Repository: Blade6570/icface Branch: master Commit: 3b9fad8560dd Files: 66 Total size: 7.4 MB Directory structure: gitextract_iiz0daxt/ ├── License ├── README.md ├── test_code_released_new/ │ ├── 00116.csv │ ├── 00296.csv │ ├── checkpoints/ │ │ └── gpubatch_resnet/ │ │ └── opt.txt │ ├── data/ │ │ ├── __init__.py │ │ ├── aligned_dataset.py │ │ ├── aligned_dataset.py~ │ │ ├── base_data_loader.py │ │ ├── base_dataset.py │ │ ├── custom_dataset_data_loader.py │ │ ├── data_loader.py │ │ ├── image_folder.py │ │ └── image_folder.py~ │ ├── image_crop.py │ ├── models/ │ │ ├── __init__.py │ │ ├── base_model.py │ │ ├── models.py │ │ ├── networks.py │ │ ├── networks.py~ │ │ ├── pix2pix_model.py │ │ ├── pix2pix_model.py~ │ │ └── test_model.py │ ├── options/ │ │ ├── __init__.py │ │ ├── base_options.py │ │ ├── test_options.py │ │ └── train_options.py │ ├── test.py │ └── util/ │ ├── __init__.py │ ├── get_data.py │ ├── html.py │ ├── image_pool.py │ ├── util.py │ └── visualizer.py └── test_code_released_old/ ├── 00116.csv ├── 00296.csv ├── checkpoints/ │ └── gpubatch_resnet/ │ └── opt.txt ├── data/ │ ├── __init__.py │ ├── aligned_dataset.py │ ├── aligned_dataset.py~ │ ├── base_data_loader.py │ ├── base_dataset.py │ ├── custom_dataset_data_loader.py │ ├── data_loader.py │ ├── image_folder.py │ └── image_folder.py~ ├── image_crop.py ├── models/ │ ├── __init__.py │ ├── base_model.py │ ├── models.py │ ├── networks.py │ ├── networks.py~ │ ├── pix2pix_model.py │ ├── pix2pix_model.py~ │ └── test_model.py ├── options/ │ ├── __init__.py │ ├── base_options.py │ ├── test_options.py │ └── train_options.py ├── test.py └── util/ ├── __init__.py ├── get_data.py ├── html.py ├── image_pool.py ├── util.py └── visualizer.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: License ================================================ Copyright (C) 2018. Soumya Tripathy, JUho Kannala, Esa Rahtu. All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for any non-commercial purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. --------------------------------------LICENSE FOR pytorch-CycleGAN-and-pix2pix------------------------------------------------ Copyright (c) 2017, Jun-Yan Zhu and Taesung Park All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. BSD License For pix2pix software Copyright (c) 2016, Phillip Isola and Jun-Yan Zhu All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. ================================================ FILE: README.md ================================================ ## ICface: Interpretable and Controllable Face Reenactment Using GANs ## **Accepted WACV-2020** [**Project**](https://tutvision.github.io/icface/) | [**Youtube**](https://www.youtube.com/watch?v=VhWrAjI6z0M) | [**Arxiv**](https://arxiv.org/abs/1904.01909) *** ![](https://github.com/Blade6570/icface/blob/master/images/tease.png?raw=true "teaser image") **Single Image Animation** Source Image-1 | Animation-1|Source Image-2 | Animation-2 :---------:|:----------:|:---------:|:----------: ![](https://github.com/Blade6570/icface/blob/master/test_code_released_new/new_crop/7.jpg?raw=true "teaser gif1") |![](https://github.com/Blade6570/icface/blob/master/images/kim.gif?raw=true "teaser gif1") | ![](https://github.com/Blade6570/icface/blob/master/test_code_released_new/new_crop/8.png?raw=true "teaser gif1") |![](https://github.com/Blade6570/icface/blob/master/images/trump.gif?raw=true "teaser gif1") This is the part of the implementation of "ICface: Interpretable and Controllable Face Reenactment Using GANs" (https://arxiv.org/abs/1904.01909). ~~_The test code is released now!_~~ :white_check_mark: **_test_code is updated_** `**Note**: The implementation and results have improved with the current modifications. The codes are updated in /test_code_released_new/. The checkpoints are also updated and added. Feel free to raise an issue if you face any difficulties in running the updated files.` **Prerequisites** 1. Python 3.5.4 2. Pytorch 0.4.1 3. Visdom and dominate 4. Natsort *The code is tested on Ubuntu 16.04 LTS* **Download the Pretrained Weights of ICface** [Google Drive Link](https://drive.google.com/drive/folders/1jvD8R-Ggo3Seg2tF-JsqlOjwropVwN1S?usp=sharing). **Testing ICface** 1. Clone the ICface repository and change the working directory to *'/test_code_released_new'* 2. Keep the pretrained weights inside the path: *./checkpoints/gpubatch_resnet*. 3. For the driving video, you can select any video file from *voxceleb* dataset, extract the action units in a .csv file using Openface and store the .csv file in the working folder. We have provided two such .csv files and thier corresponding driving videos. 4. For the source image, we have selected images from *voxceleb* test set. Three exampes are given in the folder *./new_crop*. More can be obtained from [here](http://www.robots.ox.ac.uk/~vgg/research/CMBiometrics/). In particular the "Cropped Face Images extracted at 1fps" (7.8Gb). The test identities can be downloaded here under the data section. 5. Run in terminal : *python test.py --dataroot ./ --model pix2pix --which_model_netG resnet_6blocks --which_direction AtoB --dataset_mode aligned --norm batch --display_id 0 --batchSize 1 --loadSize 128 --fineSize 128 --no_flip --name gpubatch_resnet --how_many 1 --ndf 256 --ngf 128 --which_ref ./new_crop/1.png --gpu_ids 1 --csv_path 00116.csv --results_dir results_video* 6. The resuting video will be found in *'/test_code_released_new'* under the name *'movie.mp4'* **If you are not using *voxceleb* test set** 1. In the python file *'image_crop.py'*, add your image path and run it. 2. It will create a new cropped version of your image and will store in *'./new_crop'* folder. Then follow the above steps to create youe video file. -**_If you are using this implementation for your research work then please cite us as:_** ``` #Citation @article{tripathy+kannala+rahtu, title={ICface: Interpretable and Controllable Face Reenactment Using GANs}, author={Tripathy, Soumya and Kannala, Juho and Rahtu, Esa}, journal={arXiv preprint arXiv:1904.01909}, year={2019} } ``` *NOTE:* Code framework is based on [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) ================================================ FILE: test_code_released_new/00116.csv ================================================ frame, face_id, timestamp, confidence, success, gaze_0_x, gaze_0_y, gaze_0_z, gaze_1_x, gaze_1_y, gaze_1_z, gaze_angle_x, gaze_angle_y, eye_lmk_x_0, eye_lmk_x_1, eye_lmk_x_2, eye_lmk_x_3, eye_lmk_x_4, eye_lmk_x_5, eye_lmk_x_6, eye_lmk_x_7, eye_lmk_x_8, eye_lmk_x_9, eye_lmk_x_10, eye_lmk_x_11, eye_lmk_x_12, eye_lmk_x_13, eye_lmk_x_14, eye_lmk_x_15, eye_lmk_x_16, 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214.8, 216.7, 227.4, 240.8, 218.6, 216.5, 217.0, 235.1, 217.7, 217.3, 219.7, 0.851, -0.009, 0.095, -0.042, 107.701, 86.780, -12.542, -5.013, 10.053, 0.608, -9.541, -9.601, -13.475, -0.970, -3.552, 2.889, -4.628, 3.194, 1.822, -2.684, -3.642, 0.825, -3.885, -5.926, -2.896, 0.603, 0.254, 1.668, -1.780, 1.310, -0.334, -1.442, 0.947, 0.106, -0.202, -0.098, 0.052, 0.042, -0.083, 0.067, 0.05, 0.00, 0.00, 0.00, 0.71, 0.24, 0.42, 0.00, 1.41, 0.04, 0.27, 0.09, 0.18, 0.00, 1.97, 0.00, 0.00, 0.00, 1.00, 0.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 1.00 ================================================ FILE: test_code_released_new/00296.csv ================================================ frame, face_id, timestamp, confidence, success, gaze_0_x, gaze_0_y, gaze_0_z, gaze_1_x, gaze_1_y, gaze_1_z, gaze_angle_x, gaze_angle_y, eye_lmk_x_0, eye_lmk_x_1, eye_lmk_x_2, eye_lmk_x_3, eye_lmk_x_4, eye_lmk_x_5, eye_lmk_x_6, eye_lmk_x_7, eye_lmk_x_8, eye_lmk_x_9, eye_lmk_x_10, eye_lmk_x_11, eye_lmk_x_12, eye_lmk_x_13, eye_lmk_x_14, eye_lmk_x_15, eye_lmk_x_16, eye_lmk_x_17, eye_lmk_x_18, eye_lmk_x_19, eye_lmk_x_20, eye_lmk_x_21, eye_lmk_x_22, eye_lmk_x_23, eye_lmk_x_24, eye_lmk_x_25, eye_lmk_x_26, eye_lmk_x_27, eye_lmk_x_28, eye_lmk_x_29, eye_lmk_x_30, eye_lmk_x_31, eye_lmk_x_32, eye_lmk_x_33, eye_lmk_x_34, eye_lmk_x_35, eye_lmk_x_36, eye_lmk_x_37, eye_lmk_x_38, eye_lmk_x_39, eye_lmk_x_40, eye_lmk_x_41, eye_lmk_x_42, eye_lmk_x_43, eye_lmk_x_44, eye_lmk_x_45, eye_lmk_x_46, eye_lmk_x_47, eye_lmk_x_48, eye_lmk_x_49, eye_lmk_x_50, eye_lmk_x_51, eye_lmk_x_52, eye_lmk_x_53, eye_lmk_x_54, eye_lmk_x_55, eye_lmk_y_0, eye_lmk_y_1, eye_lmk_y_2, eye_lmk_y_3, eye_lmk_y_4, eye_lmk_y_5, eye_lmk_y_6, eye_lmk_y_7, eye_lmk_y_8, eye_lmk_y_9, eye_lmk_y_10, eye_lmk_y_11, eye_lmk_y_12, eye_lmk_y_13, eye_lmk_y_14, eye_lmk_y_15, eye_lmk_y_16, eye_lmk_y_17, eye_lmk_y_18, eye_lmk_y_19, eye_lmk_y_20, eye_lmk_y_21, eye_lmk_y_22, eye_lmk_y_23, eye_lmk_y_24, eye_lmk_y_25, eye_lmk_y_26, eye_lmk_y_27, eye_lmk_y_28, eye_lmk_y_29, eye_lmk_y_30, eye_lmk_y_31, eye_lmk_y_32, eye_lmk_y_33, eye_lmk_y_34, eye_lmk_y_35, eye_lmk_y_36, eye_lmk_y_37, eye_lmk_y_38, eye_lmk_y_39, eye_lmk_y_40, eye_lmk_y_41, eye_lmk_y_42, eye_lmk_y_43, eye_lmk_y_44, eye_lmk_y_45, eye_lmk_y_46, eye_lmk_y_47, eye_lmk_y_48, eye_lmk_y_49, eye_lmk_y_50, eye_lmk_y_51, eye_lmk_y_52, eye_lmk_y_53, eye_lmk_y_54, eye_lmk_y_55, eye_lmk_X_0, eye_lmk_X_1, eye_lmk_X_2, eye_lmk_X_3, eye_lmk_X_4, eye_lmk_X_5, eye_lmk_X_6, eye_lmk_X_7, eye_lmk_X_8, eye_lmk_X_9, eye_lmk_X_10, eye_lmk_X_11, eye_lmk_X_12, eye_lmk_X_13, eye_lmk_X_14, eye_lmk_X_15, eye_lmk_X_16, eye_lmk_X_17, eye_lmk_X_18, eye_lmk_X_19, eye_lmk_X_20, eye_lmk_X_21, eye_lmk_X_22, eye_lmk_X_23, eye_lmk_X_24, eye_lmk_X_25, eye_lmk_X_26, eye_lmk_X_27, eye_lmk_X_28, eye_lmk_X_29, eye_lmk_X_30, eye_lmk_X_31, eye_lmk_X_32, eye_lmk_X_33, eye_lmk_X_34, eye_lmk_X_35, eye_lmk_X_36, eye_lmk_X_37, eye_lmk_X_38, eye_lmk_X_39, eye_lmk_X_40, eye_lmk_X_41, 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eye_lmk_Z_2, eye_lmk_Z_3, eye_lmk_Z_4, eye_lmk_Z_5, eye_lmk_Z_6, eye_lmk_Z_7, eye_lmk_Z_8, eye_lmk_Z_9, eye_lmk_Z_10, eye_lmk_Z_11, eye_lmk_Z_12, eye_lmk_Z_13, eye_lmk_Z_14, eye_lmk_Z_15, eye_lmk_Z_16, eye_lmk_Z_17, eye_lmk_Z_18, eye_lmk_Z_19, eye_lmk_Z_20, eye_lmk_Z_21, eye_lmk_Z_22, eye_lmk_Z_23, eye_lmk_Z_24, eye_lmk_Z_25, eye_lmk_Z_26, eye_lmk_Z_27, eye_lmk_Z_28, eye_lmk_Z_29, eye_lmk_Z_30, eye_lmk_Z_31, eye_lmk_Z_32, eye_lmk_Z_33, eye_lmk_Z_34, eye_lmk_Z_35, eye_lmk_Z_36, eye_lmk_Z_37, eye_lmk_Z_38, eye_lmk_Z_39, eye_lmk_Z_40, eye_lmk_Z_41, eye_lmk_Z_42, eye_lmk_Z_43, eye_lmk_Z_44, eye_lmk_Z_45, eye_lmk_Z_46, eye_lmk_Z_47, eye_lmk_Z_48, eye_lmk_Z_49, eye_lmk_Z_50, eye_lmk_Z_51, eye_lmk_Z_52, eye_lmk_Z_53, eye_lmk_Z_54, eye_lmk_Z_55, pose_Tx, pose_Ty, pose_Tz, pose_Rx, pose_Ry, pose_Rz, x_0, x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8, x_9, x_10, x_11, x_12, x_13, x_14, x_15, x_16, x_17, x_18, x_19, x_20, x_21, x_22, x_23, x_24, x_25, x_26, x_27, x_28, x_29, x_30, x_31, x_32, x_33, x_34, x_35, x_36, x_37, x_38, x_39, x_40, x_41, x_42, x_43, x_44, x_45, x_46, x_47, x_48, x_49, x_50, x_51, x_52, x_53, x_54, x_55, x_56, x_57, x_58, x_59, x_60, x_61, x_62, x_63, x_64, x_65, x_66, x_67, y_0, y_1, y_2, y_3, y_4, y_5, y_6, y_7, y_8, y_9, y_10, y_11, y_12, y_13, y_14, y_15, y_16, y_17, y_18, y_19, y_20, y_21, y_22, y_23, y_24, y_25, y_26, y_27, y_28, y_29, y_30, y_31, y_32, y_33, y_34, y_35, y_36, y_37, y_38, y_39, y_40, y_41, y_42, y_43, y_44, y_45, y_46, y_47, y_48, y_49, y_50, y_51, y_52, y_53, y_54, y_55, y_56, y_57, y_58, y_59, y_60, y_61, y_62, y_63, y_64, y_65, y_66, y_67, X_0, X_1, X_2, X_3, X_4, X_5, X_6, X_7, X_8, X_9, X_10, X_11, X_12, X_13, X_14, X_15, X_16, X_17, X_18, X_19, X_20, X_21, X_22, X_23, X_24, X_25, X_26, X_27, X_28, X_29, X_30, X_31, X_32, X_33, X_34, X_35, X_36, X_37, X_38, X_39, X_40, X_41, X_42, X_43, X_44, X_45, X_46, X_47, X_48, X_49, X_50, X_51, X_52, X_53, X_54, X_55, X_56, X_57, X_58, X_59, X_60, X_61, X_62, X_63, X_64, X_65, X_66, X_67, Y_0, Y_1, Y_2, Y_3, Y_4, Y_5, Y_6, Y_7, Y_8, Y_9, Y_10, Y_11, Y_12, Y_13, Y_14, Y_15, Y_16, Y_17, Y_18, Y_19, Y_20, Y_21, Y_22, Y_23, Y_24, Y_25, Y_26, Y_27, Y_28, Y_29, Y_30, Y_31, Y_32, Y_33, Y_34, Y_35, Y_36, Y_37, Y_38, Y_39, Y_40, Y_41, Y_42, Y_43, Y_44, Y_45, Y_46, Y_47, Y_48, Y_49, Y_50, Y_51, Y_52, Y_53, Y_54, Y_55, Y_56, Y_57, Y_58, Y_59, Y_60, Y_61, Y_62, Y_63, Y_64, Y_65, Y_66, Y_67, Z_0, Z_1, Z_2, Z_3, Z_4, Z_5, Z_6, Z_7, Z_8, Z_9, Z_10, Z_11, Z_12, Z_13, Z_14, Z_15, Z_16, Z_17, Z_18, Z_19, Z_20, Z_21, Z_22, Z_23, Z_24, Z_25, Z_26, Z_27, Z_28, Z_29, Z_30, Z_31, Z_32, Z_33, Z_34, Z_35, Z_36, Z_37, Z_38, Z_39, Z_40, Z_41, Z_42, Z_43, Z_44, Z_45, Z_46, Z_47, Z_48, Z_49, Z_50, Z_51, Z_52, Z_53, Z_54, Z_55, Z_56, Z_57, Z_58, Z_59, Z_60, Z_61, Z_62, Z_63, Z_64, Z_65, Z_66, Z_67, p_scale, p_rx, p_ry, p_rz, p_tx, p_ty, p_0, p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8, p_9, p_10, p_11, p_12, p_13, p_14, p_15, p_16, p_17, p_18, p_19, p_20, p_21, p_22, p_23, p_24, p_25, p_26, p_27, p_28, p_29, p_30, p_31, p_32, p_33, AU01_r, AU02_r, AU04_r, AU05_r, AU06_r, AU07_r, AU09_r, AU10_r, AU12_r, AU14_r, AU15_r, AU17_r, AU20_r, AU23_r, AU25_r, AU26_r, AU45_r, AU01_c, AU02_c, AU04_c, AU05_c, AU06_c, AU07_c, AU09_c, AU10_c, AU12_c, AU14_c, AU15_c, AU17_c, AU20_c, AU23_c, AU25_c, AU26_c, AU28_c, AU45_c 1, 0, 0.000, 0.98, 1, 0.076789, 0.134736, -0.987902, -0.134997, 0.151834, -0.979144, -0.030, 0.145, 80.1, 81.3, 84.7, 88.5, 90.3, 89.4, 85.6, 81.9, 75.6, 77.5, 80.5, 84.3, 88.3, 91.5, 93.9, 91.5, 88.1, 84.4, 80.9, 77.8, 83.8, 85.4, 86.9, 87.5, 86.7, 85.0, 83.5, 83.0, 127.9, 129.0, 132.6, 136.4, 138.3, 137.2, 133.6, 129.5, 124.4, 126.6, 129.8, 133.8, 137.7, 140.8, 143.0, 141.0, 138.1, 134.6, 130.8, 127.1, 131.5, 133.2, 134.8, 135.3, 134.5, 132.8, 131.2, 130.7, 62.4, 58.6, 56.8, 57.9, 61.3, 65.3, 66.9, 65.8, 65.0, 62.6, 60.8, 59.7, 59.9, 61.3, 63.3, 64.5, 65.1, 65.6, 65.9, 65.8, 63.8, 64.3, 63.4, 61.8, 60.3, 59.8, 60.6, 62.3, 58.3, 54.5, 52.8, 54.1, 57.8, 61.6, 63.3, 62.3, 60.9, 58.5, 56.6, 55.7, 56.3, 57.6, 59.6, 60.9, 61.5, 61.8, 61.8, 61.8, 60.0, 60.6, 59.8, 58.1, 56.5, 56.0, 56.7, 58.4, -37.1, -35.9, -31.9, -27.4, -25.2, -26.1, -30.4, -34.8, -43.1, -40.4, -36.5, -32.0, -27.4, -23.8, -21.1, -23.7, -27.4, -31.6, -35.9, -39.9, -32.8, -30.8, -29.1, -28.5, -29.5, -31.5, -33.2, -33.8, 17.7, 19.2, 23.3, 27.6, 29.7, 28.2, 24.1, 19.5, 13.9, 16.3, 19.8, 24.3, 28.9, 32.8, 35.6, 32.9, 29.3, 25.2, 20.9, 16.9, 21.9, 23.8, 25.6, 26.3, 25.4, 23.5, 21.7, 21.0, -57.7, -62.3, -64.5, -63.0, -58.8, -53.9, -52.0, -53.4, -55.6, -57.8, -59.4, -60.4, -60.1, -58.8, -56.8, -54.9, -53.9, -53.2, -53.2, -53.9, -56.1, -55.4, -56.4, -58.4, -60.3, -60.9, -59.9, -57.9, -60.0, -64.6, -66.9, -65.5, -61.1, -56.5, -54.3, -55.4, -57.3, -59.8, -61.7, -62.8, -62.7, -61.8, -60.2, -58.0, -56.6, -55.8, -55.7, -56.0, -58.4, -57.8, -58.8, -60.8, -62.7, -63.3, -62.3, -60.3, 237.3, 238.3, 238.6, 237.8, 236.6, 235.7, 235.3, 236.0, 241.4, 239.0, 236.7, 235.5, 235.7, 236.6, 237.8, 236.1, 234.6, 234.5, 235.8, 238.4, 237.3, 237.0, 237.1, 237.6, 238.1, 238.4, 238.3, 237.9, 228.2, 229.6, 230.7, 231.0, 230.1, 228.8, 227.6, 227.6, 229.0, 228.1, 227.5, 227.8, 229.5, 232.1, 234.8, 231.6, 228.7, 226.9, 226.6, 227.6, 229.2, 229.3, 229.8, 230.4, 230.7, 230.6, 230.2, 229.6, -1.9, -20.7, 238.2, -0.031, 0.092, -0.053, 56.3, 56.4, 58.2, 61.2, 66.0, 74.1, 84.6, 98.0, 114.2, 130.8, 145.2, 156.8, 164.5, 168.4, 169.5, 169.5, 168.9, 64.4, 70.1, 79.1, 88.9, 98.1, 117.4, 