Full Code of RomeroBarata/skeleton_based_anomaly_detection for AI

Repository: RomeroBarata/skeleton_based_anomaly_detection
Branch: master
Commit: 37c3203866d1
Files: 110
Total size: 626.7 KB

Directory structure:
gitextract_xhfl4dwo/

├── .gitignore
├── README.md
├── environment.yml
├── evaluate.py
├── pretrained/
│   └── CVPR19/
│       ├── Avenue/
│       │   └── combined_model/
│       │       └── _mp_Grobust_Lrobust_Orobust_concatdown_/
│       │           └── 01_2018_11_13_06_36_20/
│       │               ├── architecture.txt
│       │               ├── global_scaler.pkl
│       │               ├── local_scaler.pkl
│       │               ├── out_scaler.pkl
│       │               ├── training_report.csv
│       │               └── weights_082_0.18.h5
│       └── ShanghaiTech/
│           └── combined_model/
│               └── _mp_Grobust_Lrobust_Orobust_concatdown_/
│                   ├── 01_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 02_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 03_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 04_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 05_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 06_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 07_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 08_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 09_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 10_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   ├── 11_2018_11_09_10_55_13/
│                   │   ├── architecture.txt
│                   │   ├── global_scaler.pkl
│                   │   ├── local_scaler.pkl
│                   │   ├── out_scaler.pkl
│                   │   ├── training_report.csv
│                   │   └── weights_065_0.14.h5
│                   └── 12_2018_11_09_10_55_13/
│                       ├── architecture.txt
│                       ├── global_scaler.pkl
│                       ├── local_scaler.pkl
│                       ├── out_scaler.pkl
│                       ├── training_report.csv
│                       └── weights_065_0.14.h5
├── tbad/
│   ├── __init__.py
│   ├── autoencoder/
│   │   ├── __init__.py
│   │   ├── autoencoder.py
│   │   ├── data.py
│   │   ├── evaluate.py
│   │   └── train.py
│   ├── combined_model/
│   │   ├── __init__.py
│   │   ├── data.py
│   │   ├── evaluate.py
│   │   ├── fusion.py
│   │   ├── message_passing.py
│   │   └── train.py
│   ├── data.py
│   ├── eval.py
│   ├── gpu.py
│   ├── losses.py
│   ├── rnn_autoencoder/
│   │   ├── __init__.py
│   │   ├── data.py
│   │   ├── evaluate.py
│   │   ├── rnn.py
│   │   └── train.py
│   ├── train.py
│   ├── utils.py
│   └── visualisation.py
├── train.py
├── utils/
│   ├── metrics.py
│   └── score_scaling.py
└── visualise.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
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# C extensions
*.so

# Distribution / packaging
.Python
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*.egg
MANIFEST

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# Unit test / coverage reports
htmlcov/
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nosetests.xml
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*.cover
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# Translations
*.mo
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# Django stuff:
*.log
local_settings.py
db.sqlite3

# Flask stuff:
instance/
.webassets-cache

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.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
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# pyenv
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================================================
FILE: README.md
================================================
This is the code for the CVPR'19 paper ["Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos".](https://openaccess.thecvf.com/content_CVPR_2019/html/Morais_Learning_Regularity_in_Skeleton_Trajectories_for_Anomaly_Detection_in_Videos_CVPR_2019_paper.html)

# Environment Setup
First please create an appropriate environment using conda: 

> conda env create -f environment.yml

> conda activate tbad

# Download Data
Due to space constraints in the Github repository, please download the data from the following link and place the `data` folder in this directory.

Link: [trajectories](https://bit.ly/2TWCxFY)

# Test Pre-Trained Models
To evaluate pre-trained models run the evaluate.py script.
Some examples:

#### Evaluate MPED-RNN on all cameras individually and all cameras combined on HR-ShanghaiTech.
> python evaluate.py --gpu_ids 0 --gpu_memory 0.2 combined_models ./pretrained/CVPR19/ShanghaiTech/combined_model/\_mp_Grobust_Lrobust_Orobust_concatdown\_
 ./data/HR-ShanghaiTech/testing/trajectories ./data/HR-ShanghaiTech/testing/frame_level_masks --video_resolution 856x480 --overlapping_trajectories

