715,6] = 115 v[716,6] = 53 v[717,6] = 85 v[718,6] = 85 v[719,6] = 65 v[720,6] = 59 v[721,6] = 23 v[722,6] = 75 v[723,6] = 21 v[724,6] = 67 v[725,6] = 27 v[726,6] = 99 v[727,6] = 79 v[728,6] = 27 v[729,6] = 3 v[730,6] = 95 v[731,6] = 27 v[732,6] = 69 v[733,6] = 19 v[734,6] = 75 v[735,6] = 47 v[736,6] = 59 v[737,6] = 41 v[738,6] = 85 v[739,6] = 77 v[740,6] = 99 v[741,6] = 55 v[742,6] = 49 v[743,6] = 93 v[744,6] = 93 v[745,6] = 119 v[746,6] = 51 v[747,6] = 125 v[748,6] = 63 v[749,6] = 13 v[750,6] = 15 v[751,6] = 45 v[752,6] = 61 v[753,6] = 19 v[754,6] = 105 v[755,6] = 115 v[756,6] = 17 v[757,6] = 83 v[758,6] = 7 v[759,6] = 7 v[760,6] = 11 v[761,6] = 61 v[762,6] = 37 v[763,6] = 63 v[764,6] = 89 v[765,6] = 95 v[766,6] = 119 v[767,6] = 113 v[768,6] = 67 v[769,6] = 123 v[770,6] = 91 v[771,6] = 33 v[772,6] = 37 v[773,6] = 99 v[774,6] = 43 v[775,6] = 11 v[776,6] = 33 v[777,6] = 65 v[778,6] = 81 v[779,6] = 79 v[780,6] = 81 v[781,6] = 107 v[782,6] = 63 v[783,6] = 63 v[784,6] = 55 v[785,6] = 89 v[786,6] = 91 v[787,6] = 25 v[788,6] = 93 v[789,6] = 101 v[790,6] = 27 v[791,6] = 55 v[792,6] = 75 v[793,6] = 121 v[794,6] = 79 v[795,6] = 43 v[796,6] = 125 v[797,6] = 73 v[798,6] = 27 v[799,6] = 109 v[800,6] = 35 v[801,6] = 21 v[802,6] = 71 v[803,6] = 113 v[804,6] = 89 v[805,6] = 59 v[806,6] = 95 v[807,6] = 41 v[808,6] = 45 v[809,6] = 113 v[810,6] = 119 v[811,6] = 113 v[812,6] = 39 v[813,6] = 59 v[814,6] = 73 v[815,6] = 15 v[816,6] = 13 v[817,6] = 59 v[818,6] = 67 v[819,6] = 121 v[820,6] = 27 v[821,6] = 7 v[822,6] = 105 v[823,6] = 15 v[824,6] = 59 v[825,6] = 59 v[826,6] = 35 v[827,6] = 91 v[828,6] = 89 v[829,6] = 23 v[830,6] = 125 v[831,6] = 97 v[832,6] = 53 v[833,6] = 41 v[834,6] = 91 v[835,6] = 111 v[836,6] = 29 v[837,6] = 31 v[838,6] = 3 v[839,6] = 103 v[840,6] = 61 v[841,6] = 71 v[842,6] = 35 v[843,6] = 7 v[844,6] = 119 v[845,6] = 29 v[846,6] = 45 v[847,6] = 49 v[848,6] = 111 v[849,6] = 41 v[850,6] = 109 v[851,6] = 59 v[852,6] = 125 v[853,6] = 13 v[854,6] = 27 v[855,6] = 19 v[856,6] = 79 v[857,6] = 9 v[858,6] = 75 v[859,6] = 83 v[860,6] = 81 v[861,6] = 33 v[862,6] = 91 v[863,6] = 109 v[864,6] = 33 v[865,6] = 29 v[866,6] = 107 v[867,6] = 111 v[868,6] = 101 v[869,6] = 107 v[870,6] = 109 v[871,6] = 65 v[872,6] = 59 v[873,6] = 43 v[874,6] = 37 v[875,6] = 1 v[876,6] = 9 v[877,6] = 15 v[878,6] = 109 v[879,6] = 37 v[880,6] = 111 v[881,6] = 113 v[882,6] = 119 v[883,6] = 79 v[884,6] = 73 v[885,6] = 65 v[886,6] = 71 v[887,6] = 93 v[888,6] = 17 v[889,6] = 101 v[890,6] = 87 v[891,6] = 97 v[892,6] = 43 v[893,6] = 23 v[894,6] = 75 v[895,6] = 109 v[896,6] = 41 v[897,6] = 49 v[898,6] = 53 v[899,6] = 31 v[900,6] = 97 v[901,6] = 105 v[902,6] = 109 v[903,6] = 119 v[904,6] = 51 v[905,6] = 9 v[906,6] = 53 v[907,6] = 113 v[908,6] = 97 v[909,6] = 73 v[910,6] = 89 v[911,6] = 79 v[912,6] = 49 v[913,6] = 61 v[914,6] = 105 v[915,6] = 13 v[916,6] = 99 v[917,6] = 53 v[918,6] = 71 v[919,6] = 7 v[920,6] = 87 v[921,6] = 21 v[922,6] = 101 v[923,6] = 5 v[924,6] = 71 v[925,6] = 31 v[926,6] = 123 v[927,6] = 121 v[928,6] = 121 v[929,6] = 73 v[930,6] = 79 v[931,6] = 115 v[932,6] = 13 v[933,6] = 39 v[934,6] = 101 v[935,6] = 19 v[936,6] = 37 v[937,6] = 51 v[938,6] = 83 v[939,6] = 97 v[940,6] = 55 v[941,6] = 81 v[942,6] = 91 v[943,6] = 127 v[944,6] = 105 v[945,6] = 89 v[946,6] = 63 v[947,6] = 47 v[948,6] = 49 v[949,6] = 75 v[950,6] = 37 v[951,6] = 77 v[952,6] = 15 v[953,6] = 49 v[954,6] = 107 v[955,6] = 23 v[956,6] = 23 v[957,6] = 35 v[958,6] = 19 v[959,6] = 69 v[960,6] = 17 v[961,6] = 59 v[962,6] = 63 v[963,6] = 73 v[964,6] = 29 v[965,6] = 125 v[966,6] = 61 v[967,6] = 65 v[968,6] = 95 v[969,6] = 101 v[970,6] = 81 v[971,6] = 57 v[972,6] = 69 v[973,6] = 83 v[974,6] = 37 v[975,6] = 11 v[976,6] = 37 v[977,6] = 95 v[978,6] = 1 v[979,6] = 73 v[980,6] = 27 v[981,6] = 29 v[982,6] = 57 v[983,6] = 7 v[984,6] = 65 v[985,6] = 83 v[986,6] = 99 v[987,6] = 69 v[988,6] = 19 v[989,6] = 103 v[990,6] = 43 v[991,6] = 95 v[992,6] = 25 v[993,6] = 19 v[994,6] = 103 v[995,6] = 41 v[996,6] = 125 v[997,6] = 97 v[998,6] = 71 v[999,6] = 105 v[1000,6] = 83 v[1001,6] = 83 v[1002,6] = 61 v[1003,6] = 39 v[1004,6] = 9 v[1005,6] = 45 v[1006,6] = 117 v[1007,6] = 63 v[1008,6] = 31 v[1009,6] = 5 v[1010,6] = 117 v[1011,6] = 67 v[1012,6] = 125 v[1013,6] = 41 v[1014,6] = 117 v[1015,6] = 43 v[1016,6] = 77 v[1017,6] = 97 v[1018,6] = 15 v[1019,6] = 29 v[1020,6] = 5 v[1021,6] = 59 v[1022,6] = 25 v[1023,6] = 63 v[1024,6] = 87 v[1025,6] = 39 v[1026,6] = 39 v[1027,6] = 77 v[1028,6] = 85 v[1029,6] = 37 v[1030,6] = 81 v[1031,6] = 73 v[1032,6] = 89 v[1033,6] = 29 v[1034,6] = 125 v[1035,6] = 109 v[1036,6] = 21 v[1037,6] = 23 v[1038,6] = 119 v[1039,6] = 105 v[1040,6] = 43 v[1041,6] = 93 v[1042,6] = 97 v[1043,6] = 15 v[1044,6] = 125 v[1045,6] = 29 v[1046,6] = 51 v[1047,6] = 69 v[1048,6] = 37 v[1049,6] = 45 v[1050,6] = 31 v[1051,6] = 75 v[1052,6] = 109 v[1053,6] = 119 v[1054,6] = 53 v[1055,6] = 5 v[1056,6] = 101 v[1057,6] = 125 v[1058,6] = 121 v[1059,6] = 35 v[1060,6] = 29 v[1061,6] = 7 v[1062,6] = 63 v[1063,6] = 17 v[1064,6] = 63 v[1065,6] = 13 v[1066,6] = 69 v[1067,6] = 15 v[1068,6] = 105 v[1069,6] = 51 v[1070,6] = 127 v[1071,6] = 105 v[1072,6] = 9 v[1073,6] = 57 v[1074,6] = 95 v[1075,6] = 59 v[1076,6] = 109 v[1077,6] = 35 v[1078,6] = 49 v[1079,6] = 23 v[1080,6] = 33 v[1081,6] = 107 v[1082,6] = 55 v[1083,6] = 33 v[1084,6] = 57 v[1085,6] = 79 v[1086,6] = 73 v[1087,6] = 69 v[1088,6] = 59 v[1089,6] = 107 v[1090,6] = 55 v[1091,6] = 11 v[1092,6] = 63 v[1093,6] = 95 v[1094,6] = 103 v[1095,6] = 23 v[1096,6] = 125 v[1097,6] = 91 v[1098,6] = 31 v[1099,6] = 91 v[1100,6] = 51 v[1101,6] = 65 v[1102,6] = 61 v[1103,6] = 75 v[1104,6] = 69 v[1105,6] = 107 v[1106,6] = 65 v[1107,6] = 101 v[1108,6] = 59 v[1109,6] = 35 v[1110,6] = 15 v[37,7] = 7 v[38,7] = 23 v[39,7] = 39 v[40,7] = 217 v[41,7] = 141 v[42,7] = 27 v[43,7] = 53 v[44,7] = 181 v[45,7] = 169 v[46,7] = 35 v[47,7] = 15 v[48,7] = 207 v[49,7] = 45 v[50,7] = 247 v[51,7] = 185 v[52,7] = 117 v[53,7] = 41 v[54,7] = 81 v[55,7] = 223 v[56,7] = 151 v[57,7] = 81 v[58,7] = 189 v[59,7] = 61 v[60,7] = 95 v[61,7] = 185 v[62,7] = 23 v[63,7] = 73 v[64,7] = 113 v[65,7] = 239 v[66,7] = 85 v[67,7] = 9 v[68,7] = 201 v[69,7] = 83 v[70,7] = 53 v[71,7] = 183 v[72,7] = 203 v[73,7] = 91 v[74,7] = 149 v[75,7] = 101 v[76,7] = 13 v[77,7] = 111 v[78,7] = 239 v[79,7] = 3 v[80,7] = 205 v[81,7] = 253 v[82,7] = 247 v[83,7] = 121 v[84,7] = 189 v[85,7] = 169 v[86,7] = 179 v[87,7] = 197 v[88,7] = 175 v[89,7] = 217 v[90,7] = 249 v[91,7] = 195 v[92,7] = 95 v[93,7] = 63 v[94,7] = 19 v[95,7] = 7 v[96,7] = 5 v[97,7] = 75 v[98,7] = 217 v[99,7] = 245 v[100,7] = 111 v[101,7] = 189 v[102,7] = 165 v[103,7] = 169 v[104,7] = 141 v[105,7] = 221 v[106,7] = 249 v[107,7] = 159 v[108,7] = 253 v[109,7] = 207 v[110,7] = 249 v[111,7] = 219 v[112,7] = 23 v[113,7] = 49 v[114,7] = 127 v[115,7] = 237 v[116,7] = 5 v[117,7] = 25 v[118,7] = 177 v[119,7] = 37 v[120,7] = 103 v[121,7] = 65 v[122,7] = 167 v[123,7] = 81 v[124,7] = 87 v[125,7] = 119 v[126,7] = 45 v[127,7] = 79 v[128,7] = 143 v[129,7] = 57 v[130,7] = 79 v[131,7] = 187 v[132,7] = 143 v[133,7] = 183 v[134,7] = 75 v[135,7] = 97 v[136,7] = 211 v[137,7] = 149 v[138,7] = 175 v[139,7] = 37 v[140,7] = 135 v[141,7] = 189 v[142,7] = 225 v[143,7] = 241 v[144,7] = 63 v[145,7] = 33 v[146,7] = 43 v[147,7] = 13 v[148,7] = 73 v[149,7] = 213 v[150,7] = 57 v[151,7] = 239 v[152,7] = 183 v[153,7] = 117 v[154,7] = 21 v[155,7] = 29 v[156,7] = 115 v[157,7] = 43 v[158,7] = 205 v[159,7] = 223 v[160,7] = 15 v[161,7] = 3 v[162,7] = 159 v[163,7] = 51 v[164,7] = 101 v[165,7] = 127 v[166,7] = 99 v[167,7] = 239 v[168,7] = 171 v[169,7] = 113 v[170,7] = 171 v[171,7] = 119 v[172,7] = 189 v[173,7] = 245 v[174,7] = 201 v[175,7] = 27 v[176,7] = 185 v[177,7] = 229 v[178,7] = 105 v[179,7] = 153 v[180,7] = 189 v[181,7] = 33 v[182,7] = 35 v[183,7] = 137 v[184,7] = 77 v[185,7] = 97 v[186,7] = 17 v[187,7] = 181 v[188,7] = 55 v[189,7] = 197 v[190,7] = 201 v[191,7] = 155 v[192,7] = 37 v[193,7] = 197 v[194,7] = 137 v[195,7] = 223 v[196,7] = 25 v[197,7] = 179 v[198,7] = 91 v[199,7] = 23 v[200,7] = 235 v[201,7] = 53 v[202,7] = 253 v[203,7] = 49 v[204,7] = 181 v[205,7] = 249 v[206,7] = 53 v[207,7] = 173 v[208,7] = 97 v[209,7] = 247 v[210,7] = 67 v[211,7] = 115 v[212,7] = 103 v[213,7] = 159 v[214,7] = 239 v[215,7] = 69 v[216,7] = 173 v[217,7] = 217 v[218,7] = 95 v[219,7] = 221 v[220,7] = 247 v[221,7] = 97 v[222,7] = 91 v[223,7] = 123 v[224,7] = 223 v[225,7] = 213 v[226,7] = 129 v[227,7] = 181 v[228,7] = 87 v[229,7] = 239 v[230,7] = 85 v[231,7] = 89 v[232,7] = 249 v[233,7] = 141 v[234,7] = 39 v[235,7] = 57 v[236,7] = 249 v[237,7] = 71 v[238,7] = 101 v[239,7] = 159 v[240,7] = 33 v[241,7] = 137 v[242,7] = 189 v[243,7] = 71 v[244,7] = 253 v[245,7] = 205 v[246,7] = 171 v[247,7] = 13 v[248,7] = 249 v[249,7] = 109 v[250,7] = 131 v[251,7] = 199 v[252,7] = 189 v[253,7] = 179 v[254,7] = 31 v[255,7] = 99 v[256,7] = 113 v[257,7] = 41 v[258,7] = 173 v[259,7] = 23 v[260,7] = 189 v[261,7] = 197 v[262,7] = 3 v[263,7] = 135 v[264,7] = 9 v[265,7] = 95 v[266,7] = 195 v[267,7] = 27 v[268,7] = 183 v[269,7] = 1 v[270,7] = 123 v[271,7] = 73 v[272,7] = 53 v[273,7] = 99 v[274,7] = 197 v[275,7] = 59 v[276,7] = 27 v[277,7] = 101 v[278,7] = 55 v[279,7] = 193 v[280,7] = 31 v[281,7] = 61 v[282,7] = 119 v[283,7] = 11 v[284,7] = 7 v[285,7] = 255 v[286,7] = 233 v[287,7] = 53 v[288,7] = 157 v[289,7] = 193 v[290,7] = 97 v[291,7] = 83 v[292,7] = 65 v[293,7] = 81 v[294,7] = 239 v[295,7] = 167 v[296,7] = 69 v[297,7] = 71 v[298,7] = 109 v[299,7] = 97 v[300,7] = 137 v[301,7] = 71 v[302,7] = 193 v[303,7] = 189 v[304,7] = 115 v[305,7] = 79 v[306,7] = 205 v[307,7] = 37 v[308,7] = 227 v[309,7] = 53 v[310,7] = 33 v[311,7] = 91 v[312,7] = 229 v[313,7] = 245 v[314,7] = 105 v[315,7] = 77 v[316,7] = 229 v[317,7] = 161 v[318,7] = 103 v[319,7] = 93 v[320,7] = 13 v[321,7] = 161 v[322,7] = 229 v[323,7] = 223 v[324,7] = 69 v[325,7] = 15 v[326,7] = 25 v[327,7] = 23 v[328,7] = 233 v[329,7] = 93 v[330,7] = 25 v[331,7] = 217 v[332,7] = 247 v[333,7] = 61 v[334,7] = 75 v[335,7] = 27 v[336,7] = 9 v[337,7] = 223 v[338,7] = 213 v[339,7] = 55 v[340,7] = 197 v[341,7] = 145 v[342,7] = 89 v[343,7] = 199 v[344,7] = 41 v[345,7] = 201 v[346,7] = 5 v[347,7] = 149 v[348,7] = 35 v[349,7] = 119 v[350,7] = 183 v[351,7] = 53 v[352,7] = 11 v[353,7] = 13 v[354,7] = 3 v[355,7] = 179 v[356,7] = 229 v[357,7] = 43 v[358,7] = 55 v[359,7] = 187 v[360,7] = 233 v[361,7] = 47 v[362,7] = 133 v[363,7] = 91 v[364,7] = 47 v[365,7] = 71 v[366,7] = 93 v[367,7] = 105 v[368,7] = 145 v[369,7] = 45 v[370,7] = 255 v[371,7] = 221 v[372,7] = 115 v[373,7] = 175 v[374,7] = 19 v[375,7] = 129 v[376,7] = 5 v[377,7] = 209 v[378,7] = 197 v[379,7] = 57 v[380,7] = 177 v[381,7] = 115 v[382,7] = 187 v[383,7] = 119 v[384,7] = 77 v[385,7] = 211 v[386,7] = 111 v[387,7] = 33 v[388,7] = 113 v[389,7] = 23 v[390,7] = 87 v[391,7] = 137 v[392,7] = 41 v[393,7] = 7 v[394,7] = 83 v[395,7] = 43 v[396,7] = 121 v[397,7] = 145 v[398,7] = 5 v[399,7] = 219 v[400,7] = 27 v[401,7] = 11 v[402,7] = 111 v[403,7] = 207 v[404,7] = 55 v[405,7] = 97 v[406,7] = 63 v[407,7] = 229 v[408,7] = 53 v[409,7] = 33 v[410,7] = 149 v[411,7] = 23 v[412,7] = 187 v[413,7] = 153 v[414,7] = 91 v[415,7] = 193 v[416,7] = 183 v[417,7] = 59 v[418,7] = 211 v[419,7] = 93 v[420,7] = 139 v[421,7] = 59 v[422,7] = 179 v[423,7] = 163 v[424,7] = 209 v[425,7] = 77 v[426,7] = 39 v[427,7] = 111 v[428,7] = 79 v[429,7] = 229 v[430,7] = 85 v[431,7] = 237 v[432,7] = 199 v[433,7] = 137 v[434,7] = 147 v[435,7] = 25 v[436,7] = 73 v[437,7] = 121 v[438,7] = 129 v[439,7] = 83 v[440,7] = 87 v[441,7] = 93 v[442,7] = 205 v[443,7] = 167 v[444,7] = 53 v[445,7] = 107 v[446,7] = 229 v[447,7] = 213 v[448,7] = 95 v[449,7] = 219 v[450,7] = 109 v[451,7] = 175 v[452,7] = 13 v[453,7] = 209 v[454,7] = 97 v[455,7] = 61 v[456,7] = 147 v[457,7] = 19 v[458,7] = 13 v[459,7] = 123 v[460,7] = 73 v[461,7] = 35 v[462,7] = 141 v[463,7] = 81 v[464,7] = 19 v[465,7] = 171 v[466,7] = 255 v[467,7] = 111 v[468,7] = 107 v[469,7] = 233 v[470,7] = 113 v[471,7] = 133 v[472,7] = 89 v[473,7] = 9 v[474,7] = 231 v[475,7] = 95 v[476,7] = 69 v[477,7] = 33 v[478,7] = 1 v[479,7] = 253 v[480,7] = 219 v[481,7] = 253 v[482,7] = 247 v[483,7] = 129 v[484,7] = 11 v[485,7] = 251 v[486,7] = 221 v[487,7] = 153 v[488,7] = 35 v[489,7] = 103 v[490,7] = 239 v[491,7] = 7 v[492,7] = 27 v[493,7] = 235 v[494,7] = 181 v[495,7] = 5 v[496,7] = 207 v[497,7] = 53 v[498,7] = 149 v[499,7] = 155 v[500,7] = 225 v[501,7] = 165 v[502,7] = 137 v[503,7] = 155 v[504,7] = 201 v[505,7] = 97 v[506,7] = 245 v[507,7] = 203 v[508,7] = 47 v[509,7] = 39 v[510,7] = 35 v[511,7] = 105 v[512,7] = 239 v[513,7] = 49 v[514,7] = 15 v[515,7] = 253 v[516,7] = 7 v[517,7] = 237 v[518,7] = 213 v[519,7] = 55 v[520,7] = 87 v[521,7] = 199 v[522,7] = 27 v[523,7] = 175 v[524,7] = 49 v[525,7] = 41 v[526,7] = 229 v[527,7] = 85 v[528,7] = 3 v[529,7] = 149 v[530,7] = 179 v[531,7] = 129 v[532,7] = 185 v[533,7] = 249 v[534,7] = 197 v[535,7] = 15 v[536,7] = 97 v[537,7] = 197 v[538,7] = 139 v[539,7] = 203 v[540,7] = 63 v[541,7] = 33 v[542,7] = 251 v[543,7] = 217 v[544,7] = 199 v[545,7] = 199 v[546,7] = 99 v[547,7] = 249 v[548,7] = 33 v[549,7] = 229 v[550,7] = 177 v[551,7] = 13 v[552,7] = 209 v[553,7] = 147 v[554,7] = 97 v[555,7] = 31 v[556,7] = 125 v[557,7] = 177 v[558,7] = 137 v[559,7] = 187 v[560,7] = 11 v[561,7] = 91 v[562,7] = 223 v[563,7] = 29 v[564,7] = 169 v[565,7] = 231 v[566,7] = 59 v[567,7] = 31 v[568,7] = 163 v[569,7] = 41 v[570,7] = 57 v[571,7] = 87 v[572,7] = 247 v[573,7] = 25 v[574,7] = 127 v[575,7] = 101 v[576,7] = 207 v[577,7] = 187 v[578,7] = 73 v[579,7] = 61 v[580,7] = 105 v[581,7] = 27 v[582,7] = 91 v[583,7] = 171 v[584,7] = 243 v[585,7] = 33 v[586,7] = 3 v[587,7] = 1 v[588,7] = 21 v[589,7] = 229 v[590,7] = 93 v[591,7] = 71 v[592,7] = 61 v[593,7] = 37 v[594,7] = 183 v[595,7] = 65 v[596,7] = 211 v[597,7] = 53 v[598,7] = 11 v[599,7] = 151 v[600,7] = 165 v[601,7] = 47 v[602,7] = 5 v[603,7] = 129 v[604,7] = 79 v[605,7] = 101 v[606,7] = 147 v[607,7] = 169 v[608,7] = 181 v[609,7] = 19 v[610,7] = 95 v[611,7] = 77 v[612,7] = 139 v[613,7] = 197 v[614,7] = 219 v[615,7] = 97 v[616,7] = 239 v[617,7] = 183 v[618,7] = 143 v[619,7] = 9 v[620,7] = 13 v[621,7] = 209 v[622,7] = 23 v[623,7] = 215 v[624,7] = 53 v[625,7] = 137 v[626,7] = 203 v[627,7] = 19 v[628,7] = 151 v[629,7] = 171 v[630,7] = 133 v[631,7] = 219 v[632,7] = 231 v[633,7] = 3 v[634,7] = 15 v[635,7] = 253 v[636,7] = 225 v[637,7] = 33 v[638,7] = 111 v[639,7] = 183 v[640,7] = 213 v[641,7] = 169 v[642,7] = 119 v[643,7] = 111 v[644,7] = 15 v[645,7] = 201 v[646,7] = 123 v[647,7] = 121 v[648,7] = 225 v[649,7] = 113 v[650,7] = 113 v[651,7] = 225 v[652,7] = 161 v[653,7] = 165 v[654,7] = 1 v[655,7] = 139 v[656,7] = 55 v[657,7] = 3 v[658,7] = 93 v[659,7] = 217 v[660,7] = 193 v[661,7] = 97 v[662,7] = 29 v[663,7] = 69 v[664,7] = 231 v[665,7] = 161 v[666,7] = 93 v[667,7] = 69 v[668,7] = 143 v[669,7] = 137 v[670,7] = 9 v[671,7] = 87 v[672,7] = 183 v[673,7] = 113 v[674,7] = 183 v[675,7] = 73 v[676,7] = 215 v[677,7] = 137 v[678,7] = 89 v[679,7] = 251 v[680,7] = 163 v[681,7] = 41 v[682,7] = 227 v[683,7] = 145 v[684,7] = 57 v[685,7] = 81 v[686,7] = 57 v[687,7] = 11 v[688,7] = 135 v[689,7] = 145 v[690,7] = 161 v[691,7] = 175 v[692,7] = 159 v[693,7] = 25 v[694,7] = 55 v[695,7] = 167 v[696,7] = 157 v[697,7] = 211 v[698,7] = 97 v[699,7] = 247 v[700,7] = 249 v[701,7] = 23 v[702,7] = 129 v[703,7] = 159 v[704,7] = 71 v[705,7] = 197 v[706,7] = 127 v[707,7] = 141 v[708,7] = 219 v[709,7] = 5 v[710,7] = 233 v[711,7] = 131 v[712,7] = 217 v[713,7] = 101 v[714,7] = 131 v[715,7] = 33 v[716,7] = 157 v[717,7] = 173 v[718,7] = 69 v[719,7] = 207 v[720,7] = 239 v[721,7] = 81 v[722,7] = 205 v[723,7] = 11 v[724,7] = 41 v[725,7] = 169 v[726,7] = 65 v[727,7] = 193 v[728,7] = 77 v[729,7] = 201 v[730,7] = 173 v[731,7] = 1 v[732,7] = 221 v[733,7] = 157 v[734,7] = 1 v[735,7] = 15 v[736,7] = 113 v[737,7] = 147 v[738,7] = 137 v[739,7] = 205 v[740,7] = 225 v[741,7] = 73 v[742,7] = 45 v[743,7] = 49 v[744,7] = 149 v[745,7] = 113 v[746,7] = 253 v[747,7] = 99 v[748,7] = 17 v[749,7] = 119 v[750,7] = 105 v[751,7] = 117 v[752,7] = 129 v[753,7] = 243 v[754,7] = 75 v[755,7] = 203 v[756,7] = 53 v[757,7] = 29 v[758,7] = 247 v[759,7] = 35 v[760,7] = 247 v[761,7] = 171 v[762,7] = 31 v[763,7] = 199 v[764,7] = 213 v[765,7] = 29 v[766,7] = 251 v[767,7] = 7 v[768,7] = 251 v[769,7] = 187 v[770,7] = 91 v[771,7] = 11 v[772,7] = 149 v[773,7] = 13 v[774,7] = 205 v[775,7] = 37 v[776,7] = 249 v[777,7] = 137 v[778,7] = 139 v[779,7] = 9 v[780,7] = 7 v[781,7] = 113 v[782,7] = 183 v[783,7] = 205 v[784,7] = 187 v[785,7] = 39 v[786,7] = 3 v[787,7] = 79 v[788,7] = 155 v[789,7] = 227 v[790,7] = 89 v[791,7] = 185 v[792,7] = 51 v[793,7] = 127 v[794,7] = 63 v[795,7] = 83 v[796,7] = 41 v[797,7] = 133 v[798,7] = 183 v[799,7] = 181 v[800,7] = 127 v[801,7] = 19 v[802,7] = 255 v[803,7] = 219 v[804,7] = 59 v[805,7] = 251 v[806,7] = 3 v[807,7] = 187 v[808,7] = 57 v[809,7] = 217 v[810,7] = 115 v[811,7] = 217 v[812,7] = 229 v[813,7] = 181 v[814,7] = 185 v[815,7] = 149 v[816,7] = 83 v[817,7] = 115 v[818,7] = 11 v[819,7] = 123 v[820,7] = 19 v[821,7] = 109 v[822,7] = 165 v[823,7] = 103 v[824,7] = 123 v[825,7] = 219 v[826,7] = 129 v[827,7] = 155 v[828,7] = 207 v[829,7] = 177 v[830,7] = 9 v[831,7] = 49 v[832,7] = 181 v[833,7] = 231 v[834,7] = 33 v[835,7] = 233 v[836,7] = 67 v[837,7] = 155 v[838,7] = 41 v[839,7] = 9 v[840,7] = 95 v[841,7] = 123 v[842,7] = 65 v[843,7] = 117 v[844,7] = 249 v[845,7] = 85 v[846,7] = 169 v[847,7] = 129 v[848,7] = 241 v[849,7] = 173 v[850,7] = 251 v[851,7] = 225 v[852,7] = 147 v[853,7] = 165 v[854,7] = 69 v[855,7] = 81 v[856,7] = 239 v[857,7] = 95 v[858,7] = 23 v[859,7] = 83 v[860,7] = 227 v[861,7] = 249 v[862,7] = 143 v[863,7] = 171 v[864,7] = 193 v[865,7] = 9 v[866,7] = 21 v[867,7] = 57 v[868,7] = 73 v[869,7] = 97 v[870,7] = 57 v[871,7] = 29 v[872,7] = 239 v[873,7] = 151 v[874,7] = 159 v[875,7] = 191 v[876,7] = 47 v[877,7] = 51 v[878,7] = 1 v[879,7] = 223 v[880,7] = 251 v[881,7] = 251 v[882,7] = 151 v[883,7] = 41 v[884,7] = 119 v[885,7] = 127 v[886,7] = 131 v[887,7] = 33 v[888,7] = 209 v[889,7] = 123 v[890,7] = 53 v[891,7] = 241 v[892,7] = 25 v[893,7] = 31 v[894,7] = 183 v[895,7] = 107 v[896,7] = 25 v[897,7] = 115 v[898,7] = 39 v[899,7] = 11 v[900,7] = 213 v[901,7] = 239 v[902,7] = 219 v[903,7] = 109 v[904,7] = 185 v[905,7] = 35 v[906,7] = 133 v[907,7] = 123 v[908,7] = 185 v[909,7] = 27 v[910,7] = 55 v[911,7] = 245 v[912,7] = 61 v[913,7] = 75 v[914,7] = 205 v[915,7] = 213 v[916,7] = 169 v[917,7] = 163 v[918,7] = 63 v[919,7] = 55 v[920,7] = 49 v[921,7] = 83 v[922,7] = 195 v[923,7] = 51 v[924,7] = 31 v[925,7] = 41 v[926,7] = 15 v[927,7] = 203 v[928,7] = 41 v[929,7] = 63 v[930,7] = 127 v[931,7] = 161 v[932,7] = 5 v[933,7] = 143 v[934,7] = 7 v[935,7] = 199 v[936,7] = 251 v[937,7] = 95 v[938,7] = 75 v[939,7] = 101 v[940,7] = 15 v[941,7] = 43 v[942,7] = 237 v[943,7] = 197 v[944,7] = 117 v[945,7] = 167 v[946,7] = 155 v[947,7] = 21 v[948,7] = 83 v[949,7] = 205 v[950,7] = 255 v[951,7] = 49 v[952,7] = 101 v[953,7] = 213 v[954,7] = 237 v[955,7] = 135 v[956,7] = 135 v[957,7] = 21 v[958,7] = 73 v[959,7] = 93 v[960,7] = 115 v[961,7] = 7 v[962,7] = 85 v[963,7] = 223 v[964,7] = 237 v[965,7] = 79 v[966,7] = 89 v[967,7] = 5 v[968,7] = 57 v[969,7] = 239 v[970,7] = 67 v[971,7] = 65 v[972,7] = 201 v[973,7] = 155 v[974,7] = 71 v[975,7] = 85 v[976,7] = 195 v[977,7] = 89 v[978,7] = 181 v[979,7] = 119 v[980,7] = 135 v[981,7] = 147 v[982,7] = 237 v[983,7] = 173 v[984,7] = 41 v[985,7] = 155 v[986,7] = 67 v[987,7] = 113 v[988,7] = 111 v[989,7] = 21 v[990,7] = 183 v[991,7] = 23 v[992,7] = 103 v[993,7] = 207 v[994,7] = 253 v[995,7] = 69 v[996,7] = 219 v[997,7] = 205 v[998,7] = 195 v[999,7] = 43 v[1000,7] = 197 v[1001,7] = 229 v[1002,7] = 139 v[1003,7] = 177 v[1004,7] = 129 v[1005,7] = 69 v[1006,7] = 97 v[1007,7] = 201 v[1008,7] = 163 v[1009,7] = 189 v[1010,7] = 11 v[1011,7] = 99 v[1012,7] = 91 v[1013,7] = 253 v[1014,7] = 239 v[1015,7] = 91 v[1016,7] = 145 v[1017,7] = 19 v[1018,7] = 179 v[1019,7] = 231 v[1020,7] = 121 v[1021,7] = 7 v[1022,7] = 225 v[1023,7] = 237 v[1024,7] = 125 v[1025,7] = 191 v[1026,7] = 119 v[1027,7] = 59 v[1028,7] = 175 v[1029,7] = 237 v[1030,7] = 131 v[1031,7] = 79 v[1032,7] = 43 v[1033,7] = 45 v[1034,7] = 205 v[1035,7] = 199 v[1036,7] = 251 v[1037,7] = 153 v[1038,7] = 207 v[1039,7] = 37 v[1040,7] = 179 v[1041,7] = 113 v[1042,7] = 255 v[1043,7] = 107 v[1044,7] = 217 v[1045,7] = 61 v[1046,7] = 7 v[1047,7] = 181 v[1048,7] = 247 v[1049,7] = 31 v[1050,7] = 13 v[1051,7] = 113 v[1052,7] = 145 v[1053,7] = 107 v[1054,7] = 233 v[1055,7] = 233 v[1056,7] = 43 v[1057,7] = 79 v[1058,7] = 23 v[1059,7] = 169 v[1060,7] = 137 v[1061,7] = 129 v[1062,7] = 183 v[1063,7] = 53 v[1064,7] = 91 v[1065,7] = 55 v[1066,7] = 103 v[1067,7] = 223 v[1068,7] = 87 v[1069,7] = 177 v[1070,7] = 157 v[1071,7] = 79 v[1072,7] = 213 v[1073,7] = 139 v[1074,7] = 183 v[1075,7] = 231 v[1076,7] = 205 v[1077,7] = 143 v[1078,7] = 129 v[1079,7] = 243 v[1080,7] = 205 v[1081,7] = 93 v[1082,7] = 59 v[1083,7] = 15 v[1084,7] = 89 v[1085,7] = 9 v[1086,7] = 11 v[1087,7] = 47 v[1088,7] = 133 v[1089,7] = 227 v[1090,7] = 75 v[1091,7] = 9 v[1092,7] = 91 v[1093,7] = 19 v[1094,7] = 171 v[1095,7] = 163 v[1096,7] = 79 v[1097,7] = 7 v[1098,7] = 103 v[1099,7] = 5 v[1100,7] = 119 v[1101,7] = 155 v[1102,7] = 75 v[1103,7] = 11 v[1104,7] = 71 v[1105,7] = 95 v[1106,7] = 17 v[1107,7] = 13 v[1108,7] = 243 v[1109,7] = 207 v[1110,7] = 187 v[53,8] = 235 v[54,8] = 307 v[55,8] = 495 v[56,8] = 417 v[57,8] = 57 v[58,8] = 151 v[59,8] = 19 v[60,8] = 119 v[61,8] = 375 v[62,8] = 451 v[63,8] = 55 v[64,8] = 449 v[65,8] = 501 v[66,8] = 53 v[67,8] = 185 v[68,8] = 317 v[69,8] = 17 v[70,8] = 21 v[71,8] = 487 v[72,8] = 13 v[73,8] = 347 v[74,8] = 393 v[75,8] = 15 v[76,8] = 391 v[77,8] = 307 v[78,8] = 189 v[79,8] = 381 v[80,8] = 71 v[81,8] = 163 v[82,8] = 99 v[83,8] = 467 v[84,8] = 167 v[85,8] = 433 v[86,8] = 337 v[87,8] = 257 v[88,8] = 179 v[89,8] = 47 v[90,8] = 385 v[91,8] = 23 v[92,8] = 117 v[93,8] = 369 v[94,8] = 425 v[95,8] = 207 v[96,8] = 433 v[97,8] = 301 v[98,8] = 147 v[99,8] = 333 v[100,8] = 85 v[101,8] = 221 v[102,8] = 423 v[103,8] = 49 v[104,8] = 3 v[105,8] = 43 v[106,8] = 229 v[107,8] = 227 v[108,8] = 201 v[109,8] = 383 v[110,8] = 281 v[111,8] = 229 v[112,8] = 207 v[113,8] = 21 v[114,8] = 343 v[115,8] = 251 v[116,8] = 397 v[117,8] = 173 v[118,8] = 507 v[119,8] = 421 v[120,8] = 443 v[121,8] = 399 v[122,8] = 53 v[123,8] = 345 v[124,8] = 77 v[125,8] = 385 v[126,8] = 317 v[127,8] = 155 v[128,8] = 187 v[129,8] = 269 v[130,8] = 501 v[131,8] = 19 v[132,8] = 169 v[133,8] = 235 v[134,8] = 415 v[135,8] = 61 v[136,8] = 247 v[137,8] = 183 v[138,8] = 5 v[139,8] = 257 v[140,8] = 401 v[141,8] = 451 v[142,8] = 95 v[143,8] = 455 v[144,8] = 49 v[145,8] = 489 v[146,8] = 75 v[147,8] = 459 v[148,8] = 377 v[149,8] = 87 v[150,8] = 463 v[151,8] = 155 v[152,8] = 233 v[153,8] = 115 v[154,8] = 429 v[155,8] = 211 v[156,8] = 419 v[157,8] = 143 v[158,8] = 487 v[159,8] = 195 v[160,8] = 209 v[161,8] = 461 v[162,8] = 193 v[163,8] = 157 v[164,8] = 193 v[165,8] = 363 v[166,8] = 181 v[167,8] = 271 v[168,8] = 445 v[169,8] = 381 v[170,8] = 231 v[171,8] = 135 v[172,8] = 327 v[173,8] = 403 v[174,8] = 171 v[175,8] = 197 v[176,8] = 181 v[177,8] = 343 v[178,8] = 113 v[179,8] = 313 v[180,8] = 393 v[181,8] = 311 v[182,8] = 415 v[183,8] = 267 v[184,8] = 247 v[185,8] = 425 v[186,8] = 233 v[187,8] = 289 v[188,8] = 55 v[189,8] = 39 v[190,8] = 247 v[191,8] = 327 v[192,8] = 141 v[193,8] = 5 v[194,8] = 189 v[195,8] = 183 v[196,8] = 27 v[197,8] = 337 v[198,8] = 341 v[199,8] = 327 v[200,8] = 87 v[201,8] = 429 v[202,8] = 357 v[203,8] = 265 v[204,8] = 251 v[205,8] = 437 v[206,8] = 201 v[207,8] = 29 v[208,8] = 339 v[209,8] = 257 v[210,8] = 377 v[211,8] = 17 v[212,8] = 53 v[213,8] = 327 v[214,8] = 47 v[215,8] = 375 v[216,8] = 393 v[217,8] = 369 v[218,8] = 403 v[219,8] = 125 v[220,8] = 429 v[221,8] = 257 v[222,8] = 157 v[223,8] = 217 v[224,8] = 85 v[225,8] = 267 v[226,8] = 117 v[227,8] = 337 v[228,8] = 447 v[229,8] = 219 v[230,8] = 501 v[231,8] = 41 v[232,8] = 41 v[233,8] = 193 v[234,8] = 509 v[235,8] = 131 v[236,8] = 207 v[237,8] = 505 v[238,8] = 421 v[239,8] = 149 v[240,8] = 111 v[241,8] = 177 v[242,8] = 167 v[243,8] = 223 v[244,8] = 291 v[245,8] = 91 v[246,8] = 29 v[247,8] = 305 v[248,8] = 151 v[249,8] = 177 v[250,8] = 337 v[251,8] = 183 v[252,8] = 361 v[253,8] = 435 v[254,8] = 307 v[255,8] = 507 v[256,8] = 77 v[257,8] = 181 v[258,8] = 507 v[259,8] = 315 v[260,8] = 145 v[261,8] = 423 v[262,8] = 71 v[263,8] = 103 v[264,8] = 493 v[265,8] = 271 v[266,8] = 469 v[267,8] = 339 v[268,8] = 237 v[269,8] = 437 v[270,8] = 483 v[271,8] = 31 v[272,8] = 219 v[273,8] = 61 v[274,8] = 131 v[275,8] = 391 v[276,8] = 233 v[277,8] = 219 v[278,8] = 69 v[279,8] = 57 v[280,8] = 459 v[281,8] = 225 v[282,8] = 421 v[283,8] = 7 v[284,8] = 461 v[285,8] = 111 v[286,8] = 451 v[287,8] = 277 v[288,8] = 185 v[289,8] = 193 v[290,8] = 125 v[291,8] = 251 v[292,8] = 199 v[293,8] = 73 v[294,8] = 71 v[295,8] = 7 v[296,8] = 409 v[297,8] = 417 v[298,8] = 149 v[299,8] = 193 v[300,8] = 53 v[301,8] = 437 v[302,8] = 29 v[303,8] = 467 v[304,8] = 229 v[305,8] = 31 v[306,8] = 35 v[307,8] = 75 v[308,8] = 105 v[309,8] = 503 v[310,8] = 75 v[311,8] = 317 v[312,8] = 401 v[313,8] = 367 v[314,8] = 131 v[315,8] = 365 v[316,8] = 441 v[317,8] = 433 v[318,8] = 93 v[319,8] = 377 v[320,8] = 405 v[321,8] = 465 v[322,8] = 259 v[323,8] = 283 v[324,8] = 443 v[325,8] = 143 v[326,8] = 445 v[327,8] = 3 v[328,8] = 461 v[329,8] = 