127.5, 137.7, 147.6, 154.6, 108.0, 107.8, 107.6, 107.3, 97.8, 103.2, 108.8, 115.1, 120.7, 75.4, 80.9, 87.9, 94.1, 87.9, 81.1, 124.1, 130.3, 137.3, 143.2, 137.9, 131.1, 89.3, 96.4, 104.2, 109.7, 115.9, 124.8, 133.7, 125.3, 116.9, 110.2, 104.3, 96.5, 92.6, 104.3, 109.9, 116.2, 130.2, 116.2, 109.8, 104.2, 71.4, 87.3, 103.5, 119.3, 133.8, 146.4, 155.6, 161.4, 162.2, 159.4, 152.1, 141.3, 128.0, 112.4, 95.8, 78.8, 62.2, 56.7, 48.4, 44.6, 44.6, 46.8, 44.8, 40.7, 39.7, 42.1, 48.8, 57.1, 65.8, 74.5, 83.8, 95.8, 97.1, 98.2, 96.5, 94.6, 65.0, 60.6, 60.0, 63.2, 65.1, 66.0, 61.0, 56.5, 56.3, 59.6, 61.5, 61.8, 121.8, 115.3, 112.3, 113.4, 111.7, 114.0, 118.7, 124.7, 126.9, 127.6, 127.6, 126.3, 121.4, 117.6, 117.6, 116.8, 119.0, 120.0, 120.7, 120.6, -81.9, -81.4, -78.6, -73.9, -65.4, -52.0, -35.5, -16.9, 2.6, 22.4, 41.7, 58.7, 70.7, 76.8, 78.1, 77.7, 77.5, -62.6, -53.8, -41.2, -28.2, -16.7, 6.4, 18.5, 31.0, 43.3, 52.3, -4.6, -4.7, -4.8, -4.9, -16.0, -9.8, -3.4, 3.4, 9.7, -46.3, -38.8, -29.9, -22.0, -29.6, -38.3, 14.6, 21.9, 30.3, 37.6, 30.8, 22.7, -27.2, -17.6, -8.6, -2.5, 4.3, 14.2, 25.1, 14.7, 5.3, -2.0, -8.4, -17.4, -22.9, -8.5, -2.3, 4.6, 20.9, 4.5, -2.4, -8.6, -59.6, -36.2, -12.4, 10.6, 31.1, 47.3, 56.5, 59.6, 58.7, 56.4, 50.3, 38.4, 21.5, 0.6, -22.0, -44.8, -67.8, -72.7, -81.7, -84.4, -82.3, -77.9, -79.3, -84.8, -87.1, -85.0, -77.5, -63.8, -51.8, -40.4, -29.3, -18.2, -16.4, -15.0, -17.0, -19.3, -59.4, -64.0, -64.3, -59.9, -57.6, -57.0, -61.5, -66.6, -66.7, -63.1, -60.1, -59.9, 11.7, 3.8, 0.3, 1.5, -0.3, 2.3, 7.7, 14.1, 16.0, 16.8, 17.0, 16.0, 11.1, 6.1, 6.1, 5.3, 8.0, 8.7, 9.5, 9.5, 300.1, 298.9, 298.5, 297.0, 290.3, 280.4, 264.7, 246.2, 238.8, 243.1, 256.3, 267.5, 274.7, 278.0, 277.3, 275.8, 278.0, 268.1, 262.0, 255.9, 249.2, 244.0, 240.9, 242.8, 246.0, 248.1, 250.5, 237.5, 229.0, 220.3, 211.9, 229.7, 225.5, 222.0, 223.7, 226.2, 257.9, 254.6, 252.5, 250.7, 250.8, 252.8, 246.1, 244.7, 244.4, 245.7, 243.1, 243.5, 244.2, 230.1, 224.3, 222.4, 222.5, 227.0, 236.3, 225.1, 220.1, 219.7, 222.0, 229.5, 240.7, 225.0, 223.0, 223.3, 234.1, 222.5, 222.3, 224.5, 0.830, -0.044, 0.134, -0.054, 110.678, 94.444, -15.099, 3.582, -12.143, 7.822, -21.197, 2.507, 4.479, -6.777, 2.699, 3.343, -4.273, -0.539, -0.310, -6.324, -1.333, 3.828, -0.026, 1.675, -2.479, -2.005, 0.991, -0.967, -0.688, -0.477, -0.515, -0.353, -0.342, -0.068, -0.037, 0.152, -0.009, 0.044, 0.001, -0.001, 0.22, 0.00, 0.00, 0.00, 0.00, 1.72, 0.00, 0.00, 0.00, 0.00, 0.00, 0.48, 0.00, 0.25, 0.48, 0.59, 0.16, 0.00, 0.00, 0.00, 1.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00 2, 0, 0.040, 0.98, 1, 0.070312, 0.141136, -0.987490, -0.139634, 0.158903, -0.977370, -0.035, 0.152, 80.3, 81.5, 84.9, 88.6, 90.4, 89.5, 85.8, 82.1, 75.8, 77.8, 80.7, 84.4, 88.4, 91.6, 93.9, 91.6, 88.3, 84.6, 81.2, 78.1, 84.0, 85.6, 87.1, 87.6, 86.8, 85.2, 83.7, 83.2, 127.7, 128.9, 132.4, 136.2, 138.0, 136.9, 133.3, 129.3, 124.3, 126.5, 129.6, 133.6, 137.4, 140.5, 142.6, 140.7, 137.8, 134.4, 130.6, 127.0, 131.3, 133.0, 134.5, 135.0, 134.2, 132.6, 131.0, 130.5, 62.4, 58.7, 56.9, 58.0, 61.3, 65.3, 66.9, 65.8, 65.0, 62.7, 60.9, 59.8, 60.0, 61.3, 63.3, 64.5, 65.1, 65.7, 65.9, 65.8, 63.8, 64.3, 63.5, 61.8, 60.4, 59.9, 60.7, 62.3, 58.3, 54.6, 52.9, 54.2, 57.8, 61.5, 63.2, 62.2, 60.8, 58.5, 56.7, 55.8, 56.3, 57.6, 59.6, 60.9, 61.5, 61.7, 61.8, 61.7, 59.9, 60.5, 59.8, 58.1, 56.6, 56.0, 56.7, 58.4, -37.3, -36.1, -32.1, -27.6, -25.4, -26.3, -30.6, -35.0, -43.3, -40.6, -36.8, -32.2, -27.6, -24.0, -21.3, -23.9, -27.6, -31.8, -36.1, -40.1, -33.0, -31.0, -29.3, -28.8, -29.7, -31.7, -33.5, -34.0, 17.8, 19.2, 23.4, 27.7, 29.7, 28.2, 24.1, 19.5, 13.9, 16.4, 19.9, 24.4, 29.0, 32.8, 35.7, 33.0, 29.3, 25.2, 20.9, 16.9, 22.0, 23.9, 25.7, 26.3, 25.5, 23.5, 21.7, 21.1, -58.3, -62.9, -65.2, -63.7, -59.5, -54.6, -52.7, -54.1, -56.3, -58.5, -60.0, -61.0, -60.8, -59.5, -57.5, -55.6, -54.6, -53.9, -53.9, -54.6, -56.7, -56.1, -57.1, -59.1, -61.0, -61.6, -60.6, -58.6, -60.8, -65.5, -67.7, -66.3, -61.9, -57.3, -55.1, -56.2, -58.2, -60.6, -62.5, -63.6, -63.4, -62.6, -61.0, -58.8, -57.4, -56.6, -56.5, -56.8, -59.2, -58.6, -59.6, -61.6, -63.5, -64.1, -63.1, -61.1, 240.3, 241.3, 241.5, 240.8, 239.6, 238.8, 238.3, 239.0, 244.4, 242.0, 239.7, 238.4, 238.6, 239.6, 240.8, 239.1, 237.6, 237.5, 238.9, 241.4, 240.3, 240.0, 240.1, 240.6, 241.1, 241.4, 241.3, 240.9, 231.4, 232.7, 233.8, 234.0, 233.2, 231.8, 230.8, 230.7, 232.3, 231.3, 230.6, 230.8, 232.5, 235.2, 237.8, 234.6, 231.7, 230.0, 229.7, 230.8, 232.3, 232.4, 232.9, 233.5, 233.8, 233.7, 233.3, 232.7, -2.0, -20.9, 238.6, -0.026, 0.102, -0.057, 56.7, 56.7, 58.3, 61.2, 65.9, 74.0, 84.5, 98.2, 114.7, 131.8, 146.5, 158.1, 165.6, 169.1, 169.7, 169.3, 168.4, 64.9, 70.4, 79.0, 88.5, 97.5, 117.9, 127.8, 137.8, 147.3, 154.1, 107.9, 107.6, 107.3, 107.0, 97.8, 103.1, 108.7, 114.9, 120.5, 75.6, 81.1, 88.0, 94.2, 88.1, 81.3, 124.0, 130.1, 137.0, 142.9, 137.6, 130.8, 89.1, 96.1, 104.0, 109.4, 115.5, 124.6, 133.8, 125.2, 116.5, 109.9, 104.1, 96.3, 92.4, 104.1, 109.6, 115.9, 130.3, 115.8, 109.5, 104.0, 71.6, 87.4, 103.4, 119.2, 133.7, 146.4, 155.6, 161.5, 162.2, 159.1, 151.6, 140.5, 127.0, 111.2, 94.5, 77.4, 60.7, 56.4, 48.4, 44.7, 44.7, 46.7, 44.7, 40.7, 39.7, 42.1, 48.6, 57.1, 65.7, 74.2, 83.4, 95.6, 96.9, 98.0, 96.3, 94.5, 65.0, 60.7, 60.1, 63.2, 65.1, 66.0, 60.9, 56.5, 56.4, 59.6, 61.5, 61.8, 121.7, 115.2, 112.2, 113.2, 111.6, 113.9, 118.6, 125.0, 127.2, 127.9, 127.8, 126.4, 121.4, 117.6, 117.6, 116.8, 118.9, 120.2, 120.9, 120.7, -81.5, -81.3, -78.8, -74.2, -65.8, -52.5, -35.7, -16.7, 3.2, 23.5, 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274.5, 276.1, 267.7, 260.4, 253.7, 246.8, 241.0, 237.2, 239.2, 242.2, 244.4, 247.4, 235.1, 227.3, 219.2, 211.5, 230.2, 226.0, 222.6, 224.3, 226.5, 256.9, 253.2, 251.1, 249.4, 249.4, 251.5, 244.0, 242.2, 241.8, 243.2, 240.8, 241.3, 248.5, 233.2, 226.4, 224.3, 224.0, 229.0, 239.1, 227.0, 221.0, 220.8, 223.6, 232.6, 245.0, 227.2, 225.0, 225.0, 236.7, 223.3, 223.3, 225.9, 0.828, 0.008, 0.149, -0.052, 109.493, 95.341, -8.730, 5.451, -10.526, 0.313, -20.059, 5.206, -0.719, -9.976, 1.482, 3.234, -4.603, -1.265, -0.044, -6.273, -2.568, 5.551, 1.742, 1.428, -2.056, -2.870, 0.600, -1.617, -1.574, -0.694, -0.519, -0.413, -0.761, -0.096, -0.054, 0.174, 0.015, 0.053, 0.009, 0.006, 0.00, 0.06, 0.00, 0.00, 0.00, 1.64, 0.00, 0.00, 0.00, 0.11, 0.00, 0.15, 0.00, 0.23, 0.00, 0.00, 0.61, 0.00, 0.00, 0.00, 1.00, 0.00, 1.00, 0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00 ================================================ FILE: test_code_released_new/checkpoints/gpubatch_resnet/opt.txt ================================================ ------------ Options ------------- aspect_ratio: 1.0 batchSize: 1 checkpoints_dir: ./checkpoints csv_path: /home/esa/Downloads/higher_resolution/test_code_released/00116.csv dataroot: ./ dataset_mode: aligned display_id: 0 display_port: 8097 display_winsize: 256 fineSize: 128 gpu_ids: [1] how_many: 1 init_type: normal input_nc: 3 isTrain: False loadSize: 128 max_dataset_size: inf model: pix2pix nThreads: 8 n_layers_D: 3 name: gpubatch_resnet ndf: 256 ngf: 128 no_dropout: False no_flip: True norm: batch ntest: inf output_nc: 3 phase: test resize_or_crop: resize_and_crop results_dir: results_video serial_batches: False which_direction: AtoB which_epoch: latest which_model_netD: basic which_model_netG: resnet_6blocks which_ref: /home/esa/Downloads/higher_resolution/test_code_released/new_crop/1235.png which_ref_d1: path/to/reference/image which_ref_d2: path/to/reference/image -------------- End ---------------- ================================================ FILE: test_code_released_new/data/__init__.py ================================================ ================================================ FILE: test_code_released_new/data/aligned_dataset.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Mar 13 14:30:12 2019 @author:Soumya """ import os.path import random import torchvision.transforms as transforms import torch from data.base_dataset import BaseDataset #from data.image_folder import make_dataset from PIL import Image import pandas #from natsort import natsorted, ns #import glob #import numpy as np #import sys #sys.path.append('/home/esa/Downloads/face-alignment') class AlignedDataset(BaseDataset): def initialize(self, opt): self.opt = opt self.root = opt.dataroot self.dir_AB = os.path.join(opt.dataroot) self.ref_path=opt.which_ref self.csv_path=opt.csv_path def __getitem__(self, index): SI_path= self.ref_path df2 = pandas.read_csv(self.csv_path) a=list(range(296,299))+ list(range(679,696))#+ list(range(299,435)) refdf = df2[df2.columns[a]] f=0 A1 = Image.open(SI_path).convert('RGB') A1 = A1.resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC) A1 = transforms.ToTensor()(A1) A = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(A1) for i in range(0,len(refdf)): paramA=torch.tensor(refdf.values[i]).view(1,-1) paramA[0,0:3]=(paramA[0,0:3]-(-0.70))/1.4 paramA[0,3:20]=paramA[0,3:20]/5 if f==0: P1=paramA else: P1=torch.cat([P1,paramA],dim=0) f=f+1 if (not self.opt.no_flip) and random.random() < 0.5: idx = [i for i in range(A.size(2) - 1, -1, -1)] idx = torch.LongTensor(idx) A = A.index_select(2, idx) return {'A': A, 'PB': P1, 'A_paths': SI_path}#SAB_path[0] def __len__(self): return len(self.ref_path) def name(self): return 'AlignedDataset' ================================================ FILE: test_code_released_new/data/aligned_dataset.py~ ================================================ import os.path import random import torchvision.transforms as transforms import torch from data.base_dataset import BaseDataset from data.image_folder import make_dataset, make_grouped_dataset from PIL import Image import pandas from natsort import natsorted, ns import glob import numpy as np class AlignedDataset(BaseDataset): def initialize(self, opt): self.opt = opt self.root = opt.dataroot self.dir_AB = os.path.join(opt.dataroot) self.AB_paths = sorted(make_dataset(self.dir_AB)) self.all_iden=np.loadtxt('/home/esa/Downloads/files/train_vox1.txt',dtype=str) assert(opt.resize_or_crop == 'resize_and_crop') # pdb.set_trace() def __getitem__(self, index): # print(index) IM_path=self.AB_paths[index] ind=os.path.basename(os.path.dirname(os.path.dirname((os.path.dirname(IM_path))))) identity=torch.tensor(np.where(self.all_iden == ind)[0]).unsqueeze(0)#torch.tensor(self.all_iden.index(ind)).unsqueeze(0) identity_code=identity#torch.zeros(64) # identity_code[identity]=1 all_images_infolder=glob.glob(os.path.dirname(IM_path)+'/*') AB_path = natsorted(all_images_infolder, alg=ns.IGNORECASE) current_index_number=AB_path.index(IM_path)#int(os.path.basename(IM_path)[:-4]) # last_fra_number = int(os.path.basename(AB_path[len(AB_path)])[:-4]) if(len(AB_path)>1): new_list=AB_path[0:current_index_number] + AB_path[current_index_number+1:len(AB_path)] # if current_index_number= self.opt.max_dataset_size: break yield data ================================================ FILE: test_code_released_new/data/data_loader.py ================================================ def CreateDataLoader(opt): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt) return data_loader ================================================ FILE: test_code_released_new/data/image_folder.py ================================================ ############################################################################### # Code from # https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py # Modified the original code so that it also loads images from the current # directory as well as the subdirectories ############################################################################### import torch.utils.data as data from PIL import Image import os import os.path import numpy as np IMG_EXTENSIONS = [ '.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', ] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def make_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir train=np.loadtxt('/home/esa/Downloads/files/test_vox1.txt',dtype=str) for root, _, fnames in sorted(os.walk(dir)): identity=os.path.basename(os.path.dirname(os.path.dirname(root))) if np.isin(identity,train): for fname in fnames: if is_image_file(fname): path = os.path.join(root, fname) images.append(path) return images def make_grouped_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir fnames = sorted(os.walk(dir)) for fname in sorted(fnames): paths = [] root = fname[0] for f in sorted(fname[2]): if is_image_file(f): paths.append(os.path.join(root, f)) if len(paths) > 0: images.append(paths) return images def default_loader(path): return Image.open(path).convert('RGB') class ImageFolder(data.Dataset): def __init__(self, root, transform=None, return_paths=False, loader=default_loader): imgs = make_dataset(root) if len(imgs) == 0: raise(RuntimeError("Found 0 images in: " + root + "\n" "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) self.root = root self.imgs = imgs self.transform = transform self.return_paths = return_paths self.loader = loader def __getitem__(self, index): path = self.imgs[index] img = self.loader(path) if self.transform is not None: img = self.transform(img) if self.return_paths: return img, path else: return img def __len__(self): return len(self.imgs) ================================================ FILE: test_code_released_new/data/image_folder.py~ ================================================ ############################################################################### # Code from # https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py # Modified the original code so that it also loads images from the current # directory as well as the subdirectories ############################################################################### import torch.utils.data as data from PIL import Image import os import os.path import numpy as np IMG_EXTENSIONS = [ '.