#### Evaluate MPED-RNN on HR-Avenue.
> python evaluate.py --gpu_ids 0 --gpu_memory 0.2 combined_model ./pretrained/CVPR19/Avenue/combined_model/\_mp_Grobust_Lrobust_Orobust_concatdown\_/01_2018_11_13_06_36_20 
./data/HR-Avenue/testing/trajectories/01 ./data/HR-Avenue/testing/frame_level_masks/01 --video_resolution 640x360 --overlapping_trajectories

# Train Models from Scratch
To train a model from scratch you should look up the model's configuration options using the option --help on the training.py script. Here is one example:

#### Train MPED-RNN on the ShanghaiTech data set.
> python train.py --gpu_ids 0 --gpu_memory 0.1 combined_model ./data/HR-ShanghaiTech/training/trajectories/00 
--video_resolution 856x480 --message_passing --reconstruct_original_data --multiple_outputs --multiple_outputs_before_concatenation --input_length 12
 --rec_length 12 --pred_length 6 --reconstruct_reverse --cell gru --global_hidden 8 --local_hidden 16 --output_activation linear 
 --optimiser adam --learning_rate 0.001 --loss mse --epochs 5 --batch_size 256 --global_normalisation robust --local_normalisation robust 
 --out_normalisation robust

================================================
FILE: environment.yml
================================================
name: tbad
channels:
  - defaults
dependencies:
  - _tflow_180_select=1.0=gpu
  - absl-py=0.2.2=py36_0
  - astor=0.6.2=py36_0
  - blas=1.0=mkl
  - bleach=1.5.0=py36_0
  - bzip2=1.0.6=h14c3975_5
  - ca-certificates=2018.03.07=0
  - cairo=1.14.12=h7636065_2
  - certifi=2018.10.15=py36_0
  - cloudpickle=0.6.1=py36_0
  - cudatoolkit=9.0=h13b8566_0
  - cudnn=7.1.2=cuda9.0_0
  - cupti=9.0.176=0
  - cycler=0.10.0=py36h93f1223_0
  - dask-core=0.20.1=py36_0
  - dbus=1.13.2=h714fa37_1
  - decorator=4.3.0=py36_0
  - expat=2.2.5=he0dffb1_0
  - ffmpeg=4.0=h04d0a96_0
  - fontconfig=2.12.6=h49f89f6_0
  - freetype=2.8=hab7d2ae_1
  - gast=0.2.0=py36_0
  - glib=2.56.1=h000015b_0
  - graphite2=1.3.11=h16798f4_2
  - grpcio=1.12.0=py36hdbcaa40_0
  - gst-plugins-base=1.14.0=hbbd80ab_1
  - gstreamer=1.14.0=hb453b48_1
  - h5py=2.8.0=py36ha1f6525_0
  - harfbuzz=1.7.6=h5f0a787_1
  - hdf5=1.10.2=hba1933b_1
  - html5lib=0.9999999=py36_0
  - icu=58.2=h9c2bf20_1
  - imageio=2.4.1=py36_0
  - intel-openmp=2018.0.3=0
  - jasper=1.900.1=hd497a04_4
  - jpeg=9b=h024ee3a_2
  - keras=2.1.6=py36_0
  - kiwisolver=1.0.1=py36h764f252_0
  - libedit=3.1.20170329=h6b74fdf_2
  - libffi=3.2.1=hd88cf55_4
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  - libgfortran-ng=7.2.0=hdf63c60_3
  - libopenblas=0.2.20=h9ac9557_7
  - libopencv=3.4.1=h1a3b859_1
  - libopus=1.2.1=hb9ed12e_0
  - libpng=1.6.34=hb9fc6fc_0
  - libprotobuf=3.5.2=h6f1eeef_0
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  - libtiff=4.0.9=he85c1e1_1
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  - libxcb=1.13=h1bed415_1
  - libxml2=2.9.8=h26e45fe_1
  - markdown=2.6.11=py36_0
  - matplotlib=2.2.2=py36h0e671d2_1
  - mkl=2018.0.3=1
  - mkl_fft=1.0.6=py36h7dd41cf_0
  - mkl_random=1.0.1=py36h4414c95_1
  - ncurses=6.1=hf484d3e_0
  - networkx=2.2=py36_1
  - numpy=1.15.3=py36h1d66e8a_0
  - numpy-base=1.15.3=py36h81de0dd_0
  - olefile=0.46=py36_0
  - opencv=3.4.1=py36h6fd60c2_2
  - openssl=1.0.2p=h14c3975_0
  - pcre=8.42=h439df22_0
  - pillow=5.1.0=py36h3deb7b8_0
  - pip=10.0.1=py36_0
  - pixman=0.34.0=hceecf20_3
  - protobuf=3.5.2=py36hf484d3e_0
  - py-opencv=3.4.1=py36h0676e08_1
  - pyparsing=2.2.0=py36hee85983_1
  - pyqt=5.9.2=py36h751905a_0
  - python=3.6.5=hc3d631a_2
  - python-dateutil=2.7.3=py36_0
  - pytz=2018.4=py36_0
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  - tensorflow-base=1.8.0=py36hc1a7637_0
  - tensorflow-gpu=1.8.0=h7b35bdc_0
  - termcolor=1.1.0=py36_1
  - tk=8.6.7=hc745277_3
  - toolz=0.9.0=py36_0
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  - werkzeug=0.14.1=py36_0
  - wheel=0.31.1=py36_0
  - xz=5.2.4=h14c3975_4
  - yaml=0.1.7=had09818_2
  - zlib=1.2.11=ha838bed_2
  - pip:
    - chardet==3.0.4
    - dask==0.20.1
    - easydict==1.7
    - idna==2.7
    - msgpack==0.5.6
    - nibabel==2.3.0
    - pandas==0.23.4
    - pyzmq==17.1.2
    - requests==2.19.1
    - tqdm==4.23.4
    - urllib3==1.23
    - visdom==0.1.8.5
    - websocket-client==0.52.0