329 v[330,8] = 309 v[331,8] = 77 v[332,8] = 323 v[333,8] = 155 v[334,8] = 347 v[335,8] = 45 v[336,8] = 381 v[337,8] = 315 v[338,8] = 463 v[339,8] = 207 v[340,8] = 321 v[341,8] = 157 v[342,8] = 109 v[343,8] = 479 v[344,8] = 313 v[345,8] = 345 v[346,8] = 167 v[347,8] = 439 v[348,8] = 307 v[349,8] = 235 v[350,8] = 473 v[351,8] = 79 v[352,8] = 101 v[353,8] = 245 v[354,8] = 19 v[355,8] = 381 v[356,8] = 251 v[357,8] = 35 v[358,8] = 25 v[359,8] = 107 v[360,8] = 187 v[361,8] = 115 v[362,8] = 113 v[363,8] = 321 v[364,8] = 115 v[365,8] = 445 v[366,8] = 61 v[367,8] = 77 v[368,8] = 293 v[369,8] = 405 v[370,8] = 13 v[371,8] = 53 v[372,8] = 17 v[373,8] = 171 v[374,8] = 299 v[375,8] = 41 v[376,8] = 79 v[377,8] = 3 v[378,8] = 485 v[379,8] = 331 v[380,8] = 13 v[381,8] = 257 v[382,8] = 59 v[383,8] = 201 v[384,8] = 497 v[385,8] = 81 v[386,8] = 451 v[387,8] = 199 v[388,8] = 171 v[389,8] = 81 v[390,8] = 253 v[391,8] = 365 v[392,8] = 75 v[393,8] = 451 v[394,8] = 149 v[395,8] = 483 v[396,8] = 81 v[397,8] = 453 v[398,8] = 469 v[399,8] = 485 v[400,8] = 305 v[401,8] = 163 v[402,8] = 401 v[403,8] = 15 v[404,8] = 91 v[405,8] = 3 v[406,8] = 129 v[407,8] = 35 v[408,8] = 239 v[409,8] = 355 v[410,8] = 211 v[411,8] = 387 v[412,8] = 101 v[413,8] = 299 v[414,8] = 67 v[415,8] = 375 v[416,8] = 405 v[417,8] = 357 v[418,8] = 267 v[419,8] = 363 v[420,8] = 79 v[421,8] = 83 v[422,8] = 437 v[423,8] = 457 v[424,8] = 39 v[425,8] = 97 v[426,8] = 473 v[427,8] = 289 v[428,8] = 179 v[429,8] = 57 v[430,8] = 23 v[431,8] = 49 v[432,8] = 79 v[433,8] = 71 v[434,8] = 341 v[435,8] = 287 v[436,8] = 95 v[437,8] = 229 v[438,8] = 271 v[439,8] = 475 v[440,8] = 49 v[441,8] = 241 v[442,8] = 261 v[443,8] = 495 v[444,8] = 353 v[445,8] = 381 v[446,8] = 13 v[447,8] = 291 v[448,8] = 37 v[449,8] = 251 v[450,8] = 105 v[451,8] = 399 v[452,8] = 81 v[453,8] = 89 v[454,8] = 265 v[455,8] = 507 v[456,8] = 205 v[457,8] = 145 v[458,8] = 331 v[459,8] = 129 v[460,8] = 119 v[461,8] = 503 v[462,8] = 249 v[463,8] = 1 v[464,8] = 289 v[465,8] = 463 v[466,8] = 163 v[467,8] = 443 v[468,8] = 63 v[469,8] = 123 v[470,8] = 361 v[471,8] = 261 v[472,8] = 49 v[473,8] = 429 v[474,8] = 137 v[475,8] = 355 v[476,8] = 175 v[477,8] = 507 v[478,8] = 59 v[479,8] = 277 v[480,8] = 391 v[481,8] = 25 v[482,8] = 185 v[483,8] = 381 v[484,8] = 197 v[485,8] = 39 v[486,8] = 5 v[487,8] = 429 v[488,8] = 119 v[489,8] = 247 v[490,8] = 177 v[491,8] = 329 v[492,8] = 465 v[493,8] = 421 v[494,8] = 271 v[495,8] = 467 v[496,8] = 151 v[497,8] = 45 v[498,8] = 429 v[499,8] = 137 v[500,8] = 471 v[501,8] = 11 v[502,8] = 17 v[503,8] = 409 v[504,8] = 347 v[505,8] = 199 v[506,8] = 463 v[507,8] = 177 v[508,8] = 11 v[509,8] = 51 v[510,8] = 361 v[511,8] = 95 v[512,8] = 497 v[513,8] = 163 v[514,8] = 351 v[515,8] = 127 v[516,8] = 395 v[517,8] = 511 v[518,8] = 327 v[519,8] = 353 v[520,8] = 49 v[521,8] = 105 v[522,8] = 151 v[523,8] = 321 v[524,8] = 331 v[525,8] = 329 v[526,8] = 509 v[527,8] = 107 v[528,8] = 109 v[529,8] = 303 v[530,8] = 467 v[531,8] = 287 v[532,8] = 161 v[533,8] = 45 v[534,8] = 385 v[535,8] = 289 v[536,8] = 363 v[537,8] = 331 v[538,8] = 265 v[539,8] = 407 v[540,8] = 37 v[541,8] = 433 v[542,8] = 315 v[543,8] = 343 v[544,8] = 63 v[545,8] = 51 v[546,8] = 185 v[547,8] = 71 v[548,8] = 27 v[549,8] = 267 v[550,8] = 503 v[551,8] = 239 v[552,8] = 293 v[553,8] = 245 v[554,8] = 281 v[555,8] = 297 v[556,8] = 75 v[557,8] = 461 v[558,8] = 371 v[559,8] = 129 v[560,8] = 189 v[561,8] = 189 v[562,8] = 339 v[563,8] = 287 v[564,8] = 111 v[565,8] = 111 v[566,8] = 379 v[567,8] = 93 v[568,8] = 27 v[569,8] = 185 v[570,8] = 347 v[571,8] = 337 v[572,8] = 247 v[573,8] = 507 v[574,8] = 161 v[575,8] = 231 v[576,8] = 43 v[577,8] = 499 v[578,8] = 73 v[579,8] = 327 v[580,8] = 263 v[581,8] = 331 v[582,8] = 249 v[583,8] = 493 v[584,8] = 37 v[585,8] = 25 v[586,8] = 115 v[587,8] = 3 v[588,8] = 167 v[589,8] = 197 v[590,8] = 127 v[591,8] = 357 v[592,8] = 497 v[593,8] = 103 v[594,8] = 125 v[595,8] = 191 v[596,8] = 165 v[597,8] = 55 v[598,8] = 101 v[599,8] = 95 v[600,8] = 79 v[601,8] = 351 v[602,8] = 341 v[603,8] = 43 v[604,8] = 125 v[605,8] = 135 v[606,8] = 173 v[607,8] = 289 v[608,8] = 373 v[609,8] = 133 v[610,8] = 421 v[611,8] = 241 v[612,8] = 281 v[613,8] = 213 v[614,8] = 177 v[615,8] = 363 v[616,8] = 151 v[617,8] = 227 v[618,8] = 145 v[619,8] = 363 v[620,8] = 239 v[621,8] = 431 v[622,8] = 81 v[623,8] = 397 v[624,8] = 241 v[625,8] = 67 v[626,8] = 291 v[627,8] = 255 v[628,8] = 405 v[629,8] = 421 v[630,8] = 399 v[631,8] = 75 v[632,8] = 399 v[633,8] = 105 v[634,8] = 329 v[635,8] = 41 v[636,8] = 425 v[637,8] = 7 v[638,8] = 283 v[639,8] = 375 v[640,8] = 475 v[641,8] = 427 v[642,8] = 277 v[643,8] = 209 v[644,8] = 411 v[645,8] = 3 v[646,8] = 137 v[647,8] = 195 v[648,8] = 289 v[649,8] = 509 v[650,8] = 121 v[651,8] = 55 v[652,8] = 147 v[653,8] = 275 v[654,8] = 251 v[655,8] = 19 v[656,8] = 129 v[657,8] = 285 v[658,8] = 415 v[659,8] = 487 v[660,8] = 491 v[661,8] = 193 v[662,8] = 219 v[663,8] = 403 v[664,8] = 23 v[665,8] = 97 v[666,8] = 65 v[667,8] = 285 v[668,8] = 75 v[669,8] = 21 v[670,8] = 373 v[671,8] = 261 v[672,8] = 339 v[673,8] = 239 v[674,8] = 495 v[675,8] = 415 v[676,8] = 333 v[677,8] = 107 v[678,8] = 435 v[679,8] = 297 v[680,8] = 213 v[681,8] = 149 v[682,8] = 463 v[683,8] = 199 v[684,8] = 323 v[685,8] = 45 v[686,8] = 19 v[687,8] = 301 v[688,8] = 121 v[689,8] = 499 v[690,8] = 187 v[691,8] = 229 v[692,8] = 63 v[693,8] = 425 v[694,8] = 99 v[695,8] = 281 v[696,8] = 35 v[697,8] = 125 v[698,8] = 349 v[699,8] = 87 v[700,8] = 101 v[701,8] = 59 v[702,8] = 195 v[703,8] = 511 v[704,8] = 355 v[705,8] = 73 v[706,8] = 263 v[707,8] = 243 v[708,8] = 101 v[709,8] = 165 v[710,8] = 141 v[711,8] = 11 v[712,8] = 389 v[713,8] = 219 v[714,8] = 187 v[715,8] = 449 v[716,8] = 447 v[717,8] = 393 v[718,8] = 477 v[719,8] = 305 v[720,8] = 221 v[721,8] = 51 v[722,8] = 355 v[723,8] = 209 v[724,8] = 499 v[725,8] = 479 v[726,8] = 265 v[727,8] = 377 v[728,8] = 145 v[729,8] = 411 v[730,8] = 173 v[731,8] = 11 v[732,8] = 433 v[733,8] = 483 v[734,8] = 135 v[735,8] = 385 v[736,8] = 341 v[737,8] = 89 v[738,8] = 209 v[739,8] = 391 v[740,8] = 33 v[741,8] = 395 v[742,8] = 319 v[743,8] = 451 v[744,8] = 119 v[745,8] = 341 v[746,8] = 227 v[747,8] = 375 v[748,8] = 61 v[749,8] = 331 v[750,8] = 493 v[751,8] = 411 v[752,8] = 293 v[753,8] = 47 v[754,8] = 203 v[755,8] = 375 v[756,8] = 167 v[757,8] = 395 v[758,8] = 155 v[759,8] = 5 v[760,8] = 237 v[761,8] = 361 v[762,8] = 489 v[763,8] = 127 v[764,8] = 21 v[765,8] = 345 v[766,8] = 101 v[767,8] = 371 v[768,8] = 233 v[769,8] = 431 v[770,8] = 109 v[771,8] = 119 v[772,8] = 277 v[773,8] = 125 v[774,8] = 263 v[775,8] = 73 v[776,8] = 135 v[777,8] = 123 v[778,8] = 83 v[779,8] = 123 v[780,8] = 405 v[781,8] = 69 v[782,8] = 75 v[783,8] = 287 v[784,8] = 401 v[785,8] = 23 v[786,8] = 283 v[787,8] = 393 v[788,8] = 41 v[789,8] = 379 v[790,8] = 431 v[791,8] = 11 v[792,8] = 475 v[793,8] = 505 v[794,8] = 19 v[795,8] = 365 v[796,8] = 265 v[797,8] = 271 v[798,8] = 499 v[799,8] = 489 v[800,8] = 443 v[801,8] = 165 v[802,8] = 91 v[803,8] = 83 v[804,8] = 291 v[805,8] = 319 v[806,8] = 199 v[807,8] = 107 v[808,8] = 245 v[809,8] = 389 v[810,8] = 143 v[811,8] = 137 v[812,8] = 89 v[813,8] = 125 v[814,8] = 281 v[815,8] = 381 v[816,8] = 215 v[817,8] = 131 v[818,8] = 299 v[819,8] = 249 v[820,8] = 375 v[821,8] = 455 v[822,8] = 43 v[823,8] = 73 v[824,8] = 281 v[825,8] = 217 v[826,8] = 297 v[827,8] = 229 v[828,8] = 431 v[829,8] = 357 v[830,8] = 81 v[831,8] = 357 v[832,8] = 171 v[833,8] = 451 v[834,8] = 481 v[835,8] = 13 v[836,8] = 387 v[837,8] = 491 v[838,8] = 489 v[839,8] = 439 v[840,8] = 385 v[841,8] = 487 v[842,8] = 177 v[843,8] = 393 v[844,8] = 33 v[845,8] = 71 v[846,8] = 375 v[847,8] = 443 v[848,8] = 129 v[849,8] = 407 v[850,8] = 395 v[851,8] = 127 v[852,8] = 65 v[853,8] = 333 v[854,8] = 309 v[855,8] = 119 v[856,8] = 197 v[857,8] = 435 v[858,8] = 497 v[859,8] = 373 v[860,8] = 71 v[861,8] = 379 v[862,8] = 509 v[863,8] = 387 v[864,8] = 159 v[865,8] = 265 v[866,8] = 477 v[867,8] = 463 v[868,8] = 449 v[869,8] = 47 v[870,8] = 353 v[871,8] = 249 v[872,8] = 335 v[873,8] = 505 v[874,8] = 89 v[875,8] = 141 v[876,8] = 55 v[877,8] = 235 v[878,8] = 187 v[879,8] = 87 v[880,8] = 363 v[881,8] = 93 v[882,8] = 363 v[883,8] = 101 v[884,8] = 67 v[885,8] = 215 v[886,8] = 321 v[887,8] = 331 v[888,8] = 305 v[889,8] = 261 v[890,8] = 411 v[891,8] = 491 v[892,8] = 479 v[893,8] = 65 v[894,8] = 307 v[895,8] = 469 v[896,8] = 415 v[897,8] = 131 v[898,8] = 315 v[899,8] = 487 v[900,8] = 83 v[901,8] = 455 v[902,8] = 19 v[903,8] = 113 v[904,8] = 163 v[905,8] = 503 v[906,8] = 99 v[907,8] = 499 v[908,8] = 251 v[909,8] = 239 v[910,8] = 81 v[911,8] = 167 v[912,8] = 391 v[913,8] = 255 v[914,8] = 317 v[915,8] = 363 v[916,8] = 359 v[917,8] = 395 v[918,8] = 419 v[919,8] = 307 v[920,8] = 251 v[921,8] = 267 v[922,8] = 171 v[923,8] = 461 v[924,8] = 183 v[925,8] = 465 v[926,8] = 165 v[927,8] = 163 v[928,8] = 293 v[929,8] = 477 v[930,8] = 223 v[931,8] = 403 v[932,8] = 389 v[933,8] = 97 v[934,8] = 335 v[935,8] = 357 v[936,8] = 297 v[937,8] = 19 v[938,8] = 469 v[939,8] = 501 v[940,8] = 249 v[941,8] = 85 v[942,8] = 213 v[943,8] = 311 v[944,8] = 265 v[945,8] = 379 v[946,8] = 297 v[947,8] = 283 v[948,8] = 393 v[949,8] = 449 v[950,8] = 463 v[951,8] = 289 v[952,8] = 159 v[953,8] = 289 v[954,8] = 499 v[955,8] = 407 v[956,8] = 129 v[957,8] = 137 v[958,8] = 221 v[959,8] = 43 v[960,8] = 89 v[961,8] = 403 v[962,8] = 271 v[963,8] = 75 v[964,8] = 83 v[965,8] = 445 v[966,8] = 453 v[967,8] = 389 v[968,8] = 149 v[969,8] = 143 v[970,8] = 423 v[971,8] = 499 v[972,8] = 317 v[973,8] = 445 v[974,8] = 157 v[975,8] = 137 v[976,8] = 453 v[977,8] = 163 v[978,8] = 87 v[979,8] = 23 v[980,8] = 391 v[981,8] = 119 v[982,8] = 427 v[983,8] = 323 v[984,8] = 173 v[985,8] = 89 v[986,8] = 259 v[987,8] = 377 v[988,8] = 511 v[989,8] = 249 v[990,8] = 31 v[991,8] = 363 v[992,8] = 229 v[993,8] = 353 v[994,8] = 329 v[995,8] = 493 v[996,8] = 427 v[997,8] = 57 v[998,8] = 205 v[999,8] = 389 v[1000,8] = 91 v[1001,8] = 83 v[1002,8] = 13 v[1003,8] = 219 v[1004,8] = 439 v[1005,8] = 45 v[1006,8] = 35 v[1007,8] = 371 v[1008,8] = 441 v[1009,8] = 17 v[1010,8] = 267 v[1011,8] = 501 v[1012,8] = 53 v[1013,8] = 25 v[1014,8] = 333 v[1015,8] = 17 v[1016,8] = 201 v[1017,8] = 475 v[1018,8] = 257 v[1019,8] = 417 v[1020,8] = 345 v[1021,8] = 381 v[1022,8] = 377 v[1023,8] = 55 v[1024,8] = 403 v[1025,8] = 77 v[1026,8] = 389 v[1027,8] = 347 v[1028,8] = 363 v[1029,8] = 211 v[1030,8] = 413 v[1031,8] = 419 v[1032,8] = 5 v[1033,8] = 167 v[1034,8] = 219 v[1035,8] = 201 v[1036,8] = 285 v[1037,8] = 425 v[1038,8] = 11 v[1039,8] = 77 v[1040,8] = 269 v[1041,8] = 489 v[1042,8] = 281 v[1043,8] = 403 v[1044,8] = 79 v[1045,8] = 425 v[1046,8] = 125 v[1047,8] = 81 v[1048,8] = 331 v[1049,8] = 437 v[1050,8] = 271 v[1051,8] = 397 v[1052,8] = 299 v[1053,8] = 475 v[1054,8] = 271 v[1055,8] = 249 v[1056,8] = 413 v[1057,8] = 233 v[1058,8] = 261 v[1059,8] = 495 v[1060,8] = 171 v[1061,8] = 69 v[1062,8] = 27 v[1063,8] = 409 v[1064,8] = 21 v[1065,8] = 421 v[1066,8] = 367 v[1067,8] = 81 v[1068,8] = 483 v[1069,8] = 255 v[1070,8] = 15 v[1071,8] = 219 v[1072,8] = 365 v[1073,8] = 497 v[1074,8] = 181 v[1075,8] = 75 v[1076,8] = 431 v[1077,8] = 99 v[1078,8] = 325 v[1079,8] = 407 v[1080,8] = 229 v[1081,8] = 281 v[1082,8] = 63 v[1083,8] = 83 v[1084,8] = 493 v[1085,8] = 5 v[1086,8] = 113 v[1087,8] = 15 v[1088,8] = 271 v[1089,8] = 37 v[1090,8] = 87 v[1091,8] = 451 v[1092,8] = 299 v[1093,8] = 83 v[1094,8] = 451 v[1095,8] = 311 v[1096,8] = 441 v[1097,8] = 47 v[1098,8] = 455 v[1099,8] = 47 v[1100,8] = 253 v[1101,8] = 13 v[1102,8] = 109 v[1103,8] = 369 v[1104,8] = 347 v[1105,8] = 11 v[1106,8] = 409 v[1107,8] = 275 v[1108,8] = 63 v[1109,8] = 441 v[1110,8] = 15 v[101,9] = 519 v[102,9] = 307 v[103,9] = 931 v[104,9] = 1023 v[105,9] = 517 v[106,9] = 771 v[107,9] = 151 v[108,9] = 1023 v[109,9] = 539 v[110,9] = 725 v[111,9] = 45 v[112,9] = 927 v[113,9] = 707 v[114,9] = 29 v[115,9] = 125 v[116,9] = 371 v[117,9] = 275 v[118,9] = 279 v[119,9] = 817 v[120,9] = 389 v[121,9] = 453 v[122,9] = 989 v[123,9] = 1015 v[124,9] = 29 v[125,9] = 169 v[126,9] = 743 v[127,9] = 99 v[128,9] = 923 v[129,9] = 981 v[130,9] = 181 v[131,9] = 693 v[132,9] = 309 v[133,9] = 227 v[134,9] = 111 v[135,9] = 219 v[136,9] = 897 v[137,9] = 377 v[138,9] = 425 v[139,9] = 609 v[140,9] = 227 v[141,9] = 19 v[142,9] = 221 v[143,9] = 143 v[144,9] = 581 v[145,9] = 147 v[146,9] = 919 v[147,9] = 127 v[148,9] = 725 v[149,9] = 793 v[150,9] = 289 v[151,9] = 411 v[152,9] = 835 v[153,9] = 921 v[154,9] = 957 v[155,9] = 443 v[156,9] = 349 v[157,9] = 813 v[158,9] = 5 v[159,9] = 105 v[160,9] = 457 v[161,9] = 393 v[162,9] = 539 v[163,9] = 101 v[164,9] = 197 v[165,9] = 697 v[166,9] = 27 v[167,9] = 343 v[168,9] = 515 v[169,9] = 69 v[170,9] = 485 v[171,9] = 383 v[172,9] = 855 v[173,9] = 693 v[174,9] = 133 v[175,9] = 87 v[176,9] = 743 v[177,9] = 747 v[178,9] = 475 v[179,9] = 87 v[180,9] = 469 v[181,9] = 763 v[182,9] = 721 v[183,9] = 345 v[184,9] = 479 v[185,9] = 965 v[186,9] = 527 v[187,9] = 121 v[188,9] = 271 v[189,9] = 353 v[190,9] = 467 v[191,9] = 177 v[192,9] = 245 v[193,9] = 627 v[194,9] = 113 v[195,9] = 357 v[196,9] = 7 v[197,9] = 691 v[198,9] = 725 v[199,9] = 355 v[200,9] = 889 v[201,9] = 635 v[202,9] = 737 v[203,9] = 429 v[204,9] = 545 v[205,9] = 925 v[206,9] = 357 v[207,9] = 873 v[208,9] = 187 v[209,9] = 351 v[210,9] = 677 v[211,9] = 999 v[212,9] = 921 v[213,9] = 477 v[214,9] = 233 v[215,9] = 765 v[216,9] = 495 v[217,9] = 81 v[218,9] = 953 v[219,9] = 479 v[220,9] = 89 v[221,9] = 173 v[222,9] = 473 v[223,9] = 131 v[224,9] = 961 v[225,9] = 411 v[226,9] = 291 v[227,9] = 967 v[228,9] = 65 v[229,9] = 511 v[230,9] = 13 v[231,9] = 805 v[232,9] = 945 v[233,9] = 369 v[234,9] = 827 v[235,9] = 295 v[236,9] = 163 v[237,9] = 835 v[238,9] = 259 v[239,9] = 207 v[240,9] = 331 v[241,9] = 29 v[242,9] = 315 v[243,9] = 999 v[244,9] = 133 v[245,9] = 967 v[246,9] = 41 v[247,9] = 117 v[248,9] = 677 v[249,9] = 471 v[250,9] = 717 v[251,9] = 881 v[252,9] = 755 v[253,9] = 351 v[254,9] = 723 v[255,9] = 259 v[256,9] = 879 v[257,9] = 455 v[258,9] = 721 v[259,9] = 289 v[260,9] = 149 v[261,9] = 199 v[262,9] = 805 v[263,9] = 987 v[264,9] = 851 v[265,9] = 423 v[266,9] = 597 v[267,9] = 129 v[268,9] = 11 v[269,9] = 733 v[270,9] = 549 v[271,9] = 153 v[272,9] = 285 v[273,9] = 451 v[274,9] = 559 v[275,9] = 377 v[276,9] = 109 v[277,9] = 357 v[278,9] = 143 v[279,9] = 693 v[280,9] = 615 v[281,9] = 677 v[282,9] = 701 v[283,9] = 475 v[284,9] = 767 v[285,9] = 85 v[286,9] = 229 v[287,9] = 509 v[288,9] = 547 v[289,9] = 151 v[290,9] = 389 v[291,9] = 711 v[292,9] = 785 v[293,9] = 657 v[294,9] = 319 v[295,9] = 509 v[296,9] = 99 v[297,9] = 1007 v[298,9] = 775 v[299,9] = 359 v[300,9] = 697 v[301,9] = 677 v[302,9] = 85 v[303,9] = 497 v[304,9] = 105 v[305,9] = 615 v[306,9] = 891 v[307,9] = 71 v[308,9] = 449 v[309,9] = 835 v[310,9] = 609 v[311,9] = 377 v[312,9] = 693 v[313,9] = 665 v[314,9] = 627 v[315,9] = 215 v[316,9] = 911 v[317,9] = 503 v[318,9] = 729 v[319,9] = 131 v[320,9] = 19 v[321,9] = 895 v[322,9] = 199 v[323,9] = 161 v[324,9] = 239 v[325,9] = 633 v[326,9] = 1013 v[327,9] = 537 v[328,9] = 255 v[329,9] = 23 v[330,9] = 149 v[331,9] = 679 v[332,9] = 1021 v[333,9] = 595 v[334,9] = 199 v[335,9] = 557 v[336,9] = 659 v[337,9] = 251 v[338,9] = 829 v[339,9] = 727 v[340,9] = 439 v[341,9] = 495 v[342,9] = 647 v[343,9] = 223 v[344,9] = 949 v[345,9] = 625 v[346,9] = 87 v[347,9] = 481 v[348,9] = 85 v[349,9] = 799 v[350,9] = 917 v[351,9] = 769 v[352,9] = 949 v[353,9] = 739 v[354,9] = 115 v[355,9] = 499 v[356,9] = 945 v[357,9] = 547 v[358,9] = 225 v[359,9] = 1015 v[360,9] = 469 v[361,9] = 737 v[362,9] = 495 v[363,9] = 353 v[364,9] = 103 v[365,9] = 17 v[366,9] = 665 v[367,9] = 639 v[368,9] = 525 v[369,9] = 75 v[370,9] = 447 v[371,9] = 185 v[372,9] = 43 v[373,9] = 729 v[374,9] = 577 v[375,9] = 863 v[376,9] = 735 v[377,9] = 317 v[378,9] = 99 v[379,9] = 17 v[380,9] = 477 v[381,9] = 893 v[382,9] = 537 v[383,9] = 519 v[384,9] = 1017 v[385,9] = 375 v[386,9] = 297 v[387,9] = 325 v[388,9] = 999 v[389,9] = 353 v[390,9] = 343 v[391,9] = 729 v[392,9] = 135 v[393,9] = 489 v[394,9] = 859 v[395,9] = 267 v[396,9] = 141 v[397,9] = 831 v[398,9] = 141 v[399,9] = 893 v[400,9] = 249 v[401,9] = 807 v[402,9] = 53 v[403,9] = 613 v[404,9] = 131 v[405,9] = 547 v[406,9] = 977 v[407,9] = 131 v[408,9] = 999 v[409,9] = 175 v[410,9] = 31 v[411,9] = 341 v[412,9] = 739 v[413,9] = 467 v[414,9] = 675 v[415,9] = 241 v[416,9] = 645 v[417,9] = 247 v[418,9] = 391 v[419,9] = 583 v[420,9] = 183 v[421,9] = 973 v[422,9] = 433 v[423,9] = 367 v[424,9] = 131 v[425,9] = 467 v[426,9] = 571 v[427,9] = 309 v[428,9] = 385 v[429,9] = 977 v[430,9] = 111 v[431,9] = 917 v[432,9] = 935 v[433,9] = 473 v[434,9] = 345 v[435,9] = 411 v[436,9] = 313 v[437,9] = 97 v[438,9] = 149 v[439,9] = 959 v[440,9] = 841 v[441,9] = 839 v[442,9] = 669 v[443,9] = 431 v[444,9] = 51 v[445,9] = 41 v[446,9] = 301 v[447,9] = 247 v[448,9] = 1015 v[449,9] = 377 v[450,9] = 329 v[451,9] = 945 v[452,9] = 269 v[453,9] = 67 v[454,9] = 979 v[455,9] = 581 v[456,9] = 643 v[457,9] = 823 v[458,9] = 557 v[459,9] = 91 v[460,9] = 405 v[461,9] = 117 v[462,9] = 801 v[463,9] = 509 v[464,9] = 347 v[465,9] = 893 v[466,9] = 303 v[467,9] = 227 v[468,9] = 783 v[469,9] = 555 v[470,9] = 867 v[471,9] = 99 v[472,9] = 703 v[473,9] = 111 v[474,9] = 797 v[475,9] = 873 v[476,9] = 541 v[477,9] = 919 v[478,9] = 513 v[479,9] = 343 v[480,9] = 319 v[481,9] = 517 v[482,9] = 135 v[483,9] = 871 v[484,9] = 917 v[485,9] = 285 v[486,9] = 663 v[487,9] = 301 v[488,9] = 15 v[489,9] = 763 v[490,9] = 89 v[491,9] = 323 v[492,9] = 757 v[493,9] = 317 v[494,9] = 807 v[495,9] = 309 v[496,9] = 1013 v[497,9] = 345 v[498,9] = 499 v[499,9] = 279 v[500,9] = 711 v[501,9] = 915 v[502,9] = 411 v[503,9] = 281 v[504,9] = 193 v[505,9] = 739 v[506,9] = 365 v[507,9] = 315 v[508,9] = 375 v[509,9] = 809 v[510,9] = 469 v[511,9] = 487 v[512,9] = 621 v[513,9] = 857 v[514,9] = 975 v[515,9] = 537 v[516,9] = 939 v[517,9] = 585 v[518,9] = 129 v[519,9] = 625 v[520,9] = 447 v[521,9] = 129 v[522,9] = 1017 v[523,9] = 133 v[524,9] = 83 v[525,9] = 3 v[526,9] = 415 v[527,9] = 661 v[528,9] = 53 v[529,9] = 115 v[530,9] = 903 v[531,9] = 49 v[532,9] = 79 v[533,9] = 55 v[534,9] = 385 v[535,9] = 261 v[536,9] = 345 v[537,9] = 297 v[538,9] = 199 v[539,9] = 385 v[540,9] = 617 v[541,9] = 25 v[542,9] = 515 v[543,9] = 275 v[544,9] = 849 v[545,9] = 401 v[546,9] = 471 v[547,9] = 377 v[548,9] = 661 v[549,9] = 535 v[550,9] = 505 v[551,9] = 939 v[552,9] = 465 v[553,9] = 225 v[554,9] = 929 v[555,9] = 219 v[556,9] = 955 v[557,9] = 659 v[558,9] = 441 v[559,9] = 117 v[560,9] = 527 v[561,9] = 427 v[562,9] = 515 v[563,9] = 287 v[564,9] = 191 v[565,9] = 33 v[566,9] = 389 v[567,9] = 197 v[568,9] = 825 v[569,9] = 63 v[570,9] = 417 v[571,9] = 949 v[572,9] = 35 v[573,9] = 571 v[574,9] = 9 v[575,9] = 131 v[576,9] = 609 v[577,9] = 439 v[578,9] = 95 v[579,9] = 19 v[580,9] = 569 v[581,9] = 893 v[582,9] = 451 v[583,9] = 397 v[584,9] = 971 v[585,9] = 801 v[586,9] = 125 v[587,9] = 471 v[588,9] = 187 v[589,9] = 257 v[590,9] = 67 v[591,9] = 949 v[592,9] = 621 v[593,9] = 453 v[594,9] = 411 v[595,9] = 621 v[596,9] = 955 v[597,9] = 309 v[598,9] = 783 v[599,9] = 893 v[600,9] = 597 v[601,9] = 377 v[602,9] = 753 v[603,9] = 145 v[604,9] = 637 v[605,9] = 941 v[606,9] = 593 v[607,9] = 317 v[608,9] = 555 v[609,9] = 375 v[610,9] = 575 v[611,9] = 175 v[612,9] = 403 v[613,9] = 571 v[614,9] = 555 v[615,9] = 109 v[616,9] = 377 v[617,9] = 931 v[618,9] = 499 v[619,9] = 649 v[620,9] = 653 v[621,9] = 329 v[622,9] = 279 v[623,9] = 271 v[624,9] = 647 v[625,9] = 721 v[626,9] = 665 v[627,9] = 429 v[628,9] = 957 v[629,9] = 803 v[630,9] = 767 v[631,9] = 425 v[632,9] = 477 v[633,9] = 995 v[634,9] = 105 v[635,9] = 495 v[636,9] = 575 v[637,9] = 687 v[638,9] = 385 v[639,9] = 227 v[640,9] = 923 v[641,9] = 563 v[642,9] = 723 v[643,9] = 481 v[644,9] = 717 v[645,9] = 111 v[646,9] = 633 v[647,9] = 113 v[648,9] = 369 v[649,9] = 955 v[650,9] = 253 v[651,9] = 321 v[652,9] = 409 v[653,9] = 909 v[654,9] = 367 v[655,9] = 33 v[656,9] = 967 v[657,9] = 453 v[658,9] = 863 v[659,9] = 449 v[660,9] = 539 v[661,9] = 781 v[662,9] = 911 v[663,9] = 113 v[664,9] = 7 v[665,9] = 219 v[666,9] = 725 v[667,9] = 1015 v[668,9] = 971 v[669,9] = 1021 v[670,9] = 525 v[671,9] = 785 v[672,9] = 873 v[673,9] = 191 v[674,9] = 893 v[675,9] = 297 v[676,9] = 507 v[677,9] = 215 v[678,9] = 21 v[679,9] = 153 v[680,9] = 645 v[681,9] = 913 v[682,9] = 755 v[683,9] = 371 v[684,9] = 881 v[685,9] = 113 v[686,9] = 903 v[687,9] = 225 v[688,9] = 49 v[689,9] = 587 v[690,9] = 201 v[691,9] = 927 v[692,9] = 429 v[693,9] = 599 v[694,9] = 513 v[695,9] = 97 v[696,9] = 319 v[697,9] = 331 v[698,9] = 833 v[699,9] = 325 v[700,9] = 887 v[701,9] = 139 v[702,9] = 927 v[703,9] = 399 v[704,9] = 163 v[705,9] = 307 v[706,9] = 803 v[707,9] = 169 v[708,9] = 1019 v[709,9] = 869 v[710,9] = 537 v[711,9] = 907 v[712,9] = 479 v[713,9] = 335 v[714,9] = 697 v[715,9] = 479 v[716,9] = 353 v[717,9] = 769 v[718,9] = 787 v[719,9] = 1023 v[720,9] = 855 v[721,9] = 493 v[722,9] = 883 v[723,9] = 521 v[724,9] = 735 v[725,9] = 297 v[726,9] = 1011 v[727,9] = 991 v[728,9] = 879 v[729,9] = 855 v[730,9] = 591 v[731,9] = 415 v[732,9] = 917 v[733,9] = 375 v[734,9] = 453 v[735,9] = 553 v[736,9] = 189 v[737,9] = 841 v[738,9] = 339 v[739,9] = 211 v[740,9] = 601 v[741,9] = 57 v[742,9] = 765 v[743,9] = 745 v[744,9] = 621 v[745,9] = 209 v[746,9] = 875 v[747,9] = 639 v[748,9] = 7 v[749,9] = 595 v[750,9] = 971 v[751,9] = 263 v[752,9] = 1009 v[753,9] = 201 v[754,9] = 23 v[755,9] = 77 v[756,9] = 621 v[757,9] = 33 v[758,9] = 535 v[759,9] = 963 v[760,9] = 661 v[761,9] = 523 v[762,9] = 263 v[763,9] = 917 v[764,9] = 103 v[765,9] = 623 v[766,9] = 231 v[767,9] = 47 v[768,9] = 301 v[769,9] = 549 v[770,9] = 337 v[771,9] = 675 v[772,9] = 189 v[773,9] = 357 v[774,9] = 1005 v[775,9] = 789 v[776,9] = 189 v[777,9] = 319 v[778,9] = 721 v[779,9] = 1005 v[780,9] = 525 v[781,9] = 675 v[782,9] = 539 v[783,9] = 191 v[784,9] = 813 v[785,9] = 917 v[786,9] = 51 v[787,9] = 167 v[788,9] = 415 v[789,9] = 579 v[790,9] = 755 v[791,9] = 605 v[792,9] = 721 v[793,9] = 837 v[794,9] = 529 v[795,9] = 31 v[796,9] = 327 v[797,9] = 799 v[798,9] = 961 v[799,9] = 279 v[800,9] = 409 v[801,9] = 847 v[802,9] = 649 v[803,9] = 241 v[804,9] = 285 v[805,9] = 545 v[806,9] = 407 v[807,9] = 161 v[808,9] = 591 v[809,9] = 73 v[810,9] = 313 v[811,9] = 811 v[812,9] = 17 v[813,9] = 663 v[814,9] = 269 v[815,9] = 261 v[816,9] = 37 v[817,9] = 783 v[818,9] = 127 v[819,9] = 917 v[820,9] = 231 v[821,9] = 577 v[822,9] = 975 v[823,9] = 793 v[824,9] = 921 v[825,9] = 343 v[826,9] = 751 v[827,9] = 139 v[828,9] = 221 v[829,9] = 79 v[830,9] = 817 v[831,9] = 393 v[832,9] = 545 v[833,9] = 11 v[834,9] = 781 v[835,9] = 71 v[836,9] = 1 v[837,9] = 699 v[838,9] = 767 v[839,9] = 917 v[840,9] = 9 v[841,9] = 107 v[842,9] = 341 v[843,9] = 587 v[844,9] = 903 v[845,9] = 965 v[846,9] = 599 v[847,9] = 507 v[848,9] = 843 v[849,9] = 739 v[850,9] = 579 v[851,9] = 397 v[852,9] = 397 v[853,9] = 325 v[854,9] = 775 v[855,9] = 565 v[856,9] = 925 v[857,9] = 75 v[858,9] = 55 v[859,9] = 979 v[860,9] = 931 v[861,9] = 93 v[862,9] = 957 v[863,9] = 857 v[864,9] = 753 v[865,9] = 965 v[866,9] = 795 v[867,9] = 67 v[868,9] = 5 v[869,9] = 87 v[870,9] = 909 v[871,9] = 97 v[872,9] = 995 v[873,9] = 271 v[874,9] = 875 v[875,9] = 671 v[876,9] = 613 v[877,9] = 33 v[878,9] = 351 v[879,9] = 69 v[880,9] = 811 v[881,9] = 669 v[882,9] = 729 v[883,9] = 401 v[884,9] = 647 v[885,9] = 241 v[886,9] = 435 v[887,9] = 447 v[888,9] = 721 v[889,9] = 271 v[890,9] = 745 v[891,9] = 53 v[892,9] = 775 v[893,9] = 99 v[894,9] = 343 v[895,9] = 451 v[896,9] = 427 v[897,9] = 593 v[898,9] = 339 v[899,9] = 845 v[900,9] = 243 v[901,9] = 345 v[902,9] = 17 v[903,9] = 573 v[904,9] = 421 v[905,9] = 517 v[906,9] = 971 v[907,9] = 499 v[908,9] = 435 v[909,9] = 769 v[910,9] = 75 v[911,9] = 203 v[912,9] = 793 v[913,9] = 985 v[914,9] = 343 v[915,9] = 955 v[916,9] = 735 v[917,9] = 523 v[918,9] = 659 v[919,9] = 703 v[920,9] = 303 v[921,9] = 421 v[922,9] = 951 v[923,9] = 405 v[924,9] = 631 v[925,9] = 825 v[926,9] = 735 v[927,9] = 433 v[928,9] = 841 v[929,9] = 485 v[930,9] = 49 v[931,9] = 749 v[932,9] = 107 v[933,9] = 669 v[934,9] = 211 v[935,9] = 497 v[936,9] = 143 v[937,9] = 99 v[938,9] = 57 v[939,9] = 277 v[940,9] = 969 v[941,9] = 107 v[942,9] = 397 v[943,9] = 563 v[944,9] = 551 v[945,9] = 447 v[946,9] = 381 v[947,9] = 187 v[948,9] = 57 v[949,9] = 405 v[950,9] = 731 v[951,9] = 769 v[952,9] = 923 v[953,9] = 955 v[954,9] = 915 v[955,9] = 737 v[956,9] = 595 v[957,9] = 341 v[958,9] = 253 v[959,9] = 823 v[960,9] = 197 v[961,9] = 321 v[962,9] = 315 v[963,9] = 181 v[964,9] = 885 v[965,9] = 497 v[966,9] = 159 v[967,9] = 571 v[968,9] = 981 v[969,9] = 899 v[970,9] = 785 v[971,9] = 947 v[972,9] = 217 v[973,9] = 217 v[974,9] = 135 v[975,9] = 753 v[976,9] = 623 v[977,9] = 565 v[978,9] = 717 v[979,9] = 903 v[980,9] = 581 v[981,9] = 955 v[982,9] = 621 v[983,9] = 361 v[984,9] = 869 v[985,9] = 87 v[986,9] = 943 v[987,9] = 907 v[988,9] = 853 v[989,9] = 353 v[990,9] = 335 v[991,9] = 197 v[992,9] = 771 v[993,9] = 433 v[994,9] = 743 v[995,9] = 195 v[996,9] = 91 v[997,9] = 1023 v[998,9] = 63 v[999,9] = 301 v[1000,9] = 647 v[1001,9] = 205 v[1002,9] = 485 v[1003,9] = 927 v[1004,9] = 1003 v[1005,9] = 987 v[1006,9] = 359 v[1007,9] = 577 v[1008,9] = 147 v[1009,9] = 141 v[1010,9] = 1017 v[1011,9] = 701 v[1012,9] = 273 v[1013,9] = 89 v[1014,9] = 589 v[1015,9] = 487 v[1016,9] = 859 v[1017,9] = 343 v[1018,9] = 91 v[1019,9] = 847 v[1020,9] = 341 v[1021,9] = 173 v[1022,9] = 