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', ] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def make_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir train=np.loadtxt('/home/esa/Downloads/files/train_vox1.txt',dtype=str) for root, _, fnames in sorted(os.walk(dir)): identity=os.path.basename(os.path.dirname(os.path.dirname(root))) if np.isin(identity,train): for fname in fnames: if is_image_file(fname): path = os.path.join(root, fname) images.append(path) return images def make_grouped_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir fnames = sorted(os.walk(dir)) for fname in sorted(fnames): paths = [] root = fname[0] for f in sorted(fname[2]): if is_image_file(f): paths.append(os.path.join(root, f)) if len(paths) > 0: images.append(paths) return images def default_loader(path): return Image.open(path).convert('RGB') class ImageFolder(data.Dataset): def __init__(self, root, transform=None, return_paths=False, loader=default_loader): imgs = make_dataset(root) if len(imgs) == 0: raise(RuntimeError("Found 0 images in: " + root + "\n" "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) self.root = root self.imgs = imgs self.transform = transform self.return_paths = return_paths self.loader = loader def __getitem__(self, index): path = self.imgs[index] img = self.loader(path) if self.transform is not None: img = self.transform(img) if self.return_paths: return img, path else: return img def __len__(self): return len(self.imgs) ================================================ FILE: test_code_released_new/image_crop.py ================================================ #from imutils.face_utils import FaceAligner from imutils.face_utils import rect_to_bb import imutils import dlib import cv2 #from imutils import face_utils import numpy as np from PIL import Image #import pdb from imutils.face_utils import FaceAligner #def pre_process(path): for i in range(2): predictor = dlib.shape_predictor('./shape_predictor_5_face_landmarks.dat') fa = FaceAligner(predictor, desiredFaceWidth=256) # detector = dlib.get_frontal_face_detector() detector = dlib.cnn_face_detection_model_v1('./mmod_human_face_detector.dat') image = cv2.imread('/home/nvme/soumya/test_ICface2/new_crop/pd.jpg') # add your image here #image= cv2.resize(cap, (256, 256)) RGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) c=1235 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) rects = detector(RGB, 1) faces = dlib.full_object_detections() # faceAligned = fa.align(RGB, gray, rects[0].rect) # rects_a = detector(faceAligned, 1) for rect in rects: c1=rect.rect.dcenter() #(x, y, w, h) = rect_to_bb(rect) x=rect.rect.left() y=rect.rect.top() w=rect.rect.right()-x h=rect.rect.bottom()-y w=np.int(w*1.6) h=np.int(h*1.6) x=c1.x-np.int(w/2.0) y=c1.y-np.int(h/2.0) if y<0: y=0 if x<0: x=0 faceOrig = imutils.resize(RGB[y:y+h, x:x+w],height=256) #y=10,h+60,W+40 d_num = np.asarray(faceOrig) f_im = Image.fromarray(d_num) f_im.save('./new_crop/'+str(c)+'.png') ================================================ FILE: test_code_released_new/models/__init__.py ================================================ ================================================ FILE: test_code_released_new/models/base_model.py ================================================ import os import torch class BaseModel(): def name(self): return 'BaseModel' def initialize(self, opt): self.opt = opt self.gpu_ids = opt.gpu_ids self.isTrain = opt.isTrain self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) def set_input(self, input): self.input = input def forward(self): pass # used in test time, no backprop def test(self): pass def get_image_paths(self): pass def optimize_parameters(self): pass def get_current_visuals(self): return self.input def get_current_errors(self): return {} def save(self, label): pass # helper saving function that can be used by subclasses def save_network(self, network, network_label, epoch_label, gpu_ids): save_filename = '%s_net_%s.pth' % (epoch_label, network_label) save_path = os.path.join(self.save_dir, save_filename) torch.save(network.cpu().state_dict(), save_path) if len(gpu_ids) and torch.cuda.is_available(): network.cuda(gpu_ids[0]) # helper loading function that can be used by subclasses def load_network(self, network, network_label, epoch_label,optimizer): save_filename = '%s_net_%s.pth' % (epoch_label, network_label) save_path = os.path.join(self.save_dir, save_filename) network.load_state_dict(torch.load(save_path)['state_dict'],strict=False) optimizer.load_state_dict(torch.load(save_path)['optimizer']) # network.load_state_dict(torch.load(save_path)) # update learning rate (called once every epoch) def update_learning_rate(self): for scheduler in self.schedulers: scheduler.step() lr = self.optimizers[0].param_groups[0]['lr'] print('learning rate = %.7f' % lr) ================================================ FILE: test_code_released_new/models/models.py ================================================ def create_model(opt): model = None print(opt.model) if opt.model == 'cycle_gan': assert(opt.dataset_mode == 'unaligned') from .cycle_gan_model import CycleGANModel model = CycleGANModel() elif opt.model == 'pix2pix': assert(opt.dataset_mode == 'aligned') from .pix2pix_model import Pix2PixModel model = Pix2PixModel() elif opt.model == 'test': assert(opt.dataset_mode == 'single') from .test_model import TestModel model = TestModel() else: raise ValueError("Model [%s] not recognized." % opt.model) model.initialize(opt) print("model [%s] was created" % (model.name())) return model ================================================ FILE: test_code_released_new/models/networks.py ================================================ import torch import torch.nn as nn from torch.nn import init import functools from torch.autograd import Variable from torch.optim import lr_scheduler import torch.nn.functional as F import pdb import numpy as np ############################################################################### # Functions ############################################################################### def weights_init_normal(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('Linear') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_xavier(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('Linear') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_kaiming(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('Linear') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_orthogonal(m): classname = m.__class__.__name__ print(classname) if classname.find('Conv') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('Linear') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def init_weights(net, init_type='normal'): print('initialization method [%s]' % init_type) if init_type == 'normal': net.apply(weights_init_normal) elif init_type == 'xavier': net.apply(weights_init_xavier) elif init_type == 'kaiming': net.apply(weights_init_kaiming) elif init_type == 'orthogonal': net.apply(weights_init_orthogonal) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) def get_norm_layer(norm_type='instance'): if norm_type == 'batch': norm_layer = functools.partial(nn.BatchNorm2d, affine=True) elif norm_type == 'instance': norm_layer = functools.partial(nn.InstanceNorm2d, affine=False) elif norm_type == 'none': norm_layer = None else: raise NotImplementedError('normalization layer [%s] is not found' % norm_type) return norm_layer def get_scheduler(optimizer, opt): if opt.lr_policy == 'lambda': def lambda_rule(epoch): lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) return lr_l scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) elif opt.lr_policy == 'step': # scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) scheduler = lr_scheduler.StepLR(optimizer, step_size=150000, gamma=0.1) elif opt.lr_policy == 'plateau': scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) else: return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) return scheduler def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', gpu_ids=[]): netG = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netG == 'resnet_9blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, gpu_ids=gpu_ids) elif which_model_netG == 'resnet_6blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, gpu_ids=gpu_ids) else: raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG) if len(gpu_ids) > 0: netG.cuda(gpu_ids[0]) init_weights(netG, init_type=init_type) return netG def define_D(input_nc, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', gpu_ids=[]): netD = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netD == 'basic': netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'n_layers': netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'pixel': netD = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) else: raise NotImplementedError('Discriminator model name [%s] is not recognized' % which_model_netD) if use_gpu: netD.cuda(gpu_ids[0]) init_weights(netD, init_type=init_type) return netD def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print(net) print('Total number of parameters: %d' % num_params) ############################################################################## # Classes ############################################################################## # Defines the GAN loss which uses either LSGAN or the regular GAN. # When LSGAN is used, it is basically same as MSELoss, # but it abstracts away the need to create the target label tensor # that has the same size as the input class GANLoss(nn.Module): def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor): super(GANLoss, self).__init__() self.real_label = target_real_label self.fake_label = target_fake_label # self.real_label =torch.empty(1).uniform_(0.8, 1)[0]# target_real_label # # self.fake_label = torch.empty(1).uniform_(0, 0.2)[0]#target_fake_label # self.real_label_var = None self.fake_label_var = None self.Tensor = tensor if use_lsgan: self.loss = nn.MSELoss() else: self.loss = nn.BCEWithLogitsLoss()#nn.BCELoss() def get_target_tensor(self, input, target_is_real): target_tensor = None if target_is_real: create_label = ((self.real_label_var is None) or (self.real_label_var.numel() != input.numel())) if create_label: real_tensor = self.Tensor(input.size()).fill_(self.real_label) self.real_label_var = Variable(real_tensor, requires_grad=False) target_tensor = self.real_label_var else: create_label = ((self.fake_label_var is None) or (self.fake_label_var.numel() != input.numel())) if create_label: fake_tensor = self.Tensor(input.size()).fill_(self.fake_label) self.fake_label_var = Variable(fake_tensor, requires_grad=False) target_tensor = self.fake_label_var return target_tensor def __call__(self, input, target_is_real): target_tensor = self.get_target_tensor(input, target_is_real) return self.loss(input, target_tensor) class VGGLoss(nn.Module): def __init__(self, gpu_ids): super(VGGLoss, self).__init__() self.vgg = Vgg19().cuda() self.criterion = nn.L1Loss() self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] def forward(self, x, y): x_vgg, y_vgg = self.vgg(x), self.vgg(y) loss = 0 for i in range(len(x_vgg)): loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) return loss # Defines the generator that consists of Resnet blocks between a few # downsampling/upsampling operations. # Code and idea originally from Justin Johnson's architecture. # https://github.com/jcjohnson/fast-neural-style/ class ResnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, gpu_ids=[], padding_type='reflect'): assert(n_blocks >= 0) super(ResnetGenerator, self).__init__() self.input_nc = input_nc self.output_nc = output_nc self.ngf = ngf self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), norm_layer(ngf), nn.ReLU(True)] n_downsampling = 2 for i in range(n_downsampling): mult = 2**i model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), norm_layer(ngf * mult * 2), nn.ReLU(True)] mult = 2**n_downsampling for i in range(n_blocks): model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] for i in range(n_downsampling): mult = 2**(n_downsampling - i) model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1, bias=use_bias), norm_layer(int(ngf * mult / 2)), nn.ReLU(True)] model += [nn.ReflectionPad2d(3)] model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] model += [nn.Tanh()] self.model = nn.Sequential(*model) def forward(self, input): # if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): # return nn.parallel.data_parallel(self.model, input, self.gpu_ids) # else: return self.model(input) # Define a resnet block class ResnetBlock(nn.Module): def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): super(ResnetBlock, self).__init__() self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): conv_block = [] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)] if use_dropout: conv_block += [nn.Dropout(0.5)] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)] return nn.Sequential(*conv_block) def forward(self, x): out = x + self.conv_block(x) return out # Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(NLayerDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 3 padw = 1 sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.Conv2d(ndf, ndf, kernel_size=kw, stride=2, padding=padw), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2**n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), nn.Conv2d(ndf * nf_mult, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), # nn.LayerNorm([ndf * nf_mult,np.int(ndf/nf_mult),np.int(ndf/nf_mult)]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult_prev = nf_mult nf_mult = min(2**n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), # nn.LayerNorm([ndf*nf_mult,15,15]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] sequence += [nn.Conv2d(ndf * nf_mult,ndf * nf_mult, kernel_size=kw, stride=1, padding=padw)] sequence += [nn.Conv2d(ndf * nf_mult, 128, kernel_size=kw, stride=2, padding=padw)] # if use_sigmoid: # sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) self.conv1=nn.Conv2d(128, 1, kernel_size=4, stride=1, padding=1, bias=True) self.conv2=nn.Linear(128*8**2,20)#nn.Conv2d(128, 20, kernel_size=4, stride=1, padding=0, bias=True) self.aux_layer = nn.Sequential(nn.Linear(128*8**2, 939), nn.Softmax()) def forward(self, input): out1=self.model(input)#nn.parallel.data_parallel(self.model, input, self.gpu_ids)# # out=F.sigmoid(self.conv1(out1)) out=self.conv1(out1) cond=F.sigmoid(self.conv2(out1.view(out1.shape[0],-1))) class_pred=self.aux_layer(out1.view(out1.shape[0], -1)) return out,cond,class_pred # self.model = nn.Sequential(*sequence) # class PixelDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(PixelDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.net = [ nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), norm_layer(ndf * 2), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] if use_sigmoid: self.net.append(nn.Sigmoid()) self.net = nn.Sequential(*self.net) def forward(self, input): if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.net, input, self.gpu_ids) else: return self.net(input) from torchvision import models class Vgg19(torch.nn.Module): def __init__(self, requires_grad=False): super(Vgg19, self).__init__() vgg_pretrained_features = models.vgg19(pretrained=True).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() for x in range(2): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(2, 7): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(7, 12): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(12, 21): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(21, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h_relu1 = self.slice1(X) h_relu2 = self.slice2(h_relu1) h_relu3 = self.slice3(h_relu2) h_relu4 = self.slice4(h_relu3) h_relu5 = self.slice5(h_relu4) out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] return out ================================================ FILE: test_code_released_new/models/networks.py~ ================================================ import torch import torch.nn as nn from torch.nn import init import functools from torch.autograd import Variable from torch.optim import lr_scheduler import torch.nn.functional as F import pdb import numpy as np ############################################################################### # Functions ############################################################################### def normalize_to_distribution(x): x = x.view(x.size(0), -1) x_max, idx = torch.max(x, dim=1, keepdim=True) x = x / (x_max.expand_as(x) + 1e-8) x_sum = torch.sum(x, dim=1) x = x / x_sum.unsqueeze(1).expand_as(x) return x def normalize_scale(x): x = x.view(x.size(0), -1) x_max, idx = torch.max(x, dim=1, keepdim=True) x = x / (x_max.expand_as(x) + 1e-8) return x class KLLoss(nn.Module): def __init__(self): super(KLLoss, self).