================================================
FILE: evaluate.py
================================================
import argparse

from tbad.autoencoder.evaluate import eval_ae, eval_aes
from tbad.rnn_autoencoder.evaluate import eval_rnn_ae, eval_rnn_aes
from tbad.combined_model.evaluate import eval_combined_model, eval_combined_models
from tbad.gpu import configure_gpu_resources


def create_arg_parser():
    parser = argparse.ArgumentParser(description='Functions for Evaluation of Trained Trajectory-Based Anomaly Models.')

    gp_gpu = parser.add_argument_group('GPU')
    gp_gpu.add_argument('--gpu_ids', default='0', type=str, help='Which GPUs to use.')
    gp_gpu.add_argument('--gpu_memory_fraction', default=0.20, type=float,
                        help='Fraction of the memory to grab from each GPU.')

    subparsers = parser.add_subparsers(title='sub-commands', description='Valid sub-commands.')

    # Create sub-parser for evaluation of a pre-trained Autoencoder model
    parser_ae = subparsers.add_parser('autoencoder',
                                      help='Evaluate a Trained Autoencoder Model.')
    parser_ae.add_argument('pretrained_model', type=str,
                           help='Directory containing pre-trained model architecture definition, model weights and '
                                'data scaler.')
    parser_ae.add_argument('trajectories', type=str,
                           help='Directory containing skeleton\'s trajectories.')
    parser_ae.add_argument('frame_level_anomaly_masks', type=str,
                           help='Directory containing .npy files for each video in the specific camera.')
    parser_ae.add_argument('--video_resolution', default='856x480', type=str,
                           help='Resolution of the trajectories\' original video(s). It should be specified '
                                'as WxH, where W is the width and H the height of the video.')

    parser_ae.set_defaults(func=eval_ae)

    # Create sub-parser for evaluation of multiple pre-trained Autoencoder models
    parser_aes = subparsers.add_parser('autoencoders',
                                       help='Evaluate Several Trained Autoencoder Models.')
    parser_aes.add_argument('pretrained_models', type=str,
                            help='Directory containing a folder for each pre-trained model.')
    parser_aes.add_argument('all_trajectories', type=str,
                            help='Directory containing a folder for each camera, where each folder contains the '
                                 'trajectories for the associated camera.')
    parser_aes.add_argument('all_frame_level_anomaly_masks', type=str,
                            help='Directory containing a folder for the anomaly masks of each camera.')
    parser_aes.add_argument('--video_resolution', default='856x480', type=str,
                            help='Resolution of the trajectories\' original video(s). It should be specified '
                                 'as WxH, where W is the width and H the height of the video.')

    parser_aes.set_defaults(func=eval_aes)