287 v[1023,9] = 1003 v[1024,9] = 289 v[1025,9] = 639 v[1026,9] = 983 v[1027,9] = 685 v[1028,9] = 697 v[1029,9] = 35 v[1030,9] = 701 v[1031,9] = 645 v[1032,9] = 911 v[1033,9] = 501 v[1034,9] = 705 v[1035,9] = 873 v[1036,9] = 763 v[1037,9] = 745 v[1038,9] = 657 v[1039,9] = 559 v[1040,9] = 699 v[1041,9] = 315 v[1042,9] = 347 v[1043,9] = 429 v[1044,9] = 197 v[1045,9] = 165 v[1046,9] = 955 v[1047,9] = 859 v[1048,9] = 167 v[1049,9] = 303 v[1050,9] = 833 v[1051,9] = 531 v[1052,9] = 473 v[1053,9] = 635 v[1054,9] = 641 v[1055,9] = 195 v[1056,9] = 589 v[1057,9] = 821 v[1058,9] = 205 v[1059,9] = 3 v[1060,9] = 635 v[1061,9] = 371 v[1062,9] = 891 v[1063,9] = 249 v[1064,9] = 123 v[1065,9] = 77 v[1066,9] = 623 v[1067,9] = 993 v[1068,9] = 401 v[1069,9] = 525 v[1070,9] = 427 v[1071,9] = 71 v[1072,9] = 655 v[1073,9] = 951 v[1074,9] = 357 v[1075,9] = 851 v[1076,9] = 899 v[1077,9] = 535 v[1078,9] = 493 v[1079,9] = 323 v[1080,9] = 1003 v[1081,9] = 343 v[1082,9] = 515 v[1083,9] = 859 v[1084,9] = 1017 v[1085,9] = 5 v[1086,9] = 423 v[1087,9] = 315 v[1088,9] = 1011 v[1089,9] = 703 v[1090,9] = 41 v[1091,9] = 777 v[1092,9] = 163 v[1093,9] = 95 v[1094,9] = 831 v[1095,9] = 79 v[1096,9] = 975 v[1097,9] = 235 v[1098,9] = 633 v[1099,9] = 723 v[1100,9] = 297 v[1101,9] = 589 v[1102,9] = 317 v[1103,9] = 679 v[1104,9] = 981 v[1105,9] = 195 v[1106,9] = 399 v[1107,9] = 1003 v[1108,9] = 121 v[1109,9] = 501 v[1110,9] = 155 v[161,10] = 7 v[162,10] = 2011 v[163,10] = 1001 v[164,10] = 49 v[165,10] = 825 v[166,10] = 415 v[167,10] = 1441 v[168,10] = 383 v[169,10] = 1581 v[170,10] = 623 v[171,10] = 1621 v[172,10] = 1319 v[173,10] = 1387 v[174,10] = 619 v[175,10] = 839 v[176,10] = 217 v[177,10] = 75 v[178,10] = 1955 v[179,10] = 505 v[180,10] = 281 v[181,10] = 1629 v[182,10] = 1379 v[183,10] = 53 v[184,10] = 1111 v[185,10] = 1399 v[186,10] = 301 v[187,10] = 209 v[188,10] = 49 v[189,10] = 155 v[190,10] = 1647 v[191,10] = 631 v[192,10] = 129 v[193,10] = 1569 v[194,10] = 335 v[195,10] = 67 v[196,10] = 1955 v[197,10] = 1611 v[198,10] = 2021 v[199,10] = 1305 v[200,10] = 121 v[201,10] = 37 v[202,10] = 877 v[203,10] = 835 v[204,10] = 1457 v[205,10] = 669 v[206,10] = 1405 v[207,10] = 935 v[208,10] = 1735 v[209,10] = 665 v[210,10] = 551 v[211,10] = 789 v[212,10] = 1543 v[213,10] = 1267 v[214,10] = 1027 v[215,10] = 1 v[216,10] = 1911 v[217,10] = 163 v[218,10] = 1929 v[219,10] = 67 v[220,10] = 1975 v[221,10] = 1681 v[222,10] = 1413 v[223,10] = 191 v[224,10] = 1711 v[225,10] = 1307 v[226,10] = 401 v[227,10] = 725 v[228,10] = 1229 v[229,10] = 1403 v[230,10] = 1609 v[231,10] = 2035 v[232,10] = 917 v[233,10] = 921 v[234,10] = 1789 v[235,10] = 41 v[236,10] = 2003 v[237,10] = 187 v[238,10] = 67 v[239,10] = 1635 v[240,10] = 717 v[241,10] = 1449 v[242,10] = 277 v[243,10] = 1903 v[244,10] = 1179 v[245,10] = 363 v[246,10] = 1211 v[247,10] = 1231 v[248,10] = 647 v[249,10] = 1261 v[250,10] = 1029 v[251,10] = 1485 v[252,10] = 1309 v[253,10] = 1149 v[254,10] = 317 v[255,10] = 1335 v[256,10] = 171 v[257,10] = 243 v[258,10] = 271 v[259,10] = 1055 v[260,10] = 1601 v[261,10] = 1129 v[262,10] = 1653 v[263,10] = 205 v[264,10] = 1463 v[265,10] = 1681 v[266,10] = 1621 v[267,10] = 197 v[268,10] = 951 v[269,10] = 573 v[270,10] = 1697 v[271,10] = 1265 v[272,10] = 1321 v[273,10] = 1805 v[274,10] = 1235 v[275,10] = 1853 v[276,10] = 1307 v[277,10] = 945 v[278,10] = 1197 v[279,10] = 1411 v[280,10] = 833 v[281,10] = 273 v[282,10] = 1517 v[283,10] = 1747 v[284,10] = 1095 v[285,10] = 1345 v[286,10] = 869 v[287,10] = 57 v[288,10] = 1383 v[289,10] = 221 v[290,10] = 1713 v[291,10] = 335 v[292,10] = 1751 v[293,10] = 1141 v[294,10] = 839 v[295,10] = 523 v[296,10] = 1861 v[297,10] = 1105 v[298,10] = 389 v[299,10] = 1177 v[300,10] = 1877 v[301,10] = 805 v[302,10] = 93 v[303,10] = 1591 v[304,10] = 423 v[305,10] = 1835 v[306,10] = 99 v[307,10] = 1781 v[308,10] = 1515 v[309,10] = 1909 v[310,10] = 1011 v[311,10] = 303 v[312,10] = 385 v[313,10] = 1635 v[314,10] = 357 v[315,10] = 973 v[316,10] = 1781 v[317,10] = 1707 v[318,10] = 1363 v[319,10] = 1053 v[320,10] = 649 v[321,10] = 1469 v[322,10] = 623 v[323,10] = 1429 v[324,10] = 1241 v[325,10] = 1151 v[326,10] = 1055 v[327,10] = 503 v[328,10] = 921 v[329,10] = 3 v[330,10] = 349 v[331,10] = 1149 v[332,10] = 293 v[333,10] = 45 v[334,10] = 303 v[335,10] = 877 v[336,10] = 1565 v[337,10] = 1583 v[338,10] = 1001 v[339,10] = 663 v[340,10] = 1535 v[341,10] = 395 v[342,10] = 1141 v[343,10] = 1481 v[344,10] = 1797 v[345,10] = 643 v[346,10] = 1507 v[347,10] = 465 v[348,10] = 2027 v[349,10] = 1695 v[350,10] = 367 v[351,10] = 937 v[352,10] = 719 v[353,10] = 545 v[354,10] = 1991 v[355,10] = 83 v[356,10] = 819 v[357,10] = 239 v[358,10] = 1791 v[359,10] = 1461 v[360,10] = 1647 v[361,10] = 1501 v[362,10] = 1161 v[363,10] = 1629 v[364,10] = 139 v[365,10] = 1595 v[366,10] = 1921 v[367,10] = 1267 v[368,10] = 1415 v[369,10] = 509 v[370,10] = 347 v[371,10] = 777 v[372,10] = 1083 v[373,10] = 363 v[374,10] = 269 v[375,10] = 1015 v[376,10] = 1809 v[377,10] = 1105 v[378,10] = 1429 v[379,10] = 1471 v[380,10] = 2019 v[381,10] = 381 v[382,10] = 2025 v[383,10] = 1223 v[384,10] = 827 v[385,10] = 1733 v[386,10] = 887 v[387,10] = 1321 v[388,10] = 803 v[389,10] = 1951 v[390,10] = 1297 v[391,10] = 1995 v[392,10] = 833 v[393,10] = 1107 v[394,10] = 1135 v[395,10] = 1181 v[396,10] = 1251 v[397,10] = 983 v[398,10] = 1389 v[399,10] = 1565 v[400,10] = 273 v[401,10] = 137 v[402,10] = 71 v[403,10] = 735 v[404,10] = 1005 v[405,10] = 933 v[406,10] = 67 v[407,10] = 1471 v[408,10] = 551 v[409,10] = 457 v[410,10] = 1667 v[411,10] = 1729 v[412,10] = 919 v[413,10] = 285 v[414,10] = 1629 v[415,10] = 1815 v[416,10] = 653 v[417,10] = 1919 v[418,10] = 1039 v[419,10] = 531 v[420,10] = 393 v[421,10] = 1411 v[422,10] = 359 v[423,10] = 221 v[424,10] = 699 v[425,10] = 1485 v[426,10] = 471 v[427,10] = 1357 v[428,10] = 1715 v[429,10] = 595 v[430,10] = 1677 v[431,10] = 153 v[432,10] = 1903 v[433,10] = 1281 v[434,10] = 215 v[435,10] = 781 v[436,10] = 543 v[437,10] = 293 v[438,10] = 1807 v[439,10] = 965 v[440,10] = 1695 v[441,10] = 443 v[442,10] = 1985 v[443,10] = 321 v[444,10] = 879 v[445,10] = 1227 v[446,10] = 1915 v[447,10] = 839 v[448,10] = 1945 v[449,10] = 1993 v[450,10] = 1165 v[451,10] = 51 v[452,10] = 557 v[453,10] = 723 v[454,10] = 1491 v[455,10] = 817 v[456,10] = 1237 v[457,10] = 947 v[458,10] = 1215 v[459,10] = 1911 v[460,10] = 1225 v[461,10] = 1965 v[462,10] = 1889 v[463,10] = 1503 v[464,10] = 1177 v[465,10] = 73 v[466,10] = 1767 v[467,10] = 303 v[468,10] = 177 v[469,10] = 1897 v[470,10] = 1401 v[471,10] = 321 v[472,10] = 921 v[473,10] = 217 v[474,10] = 1779 v[475,10] = 327 v[476,10] = 1889 v[477,10] = 333 v[478,10] = 615 v[479,10] = 1665 v[480,10] = 1825 v[481,10] = 1639 v[482,10] = 237 v[483,10] = 1205 v[484,10] = 361 v[485,10] = 129 v[486,10] = 1655 v[487,10] = 983 v[488,10] = 1089 v[489,10] = 1171 v[490,10] = 401 v[491,10] = 677 v[492,10] = 643 v[493,10] = 749 v[494,10] = 303 v[495,10] = 1407 v[496,10] = 1873 v[497,10] = 1579 v[498,10] = 1491 v[499,10] = 1393 v[500,10] = 1247 v[501,10] = 789 v[502,10] = 763 v[503,10] = 49 v[504,10] = 5 v[505,10] = 1607 v[506,10] = 1891 v[507,10] = 735 v[508,10] = 1557 v[509,10] = 1909 v[510,10] = 1765 v[511,10] = 1777 v[512,10] = 1127 v[513,10] = 813 v[514,10] = 695 v[515,10] = 97 v[516,10] = 731 v[517,10] = 1503 v[518,10] = 1751 v[519,10] = 333 v[520,10] = 769 v[521,10] = 865 v[522,10] = 693 v[523,10] = 377 v[524,10] = 1919 v[525,10] = 957 v[526,10] = 1359 v[527,10] = 1627 v[528,10] = 1039 v[529,10] = 1783 v[530,10] = 1065 v[531,10] = 1665 v[532,10] = 1917 v[533,10] = 1947 v[534,10] = 991 v[535,10] = 1997 v[536,10] = 841 v[537,10] = 459 v[538,10] = 221 v[539,10] = 327 v[540,10] = 1595 v[541,10] = 1881 v[542,10] = 1269 v[543,10] = 1007 v[544,10] = 129 v[545,10] = 1413 v[546,10] = 475 v[547,10] = 1105 v[548,10] = 791 v[549,10] = 1983 v[550,10] = 1359 v[551,10] = 503 v[552,10] = 691 v[553,10] = 659 v[554,10] = 691 v[555,10] = 343 v[556,10] = 1375 v[557,10] = 1919 v[558,10] = 263 v[559,10] = 1373 v[560,10] = 603 v[561,10] = 1383 v[562,10] = 297 v[563,10] = 781 v[564,10] = 145 v[565,10] = 285 v[566,10] = 767 v[567,10] = 1739 v[568,10] = 1715 v[569,10] = 715 v[570,10] = 317 v[571,10] = 1333 v[572,10] = 85 v[573,10] = 831 v[574,10] = 1615 v[575,10] = 81 v[576,10] = 1667 v[577,10] = 1467 v[578,10] = 1457 v[579,10] = 1453 v[580,10] = 1825 v[581,10] = 109 v[582,10] = 387 v[583,10] = 1207 v[584,10] = 2039 v[585,10] = 213 v[586,10] = 1351 v[587,10] = 1329 v[588,10] = 1173 v[589,10] = 57 v[590,10] = 1769 v[591,10] = 951 v[592,10] = 183 v[593,10] = 23 v[594,10] = 451 v[595,10] = 1155 v[596,10] = 1551 v[597,10] = 2037 v[598,10] = 811 v[599,10] = 635 v[600,10] = 1671 v[601,10] = 1451 v[602,10] = 863 v[603,10] = 1499 v[604,10] = 1673 v[605,10] = 363 v[606,10] = 1029 v[607,10] = 1077 v[608,10] = 1525 v[609,10] = 277 v[610,10] = 1023 v[611,10] = 655 v[612,10] = 665 v[613,10] = 1869 v[614,10] = 1255 v[615,10] = 965 v[616,10] = 277 v[617,10] = 1601 v[618,10] = 329 v[619,10] = 1603 v[620,10] = 1901 v[621,10] = 395 v[622,10] = 65 v[623,10] = 1307 v[624,10] = 2029 v[625,10] = 21 v[626,10] = 1321 v[627,10] = 543 v[628,10] = 1569 v[629,10] = 1185 v[630,10] = 1905 v[631,10] = 1701 v[632,10] = 413 v[633,10] = 2041 v[634,10] = 1697 v[635,10] = 725 v[636,10] = 1417 v[637,10] = 1847 v[638,10] = 411 v[639,10] = 211 v[640,10] = 915 v[641,10] = 1891 v[642,10] = 17 v[643,10] = 1877 v[644,10] = 1699 v[645,10] = 687 v[646,10] = 1089 v[647,10] = 1973 v[648,10] = 1809 v[649,10] = 851 v[650,10] = 1495 v[651,10] = 1257 v[652,10] = 63 v[653,10] = 1323 v[654,10] = 1307 v[655,10] = 609 v[656,10] = 881 v[657,10] = 1543 v[658,10] = 177 v[659,10] = 617 v[660,10] = 1505 v[661,10] = 1747 v[662,10] = 1537 v[663,10] = 925 v[664,10] = 183 v[665,10] = 77 v[666,10] = 1723 v[667,10] = 1877 v[668,10] = 1703 v[669,10] = 397 v[670,10] = 459 v[671,10] = 521 v[672,10] = 257 v[673,10] = 1177 v[674,10] = 389 v[675,10] = 1947 v[676,10] = 1553 v[677,10] = 1583 v[678,10] = 1831 v[679,10] = 261 v[680,10] = 485 v[681,10] = 289 v[682,10] = 1281 v[683,10] = 1543 v[684,10] = 1591 v[685,10] = 1123 v[686,10] = 573 v[687,10] = 821 v[688,10] = 1065 v[689,10] = 1933 v[690,10] = 1373 v[691,10] = 2005 v[692,10] = 905 v[693,10] = 207 v[694,10] = 173 v[695,10] = 1573 v[696,10] = 1597 v[697,10] = 573 v[698,10] = 1883 v[699,10] = 1795 v[700,10] = 1499 v[701,10] = 1743 v[702,10] = 553 v[703,10] = 335 v[704,10] = 333 v[705,10] = 1645 v[706,10] = 791 v[707,10] = 871 v[708,10] = 1157 v[709,10] = 969 v[710,10] = 557 v[711,10] = 141 v[712,10] = 223 v[713,10] = 1129 v[714,10] = 1685 v[715,10] = 423 v[716,10] = 1069 v[717,10] = 391 v[718,10] = 99 v[719,10] = 95 v[720,10] = 1847 v[721,10] = 531 v[722,10] = 1859 v[723,10] = 1833 v[724,10] = 1833 v[725,10] = 341 v[726,10] = 237 v[727,10] = 1997 v[728,10] = 1799 v[729,10] = 409 v[730,10] = 431 v[731,10] = 1917 v[732,10] = 363 v[733,10] = 335 v[734,10] = 1039 v[735,10] = 1085 v[736,10] = 1657 v[737,10] = 1975 v[738,10] = 1527 v[739,10] = 1111 v[740,10] = 659 v[741,10] = 389 v[742,10] = 899 v[743,10] = 595 v[744,10] = 1439 v[745,10] = 1861 v[746,10] = 1979 v[747,10] = 1569 v[748,10] = 1087 v[749,10] = 1009 v[750,10] = 165 v[751,10] = 1895 v[752,10] = 1481 v[753,10] = 1583 v[754,10] = 29 v[755,10] = 1193 v[756,10] = 1673 v[757,10] = 1075 v[758,10] = 301 v[759,10] = 1081 v[760,10] = 1377 v[761,10] = 1747 v[762,10] = 1497 v[763,10] = 1103 v[764,10] = 1789 v[765,10] = 887 v[766,10] = 739 v[767,10] = 1577 v[768,10] = 313 v[769,10] = 1367 v[770,10] = 1299 v[771,10] = 1801 v[772,10] = 1131 v[773,10] = 1837 v[774,10] = 73 v[775,10] = 1865 v[776,10] = 1065 v[777,10] = 843 v[778,10] = 635 v[779,10] = 55 v[780,10] = 1655 v[781,10] = 913 v[782,10] = 1037 v[783,10] = 223 v[784,10] = 1871 v[785,10] = 1161 v[786,10] = 461 v[787,10] = 479 v[788,10] = 511 v[789,10] = 1721 v[790,10] = 1107 v[791,10] = 389 v[792,10] = 151 v[793,10] = 35 v[794,10] = 375 v[795,10] = 1099 v[796,10] = 937 v[797,10] = 1185 v[798,10] = 1701 v[799,10] = 769 v[800,10] = 639 v[801,10] = 1633 v[802,10] = 1609 v[803,10] = 379 v[804,10] = 1613 v[805,10] = 2031 v[806,10] = 685 v[807,10] = 289 v[808,10] = 975 v[809,10] = 671 v[810,10] = 1599 v[811,10] = 1447 v[812,10] = 871 v[813,10] = 647 v[814,10] = 99 v[815,10] = 139 v[816,10] = 1427 v[817,10] = 959 v[818,10] = 89 v[819,10] = 117 v[820,10] = 841 v[821,10] = 891 v[822,10] = 1959 v[823,10] = 223 v[824,10] = 1697 v[825,10] = 1145 v[826,10] = 499 v[827,10] = 1435 v[828,10] = 1809 v[829,10] = 1413 v[830,10] = 1445 v[831,10] = 1675 v[832,10] = 171 v[833,10] = 1073 v[834,10] = 1349 v[835,10] = 1545 v[836,10] = 2039 v[837,10] = 1027 v[838,10] = 1563 v[839,10] = 859 v[840,10] = 215 v[841,10] = 1673 v[842,10] = 1919 v[843,10] = 1633 v[844,10] = 779 v[845,10] = 411 v[846,10] = 1845 v[847,10] = 1477 v[848,10] = 1489 v[849,10] = 447 v[850,10] = 1545 v[851,10] = 351 v[852,10] = 1989 v[853,10] = 495 v[854,10] = 183 v[855,10] = 1639 v[856,10] = 1385 v[857,10] = 1805 v[858,10] = 1097 v[859,10] = 1249 v[860,10] = 1431 v[861,10] = 1571 v[862,10] = 591 v[863,10] = 697 v[864,10] = 1509 v[865,10] = 709 v[866,10] = 31 v[867,10] = 1563 v[868,10] = 165 v[869,10] = 513 v[870,10] = 1425 v[871,10] = 1299 v[872,10] = 1081 v[873,10] = 145 v[874,10] = 1841 v[875,10] = 1211 v[876,10] = 941 v[877,10] = 609 v[878,10] = 845 v[879,10] = 1169 v[880,10] = 1865 v[881,10] = 1593 v[882,10] = 347 v[883,10] = 293 v[884,10] = 1277 v[885,10] = 157 v[886,10] = 211 v[887,10] = 93 v[888,10] = 1679 v[889,10] = 1799 v[890,10] = 527 v[891,10] = 41 v[892,10] = 473 v[893,10] = 563 v[894,10] = 187 v[895,10] = 1525 v[896,10] = 575 v[897,10] = 1579 v[898,10] = 857 v[899,10] = 703 v[900,10] = 1211 v[901,10] = 647 v[902,10] = 709 v[903,10] = 981 v[904,10] = 285 v[905,10] = 697 v[906,10] = 163 v[907,10] = 981 v[908,10] = 153 v[909,10] = 1515 v[910,10] = 47 v[911,10] = 1553 v[912,10] = 599 v[913,10] = 225 v[914,10] = 1147 v[915,10] = 381 v[916,10] = 135 v[917,10] = 821 v[918,10] = 1965 v[919,10] = 609 v[920,10] = 1033 v[921,10] = 983 v[922,10] = 503 v[923,10] = 1117 v[924,10] = 327 v[925,10] = 453 v[926,10] = 2005 v[927,10] = 1257 v[928,10] = 343 v[929,10] = 1649 v[930,10] = 1199 v[931,10] = 599 v[932,10] = 1877 v[933,10] = 569 v[934,10] = 695 v[935,10] = 1587 v[936,10] = 1475 v[937,10] = 187 v[938,10] = 973 v[939,10] = 233 v[940,10] = 511 v[941,10] = 51 v[942,10] = 1083 v[943,10] = 665 v[944,10] = 1321 v[945,10] = 531 v[946,10] = 1875 v[947,10] = 1939 v[948,10] = 859 v[949,10] = 1507 v[950,10] = 1979 v[951,10] = 1203 v[952,10] = 1965 v[953,10] = 737 v[954,10] = 921 v[955,10] = 1565 v[956,10] = 1943 v[957,10] = 819 v[958,10] = 223 v[959,10] = 365 v[960,10] = 167 v[961,10] = 1705 v[962,10] = 413 v[963,10] = 1577 v[964,10] = 745 v[965,10] = 1573 v[966,10] = 655 v[967,10] = 1633 v[968,10] = 1003 v[969,10] = 91 v[970,10] = 1123 v[971,10] = 477 v[972,10] = 1741 v[973,10] = 1663 v[974,10] = 35 v[975,10] = 715 v[976,10] = 37 v[977,10] = 1513 v[978,10] = 815 v[979,10] = 941 v[980,10] = 1379 v[981,10] = 263 v[982,10] = 1831 v[983,10] = 1735 v[984,10] = 1111 v[985,10] = 1449 v[986,10] = 353 v[987,10] = 1941 v[988,10] = 1655 v[989,10] = 1349 v[990,10] = 877 v[991,10] = 285 v[992,10] = 1723 v[993,10] = 125 v[994,10] = 1753 v[995,10] = 985 v[996,10] = 723 v[997,10] = 175 v[998,10] = 439 v[999,10] = 791 v[1000,10] = 1051 v[1001,10] = 1261 v[1002,10] = 717 v[1003,10] = 1555 v[1004,10] = 1757 v[1005,10] = 1777 v[1006,10] = 577 v[1007,10] = 1583 v[1008,10] = 1957 v[1009,10] = 873 v[1010,10] = 331 v[1011,10] = 1163 v[1012,10] = 313 v[1013,10] = 1 v[1014,10] = 1963 v[1015,10] = 963 v[1016,10] = 1905 v[1017,10] = 821 v[1018,10] = 1677 v[1019,10] = 185 v[1020,10] = 709 v[1021,10] = 545 v[1022,10] = 1723 v[1023,10] = 215 v[1024,10] = 1885 v[1025,10] = 1249 v[1026,10] = 583 v[1027,10] = 1803 v[1028,10] = 839 v[1029,10] = 885 v[1030,10] = 485 v[1031,10] = 413 v[1032,10] = 1767 v[1033,10] = 425 v[1034,10] = 129 v[1035,10] = 1035 v[1036,10] = 329 v[1037,10] = 1263 v[1038,10] = 1881 v[1039,10] = 1779 v[1040,10] = 1565 v[1041,10] = 359 v[1042,10] = 367 v[1043,10] = 453 v[1044,10] = 707 v[1045,10] = 1419 v[1046,10] = 831 v[1047,10] = 1889 v[1048,10] = 887 v[1049,10] = 1871 v[1050,10] = 1869 v[1051,10] = 747 v[1052,10] = 223 v[1053,10] = 1547 v[1054,10] = 1799 v[1055,10] = 433 v[1056,10] = 1441 v[1057,10] = 553 v[1058,10] = 2021 v[1059,10] = 1303 v[1060,10] = 1505 v[1061,10] = 1735 v[1062,10] = 1619 v[1063,10] = 1065 v[1064,10] = 1161 v[1065,10] = 2047 v[1066,10] = 347 v[1067,10] = 867 v[1068,10] = 881 v[1069,10] = 1447 v[1070,10] = 329 v[1071,10] = 781 v[1072,10] = 1065 v[1073,10] = 219 v[1074,10] = 589 v[1075,10] = 645 v[1076,10] = 1257 v[1077,10] = 1833 v[1078,10] = 749 v[1079,10] = 1841 v[1080,10] = 1733 v[1081,10] = 1179 v[1082,10] = 1191 v[1083,10] = 1025 v[1084,10] = 1639 v[1085,10] = 1955 v[1086,10] = 1423 v[1087,10] = 1685 v[1088,10] = 1711 v[1089,10] = 493 v[1090,10] = 549 v[1091,10] = 783 v[1092,10] = 1653 v[1093,10] = 397 v[1094,10] = 895 v[1095,10] = 233 v[1096,10] = 759 v[1097,10] = 1505 v[1098,10] = 677 v[1099,10] = 1449 v[1100,10] = 1573 v[1101,10] = 1297 v[1102,10] = 1821 v[1103,10] = 1691 v[1104,10] = 791 v[1105,10] = 289 v[1106,10] = 1187 v[1107,10] = 867 v[1108,10] = 1535 v[1109,10] = 575 v[1110,10] = 183 v[337,11] = 3915 v[338,11] = 97 v[339,11] = 3047 v[340,11] = 937 v[341,11] = 2897 v[342,11] = 953 v[343,11] = 127 v[344,11] = 1201 v[345,11] = 3819 v[346,11] = 193 v[347,11] = 2053 v[348,11] = 3061 v[349,11] = 3759 v[350,11] = 1553 v[351,11] = 2007 v[352,11] = 2493 v[353,11] = 603 v[354,11] = 3343 v[355,11] = 3751 v[356,11] = 1059 v[357,11] = 783 v[358,11] = 1789 v[359,11] = 1589 v[360,11] = 283 v[361,11] = 1093 v[362,11] = 3919 v[363,11] = 2747 v[364,11] = 277 v[365,11] = 2605 v[366,11] = 2169 v[367,11] = 2905 v[368,11] = 721 v[369,11] = 4069 v[370,11] = 233 v[371,11] = 261 v[372,11] = 1137 v[373,11] = 3993 v[374,11] = 3619 v[375,11] = 2881 v[376,11] = 1275 v[377,11] = 3865 v[378,11] = 1299 v[379,11] = 3757 v[380,11] = 1193 v[381,11] = 733 v[382,11] = 993 v[383,11] = 1153 v[384,11] = 2945 v[385,11] = 3163 v[386,11] = 3179 v[387,11] = 437 v[388,11] = 271 v[389,11] = 3493 v[390,11] = 3971 v[391,11] = 1005 v[392,11] = 2615 v[393,11] = 2253 v[394,11] = 1131 v[395,11] = 585 v[396,11] = 2775 v[397,11] = 2171 v[398,11] = 2383 v[399,11] = 2937 v[400,11] = 2447 v[401,11] = 1745 v[402,11] = 663 v[403,11] = 1515 v[404,11] = 3767 v[405,11] = 2709 v[406,11] = 1767 v[407,11] = 3185 v[408,11] = 3017 v[409,11] = 2815 v[410,11] = 1829 v[411,11] = 87 v[412,11] = 3341 v[413,11] = 793 v[414,11] = 2627 v[415,11] = 2169 v[416,11] = 1875 v[417,11] = 3745 v[418,11] = 367 v[419,11] = 3783 v[420,11] = 783 v[421,11] = 827 v[422,11] = 3253 v[423,11] = 2639 v[424,11] = 2955 v[425,11] = 3539 v[426,11] = 1579 v[427,11] = 2109 v[428,11] = 379 v[429,11] = 2939 v[430,11] = 3019 v[431,11] = 1999 v[432,11] = 2253 v[433,11] = 2911 v[434,11] = 3733 v[435,11] = 481 v[436,11] = 1767 v[437,11] = 1055 v[438,11] = 4019 v[439,11] = 4085 v[440,11] = 105 v[441,11] = 1829 v[442,11] = 2097 v[443,11] = 2379 v[444,11] = 1567 v[445,11] = 2713 v[446,11] = 737 v[447,11] = 3423 v[448,11] = 3941 v[449,11] = 2659 v[450,11] = 3961 v[451,11] = 1755 v[452,11] = 3613 v[453,11] = 1937 v[454,11] = 1559 v[455,11] = 2287 v[456,11] = 2743 v[457,11] = 67 v[458,11] = 2859 v[459,11] = 325 v[460,11] = 2601 v[461,11] = 1149 v[462,11] = 3259 v[463,11] = 2403 v[464,11] = 3947 v[465,11] = 2011 v[466,11] = 175 v[467,11] = 3389 v[468,11] = 3915 v[469,11] = 1315 v[470,11] = 2447 v[471,11] = 141 v[472,11] = 359 v[473,11] = 3609 v[474,11] = 3933 v[475,11] = 729 v[476,11] = 2051 v[477,11] = 1755 v[478,11] = 2149 v[479,11] = 2107 v[480,11] = 1741 v[481,11] = 1051 v[482,11] = 3681 v[483,11] = 471 v[484,11] = 1055 v[485,11] = 845 v[486,11] = 257 v[487,11] = 1559 v[488,11] = 1061 v[489,11] = 2803 v[490,11] = 2219 v[491,11] = 1315 v[492,11] = 1369 v[493,11] = 3211 v[494,11] = 4027 v[495,11] = 105 v[496,11] = 11 v[497,11] = 1077 v[498,11] = 2857 v[499,11] = 337 v[500,11] = 3553 v[501,11] = 3503 v[502,11] = 3917 v[503,11] = 2665 v[504,11] = 3823 v[505,11] = 3403 v[506,11] = 3711 v[507,11] = 2085 v[508,11] = 1103 v[509,11] = 1641 v[510,11] = 701 v[511,11] = 4095 v[512,11] = 2883 v[513,11] = 1435 v[514,11] = 653 v[515,11] = 2363 v[516,11] = 1597 v[517,11] = 767 v[518,11] = 869 v[519,11] = 1825 v[520,11] = 1117 v[521,11] = 1297 v[522,11] = 501 v[523,11] = 505 v[524,11] = 149 v[525,11] = 873 v[526,11] = 2673 v[527,11] = 551 v[528,11] = 1499 v[529,11] = 2793 v[530,11] = 3277 v[531,11] = 2143 v[532,11] = 3663 v[533,11] = 533 v[534,11] = 3991 v[535,11] = 575 v[536,11] = 1877 v[537,11] = 1009 v[538,11] = 3929 v[539,11] = 473 v[540,11] = 3009 v[541,11] = 2595 v[542,11] = 3249 v[543,11] = 675 v[544,11] = 3593 v[545,11] = 2453 v[546,11] = 1567 v[547,11] = 973 v[548,11] = 595 v[549,11] = 1335 v[550,11] = 1715 v[551,11] = 589 v[552,11] = 85 v[553,11] = 2265 v[554,11] = 3069 v[555,11] = 461 v[556,11] = 1659 v[557,11] = 2627 v[558,11] = 1307 v[559,11] = 1731 v[560,11] = 1501 v[561,11] = 1699 v[562,11] = 3545 v[563,11] = 3803 v[564,11] = 2157 v[565,11] = 453 v[566,11] = 2813 v[567,11] = 2047 v[568,11] = 2999 v[569,11] = 3841 v[570,11] = 2361 v[571,11] = 1079 v[572,11] = 573 v[573,11] = 69 v[574,11] = 1363 v[575,11] = 1597 v[576,11] = 3427 v[577,11] = 2899 v[578,11] = 2771 v[579,11] = 1327 v[580,11] = 1117 v[581,11] = 1523 v[582,11] = 3521 v[583,11] = 2393 v[584,11] = 2537 v[585,11] = 1979 v[586,11] = 3179 v[587,11] = 683 v[588,11] = 2453 v[589,11] = 453 v[590,11] = 1227 v[591,11] = 779 v[592,11] = 671 v[593,11] = 3483 v[594,11] = 2135 v[595,11] = 3139 v[596,11] = 3381 v[597,11] = 3945 v[598,11] = 57 v[599,11] = 1541 v[600,11] = 3405 v[601,11] = 3381 v[602,11] = 2371 v[603,11] = 2879 v[604,11] = 1985 v[605,11] = 987 v[606,11] = 3017 v[607,11] = 3031 v[608,11] = 3839 v[609,11] = 1401 v[610,11] = 3749 v[611,11] = 2977 v[612,11] = 681 v[613,11] = 1175 v[614,11] = 1519 v[615,11] = 3355 v[616,11] = 907 v[617,11] = 117 v[618,11] = 771 v[619,11] = 3741 v[620,11] = 3337 v[621,11] = 1743 v[622,11] = 1227 v[623,11] = 3335 v[624,11] = 2755 v[625,11] = 1909 v[626,11] = 3603 v[627,11] = 2397 v[628,11] = 653 v[629,11] = 87 v[630,11] = 2025 v[631,11] = 2617 v[632,11] = 3257 v[633,11] = 287 v[634,11] = 3051 v[635,11] = 3809 v[636,11] = 897 v[637,11] = 2215 v[638,11] = 63 v[639,11] = 2043 v[640,11] = 1757 v[641,11] = 3671 v[642,11] = 297 v[643,11] = 3131 v[644,11] = 1305 v[645,11] = 293 v[646,11] = 3865 v[647,11] = 3173 v[648,11] = 3397 v[649,11] = 2269 v[650,11] = 3673 v[651,11] = 717 v[652,11] = 3041 v[653,11] = 3341 v[654,11] = 3595 v[655,11] = 3819 v[656,11] = 2871 v[657,11] = 3973 v[658,11] = 1129 v[659,11] = 513 v[660,11] = 871 v[661,11] = 1485 v[662,11] = 3977 v[663,11] = 2473 v[664,11] = 1171 v[665,11] = 1143 v[666,11] = 3063 v[667,11] = 3547 v[668,11] = 2183 v[669,11] = 3993 v[670,11] = 133 v[671,11] = 2529 v[672,11] = 2699 v[673,11] = 233 v[674,11] = 2355 v[675,11] = 231 v[676,11] = 3241 v[677,11] = 611 v[678,11] = 1309 v[679,11] = 3829 v[680,11] = 1839 v[681,11] = 1495 v[682,11] = 301 v[683,11] = 1169 v[684,11] = 1613 v[685,11] = 2673 v[686,11] = 243 v[687,11] = 3601 v[688,11] = 3669 v[689,11] = 2813 v[690,11] = 2671 v[691,11] = 2679 v[692,11] = 3463 v[693,11] = 2477 v[694,11] = 1795 v[695,11] = 617 v[696,11] = 2317 v[697,11] = 1855 v[698,11] = 1057 v[699,11] = 1703 v[700,11] = 1761 v[701,11] = 2515 v[702,11] = 801 v[703,11] = 1205 v[704,11] = 1311 v[705,11] = 473 v[706,11] = 3963 v[707,11] = 697 v[708,11] = 1221 v[709,11] = 251 v[710,11] = 381 v[711,11] = 3887 v[712,11] = 1761 v[713,11] = 3093 v[714,11] = 3721 v[715,11] = 2079 v[716,11] = 4085 v[717,11] = 379 v[718,11] = 3601 v[719,11] = 3845 v[720,11] = 433 v[721,11] = 1781 v[722,11] = 29 v[723,11] = 1897 v[724,11] = 1599 v[725,11] = 2163 v[726,11] = 75 v[727,11] = 3475 v[728,11] = 3957 v[729,11] = 1641 v[730,11] = 3911 v[731,11] = 2959 v[732,11] = 2833 v[733,11] = 1279 v[734,11] = 1099 v[735,11] = 403 v[736,11] = 799 v[737,11] = 2183 v[738,11] = 2699 v[739,11] = 1711 v[740,11] = 2037 v[741,11] = 727 v[742,11] = 289 v[743,11] = 1785 v[744,11] = 1575 v[745,11] = 3633 v[746,11] = 2367 v[747,11] = 1261 v[748,11] = 3953 v[749,11] = 1735 v[750,11] = 171 v[751,11] = 1959 v[752,11] = 2867 v[753,11] = 859 v[754,11] = 2951 v[755,11] = 3211 v[756,11] = 15 v[757,11] = 1279 v[758,11] = 1323 v[759,11] = 599 v[760,11] = 1651 v[761,11] = 3951 v[762,11] = 1011 v[763,11] = 315 v[764,11] = 3513 v[765,11] = 3351 v[766,11] = 1725 v[767,11] = 3793 v[768,11] = 2399 v[769,11] = 287 v[770,11] = 4017 v[771,11] = 3571 v[772,11] = 1007 v[773,11] = 541 v[774,11] = 3115 v[775,11] = 429 v[776,11] = 1585 v[777,11] = 1285 v[778,11] = 755 v[779,11] = 1211 v[780,11] = 3047 v[781,11] = 915 v[782,11] = 3611 v[783,11] = 2697 v[784,11] = 2129 v[785,11] = 3669 v[786,11] = 81 v[787,11] = 3939 v[788,11] = 2437 v[789,11] = 915 v[790,11] = 779 v[791,11] = 3567 v[792,11] = 3701 v[793,11] = 2479 v[794,11] = 3807 v[795,11] = 1893 v[796,11] = 3927 v[797,11] = 2619 v[798,11] = 2543 v[799,11] = 3633 v[800,11] = 2007 v[801,11] = 3857 v[802,11] = 3837 v[803,11] = 487 v[804,11] = 1769 v[805,11] = 3759 v[806,11] = 3105 v[807,11] = 2727 v[808,11] = 3155 v[809,11] = 2479 v[810,11] = 1341 v[811,11] = 1657 v[812,11] = 2767 v[813,11] = 2541 v[814,11] = 577 v[815,11] = 2105 v[816,11] = 799 v[817,11] = 17 v[818,11] = 2871 v[819,11] = 3637 v[820,11] = 953 v[821,11] = 65 v[822,11] = 69 v[823,11] = 2897 v[824,11] = 3841 v[825,11] = 3559 v[826,11] = 4067 v[827,11] = 2335 v[828,11] = 3409 v[829,11] = 