__init__() def forward(self, x0, y0): x0 = normalize_to_distribution(x0) y0 = normalize_to_distribution(y0) # print(x0,y0) # pdb.set_trace() x_log = torch.log(x0 + 1e-8) y_log = torch.log(y0 + 1e-8) loss = torch.sum(torch.mul(y0, (y_log - x_log)), dim=1) return loss.sum() class ACCLoss(nn.Module): def __init__(self): super(ACCLoss, self).__init__() def forward(self, x0, y0): print(x0.size(),y0.size()) x0 = normalize_to_distribution(x0) y0 = normalize_to_distribution(y0) msx = x0 - x0.mean(dim=1, keepdim=True).expand_as(x0) msy = y0 - y0.mean(dim=1, keepdim=True).expand_as(y0) denom = torch.sqrt(torch.sum(torch.pow(msx, 2), dim=1)) * torch.sqrt(torch.sum(torch.pow(msy, 2), dim=1)) # we opimize 1 - rho which means our cost function is at the range of [0-2] where 2 is total disagreement loss = 1 - (torch.sum(msx * msy, dim=1) / (denom + 1e-8) ) return loss.sum() class ENEGNSSLoss(nn.Module): def __init__(self): super(ENEGNSSLoss, self).__init__() def forward(self, pred, gt_fix): pred = normalize_scale(pred) gt_fix[gt_fix > 0.0] = 1.0 gt_fix = gt_fix.view(gt_fix.size(0), -1) p_mean = pred.mean(dim=1, keepdim=True).expand_as(pred) p_std = pred.std(dim=1, keepdim=True).expand_as(pred) pred = (pred - p_mean) / (p_std + 1e-8) nss = torch.sum(gt_fix * pred, dim=1, keepdim=True) / gt_fix.sum(dim=1, keepdim=True) nss_e = torch.exp(-nss) return nss_e.sum() def weights_init_normal(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('Linear') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_xavier(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('Linear') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_kaiming(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('Linear') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_orthogonal(m): classname = m.__class__.__name__ print(classname) if classname.find('Conv') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('Linear') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def init_weights(net, init_type='normal'): print('initialization method [%s]' % init_type) if init_type == 'normal': net.apply(weights_init_normal) elif init_type == 'xavier': net.apply(weights_init_xavier) elif init_type == 'kaiming': net.apply(weights_init_kaiming) elif init_type == 'orthogonal': net.apply(weights_init_orthogonal) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) def get_norm_layer(norm_type='instance'): if norm_type == 'batch': norm_layer = functools.partial(nn.BatchNorm2d, affine=True) elif norm_type == 'instance': norm_layer = functools.partial(nn.InstanceNorm2d, affine=False) elif norm_type == 'none': norm_layer = None else: raise NotImplementedError('normalization layer [%s] is not found' % norm_type) return norm_layer def get_scheduler(optimizer, opt): if opt.lr_policy == 'lambda': def lambda_rule(epoch): lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) return lr_l scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) elif opt.lr_policy == 'step': # scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) scheduler = lr_scheduler.StepLR(optimizer, step_size=150000, gamma=0.1) elif opt.lr_policy == 'plateau': scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) else: return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) return scheduler def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', gpu_ids=[]): netG = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netG == 'resnet_9blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, gpu_ids=gpu_ids) elif which_model_netG == 'resnet_6blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, gpu_ids=gpu_ids) elif which_model_netG == 'unet_128': netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) elif which_model_netG == 'unet_256': netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) else: raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG) if len(gpu_ids) > 0: netG.cuda(gpu_ids[0]) init_weights(netG, init_type=init_type) return netG def define_D(input_nc, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', gpu_ids=[]): netD = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netD == 'basic': netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'n_layers': netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'pixel': netD = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) else: raise NotImplementedError('Discriminator model name [%s] is not recognized' % which_model_netD) if use_gpu: netD.cuda(gpu_ids[0]) init_weights(netD, init_type=init_type) return netD def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print(net) print('Total number of parameters: %d' % num_params) ############################################################################## # Classes ############################################################################## # Defines the GAN loss which uses either LSGAN or the regular GAN. # When LSGAN is used, it is basically same as MSELoss, # but it abstracts away the need to create the target label tensor # that has the same size as the input class GANLoss(nn.Module): def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor): super(GANLoss, self).__init__() self.real_label = target_real_label self.fake_label = target_fake_label # self.real_label =torch.empty(1).uniform_(0.8, 1)[0]# target_real_label # # self.fake_label = torch.empty(1).uniform_(0, 0.2)[0]#target_fake_label # self.real_label_var = None self.fake_label_var = None self.Tensor = tensor if use_lsgan: self.loss = nn.MSELoss() else: self.loss = nn.BCEWithLogitsLoss()#nn.BCELoss() def get_target_tensor(self, input, target_is_real): target_tensor = None if target_is_real: create_label = ((self.real_label_var is None) or (self.real_label_var.numel() != input.numel())) if create_label: real_tensor = self.Tensor(input.size()).fill_(self.real_label) self.real_label_var = Variable(real_tensor, requires_grad=False) target_tensor = self.real_label_var else: create_label = ((self.fake_label_var is None) or (self.fake_label_var.numel() != input.numel())) if create_label: fake_tensor = self.Tensor(input.size()).fill_(self.fake_label) self.fake_label_var = Variable(fake_tensor, requires_grad=False) target_tensor = self.fake_label_var return target_tensor def __call__(self, input, target_is_real): target_tensor = self.get_target_tensor(input, target_is_real) return self.loss(input, target_tensor) class VGGLoss(nn.Module): def __init__(self, gpu_ids): super(VGGLoss, self).__init__() self.vgg = Vgg19().cuda() self.criterion = nn.L1Loss() self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] def forward(self, x, y): x_vgg, y_vgg = self.vgg(x), self.vgg(y) loss = 0 for i in range(len(x_vgg)): loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) return loss # Defines the generator that consists of Resnet blocks between a few # downsampling/upsampling operations. # Code and idea originally from Justin Johnson's architecture. # https://github.com/jcjohnson/fast-neural-style/ class ResnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, gpu_ids=[], padding_type='reflect'): assert(n_blocks >= 0) super(ResnetGenerator, self).__init__() self.input_nc = input_nc self.output_nc = output_nc self.ngf = ngf self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), norm_layer(ngf), nn.ReLU(True)] n_downsampling = 2 for i in range(n_downsampling): mult = 2**i model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), norm_layer(ngf * mult * 2), nn.ReLU(True)] mult = 2**n_downsampling for i in range(n_blocks): model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] for i in range(n_downsampling): mult = 2**(n_downsampling - i) model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1, bias=use_bias), norm_layer(int(ngf * mult / 2)), nn.ReLU(True)] model += [nn.ReflectionPad2d(3)] model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] model += [nn.Tanh()] self.model = nn.Sequential(*model) def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) # Define a resnet block class ResnetBlock(nn.Module): def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): super(ResnetBlock, self).__init__() self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): conv_block = [] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)] if use_dropout: conv_block += [nn.Dropout(0.5)] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)] return nn.Sequential(*conv_block) def forward(self, x): out = x + self.conv_block(x) return out # Defines the Unet generator. # |num_downs|: number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck class UnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(UnetGenerator, self).__init__() self.gpu_ids = gpu_ids # Unet encoder self.conv1 = nn.Conv2d(input_nc, ngf, 4, 2, 1) self.conv2 = nn.Conv2d(ngf, ngf * 2, 4, 2, 1) self.conv2_bn = nn.BatchNorm2d(ngf * 2) #nn.InstanceNorm2d(ngf*2, affine=True)#)# self.conv3 = nn.Conv2d(ngf * 2, ngf * 4, 4, 2, 1) self.conv3_bn = nn.BatchNorm2d(ngf * 4) #nn.InstanceNorm2d(ngf*4, affine=True)#)# self.conv4 = nn.Conv2d(ngf * 4, ngf * 8, 4, 2, 1) self.conv4_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)#)# self.conv5 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) self.conv5_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# self.conv6 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) self.conv6_bn = nn.BatchNorm2d(ngf * 8) # self.conv7 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) self.conv7_bn =nn.BatchNorm2d(ngf * 8) # nn.InstanceNorm2d(ngf*8, affine=True)# # self.conv8 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) # self.conv8_bn = nn.BatchNorm2d(d * 8) # Unet decoder # self.deconv1 = nn.ConvTranspose2d(ngf * 8, ngf * 8, 4, 2, 1) self.deconv1=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8, ngf*8, kernel_size=3, stride=1, padding=1)) self.deconv1_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# # self.deconv2 = nn.ConvTranspose2d(ngf * 8 * 2, ngf * 8, 4, 2, 1) self.deconv2=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8 * 2, ngf*8, kernel_size=3, stride=1, padding=1)) self.deconv2_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# # self.deconv3 = nn.ConvTranspose2d(ngf * 8 * 2, ngf * 8, 4, 2, 1) self.deconv1=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8*2, ngf*8, kernel_size=3, stride=1, padding=1)) self.deconv3_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# # self.deconv4 = nn.ConvTranspose2d(ngf * 8 * 2, ngf * 4, 4, 2, 1) self.deconv4=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8*2, ngf*4, kernel_size=3, stride=1, padding=1)) self.deconv4_bn = nn.BatchNorm2d(ngf * 4) #nn.InstanceNorm2d(ngf*4, affine=True)# # self.deconv5 = nn.ConvTranspose2d(ngf * 4 * 2, ngf * 2, 4, 2, 1) self.deconv5=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 4 * 2, ngf*2, kernel_size=3, stride=1, padding=1)) self.deconv5_bn = nn.BatchNorm2d(ngf * 2) #nn.InstanceNorm2d(ngf*2, affine=True)# # self.deconv6 = nn.ConvTranspose2d(ngf * 2 * 2, ngf, 4, 2, 1) self.deconv6=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 2 * 2, ngf, kernel_size=3, stride=1, padding=1)) self.deconv6_bn = nn.BatchNorm2d(ngf) #nn.InstanceNorm2d(ngf, affine=True)# # self.deconv7 = nn.ConvTranspose2d(ngf * 2, output_nc, 4, 2, 1) self.deconv7=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 2, output_nc, kernel_size=3, stride=1, padding=1)) # self.deconv7_bn = nn.BatchNorm2d(ngf) # self.deconv8 = nn.ConvTranspose2d(ngf * 2, output_nc, 4, 2, 1) # weight_init # def weight_init(self, mean, std): # for m in self._modules: # normal_init(self._modules[m], mean, std) # forward method # def forward(self, input): # e1 = self.conv1(input) # e2 = self.conv2_bn(self.conv2(F.relu(e1))) # e3 = self.conv3_bn(self.conv3(F.relu(e2))) # e4 = self.conv4_bn(self.conv4(F.relu(e3))) # e5 = self.conv5_bn(self.conv5(F.relu(e4))) # e6 = self.conv6_bn(self.conv6(F.relu(e5))) # e7 = self.conv7_bn(self.conv7(F.leaky_relu(e6, 0.2))) # e7 = self.conv7(F.leaky_relu(e6)) # e8 = self.conv8(F.leaky_relu(e7, 0.2)) # e8=e88[:,0:512,:,:] # e_return=e88[:,512:1024,:,:] # e8 = self.conv8_bn(self.conv8(F.leaky_relu(e7, 0.2))) # d1 = self.deconv1_bn(self.deconv1(F.relu(e7))) # d1 = torch.cat([d1, e6], 1) # d2 = self.deconv2_bn(self.deconv2(F.relu(d1))) # d2 = torch.cat([d2, e5], 1) # d3 = self.deconv3_bn(self.deconv3(F.relu(d2))) # d3 = torch.cat([d3, e4], 1) # d4 = self.deconv4_bn(self.deconv4(F.relu(d3))) # d4 = F.dropout(self.deconv4_bn(self.deconv4(F.relu(d3))), 0.5) # d4 = torch.cat([d4, e3], 1) # d44=self.deconv5(F.relu(d4)) # d5 = self.deconv5_bn(d44) # d5 = torch.cat([d5, e2], 1) # d6 = self.deconv6_bn(self.deconv6(F.relu(d5))) # d6 = torch.cat([d6, e1], 1) # d7 = self.deconv7(d6) # d7 = torch.cat([d7, e1], 1) # d8 = self.deconv8(F.relu(d7)) # o = F.tanh(d7) # return o#,e_return def forward(self, input): e1 = self.conv1(input) e2 = self.conv2(F.leaky_relu(e1, 0.2)) e3 = self.conv3(F.leaky_relu(e2, 0.2)) e4 = self.conv4(F.leaky_relu(e3, 0.2)) e5 = self.conv5(F.leaky_relu(e4, 0.2)) e6 = self.conv6(F.leaky_relu(e5, 0.2)) # e7 = self.conv7_bn(self.conv7(F.leaky_relu(e6, 0.2))) e7 = self.conv7(F.leaky_relu(e6, 0.2)) # e8 = self.conv8(F.leaky_relu(e7, 0.2)) # e8=e88[:,0:512,:,:] # e_return=e88[:,512:1024,:,:] # e8 = self.conv8_bn(self.conv8(F.leaky_relu(e7, 0.2))) d1 = self.deconv1(F.relu(e7)) d1 = torch.cat([d1, e6], 1) d2 = self.deconv2(F.relu(d1)) d2 = torch.cat([d2, e5], 1) d3 = self.deconv3(F.relu(d2)) d3 = torch.cat([d3, e4], 1) d4 = self.deconv4(F.relu(d3)) # d4 = F.dropout(self.deconv4_bn(self.deconv4(F.relu(d3))), 0.5) d4 = torch.cat([d4, e3], 1) d44=self.deconv5(F.relu(d4)) d5 = self.deconv5_bn(d44) d5 = torch.cat([d5, e2], 1) d6 = self.deconv6(F.relu(d5)) d6 = torch.cat([d6, e1], 1) d7 = self.deconv7(d6) # d7 = torch.cat([d7, e1], 1) # d8 = self.deconv8(F.relu(d7)) o = F.tanh(d7) return o#,e_return #class UnetGenerator(nn.Module): # def __init__(self, input_nc, output_nc, num_downs, ngf=64, # norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): # super(UnetGenerator, self).__init__() # self.gpu_ids = gpu_ids # # # construct unet structure # unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # for i in range(num_downs - 5): # unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) # unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) # unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) # unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) # unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # # self.model = unet_block # # def forward(self, input): # if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): # return nn.parallel.data_parallel(self.model, input, self.gpu_ids) # else: # return self.model(input) # # ## Defines the submodule with skip connection. ## X -------------------identity---------------------- X ## |-- downsampling -- |submodule| -- upsampling --| #class UnetSkipConnectionBlock(nn.Module): # def __init__(self, outer_nc, inner_nc, input_nc=None, # submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): # super(UnetSkipConnectionBlock, self).