    # Create sub-parser for evaluation of a pre-trained RNN Autoencoder model
    parser_rnn_ae = subparsers.add_parser('rnn_autoencoder', help='Evaluate a Trained RNN Autoencoder model.')
    parser_rnn_ae.add_argument('pretrained_model', type=str,
                               help='Directory containing pre-trained model architecture definition, model weights and '
                                    'data scaler.')
    parser_rnn_ae.add_argument('trajectories', type=str,
                               help='Directory containing skeleton\'s trajectories.')
    parser_rnn_ae.add_argument('frame_level_anomaly_masks', type=str,
                               help='Directory containing .npy files for each video in the specific camera.')
    parser_rnn_ae.add_argument('--video_resolution', default='856x480', type=str,
                               help='Resolution of the trajectories\' original video(s). It should be specified '
                                    'as WxH, where W is the width and H the height of the video.')
    parser_rnn_ae.add_argument('--overlapping_trajectories', action='store_true')

    gp_rnn_ae_logging = parser_rnn_ae.add_argument_group('Evaluation Logging')
    gp_rnn_ae_logging.add_argument('--write_reconstructions', type=str,
                                   help='TO DO')

    parser_rnn_ae.set_defaults(func=eval_rnn_ae)

    # Create sub-parser for evaluation of multiple pre-trained RNN Autoencoder models
    parser_rnn_aes = subparsers.add_parser('rnn_autoencoders',
                                           help='Evaluate Multiple Pre-Trained RNN Autoencoder models.')
    parser_rnn_aes.add_argument('pretrained_models', type=str,
                                help='Directory containing a folder for each pre-trained model.')
    parser_rnn_aes.add_argument('all_trajectories', type=str,
                                help='Directory containing a folder for each camera, where each folder contains the '
                                     'trajectories for the associated camera.')
    parser_rnn_aes.add_argument('all_frame_level_anomaly_masks', type=str,
                                help='Directory containing a folder for the anomaly masks of each camera.')
    parser_rnn_aes.add_argument('--video_resolution', default='856x480', type=str,
                                help='Resolution of the trajectories\' original video(s). It should be specified '
                                     'as WxH, where W is the width and H the height of the video.')
    parser_rnn_aes.add_argument('--overlapping_trajectories', action='store_true')

    gp_rnn_aes_logging = parser_rnn_aes.add_argument_group('Evaluation Logging')
    gp_rnn_aes_logging.add_argument('--write_reconstructions', type=str,
                                    help='TO DO')

    parser_rnn_aes.set_defaults(func=eval_rnn_aes)

    # Create a sub-parser for evaluation of a trained Combined model
    parser_combined_model = subparsers.add_parser('combined_model',
                                                  help='Evaluate a Trained Combined Model.')
    parser_combined_model.add_argument('pretrained_model', type=str,
                                       help='Directory containing pre-trained model architecture definition, model '
                                            'weights and data scaler.')
    parser_combined_model.add_argument('trajectories', type=str,
                                       help='Directory containing skeleton\'s trajectories.')
    parser_combined_model.add_argument('frame_level_anomaly_masks', type=str,
                                       help='Directory containing .npy files for each video in the specific camera.')
    parser_combined_model.add_argument('--video_resolution', default='856x480', type=str,
                                       help='Resolution of the trajectories\' original video(s). It should be '
                                            'specified as WxH, where W is the width and H the height of the video.')
    parser_combined_model.add_argument('--overlapping_trajectories', action='store_true')

    gp_combined_model_logging = parser_combined_model.add_argument_group('Evaluation Logging')
    gp_combined_model_logging.add_argument('--write_reconstructions', action='store_true')
    gp_combined_model_logging.add_argument('--write_bounding_boxes', action='store_true')
    gp_combined_model_logging.add_argument('--write_predictions', action='store_true')
    gp_combined_model_logging.add_argument('--write_predictions_bounding_boxes', action='store_true')
    gp_combined_model_logging.add_argument('--write_anomaly_masks', action='store_true')
    gp_combined_model_logging.add_argument('--write_mistakes', action='store_true')

    parser_combined_model.set_defaults(func=eval_combined_model)