1087 v[830,11] = 425 v[831,11] = 2813 v[832,11] = 1705 v[833,11] = 1701 v[834,11] = 1237 v[835,11] = 821 v[836,11] = 1375 v[837,11] = 3673 v[838,11] = 2693 v[839,11] = 3925 v[840,11] = 1541 v[841,11] = 1871 v[842,11] = 2285 v[843,11] = 847 v[844,11] = 4035 v[845,11] = 1101 v[846,11] = 2029 v[847,11] = 855 v[848,11] = 2733 v[849,11] = 2503 v[850,11] = 121 v[851,11] = 2855 v[852,11] = 1069 v[853,11] = 3463 v[854,11] = 3505 v[855,11] = 1539 v[856,11] = 607 v[857,11] = 1349 v[858,11] = 575 v[859,11] = 2301 v[860,11] = 2321 v[861,11] = 1101 v[862,11] = 333 v[863,11] = 291 v[864,11] = 2171 v[865,11] = 4085 v[866,11] = 2173 v[867,11] = 2541 v[868,11] = 1195 v[869,11] = 925 v[870,11] = 4039 v[871,11] = 1379 v[872,11] = 699 v[873,11] = 1979 v[874,11] = 275 v[875,11] = 953 v[876,11] = 1755 v[877,11] = 1643 v[878,11] = 325 v[879,11] = 101 v[880,11] = 2263 v[881,11] = 3329 v[882,11] = 3673 v[883,11] = 3413 v[884,11] = 1977 v[885,11] = 2727 v[886,11] = 2313 v[887,11] = 1419 v[888,11] = 887 v[889,11] = 609 v[890,11] = 2475 v[891,11] = 591 v[892,11] = 2613 v[893,11] = 2081 v[894,11] = 3805 v[895,11] = 3435 v[896,11] = 2409 v[897,11] = 111 v[898,11] = 3557 v[899,11] = 3607 v[900,11] = 903 v[901,11] = 231 v[902,11] = 3059 v[903,11] = 473 v[904,11] = 2959 v[905,11] = 2925 v[906,11] = 3861 v[907,11] = 2043 v[908,11] = 3887 v[909,11] = 351 v[910,11] = 2865 v[911,11] = 369 v[912,11] = 1377 v[913,11] = 2639 v[914,11] = 1261 v[915,11] = 3625 v[916,11] = 3279 v[917,11] = 2201 v[918,11] = 2949 v[919,11] = 3049 v[920,11] = 449 v[921,11] = 1297 v[922,11] = 897 v[923,11] = 1891 v[924,11] = 411 v[925,11] = 2773 v[926,11] = 749 v[927,11] = 2753 v[928,11] = 1825 v[929,11] = 853 v[930,11] = 2775 v[931,11] = 3547 v[932,11] = 3923 v[933,11] = 3923 v[934,11] = 987 v[935,11] = 3723 v[936,11] = 2189 v[937,11] = 3877 v[938,11] = 3577 v[939,11] = 297 v[940,11] = 2763 v[941,11] = 1845 v[942,11] = 3083 v[943,11] = 2951 v[944,11] = 483 v[945,11] = 2169 v[946,11] = 3985 v[947,11] = 245 v[948,11] = 3655 v[949,11] = 3441 v[950,11] = 1023 v[951,11] = 235 v[952,11] = 835 v[953,11] = 3693 v[954,11] = 3585 v[955,11] = 327 v[956,11] = 1003 v[957,11] = 543 v[958,11] = 3059 v[959,11] = 2637 v[960,11] = 2923 v[961,11] = 87 v[962,11] = 3617 v[963,11] = 1031 v[964,11] = 1043 v[965,11] = 903 v[966,11] = 2913 v[967,11] = 2177 v[968,11] = 2641 v[969,11] = 3279 v[970,11] = 389 v[971,11] = 2009 v[972,11] = 525 v[973,11] = 4085 v[974,11] = 3299 v[975,11] = 987 v[976,11] = 2409 v[977,11] = 813 v[978,11] = 2683 v[979,11] = 373 v[980,11] = 2695 v[981,11] = 3775 v[982,11] = 2375 v[983,11] = 1119 v[984,11] = 2791 v[985,11] = 223 v[986,11] = 325 v[987,11] = 587 v[988,11] = 1379 v[989,11] = 2877 v[990,11] = 2867 v[991,11] = 3793 v[992,11] = 655 v[993,11] = 831 v[994,11] = 3425 v[995,11] = 1663 v[996,11] = 1681 v[997,11] = 2657 v[998,11] = 1865 v[999,11] = 3943 v[1000,11] = 2977 v[1001,11] = 1979 v[1002,11] = 2271 v[1003,11] = 3247 v[1004,11] = 1267 v[1005,11] = 1747 v[1006,11] = 811 v[1007,11] = 159 v[1008,11] = 429 v[1009,11] = 2001 v[1010,11] = 1195 v[1011,11] = 3065 v[1012,11] = 553 v[1013,11] = 1499 v[1014,11] = 3529 v[1015,11] = 1081 v[1016,11] = 2877 v[1017,11] = 3077 v[1018,11] = 845 v[1019,11] = 1793 v[1020,11] = 2409 v[1021,11] = 3995 v[1022,11] = 2559 v[1023,11] = 4081 v[1024,11] = 1195 v[1025,11] = 2955 v[1026,11] = 1117 v[1027,11] = 1409 v[1028,11] = 785 v[1029,11] = 287 v[1030,11] = 1521 v[1031,11] = 1607 v[1032,11] = 85 v[1033,11] = 3055 v[1034,11] = 3123 v[1035,11] = 2533 v[1036,11] = 2329 v[1037,11] = 3477 v[1038,11] = 799 v[1039,11] = 3683 v[1040,11] = 3715 v[1041,11] = 337 v[1042,11] = 3139 v[1043,11] = 3311 v[1044,11] = 431 v[1045,11] = 3511 v[1046,11] = 2299 v[1047,11] = 365 v[1048,11] = 2941 v[1049,11] = 3067 v[1050,11] = 1331 v[1051,11] = 1081 v[1052,11] = 1097 v[1053,11] = 2853 v[1054,11] = 2299 v[1055,11] = 495 v[1056,11] = 1745 v[1057,11] = 749 v[1058,11] = 3819 v[1059,11] = 619 v[1060,11] = 1059 v[1061,11] = 3559 v[1062,11] = 183 v[1063,11] = 3743 v[1064,11] = 723 v[1065,11] = 949 v[1066,11] = 3501 v[1067,11] = 733 v[1068,11] = 2599 v[1069,11] = 3983 v[1070,11] = 3961 v[1071,11] = 911 v[1072,11] = 1899 v[1073,11] = 985 v[1074,11] = 2493 v[1075,11] = 1795 v[1076,11] = 653 v[1077,11] = 157 v[1078,11] = 433 v[1079,11] = 2361 v[1080,11] = 3093 v[1081,11] = 3119 v[1082,11] = 3679 v[1083,11] = 2367 v[1084,11] = 1701 v[1085,11] = 1445 v[1086,11] = 1321 v[1087,11] = 2397 v[1088,11] = 1241 v[1089,11] = 3305 v[1090,11] = 3985 v[1091,11] = 2349 v[1092,11] = 4067 v[1093,11] = 3805 v[1094,11] = 3073 v[1095,11] = 2837 v[1096,11] = 1567 v[1097,11] = 3783 v[1098,11] = 451 v[1099,11] = 2441 v[1100,11] = 1181 v[1101,11] = 487 v[1102,11] = 543 v[1103,11] = 1201 v[1104,11] = 3735 v[1105,11] = 2517 v[1106,11] = 733 v[1107,11] = 1535 v[1108,11] = 2175 v[1109,11] = 3613 v[1110,11] = 3019 v[481,12] = 2319 v[482,12] = 653 v[483,12] = 1379 v[484,12] = 1675 v[485,12] = 1951 v[486,12] = 7075 v[487,12] = 2087 v[488,12] = 7147 v[489,12] = 1427 v[490,12] = 893 v[491,12] = 171 v[492,12] = 2019 v[493,12] = 7235 v[494,12] = 5697 v[495,12] = 3615 v[496,12] = 1961 v[497,12] = 7517 v[498,12] = 6849 v[499,12] = 2893 v[500,12] = 1883 v[501,12] = 2863 v[502,12] = 2173 v[503,12] = 4543 v[504,12] = 73 v[505,12] = 381 v[506,12] = 3893 v[507,12] = 6045 v[508,12] = 1643 v[509,12] = 7669 v[510,12] = 1027 v[511,12] = 1549 v[512,12] = 3983 v[513,12] = 1985 v[514,12] = 6589 v[515,12] = 7497 v[516,12] = 2745 v[517,12] = 2375 v[518,12] = 7047 v[519,12] = 1117 v[520,12] = 1171 v[521,12] = 1975 v[522,12] = 5199 v[523,12] = 3915 v[524,12] = 3695 v[525,12] = 8113 v[526,12] = 4303 v[527,12] = 3773 v[528,12] = 7705 v[529,12] = 6855 v[530,12] = 1675 v[531,12] = 2245 v[532,12] = 2817 v[533,12] = 1719 v[534,12] = 569 v[535,12] = 1021 v[536,12] = 2077 v[537,12] = 5945 v[538,12] = 1833 v[539,12] = 2631 v[540,12] = 4851 v[541,12] = 6371 v[542,12] = 833 v[543,12] = 7987 v[544,12] = 331 v[545,12] = 1899 v[546,12] = 8093 v[547,12] = 6719 v[548,12] = 6903 v[549,12] = 5903 v[550,12] = 5657 v[551,12] = 5007 v[552,12] = 2689 v[553,12] = 6637 v[554,12] = 2675 v[555,12] = 1645 v[556,12] = 1819 v[557,12] = 689 v[558,12] = 6709 v[559,12] = 7717 v[560,12] = 6295 v[561,12] = 7013 v[562,12] = 7695 v[563,12] = 3705 v[564,12] = 7069 v[565,12] = 2621 v[566,12] = 3631 v[567,12] = 6571 v[568,12] = 6259 v[569,12] = 7261 v[570,12] = 3397 v[571,12] = 7645 v[572,12] = 1115 v[573,12] = 4753 v[574,12] = 2047 v[575,12] = 7579 v[576,12] = 2271 v[577,12] = 5403 v[578,12] = 4911 v[579,12] = 7629 v[580,12] = 4225 v[581,12] = 1209 v[582,12] = 6955 v[583,12] = 6951 v[584,12] = 1829 v[585,12] = 5579 v[586,12] = 5231 v[587,12] = 1783 v[588,12] = 4285 v[589,12] = 7425 v[590,12] = 599 v[591,12] = 5785 v[592,12] = 3275 v[593,12] = 5643 v[594,12] = 2263 v[595,12] = 657 v[596,12] = 6769 v[597,12] = 6261 v[598,12] = 1251 v[599,12] = 3249 v[600,12] = 4447 v[601,12] = 4111 v[602,12] = 3991 v[603,12] = 1215 v[604,12] = 131 v[605,12] = 4397 v[606,12] = 3487 v[607,12] = 7585 v[608,12] = 5565 v[609,12] = 7199 v[610,12] = 3573 v[611,12] = 7105 v[612,12] = 7409 v[613,12] = 1671 v[614,12] = 949 v[615,12] = 3889 v[616,12] = 5971 v[617,12] = 3333 v[618,12] = 225 v[619,12] = 3647 v[620,12] = 5403 v[621,12] = 3409 v[622,12] = 7459 v[623,12] = 6879 v[624,12] = 5789 v[625,12] = 6567 v[626,12] = 5581 v[627,12] = 4919 v[628,12] = 1927 v[629,12] = 4407 v[630,12] = 8085 v[631,12] = 4691 v[632,12] = 611 v[633,12] = 3005 v[634,12] = 591 v[635,12] = 753 v[636,12] = 589 v[637,12] = 171 v[638,12] = 5729 v[639,12] = 5891 v[640,12] = 1033 v[641,12] = 3049 v[642,12] = 6567 v[643,12] = 5257 v[644,12] = 8003 v[645,12] = 1757 v[646,12] = 4489 v[647,12] = 4923 v[648,12] = 6379 v[649,12] = 5171 v[650,12] = 1757 v[651,12] = 689 v[652,12] = 3081 v[653,12] = 1389 v[654,12] = 4113 v[655,12] = 455 v[656,12] = 2761 v[657,12] = 847 v[658,12] = 7575 v[659,12] = 5829 v[660,12] = 633 v[661,12] = 6629 v[662,12] = 1103 v[663,12] = 7635 v[664,12] = 803 v[665,12] = 6175 v[666,12] = 6587 v[667,12] = 2711 v[668,12] = 3879 v[669,12] = 67 v[670,12] = 1179 v[671,12] = 4761 v[672,12] = 7281 v[673,12] = 1557 v[674,12] = 3379 v[675,12] = 2459 v[676,12] = 4273 v[677,12] = 4127 v[678,12] = 7147 v[679,12] = 35 v[680,12] = 3549 v[681,12] = 395 v[682,12] = 3735 v[683,12] = 5787 v[684,12] = 4179 v[685,12] = 5889 v[686,12] = 5057 v[687,12] = 7473 v[688,12] = 4713 v[689,12] = 2133 v[690,12] = 2897 v[691,12] = 1841 v[692,12] = 2125 v[693,12] = 1029 v[694,12] = 1695 v[695,12] = 6523 v[696,12] = 1143 v[697,12] = 5105 v[698,12] = 7133 v[699,12] = 3351 v[700,12] = 2775 v[701,12] = 3971 v[702,12] = 4503 v[703,12] = 7589 v[704,12] = 5155 v[705,12] = 4305 v[706,12] = 1641 v[707,12] = 4717 v[708,12] = 2427 v[709,12] = 5617 v[710,12] = 1267 v[711,12] = 399 v[712,12] = 5831 v[713,12] = 4305 v[714,12] = 4241 v[715,12] = 3395 v[716,12] = 3045 v[717,12] = 4899 v[718,12] = 1713 v[719,12] = 171 v[720,12] = 411 v[721,12] = 7099 v[722,12] = 5473 v[723,12] = 5209 v[724,12] = 1195 v[725,12] = 1077 v[726,12] = 1309 v[727,12] = 2953 v[728,12] = 7343 v[729,12] = 4887 v[730,12] = 3229 v[731,12] = 6759 v[732,12] = 6721 v[733,12] = 6775 v[734,12] = 675 v[735,12] = 4039 v[736,12] = 2493 v[737,12] = 7511 v[738,12] = 3269 v[739,12] = 4199 v[740,12] = 6625 v[741,12] = 7943 v[742,12] = 2013 v[743,12] = 4145 v[744,12] = 667 v[745,12] = 513 v[746,12] = 2303 v[747,12] = 4591 v[748,12] = 7941 v[749,12] = 2741 v[750,12] = 987 v[751,12] = 8061 v[752,12] = 3161 v[753,12] = 5951 v[754,12] = 1431 v[755,12] = 831 v[756,12] = 5559 v[757,12] = 7405 v[758,12] = 1357 v[759,12] = 4319 v[760,12] = 4235 v[761,12] = 5421 v[762,12] = 2559 v[763,12] = 4415 v[764,12] = 2439 v[765,12] = 823 v[766,12] = 1725 v[767,12] = 6219 v[768,12] = 4903 v[769,12] = 6699 v[770,12] = 5451 v[771,12] = 349 v[772,12] = 7703 v[773,12] = 2927 v[774,12] = 7809 v[775,12] = 6179 v[776,12] = 1417 v[777,12] = 5987 v[778,12] = 3017 v[779,12] = 4983 v[780,12] = 3479 v[781,12] = 4525 v[782,12] = 4643 v[783,12] = 4911 v[784,12] = 227 v[785,12] = 5475 v[786,12] = 2287 v[787,12] = 5581 v[788,12] = 6817 v[789,12] = 1937 v[790,12] = 1421 v[791,12] = 4415 v[792,12] = 7977 v[793,12] = 1789 v[794,12] = 3907 v[795,12] = 6815 v[796,12] = 6789 v[797,12] = 6003 v[798,12] = 5609 v[799,12] = 4507 v[800,12] = 337 v[801,12] = 7427 v[802,12] = 7943 v[803,12] = 3075 v[804,12] = 6427 v[805,12] = 1019 v[806,12] = 7121 v[807,12] = 4763 v[808,12] = 81 v[809,12] = 3587 v[810,12] = 2929 v[811,12] = 1795 v[812,12] = 8067 v[813,12] = 2415 v[814,12] = 1265 v[815,12] = 4025 v[816,12] = 5599 v[817,12] = 4771 v[818,12] = 3025 v[819,12] = 2313 v[820,12] = 6129 v[821,12] = 7611 v[822,12] = 6881 v[823,12] = 5253 v[824,12] = 4413 v[825,12] = 7869 v[826,12] = 105 v[827,12] = 3173 v[828,12] = 1629 v[829,12] = 2537 v[830,12] = 1023 v[831,12] = 4409 v[832,12] = 7209 v[833,12] = 4413 v[834,12] = 7107 v[835,12] = 7469 v[836,12] = 33 v[837,12] = 1955 v[838,12] = 2881 v[839,12] = 5167 v[840,12] = 6451 v[841,12] = 4211 v[842,12] = 179 v[843,12] = 5573 v[844,12] = 7879 v[845,12] = 3387 v[846,12] = 7759 v[847,12] = 5455 v[848,12] = 7157 v[849,12] = 1891 v[850,12] = 5683 v[851,12] = 5689 v[852,12] = 6535 v[853,12] = 3109 v[854,12] = 6555 v[855,12] = 6873 v[856,12] = 1249 v[857,12] = 4251 v[858,12] = 6437 v[859,12] = 49 v[860,12] = 2745 v[861,12] = 1201 v[862,12] = 7327 v[863,12] = 4179 v[864,12] = 6783 v[865,12] = 623 v[866,12] = 2779 v[867,12] = 5963 v[868,12] = 2585 v[869,12] = 6927 v[870,12] = 5333 v[871,12] = 4033 v[872,12] = 285 v[873,12] = 7467 v[874,12] = 4443 v[875,12] = 4917 v[876,12] = 3 v[877,12] = 4319 v[878,12] = 5517 v[879,12] = 3449 v[880,12] = 813 v[881,12] = 5499 v[882,12] = 2515 v[883,12] = 5771 v[884,12] = 3357 v[885,12] = 2073 v[886,12] = 4395 v[887,12] = 4925 v[888,12] = 2643 v[889,12] = 7215 v[890,12] = 5817 v[891,12] = 1199 v[892,12] = 1597 v[893,12] = 1619 v[894,12] = 7535 v[895,12] = 4833 v[896,12] = 609 v[897,12] = 4797 v[898,12] = 8171 v[899,12] = 6847 v[900,12] = 793 v[901,12] = 6757 v[902,12] = 8165 v[903,12] = 3371 v[904,12] = 2431 v[905,12] = 5235 v[906,12] = 4739 v[907,12] = 7703 v[908,12] = 7223 v[909,12] = 6525 v[910,12] = 5891 v[911,12] = 5605 v[912,12] = 4433 v[913,12] = 3533 v[914,12] = 5267 v[915,12] = 5125 v[916,12] = 5037 v[917,12] = 225 v[918,12] = 6717 v[919,12] = 1121 v[920,12] = 5741 v[921,12] = 2013 v[922,12] = 4327 v[923,12] = 4839 v[924,12] = 569 v[925,12] = 5227 v[926,12] = 7677 v[927,12] = 4315 v[928,12] = 2391 v[929,12] = 5551 v[930,12] = 859 v[931,12] = 3627 v[932,12] = 6377 v[933,12] = 3903 v[934,12] = 4311 v[935,12] = 6527 v[936,12] = 7573 v[937,12] = 4905 v[938,12] = 7731 v[939,12] = 1909 v[940,12] = 1555 v[941,12] = 3279 v[942,12] = 1949 v[943,12] = 1887 v[944,12] = 6675 v[945,12] = 5509 v[946,12] = 2033 v[947,12] = 5473 v[948,12] = 3539 v[949,12] = 5033 v[950,12] = 5935 v[951,12] = 6095 v[952,12] = 4761 v[953,12] = 1771 v[954,12] = 1271 v[955,12] = 1717 v[956,12] = 4415 v[957,12] = 5083 v[958,12] = 6277 v[959,12] = 3147 v[960,12] = 7695 v[961,12] = 2461 v[962,12] = 4783 v[963,12] = 4539 v[964,12] = 5833 v[965,12] = 5583 v[966,12] = 651 v[967,12] = 1419 v[968,12] = 2605 v[969,12] = 5511 v[970,12] = 3913 v[971,12] = 5795 v[972,12] = 2333 v[973,12] = 2329 v[974,12] = 4431 v[975,12] = 3725 v[976,12] = 6069 v[977,12] = 2699 v[978,12] = 7055 v[979,12] = 6879 v[980,12] = 1017 v[981,12] = 3121 v[982,12] = 2547 v[983,12] = 4603 v[984,12] = 2385 v[985,12] = 6915 v[986,12] = 6103 v[987,12] = 5669 v[988,12] = 7833 v[989,12] = 2001 v[990,12] = 4287 v[991,12] = 6619 v[992,12] = 955 v[993,12] = 2761 v[994,12] = 5711 v[995,12] = 6291 v[996,12] = 3415 v[997,12] = 3909 v[998,12] = 2841 v[999,12] = 5627 v[1000,12] = 4939 v[1001,12] = 7671 v[1002,12] = 6059 v[1003,12] = 6275 v[1004,12] = 6517 v[1005,12] = 1931 v[1006,12] = 4583 v[1007,12] = 7301 v[1008,12] = 1267 v[1009,12] = 7509 v[1010,12] = 1435 v[1011,12] = 2169 v[1012,12] = 6939 v[1013,12] = 3515 v[1014,12] = 2985 v[1015,12] = 2787 v[1016,12] = 2123 v[1017,12] = 1969 v[1018,12] = 3307 v[1019,12] = 353 v[1020,12] = 4359 v[1021,12] = 7059 v[1022,12] = 5273 v[1023,12] = 5873 v[1024,12] = 6657 v[1025,12] = 6765 v[1026,12] = 6229 v[1027,12] = 3179 v[1028,12] = 1583 v[1029,12] = 6237 v[1030,12] = 2155 v[1031,12] = 371 v[1032,12] = 273 v[1033,12] = 7491 v[1034,12] = 3309 v[1035,12] = 6805 v[1036,12] = 3015 v[1037,12] = 6831 v[1038,12] = 7819 v[1039,12] = 713 v[1040,12] = 4747 v[1041,12] = 3935 v[1042,12] = 4109 v[1043,12] = 1311 v[1044,12] = 709 v[1045,12] = 3089 v[1046,12] = 7059 v[1047,12] = 4247 v[1048,12] = 2989 v[1049,12] = 1509 v[1050,12] = 4919 v[1051,12] = 1841 v[1052,12] = 3045 v[1053,12] = 3821 v[1054,12] = 6929 v[1055,12] = 4655 v[1056,12] = 1333 v[1057,12] = 6429 v[1058,12] = 6649 v[1059,12] = 2131 v[1060,12] = 5265 v[1061,12] = 1051 v[1062,12] = 261 v[1063,12] = 8057 v[1064,12] = 3379 v[1065,12] = 2179 v[1066,12] = 1993 v[1067,12] = 5655 v[1068,12] = 3063 v[1069,12] = 6381 v[1070,12] = 3587 v[1071,12] = 7417 v[1072,12] = 1579 v[1073,12] = 1541 v[1074,12] = 2107 v[1075,12] = 5085 v[1076,12] = 2873 v[1077,12] = 6141 v[1078,12] = 955 v[1079,12] = 3537 v[1080,12] = 2157 v[1081,12] = 841 v[1082,12] = 1999 v[1083,12] = 1465 v[1084,12] = 5171 v[1085,12] = 5651 v[1086,12] = 1535 v[1087,12] = 7235 v[1088,12] = 4349 v[1089,12] = 1263 v[1090,12] = 1453 v[1091,12] = 1005 v[1092,12] = 6893 v[1093,12] = 2919 v[1094,12] = 1947 v[1095,12] = 1635 v[1096,12] = 3963 v[1097,12] = 397 v[1098,12] = 969 v[1099,12] = 4569 v[1100,12] = 655 v[1101,12] = 6737 v[1102,12] = 2995 v[1103,12] = 7235 v[1104,12] = 7713 v[1105,12] = 973 v[1106,12] = 4821 v[1107,12] = 2377 v[1108,12] = 1673 v[1109,12] = 1 v[1110,12] = 6541 # v[0:40,0] = transpose([ \ # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \ # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \ # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \ # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]) # v[2:40,1] = transpose([ \ # 1, 3, 1, 3, 1, 3, 3, 1, \ # 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, \ # 1, 3, 1, 3, 3, 1, 3, 1, 3, 1, \ # 3, 1, 1, 3, 1, 3, 1, 3, 1, 3 ]) # v[3:40,2] = transpose([ \ # 7, 5, 1, 3, 3, 7, 5, \ # 5, 7, 7, 1, 3, 3, 7, 5, 1, 1, \ # 5, 3, 3, 1, 7, 5, 1, 3, 3, 7, \ # 5, 1, 1, 5, 7, 7, 5, 1, 3, 3 ]) # v[5:40,3] = transpose([ \ # 1, 7, 9,13,11, \ # 1, 3, 7, 9, 5,13,13,11, 3,15, \ # 5, 3,15, 7, 9,13, 9, 1,11, 7, \ # 5,15, 1,15,11, 5, 3, 1, 7, 9 ]) # v[7:40,4] = transpose([ \ # 9, 3,27, \ # 15,29,21,23,19,11,25, 7,13,17, \ # 1,25,29, 3,31,11, 5,23,27,19, \ # 21, 5, 1,17,13, 7,15, 9,31, 9 ]) # v[13:40,5] = transpose([ \ # 37,33, 7, 5,11,39,63, \ # 27,17,15,23,29, 3,21,13,31,25, \ # 9,49,33,19,29,11,19,27,15,25 ]) # v[19:40,6] = transpose([ \ # 13, \ # 33,115, 41, 79, 17, 29,119, 75, 73,105, \ # 7, 59, 65, 21, 3,113, 61, 89, 45,107 ]) # v[37:40,7] = transpose([ \ # 7, 23, 39 ]) # # Set POLY. # poly= [ \ 1, 3, 7, 11, 13, 19, 25, 37, 59, 47, \ 61, 55, 41, 67, 97, 91, 109, 103, 115, 131, \ 193, 137, 145, 143, 241, 157, 185, 167, 229, 171, \ 213, 191, 253, 203, 211, 239, 247, 285, 369, 299 ] poly = [\ 1, 3, 7, 11, 13, 19, 25, 37, 59, 47, 61, 55, 41, 67, 97, 91, 109, 103, 115, 131, 193, 137, 145, 143, 241, 157, 185, 167, 229, 171, 213, 191, 253, 203, 211, 239, 247, 285, 369, 299, 301, 333, 351, 355, 357, 361, 391, 397, 425, 451, 463, 487, 501, 529, 539, 545, 557, 563, 601, 607, 617, 623, 631, 637, 647, 661, 675, 677, 687, 695, 701, 719, 721, 731, 757, 761, 787, 789, 799, 803, 817, 827, 847, 859, 865, 875, 877, 883, 895, 901, 911, 949, 953, 967, 971, 973, 981, 985, 995, 1001, 1019, 1033, 1051, 1063, 1069, 1125, 1135, 1153, 1163, 1221, 1239, 1255, 1267, 1279, 1293, 1305, 1315, 1329, 1341, 1347, 1367, 1387, 1413, 1423, 1431, 1441, 1479, 1509, 1527, 1531, 1555, 1557, 1573, 1591, 1603, 1615, 1627, 1657, 1663, 1673, 1717, 1729, 1747, 1759, 1789, 1815, 1821, 1825, 1849, 1863, 1869, 1877, 1881, 1891, 1917, 1933, 1939, 1969, 2011, 2035, 2041, 2053, 2071, 2091, 2093, 2119, 2147, 2149, 2161, 2171, 2189, 2197, 2207, 2217, 2225, 2255, 2257, 2273, 2279, 2283, 2293, 2317, 2323, 2341, 2345, 2363, 2365, 2373, 2377, 2385, 2395, 2419, 2421, 2431, 2435, 2447, 2475, 2477, 2489, 2503, 2521, 2533, 2551, 2561, 2567, 2579, 2581, 2601, 2633, 2657, 2669, 2681, 2687, 2693, 2705, 2717, 2727, 2731, 2739, 2741, 2773, 2783, 2793, 2799, 2801, 2811, 2819, 2825, 2833, 2867, 2879, 2881, 2891, 2905, 2911, 2917, 2927, 2941, 2951, 2955, 2963, 2965, 2991, 2999, 3005, 3017, 3035, 3037, 3047, 3053, 3083, 3085, 3097, 3103, 3159, 3169, 3179, 3187, 3205, 3209, 3223, 3227, 3229, 3251, 3263, 3271, 3277, 3283, 3285, 3299, 3305, 3319, 3331, 3343, 3357, 3367, 3373, 3393, 3399, 3413, 3417, 3427, 3439, 3441, 3475, 3487, 3497, 3515, 3517, 3529, 3543, 3547, 3553, 3559, 3573, 3589, 3613, 3617, 3623, 3627, 3635, 3641, 3655, 3659, 3669, 3679, 3697, 3707, 3709, 3713, 3731, 3743, 3747, 3771, 3791, 3805, 3827, 3833, 3851, 3865, 3889, 3895, 3933, 3947, 3949, 3957, 3971, 3985, 3991, 3995, 4007, 4013, 4021, 4045, 4051, 4069, 4073, 4179, 4201, 4219, 4221, 4249, 4305, 4331, 4359, 4383, 4387, 4411, 4431, 4439, 4449, 4459, 4485, 4531, 4569, 4575, 4621, 4663, 4669, 4711, 4723, 4735, 4793, 4801, 4811, 4879, 4893, 4897, 4921, 4927, 4941, 4977, 5017, 5027, 5033, 5127, 5169, 5175, 5199, 5213, 5223, 5237, 5287, 5293, 5331, 5391, 5405, 5453, 5523, 5573, 5591, 5597, 5611, 5641, 5703, 5717, 5721, 5797, 5821, 5909, 5913, 5955, 5957, 6005, 6025, 6061, 6067, 6079, 6081, 6231, 6237, 6289, 6295, 6329, 6383, 6427, 6453, 6465, 6501, 6523, 6539, 6577, 6589, 6601, 6607, 6631, 6683, 6699, 6707, 6761, 6795, 6865, 6881, 6901, 6923, 6931, 6943, 6999, 7057, 7079, 7103, 7105, 7123, 7173, 7185, 7191, 7207, 7245, 7303, 7327, 7333, 7355, 7365, 7369, 7375, 7411, 7431, 7459, 7491, 7505, 7515, 7541, 7557, 7561, 7701, 7705, 7727, 7749, 7761, 7783, 7795, 7823, 7907, 7953, 7963, 7975, 8049, 8089, 8123, 8125, 8137, 8219, 8231, 8245, 8275, 8293, 8303, 8331, 8333, 8351, 8357, 8367, 8379, 8381, 8387, 8393, 8417, 8435, 8461, 8469, 8489, 8495, 8507, 8515, 8551, 8555, 8569, 8585, 8599, 8605, 8639, 8641, 8647, 8653, 8671, 8675, 8689, 8699, 8729, 8741, 8759, 8765, 8771, 8795, 8797, 8825, 8831, 8841, 8855, 8859, 8883, 8895, 8909, 8943, 8951, 8955, 8965, 8999, 9003, 9031, 9045, 9049, 9071, 9073, 9085, 9095, 9101, 9109, 9123, 9129, 9137, 9143, 9147, 9185, 9197, 9209, 9227, 9235, 9247, 9253, 9257, 9277, 9297, 9303, 9313, 9325, 9343, 9347, 9371, 9373, 9397, 9407, 9409, 9415, 9419, 9443, 9481, 9495, 9501, 9505, 9517, 9529, 9555, 9557, 9571, 9585, 9591, 9607, 9611, 9621, 9625, 9631, 9647, 9661, 9669, 9679, 9687, 9707, 9731, 9733, 9745, 9773, 9791, 9803, 9811, 9817, 9833, 9847, 9851, 9863, 9875, 9881, 9905, 9911, 9917, 9923, 9963, 9973,10003,10025, 10043,10063,10071,10077,10091,10099,10105,10115,10129,10145, 10169,10183,10187,10207,10223,10225,10247,10265,10271,10275, 10289,10299,10301,10309,10343,10357,10373,10411,10413,10431, 10445,10453,10463,10467,10473,10491,10505,10511,10513,10523, 10539,10549,10559,10561,10571,10581,10615,10621,10625,10643, 10655,10671,10679,10685,10691,10711,10739,10741,10755,10767, 10781,10785,10803,10805,10829,10857,10863,10865,10875,10877, 10917,10921,10929,10949,10967,10971,10987,10995,11009,11029, 11043,11045,11055,11063,11075,11081,11117,11135,11141,11159, 11163,11181,11187,11225,11237,11261,11279,11297,11307,11309, 11327,11329,11341,11377,11403,11405,11413,11427,11439,11453, 11461,11473,11479,11489,11495,11499,11533,11545,11561,11567, 11575,11579,11589,11611,11623,11637,11657,11663,11687,11691, 11701,11747,11761,11773,11783,11795,11797,11817,11849,11855, 11867,11869,11873,11883,11919,11921,11927,11933,11947,11955, 11961,11999,12027,12029,12037,12041,12049,12055,12095,12097, 12107,12109,12121,12127,12133,12137,12181,12197,12207,12209, 12239,12253,12263,12269,12277,12287,12295,12309,12313,12335, 12361,12367,12391,12409,12415,12433,12449,12469,12479,12481, 12499,12505,12517,12527,12549,12559,12597,12615,12621,12639, 12643,12657,12667,12707,12713,12727,12741,12745,12763,12769, 12779,12781,12787,12799,12809,12815,12829,12839,12857,12875, 12883,12889,12901,12929,12947,12953,12959,12969,12983,12987, 12995,13015,13019,13031,13063,13077,13103,13137,13149,13173, 13207,13211,13227,13241,13249,13255,13269,13283,13285,13303, 13307,13321,13339,13351,13377,13389,13407,13417,13431,13435, 13447,13459,13465,13477,13501,13513,13531,13543,13561,13581, 13599,13605,13617,13623,13637,13647,13661,13677,13683,13695, 13725,13729,13753,13773,13781,13785,13795,13801,13807,13825, 13835,13855,13861,13871,13883,13897,13905,13915,13939,13941, 13969,13979,13981,13997,14027,14035,14037,14051,14063,14085, 14095,14107,14113,14125,14137,14145,14151,14163,14193,14199, 14219,14229,14233,14243,14277,14287,14289,14295,14301,14305, 14323,14339,14341,14359,14365,14375,14387,14411,14425,14441, 14449,14499,14513,14523,14537,14543,14561,14579,14585,14593, 14599,14603,14611,14641,14671,14695,14701,14723,14725,14743, 14753,14759,14765,14795,14797,14803,14831,14839,14845,14855, 14889,14895,14909,14929,14941,14945,14951,14963,14965,14985, 15033,15039,15053,15059,15061,15071,15077,15081,15099,15121, 15147,15149,15157,15167,15187,15193,15203,15205,15215,15217, 15223,15243,15257,15269,15273,15287,15291,15313,15335,15347, 15359,15373,15379,15381,15391,15395,15397,15419,15439,15453, 15469,15491,15503,15517,15527,15531,15545,15559,15593,15611, 15613,15619,15639,15643,15649,15661,15667,15669,15681,15693, 15717,15721,15741,15745,15765,15793,15799,15811,15825,15835, 15847,15851,15865,15877,15881,15887,15899,15915,15935,15937, 15955,15973,15977,16011,16035,16061,16069,16087,16093,16097, 16121,16141,16153,16159,16165,16183,16189,16195,16197,16201, 16209,16215,16225,16259,16265,16273,16299,16309,16355,16375, 16381] atmost = 2**log_max - 1 # # Find the number of bits in ATMOST. # maxcol = i4_bit_hi1 ( atmost ) # # Initialize row 1 of V. # v[0,0:maxcol] = 1 # # Things to do only if the dimension changed. # if ( dim_num != dim_num_save ): # # Check parameters. # if ( dim_num < 1 or dim_max < dim_num ): print('I4_SOBOL - Fatal error!' ) print(' The spatial dimension DIM_NUM should satisfy:') print(' 1 <= DIM_NUM <= %d'%dim_max) print(' But this input value is DIM_NUM = %d'%dim_num) return dim_num_save = dim_num # # Initialize the remaining rows of V. # for i in range(2 , dim_num+1): # # The bits of the integer POLY(I) gives the form of polynomial I. # # Find the degree of polynomial I from binary encoding. # j = poly[i-1] m = 0 while ( 1 ): j = math.floor ( j / 2. ) if ( j <= 0 ): break m = m + 1 # # Expand this bit pattern to separate components of the logical array INCLUD. # j = poly[i-1] includ=zeros(m) for k in range(m, 0, -1): j2 = math.floor ( j / 2. ) includ[k-1] = (j != 2 * j2 ) j = j2 # # Calculate the remaining elements of row I as explained # in Bratley and Fox, section 2. # for j in range( m+1, maxcol+1 ): newv = v[i-1,j-m-1] l = 1 for k in range(1, m+1): l = 2 * l if ( includ[k-1] ): newv = bitwise_xor ( int(newv), int(l * v[i-1,j-k-1]) ) v[i-1,j-1] = newv # # Multiply columns of V by appropriate power of 2. # l = 1 for j in range( maxcol-1, 0, -1): l = 2 * l v[0:dim_num,j-1] = v[0:dim_num,j-1] * l # # RECIPD is 1/(common denominator of the elements in V). # recipd = 1.0 / ( 2 * l ) lastq=zeros(dim_num) seed = int(math.floor ( seed )) if ( seed < 0 ): seed = 0 if ( seed == 0 ): l = 1 lastq=zeros(dim_num) elif ( seed == seed_save + 1 ): # # Find the position of the right-hand zero in SEED. # l = i4_bit_lo0 ( seed ) elif ( seed <= seed_save ): seed_save = 0 l = 1 lastq=zeros(dim_num) for seed_temp in range( int(seed_save), int(seed)): l = i4_bit_lo0 ( seed_temp ) for i in range(1 , dim_num+1): lastq[i-1] = bitwise_xor ( int(lastq[i-1]), int(v[i-1,l-1]) ) l = i4_bit_lo0 ( seed ) elif ( seed_save + 1 < seed ): for seed_temp in range( int(seed_save + 1), int(seed) ): l = i4_bit_lo0 ( seed_temp ) for i in range(1, dim_num+1): lastq[i-1] = bitwise_xor ( int(lastq[i-1]), int(v[i-1,l-1]) ) l = i4_bit_lo0 ( seed ) # # Check that the user is not calling too many times! # if ( maxcol < l ): print('I4_SOBOL - Fatal error!') print(' Too many calls!') print(' MAXCOL = %d\n'%maxcol) print(' L = %d\n'%l) return # # Calculate the new components of QUASI. # quasi=zeros(dim_num) for i in range( 1, dim_num+1): quasi[i-1] = lastq[i-1] * recipd lastq[i-1] = bitwise_xor ( int(lastq[i-1]), int(v[i-1,l-1]) ) seed_save = seed seed = seed + 1 return [ quasi, seed ] def i4_uniform ( a, b, seed ): #*****************************************************************************80 # ## I4_UNIFORM returns a scaled pseudorandom I4. # # Discussion: # # The pseudorandom number will be scaled to be uniformly distributed # between A and B. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 22 February 2011 # # Author: # # Original MATLAB version by John Burkardt. # PYTHON version by Corrado Chisari # # Reference: # # Paul Bratley, Bennett Fox, Linus Schrage, # A Guide to Simulation, # Springer Verlag, pages 201-202, 1983. # # Pierre L'Ecuyer, # Random Number Generation, # in Handbook of Simulation, # edited by Jerry Banks, # Wiley Interscience, page 95, 1998. # # Bennett Fox, # Algorithm 647: # Implementation and Relative Efficiency of Quasirandom # Sequence Generators, # ACM Transactions on Mathematical Software, # Volume 12, Number 4, pages 362-376, 1986. # # Peter Lewis, Allen Goodman, James Miller # A Pseudo-Random Number Generator for the System/360, # IBM Systems Journal, # Volume 8, pages 136-143, 1969. # # Parameters: # # Input, integer A, B, the minimum and maximum acceptable values. # # Input, integer SEED, a seed for the random number generator. # # Output, integer C, the randomly chosen integer. # # Output, integer SEED, the updated seed. # if ( seed == 0 ): print('I4_UNIFORM - Fatal error!') print(' Input SEED = 0!') seed = math.floor ( seed ) a = round ( a ) b = round ( b ) seed = mod ( seed, 2147483647 ) if ( seed < 0 ) : seed = seed + 2147483647 k = math.floor ( seed / 127773 ) seed = 16807 * ( seed - k * 127773 ) - k * 2836 if ( seed < 0 ): seed = seed + 2147483647 r = seed * 4.656612875E-10 # # Scale R to lie between A-0.5 and B+0.5. # r = ( 1.0 - r ) * ( min ( a, b ) - 0.5 ) + r * ( max ( a, b ) + 0.5 ) # # Use rounding to convert R to an integer between A and B. # value = round ( r ) value = max ( value, min ( a, b ) ) value = min ( value, max ( a, b ) ) c = value return [ int(c), int(seed) ] def prime_ge ( n ): #*****************************************************************************80 # ## PRIME_GE returns the smallest prime greater than or equal to N. # # # Example: # # N PRIME_GE # # -10 2 # 1 2 # 2 2 # 3 3 # 4 5 # 5 5 # 6 7 # 7 7 # 8 11 # 9 11 # 10 11 # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 22 February 2011 # # Author: # # Original MATLAB version by John Burkardt. # PYTHON version by Corrado Chisari # # Parameters: # # Input, integer N, the number to be bounded. # # Output, integer P, the smallest prime number that is greater # than or equal to N. # p = max ( math.ceil ( n ), 2 ) while ( not isprime ( p ) ): p = p + 1 return p def isprime(n): #*****************************************************************************80 # ## IS_PRIME returns True if N is a prime number, False otherwise # # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 22 February 2011 # # Author: # # Corrado Chisari # # Parameters: # # Input, integer N, the number to be checked. # # Output, boolean value, True or False # if n!=int(n) or n<1: return False p=2 while p. import re import numpy as np import numpy.random as npr import logging logger = logging.getLogger(__name__) log = logger.debug def unpack_args(str): if len(str) > 1: eq_re = re.compile("\s*=\s*") return dict(map(lambda x: eq_re.split(x), re.compile("\s*,\s*").split(str))) else: return {} def slice_sample(init_x, logprob, sigma=1.0, step_out=True, max_steps_out=1000, compwise=False, verbose=False): def direction_slice(direction, init_x): def dir_logprob(z): return logprob(direction*z + init_x) upper = sigma*npr.rand() lower = upper - sigma llh_s = np.log(npr.rand()) + dir_logprob(0.0) l_steps_out = 0 u_steps_out = 0 if step_out: while dir_logprob(lower) > llh_s and l_steps_out < max_steps_out: l_steps_out += 1 lower -= sigma while dir_logprob(upper) > llh_s and u_steps_out < max_steps_out: u_steps_out += 1 upper += sigma steps_in = 0 while True: steps_in += 1 new_z = (upper - lower)*npr.rand() + lower new_llh = dir_logprob(new_z) if np.isnan(new_llh): log(new_z, direction*new_z + init_x, new_llh, llh_s, init_x, logprob(init_x)) raise Exception("Slice sampler got a NaN") if new_llh > llh_s: break elif new_z < 0: lower = new_z elif new_z > 0: upper = new_z else: raise Exception("Slice sampler shrank to zero!") if verbose: log("Steps Out:", l_steps_out, u_steps_out, " Steps In:", steps_in) return new_z*direction + init_x if not init_x.shape: init_x = np.array([init_x]) dims = init_x.shape[0] if compwise: ordering = list(range(dims)) npr.shuffle(ordering) cur_x = init_x.copy() for d in ordering: direction = np.zeros((dims)) direction[d] = 1.0 cur_x = direction_slice(direction, cur_x) return cur_x else: direction = npr.randn(dims) direction = direction / np.sqrt(np.sum(direction**2)) return direction_slice(direction, init_x) ================================================ FILE: src/aup/RestAPI/__init__.py ================================================ """ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ ================================================ FILE: src/aup/RestAPI/server.py ================================================ """ RestAPI server using Flask ============================================ .. Copyright (c) 2018 LG Electronics Inc. SPDX-License-Identifier: GPL-3.0-or-later """ from flask import render_template, jsonify, request from flask_cors import CORS import connexion import click import logging import sys import sqlite3 as sql import os import os.path from six.moves.configparser import ConfigParser import json import signal import multiprocessing from aup.compression.utils import SERIALIZATION_SEPARATOR, run_non_automatic_experiment, verify_compression_config, adjust_compression_config from aup.setup import setup from aup.utils import get_default_username, set_default_keyvalue from ..ET.Connector.SQLiteConnector import SQLiteConnector from ..EE.Experiment import Experiment from ..aup import BasicConfig from aup.Proposer import get_proposer from threading import Lock logger = logging.getLogger("RestAPI") # Create the application instance app = connexion.App(__name__, specification_dir='./') CORS(app.app) EXPS = dict() EXPS_lock = Lock() EXPERIMENT_STATUS_TRANSLATE_DICT = { "CREATED": "CREATED", "RUNNING": "RUNNING", "STOPPED": "STOPPED", "FINISHED": "FINISHED", "FAILED": "FAILED", "STOPPING": "STOPPING", "REQUEST_STOP": "STOPPING", } def fix_none_res(arg, value): return arg if arg is not None and arg != 'None' else value def get_display_names(params, exp_config=None, eid=None, cur=None): if exp_config is None and eid is not None and cur is not None: cur.execute("SELECT json_extract(exp_config, '$') as exp_config \ FROM experiment WHERE eid={eid} LIMIT 1;".format(eid=eid)) exp_config = json.loads(str(cur.fetchone()[0])) display_names = {} for param in params: param = param['name'] full_param = param.split(SERIALIZATION_SEPARATOR) index = int(full_param[0]) original_key = full_param[-1] cdict = exp_config['compression']['config_list'][index] for part in full_param[1:-1]: cdict = cdict[part] if 'op_names' in exp_config['compression']['config_list'][index]: new_key = '{} ({})'.format( SERIALIZATION_SEPARATOR.join(full_param[1:]), ", ".join([op_name for op_name in exp_config['compression']['config_list'][index]['op_names']])) else: new_key = '{} ({})'.format( SERIALIZATION_SEPARATOR.join(full_param[1:]), ", ".join([op_type for op_type in exp_config['compression']['config_list'][index]['op_types']])) display_names[param] = new_key return display_names def fix_compression_job_config(jobs, params, display_names): new_jobs = [] for job in jobs: if job['job_config'] is None: new_job = { **{display_names[param['name']]: None for param in params}, **{key: val for key, val in job.items() if key != 'job_config'}, } new_jobs += [new_job] continue new_job = {key: val for key, val in job.items() if key != 'job_config'} job_config = json.loads(job['job_config']) for param in params: param = param['name'] full_param = param.split(SERIALIZATION_SEPARATOR) index = int(full_param[0]) original_key = full_param[-1] cdict = job_config['config_list'][index] for part in full_param[1:-1]: cdict = cdict[part] new_job[display_names[param]] = cdict[original_key] new_jobs += [new_job] return new_jobs def is_compression_experiment(exp_config=None, eid=None, cur=None): if exp_config is not None: return "compression" in exp_config elif eid is not None: ret = query_db(cur, "SELECT (json_extract(exp_config, '$.compression') IS NOT NULL) as is_compression_exp \ FROM experiment WHERE eid={eid} LIMIT 1;".format(eid=eid)) return bool(ret[0]["is_compression_exp"]) else: raise ValueError("Missing parameter for is_compression_experiment") def get_params_for_experiment(cur, eid): exp_config = query_db(cur, "SELECT json_extract(exp_config, '$.parameter_config') as parameters from experiment where eid={eid};".format(eid=eid)) params_json = json.loads(exp_config[0]['parameters']) params = [] for v in params_json: name = v['name'] new_param = {} new_param['name'] = name new_param['type'] = v['type'] new_param['range'] = v.get('range', None) new_param['interval'] = v.get('interval', None) new_param['n'] = v.get('n', None) params.append(new_param) return params def query_db(cur, query, args=(), one=False): cur.execute(query, args) r = [dict((cur.description[i][0], value) \ for i, value in enumerate(row)) for row in cur.fetchall()] return (r[0] if r else None) if one else r def start_experiment_daemon(json_conf, eid, cwd, event): daemon_logger = logging.getLogger("RestAPIDaemon") is_compression_exp = "compression" in json_conf is_one_shot_compression_exp = is_compression_exp and "proposer" not in json_conf rc = 0 exp = None previous_dir = os.getcwd() os.chdir(cwd) try: os.setsid() os.umask(0) if is_compression_exp: json_conf = verify_compression_config(json_conf) json_conf["compression"] = adjust_compression_config(json_conf["compression"]) set_default_keyvalue("workingdir", cwd, json_conf, log=logger) if not is_one_shot_compression_exp: exp = Experiment(json_conf, eid=eid) exp.add_suspend_signal() exp.add_refresh_signal() else: _, finish = run_non_automatic_experiment(json_conf, os.path.join(".aup"), eid=eid) event.set() event.clear() event.wait() if not is_one_shot_compression_exp: exp.start() except Exception as e: daemon_logger.exception('Exception caught:' + str(e)) rc = 1 finally: if not is_one_shot_compression_exp: exp.finish() else: finish() os.chdir(previous_dir) os._exit(rc) def get_valid_jobs_interval(cursor, eid): fin_cond = "(score is not NULL and (score == 'EARLY STOPPED' or typeof(score)=='real')) \ and start_time is not NULL and end_time is not NULL" cursor.execute("SELECT jid, ({fin_cond}) AS stat FROM job WHERE \ eid={eid} ORDER BY jid;".format(fin_cond=fin_cond, eid=eid)) sql_res = cursor.fetchall() first = None last = None idx = None num_jobs = 0 for i in range(0, len(sql_res)): if first == None and sql_res[i][1] == 1: first = sql_res[i][0] idx = i if first != None and sql_res[i][1] == 0: last = sql_res[i-1][0] num_jobs = (i - idx) break start_job = first if first == None: num_jobs = 0 elif last == None: num_jobs = len(sql_res) return (start_job, num_jobs) # Create a URL route in our application for "/" @app.route('/') def home(): """ This function just responds to the browser ULR localhost:5000/ :return: the rendered template 'home.html' """ return render_template('home.html') @app.route('/api/resource_types', methods=['GET']) def get_resource_types(): with sql.connect(app.app.config['db_file'], check_same_thread=False) as con: cur = con.cursor() cur.execute("SELECT DISTINCT type from resource;") res = [i[0] for i in cur.fetchall()] return jsonify({'resources': res}) return None @app.route('/api/experiments/', methods=['GET']) def get_experiment(eid): with sql.connect(app.app.config['db_file'], check_same_thread=False) as con: cur = con.cursor() experiment = experiment = query_db(cur, "SELECT *, name as experiment_name, \ json_extract(exp_config, '$.script') as script_name \ from experiment where eid={eid};".format(eid=eid)) experiment[0]['status'] = EXPERIMENT_STATUS_TRANSLATE_DICT[experiment[0]['status']] cur = con.cursor() cur.execute("SELECT count(*) from job where (end_time is NULL or \ (typeof(score) is not 'real' and score is not 'EARLY STOPPED')) and eid={eid}".format(eid=eid)) unfinished = int(cur.fetchone()[0]) cur.execute("SELECT count(*) from job where (end_time is not NULL and \ (typeof(score) is 'real' or score is 'EARLY STOPPED')) and eid={eid}".format(eid=eid)) finished = int(cur.fetchone()[0]) cur.execute("SELECT json_extract(exp_config, '$.target') from experiment where eid={eid}".format(eid=eid)) order = str(cur.fetchone()[0]) order = fix_none_res(order, 'max') cur.execute("SELECT json_extract(exp_config, '$.proposer') from experiment where eid={eid}".format(eid=eid)) proposer = str(cur.fetchone()[0]) cur = con.cursor() cur.execute("SELECT exp_config \ from experiment where eid={eid};".format(eid=eid)) exp_config = json.loads(str(cur.fetchone()[0])) num_params = len(exp_config['parameter_config']) proposer_obj = None n_samples = 1 if proposer is not None and proposer != 'None': proposer_obj = get_proposer(proposer) n_samples = proposer_obj(exp_config).nSamples if proposer == 'bohb' and experiment[0]['status'] == 'FINISHED': n_samples = finished param_names = [] for p in range(0, num_params): cur.execute("SELECT json_extract(exp_config, '$.parameter_config[{idx}].name') \ from experiment where eid={eid};".format(idx=p, eid=eid)) param_names.append(str(cur.fetchone()[0])) is_compression_exp = is_compression_experiment(exp_config) best_metrics_vs_hparams = query_db(cur, "SELECT {order}(score) as score, job_config \ from job where eid={eid} and typeof(score) = 'real';".format(order=order, eid=eid)) params = get_params_for_experiment(cur, eid) new_best_score = {} best_job_config = best_metrics_vs_hparams[0]['job_config'] if is_compression_exp: display_names = get_display_names(params, exp_config) best_metrics_vs_hparams = fix_compression_job_config(best_metrics_vs_hparams, params, display_names) for param in params: if param['name'] in display_names: param['name'] = display_names[param['name']] param['value'] = best_metrics_vs_hparams[0][param['name']] else: job_configs = [ json.loads(mvh["job_config"]) if "job_config" in mvh and mvh["job_config"] is not None else {} for mvh in best_metrics_vs_hparams ] best_metrics_vs_hparams = [{ "score": mvh["score"], **{param['name']: job_config[param['name']] if param['name'] in job_config else None for param in params}, } for mvh, job_config in zip(best_metrics_vs_hparams, job_configs)] for param in params: param['value'] = best_metrics_vs_hparams[0][param['name']] new_best_score['params'] = params new_best_score['proposer'] = proposer new_best_score['score'] = best_metrics_vs_hparams[0]['score'] res = { 'experiment': experiment[0], 'best_score': new_best_score, 'job_stats': { 'finished': finished, 'unfinished': unfinished, 'total': n_samples }, } if is_compression_exp and len(params) == 0: best_config_list = query_db(cur, "SELECT json_extract(exp_config, '$.compression.config_list') as 'config_list' \ FROM experiment WHERE eid={eid} LIMIT 1;".format(eid=eid)) best_config_list = best_config_list[0]['config_list'] res['best_score'] = { 'score': res['best_score']['score'], 'config_list': best_config_list, } return res return None @app.route('/api/experiments', methods=['GET']) def get_experiments(): with sql.connect(app.app.config['db_file'], check_same_thread=False) as con: cur = con.cursor() experiment = query_db(cur, "SELECT *, json_extract(exp_config, '$.script') as script_name, \ name as experiment_name, \ (SELECT case when (SELECT json_extract(exp_config, '$.target') from experiment where eid=experiment.eid) is \"min\" \ then min(score) else max(score) end from job where job.eid=experiment.eid and typeof(score) is 'real') as best_score, \ (SELECT json_group_array(job.score) from job where job.eid=experiment.eid) as scores, \ (SELECT count(*) from job where (end_time is NULL or typeof(score) is not 'real') and job.eid=experiment.eid) as jobs_unfinished, \ (SELECT count(*) from job where end_time is not NULL and 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h=8+(r.w_bits-8<<4)<<8;h|=(r.strategy>=2||r.level<2?0:r.level<6?1:6===r.level?2:3)<<6,0!==r.strstart&&(h|=32),h+=31-h%31,r.status=u,I(r,h),0!==r.strstart&&(I(r,e.adler>>>16),I(r,65535&e.adler)),e.adler=1}if(69===r.status)if(r.gzhead.extra){for(s=r.pending;r.gzindex<(65535&r.gzhead.extra.length)&&(r.pending!==r.pending_buf_size||(r.gzhead.hcrc&&r.pending>s&&(e.adler=o(e.adler,r.pending_buf,r.pending-s,s)),C(e),s=r.pending,r.pending!==r.pending_buf_size));)v(r,255&r.gzhead.extra[r.gzindex]),r.gzindex++;r.gzhead.hcrc&&r.pending>s&&(e.adler=o(e.adler,r.pending_buf,r.pending-s,s)),r.gzindex===r.gzhead.extra.length&&(r.gzindex=0,r.status=73)}else r.status=73;if(73===r.status)if(r.gzhead.name){s=r.pending;do{if(r.pending===r.pending_buf_size&&(r.gzhead.hcrc&&r.pending>s&&(e.adler=o(e.adler,r.pending_buf,r.pending-s,s)),C(e),s=r.pending,r.pending===r.pending_buf_size)){a=1;break}a=r.gzindexs&&(e.adler=o(e.adler,r.pending_buf,r.pending-s,s)),0===a&&(r.gzindex=0,r.status=91)}else r.status=91;if(91===r.status)if(r.gzhead.comment){s=r.pending;do{if(r.pending===r.pending_buf_size&&(r.gzhead.hcrc&&r.pending>s&&(e.adler=o(e.adler,r.pending_buf,r.pending-s,s)),C(e),s=r.pending,r.pending===r.pending_buf_size)){a=1;break}a=r.gzindexs&&(e.adler=o(e.adler,r.pending_buf,r.pending-s,s)),0===a&&(r.status=d)}else r.status=d;if(r.status===d&&(r.gzhead.hcrc?(r.pending+2>r.pending_buf_size&&C(e),r.pending+2<=r.pending_buf_size&&(v(r,255&e.adler),v(r,e.adler>>8&255),e.adler=0,r.status=u)):r.status=u),0!==r.pending){if(C(e),0===e.avail_out)return r.last_flush=-1,0}else if(0===e.avail_in&&p(t)<=p(n)&&4!==t)return g(e,-5);if(r.status===f&&0!==e.avail_in)return g(e,-5);if(0!==e.avail_in||0!==r.lookahead||0!==t&&r.status!==f){var A=2===r.strategy?function(e,t){for(var n;;){if(0===e.lookahead&&(y(e),0===e.lookahead)){if(0===t)return 1;break}if(e.match_length=0,n=i._tr_tally(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(b(e,!1),0===e.strm.avail_out))return 1}return e.insert=0,4===t?(b(e,!0),0===e.strm.avail_out?3:4):e.last_lit&&(b(e,!1),0===e.strm.avail_out)?1:2}(r,t):3===r.strategy?function(e,t){for(var n,r,s,o,a=e.window;;){if(e.lookahead<=l){if(y(e),e.lookahead<=l&&0===t)return 1;if(0===e.lookahead)break}if(e.match_length=0,e.lookahead>=3&&e.strstart>0&&(r=a[s=e.strstart-1])===a[++s]&&r===a[++s]&&r===a[++s]){o=e.strstart+l;do{}while(r===a[++s]&&r===a[++s]&&r===a[++s]&&r===a[++s]&&r===a[++s]&&r===a[++s]&&r===a[++s]&&r===a[++s]&&se.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=3?(n=i._tr_tally(e,1,e.match_length-3),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=i._tr_tally(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(b(e,!1),0===e.strm.avail_out))return 1}return e.insert=0,4===t?(b(e,!0),0===e.strm.avail_out?3:4):e.last_lit&&(b(e,!1),0===e.strm.avail_out)?1:2}(r,t):x[r.level].func(r,t);if(3!==A&&4!==A||(r.status=f),1===A||3===A)return 0===e.avail_out&&(r.last_flush=-1),0;if(2===A&&(1===t?i._tr_align(r):5!==t&&(i._tr_stored_block(r,0,0,!1),3===t&&(m(r.head),0===r.lookahead&&(r.strstart=0,r.block_start=0,r.insert=0))),C(e),0===e.avail_out))return r.last_flush=-1,0}return 4!==t?0:r.wrap<=0?1:(2===r.wrap?(v(r,255&e.adler),v(r,e.adler>>8&255),v(r,e.adler>>16&255),v(r,e.adler>>24&255),v(r,255&e.total_in),v(r,e.total_in>>8&255),v(r,e.total_in>>16&255),v(r,e.total_in>>24&255)):(I(r,e.adler>>>16),I(r,65535&e.adler)),C(e),r.wrap>0&&(r.wrap=-r.wrap),0!==r.pending?0:1)},n.deflateEnd=function(e){var t;return e&&e.state?42!==(t=e.state.status)&&69!==t&&73!==t&&91!==t&&t!==d&&t!==u&&t!==f?g(e,c):(e.state=null,t===u?g(e,-3):0):c},n.deflateInfo="pako deflate (from Nodeca project)"},{"../utils/common":27,"./adler32":29,"./crc32":31,"./messages":37,"./trees":38}],33:[function(e,t,n){"use strict";t.exports=function(){this.text=0,this.time=0,this.xflags=0,this.os=0,this.extra=null,this.extra_len=0,this.name="",this.comment="",this.hcrc=0,this.done=!1}},{}],34:[function(e,t,n){"use strict";t.exports=function(e,t){var n,r,i,s,o,a,c,l,h,d,u,f,g,p,m,C,b,v,I,A,y,w,S,x,E;x=e.input,i=(r=e.next_in)+(e.avail_in-5),E=e.output,o=(s=e.next_out)-(t-e.avail_out),a=s+(e.avail_out-257),c=(n=e.state).dmax,l=n.wsize,h=n.whave,d=n.wnext,u=n.window,f=n.hold,g=n.bits,p=n.lencode,m=n.distcode,C=(1<>>=I=v>>>24,g-=I,0==(I=v>>>16&255))E[s++]=65535&v;else{if(!(16&I)){if(0==(64&I)){v=p[(65535&v)+(f&(1<>>=I,g-=I),g<15&&(f+=x[r++]<>>=I=v>>>24,g-=I,!(16&(I=v>>>16&255))){if(0==(64&I)){v=m[(65535&v)+(f&(1<c){e.msg="invalid distance too far back",n.mode=30;break e}if(f>>>=I,g-=I,y>(I=s-o)){if((I=y-I)>h&&n.sane){e.msg="invalid distance too far back",n.mode=30;break e}if(w=0,S=u,0===d){if(w+=l-I,I2;)E[s++]=S[w++],E[s++]=S[w++],E[s++]=S[w++],A-=3;A&&(E[s++]=S[w++],A>1&&(E[s++]=S[w++]))}else{w=s-y;do{E[s++]=E[w++],E[s++]=E[w++],E[s++]=E[w++],A-=3}while(A>2);A&&(E[s++]=E[w++],A>1&&(E[s++]=E[w++]))}break}}break}}while(r>3)<<3))-1,e.next_in=r-=A,e.next_out=s,e.avail_in=r>>24&255)+(e>>>8&65280)+((65280&e)<<8)+((255&e)<<24)}function u(){this.mode=0,this.last=!1,this.wrap=0,this.havedict=!1,this.flags=0,this.dmax=0,this.check=0,this.total=0,this.head=null,this.wbits=0,this.wsize=0,this.whave=0,this.wnext=0,this.window=null,this.hold=0,this.bits=0,this.length=0,this.offset=0,this.extra=0,this.lencode=null,this.distcode=null,this.lenbits=0,this.distbits=0,this.ncode=0,this.nlen=0,this.ndist=0,this.have=0,this.next=null,this.lens=new r.Buf16(320),this.work=new r.Buf16(288),this.lendyn=null,this.distdyn=null,this.sane=0,this.back=0,this.was=0}function f(e){var t;return e&&e.state?(e.total_in=e.total_out=(t=e.state).total=0,e.msg="",t.wrap&&(e.adler=1&t.wrap),t.mode=1,t.last=0,t.havedict=0,t.dmax=32768,t.head=null,t.hold=0,t.bits=0,t.lencode=t.lendyn=new r.Buf32(852),t.distcode=t.distdyn=new r.Buf32(592),t.sane=1,t.back=-1,0):c}function g(e){var t;return e&&e.state?((t=e.state).wsize=0,t.whave=0,t.wnext=0,f(e)):c}function p(e,t){var n,r;return e&&e.state?(t<0?(n=0,t=-t):(n=1+(t>>4),t<48&&(t&=15)),t&&(t<8||t>15)?c:(null!==(r=e.state).window&&r.wbits!==t&&(r.window=null),r.wrap=n,r.wbits=t,g(e))):c}function m(e,t){var n,r;return e?(r=new u,e.state=r,r.window=null,0!==(n=p(e,t))&&(e.state=null),n):c}var C,b,v=!0;function I(e){if(v){var t;for(C=new r.Buf32(512),b=new r.Buf32(32),t=0;t<144;)e.lens[t++]=8;for(;t<256;)e.lens[t++]=9;for(;t<280;)e.lens[t++]=7;for(;t<288;)e.lens[t++]=8;for(a(1,e.lens,0,288,C,0,e.work,{bits:9}),t=0;t<32;)e.lens[t++]=5;a(2,e.lens,0,32,b,0,e.work,{bits:5}),v=!1}e.lencode=C,e.lenbits=9,e.distcode=b,e.distbits=5}n.inflateReset=g,n.inflateReset2=p,n.inflateResetKeep=f,n.inflateInit=function(e){return m(e,15)},n.inflateInit2=m,n.inflate=function(e,t){var n,u,f,g,p,m,C,b,v,A,y,w,S,x,E,k,_,T,R,B,O,L,P,F,N=0,M=new r.Buf8(4),D=[16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15];if(!e||!e.state||!e.output||!e.input&&0!==e.avail_in)return c;(n=e.state).mode===l&&(n.mode=13),p=e.next_out,f=e.output,g=e.next_in,u=e.input,b=n.hold,v=n.bits,A=m=e.avail_in,y=C=e.avail_out,L=0;e:for(;;)switch(n.mode){case 1:if(0===n.wrap){n.mode=13;break}for(;v<16;){if(0===m)break e;m--,b+=u[g++]<>>8&255,n.check=s(n.check,M,2,0),b=0,v=0,n.mode=2;break}if(n.flags=0,n.head&&(n.head.done=!1),!(1&n.wrap)||(((255&b)<<8)+(b>>8))%31){e.msg="incorrect header check",n.mode=h;break}if(8!