__init__() # self.outermost = outermost # if type(norm_layer) == functools.partial: # use_bias = norm_layer.func == nn.InstanceNorm2d # else: # use_bias = norm_layer == nn.InstanceNorm2d # if input_nc is None: # input_nc = outer_nc # downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, # stride=2, padding=1, bias=use_bias) # downrelu = nn.LeakyReLU(0.2, True) # downnorm = norm_layer(inner_nc) # uprelu = nn.ReLU(True) # upnorm = norm_layer(outer_nc) # # if outermost: # upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, # kernel_size=4, stride=2, # padding=1) # down = [downconv] # up = [uprelu, upconv, nn.Tanh()] # model = down + [submodule] + up # elif innermost: # upconv = nn.ConvTranspose2d(inner_nc, outer_nc, # kernel_size=4, stride=2, # padding=1, bias=use_bias) # down = [downrelu, downconv] # up = [uprelu, upconv, upnorm] # model = down + up # else: # upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, # kernel_size=4, stride=2, # padding=1, bias=use_bias) # down = [downrelu, downconv, downnorm] # up = [uprelu, upconv, upnorm] # # if use_dropout: # model = down + [submodule] + up + [nn.Dropout(0.5)] # else: # model = down + [submodule] + up # # self.model = nn.Sequential(*model) # # def forward(self, x): # if self.outermost: # return self.model(x) # else: # return torch.cat([x, self.model(x)], 1) # Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(NLayerDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 3 padw = 1 sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.Conv2d(ndf, ndf, kernel_size=kw, stride=2, padding=padw), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2**n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), nn.Conv2d(ndf * nf_mult, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), # nn.LayerNorm([ndf * nf_mult,np.int(ndf/nf_mult),np.int(ndf/nf_mult)]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult_prev = nf_mult nf_mult = min(2**n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), # nn.LayerNorm([ndf*nf_mult,15,15]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] sequence += [nn.Conv2d(ndf * nf_mult,ndf * nf_mult, kernel_size=kw, stride=1, padding=padw)] sequence += [nn.Conv2d(ndf * nf_mult, 128, kernel_size=kw, stride=2, padding=padw)] # if use_sigmoid: # sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) self.conv1=nn.Conv2d(128, 1, kernel_size=4, stride=1, padding=1, bias=True) self.conv2=nn.Linear(128*8**2,20)#nn.Conv2d(128, 20, kernel_size=4, stride=1, padding=0, bias=True) self.aux_layer = nn.Sequential(nn.Linear(128*8**2, 939), nn.Softmax()) def forward(self, input): out1=nn.parallel.data_parallel(self.model, input, self.gpu_ids)# self.model(input) # out=F.sigmoid(self.conv1(out1)) out=self.conv1(out1) cond=F.sigmoid(self.conv2(out1.view(out1.shape[0],-1))) class_pred=self.aux_layer(out1.view(out1.shape[0], -1)) return out,cond,class_pred # self.model = nn.Sequential(*sequence) # def forward(self, input): # if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): # return nn.parallel.data_parallel(self.model, input, self.gpu_ids) # else: # return self.model(input) class PixelDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(PixelDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.net = [ nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), norm_layer(ndf * 2), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] if use_sigmoid: self.net.append(nn.Sigmoid()) self.net = nn.Sequential(*self.net) def forward(self, input): if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.net, input, self.gpu_ids) else: return self.net(input) from torchvision import models class Vgg19(torch.nn.Module): def __init__(self, requires_grad=False): super(Vgg19, self).__init__() vgg_pretrained_features = models.vgg19(pretrained=True).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() for x in range(2): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(2, 7): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(7, 12): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(12, 21): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(21, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h_relu1 = self.slice1(X) h_relu2 = self.slice2(h_relu1) h_relu3 = self.slice3(h_relu2) h_relu4 = self.slice4(h_relu3) h_relu5 = self.slice5(h_relu4) out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] return out ================================================ FILE: test_code_released_new/models/pix2pix_model.py ================================================ import torch from collections import OrderedDict #from torch.autograd import Variable import util.util as util from util.image_pool import ImagePool from .base_model import BaseModel from . import networks #import itertools import torch.nn as nn import torch.nn.functional as F import pdb import torchvision import os from torch import autograd import copy from natsort import natsorted, ns class Estimate(nn.Module): def __init__(self): super(Estimate, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, 3, stride=1, padding=1), nn.Conv2d(64, 64, 3, stride=1, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.Conv2d(64, 128, 3, stride=1, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.Conv2d(128, 3, 3, stride=1, padding=1) ) def forward(self, x): x = F.tanh(self.features(x)) return x class Pix2PixModel(BaseModel): def name(self): return 'Pix2PixModel' def _compute_loss_smooth(self, mat): return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \ torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :])) def initialize(self, opt): BaseModel.initialize(self, opt) self.isTrain = opt.isTrain self.B=opt.batchSize self.netG = torch.nn.DataParallel(networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids),device_ids=self.gpu_ids) self.netGN = torch.nn.DataParallel(networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids),device_ids=self.gpu_ids) # self.I_E=Estimate().cuda() if self.isTrain: use_sigmoid = opt.no_lsgan self.netDA = torch.nn.DataParallel(networks.define_D(opt.input_nc, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids),device_ids=self.gpu_ids) self.schedulers = [] self.optimizers = [] self.optimizer_G = torch.optim.RMSprop(self.netG.parameters(), lr=0.0002, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netG.parameters(), # lr=opt.lr, betas=(0, 0.999),amsgrad=False,weight_decay=0) self.optimizer_GN =torch.optim.RMSprop(self.netGN.parameters(), lr=0.0002, alpha=0.99, eps=1e-8)# torch.optim.Adam(self.netGN.parameters(), #torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr)# # self.optimizer_DA = torch.optim.RMSprop(self.netDA.parameters(), lr=0.0002, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netDA.parameters(), #torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr)# self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_GN) # self.optimizers.append(self.optimizer_DA) # self.optimizers.append(self.optimizer_I) # for optimizer in self.optimizers: # self.schedulers.append(networks.get_scheduler(optimizer, opt)) if not self.isTrain or opt.continue_train: self.load_network(self.netG, 'G', opt.which_epoch,self.optimizer_G) self.load_network(self.netGN, 'GN', opt.which_epoch,self.optimizer_GN) # self.load_network(self.I_E, 'I_E', opt.which_epoch) if self.isTrain: self.load_network(self.netDA, 'DA', opt.which_epoch) # self.generator_test = copy.deepcopy(self.netG) # self.generator_testN = copy.deepcopy(self.netGN) if self.isTrain: # self.fake_AB_pool = ImagePool(opt.pool_size) # define loss functions self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor) self.criterionL1_F = torch.nn.L1Loss(size_average=False) self.criterionL1 = torch.nn.L1Loss() self.criterionL2 = torch.nn.MSELoss() self.criterionVGG = networks.VGGLoss(self.gpu_ids) self.criterionCE = torch.nn.CrossEntropyLoss() # initialize optimizers # self.optimizer_I = torch.optim.Adam(self.I_E.parameters(),lr=opt.lr, betas=(opt.beta1, 0.999)) #0.1 in other program print('---------- Networks initialized -------------') networks.print_network(self.netG) if self.isTrain: networks.print_network(self.netDA) print('-----------------------------------------------') def set_input(self, input): input_A = input['A'] P_B = input['PB'] if len(self.gpu_ids) > 0: input_A = input_A.cuda(self.gpu_ids[0], async=True) P_B = P_B.cuda(self.gpu_ids[0], async=True) self.input_A = input_A self.P_B = P_B self.image_paths = input['A_paths']# if AtoB else 'B_paths'] def test(self): self.netG.eval() self.netGN.eval() # self.I_E.eval() # pdb.set_trace() desti=os.path.dirname(self.image_paths[0])+'/'+self.opt.results_dir # pdb.set_trace() if not os.path.isdir(desti): os.makedirs(desti) for i in range(0,self.P_B.size(1)): self.param_B = self.P_B[0,i,:].unsqueeze(0) self.param_B=self.param_B.view(-1,20).float() self.real_A=self.input_A[:,0:3,:,:] self.real_A.requires_grad=True # I_p=self.I_E(self.real_A) # self.param_A=self.param_A.view(-1,20).float() self.AUN = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128)/100000 self.AUN[:,0:3] = 0.5 # self.fake_B = self.netGN(torch.cat([self.real_A,self.AUN],dim=1)) #####################ORIGINAL###################################################### AUR = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128) ################################################################################### # I_f=self.I_E(self.fake_B) self.fake_B_recon=self.netG(torch.cat([self.fake_B.data,AUR],dim=1)) torchvision.utils.save_image((self.fake_B_recon*0.5)+0.5,desti+'/'+str(i)+'_re'+'.png') # #put your video_path os.system("ffmpeg -r 25 -i ./new_crop/results_video/%01d_re.png -vcodec mpeg4 -y movie.mp4") os.system('rm -r ./new_crop/results_video/') def get_image_paths(self): return self.image_paths # return self.ref_path def _compute_loss_D(self, estim, is_real): return -torch.mean(estim) if is_real else torch.mean(estim) ================================================ FILE: test_code_released_new/models/pix2pix_model.py~ ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Oct 25 17:51:34 2018 @author: esa """ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 23 17:42:29 2018 @author: esa """ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Oct 22 19:08:31 2018 @author: esa """ import torch from collections import OrderedDict #from torch.autograd import Variable import util.util as util from util.image_pool import ImagePool from .base_model import BaseModel from . import networks #import itertools import torch.nn as nn import torch.nn.functional as F import pdb import torchvision import os from torch import autograd import copy class Estimate(nn.Module): def __init__(self): super(Estimate, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, 4, stride=2, padding=1), nn.Conv2d(64, 64, 4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 128, kernel_size=4, stride=2, padding=1, bias=True), # nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True), # nn.Conv2d(64,128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 3, kernel_size=4, stride=2, padding=1, bias=True), # nn.BatchNorm2d(64), # nn.ReLU(inplace=True), ) # self.classifier = nn.Sequential( # nn.Linear(576, 236) # ) def forward(self, x): x = F.tanh(self.features(x)) # y=F.tanh(self.classifier(x.view(x.size(0),576))) # o = F.tanh(d8) # x6 = self.down4(x5) return x #class Estimate(nn.Module): # def __init__(self): # super(Estimate, self).__init__() ## self.features = nn.Sequential( # self.conv1=nn.Conv2d(3, 64, 4, stride=2, padding=1) # self.conv2 = nn.Conv2d(64, 64, 4, stride=2, padding=1) # self.conv2_bn=nn.BatchNorm2d(64) ## nn.ReLU(inplace=True), # self.deconv1=nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1, bias=True) ## nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True), ## nn.Conv2d(64,128, kernel_size=4, stride=2, padding=1), # self.deconv1_bn=nn.BatchNorm2d(64) ## nn.ReLU(inplace=True), # self.deconv2=nn.ConvTranspose2d(64*2, 3, kernel_size=4, stride=2, padding=1, bias=True) ## nn.BatchNorm2d(64), ## nn.ReLU(inplace=True), ## ) ## s ## self.classifier = nn.Sequential( ## nn.Linear(576, 236) ## ) # def forward(self, input): # # e1 = self.conv1(input) # e2 = self.conv2_bn(self.conv2(F.relu(e1))) # d1 = self.deconv1_bn(self.deconv1(F.relu(e2))) # d1 = torch.cat([d1, e1], 1) # d2 = self.deconv2(d1) # o = F.tanh(d2) ## y=F.tanh(self.classifier(x.view(x.size(0),576))) # ## o = F.tanh(d8) ## x6 = self.down4(x5) # return o class Pix2PixModel(BaseModel): def name(self): return 'Pix2PixModel' def _compute_loss_smooth(self, mat): return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \ torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :])) def initialize(self, opt): BaseModel.initialize(self, opt) self.isTrain = opt.isTrain self.B=opt.batchSize # load/define networks self.netG = networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) self.netGN = networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) # self.netGR = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, # opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) self.I_E=Estimate().cuda() # pdb.set_trace() # self.I_D=Decoder().cuda() #setting up the saliency model for loss calculations # self.u_sal=saliency_encoder().cuda() if self.isTrain: use_sigmoid = opt.no_lsgan self.netDA = networks.define_D(opt.input_nc, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids) # self.netDB = networks.define_D(opt.input_nc, opt.ndf, # opt.which_model_netD, # opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids) if not self.isTrain or opt.continue_train: self.load_network(self.netG, 'G', opt.which_epoch) self.load_network(self.netGN, 'GN', opt.which_epoch) self.load_network(self.I_E, 'I_E', opt.which_epoch) # self.load_network(self.I_D, 'I_D', opt.which_epoch) if self.isTrain: self.load_network(self.netDA, 'DA', opt.which_epoch) # self.load_network(self.netDB, 'DB', opt.which_epoch) self.generator_test = copy.deepcopy(self.netG) self.generator_testN = copy.deepcopy(self.netGN) if self.isTrain: self.fake_AB_pool = ImagePool(opt.pool_size) # define loss functions self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor) self.criterionL1_F = torch.nn.L1Loss(size_average=False) self.criterionL1 = torch.nn.L1Loss() self.criterionL2 = torch.nn.MSELoss() self.criterionVGG = networks.VGGLoss(self.gpu_ids) self.criterionCE = torch.nn.CrossEntropyLoss() # initialize optimizers self.schedulers = [] self.optimizers = [] # params = list(self.netG.parameters()) # params += list(self.I_E.parameters()) self.optimizer_I = torch.optim.Adam(self.I_E.parameters(),lr=opt.lr, betas=(opt.beta1, 0.999)) #0.1 in other program # self.optimizer_D = torch.optim.Adam(self.I_D.parameters(), # lr=0.0002, betas=(opt.beta1, 0.999),weight_decay=0) self.optimizer_G = torch.optim.RMSprop(self.netG.