    # Create a sub-parser for evaluation of multiple pre-trained Combined models
    parser_combined_models = subparsers.add_parser('combined_models',
                                                   help='Evaluate Multiple Pre-Trained Combined Models.')
    parser_combined_models.add_argument('pretrained_models', type=str,
                                        help='Directory containing a folder for each pre-trained model.')
    parser_combined_models.add_argument('all_trajectories', type=str,
                                        help='Directory containing a folder for each camera, where each folder '
                                             'contains the trajectories for the associated camera.')
    parser_combined_models.add_argument('all_frame_level_anomaly_masks', type=str,
                                        help='Directory containing a folder for the anomaly masks of each camera.')
    parser_combined_models.add_argument('--video_resolution', default='856x480', type=str,
                                        help='Resolution of the trajectories\' original video(s). It should be '
                                             'specified as WxH, where W is the width and H the height of the video.')
    parser_combined_models.add_argument('--overlapping_trajectories', action='store_true')

    gp_combined_models_logging = parser_combined_models.add_argument_group('Evaluation Logging')
    gp_combined_models_logging.add_argument('--write_reconstructions', action='store_true')
    gp_combined_models_logging.add_argument('--write_bounding_boxes', action='store_true')
    gp_combined_models_logging.add_argument('--write_predictions', action='store_true')
    gp_combined_models_logging.add_argument('--write_predictions_bounding_boxes', action='store_true')
    gp_combined_models_logging.add_argument('--write_anomaly_masks', action='store_true')
    gp_combined_models_logging.add_argument('--write_mistakes', action='store_true')

    parser_combined_models.set_defaults(func=eval_combined_models)

    return parser


def main():
    arg_parser = create_arg_parser()
    args = arg_parser.parse_args()

    configure_gpu_resources(args.gpu_ids, args.gpu_memory_fraction)
    args.func(args)


if __name__ == '__main__':
    main()


================================================
FILE: pretrained/CVPR19/Avenue/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/01_2018_11_13_06_36_20/architecture.txt
================================================
input_length 8
global_input_dim 4
local_input_dim 34
reconstruction_length 8
prediction_length 4
global_hidden_dims 4
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/Avenue/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/01_2018_11_13_06_36_20/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_17_loss,lambda_18_loss,lambda_19_loss,lambda_20_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_17_loss,val_lambda_18_loss,val_lambda_19_loss,val_lambda_20_loss,val_loss
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================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/01_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/01_2018_11_09_10_55_13/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_25_loss,lambda_26_loss,lambda_27_loss,lambda_28_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_25_loss,val_lambda_26_loss,val_lambda_27_loss,val_lambda_28_loss,val_loss
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FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/02_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/02_2018_11_09_10_55_13/training_report.csv
================================================
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================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/03_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/03_2018_11_09_10_55_13/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_25_loss,lambda_26_loss,lambda_27_loss,lambda_28_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_25_loss,val_lambda_26_loss,val_lambda_27_loss,val_lambda_28_loss,val_loss
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================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/04_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/04_2018_11_09_10_55_13/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_25_loss,lambda_26_loss,lambda_27_loss,lambda_28_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_25_loss,val_lambda_26_loss,val_lambda_27_loss,val_lambda_28_loss,val_loss
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FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/05_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/05_2018_11_09_10_55_13/training_report.csv
================================================
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================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/06_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/06_2018_11_09_10_55_13/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_25_loss,lambda_26_loss,lambda_27_loss,lambda_28_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_25_loss,val_lambda_26_loss,val_lambda_27_loss,val_lambda_28_loss,val_loss
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================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/07_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/07_2018_11_09_10_55_13/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_25_loss,lambda_26_loss,lambda_27_loss,lambda_28_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_25_loss,val_lambda_26_loss,val_lambda_27_loss,val_lambda_28_loss,val_loss
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FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/08_2018_11_09_10_55_13/architecture.txt
================================================
input_length 12
global_input_dim 4
local_input_dim 34
reconstruction_length 12
prediction_length 6
global_hidden_dims 8
local_hidden_dims 16
extra_hidden_dims
output_activation linear
reconstruct_reverse True
reconstruct_original_data True
multiple_outputs True
multiple_outputs_before_concatenation True
cell_type gru
optimiser adam
learning_rate 0.001
loss mse
l1_reg 0.0
l2_reg 0.0


================================================
FILE: pretrained/CVPR19/ShanghaiTech/combined_model/_mp_Grobust_Lrobust_Orobust_concatdown_/08_2018_11_09_10_55_13/training_report.csv
================================================
epoch,dense_14_loss,dense_9_loss,lambda_25_loss,lambda_26_loss,lambda_27_loss,lambda_28_loss,loss,val_dense_14_loss,val_dense_9_loss,val_lambda_25_loss,val_lambda_26_loss,val_lambda_27_loss,val_lambda_28_loss,val_loss
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