=(15&b)){e.msg="unknown compression method",n.mode=h;break}if(v-=4,O=8+(15&(b>>>=4)),0===n.wbits)n.wbits=O;else if(O>n.wbits){e.msg="invalid window size",n.mode=h;break}n.dmax=1<>8&1),512&n.flags&&(M[0]=255&b,M[1]=b>>>8&255,n.check=s(n.check,M,2,0)),b=0,v=0,n.mode=3;case 3:for(;v<32;){if(0===m)break e;m--,b+=u[g++]<>>8&255,M[2]=b>>>16&255,M[3]=b>>>24&255,n.check=s(n.check,M,4,0)),b=0,v=0,n.mode=4;case 4:for(;v<16;){if(0===m)break e;m--,b+=u[g++]<>8),512&n.flags&&(M[0]=255&b,M[1]=b>>>8&255,n.check=s(n.check,M,2,0)),b=0,v=0,n.mode=5;case 5:if(1024&n.flags){for(;v<16;){if(0===m)break e;m--,b+=u[g++]<>>8&255,n.check=s(n.check,M,2,0)),b=0,v=0}else n.head&&(n.head.extra=null);n.mode=6;case 6:if(1024&n.flags&&((w=n.length)>m&&(w=m),w&&(n.head&&(O=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new Array(n.head.extra_len)),r.arraySet(n.head.extra,u,g,w,O)),512&n.flags&&(n.check=s(n.check,u,w,g)),m-=w,g+=w,n.length-=w),n.length))break e;n.length=0,n.mode=7;case 7:if(2048&n.flags){if(0===m)break e;w=0;do{O=u[g+w++],n.head&&O&&n.length<65536&&(n.head.name+=String.fromCharCode(O))}while(O&&w>9&1,n.head.done=!0),e.adler=n.check=0,n.mode=l;break;case 10:for(;v<32;){if(0===m)break e;m--,b+=u[g++]<>>=7&v,v-=7&v,n.mode=27;break}for(;v<3;){if(0===m)break e;m--,b+=u[g++]<>>=1)){case 0:n.mode=14;break;case 1:if(I(n),n.mode=20,6===t){b>>>=2,v-=2;break e}break;case 2:n.mode=17;break;case 3:e.msg="invalid block type",n.mode=h}b>>>=2,v-=2;break;case 14:for(b>>>=7&v,v-=7&v;v<32;){if(0===m)break e;m--,b+=u[g++]<>>16^65535)){e.msg="invalid stored block lengths",n.mode=h;break}if(n.length=65535&b,b=0,v=0,n.mode=15,6===t)break e;case 15:n.mode=16;case 16:if(w=n.length){if(w>m&&(w=m),w>C&&(w=C),0===w)break e;r.arraySet(f,u,g,w,p),m-=w,g+=w,C-=w,p+=w,n.length-=w;break}n.mode=l;break;case 17:for(;v<14;){if(0===m)break e;m--,b+=u[g++]<>>=5)),v-=5,n.ncode=4+(15&(b>>>=5)),b>>>=4,v-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=h;break}n.have=0,n.mode=18;case 18:for(;n.have>>=3,v-=3}for(;n.have<19;)n.lens[D[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,L=a(0,n.lens,0,19,n.lencode,0,n.work,P={bits:n.lenbits}),n.lenbits=P.bits,L){e.msg="invalid code lengths set",n.mode=h;break}n.have=0,n.mode=19;case 19:for(;n.have>>16&255,_=65535&N,!((E=N>>>24)<=v);){if(0===m)break e;m--,b+=u[g++]<>>=E,v-=E,n.lens[n.have++]=_;else{if(16===_){for(F=E+2;v>>=E,v-=E,0===n.have){e.msg="invalid bit length repeat",n.mode=h;break}O=n.lens[n.have-1],w=3+(3&b),b>>>=2,v-=2}else if(17===_){for(F=E+3;v>>=E)),b>>>=3,v-=3}else{for(F=E+7;v>>=E)),b>>>=7,v-=7}if(n.have+w>n.nlen+n.ndist){e.msg="invalid bit length repeat",n.mode=h;break}for(;w--;)n.lens[n.have++]=O}}if(n.mode===h)break;if(0===n.lens[256]){e.msg="invalid code -- missing end-of-block",n.mode=h;break}if(n.lenbits=9,L=a(1,n.lens,0,n.nlen,n.lencode,0,n.work,P={bits:n.lenbits}),n.lenbits=P.bits,L){e.msg="invalid literal/lengths set",n.mode=h;break}if(n.distbits=6,n.distcode=n.distdyn,L=a(2,n.lens,n.nlen,n.ndist,n.distcode,0,n.work,P={bits:n.distbits}),n.distbits=P.bits,L){e.msg="invalid distances set",n.mode=h;break}if(n.mode=20,6===t)break e;case 20:n.mode=21;case 21:if(m>=6&&C>=258){e.next_out=p,e.avail_out=C,e.next_in=g,e.avail_in=m,n.hold=b,n.bits=v,o(e,y),p=e.next_out,f=e.output,C=e.avail_out,g=e.next_in,u=e.input,m=e.avail_in,b=n.hold,v=n.bits,n.mode===l&&(n.back=-1);break}for(n.back=0;k=(N=n.lencode[b&(1<>>16&255,_=65535&N,!((E=N>>>24)<=v);){if(0===m)break e;m--,b+=u[g++]<>T)])>>>16&255,_=65535&N,!(T+(E=N>>>24)<=v);){if(0===m)break e;m--,b+=u[g++]<>>=T,v-=T,n.back+=T}if(b>>>=E,v-=E,n.back+=E,n.length=_,0===k){n.mode=26;break}if(32&k){n.back=-1,n.mode=l;break}if(64&k){e.msg="invalid literal/length code",n.mode=h;break}n.extra=15&k,n.mode=22;case 22:if(n.extra){for(F=n.extra;v>>=n.extra,v-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=23;case 23:for(;k=(N=n.distcode[b&(1<>>16&255,_=65535&N,!((E=N>>>24)<=v);){if(0===m)break e;m--,b+=u[g++]<>T)])>>>16&255,_=65535&N,!(T+(E=N>>>24)<=v);){if(0===m)break e;m--,b+=u[g++]<>>=T,v-=T,n.back+=T}if(b>>>=E,v-=E,n.back+=E,64&k){e.msg="invalid distance code",n.mode=h;break}n.offset=_,n.extra=15&k,n.mode=24;case 24:if(n.extra){for(F=n.extra;v>>=n.extra,v-=n.extra,n.back+=n.extra}if(n.offset>n.dmax){e.msg="invalid distance too far back",n.mode=h;break}n.mode=25;case 25:if(0===C)break e;if(n.offset>(w=y-C)){if((w=n.offset-w)>n.whave&&n.sane){e.msg="invalid distance too far back",n.mode=h;break}S=w>n.wnext?n.wsize-(w-=n.wnext):n.wnext-w,w>n.length&&(w=n.length),x=n.window}else x=f,S=p-n.offset,w=n.length;w>C&&(w=C),C-=w,n.length-=w;do{f[p++]=x[S++]}while(--w);0===n.length&&(n.mode=21);break;case 26:if(0===C)break e;f[p++]=n.length,C--,n.mode=21;break;case 27:if(n.wrap){for(;v<32;){if(0===m)break e;m--,b|=u[g++]<=o.wsize?(r.arraySet(o.window,t,n-o.wsize,o.wsize,0),o.wnext=0,o.whave=o.wsize):((s=o.wsize-o.wnext)>i&&(s=i),r.arraySet(o.window,t,n-i,s,o.wnext),(i-=s)?(r.arraySet(o.window,t,n-i,i,0),o.wnext=i,o.whave=o.wsize):(o.wnext+=s,o.wnext===o.wsize&&(o.wnext=0),o.whave=1&&0===F[E];E--);if(k>E&&(k=E),0===E)return l[h++]=20971520,l[h++]=20971520,u.bits=1,0;for(x=1;x0&&(0===e||1!==E))return-1;for(N[1]=0,w=1;w<15;w++)N[w+1]=N[w]+F[w];for(S=0;S852||2===e&&B>592)return 1;for(;;){v=w-T,d[S]b?(I=M[D+d[S]],A=L[P+d[S]]):(I=96,A=0),f=1<>T)+(g-=f)]=v<<24|I<<16|A|0}while(0!==g);for(f=1<>=1;if(0!==f?(O&=f-1,O+=f):O=0,S++,0==--F[w]){if(w===E)break;w=t[n+d[S]]}if(w>k&&(O&m)!==p){for(0===T&&(T=k),C+=x,R=1<<(_=w-T);_+T852||2===e&&B>592)return 1;l[p=O&m]=k<<24|_<<16|C-h|0}}return 0!==O&&(l[C+O]=w-T<<24|64<<16|0),u.bits=k,0}},{"../utils/common":27}],37:[function(e,t,n){"use strict";t.exports={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data 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e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f 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e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc7\xfc\xe9\xe2\xe4\xe0\xe5\xe7\xea\xeb\xe8\xef\xee\xec\xc4\xc5\xc9\xe6\xc6\xf4\xf6\xf2\xfb\xf9\xff\xd6\xdc\xf8\xa3\xd8\xd7\u0192\xe1\xed\xf3\xfa\xf1\xd1\xaa\xba\xbf\xae\xac\xbd\xbc\xa1\xab\xbb\u2591\u2592\u2593\u2502\u2524\xc1\xc2\xc0\xa9\u2563\u2551\u2557\u255d\xa2\xa5\u2510\u2514\u2534\u252c\u251c\u2500\u253c\xe3\xc3\u255a\u2554\u2569\u2566\u2560\u2550\u256c\xa4\xf0\xd0\xca\xcb\xc8\u0131\xcd\xce\xcf\u2518\u250c\u2588\u2584\xa6\xcc\u2580\xd3\xdf\xd4\xd2\xf5\xd5\xb5\xfe\xde\xda\xdb\xd9\xfd\xdd\xaf\xb4\xad\xb1\u2017\xbe\xb6\xa7\xf7\xb8\xb0\xa8\xb7\xb9\xb3\xb2\u25a0\xa0",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[852]=function(){for(var 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!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc7\xfc\xe9\xe2\xe4\u016f\u0107\xe7\u0142\xeb\u0150\u0151\xee\u0179\xc4\u0106\xc9\u0139\u013a\xf4\xf6\u013d\u013e\u015a\u015b\xd6\xdc\u0164\u0165\u0141\xd7\u010d\xe1\xed\xf3\xfa\u0104\u0105\u017d\u017e\u0118\u0119\xac\u017a\u010c\u015f\xab\xbb\u2591\u2592\u2593\u2502\u2524\xc1\xc2\u011a\u015e\u2563\u2551\u2557\u255d\u017b\u017c\u2510\u2514\u2534\u252c\u251c\u2500\u253c\u0102\u0103\u255a\u2554\u2569\u2566\u2560\u2550\u256c\xa4\u0111\u0110\u010e\xcb\u010f\u0147\xcd\xce\u011b\u2518\u250c\u2588\u2584\u0162\u016e\u2580\xd3\xdf\xd4\u0143\u0144\u0148\u0160\u0161\u0154\xda\u0155\u0170\xfd\xdd\u0163\xb4\xad\u02dd\u02db\u02c7\u02d8\xa7\xf7\xb8\xb0\xa8\u02d9\u0171\u0158\u0159\u25a0\xa0",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[857]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc7\xfc\xe9\xe2\xe4\xe0\xe5\xe7\xea\xeb\xe8\xef\xee\u0131\xc4\xc5\xc9\xe6\xc6\xf4\xf6\xf2\xfb\xf9\u0130\xd6\xdc\xf8\xa3\xd8\u015e\u015f\xe1\xed\xf3\xfa\xf1\xd1\u011e\u011f\xbf\xae\xac\xbd\xbc\xa1\xab\xbb\u2591\u2592\u2593\u2502\u2524\xc1\xc2\xc0\xa9\u2563\u2551\u2557\u255d\xa2\xa5\u2510\u2514\u2534\u252c\u251c\u2500\u253c\xe3\xc3\u255a\u2554\u2569\u2566\u2560\u2550\u256c\xa4\xba\xaa\xca\xcb\xc8\ufffd\xcd\xce\xcf\u2518\u250c\u2588\u2584\xa6\xcc\u2580\xd3\xdf\xd4\xd2\xf5\xd5\xb5\ufffd\xd7\xda\xdb\xd9\xec\xff\xaf\xb4\xad\xb1\ufffd\xbe\xb6\xa7\xf7\xb8\xb0\xa8\xb7\xb9\xb3\xb2\u25a0\xa0",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[861]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc7\xfc\xe9\xe2\xe4\xe0\xe5\xe7\xea\xeb\xe8\xd0\xf0\xde\xc4\xc5\xc9\xe6\xc6\xf4\xf6\xfe\xfb\xdd\xfd\xd6\xdc\xf8\xa3\xd8\u20a7\u0192\xe1\xed\xf3\xfa\xc1\xcd\xd3\xda\xbf\u2310\xac\xbd\xbc\xa1\xab\xbb\u2591\u2592\u2593\u2502\u2524\u2561\u2562\u2556\u2555\u2563\u2551\u2557\u255d\u255c\u255b\u2510\u2514\u2534\u252c\u251c\u2500\u253c\u255e\u255f\u255a\u2554\u2569\u2566\u2560\u2550\u256c\u2567\u2568\u2564\u2565\u2559\u2558\u2552\u2553\u256b\u256a\u2518\u250c\u2588\u2584\u258c\u2590\u2580\u03b1\xdf\u0393\u03c0\u03a3\u03c3\xb5\u03c4\u03a6\u0398\u03a9\u03b4\u221e\u03c6\u03b5\u2229\u2261\xb1\u2265\u2264\u2320\u2321\xf7\u2248\xb0\u2219\xb7\u221a\u207f\xb2\u25a0\xa0",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[865]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc7\xfc\xe9\xe2\xe4\xe0\xe5\xe7\xea\xeb\xe8\xef\xee\xec\xc4\xc5\xc9\xe6\xc6\xf4\xf6\xf2\xfb\xf9\xff\xd6\xdc\xf8\xa3\xd8\u20a7\u0192\xe1\xed\xf3\xfa\xf1\xd1\xaa\xba\xbf\u2310\xac\xbd\xbc\xa1\xab\xa4\u2591\u2592\u2593\u2502\u2524\u2561\u2562\u2556\u2555\u2563\u2551\u2557\u255d\u255c\u255b\u2510\u2514\u2534\u252c\u251c\u2500\u253c\u255e\u255f\u255a\u2554\u2569\u2566\u2560\u2550\u256c\u2567\u2568\u2564\u2565\u2559\u2558\u2552\u2553\u256b\u256a\u2518\u250c\u2588\u2584\u258c\u2590\u2580\u03b1\xdf\u0393\u03c0\u03a3\u03c3\xb5\u03c4\u03a6\u0398\u03a9\u03b4\u221e\u03c6\u03b5\u2229\u2261\xb1\u2265\u2264\u2320\u2321\xf7\u2248\xb0\u2219\xb7\u221a\u207f\xb2\u25a0\xa0",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[866]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u0410\u0411\u0412\u0413\u0414\u0415\u0416\u0417\u0418\u0419\u041a\u041b\u041c\u041d\u041e\u041f\u0420\u0421\u0422\u0423\u0424\u0425\u0426\u0427\u0428\u0429\u042a\u042b\u042c\u042d\u042e\u042f\u0430\u0431\u0432\u0433\u0434\u0435\u0436\u0437\u0438\u0439\u043a\u043b\u043c\u043d\u043e\u043f\u2591\u2592\u2593\u2502\u2524\u2561\u2562\u2556\u2555\u2563\u2551\u2557\u255d\u255c\u255b\u2510\u2514\u2534\u252c\u251c\u2500\u253c\u255e\u255f\u255a\u2554\u2569\u2566\u2560\u2550\u256c\u2567\u2568\u2564\u2565\u2559\u2558\u2552\u2553\u256b\u256a\u2518\u250c\u2588\u2584\u258c\u2590\u2580\u0440\u0441\u0442\u0443\u0444\u0445\u0446\u0447\u0448\u0449\u044a\u044b\u044c\u044d\u044e\u044f\u0401\u0451\u0404\u0454\u0407\u0457\u040e\u045e\xb0\u2219\xb7\u221a\u2116\xa4\u25a0\xa0",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[874]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\ufffd\ufffd\ufffd\u2026\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\xa0\u0e01\u0e02\u0e03\u0e04\u0e05\u0e06\u0e07\u0e08\u0e09\u0e0a\u0e0b\u0e0c\u0e0d\u0e0e\u0e0f\u0e10\u0e11\u0e12\u0e13\u0e14\u0e15\u0e16\u0e17\u0e18\u0e19\u0e1a\u0e1b\u0e1c\u0e1d\u0e1e\u0e1f\u0e20\u0e21\u0e22\u0e23\u0e24\u0e25\u0e26\u0e27\u0e28\u0e29\u0e2a\u0e2b\u0e2c\u0e2d\u0e2e\u0e2f\u0e30\u0e31\u0e32\u0e33\u0e34\u0e35\u0e36\u0e37\u0e38\u0e39\u0e3a\ufffd\ufffd\ufffd\ufffd\u0e3f\u0e40\u0e41\u0e42\u0e43\u0e44\u0e45\u0e46\u0e47\u0e48\u0e49\u0e4a\u0e4b\u0e4c\u0e4d\u0e4e\u0e4f\u0e50\u0e51\u0e52\u0e53\u0e54\u0e55\u0e56\u0e57\u0e58\u0e59\u0e5a\u0e5b\ufffd\ufffd\ufffd\ufffd",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[895]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f 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5bcb\u5bcd\u5bce\u5bcf\ufffd\u5bd1\u5bd4\u5bd5\u5bd6\u5bd7\u5bd8\u5bd9\u5bda\u5bdb\u5bdc\u5be0\u5be2\u5be3\u5be6\u5be7\u5be9\u5bea\u5beb\u5bec\u5bed\u5bef\u5bf1\u5bf2\u5bf3\u5bf4\u5bf5\u5bf6\u5bf7\u5bfd\u5bfe\u5c00\u5c02\u5c03\u5c05\u5c07\u5c08\u5c0b\u5c0c\u5c0d\u5c0e\u5c10\u5c12\u5c13\u5c17\u5c19\u5c1b\u5c1e\u5c1f\u5c20\u5c21\u5c23\u5c26\u5c28\u5c29\u5c2a\u5c2b\u5c2d\u5c2e\u5c2f\u5c30\u5c32\u5c33\u5c35\u5c36\u5c37\u5c43\u5c44\u5c46\u5c47\u5c4c\u5c4d\u5c52\u5c53\u5c54\u5c56\u5c57\u5c58\u5c5a\u5c5b\u5c5c\u5c5d\u5c5f\u5c62\u5c64\u5c67\u5c68\u5c69\u5c6a\u5c6b\u5c6c\u5c6d\u5c70\u5c72\u5c73\u5c74\u5c75\u5c76\u5c77\u5c78\u5c7b\u5c7c\u5c7d\u5c7e\u5c80\u5c83\u5c84\u5c85\u5c86\u5c87\u5c89\u5c8a\u5c8b\u5c8e\u5c8f\u5c92\u5c93\u5c95\u5c9d\u5c9e\u5c9f\u5ca0\u5ca1\u5ca4\u5ca5\u5ca6\u5ca7\u5ca8\ufffd".split(""),e=0;e!=r[140].length;++e)65533!==r[140][e].charCodeAt(0)&&(n[r[140][e]]=35840+e,t[35840+e]=r[140][e]);for(r[141]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u5caa\u5cae\u5caf\u5cb0\u5cb2\u5cb4\u5cb6\u5cb9\u5cba\u5cbb\u5cbc\u5cbe\u5cc0\u5cc2\u5cc3\u5cc5\u5cc6\u5cc7\u5cc8\u5cc9\u5cca\u5ccc\u5ccd\u5cce\u5ccf\u5cd0\u5cd1\u5cd3\u5cd4\u5cd5\u5cd6\u5cd7\u5cd8\u5cda\u5cdb\u5cdc\u5cdd\u5cde\u5cdf\u5ce0\u5ce2\u5ce3\u5ce7\u5ce9\u5ceb\u5cec\u5cee\u5cef\u5cf1\u5cf2\u5cf3\u5cf4\u5cf5\u5cf6\u5cf7\u5cf8\u5cf9\u5cfa\u5cfc\u5cfd\u5cfe\u5cff\u5d00\ufffd\u5d01\u5d04\u5d05\u5d08\u5d09\u5d0a\u5d0b\u5d0c\u5d0d\u5d0f\u5d10\u5d11\u5d12\u5d13\u5d15\u5d17\u5d18\u5d19\u5d1a\u5d1c\u5d1d\u5d1f\u5d20\u5d21\u5d22\u5d23\u5d25\u5d28\u5d2a\u5d2b\u5d2c\u5d2f\u5d30\u5d31\u5d32\u5d33\u5d35\u5d36\u5d37\u5d38\u5d39\u5d3a\u5d3b\u5d3c\u5d3f\u5d40\u5d41\u5d42\u5d43\u5d44\u5d45\u5d46\u5d48\u5d49\u5d4d\u5d4e\u5d4f\u5d50\u5d51\u5d52\u5d53\u5d54\u5d55\u5d56\u5d57\u5d59\u5d5a\u5d5c\u5d5e\u5d5f\u5d60\u5d61\u5d62\u5d63\u5d64\u5d65\u5d66\u5d67\u5d68\u5d6a\u5d6d\u5d6e\u5d70\u5d71\u5d72\u5d73\u5d75\u5d76\u5d77\u5d78\u5d79\u5d7a\u5d7b\u5d7c\u5d7d\u5d7e\u5d7f\u5d80\u5d81\u5d83\u5d84\u5d85\u5d86\u5d87\u5d88\u5d89\u5d8a\u5d8b\u5d8c\u5d8d\u5d8e\u5d8f\u5d90\u5d91\u5d92\u5d93\u5d94\u5d95\u5d96\u5d97\u5d98\u5d9a\u5d9b\u5d9c\u5d9e\u5d9f\u5da0\ufffd".split(""),e=0;e!=r[141].length;++e)65533!==r[141][e].charCodeAt(0)&&(n[r[141][e]]=36096+e,t[36096+e]=r[141][e]);for(r[142]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u5da1\u5da2\u5da3\u5da4\u5da5\u5da6\u5da7\u5da8\u5da9\u5daa\u5dab\u5dac\u5dad\u5dae\u5daf\u5db0\u5db1\u5db2\u5db3\u5db4\u5db5\u5db6\u5db8\u5db9\u5dba\u5dbb\u5dbc\u5dbd\u5dbe\u5dbf\u5dc0\u5dc1\u5dc2\u5dc3\u5dc4\u5dc6\u5dc7\u5dc8\u5dc9\u5dca\u5dcb\u5dcc\u5dce\u5dcf\u5dd0\u5dd1\u5dd2\u5dd3\u5dd4\u5dd5\u5dd6\u5dd7\u5dd8\u5dd9\u5dda\u5ddc\u5ddf\u5de0\u5de3\u5de4\u5dea\u5dec\u5ded\ufffd\u5df0\u5df5\u5df6\u5df8\u5df9\u5dfa\u5dfb\u5dfc\u5dff\u5e00\u5e04\u5e07\u5e09\u5e0a\u5e0b\u5e0d\u5e0e\u5e12\u5e13\u5e17\u5e1e\u5e1f\u5e20\u5e21\u5e22\u5e23\u5e24\u5e25\u5e28\u5e29\u5e2a\u5e2b\u5e2c\u5e2f\u5e30\u5e32\u5e33\u5e34\u5e35\u5e36\u5e39\u5e3a\u5e3e\u5e3f\u5e40\u5e41\u5e43\u5e46\u5e47\u5e48\u5e49\u5e4a\u5e4b\u5e4d\u5e4e\u5e4f\u5e50\u5e51\u5e52\u5e53\u5e56\u5e57\u5e58\u5e59\u5e5a\u5e5c\u5e5d\u5e5f\u5e60\u5e63\u5e64\u5e65\u5e66\u5e67\u5e68\u5e69\u5e6a\u5e6b\u5e6c\u5e6d\u5e6e\u5e6f\u5e70\u5e71\u5e75\u5e77\u5e79\u5e7e\u5e81\u5e82\u5e83\u5e85\u5e88\u5e89\u5e8c\u5e8d\u5e8e\u5e92\u5e98\u5e9b\u5e9d\u5ea1\u5ea2\u5ea3\u5ea4\u5ea8\u5ea9\u5eaa\u5eab\u5eac\u5eae\u5eaf\u5eb0\u5eb1\u5eb2\u5eb4\u5eba\u5ebb\u5ebc\u5ebd\u5ebf\u5ec0\u5ec1\u5ec2\u5ec3\u5ec4\u5ec5\ufffd".split(""),e=0;e!=r[142].length;++e)65533!==r[142][e].charCodeAt(0)&&(n[r[142][e]]=36352+e,t[36352+e]=r[142][e]);for(r[143]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u5ec6\u5ec7\u5ec8\u5ecb\u5ecc\u5ecd\u5ece\u5ecf\u5ed0\u5ed4\u5ed5\u5ed7\u5ed8\u5ed9\u5eda\u5edc\u5edd\u5ede\u5edf\u5ee0\u5ee1\u5ee2\u5ee3\u5ee4\u5ee5\u5ee6\u5ee7\u5ee9\u5eeb\u5eec\u5eed\u5eee\u5eef\u5ef0\u5ef1\u5ef2\u5ef3\u5ef5\u5ef8\u5ef9\u5efb\u5efc\u5efd\u5f05\u5f06\u5f07\u5f09\u5f0c\u5f0d\u5f0e\u5f10\u5f12\u5f14\u5f16\u5f19\u5f1a\u5f1c\u5f1d\u5f1e\u5f21\u5f22\u5f23\u5f24\ufffd\u5f28\u5f2b\u5f2c\u5f2e\u5f30\u5f32\u5f33\u5f34\u5f35\u5f36\u5f37\u5f38\u5f3b\u5f3d\u5f3e\u5f3f\u5f41\u5f42\u5f43\u5f44\u5f45\u5f46\u5f47\u5f48\u5f49\u5f4a\u5f4b\u5f4c\u5f4d\u5f4e\u5f4f\u5f51\u5f54\u5f59\u5f5a\u5f5b\u5f5c\u5f5e\u5f5f\u5f60\u5f63\u5f65\u5f67\u5f68\u5f6b\u5f6e\u5f6f\u5f72\u5f74\u5f75\u5f76\u5f78\u5f7a\u5f7d\u5f7e\u5f7f\u5f83\u5f86\u5f8d\u5f8e\u5f8f\u5f91\u5f93\u5f94\u5f96\u5f9a\u5f9b\u5f9d\u5f9e\u5f9f\u5fa0\u5fa2\u5fa3\u5fa4\u5fa5\u5fa6\u5fa7\u5fa9\u5fab\u5fac\u5faf\u5fb0\u5fb1\u5fb2\u5fb3\u5fb4\u5fb6\u5fb8\u5fb9\u5fba\u5fbb\u5fbe\u5fbf\u5fc0\u5fc1\u5fc2\u5fc7\u5fc8\u5fca\u5fcb\u5fce\u5fd3\u5fd4\u5fd5\u5fda\u5fdb\u5fdc\u5fde\u5fdf\u5fe2\u5fe3\u5fe5\u5fe6\u5fe8\u5fe9\u5fec\u5fef\u5ff0\u5ff2\u5ff3\u5ff4\u5ff6\u5ff7\u5ff9\u5ffa\u5ffc\u6007\ufffd".split(""),e=0;e!=r[143].length;++e)65533!==r[143][e].charCodeAt(0)&&(n[r[143][e]]=36608+e,t[36608+e]=r[143][e]);for(r[144]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u6008\u6009\u600b\u600c\u6010\u6011\u6013\u6017\u6018\u601a\u601e\u601f\u6022\u6023\u6024\u602c\u602d\u602e\u6030\u6031\u6032\u6033\u6034\u6036\u6037\u6038\u6039\u603a\u603d\u603e\u6040\u6044\u6045\u6046\u6047\u6048\u6049\u604a\u604c\u604e\u604f\u6051\u6053\u6054\u6056\u6057\u6058\u605b\u605c\u605e\u605f\u6060\u6061\u6065\u6066\u606e\u6071\u6072\u6074\u6075\u6077\u607e\u6080\ufffd\u6081\u6082\u6085\u6086\u6087\u6088\u608a\u608b\u608e\u608f\u6090\u6091\u6093\u6095\u6097\u6098\u6099\u609c\u609e\u60a1\u60a2\u60a4\u60a5\u60a7\u60a9\u60aa\u60ae\u60b0\u60b3\u60b5\u60b6\u60b7\u60b9\u60ba\u60bd\u60be\u60bf\u60c0\u60c1\u60c2\u60c3\u60c4\u60c7\u60c8\u60c9\u60cc\u60cd\u60ce\u60cf\u60d0\u60d2\u60d3\u60d4\u60d6\u60d7\u60d9\u60db\u60de\u60e1\u60e2\u60e3\u60e4\u60e5\u60ea\u60f1\u60f2\u60f5\u60f7\u60f8\u60fb\u60fc\u60fd\u60fe\u60ff\u6102\u6103\u6104\u6105\u6107\u610a\u610b\u610c\u6110\u6111\u6112\u6113\u6114\u6116\u6117\u6118\u6119\u611b\u611c\u611d\u611e\u6121\u6122\u6125\u6128\u6129\u612a\u612c\u612d\u612e\u612f\u6130\u6131\u6132\u6133\u6134\u6135\u6136\u6137\u6138\u6139\u613a\u613b\u613c\u613d\u613e\u6140\u6141\u6142\u6143\u6144\u6145\u6146\ufffd".split(""),e=0;e!=r[144].length;++e)65533!==r[144][e].charCodeAt(0)&&(n[r[144][e]]=36864+e,t[36864+e]=r[144][e]);for(r[145]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u6147\u6149\u614b\u614d\u614f\u6150\u6152\u6153\u6154\u6156\u6157\u6158\u6159\u615a\u615b\u615c\u615e\u615f\u6160\u6161\u6163\u6164\u6165\u6166\u6169\u616a\u616b\u616c\u616d\u616e\u616f\u6171\u6172\u6173\u6174\u6176\u6178\u6179\u617a\u617b\u617c\u617d\u617e\u617f\u6180\u6181\u6182\u6183\u6184\u6185\u6186\u6187\u6188\u6189\u618a\u618c\u618d\u618f\u6190\u6191\u6192\u6193\u6195\ufffd\u6196\u6197\u6198\u6199\u619a\u619b\u619c\u619e\u619f\u61a0\u61a1\u61a2\u61a3\u61a4\u61a5\u61a6\u61aa\u61ab\u61ad\u61ae\u61af\u61b0\u61b1\u61b2\u61b3\u61b4\u61b5\u61b6\u61b8\u61b9\u61ba\u61bb\u61bc\u61bd\u61bf\u61c0\u61c1\u61c3\u61c4\u61c5\u61c6\u61c7\u61c9\u61cc\u61cd\u61ce\u61cf\u61d0\u61d3\u61d5\u61d6\u61d7\u61d8\u61d9\u61da\u61db\u61dc\u61dd\u61de\u61df\u61e0\u61e1\u61e2\u61e3\u61e4\u61e5\u61e7\u61e8\u61e9\u61ea\u61eb\u61ec\u61ed\u61ee\u61ef\u61f0\u61f1\u61f2\u61f3\u61f4\u61f6\u61f7\u61f8\u61f9\u61fa\u61fb\u61fc\u61fd\u61fe\u6200\u6201\u6202\u6203\u6204\u6205\u6207\u6209\u6213\u6214\u6219\u621c\u621d\u621e\u6220\u6223\u6226\u6227\u6228\u6229\u622b\u622d\u622f\u6230\u6231\u6232\u6235\u6236\u6238\u6239\u623a\u623b\u623c\u6242\u6244\u6245\u6246\u624a\ufffd".split(""),e=0;e!=r[145].length;++e)65533!==r[145][e].charCodeAt(0)&&(n[r[145][e]]=37120+e,t[37120+e]=r[145][e]);for(r[146]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u624f\u6250\u6255\u6256\u6257\u6259\u625a\u625c\u625d\u625e\u625f\u6260\u6261\u6262\u6264\u6265\u6268\u6271\u6272\u6274\u6275\u6277\u6278\u627a\u627b\u627d\u6281\u6282\u6283\u6285\u6286\u6287\u6288\u628b\u628c\u628d\u628e\u628f\u6290\u6294\u6299\u629c\u629d\u629e\u62a3\u62a6\u62a7\u62a9\u62aa\u62ad\u62ae\u62af\u62b0\u62b2\u62b3\u62b4\u62b6\u62b7\u62b8\u62ba\u62be\u62c0\u62c1\ufffd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51c6\u6349\u62d9\u5353\u684c\u7422\u8301\u914c\u5544\u7740\u707c\u6d4a\u5179\u54a8\u8d44\u59ff\u6ecb\u6dc4\u5b5c\u7d2b\u4ed4\u7c7d\u6ed3\u5b50\u81ea\u6e0d\u5b57\u9b03\u68d5\u8e2a\u5b97\u7efc\u603b\u7eb5\u90b9\u8d70\u594f\u63cd\u79df\u8db3\u5352\u65cf\u7956\u8bc5\u963b\u7ec4\u94bb\u7e82\u5634\u9189\u6700\u7f6a\u5c0a\u9075\u6628\u5de6\u4f50\u67de\u505a\u4f5c\u5750\u5ea7\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd".split(""),e=0;e!=r[215].length;++e)65533!==r[215][e].charCodeAt(0)&&(n[r[215][e]]=55040+e,t[55040+e]=r[215][e]);for(r[216]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u8c38\u8c39\u8c3a\u8c3b\u8c3c\u8c3d\u8c3e\u8c3f\u8c40\u8c42\u8c43\u8c44\u8c45\u8c48\u8c4a\u8c4b\u8c4d\u8c4e\u8c4f\u8c50\u8c51\u8c52\u8c53\u8c54\u8c56\u8c57\u8c58\u8c59\u8c5b\u8c5c\u8c5d\u8c5e\u8c5f\u8c60\u8c63\u8c64\u8c65\u8c66\u8c67\u8c68\u8c69\u8c6c\u8c6d\u8c6e\u8c6f\u8c70\u8c71\u8c72\u8c74\u8c75\u8c76\u8c77\u8c7b\u8c7c\u8c7d\u8c7e\u8c7f\u8c80\u8c81\u8c83\u8c84\u8c86\u8c87\ufffd\u8c88\u8c8b\u8c8d\u8c8e\u8c8f\u8c90\u8c91\u8c92\u8c93\u8c95\u8c96\u8c97\u8c99\u8c9a\u8c9b\u8c9c\u8c9d\u8c9e\u8c9f\u8ca0\u8ca1\u8ca2\u8ca3\u8ca4\u8ca5\u8ca6\u8ca7\u8ca8\u8ca9\u8caa\u8cab\u8cac\u8cad\u4e8d\u4e0c\u5140\u4e10\u5eff\u5345\u4e15\u4e98\u4e1e\u9b32\u5b6c\u5669\u4e28\u79ba\u4e3f\u5315\u4e47\u592d\u723b\u536e\u6c10\u56df\u80e4\u9997\u6bd3\u777e\u9f17\u4e36\u4e9f\u9f10\u4e5c\u4e69\u4e93\u8288\u5b5b\u556c\u560f\u4ec4\u538d\u539d\u53a3\u53a5\u53ae\u9765\u8d5d\u531a\u53f5\u5326\u532e\u533e\u8d5c\u5366\u5363\u5202\u5208\u520e\u522d\u5233\u523f\u5240\u524c\u525e\u5261\u525c\u84af\u527d\u5282\u5281\u5290\u5293\u5182\u7f54\u4ebb\u4ec3\u4ec9\u4ec2\u4ee8\u4ee1\u4eeb\u4ede\u4f1b\u4ef3\u4f22\u4f64\u4ef5\u4f25\u4f27\u4f09\u4f2b\u4f5e\u4f67\u6538\u4f5a\u4f5d\ufffd".split(""),e=0;e!=r[216].length;++e)65533!==r[216][e].charCodeAt(0)&&(n[r[216][e]]=55296+e,t[55296+e]=r[216][e]);for(r[217]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u8cae\u8caf\u8cb0\u8cb1\u8cb2\u8cb3\u8cb4\u8cb5\u8cb6\u8cb7\u8cb8\u8cb9\u8cba\u8cbb\u8cbc\u8cbd\u8cbe\u8cbf\u8cc0\u8cc1\u8cc2\u8cc3\u8cc4\u8cc5\u8cc6\u8cc7\u8cc8\u8cc9\u8cca\u8ccb\u8ccc\u8ccd\u8cce\u8ccf\u8cd0\u8cd1\u8cd2\u8cd3\u8cd4\u8cd5\u8cd6\u8cd7\u8cd8\u8cd9\u8cda\u8cdb\u8cdc\u8cdd\u8cde\u8cdf\u8ce0\u8ce1\u8ce2\u8ce3\u8ce4\u8ce5\u8ce6\u8ce7\u8ce8\u8ce9\u8cea\u8ceb\u8cec\ufffd\u8ced\u8cee\u8cef\u8cf0\u8cf1\u8cf2\u8cf3\u8cf4\u8cf5\u8cf6\u8cf7\u8cf8\u8cf9\u8cfa\u8cfb\u8cfc\u8cfd\u8cfe\u8cff\u8d00\u8d01\u8d02\u8d03\u8d04\u8d05\u8d06\u8d07\u8d08\u8d09\u8d0a\u8d0b\u8d0c\u8d0d\u4f5f\u4f57\u4f32\u4f3d\u4f76\u4f74\u4f91\u4f89\u4f83\u4f8f\u4f7e\u4f7b\u4faa\u4f7c\u4fac\u4f94\u4fe6\u4fe8\u4fea\u4fc5\u4fda\u4fe3\u4fdc\u4fd1\u4fdf\u4ff8\u5029\u504c\u4ff3\u502c\u500f\u502e\u502d\u4ffe\u501c\u500c\u5025\u5028\u507e\u5043\u5055\u5048\u504e\u506c\u507b\u50a5\u50a7\u50a9\u50ba\u50d6\u5106\u50ed\u50ec\u50e6\u50ee\u5107\u510b\u4edd\u6c3d\u4f58\u4f65\u4fce\u9fa0\u6c46\u7c74\u516e\u5dfd\u9ec9\u9998\u5181\u5914\u52f9\u530d\u8a07\u5310\u51eb\u5919\u5155\u4ea0\u5156\u4eb3\u886e\u88a4\u4eb5\u8114\u88d2\u7980\u5b34\u8803\u7fb8\u51ab\u51b1\u51bd\u51bc\ufffd".split(""),e=0;e!=r[217].length;++e)65533!==r[217][e].charCodeAt(0)&&(n[r[217][e]]=55552+e,t[55552+e]=r[217][e]);for(r[218]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u8d0e\u8d0f\u8d10\u8d11\u8d12\u8d13\u8d14\u8d15\u8d16\u8d17\u8d18\u8d19\u8d1a\u8d1b\u8d1c\u8d20\u8d51\u8d52\u8d57\u8d5f\u8d65\u8d68\u8d69\u8d6a\u8d6c\u8d6e\u8d6f\u8d71\u8d72\u8d78\u8d79\u8d7a\u8d7b\u8d7c\u8d7d\u8d7e\u8d7f\u8d80\u8d82\u8d83\u8d86\u8d87\u8d88\u8d89\u8d8c\u8d8d\u8d8e\u8d8f\u8d90\u8d92\u8d93\u8d95\u8d96\u8d97\u8d98\u8d99\u8d9a\u8d9b\u8d9c\u8d9d\u8d9e\u8da0\u8da1\ufffd\u8da2\u8da4\u8da5\u8da6\u8da7\u8da8\u8da9\u8daa\u8dab\u8dac\u8dad\u8dae\u8daf\u8db0\u8db2\u8db6\u8db7\u8db9\u8dbb\u8dbd\u8dc0\u8dc1\u8dc2\u8dc5\u8dc7\u8dc8\u8dc9\u8dca\u8dcd\u8dd0\u8dd2\u8dd3\u8dd4\u51c7\u5196\u51a2\u51a5\u8ba0\u8ba6\u8ba7\u8baa\u8bb4\u8bb5\u8bb7\u8bc2\u8bc3\u8bcb\u8bcf\u8bce\u8bd2\u8bd3\u8bd4\u8bd6\u8bd8\u8bd9\u8bdc\u8bdf\u8be0\u8be4\u8be8\u8be9\u8bee\u8b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fd\ufffd\ufffd\ufffd\ub8be\ub8bf\ub8c2\ub8c4\ub8c6\ub8c7\ub8c8\ub8c9\ub8ca\ub8cb\ub8cd\ub8ce\ub8cf\ub8d1\ub8d2\ub8d3\ub8d5\ub8d6\ub8d7\ub8d8\ub8d9\ub8da\ub8db\ub8dc\ub8de\ub8e0\ub8e2\ub8e3\ub8e4\ub8e5\ub8e6\ub8e7\ub8ea\ub8eb\ub8ed\ub8ee\ub8ef\ub8f1\ub8f2\ub8f3\ub8f4\ub8f5\ub8f6\ub8f7\ub8fa\ub8fc\ub8fe\ub8ff\ub900\ub901\ub902\ub903\ub905\ub906\ub907\ub908\ub909\ub90a\ub90b\ub90c\ub90d\ub90e\ub90f\ub910\ub911\ub912\ub913\ub914\ub915\ub916\ub917\ub919\ub91a\ub91b\ub91c\ub91d\ub91e\ub91f\ub921\ub922\ub923\ub924\ub925\ub926\ub927\ub928\ub929\ub92a\ub92b\ub92c\ub92d\ub92e\ub92f\ub930\ub931\ub932\ub933\ub934\ub935\ub936\ub937\ub938\ub939\ub93a\ub93b\ub93e\ub93f\ub941\ub942\ub943\ub945\ub946\ub947\ub948\ub949\ub94a\ub94b\ub94d\ub94e\ub950\ub952\ub953\ub954\ub955\ub956\ub957\ufffd".split(""),e=0;e!=r[143].length;++e)65533!==r[143][e].charCodeAt(0)&&(n[r[143][e]]=36608+e,t[36608+e]=r[143][e]);for(r[144]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ub95a\ub95b\ub95d\ub95e\ub95f\ub961\ub962\ub963\ub964\ub965\ub966\ub967\ub96a\ub96c\ub96e\ub96f\ub970\ub971\ub972\ub973\ub976\ub977\ub979\ub97a\ub97b\ub97d\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ub97e\ub97f\ub980\ub981\ub982\ub983\ub986\ub988\ub98b\ub98c\ub98f\ub990\ub991\ub992\ub993\ub994\ub995\ub996\ub997\ub998\ub999\ub99a\ub99b\ub99c\ub99d\ub99e\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ub99f\ub9a0\ub9a1\ub9a2\ub9a3\ub9a4\ub9a5\ub9a6\ub9a7\ub9a8\ub9a9\ub9aa\ub9ab\ub9ae\ub9af\ub9b1\ub9b2\ub9b3\ub9b5\ub9b6\ub9b7\ub9b8\ub9b9\ub9ba\ub9bb\ub9be\ub9c0\ub9c2\ub9c3\ub9c4\ub9c5\ub9c6\ub9c7\ub9ca\ub9cb\ub9cd\ub9d3\ub9d4\ub9d5\ub9d6\ub9d7\ub9da\ub9dc\ub9df\ub9e0\ub9e2\ub9e6\ub9e7\ub9e9\ub9ea\ub9eb\ub9ed\ub9ee\ub9ef\ub9f0\ub9f1\ub9f2\ub9f3\ub9f6\ub9fb\ub9fc\ub9fd\ub9fe\ub9ff\uba02\uba03\uba04\uba05\uba06\uba07\uba09\uba0a\uba0b\uba0c\uba0d\uba0e\uba0f\uba10\uba11\uba12\uba13\uba14\uba16\uba17\uba18\uba19\uba1a\uba1b\uba1c\uba1d\uba1e\uba1f\uba20\uba21\uba22\uba23\uba24\uba25\uba26\uba27\uba28\uba29\uba2a\uba2b\uba2c\uba2d\uba2e\uba2f\uba30\uba31\uba32\uba33\uba34\uba35\uba36\uba37\uba3a\uba3b\uba3d\uba3e\uba3f\uba41\uba43\uba44\uba45\uba46\ufffd".split(""),e=0;e!=r[144].length;++e)65533!==r[144][e].charCodeAt(0)&&(n[r[144][e]]=36864+e,t[36864+e]=r[144][e]);for(r[145]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uba47\uba4a\uba4c\uba4f\uba50\uba51\uba52\uba56\uba57\uba59\uba5a\uba5b\uba5d\uba5e\uba5f\uba60\uba61\uba62\uba63\uba66\uba6a\uba6b\uba6c\uba6d\uba6e\uba6f\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uba72\uba73\uba75\uba76\uba77\uba79\uba7a\uba7b\uba7c\uba7d\uba7e\uba7f\uba80\uba81\uba82\uba86\uba88\uba89\uba8a\uba8b\uba8d\uba8e\uba8f\uba90\uba91\uba92\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uba93\uba94\uba95\uba96\uba97\uba98\uba99\uba9a\uba9b\uba9c\uba9d\uba9e\uba9f\ubaa0\ubaa1\ubaa2\ubaa3\ubaa4\ubaa5\ubaa6\ubaa7\ubaaa\ubaad\ubaae\ubaaf\ubab1\ubab3\ubab4\ubab5\ubab6\ubab7\ubaba\ubabc\ubabe\ubabf\ubac0\ubac1\ubac2\ubac3\ubac5\ubac6\ubac7\ubac9\ubaca\ubacb\ubacc\ubacd\ubace\ubacf\ubad0\ubad1\ubad2\ubad3\ubad4\ubad5\ubad6\ubad7\ubada\ubadb\ubadc\ubadd\ubade\ubadf\ubae0\ubae1\ubae2\ubae3\ubae4\ubae5\ubae6\ubae7\ubae8\ubae9\ubaea\ubaeb\ubaec\ubaed\ubaee\ubaef\ubaf0\ubaf1\ubaf2\ubaf3\ubaf4\ubaf5\ubaf6\ubaf7\ubaf8\ubaf9\ubafa\ubafb\ubafd\ubafe\ubaff\ubb01\ubb02\ubb03\ubb05\ubb06\ubb07\ubb08\ubb09\ubb0a\ubb0b\ubb0c\ubb0e\ubb10\ubb12\ubb13\ubb14\ubb15\ubb16\ubb17\ubb19\ubb1a\ubb1b\ubb1d\ubb1e\ubb1f\ubb21\ubb22\ubb23\ubb24\ubb25\ubb26\ubb27\ufffd".split(""),e=0;e!