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netG.parameters(), # lr=opt.lr, betas=(0, 0.999),amsgrad=False,weight_decay=0) self.optimizer_GN =torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)# torch.optim.Adam(self.netGN.parameters(), #torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr)# # lr=opt.lr, betas=(0, 0.999),amsgrad=False) # pdb.set_trace() self.optimizer_DA = torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netDA.parameters(), #torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr)# # lr=opt.lr, betas=(0, 0.999),amsgrad=False,weight_decay=0) # self.optimizer_DB = torch.optim.Adam(self.netDB.parameters(), # lr=opt.lrD, betas=(opt.beta1, 0.999),amsgrad=True) self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_GN) # self.optimizers.append(self.optimizer_DB) self.optimizers.append(self.optimizer_DA) self.optimizers.append(self.optimizer_I) # self.optimizers.append(self.optimizer_D) for optimizer in self.optimizers: self.schedulers.append(networks.get_scheduler(optimizer, opt)) print('---------- Networks initialized -------------') networks.print_network(self.netG) if self.isTrain: networks.print_network(self.netDA) print('-----------------------------------------------') def set_input(self, input): # AtoB = self.opt.which_direction == 'AtoB' input_A = input['A'] #if AtoB else 'B'] input_B = input['B'] P_A = input['PA'] P_B = input['PB'] I = input['I'] C=input['C'] #if AtoB elself.criterionL1 = torch.nn.L1Loss(size_average=False)se 'A'] # pdb.set_trace() # input_B[:,0,0:3]=(input_B[:,0,0:3]-(-0.50))/1 # input_B[:,0,3:20]=input_B[:,0,3:20]/5 # pdb.set_trace() # print(input_B) # input_S = input['S'] if len(self.gpu_ids) > 0: input_A = input_A.cuda(self.gpu_ids[0], async=True) input_B = input_B.cuda(self.gpu_ids[0], async=True) P_A = P_A.cuda(self.gpu_ids[0], async=True) P_B = P_B.cuda(self.gpu_ids[0], async=True) I = I.cuda(self.gpu_ids[0], async=True) C = C.cuda(self.gpu_ids[0], async=True) # input_S = input_S.cuda(self.gpu_ids[0], async=True) self.input_A = input_A self.input_B = input_B self.P_A = P_A self.P_B = P_B self.I = I/939 self.C = C.long().view(-1) # self.input_S = input_S # print(self.input_A.size()) self.image_paths = input['A_paths']# if AtoB else 'B_paths'] self.ref_paths = input['B_paths'] # pdb.set_trace() # def test(self): # # self.real_A = self.input_A # self.real_A.requires_grad=True # # self.real_B = self.input_B # self.real_B.requires_grad=True # # self.param_A = self.P_A.view(self.B,20) # self.param_B = self.P_B.view(self.B,20) # self.netG.eval() # self.netGN.eval() # self.I_E.eval() ## self.param_A.requires_grad=True # # with torch.no_grad(): # I_p=self.I_E(self.real_A) ## pdb.set_trace() # self.AUN=self.param_A.float()/100000 # self.AUN[:,0:3] = 0.5 # ## self.AUN=self.real_B.float()+torch.randn(self.B,20).cuda()/10 ## self.AUN=torch.clamp(self.AUN,0,1) # I_pd=I_p.detach() ## pdb.set_trace() ## I_pd.requires_grad=True # self.fake_B = self.netGN(torch.cat([I_pd.data,self.AUN],dim=1)) # AUR=self.param_B.view(-1,20).float() # # with torch.no_grad(): # I_f=self.I_E(self.fake_B) # I_fd=I_f.detach() ## I_fd.requires_grad=True # ## pdb.set_trace() ## self.new_z2=z.view(z.size(0), z.size(1), 1, 1).expand(z.size(0), z.size(1), self.fake_B.size(2), self.fake_B.size(3)) ## with torch.no_grad(): # self.fake_B_recon=self.netG(torch.cat([I_fd.data,AUR],dim=1)) def test(self): # self.real_A = self.input_A # self.real_A.requires_grad=True # self.real_B = self.input_B # self.real_B.requires_grad=True self.netG.eval() self.netGN.eval() self.I_E.eval() # self.up1.eval() # self.param_A.requires_grad=True pdb.set_trace() desti=os.path.dirname(self.image_paths[0].replace('/home/esa/Downloads/unzippedFaces','./results')) if not os.path.isdir(desti): os.makedirs(desti) self.U=nn.Upsample(size=(256,256), scale_factor=None, mode='bilinear', align_corners=None) for i in range(0,self.P_A.size(1)): # pdb.set_trace() self.param_A = self.P_A[0,0,:].unsqueeze(0)#.view(self.B,i,20) self.param_B = self.P_B[0,i,:].unsqueeze(0)#.view(self.B,i,20) self.real_B=self.input_B[:,i*3:(i*3)+3,:,:] self.real_A=self.input_A[:,0:3,:,:] self.real_A.requires_grad=True # pdb.set_trace() # with torch.no_grad(): I_p=self.I_E(self.real_A) # pdb.set_trace() self.AUN=self.param_A.float()/100000 self.AUN[:,0:3] = 0.5 # # self.AUN=self.real_B.float()+torch.randn(self.B,20).cuda()/10 # self.AUN=torch.clamp(self.AUN,0,1) # I_pd=I_p.detach() # pdb.set_trace() # I_pd.requires_grad=True self.fake_B = self.netGN(torch.cat([I_p,self.AUN],dim=1)) AUR=self.param_B.view(-1,20).float() # AUR[:,2]=i/100 # AUR[:,3:20]=self.param_A.float()[:,3:20] # AUR[:,0]=0.75 # AUR[:,1]=0.5 # # AUR[:,2]=0.5 # AUR[:,0]=torch.rand(1)[0] # AUR[:,1]=torch.rand(1)[0] # AUR[:,2]=torch.rand(1)[0] # AUR[:,2]=(i+1)/10 # AUR[:,3]=0.2 # AUR[:,5]=0.8 # AUR[:,3:19]=torch.rand(16)/10 # AUR[:,3]=0.7 # AUR[:,16]=0 # AUR[:,18]=0.8 # with torch.no_grad(): I_f=self.I_E(self.fake_B) # I_fd=I_f.detach() # I_fd.requires_grad=True # pdb.set_trace() # self.new_z2=z.view(z.size(0), z.size(1), 1, 1).expand(z.size(0), z.size(1), self.fake_B.size(2), self.fake_B.size(3)) # with torch.no_grad(): self.real_A_up=self.U(self.real_A) self.fake_B_recon=self.netG(torch.cat([I_f.data,AUR],dim=1)) # self.fake_B_256=self.up1(torch.cat([self.real_A_up,self.U(self.fake_B_recon)],dim=1)) # pdb.set_trace() torchvision.utils.save_image((self.fake_B_recon*0.5)+0.5,desti+'/'+str(i)+'_re'+'.png') torchvision.utils.save_image((self.fake_B*0.5)+0.5,desti+'/'+str(i)+'_neut'+'.png') torchvision.utils.save_image((self.real_B*0.5)+0.5,desti+'/'+str(i)+'_gt'+'.png') # torchvision.utils.save_image((self.fake_B_256*0.5)+0.5,desti+'/'+str(i)+'_256'+'.png') # torchvision.utils.save_image((self.real_A_up*0.5)+0.5,desti+'/'+str(i)+'_A_up'+'.png') torchvision.utils.save_image((self.real_A*0.5)+0.5,desti+'/'+str(i)+'_ref'+'.png') # pdb.set_trace() torch.__version__ # pdb.set_trace() # os.system('export PATH="/home/esa/anaconda3/bin:$PATH"') # os.system('source activate x2face') # os.system('/home/esa/anaconda3/envs/x2face/bin/python /home/esa/Downloads/X2Face-master/UnwrapMosaic/Face2Face_UnwrapMosaic_run.py --driver_path %(language)s --source_path %(sp)s' %{'language': os.path.dirname(self.image_paths[0])+'/','sp': os.path.dirname(self.ref_paths[0])+'/'}) # os.system('source deactivate') # pdb.set_trace() def forward(self): self.real_A = self.input_A self.real_A.requires_grad=True self.real_B = self.input_B self.real_B.requires_grad=True self.param_A = self.P_A.view(self.B,20) self.param_B = self.P_B.view(self.B,20) # self.param_A.requires_grad=True # with torch.no_grad(): I_p=self.I_E(self.real_A) # pdb.set_trace() self.param_A=self.param_A.view(-1,20).float() self.AUN = self.param_A.view(self.param_A.size(0), self.param_A.size(1), 1, 1).expand( self.param_A.size(0), self.param_A.size(1), 128, 128)/100000000 # self.AUN=self.param_A.float()/100000 self.AUN[:,0:3] = 0.5 # # self.AUN=self.real_B.float()+torch.randn(self.B,20).cuda()/10 # self.AUN=torch.clamp(self.AUN,0,1) # I_pd=I_p.detach() # pdb.set_trace() # I_pd.requires_grad=True self.fake_B = self.netGN(torch.cat([I_p,self.AUN],dim=1)) self.param_B=self.param_B.view(-1,20).float() AUR = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128) # with torch.no_grad(): I_f=self.I_E(self.fake_B) # I_fd=I_f.detach() # I_fd.requires_grad=True # pdb.set_trace() # self.new_z2=z.view(z.size(0), z.size(1), 1, 1).expand(z.size(0), z.size(1), self.fake_B.size(2), self.fake_B.size(3)) # with torch.no_grad(): self.fake_B_recon=self.netG(torch.cat([I_f,AUR],dim=1)) # pdb.set_trace() # self.gar_con=self.u_sal(self.garbage) # pdb.set_trace() # get image paths def get_image_paths(self): return self.image_paths def _compute_loss_D(self, estim, is_real): return -torch.mean(estim) if is_real else torch.mean(estim) def backward_DA(self): def compute_grad2(d_out, x_in): batch_size = x_in.size(0) grad_dout = autograd.grad( outputs=d_out.sum(), inputs=x_in, create_graph=True, retain_graph=True, only_inputs=True )[0] grad_dout2 = grad_dout.pow(2) assert(grad_dout2.size() == x_in.size()) reg = grad_dout2.view(batch_size, -1).sum(1) return reg # Fake # stop backprop to the generator by detaching fake_B pred_real, cond_real,class_pred = self.netDA(self.real_A) # self.D_loss1 = -(torch.mean(pred_real) - torch.mean(pred_fake)) # self.D_loss2 = -(torch.mean(pred_real) - torch.mean(pred_fake2)) self.AUX_loss=self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C # pdb.set_trace() self.pred_loss_B=self.criterionL1(cond_real,self.param_A.float().detach())/self.B *self.opt.lambda_A self.loss_D_realB = self.criterionGAN(pred_real, True) + self.AUX_loss + self.pred_loss_B self.loss_D_realB.backward(retain_graph=True) self.reg = 0.05*compute_grad2(pred_real, self.real_A).mean() self.reg.backward() fake_AB = self.fake_B_recon.detach() fake_B=self.fake_B.detach() fake_AB.requires_grad=True fake_B.requires_gard=True # pdb.set_trace() pred_fake, cond_fake, class_pred1 = self.netDA(fake_AB) pred_fake2, cond_fake2,class_pred2 = self.netDA(fake_B) # pred_real, cond_real,class_pred = self.netDA(self.real_A) self.loss_D_fakeB = self.criterionGAN(pred_fake, False) self.loss_D_fakeB2 = self.criterionGAN(pred_fake2, False) loss_fake=self.loss_D_fakeB + self.loss_D_fakeB2 loss_fake.backward()#(retain_graph=True) # pdb.set_trace() # Real # real_AB = self.real_A#self.real_A # real_pred=self.real_B.float() + (torch.rand(self.B,20)/torch.randint(10, 100, (1,))).cuda() # pdb.set_trace() # self.AUX_loss=self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C ## pdb.set_trace() # self.pred_loss_B=self.criterionL1(cond_real,self.param_A.float().detach())/self.B *self.opt.lambda_A # m = nn.LogSoftmax() # Combined loss # self.loss_DB = ((self.loss_D_fakeB + self.loss_D_realB)*0.5) + self.pred_loss_B + self.loss_D_fakeB2 + self.AUX_loss # self.loss_DB = self.D_loss1 + self.D_loss2 + self.AUX_loss + self.pred_loss_B #+ self.loss_D_fakeB2 # pdb.set_trace() # self.loss_DB.backward() def backward_GR(self): # fake_B=self.fake_B # self.fake_B_re=self.netGR(torch.cat([fake_B.detach(),self.new_z2.detach()],dim=1)) # fake_BA = self.fake_B_re # pdb.set_trace() pred_fake, E_con, class_pred = self.netDA(self.fake_B) # gar_con.register_hook(print) self.loss_G_GANE = self.criterionGAN(pred_fake, True) #-torch.mean(pred_fake)# self.sal_loss2= self.criterionL1(E_con,self.param_A.detach()/1000000)/self.B*self.opt.lambda_A self.class_pred_loss =self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C # self.sal_loss_N1=self.criterionL1(gar_con,self.real_B.float().detach())/self.B*self.opt.lambda_A # self.recon_loss=self.criterionL1(self.fake_B_re,self.real_A.detach())*self.opt.lambda_B*0.1 with torch.no_grad(): I_f=self.I_E(self.fake_B) self.tar=self.netG(torch.cat([I_f.data,self.AUN],dim=1)) # pdb.set_trace() self.recon=self.criterionL1(self.fake_B,self.tar.detach())*self.opt.lambda_D self.recon_vgg=self.criterionVGG(self.fake_B,self.real_B.detach())*0.1 # AUR=self.param_B.view(-1,20).float() AUR = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128) I_f=self.I_E(self.fake_B) # pdb.set_trace() self.fake_B_re=self.netG(torch.cat([I_f,AUR],dim=1)) self.R=self.criterionL1(self.fake_B_re,self.real_B.detach())*self.opt.lambda_B self.loss_GR= self.loss_G_GANE + self.sal_loss2 +self.recon + self.R + self.class_pred_loss + self.recon_vgg # self.loss_GR= self.loss_G_GANE + self.sal_loss2 # pdb.set_trace() self.loss_GR.backward() def backward_G(self): def compute_loss_smooth(mat): return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \ torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :])) # First, G(A) should fake the discriminator # fake_B = self.netG(torch.cat([self.real_A,self.D_au],dim=1)) # fake_AB = fake_B # pred_fake, E_con = self.netDA(self.fake_B) pred_fake_recon, gar_con, class_pred = self.netDA(self.fake_B_recon) # pdb.set_trace() # self.loss_G_GANE = self.criterionGAN(pred_fake_recon, True) # pdb.set_trace() self.loss_G_GAN = self.criterionGAN(pred_fake_recon, True) #-torch.mean(pred_fake_recon)# # pdb.set_trace() # self.sal_loss2= self.criterionL1(E_con,self.AUN.detach())/self.B*self.opt.lambda_A self.sal_loss_N1=self.criterionL1(gar_con,self.param_B.float().detach())/self.B*self.opt.lambda_A self.recon_loss=self.criterionL1(self.fake_B_recon,self.real_B.detach())*self.opt.lambda_D self.class_pred_loss_recon=self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C # self.loss_smooth=compute_loss_smooth(self.fake_B)*self.opt.lambda_B # weight_regur=torch.mean(self.fake_B) self._loss_g_mask_1_smooth = self._compute_loss_smooth(self.fake_B_recon)*0.1 #* self.opt.lambda_A # print(weight_regur) #torch.mean(torch.pow(self.fake_B, 2))*self.opt.lambda_B self.loss_G = self.loss_G_GAN + self.recon_loss + self.sal_loss_N1 + self.class_pred_loss_recon #+ self._loss_g_mask_1_smooth #+weight_regur#+ self.loss_G_L1 # pdb.set_trace() self.loss_G.backward(retain_graph=True) def _gradinet_penalty_D(self): # interpolate sample alpha = torch.rand(self.B, 1, 1, 1).cuda().expand_as(self.real_B) interpolated = alpha * self.real_B.data + (1 - alpha) * self.fake_B_recon.data interpolated.requires_grad=True # p=self.fake_B.detach() # p.requires_grad=True # pdb.set_trace() interpolated_prob, cond_real,class_pred = self.netDA(interpolated) # compute gradients grad = torch.autograd.grad(outputs=interpolated_prob, inputs=interpolated, grad_outputs=torch.ones(interpolated_prob.size()).cuda(), retain_graph=True, create_graph=True, only_inputs=True)[0] # penalize gradients grad = grad.view(grad.size(0), -1) grad_l2norm = torch.sqrt(torch.sum(grad ** 2, dim=1)) self._loss_d_gp = torch.mean((grad_l2norm - 1) ** 2) * 1 self._loss_d_gp.backward() def optimize_parameters_D(self): self.forward() self.optimizer_DA.zero_grad() self.backward_DA() self.optimizer_DA.step() # for p in self.netDA.parameters(): # p.data.clamp_(-0.01, 0.01) # self.optimizer_DA.zero_grad() # self._gradinet_penalty_D() ## loss_D_gp.backward() # self.optimizer_DA.step() # self.optimizer_D.zero_grad() # self.backward_I() # for param in self.netG.parameters(): # print(param.grad.data.sum()) # pdb.set_trace() # self.optimizer_D.step() def optimize_parameters_G(self): def toogle_grad(model, requires_grad): for p in model.parameters(): p.requires_grad_(requires_grad) def update_average(model_tgt, model_src, beta): toogle_grad(model_src, False) toogle_grad(model_tgt, False) param_dict_src = dict(model_src.named_parameters()) for p_name, p_tgt in model_tgt.named_parameters(): p_src = param_dict_src[p_name] assert(p_src is not p_tgt) p_tgt.copy_(beta*p_tgt + (1. - beta)*p_src) # self.generator_test = copy.deepcopy(self.netG) # self.generator_testN = copy.deepcopy(self.netGN) self.optimizer_G.zero_grad() self.optimizer_I.zero_grad() self.backward_G() self.optimizer_G.step() # for param in self.netG.parameters(): # print(param.grad.data.sum()) # pdb.set_trace() # update_average(self.generator_test, self.netG, # beta=0.99) # self.optimizer_I.zero_grad() self.optimizer_I.step() # self.optimizer_GN.step() self.optimizer_GN.zero_grad() self.backward_GR() # for param in self.netG.parameters(): # print(param.grad.data.sum()) # pdb.set_trace() self.optimizer_GN.step() # update_average(self.generator_testN, self.netGN, # beta=0.99) def