=r[145].length;++e)65533!==r[145][e].charCodeAt(0)&&(n[r[145][e]]=37120+e,t[37120+e]=r[145][e]);for(r[146]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubb28\ubb2a\ubb2c\ubb2d\ubb2e\ubb2f\ubb30\ubb31\ubb32\ubb33\ubb37\ubb39\ubb3a\ubb3f\ubb40\ubb41\ubb42\ubb43\ubb46\ubb48\ubb4a\ubb4b\ubb4c\ubb4e\ubb51\ubb52\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubb53\ubb55\ubb56\ubb57\ubb59\ubb5a\ubb5b\ubb5c\ubb5d\ubb5e\ubb5f\ubb60\ubb62\ubb64\ubb65\ubb66\ubb67\ubb68\ubb69\ubb6a\ubb6b\ubb6d\ubb6e\ubb6f\ubb70\ubb71\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubb72\ubb73\ubb74\ubb75\ubb76\ubb77\ubb78\ubb79\ubb7a\ubb7b\ubb7c\ubb7d\ubb7e\ubb7f\ubb80\ubb81\ubb82\ubb83\ubb84\ubb85\ubb86\ubb87\ubb89\ubb8a\ubb8b\ubb8d\ubb8e\ubb8f\ubb91\ubb92\ubb93\ubb94\ubb95\ubb96\ubb97\ubb98\ubb99\ubb9a\ubb9b\ubb9c\ubb9d\ubb9e\ubb9f\ubba0\ubba1\ubba2\ubba3\ubba5\ubba6\ubba7\ubba9\ubbaa\ubbab\ubbad\ubbae\ubbaf\ubbb0\ubbb1\ubbb2\ubbb3\ubbb5\ubbb6\ubbb8\ubbb9\ubbba\ubbbb\ubbbc\ubbbd\ubbbe\ubbbf\ubbc1\ubbc2\ubbc3\ubbc5\ubbc6\ubbc7\ubbc9\ubbca\ubbcb\ubbcc\ubbcd\ubbce\ubbcf\ubbd1\ubbd2\ubbd4\ubbd5\ubbd6\ubbd7\ubbd8\ubbd9\ubbda\ubbdb\ubbdc\ubbdd\ubbde\ubbdf\ubbe0\ubbe1\ubbe2\ubbe3\ubbe4\ubbe5\ubbe6\ubbe7\ubbe8\ubbe9\ubbea\ubbeb\ubbec\ubbed\ubbee\ubbef\ubbf0\ubbf1\ubbf2\ubbf3\ubbf4\ubbf5\ubbf6\ubbf7\ubbfa\ubbfb\ubbfd\ubbfe\ubc01\ufffd".split(""),e=0;e!=r[146].length;++e)65533!==r[146][e].charCodeAt(0)&&(n[r[146][e]]=37376+e,t[37376+e]=r[146][e]);for(r[147]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubc03\ubc04\ubc05\ubc06\ubc07\ubc0a\ubc0e\ubc10\ubc12\ubc13\ubc19\ubc1a\ubc20\ubc21\ubc22\ubc23\ubc26\ubc28\ubc2a\ubc2b\ubc2c\ubc2e\ubc2f\ubc32\ubc33\ubc35\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubc36\ubc37\ubc39\ubc3a\ubc3b\ubc3c\ubc3d\ubc3e\ubc3f\ubc42\ubc46\ubc47\ubc48\ubc4a\ubc4b\ubc4e\ubc4f\ubc51\ubc52\ubc53\ubc54\ubc55\ubc56\ubc57\ubc58\ubc59\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubc5a\ubc5b\ubc5c\ubc5e\ubc5f\ubc60\ubc61\ubc62\ubc63\ubc64\ubc65\ubc66\ubc67\ubc68\ubc69\ubc6a\ubc6b\ubc6c\ubc6d\ubc6e\ubc6f\ubc70\ubc71\ubc72\ubc73\ubc74\ubc75\ubc76\ubc77\ubc78\ubc79\ubc7a\ubc7b\ubc7c\ubc7d\ubc7e\ubc7f\ubc80\ubc81\ubc82\ubc83\ubc86\ubc87\ubc89\ubc8a\ubc8d\ubc8f\ubc90\ubc91\ubc92\ubc93\ubc96\ubc98\ubc9b\ubc9c\ubc9d\ubc9e\ubc9f\ubca2\ubca3\ubca5\ubca6\ubca9\ubcaa\ubcab\ubcac\ubcad\ubcae\ubcaf\ubcb2\ubcb6\ubcb7\ubcb8\ubcb9\ubcba\ubcbb\ubcbe\ubcbf\ubcc1\ubcc2\ubcc3\ubcc5\ubcc6\ubcc7\ubcc8\ubcc9\ubcca\ubccb\ubccc\ubcce\ubcd2\ubcd3\ubcd4\ubcd6\ubcd7\ubcd9\ubcda\ubcdb\ubcdd\ubcde\ubcdf\ubce0\ubce1\ubce2\ubce3\ubce4\ubce5\ubce6\ubce7\ubce8\ubce9\ubcea\ubceb\ubcec\ubced\ubcee\ubcef\ubcf0\ubcf1\ubcf2\ubcf3\ubcf7\ubcf9\ubcfa\ubcfb\ubcfd\ufffd".split(""),e=0;e!=r[147].length;++e)65533!==r[147][e].charCodeAt(0)&&(n[r[147][e]]=37632+e,t[37632+e]=r[147][e]);for(r[148]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubcfe\ubcff\ubd00\ubd01\ubd02\ubd03\ubd06\ubd08\ubd0a\ubd0b\ubd0c\ubd0d\ubd0e\ubd0f\ubd11\ubd12\ubd13\ubd15\ubd16\ubd17\ubd18\ubd19\ubd1a\ubd1b\ubd1c\ubd1d\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubd1e\ubd1f\ubd20\ubd21\ubd22\ubd23\ubd25\ubd26\ubd27\ubd28\ubd29\ubd2a\ubd2b\ubd2d\ubd2e\ubd2f\ubd30\ubd31\ubd32\ubd33\ubd34\ubd35\ubd36\ubd37\ubd38\ubd39\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubd3a\ubd3b\ubd3c\ubd3d\ubd3e\ubd3f\ubd41\ubd42\ubd43\ubd44\ubd45\ubd46\ubd47\ubd4a\ubd4b\ubd4d\ubd4e\ubd4f\ubd51\ubd52\ubd53\ubd54\ubd55\ubd56\ubd57\ubd5a\ubd5b\ubd5c\ubd5d\ubd5e\ubd5f\ubd60\ubd61\ubd62\ubd63\ubd65\ubd66\ubd67\ubd69\ubd6a\ubd6b\ubd6c\ubd6d\ubd6e\ubd6f\ubd70\ubd71\ubd72\ubd73\ubd74\ubd75\ubd76\ubd77\ubd78\ubd79\ubd7a\ubd7b\ubd7c\ubd7d\ubd7e\ubd7f\ubd82\ubd83\ubd85\ubd86\ubd8b\ubd8c\ubd8d\ubd8e\ubd8f\ubd92\ubd94\ubd96\ubd97\ubd98\ubd9b\ubd9d\ubd9e\ubd9f\ubda0\ubda1\ubda2\ubda3\ubda5\ubda6\ubda7\ubda8\ubda9\ubdaa\ubdab\ubdac\ubdad\ubdae\ubdaf\ubdb1\ubdb2\ubdb3\ubdb4\ubdb5\ubdb6\ubdb7\ubdb9\ubdba\ubdbb\ubdbc\ubdbd\ubdbe\ubdbf\ubdc0\ubdc1\ubdc2\ubdc3\ubdc4\ubdc5\ubdc6\ubdc7\ubdc8\ubdc9\ubdca\ubdcb\ubdcc\ubdcd\ubdce\ubdcf\ubdd0\ubdd1\ufffd".split(""),e=0;e!=r[148].length;++e)65533!==r[148][e].charCodeAt(0)&&(n[r[148][e]]=37888+e,t[37888+e]=r[148][e]);for(r[149]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubdd2\ubdd3\ubdd6\ubdd7\ubdd9\ubdda\ubddb\ubddd\ubdde\ubddf\ubde0\ubde1\ubde2\ubde3\ubde4\ubde5\ubde6\ubde7\ubde8\ubdea\ubdeb\ubdec\ubded\ubdee\ubdef\ubdf1\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubdf2\ubdf3\ubdf5\ubdf6\ubdf7\ubdf9\ubdfa\ubdfb\ubdfc\ubdfd\ubdfe\ubdff\ube01\ube02\ube04\ube06\ube07\ube08\ube09\ube0a\ube0b\ube0e\ube0f\ube11\ube12\ube13\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ube15\ube16\ube17\ube18\ube19\ube1a\ube1b\ube1e\ube20\ube21\ube22\ube23\ube24\ube25\ube26\ube27\ube28\ube29\ube2a\ube2b\ube2c\ube2d\ube2e\ube2f\ube30\ube31\ube32\ube33\ube34\ube35\ube36\ube37\ube38\ube39\ube3a\ube3b\ube3c\ube3d\ube3e\ube3f\ube40\ube41\ube42\ube43\ube46\ube47\ube49\ube4a\ube4b\ube4d\ube4f\ube50\ube51\ube52\ube53\ube56\ube58\ube5c\ube5d\ube5e\ube5f\ube62\ube63\ube65\ube66\ube67\ube69\ube6b\ube6c\ube6d\ube6e\ube6f\ube72\ube76\ube77\ube78\ube79\ube7a\ube7e\ube7f\ube81\ube82\ube83\ube85\ube86\ube87\ube88\ube89\ube8a\ube8b\ube8e\ube92\ube93\ube94\ube95\ube96\ube97\ube9a\ube9b\ube9c\ube9d\ube9e\ube9f\ubea0\ubea1\ubea2\ubea3\ubea4\ubea5\ubea6\ubea7\ubea9\ubeaa\ubeab\ubeac\ubead\ubeae\ubeaf\ubeb0\ubeb1\ubeb2\ubeb3\ubeb4\ubeb5\ubeb6\ubeb7\ufffd".split(""),e=0;e!=r[149].length;++e)65533!==r[149][e].charCodeAt(0)&&(n[r[149][e]]=38144+e,t[38144+e]=r[149][e]);for(r[150]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubeb8\ubeb9\ubeba\ubebb\ubebc\ubebd\ubebe\ubebf\ubec0\ubec1\ubec2\ubec3\ubec4\ubec5\ubec6\ubec7\ubec8\ubec9\ubeca\ubecb\ubecc\ubecd\ubece\ubecf\ubed2\ubed3\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubed5\ubed6\ubed9\ubeda\ubedb\ubedc\ubedd\ubede\ubedf\ubee1\ubee2\ubee6\ubee7\ubee8\ubee9\ubeea\ubeeb\ubeed\ubeee\ubeef\ubef0\ubef1\ubef2\ubef3\ubef4\ubef5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubef6\ubef7\ubef8\ubef9\ubefa\ubefb\ubefc\ubefd\ubefe\ubeff\ubf00\ubf02\ubf03\ubf04\ubf05\ubf06\ubf07\ubf0a\ubf0b\ubf0c\ubf0d\ubf0e\ubf0f\ubf10\ubf11\ubf12\ubf13\ubf14\ubf15\ubf16\ubf17\ubf1a\ubf1e\ubf1f\ubf20\ubf21\ubf22\ubf23\ubf24\ubf25\ubf26\ubf27\ubf28\ubf29\ubf2a\ubf2b\ubf2c\ubf2d\ubf2e\ubf2f\ubf30\ubf31\ubf32\ubf33\ubf34\ubf35\ubf36\ubf37\ubf38\ubf39\ubf3a\ubf3b\ubf3c\ubf3d\ubf3e\ubf3f\ubf42\ubf43\ubf45\ubf46\ubf47\ubf49\ubf4a\ubf4b\ubf4c\ubf4d\ubf4e\ubf4f\ubf52\ubf53\ubf54\ubf56\ubf57\ubf58\ubf59\ubf5a\ubf5b\ubf5c\ubf5d\ubf5e\ubf5f\ubf60\ubf61\ubf62\ubf63\ubf64\ubf65\ubf66\ubf67\ubf68\ubf69\ubf6a\ubf6b\ubf6c\ubf6d\ubf6e\ubf6f\ubf70\ubf71\ubf72\ubf73\ubf74\ubf75\ubf76\ubf77\ubf78\ubf79\ubf7a\ubf7b\ubf7c\ubf7d\ubf7e\ubf7f\ubf80\ubf81\ubf82\ufffd".split(""),e=0;e!=r[150].length;++e)65533!==r[150][e].charCodeAt(0)&&(n[r[150][e]]=38400+e,t[38400+e]=r[150][e]);for(r[151]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubf83\ubf84\ubf85\ubf86\ubf87\ubf88\ubf89\ubf8a\ubf8b\ubf8c\ubf8d\ubf8e\ubf8f\ubf90\ubf91\ubf92\ubf93\ubf95\ubf96\ubf97\ubf98\ubf99\ubf9a\ubf9b\ubf9c\ubf9d\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubf9e\ubf9f\ubfa0\ubfa1\ubfa2\ubfa3\ubfa4\ubfa5\ubfa6\ubfa7\ubfa8\ubfa9\ubfaa\ubfab\ubfac\ubfad\ubfae\ubfaf\ubfb1\ubfb2\ubfb3\ubfb4\ubfb5\ubfb6\ubfb7\ubfb8\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ubfb9\ubfba\ubfbb\ubfbc\ubfbd\ubfbe\ubfbf\ubfc0\ubfc1\ubfc2\ubfc3\ubfc4\ubfc6\ubfc7\ubfc8\ubfc9\ubfca\ubfcb\ubfce\ubfcf\ubfd1\ubfd2\ubfd3\ubfd5\ubfd6\ubfd7\ubfd8\ubfd9\ubfda\ubfdb\ubfdd\ubfde\ubfe0\ubfe2\ubfe3\ubfe4\ubfe5\ubfe6\ubfe7\ubfe8\ubfe9\ubfea\ubfeb\ubfec\ubfed\ubfee\ubfef\ubff0\ubff1\ubff2\ubff3\ubff4\ubff5\ubff6\ubff7\ubff8\ubff9\ubffa\ubffb\ubffc\ubffd\ubffe\ubfff\uc000\uc001\uc002\uc003\uc004\uc005\uc006\uc007\uc008\uc009\uc00a\uc00b\uc00c\uc00d\uc00e\uc00f\uc010\uc011\uc012\uc013\uc014\uc015\uc016\uc017\uc018\uc019\uc01a\uc01b\uc01c\uc01d\uc01e\uc01f\uc020\uc021\uc022\uc023\uc024\uc025\uc026\uc027\uc028\uc029\uc02a\uc02b\uc02c\uc02d\uc02e\uc02f\uc030\uc031\uc032\uc033\uc034\uc035\uc036\uc037\uc038\uc039\uc03a\uc03b\uc03d\uc03e\uc03f\ufffd".split(""),e=0;e!=r[151].length;++e)65533!==r[151][e].charCodeAt(0)&&(n[r[151][e]]=38656+e,t[38656+e]=r[151][e]);for(r[152]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc040\uc041\uc042\uc043\uc044\uc045\uc046\uc047\uc048\uc049\uc04a\uc04b\uc04c\uc04d\uc04e\uc04f\uc050\uc052\uc053\uc054\uc055\uc056\uc057\uc059\uc05a\uc05b\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc05d\uc05e\uc05f\uc061\uc062\uc063\uc064\uc065\uc066\uc067\uc06a\uc06b\uc06c\uc06d\uc06e\uc06f\uc070\uc071\uc072\uc073\uc074\uc075\uc076\uc077\uc078\uc079\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc07a\uc07b\uc07c\uc07d\uc07e\uc07f\uc080\uc081\uc082\uc083\uc084\uc085\uc086\uc087\uc088\uc089\uc08a\uc08b\uc08c\uc08d\uc08e\uc08f\uc092\uc093\uc095\uc096\uc097\uc099\uc09a\uc09b\uc09c\uc09d\uc09e\uc09f\uc0a2\uc0a4\uc0a6\uc0a7\uc0a8\uc0a9\uc0aa\uc0ab\uc0ae\uc0b1\uc0b2\uc0b7\uc0b8\uc0b9\uc0ba\uc0bb\uc0be\uc0c2\uc0c3\uc0c4\uc0c6\uc0c7\uc0ca\uc0cb\uc0cd\uc0ce\uc0cf\uc0d1\uc0d2\uc0d3\uc0d4\uc0d5\uc0d6\uc0d7\uc0da\uc0de\uc0df\uc0e0\uc0e1\uc0e2\uc0e3\uc0e6\uc0e7\uc0e9\uc0ea\uc0eb\uc0ed\uc0ee\uc0ef\uc0f0\uc0f1\uc0f2\uc0f3\uc0f6\uc0f8\uc0fa\uc0fb\uc0fc\uc0fd\uc0fe\uc0ff\uc101\uc102\uc103\uc105\uc106\uc107\uc109\uc10a\uc10b\uc10c\uc10d\uc10e\uc10f\uc111\uc112\uc113\uc114\uc116\uc117\uc118\uc119\uc11a\uc11b\uc121\uc122\uc125\uc128\uc129\uc12a\uc12b\uc12e\ufffd".split(""),e=0;e!=r[152].length;++e)65533!==r[152][e].charCodeAt(0)&&(n[r[152][e]]=38912+e,t[38912+e]=r[152][e]);for(r[153]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc132\uc133\uc134\uc135\uc137\uc13a\uc13b\uc13d\uc13e\uc13f\uc141\uc142\uc143\uc144\uc145\uc146\uc147\uc14a\uc14e\uc14f\uc150\uc151\uc152\uc153\uc156\uc157\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc159\uc15a\uc15b\uc15d\uc15e\uc15f\uc160\uc161\uc162\uc163\uc166\uc16a\uc16b\uc16c\uc16d\uc16e\uc16f\uc171\uc172\uc173\uc175\uc176\uc177\uc179\uc17a\uc17b\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc17c\uc17d\uc17e\uc17f\uc180\uc181\uc182\uc183\uc184\uc186\uc187\uc188\uc189\uc18a\uc18b\uc18f\uc191\uc192\uc193\uc195\uc197\uc198\uc199\uc19a\uc19b\uc19e\uc1a0\uc1a2\uc1a3\uc1a4\uc1a6\uc1a7\uc1aa\uc1ab\uc1ad\uc1ae\uc1af\uc1b1\uc1b2\uc1b3\uc1b4\uc1b5\uc1b6\uc1b7\uc1b8\uc1b9\uc1ba\uc1bb\uc1bc\uc1be\uc1bf\uc1c0\uc1c1\uc1c2\uc1c3\uc1c5\uc1c6\uc1c7\uc1c9\uc1ca\uc1cb\uc1cd\uc1ce\uc1cf\uc1d0\uc1d1\uc1d2\uc1d3\uc1d5\uc1d6\uc1d9\uc1da\uc1db\uc1dc\uc1dd\uc1de\uc1df\uc1e1\uc1e2\uc1e3\uc1e5\uc1e6\uc1e7\uc1e9\uc1ea\uc1eb\uc1ec\uc1ed\uc1ee\uc1ef\uc1f2\uc1f4\uc1f5\uc1f6\uc1f7\uc1f8\uc1f9\uc1fa\uc1fb\uc1fe\uc1ff\uc201\uc202\uc203\uc205\uc206\uc207\uc208\uc209\uc20a\uc20b\uc20e\uc210\uc212\uc213\uc214\uc215\uc216\uc217\uc21a\uc21b\uc21d\uc21e\uc221\uc222\uc223\ufffd".split(""),e=0;e!=r[153].length;++e)65533!==r[153][e].charCodeAt(0)&&(n[r[153][e]]=39168+e,t[39168+e]=r[153][e]);for(r[154]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc224\uc225\uc226\uc227\uc22a\uc22c\uc22e\uc230\uc233\uc235\uc236\uc237\uc238\uc239\uc23a\uc23b\uc23c\uc23d\uc23e\uc23f\uc240\uc241\uc242\uc243\uc244\uc245\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc246\uc247\uc249\uc24a\uc24b\uc24c\uc24d\uc24e\uc24f\uc252\uc253\uc255\uc256\uc257\uc259\uc25a\uc25b\uc25c\uc25d\uc25e\uc25f\uc261\uc262\uc263\uc264\uc266\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc267\uc268\uc269\uc26a\uc26b\uc26e\uc26f\uc271\uc272\uc273\uc275\uc276\uc277\uc278\uc279\uc27a\uc27b\uc27e\uc280\uc282\uc283\uc284\uc285\uc286\uc287\uc28a\uc28b\uc28c\uc28d\uc28e\uc28f\uc291\uc292\uc293\uc294\uc295\uc296\uc297\uc299\uc29a\uc29c\uc29e\uc29f\uc2a0\uc2a1\uc2a2\uc2a3\uc2a6\uc2a7\uc2a9\uc2aa\uc2ab\uc2ae\uc2af\uc2b0\uc2b1\uc2b2\uc2b3\uc2b6\uc2b8\uc2ba\uc2bb\uc2bc\uc2bd\uc2be\uc2bf\uc2c0\uc2c1\uc2c2\uc2c3\uc2c4\uc2c5\uc2c6\uc2c7\uc2c8\uc2c9\uc2ca\uc2cb\uc2cc\uc2cd\uc2ce\uc2cf\uc2d0\uc2d1\uc2d2\uc2d3\uc2d4\uc2d5\uc2d6\uc2d7\uc2d8\uc2d9\uc2da\uc2db\uc2de\uc2df\uc2e1\uc2e2\uc2e5\uc2e6\uc2e7\uc2e8\uc2e9\uc2ea\uc2ee\uc2f0\uc2f2\uc2f3\uc2f4\uc2f5\uc2f7\uc2fa\uc2fd\uc2fe\uc2ff\uc301\uc302\uc303\uc304\uc305\uc306\uc307\uc30a\uc30b\uc30e\uc30f\ufffd".split(""),e=0;e!=r[154].length;++e)65533!==r[154][e].charCodeAt(0)&&(n[r[154][e]]=39424+e,t[39424+e]=r[154][e]);for(r[155]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc310\uc311\uc312\uc316\uc317\uc319\uc31a\uc31b\uc31d\uc31e\uc31f\uc320\uc321\uc322\uc323\uc326\uc327\uc32a\uc32b\uc32c\uc32d\uc32e\uc32f\uc330\uc331\uc332\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc333\uc334\uc335\uc336\uc337\uc338\uc339\uc33a\uc33b\uc33c\uc33d\uc33e\uc33f\uc340\uc341\uc342\uc343\uc344\uc346\uc347\uc348\uc349\uc34a\uc34b\uc34c\uc34d\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc34e\uc34f\uc350\uc351\uc352\uc353\uc354\uc355\uc356\uc357\uc358\uc359\uc35a\uc35b\uc35c\uc35d\uc35e\uc35f\uc360\uc361\uc362\uc363\uc364\uc365\uc366\uc367\uc36a\uc36b\uc36d\uc36e\uc36f\uc371\uc373\uc374\uc375\uc376\uc377\uc37a\uc37b\uc37e\uc37f\uc380\uc381\uc382\uc383\uc385\uc386\uc387\uc389\uc38a\uc38b\uc38d\uc38e\uc38f\uc390\uc391\uc392\uc393\uc394\uc395\uc396\uc397\uc398\uc399\uc39a\uc39b\uc39c\uc39d\uc39e\uc39f\uc3a0\uc3a1\uc3a2\uc3a3\uc3a4\uc3a5\uc3a6\uc3a7\uc3a8\uc3a9\uc3aa\uc3ab\uc3ac\uc3ad\uc3ae\uc3af\uc3b0\uc3b1\uc3b2\uc3b3\uc3b4\uc3b5\uc3b6\uc3b7\uc3b8\uc3b9\uc3ba\uc3bb\uc3bc\uc3bd\uc3be\uc3bf\uc3c1\uc3c2\uc3c3\uc3c4\uc3c5\uc3c6\uc3c7\uc3c8\uc3c9\uc3ca\uc3cb\uc3cc\uc3cd\uc3ce\uc3cf\uc3d0\uc3d1\uc3d2\uc3d3\uc3d4\uc3d5\uc3d6\uc3d7\uc3da\ufffd".split(""),e=0;e!=r[155].length;++e)65533!==r[155][e].charCodeAt(0)&&(n[r[155][e]]=39680+e,t[39680+e]=r[155][e]);for(r[156]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc3db\uc3dd\uc3de\uc3e1\uc3e3\uc3e4\uc3e5\uc3e6\uc3e7\uc3ea\uc3eb\uc3ec\uc3ee\uc3ef\uc3f0\uc3f1\uc3f2\uc3f3\uc3f6\uc3f7\uc3f9\uc3fa\uc3fb\uc3fc\uc3fd\uc3fe\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc3ff\uc400\uc401\uc402\uc403\uc404\uc405\uc406\uc407\uc409\uc40a\uc40b\uc40c\uc40d\uc40e\uc40f\uc411\uc412\uc413\uc414\uc415\uc416\uc417\uc418\uc419\uc41a\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc41b\uc41c\uc41d\uc41e\uc41f\uc420\uc421\uc422\uc423\uc425\uc426\uc427\uc428\uc429\uc42a\uc42b\uc42d\uc42e\uc42f\uc431\uc432\uc433\uc435\uc436\uc437\uc438\uc439\uc43a\uc43b\uc43e\uc43f\uc440\uc441\uc442\uc443\uc444\uc445\uc446\uc447\uc449\uc44a\uc44b\uc44c\uc44d\uc44e\uc44f\uc450\uc451\uc452\uc453\uc454\uc455\uc456\uc457\uc458\uc459\uc45a\uc45b\uc45c\uc45d\uc45e\uc45f\uc460\uc461\uc462\uc463\uc466\uc467\uc469\uc46a\uc46b\uc46d\uc46e\uc46f\uc470\uc471\uc472\uc473\uc476\uc477\uc478\uc47a\uc47b\uc47c\uc47d\uc47e\uc47f\uc481\uc482\uc483\uc484\uc485\uc486\uc487\uc488\uc489\uc48a\uc48b\uc48c\uc48d\uc48e\uc48f\uc490\uc491\uc492\uc493\uc495\uc496\uc497\uc498\uc499\uc49a\uc49b\uc49d\uc49e\uc49f\uc4a0\uc4a1\uc4a2\uc4a3\uc4a4\uc4a5\uc4a6\uc4a7\uc4a8\uc4a9\ufffd".split(""),e=0;e!=r[156].length;++e)65533!==r[156][e].charCodeAt(0)&&(n[r[156][e]]=39936+e,t[39936+e]=r[156][e]);for(r[157]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc4aa\uc4ab\uc4ac\uc4ad\uc4ae\uc4af\uc4b0\uc4b1\uc4b2\uc4b3\uc4b4\uc4b5\uc4b6\uc4b7\uc4b9\uc4ba\uc4bb\uc4bd\uc4be\uc4bf\uc4c0\uc4c1\uc4c2\uc4c3\uc4c4\uc4c5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc4c6\uc4c7\uc4c8\uc4c9\uc4ca\uc4cb\uc4cc\uc4cd\uc4ce\uc4cf\uc4d0\uc4d1\uc4d2\uc4d3\uc4d4\uc4d5\uc4d6\uc4d7\uc4d8\uc4d9\uc4da\uc4db\uc4dc\uc4dd\uc4de\uc4df\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc4e0\uc4e1\uc4e2\uc4e3\uc4e4\uc4e5\uc4e6\uc4e7\uc4e8\uc4ea\uc4eb\uc4ec\uc4ed\uc4ee\uc4ef\uc4f2\uc4f3\uc4f5\uc4f6\uc4f7\uc4f9\uc4fb\uc4fc\uc4fd\uc4fe\uc502\uc503\uc504\uc505\uc506\uc507\uc508\uc509\uc50a\uc50b\uc50d\uc50e\uc50f\uc511\uc512\uc513\uc515\uc516\uc517\uc518\uc519\uc51a\uc51b\uc51d\uc51e\uc51f\uc520\uc521\uc522\uc523\uc524\uc525\uc526\uc527\uc52a\uc52b\uc52d\uc52e\uc52f\uc531\uc532\uc533\uc534\uc535\uc536\uc537\uc53a\uc53c\uc53e\uc53f\uc540\uc541\uc542\uc543\uc546\uc547\uc54b\uc54f\uc550\uc551\uc552\uc556\uc55a\uc55b\uc55c\uc55f\uc562\uc563\uc565\uc566\uc567\uc569\uc56a\uc56b\uc56c\uc56d\uc56e\uc56f\uc572\uc576\uc577\uc578\uc579\uc57a\uc57b\uc57e\uc57f\uc581\uc582\uc583\uc585\uc586\uc588\uc589\uc58a\uc58b\uc58e\uc590\uc592\uc593\uc594\ufffd".split(""),e=0;e!=r[157].length;++e)65533!==r[157][e].charCodeAt(0)&&(n[r[157][e]]=40192+e,t[40192+e]=r[157][e]);for(r[158]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc596\uc599\uc59a\uc59b\uc59d\uc59e\uc59f\uc5a1\uc5a2\uc5a3\uc5a4\uc5a5\uc5a6\uc5a7\uc5a8\uc5aa\uc5ab\uc5ac\uc5ad\uc5ae\uc5af\uc5b0\uc5b1\uc5b2\uc5b3\uc5b6\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc5b7\uc5ba\uc5bf\uc5c0\uc5c1\uc5c2\uc5c3\uc5cb\uc5cd\uc5cf\uc5d2\uc5d3\uc5d5\uc5d6\uc5d7\uc5d9\uc5da\uc5db\uc5dc\uc5dd\uc5de\uc5df\uc5e2\uc5e4\uc5e6\uc5e7\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\uc5e8\uc5e9\uc5ea\uc5eb\uc5ef\uc5f1\uc5f2\uc5f3\u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b080\ub084\ub08c\ub08d\ub08f\ub091\ub098\ub099\ub09a\ub09c\ub09f\ub0a0\ub0a1\ub0a2\ub0a8\ub0a9\ub0ab\ub0ac\ub0ad\ub0ae\ub0af\ub0b1\ub0b3\ub0b4\ub0b5\ub0b8\ub0bc\ub0c4\ub0c5\ub0c7\ub0c8\ub0c9\ub0d0\ub0d1\ub0d4\ub0d8\ub0e0\ub0e5\ub108\ub109\ub10b\ub10c\ub110\ub112\ub113\ub118\ub119\ub11b\ub11c\ub11d\ub123\ub124\ub125\ub128\ub12c\ub134\ub135\ub137\ub138\ub139\ub140\ub141\ub144\ub148\ub150\ub151\ub154\ub155\ub158\ub15c\ub160\ub178\ub179\ub17c\ub180\ub182\ub188\ub189\ub18b\ub18d\ub192\ub193\ub194\ub198\ub19c\ub1a8\ub1cc\ub1d0\ub1d4\ub1dc\ub1dd\ufffd".split(""),e=0;e!=r[179].length;++e)65533!==r[179][e].charCodeAt(0)&&(n[r[179][e]]=45824+e,t[45824+e]=r[179][e]);for(r[180]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud02e\ud02f\ud030\ud031\ud032\ud033\ud036\ud037\ud039\ud03a\ud03b\ud03d\ud03e\ud03f\ud040\ud041\ud042\ud043\ud046\ud048\ud04a\ud04b\ud04c\ud04d\ud04e\ud04f\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud051\ud052\ud053\ud055\ud056\ud057\ud059\ud05a\ud05b\ud05c\ud05d\ud05e\ud05f\ud061\ud062\ud063\ud064\ud065\ud066\ud067\ud068\ud069\ud06a\ud06b\ud06e\ud06f\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud071\ud072\ud073\ud075\ud076\ud077\ud078\ud079\ud07a\ud07b\ud07e\ud07f\ud080\ud082\ud083\ud084\ud085\ud086\ud087\ud088\ud089\ud08a\ud08b\ud08c\ud08d\ud08e\ud08f\ud090\ud091\ud092\ud093\ud094\ub1df\ub1e8\ub1e9\ub1ec\ub1f0\ub1f9\ub1fb\ub1fd\ub204\ub205\ub208\ub20b\ub20c\ub214\ub215\ub217\ub219\ub220\ub234\ub23c\ub258\ub25c\ub260\ub268\ub269\ub274\ub275\ub27c\ub284\ub285\ub289\ub290\ub291\ub294\ub298\ub299\ub29a\ub2a0\ub2a1\ub2a3\ub2a5\ub2a6\ub2aa\ub2ac\ub2b0\ub2b4\ub2c8\ub2c9\ub2cc\ub2d0\ub2d2\ub2d8\ub2d9\ub2db\ub2dd\ub2e2\ub2e4\ub2e5\ub2e6\ub2e8\ub2eb\ub2ec\ub2ed\ub2ee\ub2ef\ub2f3\ub2f4\ub2f5\ub2f7\ub2f8\ub2f9\ub2fa\ub2fb\ub2ff\ub300\ub301\ub304\ub308\ub310\ub311\ub313\ub314\ub315\ub31c\ub354\ub355\ub356\ub358\ub35b\ub35c\ub35e\ub35f\ub364\ub365\ufffd".split(""),e=0;e!=r[180].length;++e)65533!==r[180][e].charCodeAt(0)&&(n[r[180][e]]=46080+e,t[46080+e]=r[180][e]);for(r[181]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud095\ud096\ud097\ud098\ud099\ud09a\ud09b\ud09c\ud09d\ud09e\ud09f\ud0a0\ud0a1\ud0a2\ud0a3\ud0a6\ud0a7\ud0a9\ud0aa\ud0ab\ud0ad\ud0ae\ud0af\ud0b0\ud0b1\ud0b2\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud0b3\ud0b6\ud0b8\ud0ba\ud0bb\ud0bc\ud0bd\ud0be\ud0bf\ud0c2\ud0c3\ud0c5\ud0c6\ud0c7\ud0ca\ud0cb\ud0cc\ud0cd\ud0ce\ud0cf\ud0d2\ud0d6\ud0d7\ud0d8\ud0d9\ud0da\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud0db\ud0de\ud0df\ud0e1\ud0e2\ud0e3\ud0e5\ud0e6\ud0e7\ud0e8\ud0e9\ud0ea\ud0eb\ud0ee\ud0f2\ud0f3\ud0f4\ud0f5\ud0f6\ud0f7\ud0f9\ud0fa\ud0fb\ud0fc\ud0fd\ud0fe\ud0ff\ud100\ud101\ud102\ud103\ud104\ub367\ub369\ub36b\ub36e\ub370\ub371\ub374\ub378\ub380\ub381\ub383\ub384\ub385\ub38c\ub390\ub394\ub3a0\ub3a1\ub3a8\ub3ac\ub3c4\ub3c5\ub3c8\ub3cb\ub3cc\ub3ce\ub3d0\ub3d4\ub3d5\ub3d7\ub3d9\ub3db\ub3dd\ub3e0\ub3e4\ub3e8\ub3fc\ub410\ub418\ub41c\ub420\ub428\ub429\ub42b\ub434\ub450\ub451\ub454\ub458\ub460\ub461\ub463\ub465\ub46c\ub480\ub488\ub49d\ub4a4\ub4a8\ub4ac\ub4b5\ub4b7\ub4b9\ub4c0\ub4c4\ub4c8\ub4d0\ub4d5\ub4dc\ub4dd\ub4e0\ub4e3\ub4e4\ub4e6\ub4ec\ub4ed\ub4ef\ub4f1\ub4f8\ub514\ub515\ub518\ub51b\ub51c\ub524\ub525\ub527\ub528\ub529\ub52a\ub530\ub531\ub534\ub538\ufffd".split(""),e=0;e!=r[181].length;++e)65533!==r[181][e].charCodeAt(0)&&(n[r[181][e]]=46336+e,t[46336+e]=r[181][e]);for(r[182]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud105\ud106\ud107\ud108\ud109\ud10a\ud10b\ud10c\ud10e\ud10f\ud110\ud111\ud112\ud113\ud114\ud115\ud116\ud117\ud118\ud119\ud11a\ud11b\ud11c\ud11d\ud11e\ud11f\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud120\ud121\ud122\ud123\ud124\ud125\ud126\ud127\ud128\ud129\ud12a\ud12b\ud12c\ud12d\ud12e\ud12f\ud132\ud133\ud135\ud136\ud137\ud139\ud13b\ud13c\ud13d\ud13e\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud13f\ud142\ud146\ud147\ud148\ud149\ud14a\ud14b\ud14e\ud14f\ud151\ud152\ud153\ud155\ud156\ud157\ud158\ud159\ud15a\ud15b\ud15e\ud160\ud162\ud163\ud164\ud165\ud166\ud167\ud169\ud16a\ud16b\ud16d\ub540\ub541\ub543\ub544\ub545\ub54b\ub54c\ub54d\ub550\ub554\ub55c\ub55d\ub55f\ub560\ub561\ub5a0\ub5a1\ub5a4\ub5a8\ub5aa\ub5ab\ub5b0\ub5b1\ub5b3\ub5b4\ub5b5\ub5bb\ub5bc\ub5bd\ub5c0\ub5c4\ub5cc\ub5cd\ub5cf\ub5d0\ub5d1\ub5d8\ub5ec\ub610\ub611\ub614\ub618\ub625\ub62c\ub634\ub648\ub664\ub668\ub69c\ub69d\ub6a0\ub6a4\ub6ab\ub6ac\ub6b1\ub6d4\ub6f0\ub6f4\ub6f8\ub700\ub701\ub705\ub728\ub729\ub72c\ub72f\ub730\ub738\ub739\ub73b\ub744\ub748\ub74c\ub754\ub755\ub760\ub764\ub768\ub770\ub771\ub773\ub775\ub77c\ub77d\ub780\ub784\ub78c\ub78d\ub78f\ub790\ub791\ub792\ub796\ub797\ufffd".split(""),e=0;e!=r[182].length;++e)65533!==r[182][e].charCodeAt(0)&&(n[r[182][e]]=46592+e,t[46592+e]=r[182][e]);for(r[183]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud16e\ud16f\ud170\ud171\ud172\ud173\ud174\ud175\ud176\ud177\ud178\ud179\ud17a\ud17b\ud17d\ud17e\ud17f\ud180\ud181\ud182\ud183\ud185\ud186\ud187\ud189\ud18a\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud18b\ud18c\ud18d\ud18e\ud18f\ud190\ud191\ud192\ud193\ud194\ud195\ud196\ud197\ud198\ud199\ud19a\ud19b\ud19c\ud19d\ud19e\ud19f\ud1a2\ud1a3\ud1a5\ud1a6\ud1a7\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud1a9\ud1aa\ud1ab\ud1ac\ud1ad\ud1ae\ud1af\ud1b2\ud1b4\ud1b6\ud1b7\ud1b8\ud1b9\ud1bb\ud1bd\ud1be\ud1bf\ud1c1\ud1c2\ud1c3\ud1c4\ud1c5\ud1c6\ud1c7\ud1c8\ud1c9\ud1ca\ud1cb\ud1cc\ud1cd\ud1ce\ud1cf\ub798\ub799\ub79c\ub7a0\ub7a8\ub7a9\ub7ab\ub7ac\ub7ad\ub7b4\ub7b5\ub7b8\ub7c7\ub7c9\ub7ec\ub7ed\ub7f0\ub7f4\ub7fc\ub7fd\ub7ff\ub800\ub801\ub807\ub808\ub809\ub80c\ub810\ub818\ub819\ub81b\ub81d\ub824\ub825\ub828\ub82c\ub834\ub835\ub837\ub838\ub839\ub840\ub844\ub851\ub853\ub85c\ub85d\ub860\ub864\ub86c\ub86d\ub86f\ub871\ub878\ub87c\ub88d\ub8a8\ub8b0\ub8b4\ub8b8\ub8c0\ub8c1\ub8c3\ub8c5\ub8cc\ub8d0\ub8d4\ub8dd\ub8df\ub8e1\ub8e8\ub8e9\ub8ec\ub8f0\ub8f8\ub8f9\ub8fb\ub8fd\ub904\ub918\ub920\ub93c\ub93d\ub940\ub944\ub94c\ub94f\ub951\ub958\ub959\ub95c\ub960\ub968\ub969\ufffd".split(""),e=0;e!=r[183].length;++e)65533!==r[183][e].charCodeAt(0)&&(n[r[183][e]]=46848+e,t[46848+e]=r[183][e]);for(r[184]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud1d0\ud1d1\ud1d2\ud1d3\ud1d4\ud1d5\ud1d6\ud1d7\ud1d9\ud1da\ud1db\ud1dc\ud1dd\ud1de\ud1df\ud1e0\ud1e1\ud1e2\ud1e3\ud1e4\ud1e5\ud1e6\ud1e7\ud1e8\ud1e9\ud1ea\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud1eb\ud1ec\ud1ed\ud1ee\ud1ef\ud1f0\ud1f1\ud1f2\ud1f3\ud1f5\ud1f6\ud1f7\ud1f9\ud1fa\ud1fb\ud1fc\ud1fd\ud1fe\ud1ff\ud200\ud201\ud202\ud203\ud204\ud205\ud206\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud208\ud20a\ud20b\ud20c\ud20d\ud20e\ud20f\ud211\ud212\ud213\ud214\ud215\ud216\ud217\ud218\ud219\ud21a\ud21b\ud21c\ud21d\ud21e\ud21f\ud220\ud221\ud222\ud223\ud224\ud225\ud226\ud227\ud228\ud229\ub96b\ub96d\ub974\ub975\ub978\ub97c\ub984\ub985\ub987\ub989\ub98a\ub98d\ub98e\ub9ac\ub9ad\ub9b0\ub9b4\ub9bc\ub9bd\ub9bf\ub9c1\ub9c8\ub9c9\ub9cc\ub9ce\ub9cf\ub9d0\ub9d1\ub9d2\ub9d8\ub9d9\ub9db\ub9dd\ub9de\ub9e1\ub9e3\ub9e4\ub9e5\ub9e8\ub9ec\ub9f4\ub9f5\ub9f7\ub9f8\ub9f9\ub9fa\uba00\uba01\uba08\uba15\uba38\uba39\uba3c\uba40\uba42\uba48\uba49\uba4b\uba4d\uba4e\uba53\uba54\uba55\uba58\uba5c\uba64\uba65\uba67\uba68\uba69\uba70\uba71\uba74\uba78\uba83\uba84\uba85\uba87\uba8c\ubaa8\ubaa9\ubaab\ubaac\ubab0\ubab2\ubab8\ubab9\ubabb\ubabd\ubac4\ubac8\ubad8\ubad9\ubafc\ufffd".split(""),e=0;e!=r[184].length;++e)65533!