get_current_errors(self): return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]), ('G_GANE', self.loss_G_GANE.data[0]), # ('sal_loss', self.sal_loss.data[0]), ('sal_loss2', self.sal_loss2.data[0]), ('sal_lossN1', self.sal_loss_N1.data[0]), ('recon_loss', self.recon_loss.data[0]), # ('d_gp', self._loss_d_gp.data[0]), # ('recon_loss2', self.recon_loss2.data[0]), ('grad_pen', self.reg.data[0]), ('D_loss_fake', self.loss_D_fakeB.data[0]), ('D_loss_real', self.loss_D_realB.data[0]), ('vgg', self.recon_vgg.data[0]), ('class_loss',self.class_pred_loss.data[0]) # ('smooth', self._loss_g_mask_1_smooth.data[0]), # ('D_loss_fake_A',self.loss_D_fake.data[0]) ]) def get_current_visuals(self): torchvision.utils.save_image((self.fake_B_recon*0.5)+0.5,'recon_WGAN_dirac_steroid.png') torchvision.utils.save_image((self.fake_B*0.5)+0.5,'neut_WGAN_dirac_steroid.png') torchvision.utils.save_image((self.real_A*0.5)+0.5,'real_WGAN_dirac_steroid.png') # torchvision.utils.save_image((self.tar*0.5)+0.5,'tar.png') # real_A = util.tensor2im(self.input_A.data) fake_B = util.tensor2im(self.fake_B.data) fake_B_recon = util.tensor2im(self.fake_B_recon.data) return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('fake_B_recon', fake_B_recon)]) def save(self, label): self.save_network(self.netG, 'G', label, self.gpu_ids) self.save_network(self.netDA, 'DA', label, self.gpu_ids) self.save_network(self.I_E, 'I_E', label, self.gpu_ids) # self.save_network(self.I_D, 'I_D', label, self.gpu_ids) # self.save_network(self.netDB, 'DB', label, self.gpu_ids) self.save_network(self.netGN, 'GN', label, self.gpu_ids) ================================================ FILE: test_code_released_new/models/test_model.py ================================================ from torch.autograd import Variable from collections import OrderedDict import util.util as util from .base_model import BaseModel from . import networks class TestModel(BaseModel): def name(self): return 'TestModel' def initialize(self, opt): assert(not opt.isTrain) BaseModel.initialize(self, opt) self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) which_epoch = opt.which_epoch self.load_network(self.netG, 'G', which_epoch) print('---------- Networks initialized -------------') networks.print_network(self.netG) print('-----------------------------------------------') def set_input(self, input): # we need to use single_dataset mode input_A = input['A'] if len(self.gpu_ids) > 0: input_A.cuda(self.gpu_ids[0], async=True) self.input_A = input_A self.image_paths = input['A_paths'] def test(self): self.real_A = Variable(self.input_A) self.fake_B = self.netG(self.real_A) # get image paths def get_image_paths(self): return self.image_paths def get_current_visuals(self): real_A = util.tensor2im(self.real_A.data) fake_B = util.tensor2im(self.fake_B.data) return OrderedDict([('real_A', real_A), ('fake_B', fake_B)]) ================================================ FILE: test_code_released_new/options/__init__.py ================================================ ================================================ FILE: test_code_released_new/options/base_options.py ================================================ import argparse import os from util import util import torch class BaseOptions(): def __init__(self): self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) self.initialized = False def initialize(self): self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size') self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size') self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size') self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') self.parser.add_argument('--which_model_netD', type=str, default='basic', help='selects model to use for netD') self.parser.add_argument('--which_model_netG', type=str, default='resnet_9blocks', help='selects model to use for netG') self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers') self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') self.parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') self.parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single]') self.parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. cycle_gan, pix2pix, test') self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA') self.parser.add_argument('--nThreads', default=8, type=int, help='# threads for loading data') self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size') self.parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') self.parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') self.parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]') self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') self.parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]') self.initialized = True def parse(self): if not self.initialized: self.initialize() self.opt = self.parser.parse_args() self.opt.isTrain = self.isTrain # train or test str_ids = self.opt.gpu_ids.split(',') self.opt.gpu_ids = [] for str_id in str_ids: id = int(str_id) if id >= 0: self.opt.gpu_ids.append(id) # set gpu ids if len(self.opt.gpu_ids) > 0: torch.cuda.set_device(self.opt.gpu_ids[0]) args = vars(self.opt) print('------------ Options -------------') for k, v in sorted(args.items()): print('%s: %s' % (str(k), str(v))) print('-------------- End ----------------') # save to the disk expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) util.mkdirs(expr_dir) file_name = os.path.join(expr_dir, 'opt.txt') with open(file_name, 'wt') as opt_file: opt_file.write('------------ Options -------------\n') for k, v in sorted(args.items()): opt_file.write('%s: %s\n' % (str(k), str(v))) opt_file.write('-------------- End ----------------\n') return self.opt ================================================ FILE: test_code_released_new/options/test_options.py ================================================ from .base_options import BaseOptions class TestOptions(BaseOptions): def initialize(self): BaseOptions.initialize(self) self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') self.parser.add_argument('--which_ref', type=str, default='path/to/reference/image', help='path to customized reference image') self.parser.add_argument('--csv_path', type=str, default='path/to/csv/driving', help='path to csv file containing AUs of driving video') self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run') self.parser.add_argument('--which_ref_d1', type=str, default='path/to/reference/image', help='path to customized driving_1 image') self.parser.add_argument('--which_ref_d2', type=str, default='path/to/reference/image', help='path to customized driving_2 image') self.isTrain = False ================================================ FILE: test_code_released_new/options/train_options.py ================================================ from .base_options import BaseOptions class TrainOptions(BaseOptions): def initialize(self): BaseOptions.initialize(self) self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen') self.parser.add_argument('--display_single_pane_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.') self.parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') self.parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') self.parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') self.parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') self.parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)') self.parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)') self.parser.add_argument('--lambda_C', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)') self.parser.add_argument('--lambda_D', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)') self.parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') self.parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau') self.parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') self.parser.add_argument('--identity', type=float, default=0.5, help='use identity mapping. Setting identity other than 1 has an effect of scaling the weight of the identity mapping loss.' 'For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set optidentity = 0.1') self.isTrain = True ================================================ FILE: test_code_released_new/test.py ================================================ import os from options.test_options import TestOptions from data.data_loader import CreateDataLoader from models.models import create_model #from util.visualizer import Visualizer #from util import html import pdb opt = TestOptions().parse() opt.nThreads = 1 # test code only supports nThreads = 1 opt.batchSize = 1 # test code only supports batchSize = 1 opt.serial_batches = True # no shuffle opt.no_flip = True # no flip data_loader = CreateDataLoader(opt) dataset = data_loader.load_data() model = create_model(opt) #visualizer = Visualizer(opt) # create website web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch)) #webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch)) # test def main(): for i, data in enumerate(dataset): # pdb.set_trace() if i >= opt.how_many: break model.set_input(data) img_path = model.get_image_paths() model.test() img_path = model.get_image_paths() # print('%04d: process image... %s' % (i, img_path)) print("Done!") if __name__ == '__main__': main() ================================================ FILE: test_code_released_new/util/__init__.py ================================================ ================================================ FILE: test_code_released_new/util/get_data.py ================================================ from __future__ import print_function import os import tarfile import requests from warnings import warn from zipfile import ZipFile from bs4 import BeautifulSoup from os.path import abspath, isdir, join, basename class GetData(object): """ Download CycleGAN or Pix2Pix Data. Args: technique : str One of: 'cyclegan' or 'pix2pix'. verbose : bool If True, print additional information. Examples: >>> from util.get_data import GetData >>> gd = GetData(technique='cyclegan') >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. """ def __init__(self, technique='cyclegan', verbose=True): url_dict = { 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets', 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' } self.url = url_dict.get(technique.lower()) self._verbose = verbose def _print(self, text): if self._verbose: print(text) @staticmethod def _get_options(r): soup = BeautifulSoup(r.text, 'lxml') options = [h.text for h in soup.find_all('a', href=True) if h.text.endswith(('.zip', 'tar.gz'))] return options def _present_options(self): r = requests.get(self.url) options = self._get_options(r) print('Options:\n') for i, o in enumerate(options): print("{0}: {1}".format(i, o)) choice = input("\nPlease enter the number of the " "dataset above you wish to download:") return options[int(choice)] def _download_data(self, dataset_url, save_path): if not isdir(save_path): os.makedirs(save_path) base = basename(dataset_url) temp_save_path = join(save_path, base) with open(temp_save_path, "wb") as f: r = requests.get(dataset_url) f.write(r.content) if base.endswith('.tar.gz'): obj = tarfile.open(temp_save_path) elif base.endswith('.zip'): obj = ZipFile(temp_save_path, 'r') else: raise ValueError("Unknown File Type: {0}.".format(base)) self._print("Unpacking Data...") obj.extractall(save_path) obj.close() os.remove(temp_save_path) def get(self, save_path, dataset=None): """ Download a dataset. Args: save_path : str A directory to save the data to. dataset : str, optional A specific dataset to download. Note: this must include the file extension. If None, options will be presented for you to choose from. Returns: save_path_full : str The absolute path to the downloaded data. """ if dataset is None: selected_dataset = self._present_options() else: selected_dataset = dataset save_path_full = join(save_path, selected_dataset.split('.')[0]) if isdir(save_path_full): warn("\n'{0}' already exists. Voiding Download.".format( save_path_full)) else: self._print('Downloading Data...') url = "{0}/{1}".format(self.url, selected_dataset) self._download_data(url, save_path=save_path) return abspath(save_path_full) ================================================ FILE: test_code_released_new/util/html.py ================================================ import dominate from dominate.tags import * import os class HTML: def __init__(self, web_dir, title, reflesh=0): self.title = title self.web_dir = web_dir self.img_dir = os.path.join(self.web_dir, 'images') if not os.path.exists(self.web_dir): os.makedirs(self.web_dir) if not os.path.exists(self.img_dir): os.makedirs(self.img_dir) # print(self.img_dir) self.doc = dominate.document(title=title) if reflesh > 0: with self.doc.head: meta(http_equiv="reflesh", content=str(reflesh)) def get_image_dir(self): return self.img_dir def add_header(self, str): with self.doc: h3(str) def add_table(self, border=1): self.t = table(border=border, style="table-layout: fixed;") self.doc.add(self.t) def add_images(self, ims, txts, links, width=400): self.add_table() with self.t: with tr(): for im, txt, link in zip(ims, txts, links): with td(style="word-wrap: break-word;", halign="center", valign="top"): with p(): with a(href=os.path.join('images', link)): img(style="width:%dpx" % width, src=os.path.join('images', im)) br() p(txt) def save(self): html_file = '%s/index.html' % self.web_dir f = open(html_file, 'wt') f.write(self.doc.render()) f.close() if __name__ == '__main__': html = HTML('web/', 'test_html') html.add_header('hello world') ims = [] txts = [] links = [] for n in range(4): ims.append('image_%d.png' % n) txts.append('text_%d' % n) links.append('image_%d.png' % n) html.add_images(ims, txts, links) html.save() ================================================ FILE: test_code_released_new/util/image_pool.py ================================================ import random import torch from torch.autograd import Variable class ImagePool(): def __init__(self, pool_size): self.pool_size = pool_size if self.pool_size > 0: self.num_imgs = 0 self.images = [] def query(self, images): if self.pool_size == 0: images.requires_grad=True return images return_images = [] for image in images: image = torch.unsqueeze(image, 0) if self.num_imgs < self.pool_size: self.num_imgs = self.num_imgs + 1 self.images.append(image) return_images.append(image) else: p = random.uniform(0, 1) if p > 0.5: random_id = random.randint(0, self.pool_size - 1) tmp = self.images[random_id].clone() self.images[random_id] = image return_images.append(tmp) else: return_images.append(image) return_images = Variable(torch.cat(return_images, 0)) return_images.requires_grad=True return return_images ================================================ FILE: test_code_released_new/util/util.py ================================================ from __future__ import print_function import torch import numpy as np from PIL import Image import os # Converts a Tensor into a Numpy array # |imtype|: the desired type of the converted numpy array def tensor2im(image_tensor, imtype=np.uint8): image_numpy = image_tensor[0].cpu().float().numpy() if image_numpy.shape[0] == 1: image_numpy = np.tile(image_numpy, (3, 1, 1)) image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 return image_numpy.astype(imtype) def diagnose_network(net, name='network'): mean = 0.0 count = 0 for param in net.parameters(): if param.grad is not None: mean += torch.mean(torch.abs(param.grad.data)) count += 1 if