==r[184][e].charCodeAt(0)&&(n[r[184][e]]=47104+e,t[47104+e]=r[184][e]);for(r[185]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud22a\ud22b\ud22e\ud22f\ud231\ud232\ud233\ud235\ud236\ud237\ud238\ud239\ud23a\ud23b\ud23e\ud240\ud242\ud243\ud244\ud245\ud246\ud247\ud249\ud24a\ud24b\ud24c\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud24d\ud24e\ud24f\ud250\ud251\ud252\ud253\ud254\ud255\ud256\ud257\ud258\ud259\ud25a\ud25b\ud25d\ud25e\ud25f\ud260\ud261\ud262\ud263\ud265\ud266\ud267\ud268\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud269\ud26a\ud26b\ud26c\ud26d\ud26e\ud26f\ud270\ud271\ud272\ud273\ud274\ud275\ud276\ud277\ud278\ud279\ud27a\ud27b\ud27c\ud27d\ud27e\ud27f\ud282\ud283\ud285\ud286\ud287\ud289\ud28a\ud28b\ud28c\ubb00\ubb04\ubb0d\ubb0f\ubb11\ubb18\ubb1c\ubb20\ubb29\ubb2b\ubb34\ubb35\ubb36\ubb38\ubb3b\ubb3c\ubb3d\ubb3e\ubb44\ubb45\ubb47\ubb49\ubb4d\ubb4f\ubb50\ubb54\ubb58\ubb61\ubb63\ubb6c\ubb88\ubb8c\ubb90\ubba4\ubba8\ubbac\ubbb4\ubbb7\ubbc0\ubbc4\ubbc8\ubbd0\ubbd3\ubbf8\ubbf9\ubbfc\ubbff\ubc00\ubc02\ubc08\ubc09\ubc0b\ubc0c\ubc0d\ubc0f\ubc11\ubc14\ubc15\ubc16\ubc17\ubc18\ubc1b\ubc1c\ubc1d\ubc1e\ubc1f\ubc24\ubc25\ubc27\ubc29\ubc2d\ubc30\ubc31\ubc34\ubc38\ubc40\ubc41\ubc43\ubc44\ubc45\ubc49\ubc4c\ubc4d\ubc50\ubc5d\ubc84\ubc85\ubc88\ubc8b\ubc8c\ubc8e\ubc94\ubc95\ubc97\ufffd".split(""),e=0;e!=r[185].length;++e)65533!==r[185][e].charCodeAt(0)&&(n[r[185][e]]=47360+e,t[47360+e]=r[185][e]);for(r[186]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud28d\ud28e\ud28f\ud292\ud293\ud294\ud296\ud297\ud298\ud299\ud29a\ud29b\ud29d\ud29e\ud29f\ud2a1\ud2a2\ud2a3\ud2a5\ud2a6\ud2a7\ud2a8\ud2a9\ud2aa\ud2ab\ud2ad\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud2ae\ud2af\ud2b0\ud2b2\ud2b3\ud2b4\ud2b5\ud2b6\ud2b7\ud2ba\ud2bb\ud2bd\ud2be\ud2c1\ud2c3\ud2c4\ud2c5\ud2c6\ud2c7\ud2ca\ud2cc\ud2cd\ud2ce\ud2cf\ud2d0\ud2d1\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud2d2\ud2d3\ud2d5\ud2d6\ud2d7\ud2d9\ud2da\ud2db\ud2dd\ud2de\ud2df\ud2e0\ud2e1\ud2e2\ud2e3\ud2e6\ud2e7\ud2e8\ud2e9\ud2ea\ud2eb\ud2ec\ud2ed\ud2ee\ud2ef\ud2f2\ud2f3\ud2f5\ud2f6\ud2f7\ud2f9\ud2fa\ubc99\ubc9a\ubca0\ubca1\ubca4\ubca7\ubca8\ubcb0\ubcb1\ubcb3\ubcb4\ubcb5\ubcbc\ubcbd\ubcc0\ubcc4\ubccd\ubccf\ubcd0\ubcd1\ubcd5\ubcd8\ubcdc\ubcf4\ubcf5\ubcf6\ubcf8\ubcfc\ubd04\ubd05\ubd07\ubd09\ubd10\ubd14\ubd24\ubd2c\ubd40\ubd48\ubd49\ubd4c\ubd50\ubd58\ubd59\ubd64\ubd68\ubd80\ubd81\ubd84\ubd87\ubd88\ubd89\ubd8a\ubd90\ubd91\ubd93\ubd95\ubd99\ubd9a\ubd9c\ubda4\ubdb0\ubdb8\ubdd4\ubdd5\ubdd8\ubddc\ubde9\ubdf0\ubdf4\ubdf8\ube00\ube03\ube05\ube0c\ube0d\ube10\ube14\ube1c\ube1d\ube1f\ube44\ube45\ube48\ube4c\ube4e\ube54\ube55\ube57\ube59\ube5a\ube5b\ube60\ube61\ube64\ufffd".split(""),e=0;e!=r[186].length;++e)65533!==r[186][e].charCodeAt(0)&&(n[r[186][e]]=47616+e,t[47616+e]=r[186][e]);for(r[187]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud2fb\ud2fc\ud2fd\ud2fe\ud2ff\ud302\ud304\ud306\ud307\ud308\ud309\ud30a\ud30b\ud30f\ud311\ud312\ud313\ud315\ud317\ud318\ud319\ud31a\ud31b\ud31e\ud322\ud323\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud324\ud326\ud327\ud32a\ud32b\ud32d\ud32e\ud32f\ud331\ud332\ud333\ud334\ud335\ud336\ud337\ud33a\ud33e\ud33f\ud340\ud341\ud342\ud343\ud346\ud347\ud348\ud349\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud34a\ud34b\ud34c\ud34d\ud34e\ud34f\ud350\ud351\ud352\ud353\ud354\ud355\ud356\ud357\ud358\ud359\ud35a\ud35b\ud35c\ud35d\ud35e\ud35f\ud360\ud361\ud362\ud363\ud364\ud365\ud366\ud367\ud368\ud369\ube68\ube6a\ube70\ube71\ube73\ube74\ube75\ube7b\ube7c\ube7d\ube80\ube84\ube8c\ube8d\ube8f\ube90\ube91\ube98\ube99\ubea8\ubed0\ubed1\ubed4\ubed7\ubed8\ubee0\ubee3\ubee4\ubee5\ubeec\ubf01\ubf08\ubf09\ubf18\ubf19\ubf1b\ubf1c\ubf1d\ubf40\ubf41\ubf44\ubf48\ubf50\ubf51\ubf55\ubf94\ubfb0\ubfc5\ubfcc\ubfcd\ubfd0\ubfd4\ubfdc\ubfdf\ubfe1\uc03c\uc051\uc058\uc05c\uc060\uc068\uc069\uc090\uc091\uc094\uc098\uc0a0\uc0a1\uc0a3\uc0a5\uc0ac\uc0ad\uc0af\uc0b0\uc0b3\uc0b4\uc0b5\uc0b6\uc0bc\uc0bd\uc0bf\uc0c0\uc0c1\uc0c5\uc0c8\uc0c9\uc0cc\uc0d0\uc0d8\uc0d9\uc0db\uc0dc\uc0dd\uc0e4\ufffd".split(""),e=0;e!=r[187].length;++e)65533!==r[187][e].charCodeAt(0)&&(n[r[187][e]]=47872+e,t[47872+e]=r[187][e]);for(r[188]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud36a\ud36b\ud36c\ud36d\ud36e\ud36f\ud370\ud371\ud372\ud373\ud374\ud375\ud376\ud377\ud378\ud379\ud37a\ud37b\ud37e\ud37f\ud381\ud382\ud383\ud385\ud386\ud387\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud388\ud389\ud38a\ud38b\ud38e\ud392\ud393\ud394\ud395\ud396\ud397\ud39a\ud39b\ud39d\ud39e\ud39f\ud3a1\ud3a2\ud3a3\ud3a4\ud3a5\ud3a6\ud3a7\ud3aa\ud3ac\ud3ae\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud3af\ud3b0\ud3b1\ud3b2\ud3b3\ud3b5\ud3b6\ud3b7\ud3b9\ud3ba\ud3bb\ud3bd\ud3be\ud3bf\ud3c0\ud3c1\ud3c2\ud3c3\ud3c6\ud3c7\ud3ca\ud3cb\ud3cc\ud3cd\ud3ce\ud3cf\ud3d1\ud3d2\ud3d3\ud3d4\ud3d5\ud3d6\uc0e5\uc0e8\uc0ec\uc0f4\uc0f5\uc0f7\uc0f9\uc100\uc104\uc108\uc110\uc115\uc11c\uc11d\uc11e\uc11f\uc120\uc123\uc124\uc126\uc127\uc12c\uc12d\uc12f\uc130\uc131\uc136\uc138\uc139\uc13c\uc140\uc148\uc149\uc14b\uc14c\uc14d\uc154\uc155\uc158\uc15c\uc164\uc165\uc167\uc168\uc169\uc170\uc174\uc178\uc185\uc18c\uc18d\uc18e\uc190\uc194\uc196\uc19c\uc19d\uc19f\uc1a1\uc1a5\uc1a8\uc1a9\uc1ac\uc1b0\uc1bd\uc1c4\uc1c8\uc1cc\uc1d4\uc1d7\uc1d8\uc1e0\uc1e4\uc1e8\uc1f0\uc1f1\uc1f3\uc1fc\uc1fd\uc200\uc204\uc20c\uc20d\uc20f\uc211\uc218\uc219\uc21c\uc21f\uc220\uc228\uc229\uc22b\uc22d\ufffd".split(""),e=0;e!=r[188].length;++e)65533!==r[188][e].charCodeAt(0)&&(n[r[188][e]]=48128+e,t[48128+e]=r[188][e]);for(r[189]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud3d7\ud3d9\ud3da\ud3db\ud3dc\ud3dd\ud3de\ud3df\ud3e0\ud3e2\ud3e4\ud3e5\ud3e6\ud3e7\ud3e8\ud3e9\ud3ea\ud3eb\ud3ee\ud3ef\ud3f1\ud3f2\ud3f3\ud3f5\ud3f6\ud3f7\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud3f8\ud3f9\ud3fa\ud3fb\ud3fe\ud400\ud402\ud403\ud404\ud405\ud406\ud407\ud409\ud40a\ud40b\ud40c\ud40d\ud40e\ud40f\ud410\ud411\ud412\ud413\ud414\ud415\ud416\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud417\ud418\ud419\ud41a\ud41b\ud41c\ud41e\ud41f\ud420\ud421\ud422\ud423\ud424\ud425\ud426\ud427\ud428\ud429\ud42a\ud42b\ud42c\ud42d\ud42e\ud42f\ud430\ud431\ud432\ud433\ud434\ud435\ud436\ud437\uc22f\uc231\uc232\uc234\uc248\uc250\uc251\uc254\uc258\uc260\uc265\uc26c\uc26d\uc270\uc274\uc27c\uc27d\uc27f\uc281\uc288\uc289\uc290\uc298\uc29b\uc29d\uc2a4\uc2a5\uc2a8\uc2ac\uc2ad\uc2b4\uc2b5\uc2b7\uc2b9\uc2dc\uc2dd\uc2e0\uc2e3\uc2e4\uc2eb\uc2ec\uc2ed\uc2ef\uc2f1\uc2f6\uc2f8\uc2f9\uc2fb\uc2fc\uc300\uc308\uc309\uc30c\uc30d\uc313\uc314\uc315\uc318\uc31c\uc324\uc325\uc328\uc329\uc345\uc368\uc369\uc36c\uc370\uc372\uc378\uc379\uc37c\uc37d\uc384\uc388\uc38c\uc3c0\uc3d8\uc3d9\uc3dc\uc3df\uc3e0\uc3e2\uc3e8\uc3e9\uc3ed\uc3f4\uc3f5\uc3f8\uc408\uc410\uc424\uc42c\uc430\ufffd".split(""),e=0;e!=r[189].length;++e)65533!==r[189][e].charCodeAt(0)&&(n[r[189][e]]=48384+e,t[48384+e]=r[189][e]);for(r[190]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud438\ud439\ud43a\ud43b\ud43c\ud43d\ud43e\ud43f\ud441\ud442\ud443\ud445\ud446\ud447\ud448\ud449\ud44a\ud44b\ud44c\ud44d\ud44e\ud44f\ud450\ud451\ud452\ud453\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud454\ud455\ud456\ud457\ud458\ud459\ud45a\ud45b\ud45d\ud45e\ud45f\ud461\ud462\ud463\ud465\ud466\ud467\ud468\ud469\ud46a\ud46b\ud46c\ud46e\ud470\ud471\ud472\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud473\ud474\ud475\ud476\ud477\ud47a\ud47b\ud47d\ud47e\ud481\ud483\ud484\ud485\ud486\ud487\ud48a\ud48c\ud48e\ud48f\ud490\ud491\ud492\ud493\ud495\ud496\ud497\ud498\ud499\ud49a\ud49b\ud49c\ud49d\uc434\uc43c\uc43d\uc448\uc464\uc465\uc468\uc46c\uc474\uc475\uc479\uc480\uc494\uc49c\uc4b8\uc4bc\uc4e9\uc4f0\uc4f1\uc4f4\uc4f8\uc4fa\uc4ff\uc500\uc501\uc50c\uc510\uc514\uc51c\uc528\uc529\uc52c\uc530\uc538\uc539\uc53b\uc53d\uc544\uc545\uc548\uc549\uc54a\uc54c\uc54d\uc54e\uc553\uc554\uc555\uc557\uc558\uc559\uc55d\uc55e\uc560\uc561\uc564\uc568\uc570\uc571\uc573\uc574\uc575\uc57c\uc57d\uc580\uc584\uc587\uc58c\uc58d\uc58f\uc591\uc595\uc597\uc598\uc59c\uc5a0\uc5a9\uc5b4\uc5b5\uc5b8\uc5b9\uc5bb\uc5bc\uc5bd\uc5be\uc5c4\uc5c5\uc5c6\uc5c7\uc5c8\uc5c9\uc5ca\uc5cc\uc5ce\ufffd".split(""),e=0;e!=r[190].length;++e)65533!==r[190][e].charCodeAt(0)&&(n[r[190][e]]=48640+e,t[48640+e]=r[190][e]);for(r[191]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud49e\ud49f\ud4a0\ud4a1\ud4a2\ud4a3\ud4a4\ud4a5\ud4a6\ud4a7\ud4a8\ud4aa\ud4ab\ud4ac\ud4ad\ud4ae\ud4af\ud4b0\ud4b1\ud4b2\ud4b3\ud4b4\ud4b5\ud4b6\ud4b7\ud4b8\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud4b9\ud4ba\ud4bb\ud4bc\ud4bd\ud4be\ud4bf\ud4c0\ud4c1\ud4c2\ud4c3\ud4c4\ud4c5\ud4c6\ud4c7\ud4c8\ud4c9\ud4ca\ud4cb\ud4cd\ud4ce\ud4cf\ud4d1\ud4d2\ud4d3\ud4d5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud4d6\ud4d7\ud4d8\ud4d9\ud4da\ud4db\ud4dd\ud4de\ud4e0\ud4e1\ud4e2\ud4e3\ud4e4\ud4e5\ud4e6\ud4e7\ud4e9\ud4ea\ud4eb\ud4ed\ud4ee\ud4ef\ud4f1\ud4f2\ud4f3\ud4f4\ud4f5\ud4f6\ud4f7\ud4f9\ud4fa\ud4fc\uc5d0\uc5d1\uc5d4\uc5d8\uc5e0\uc5e1\uc5e3\uc5e5\uc5ec\uc5ed\uc5ee\uc5f0\uc5f4\uc5f6\uc5f7\uc5fc\uc5fd\uc5fe\uc5ff\uc600\uc601\uc605\uc606\uc607\uc608\uc60c\uc610\uc618\uc619\uc61b\uc61c\uc624\uc625\uc628\uc62c\uc62d\uc62e\uc630\uc633\uc634\uc635\uc637\uc639\uc63b\uc640\uc641\uc644\uc648\uc650\uc651\uc653\uc654\uc655\uc65c\uc65d\uc660\uc66c\uc66f\uc671\uc678\uc679\uc67c\uc680\uc688\uc689\uc68b\uc68d\uc694\uc695\uc698\uc69c\uc6a4\uc6a5\uc6a7\uc6a9\uc6b0\uc6b1\uc6b4\uc6b8\uc6b9\uc6ba\uc6c0\uc6c1\uc6c3\uc6c5\uc6cc\uc6cd\uc6d0\uc6d4\uc6dc\uc6dd\uc6e0\uc6e1\uc6e8\ufffd".split(""),e=0;e!=r[191].length;++e)65533!==r[191][e].charCodeAt(0)&&(n[r[191][e]]=48896+e,t[48896+e]=r[191][e]);for(r[192]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud4fe\ud4ff\ud500\ud501\ud502\ud503\ud505\ud506\ud507\ud509\ud50a\ud50b\ud50d\ud50e\ud50f\ud510\ud511\ud512\ud513\ud516\ud518\ud519\ud51a\ud51b\ud51c\ud51d\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud51e\ud51f\ud520\ud521\ud522\ud523\ud524\ud525\ud526\ud527\ud528\ud529\ud52a\ud52b\ud52c\ud52d\ud52e\ud52f\ud530\ud531\ud532\ud533\ud534\ud535\ud536\ud537\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud538\ud539\ud53a\ud53b\ud53e\ud53f\ud541\ud542\ud543\ud545\ud546\ud547\ud548\ud549\ud54a\ud54b\ud54e\ud550\ud552\ud553\ud554\ud555\ud556\ud557\ud55a\ud55b\ud55d\ud55e\ud55f\ud561\ud562\ud563\uc6e9\uc6ec\uc6f0\uc6f8\uc6f9\uc6fd\uc704\uc705\uc708\uc70c\uc714\uc715\uc717\uc719\uc720\uc721\uc724\uc728\uc730\uc731\uc733\uc735\uc737\uc73c\uc73d\uc740\uc744\uc74a\uc74c\uc74d\uc74f\uc751\uc752\uc753\uc754\uc755\uc756\uc757\uc758\uc75c\uc760\uc768\uc76b\uc774\uc775\uc778\uc77c\uc77d\uc77e\uc783\uc784\uc785\uc787\uc788\uc789\uc78a\uc78e\uc790\uc791\uc794\uc796\uc797\uc798\uc79a\uc7a0\uc7a1\uc7a3\uc7a4\uc7a5\uc7a6\uc7ac\uc7ad\uc7b0\uc7b4\uc7bc\uc7bd\uc7bf\uc7c0\uc7c1\uc7c8\uc7c9\uc7cc\uc7ce\uc7d0\uc7d8\uc7dd\uc7e4\uc7e8\uc7ec\uc800\uc801\uc804\uc808\uc80a\ufffd".split(""),e=0;e!=r[192].length;++e)65533!==r[192][e].charCodeAt(0)&&(n[r[192][e]]=49152+e,t[49152+e]=r[192][e]);for(r[193]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud564\ud566\ud567\ud56a\ud56c\ud56e\ud56f\ud570\ud571\ud572\ud573\ud576\ud577\ud579\ud57a\ud57b\ud57d\ud57e\ud57f\ud580\ud581\ud582\ud583\ud586\ud58a\ud58b\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud58c\ud58d\ud58e\ud58f\ud591\ud592\ud593\ud594\ud595\ud596\ud597\ud598\ud599\ud59a\ud59b\ud59c\ud59d\ud59e\ud59f\ud5a0\ud5a1\ud5a2\ud5a3\ud5a4\ud5a6\ud5a7\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud5a8\ud5a9\ud5aa\ud5ab\ud5ac\ud5ad\ud5ae\ud5af\ud5b0\ud5b1\ud5b2\ud5b3\ud5b4\ud5b5\ud5b6\ud5b7\ud5b8\ud5b9\ud5ba\ud5bb\ud5bc\ud5bd\ud5be\ud5bf\ud5c0\ud5c1\ud5c2\ud5c3\ud5c4\ud5c5\ud5c6\ud5c7\uc810\uc811\uc813\uc815\uc816\uc81c\uc81d\uc820\uc824\uc82c\uc82d\uc82f\uc831\uc838\uc83c\uc840\uc848\uc849\uc84c\uc84d\uc854\uc870\uc871\uc874\uc878\uc87a\uc880\uc881\uc883\uc885\uc886\uc887\uc88b\uc88c\uc88d\uc894\uc89d\uc89f\uc8a1\uc8a8\uc8bc\uc8bd\uc8c4\uc8c8\uc8cc\uc8d4\uc8d5\uc8d7\uc8d9\uc8e0\uc8e1\uc8e4\uc8f5\uc8fc\uc8fd\uc900\uc904\uc905\uc906\uc90c\uc90d\uc90f\uc911\uc918\uc92c\uc934\uc950\uc951\uc954\uc958\uc960\uc961\uc963\uc96c\uc970\uc974\uc97c\uc988\uc989\uc98c\uc990\uc998\uc999\uc99b\uc99d\uc9c0\uc9c1\uc9c4\uc9c7\uc9c8\uc9ca\uc9d0\uc9d1\uc9d3\ufffd".split(""),e=0;e!=r[193].length;++e)65533!==r[193][e].charCodeAt(0)&&(n[r[193][e]]=49408+e,t[49408+e]=r[193][e]);for(r[194]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud5ca\ud5cb\ud5cd\ud5ce\ud5cf\ud5d1\ud5d3\ud5d4\ud5d5\ud5d6\ud5d7\ud5da\ud5dc\ud5de\ud5df\ud5e0\ud5e1\ud5e2\ud5e3\ud5e6\ud5e7\ud5e9\ud5ea\ud5eb\ud5ed\ud5ee\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud5ef\ud5f0\ud5f1\ud5f2\ud5f3\ud5f6\ud5f8\ud5fa\ud5fb\ud5fc\ud5fd\ud5fe\ud5ff\ud602\ud603\ud605\ud606\ud607\ud609\ud60a\ud60b\ud60c\ud60d\ud60e\ud60f\ud612\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud616\ud617\ud618\ud619\ud61a\ud61b\ud61d\ud61e\ud61f\ud621\ud622\ud623\ud625\ud626\ud627\ud628\ud629\ud62a\ud62b\ud62c\ud62e\ud62f\ud630\ud631\ud632\ud633\ud634\ud635\ud636\ud637\ud63a\ud63b\uc9d5\uc9d6\uc9d9\uc9da\uc9dc\uc9dd\uc9e0\uc9e2\uc9e4\uc9e7\uc9ec\uc9ed\uc9ef\uc9f0\uc9f1\uc9f8\uc9f9\uc9fc\uca00\uca08\uca09\uca0b\uca0c\uca0d\uca14\uca18\uca29\uca4c\uca4d\uca50\uca54\uca5c\uca5d\uca5f\uca60\uca61\uca68\uca7d\uca84\uca98\ucabc\ucabd\ucac0\ucac4\ucacc\ucacd\ucacf\ucad1\ucad3\ucad8\ucad9\ucae0\ucaec\ucaf4\ucb08\ucb10\ucb14\ucb18\ucb20\ucb21\ucb41\ucb48\ucb49\ucb4c\ucb50\ucb58\ucb59\ucb5d\ucb64\ucb78\ucb79\ucb9c\ucbb8\ucbd4\ucbe4\ucbe7\ucbe9\ucc0c\ucc0d\ucc10\ucc14\ucc1c\ucc1d\ucc21\ucc22\ucc27\ucc28\ucc29\ucc2c\ucc2e\ucc30\ucc38\ucc39\ucc3b\ufffd".split(""),e=0;e!=r[194].length;++e)65533!==r[194][e].charCodeAt(0)&&(n[r[194][e]]=49664+e,t[49664+e]=r[194][e]);for(r[195]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud63d\ud63e\ud63f\ud641\ud642\ud643\ud644\ud646\ud647\ud64a\ud64c\ud64e\ud64f\ud650\ud652\ud653\ud656\ud657\ud659\ud65a\ud65b\ud65d\ud65e\ud65f\ud660\ud661\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud662\ud663\ud664\ud665\ud666\ud668\ud66a\ud66b\ud66c\ud66d\ud66e\ud66f\ud672\ud673\ud675\ud676\ud677\ud678\ud679\ud67a\ud67b\ud67c\ud67d\ud67e\ud67f\ud680\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud681\ud682\ud684\ud686\ud687\ud688\ud689\ud68a\ud68b\ud68e\ud68f\ud691\ud692\ud693\ud695\ud696\ud697\ud698\ud699\ud69a\ud69b\ud69c\ud69e\ud6a0\ud6a2\ud6a3\ud6a4\ud6a5\ud6a6\ud6a7\ud6a9\ud6aa\ucc3c\ucc3d\ucc3e\ucc44\ucc45\ucc48\ucc4c\ucc54\ucc55\ucc57\ucc58\ucc59\ucc60\ucc64\ucc66\ucc68\ucc70\ucc75\ucc98\ucc99\ucc9c\ucca0\ucca8\ucca9\uccab\uccac\uccad\uccb4\uccb5\uccb8\uccbc\uccc4\uccc5\uccc7\uccc9\uccd0\uccd4\ucce4\uccec\uccf0\ucd01\ucd08\ucd09\ucd0c\ucd10\ucd18\ucd19\ucd1b\ucd1d\ucd24\ucd28\ucd2c\ucd39\ucd5c\ucd60\ucd64\ucd6c\ucd6d\ucd6f\ucd71\ucd78\ucd88\ucd94\ucd95\ucd98\ucd9c\ucda4\ucda5\ucda7\ucda9\ucdb0\ucdc4\ucdcc\ucdd0\ucde8\ucdec\ucdf0\ucdf8\ucdf9\ucdfb\ucdfd\uce04\uce08\uce0c\uce14\uce19\uce20\uce21\uce24\uce28\uce30\uce31\uce33\uce35\ufffd".split(""),e=0;e!=r[195].length;++e)65533!==r[195][e].charCodeAt(0)&&(n[r[195][e]]=49920+e,t[49920+e]=r[195][e]);for(r[196]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud6ab\ud6ad\ud6ae\ud6af\ud6b1\ud6b2\ud6b3\ud6b4\ud6b5\ud6b6\ud6b7\ud6b8\ud6ba\ud6bc\ud6bd\ud6be\ud6bf\ud6c0\ud6c1\ud6c2\ud6c3\ud6c6\ud6c7\ud6c9\ud6ca\ud6cb\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud6cd\ud6ce\ud6cf\ud6d0\ud6d2\ud6d3\ud6d5\ud6d6\ud6d8\ud6da\ud6db\ud6dc\ud6dd\ud6de\ud6df\ud6e1\ud6e2\ud6e3\ud6e5\ud6e6\ud6e7\ud6e9\ud6ea\ud6eb\ud6ec\ud6ed\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud6ee\ud6ef\ud6f1\ud6f2\ud6f3\ud6f4\ud6f6\ud6f7\ud6f8\ud6f9\ud6fa\ud6fb\ud6fe\ud6ff\ud701\ud702\ud703\ud705\ud706\ud707\ud708\ud709\ud70a\ud70b\ud70c\ud70d\ud70e\ud70f\ud710\ud712\ud713\ud714\uce58\uce59\uce5c\uce5f\uce60\uce61\uce68\uce69\uce6b\uce6d\uce74\uce75\uce78\uce7c\uce84\uce85\uce87\uce89\uce90\uce91\uce94\uce98\ucea0\ucea1\ucea3\ucea4\ucea5\uceac\ucead\ucec1\ucee4\ucee5\ucee8\uceeb\uceec\ucef4\ucef5\ucef7\ucef8\ucef9\ucf00\ucf01\ucf04\ucf08\ucf10\ucf11\ucf13\ucf15\ucf1c\ucf20\ucf24\ucf2c\ucf2d\ucf2f\ucf30\ucf31\ucf38\ucf54\ucf55\ucf58\ucf5c\ucf64\ucf65\ucf67\ucf69\ucf70\ucf71\ucf74\ucf78\ucf80\ucf85\ucf8c\ucfa1\ucfa8\ucfb0\ucfc4\ucfe0\ucfe1\ucfe4\ucfe8\ucff0\ucff1\ucff3\ucff5\ucffc\ud000\ud004\ud011\ud018\ud02d\ud034\ud035\ud038\ud03c\ufffd".split(""),e=0;e!=r[196].length;++e)65533!==r[196][e].charCodeAt(0)&&(n[r[196][e]]=50176+e,t[50176+e]=r[196][e]);for(r[197]="\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud715\ud716\ud717\ud71a\ud71b\ud71d\ud71e\ud71f\ud721\ud722\ud723\ud724\ud725\ud726\ud727\ud72a\ud72c\ud72e\ud72f\ud730\ud731\ud732\ud733\ud736\ud737\ud739\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud73a\ud73b\ud73d\ud73e\ud73f\ud740\ud741\ud742\ud743\ud745\ud746\ud748\ud74a\ud74b\ud74c\ud74d\ud74e\ud74f\ud752\ud753\ud755\ud75a\ud75b\ud75c\ud75d\ud75e\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ud75f\ud762\ud764\ud766\ud767\ud768\ud76a\ud76b\ud76d\ud76e\ud76f\ud771\ud772\ud773\ud775\ud776\ud777\ud778\ud779\ud77a\ud77b\ud77e\ud77f\ud780\ud782\ud783\ud784\ud785\ud786\ud787\ud78a\ud78b\ud044\ud045\ud047\ud049\ud050\ud054\ud058\ud060\ud06c\ud06d\ud070\ud074\ud07c\ud07d\ud081\ud0a4\ud0a5\u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!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\ufffd\u201e\u2026\u2020\u2021\ufffd\u2030\u0160\u2039\u015a\u0164\u017d\u0179\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\ufffd\u2122\u0161\u203a\u015b\u0165\u017e\u017a\xa0\u02c7\u02d8\u0141\xa4\u0104\xa6\xa7\xa8\xa9\u015e\xab\xac\xad\xae\u017b\xb0\xb1\u02db\u0142\xb4\xb5\xb6\xb7\xb8\u0105\u015f\xbb\u013d\u02dd\u013e\u017c\u0154\xc1\xc2\u0102\xc4\u0139\u0106\xc7\u010c\xc9\u0118\xcb\u011a\xcd\xce\u010e\u0110\u0143\u0147\xd3\xd4\u0150\xd6\xd7\u0158\u016e\xda\u0170\xdc\xdd\u0162\xdf\u0155\xe1\xe2\u0103\xe4\u013a\u0107\xe7\u010d\xe9\u0119\xeb\u011b\xed\xee\u010f\u0111\u0144\u0148\xf3\xf4\u0151\xf6\xf7\u0159\u016f\xfa\u0171\xfc\xfd\u0163\u02d9",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1251]=function(){for(var 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!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u0402\u0403\u201a\u0453\u201e\u2026\u2020\u2021\u20ac\u2030\u0409\u2039\u040a\u040c\u040b\u040f\u0452\u2018\u2019\u201c\u201d\u2022\u2013\u2014\ufffd\u2122\u0459\u203a\u045a\u045c\u045b\u045f\xa0\u040e\u045e\u0408\xa4\u0490\xa6\xa7\u0401\xa9\u0404\xab\xac\xad\xae\u0407\xb0\xb1\u0406\u0456\u0491\xb5\xb6\xb7\u0451\u2116\u0454\xbb\u0458\u0405\u0455\u0457\u0410\u0411\u0412\u0413\u0414\u0415\u0416\u0417\u0418\u0419\u041a\u041b\u041c\u041d\u041e\u041f\u0420\u0421\u0422\u0423\u0424\u0425\u0426\u0427\u0428\u0429\u042a\u042b\u042c\u042d\u042e\u042f\u0430\u0431\u0432\u0433\u0434\u0435\u0436\u0437\u0438\u0439\u043a\u043b\u043c\u043d\u043e\u043f\u0440\u0441\u0442\u0443\u0444\u0445\u0446\u0447\u0448\u0449\u044a\u044b\u044c\u044d\u044e\u044f",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1252]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\u0192\u201e\u2026\u2020\u2021\u02c6\u2030\u0160\u2039\u0152\ufffd\u017d\ufffd\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\u02dc\u2122\u0161\u203a\u0153\ufffd\u017e\u0178\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9\xaa\xab\xac\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf\xc0\xc1\xc2\xc3\xc4\xc5\xc6\xc7\xc8\xc9\xca\xcb\xcc\xcd\xce\xcf\xd0\xd1\xd2\xd3\xd4\xd5\xd6\xd7\xd8\xd9\xda\xdb\xdc\xdd\xde\xdf\xe0\xe1\xe2\xe3\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\xec\xed\xee\xef\xf0\xf1\xf2\xf3\xf4\xf5\xf6\xf7\xf8\xf9\xfa\xfb\xfc\xfd\xfe\xff",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1253]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\u0192\u201e\u2026\u2020\u2021\ufffd\u2030\ufffd\u2039\ufffd\ufffd\ufffd\ufffd\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\ufffd\u2122\ufffd\u203a\ufffd\ufffd\ufffd\ufffd\xa0\u0385\u0386\xa3\xa4\xa5\xa6\xa7\xa8\xa9\ufffd\xab\xac\xad\xae\u2015\xb0\xb1\xb2\xb3\u0384\xb5\xb6\xb7\u0388\u0389\u038a\xbb\u038c\xbd\u038e\u038f\u0390\u0391\u0392\u0393\u0394\u0395\u0396\u0397\u0398\u0399\u039a\u039b\u039c\u039d\u039e\u039f\u03a0\u03a1\ufffd\u03a3\u03a4\u03a5\u03a6\u03a7\u03a8\u03a9\u03aa\u03ab\u03ac\u03ad\u03ae\u03af\u03b0\u03b1\u03b2\u03b3\u03b4\u03b5\u03b6\u03b7\u03b8\u03b9\u03ba\u03bb\u03bc\u03bd\u03be\u03bf\u03c0\u03c1\u03c2\u03c3\u03c4\u03c5\u03c6\u03c7\u03c8\u03c9\u03ca\u03cb\u03cc\u03cd\u03ce\ufffd",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1254]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\u0192\u201e\u2026\u2020\u2021\u02c6\u2030\u0160\u2039\u0152\ufffd\ufffd\ufffd\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\u02dc\u2122\u0161\u203a\u0153\ufffd\ufffd\u0178\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9\xaa\xab\xac\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf\xc0\xc1\xc2\xc3\xc4\xc5\xc6\xc7\xc8\xc9\xca\xcb\xcc\xcd\xce\xcf\u011e\xd1\xd2\xd3\xd4\xd5\xd6\xd7\xd8\xd9\xda\xdb\xdc\u0130\u015e\xdf\xe0\xe1\xe2\xe3\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\xec\xed\xee\xef\u011f\xf1\xf2\xf3\xf4\xf5\xf6\xf7\xf8\xf9\xfa\xfb\xfc\u0131\u015f\xff",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1255]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\u0192\u201e\u2026\u2020\u2021\u02c6\u2030\ufffd\u2039\ufffd\ufffd\ufffd\ufffd\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\u02dc\u2122\ufffd\u203a\ufffd\ufffd\ufffd\ufffd\xa0\xa1\xa2\xa3\u20aa\xa5\xa6\xa7\xa8\xa9\xd7\xab\xac\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\xf7\xbb\xbc\xbd\xbe\xbf\u05b0\u05b1\u05b2\u05b3\u05b4\u05b5\u05b6\u05b7\u05b8\u05b9\ufffd\u05bb\u05bc\u05bd\u05be\u05bf\u05c0\u05c1\u05c2\u05c3\u05f0\u05f1\u05f2\u05f3\u05f4\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u05d0\u05d1\u05d2\u05d3\u05d4\u05d5\u05d6\u05d7\u05d8\u05d9\u05da\u05db\u05dc\u05dd\u05de\u05df\u05e0\u05e1\u05e2\u05e3\u05e4\u05e5\u05e6\u05e7\u05e8\u05e9\u05ea\ufffd\ufffd\u200e\u200f\ufffd",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1256]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\u067e\u201a\u0192\u201e\u2026\u2020\u2021\u02c6\u2030\u0679\u2039\u0152\u0686\u0698\u0688\u06af\u2018\u2019\u201c\u201d\u2022\u2013\u2014\u06a9\u2122\u0691\u203a\u0153\u200c\u200d\u06ba\xa0\u060c\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9\u06be\xab\xac\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\u061b\xbb\xbc\xbd\xbe\u061f\u06c1\u0621\u0622\u0623\u0624\u0625\u0626\u0627\u0628\u0629\u062a\u062b\u062c\u062d\u062e\u062f\u0630\u0631\u0632\u0633\u0634\u0635\u0636\xd7\u0637\u0638\u0639\u063a\u0640\u0641\u0642\u0643\xe0\u0644\xe2\u0645\u0646\u0647\u0648\xe7\xe8\xe9\xea\xeb\u0649\u064a\xee\xef\u064b\u064c\u064d\u064e\xf4\u064f\u0650\xf7\u0651\xf9\u0652\xfb\xfc\u200e\u200f\u06d2",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1257]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\ufffd\u201e\u2026\u2020\u2021\ufffd\u2030\ufffd\u2039\ufffd\xa8\u02c7\xb8\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\ufffd\u2122\ufffd\u203a\ufffd\xaf\u02db\ufffd\xa0\ufffd\xa2\xa3\xa4\ufffd\xa6\xa7\xd8\xa9\u0156\xab\xac\xad\xae\xc6\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xf8\xb9\u0157\xbb\xbc\xbd\xbe\xe6\u0104\u012e\u0100\u0106\xc4\xc5\u0118\u0112\u010c\xc9\u0179\u0116\u0122\u0136\u012a\u013b\u0160\u0143\u0145\xd3\u014c\xd5\xd6\xd7\u0172\u0141\u015a\u016a\xdc\u017b\u017d\xdf\u0105\u012f\u0101\u0107\xe4\xe5\u0119\u0113\u010d\xe9\u017a\u0117\u0123\u0137\u012b\u013c\u0161\u0144\u0146\xf3\u014d\xf5\xf6\xf7\u0173\u0142\u015b\u016b\xfc\u017c\u017e\u02d9",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1258]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffd\u201a\u0192\u201e\u2026\u2020\u2021\u02c6\u2030\ufffd\u2039\u0152\ufffd\ufffd\ufffd\ufffd\u2018\u2019\u201c\u201d\u2022\u2013\u2014\u02dc\u2122\ufffd\u203a\u0153\ufffd\ufffd\u0178\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9\xaa\xab\xac\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf\xc0\xc1\xc2\u0102\xc4\xc5\xc6\xc7\xc8\xc9\xca\xcb\u0300\xcd\xce\xcf\u0110\xd1\u0309\xd3\xd4\u01a0\xd6\xd7\xd8\xd9\xda\xdb\xdc\u01af\u0303\xdf\xe0\xe1\xe2\u0103\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\u0301\xed\xee\xef\u0111\xf1\u0323\xf3\xf4\u01a1\xf6\xf7\xf8\xf9\xfa\xfb\xfc\u01b0\u20ab\xff",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[1e4]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc4\xc5\xc7\xc9\xd1\xd6\xdc\xe1\xe0\xe2\xe4\xe3\xe5\xe7\xe9\xe8\xea\xeb\xed\xec\xee\xef\xf1\xf3\xf2\xf4\xf6\xf5\xfa\xf9\xfb\xfc\u2020\xb0\xa2\xa3\xa7\u2022\xb6\xdf\xae\xa9\u2122\xb4\xa8\u2260\xc6\xd8\u221e\xb1\u2264\u2265\xa5\xb5\u2202\u2211\u220f\u03c0\u222b\xaa\xba\u2126\xe6\xf8\xbf\xa1\xac\u221a\u0192\u2248\u2206\xab\xbb\u2026\xa0\xc0\xc3\xd5\u0152\u0153\u2013\u2014\u201c\u201d\u2018\u2019\xf7\u25ca\xff\u0178\u2044\xa4\u2039\u203a\ufb01\ufb02\u2021\xb7\u201a\u201e\u2030\xc2\xca\xc1\xcb\xc8\xcd\xce\xcf\xcc\xd3\xd4\ufffd\xd2\xda\xdb\xd9\u0131\u02c6\u02dc\xaf\u02d8\u02d9\u02da\xb8\u02dd\u02db\u02c7",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[10006]=function(){for(var e="\0\x01\x02\x03\x04\x05\x06\x07\b\t\n\v\f\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\xc4\xb9\xb2\xc9\xb3\xd6\xdc\u0385\xe0\xe2\xe4\u0384\xa8\xe7\xe9\xe8\xea\xeb\xa3\u2122\xee\xef\u2022\xbd\u2030\xf4\xf6\xa6\xad\xf9\xfb\xfc\u2020\u0393\u0394\u0398\u039b\u039e\u03a0\xdf\xae\xa9\u03a3\u03aa\xa7\u2260\xb0\u0387\u0391\xb1\u2264\u2265\xa5\u0392\u0395\u0396\u0397\u0399\u039a\u039c\u03a6\u03ab\u03a8\u03a9\u03ac\u039d\xac\u039f\u03a1\u2248\u03a4\xab\xbb\u2026\xa0\u03a5\u03a7\u0386\u0388\u0153\u2013\u2015\u201c\u201d\u2018\u2019\xf7\u0389\u038a\u038c\u038e\u03ad\u03ae\u03af\u03cc\u038f\u03cd\u03b1\u03b2\u03c8\u03b4\u03b5\u03c6\u03b3\u03b7\u03b9\u03be\u03ba\u03bb\u03bc\u03bd\u03bf\u03c0\u03ce\u03c1\u03c3\u03c4\u03b8\u03c9\u03c2\u03c7\u03c5\u03b6\u03ca\u03cb\u0390\u03b0\ufffd",t=[],n={},r=0;r!=e.length;++r)65533!==e.charCodeAt(r)&&(n[e.charAt(r)]=r),t[r]=e.charAt(r);return{enc:n,dec:t}}(),r[10007]=function(){for(var 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