count > 0: mean = mean / count print(name) print(mean) def save_image(image_numpy, image_path): image_pil = Image.fromarray(image_numpy) image_pil.save(image_path) def print_numpy(x, val=True, shp=False): x = x.astype(np.float64) if shp: print('shape,', x.shape) if val: x = x.flatten() print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) def mkdirs(paths): if isinstance(paths, list) and not isinstance(paths, str): for path in paths: mkdir(path) else: mkdir(paths) def mkdir(path): if not os.path.exists(path): os.makedirs(path) ================================================ FILE: test_code_released_new/util/visualizer.py ================================================ import numpy as np import os import ntpath import time from . import util from . import html from scipy.misc import imresize class Visualizer(): def __init__(self, opt): # self.opt = opt self.display_id = opt.display_id self.use_html = opt.isTrain and not opt.no_html self.win_size = opt.display_winsize self.name = opt.name self.opt = opt self.saved = False if self.display_id > 0: import visdom self.vis = visdom.Visdom(port=opt.display_port) if self.use_html: self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') self.img_dir = os.path.join(self.web_dir, 'images') print('create web directory %s...' % self.web_dir) util.mkdirs([self.web_dir, self.img_dir]) self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') with open(self.log_name, "a") as log_file: now = time.strftime("%c") log_file.write('================ Training Loss (%s) ================\n' % now) def reset(self): self.saved = False # |visuals|: dictionary of images to display or save def display_current_results(self, visuals, epoch, save_result): if self.display_id > 0: # show images in the browser ncols = self.opt.display_single_pane_ncols if ncols > 0: h, w = next(iter(visuals.values())).shape[:2] table_css = """""" % (w, h) title = self.name label_html = '' label_html_row = '' nrows = int(np.ceil(len(visuals.items()) / ncols)) images = [] idx = 0 for label, image_numpy in visuals.items(): label_html_row += '%s' % label images.append(image_numpy.transpose([2, 0, 1])) idx += 1 if idx % ncols == 0: label_html += '%s' % label_html_row label_html_row = '' white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255 while idx % ncols != 0: images.append(white_image) label_html_row += '' idx += 1 if label_html_row != '': label_html += '%s' % label_html_row # pane col = image row self.vis.images(images, nrow=ncols, win=self.display_id + 1, padding=2, opts=dict(title=title + ' images')) label_html = '%s
' % label_html self.vis.text(table_css + label_html, win=self.display_id + 2, opts=dict(title=title + ' labels')) else: idx = 1 for label, image_numpy in visuals.items(): self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), win=self.display_id + idx) idx += 1 if self.use_html and (save_result or not self.saved): # save images to a html file self.saved = True for label, image_numpy in visuals.items(): img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) util.save_image(image_numpy, img_path) # update website webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) for n in range(epoch, 0, -1): webpage.add_header('epoch [%d]' % n) ims = [] txts = [] links = [] for label, image_numpy in visuals.items(): img_path = 'epoch%.3d_%s.png' % (n, label) ims.append(img_path) txts.append(label) links.append(img_path) webpage.add_images(ims, txts, links, width=self.win_size) webpage.save() # errors: dictionary of error labels and values def plot_current_errors(self, epoch, counter_ratio, opt, errors): if not hasattr(self, 'plot_data'): self.plot_data = {'X': [], 'Y': [], 'legend': list(errors.keys())} self.plot_data['X'].append(epoch + counter_ratio) self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']]) self.vis.line( X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), Y=np.array(self.plot_data['Y']), opts={ 'title': self.name + ' loss over time', 'legend': self.plot_data['legend'], 'xlabel': 'epoch', 'ylabel': 'loss'}, win=self.display_id) # errors: same format as |errors| of plotCurrentErrors def print_current_errors(self, epoch, i, errors, t): message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t) for k, v in errors.items(): message += '%s: %.3f ' % (k, v) print(message) with open(self.log_name, "a") as log_file: log_file.write('%s\n' % message) # save image to the disk def save_images(self, webpage, visuals, image_path, aspect_ratio=1.0): image_dir = webpage.get_image_dir() short_path = ntpath.basename(image_path[0]) name = os.path.splitext(short_path)[0] webpage.add_header(name) ims = [] txts = [] links = [] for label, im in visuals.items(): image_name = '%s_%s.png' % (name, label) save_path = os.path.join(image_dir, image_name) h, w, _ = im.shape if aspect_ratio > 1.0: im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') if aspect_ratio < 1.0: im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') util.save_image(im, save_path) ims.append(image_name) txts.append(label) links.append(image_name) webpage.add_images(ims, txts, links, width=self.win_size) ================================================ FILE: test_code_released_old/00116.csv ================================================ frame, face_id, timestamp, confidence, success, gaze_0_x, gaze_0_y, gaze_0_z, gaze_1_x, gaze_1_y, gaze_1_z, gaze_angle_x, gaze_angle_y, eye_lmk_x_0, eye_lmk_x_1, eye_lmk_x_2, eye_lmk_x_3, eye_lmk_x_4, eye_lmk_x_5, eye_lmk_x_6, eye_lmk_x_7, eye_lmk_x_8, eye_lmk_x_9, eye_lmk_x_10, eye_lmk_x_11, eye_lmk_x_12, eye_lmk_x_13, eye_lmk_x_14, eye_lmk_x_15, eye_lmk_x_16, eye_lmk_x_17, eye_lmk_x_18, eye_lmk_x_19, eye_lmk_x_20, eye_lmk_x_21, eye_lmk_x_22, eye_lmk_x_23, eye_lmk_x_24, eye_lmk_x_25, eye_lmk_x_26, eye_lmk_x_27, eye_lmk_x_28, eye_lmk_x_29, eye_lmk_x_30, eye_lmk_x_31, eye_lmk_x_32, eye_lmk_x_33, eye_lmk_x_34, eye_lmk_x_35, eye_lmk_x_36, eye_lmk_x_37, eye_lmk_x_38, eye_lmk_x_39, eye_lmk_x_40, eye_lmk_x_41, eye_lmk_x_42, eye_lmk_x_43, eye_lmk_x_44, eye_lmk_x_45, eye_lmk_x_46, eye_lmk_x_47, eye_lmk_x_48, eye_lmk_x_49, eye_lmk_x_50, eye_lmk_x_51, eye_lmk_x_52, eye_lmk_x_53, eye_lmk_x_54, eye_lmk_x_55, eye_lmk_y_0, eye_lmk_y_1, eye_lmk_y_2, eye_lmk_y_3, eye_lmk_y_4, eye_lmk_y_5, eye_lmk_y_6, eye_lmk_y_7, eye_lmk_y_8, eye_lmk_y_9, eye_lmk_y_10, eye_lmk_y_11, eye_lmk_y_12, eye_lmk_y_13, eye_lmk_y_14, eye_lmk_y_15, eye_lmk_y_16, eye_lmk_y_17, eye_lmk_y_18, eye_lmk_y_19, eye_lmk_y_20, eye_lmk_y_21, eye_lmk_y_22, eye_lmk_y_23, eye_lmk_y_24, eye_lmk_y_25, eye_lmk_y_26, eye_lmk_y_27, eye_lmk_y_28, eye_lmk_y_29, eye_lmk_y_30, eye_lmk_y_31, eye_lmk_y_32, eye_lmk_y_33, eye_lmk_y_34, eye_lmk_y_35, eye_lmk_y_36, eye_lmk_y_37, eye_lmk_y_38, eye_lmk_y_39, eye_lmk_y_40, eye_lmk_y_41, eye_lmk_y_42, eye_lmk_y_43, eye_lmk_y_44, eye_lmk_y_45, eye_lmk_y_46, eye_lmk_y_47, eye_lmk_y_48, eye_lmk_y_49, eye_lmk_y_50, eye_lmk_y_51, eye_lmk_y_52, eye_lmk_y_53, eye_lmk_y_54, eye_lmk_y_55, eye_lmk_X_0, eye_lmk_X_1, eye_lmk_X_2, eye_lmk_X_3, eye_lmk_X_4, eye_lmk_X_5, eye_lmk_X_6, eye_lmk_X_7, eye_lmk_X_8, eye_lmk_X_9, eye_lmk_X_10, eye_lmk_X_11, eye_lmk_X_12, eye_lmk_X_13, eye_lmk_X_14, eye_lmk_X_15, eye_lmk_X_16, eye_lmk_X_17, eye_lmk_X_18, eye_lmk_X_19, eye_lmk_X_20, eye_lmk_X_21, eye_lmk_X_22, eye_lmk_X_23, eye_lmk_X_24, eye_lmk_X_25, eye_lmk_X_26, eye_lmk_X_27, eye_lmk_X_28, eye_lmk_X_29, eye_lmk_X_30, eye_lmk_X_31, eye_lmk_X_32, eye_lmk_X_33, eye_lmk_X_34, eye_lmk_X_35, eye_lmk_X_36, eye_lmk_X_37, eye_lmk_X_38, eye_lmk_X_39, eye_lmk_X_40, eye_lmk_X_41, eye_lmk_X_42, eye_lmk_X_43, eye_lmk_X_44, eye_lmk_X_45, eye_lmk_X_46, eye_lmk_X_47, eye_lmk_X_48, eye_lmk_X_49, eye_lmk_X_50, eye_lmk_X_51, eye_lmk_X_52, eye_lmk_X_53, eye_lmk_X_54, eye_lmk_X_55, eye_lmk_Y_0, eye_lmk_Y_1, eye_lmk_Y_2, eye_lmk_Y_3, eye_lmk_Y_4, eye_lmk_Y_5, eye_lmk_Y_6, eye_lmk_Y_7, eye_lmk_Y_8, eye_lmk_Y_9, eye_lmk_Y_10, eye_lmk_Y_11, eye_lmk_Y_12, eye_lmk_Y_13, eye_lmk_Y_14, eye_lmk_Y_15, eye_lmk_Y_16, eye_lmk_Y_17, eye_lmk_Y_18, eye_lmk_Y_19, eye_lmk_Y_20, eye_lmk_Y_21, eye_lmk_Y_22, eye_lmk_Y_23, eye_lmk_Y_24, eye_lmk_Y_25, eye_lmk_Y_26, eye_lmk_Y_27, eye_lmk_Y_28, eye_lmk_Y_29, eye_lmk_Y_30, eye_lmk_Y_31, eye_lmk_Y_32, eye_lmk_Y_33, eye_lmk_Y_34, eye_lmk_Y_35, eye_lmk_Y_36, eye_lmk_Y_37, eye_lmk_Y_38, eye_lmk_Y_39, eye_lmk_Y_40, eye_lmk_Y_41, eye_lmk_Y_42, eye_lmk_Y_43, eye_lmk_Y_44, eye_lmk_Y_45, eye_lmk_Y_46, eye_lmk_Y_47, eye_lmk_Y_48, eye_lmk_Y_49, eye_lmk_Y_50, eye_lmk_Y_51, eye_lmk_Y_52, eye_lmk_Y_53, eye_lmk_Y_54, eye_lmk_Y_55, eye_lmk_Z_0, eye_lmk_Z_1, eye_lmk_Z_2, eye_lmk_Z_3, eye_lmk_Z_4, eye_lmk_Z_5, eye_lmk_Z_6, eye_lmk_Z_7, eye_lmk_Z_8, eye_lmk_Z_9, eye_lmk_Z_10, eye_lmk_Z_11, eye_lmk_Z_12, eye_lmk_Z_13, eye_lmk_Z_14, eye_lmk_Z_15, eye_lmk_Z_16, eye_lmk_Z_17, eye_lmk_Z_18, eye_lmk_Z_19, eye_lmk_Z_20, eye_lmk_Z_21, eye_lmk_Z_22, eye_lmk_Z_23, eye_lmk_Z_24, eye_lmk_Z_25, eye_lmk_Z_26, eye_lmk_Z_27, eye_lmk_Z_28, eye_lmk_Z_29, eye_lmk_Z_30, eye_lmk_Z_31, eye_lmk_Z_32, eye_lmk_Z_33, eye_lmk_Z_34, eye_lmk_Z_35, eye_lmk_Z_36, eye_lmk_Z_37, eye_lmk_Z_38, eye_lmk_Z_39, eye_lmk_Z_40, eye_lmk_Z_41, eye_lmk_Z_42, eye_lmk_Z_43, eye_lmk_Z_44, eye_lmk_Z_45, eye_lmk_Z_46, eye_lmk_Z_47, eye_lmk_Z_48, eye_lmk_Z_49, eye_lmk_Z_50, eye_lmk_Z_51, eye_lmk_Z_52, eye_lmk_Z_53, eye_lmk_Z_54, eye_lmk_Z_55, pose_Tx, pose_Ty, pose_Tz, pose_Rx, pose_Ry, pose_Rz, x_0, x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8, x_9, x_10, x_11, x_12, x_13, x_14, x_15, x_16, x_17, x_18, x_19, x_20, x_21, x_22, x_23, x_24, x_25, x_26, x_27, x_28, x_29, x_30, x_31, x_32, x_33, x_34, x_35, x_36, x_37, x_38, x_39, x_40, x_41, x_42, x_43, x_44, x_45, x_46, x_47, x_48, x_49, x_50, x_51, x_52, x_53, x_54, x_55, x_56, x_57, x_58, x_59, x_60, x_61, x_62, x_63, x_64, x_65, x_66, x_67, y_0, y_1, y_2, y_3, y_4, y_5, y_6, y_7, y_8, y_9, y_10, y_11, y_12, y_13, y_14, y_15, y_16, y_17, y_18, y_19, y_20, y_21, y_22, y_23, y_24, y_25, y_26, y_27, y_28, y_29, y_30, y_31, y_32, y_33, y_34, y_35, y_36, y_37, y_38, y_39, y_40, y_41, y_42, y_43, y_44, y_45, y_46, y_47, y_48, y_49, y_50, y_51, y_52, y_53, y_54, y_55, y_56, y_57, y_58, y_59, y_60, y_61, y_62, y_63, y_64, y_65, y_66, y_67, X_0, X_1, X_2, X_3, X_4, X_5, X_6, X_7, X_8, X_9, X_10, X_11, X_12, X_13, X_14, X_15, X_16, X_17, X_18, X_19, X_20, X_21, X_22, X_23, X_24, X_25, X_26, X_27, X_28, X_29, X_30, X_31, X_32, X_33, X_34, X_35, X_36, X_37, X_38, X_39, X_40, X_41, X_42, X_43, X_44, X_45, X_46, X_47, X_48, X_49, X_50, X_51, X_52, X_53, X_54, X_55, X_56, X_57, X_58, X_59, X_60, X_61, X_62, X_63, X_64, X_65, X_66, X_67, Y_0, Y_1, Y_2, Y_3, Y_4, Y_5, Y_6, Y_7, Y_8, Y_9, Y_10, Y_11, Y_12, Y_13, Y_14, Y_15, Y_16, Y_17, Y_18, Y_19, Y_20, Y_21, Y_22, Y_23, Y_24, Y_25, Y_26, Y_27, Y_28, Y_29, Y_30, Y_31, Y_32, Y_33, Y_34, Y_35, Y_36, Y_37, Y_38, Y_39, Y_40, Y_41, Y_42, Y_43, Y_44, Y_45, Y_46, Y_47, Y_48, Y_49, Y_50, Y_51, Y_52, Y_53, Y_54, Y_55, Y_56, Y_57, Y_58, Y_59, Y_60, Y_61, Y_62, Y_63, Y_64, Y_65, Y_66, Y_67, Z_0, Z_1, Z_2, Z_3, Z_4, Z_5, Z_6, Z_7, Z_8, Z_9, Z_10, Z_11, Z_12, Z_13, Z_14, Z_15, Z_16, Z_17, Z_18, Z_19, Z_20, Z_21, Z_22, Z_23, Z_24, Z_25, Z_26, Z_27, Z_28, Z_29, Z_30, Z_31, Z_32, Z_33, Z_34, Z_35, Z_36, Z_37, Z_38, Z_39, Z_40, Z_41, Z_42, Z_43, Z_44, Z_45, Z_46, Z_47, Z_48, Z_49, Z_50, Z_51, Z_52, Z_53, Z_54, Z_55, Z_56, Z_57, Z_58, Z_59, Z_60, Z_61, Z_62, Z_63, Z_64, Z_65, Z_66, Z_67, p_scale, p_rx, p_ry, p_rz, p_tx, p_ty, p_0, p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8, p_9, p_10, p_11, p_12, p_13, p_14, p_15, p_16, p_17, p_18, p_19, p_20, p_21, p_22, p_23, p_24, p_25, p_26, p_27, p_28, p_29, p_30, p_31, p_32, p_33, AU01_r, AU02_r, AU04_r, AU05_r, AU06_r, AU07_r, AU09_r, AU10_r, AU12_r, AU14_r, AU15_r, AU17_r, AU20_r, AU23_r, AU25_r, AU26_r, AU45_r, AU01_c, AU02_c, AU04_c, AU05_c, AU06_c, AU07_c, AU09_c, AU10_c, AU12_c, AU14_c, AU15_c, AU17_c, AU20_c, AU23_c, AU25_c, AU26_c, AU28_c, AU45_c 1, 0, 0.000, 0.98, 1, 0.095270, 0.229416, -0.968655, -0.145073, 0.218756, -0.964935, -0.026, 0.228, 79.9, 81.9, 86.0, 89.9, 91.2, 89.6, 85.1, 81.3, 74.8, 77.2, 80.6, 85.0, 89.3, 92.7, 94.9, 92.0, 88.1, 84.0, 80.2, 76.9, 83.8, 85.5, 87.3, 88.1, 87.6, 85.9, 84.1, 83.2, 130.9, 132.8, 137.0, 141.1, 142.6, 140.6, 136.4, 132.1, 126.7, 129.5, 133.3, 137.9, 142.2, 145.4, 147.4, 145.1, 141.7, 137.8, 133.6, 129.6, 134.7, 136.5, 138.3, 139.2, 138.5, 136.7, 134.9, 134.1, 49.8, 45.8, 44.4, 46.3, 50.5, 54.8, 55.9, 54.0, 51.5, 49.2, 47.8, 47.3, 48.3, 50.5, 53.3, 54.6, 54.8, 54.8, 54.3, 53.3, 52.0, 52.8, 52.2, 50.5, 48.6, 47.8, 48.4, 50.2, 53.2, 49.1, 47.7, 49.8, 54.1, 58.2, 59.6, 57.9, 55.4, 53.0, 51.4, 51.0, 52.1, 54.0, 56.5, 58.0, 58.5, 58.4, 57.8, 57.1, 55.5, 56.4, 55.8, 54.0, 52.1, 51.2, 51.8, 53.6, -33.2, -31.3, -26.9, -22.9, -21.4, -23.1, -27.6, -31.7, -39.4, -36.4, -32.4, -27.7, -23.2, -19.9, -17.7, -20.6, -24.5, -28.7, -32.8, -36.6, -29.3, -27.5, -25.6, -24.7, -25.4, -27.2, -29.0, -29.9, 18.9, 20.9, 25.2, 29.3, 30.8, 28.7, 24.4, 20.0, 14.7, 17.5, 21.2, 25.8, 30.2, 33.9, 36.5, 33.6, 29.8, 25.6, 21.4, 17.5, 22.8, 24.6, 26.5, 27.4, 26.7, 24.9, 23.0, 22.1, -64.4, -68.7, -70.2, -67.9, -63.4, -58.8, -57.7, -59.9, -64.1, -65.7, -66.3, -66.4, -65.3, -63.4, -60.8, -59.1, -58.5, -58.6, -59.5, -61.3, -62.2, -61.3, -61.9, -63.7, -65.8, -66.7, -66.1, -64.2, -58.7, -63.0, -64.8, -62.8, -58.3, -53.9, -52.2, -53.9, -56.7, -58.9, -60.2, -60.7, -60.1, -59.0, -57.1, -54.8, -53.6, -53.2, -53.7, -54.7, -56.6, -55.8, -56.5, -58.4, -60.4, -61.3, -60.5, -58.6, 211.6, 212.1, 211.9, 211.2, 210.3, 210.0, 210.0, 210.7, 216.3, 213.5, 210.9, 209.3, 209.4, 210.3, 211.6, 210.1, 209.0, 209.1, 210.6, 213.3, 211.6, 211.3, 211.2, 211.5, 211.8, 212.2, 212.2, 212.0, 203.7, 204.7, 205.6, 206.0, 205.5, 204.5, 203.6, 203.4, 204.7, 203.6, 202.8, 203.0, 204.7, 207.5, 210.3, 207.2, 204.4, 202.7, 202.4, 203.4, 204.8, 204.9, 205.4, 205.8, 206.0, 205.8, 205.4, 205.0, -3.7, -30.9, 238.4, 0.070, 0.064, 0.063, 55.8, 54.9, 55.5, 57.2, 61.5, 69.0, 78.3, 89.6, 104.0, 119.5, 134.2, 147.7, 157.6, 163.9, 167.6, 170.2, 171.9, 63.9, 71.2, 81.5, 92.1, 101.5, 121.9, 133.2, 144.4, 154.6, 160.9, 110.9, 109.7, 108.5, 107.3, 95.9, 101.4, 106.9, 113.1, 118.7, 74.6, 81.1, 89.0, 95.1, 88.1, 80.1, 126.5, 133.7, 141.6, 147.7, 141.6, 133.8, 85.8, 93.8, 101.6, 106.3, 111.9, 119.9, 127.9, 119.1, 111.0, 105.1, 99.8, 92.5, 89.1, 101.2, 106.1, 111.7, 124.1, 111.3, 105.6, 100.7, 53.4, 70.0, 86.8, 103.0, 118.0, 130.9, 141.3, 149.6, 152.6, 151.3, 144.3, 134.3, 121.5, 106.2, 90.2, 74.3, 58.4, 39.1, 31.4, 28.9, 30.6, 34.9, 36.0, 33.4, 33.6, 37.6, 45.9, 49.8, 58.7, 67.4, 76.7, 86.5, 88.6, 90.5, 89.5, 88.3, 51.5, 47.7, 48.3, 53.3, 54.8, 54.5, 55.4, 51.2, 52.1, 56.5, 58.5, 57.9, 109.5, 104.5, 102.3, 104.1, 103.1, 106.5, 111.7, 117.8, 119.4, 119.5, 118.7, 115.9, 109.6, 107.9, 108.6, 108.5, 111.5, 112.1, 112.2, 111.4, -81.8, -83.1, -82.3, -79.2, -71.6, -59.1, -44.4, -27.9, -9.8, 9.3, 28.6, 47.5, 61.6, 70.1, 75.2, 78.7, 81.4, -62.2, -51.3, -37.1, -23.5, -12.1, 11.3, 24.4, 38.0, 50.9, 59.6, -1.2, -2.6, -3.7, -4.8, -17.8, -11.6, -5.5, 1.2, 7.4, -46.6, -37.8, -27.9, -20.4, -28.9, -38.9, 17.2, 25.7, 35.1, 42.7, 35.0, 25.7, -31.2, -20.4, -11.4, -6.1, -0.1, 8.8, 18.5, 7.9, -1.0, -7.5, -13.3, -21.9, -26.9, -11.9, -6.5, -0.3, 13.9, -0.7, -6.9, -12.5, -85.3, -61.2, -36.7, -13.0, 8.5, 26.1, 38.6, 46.8, 49.4, 48.6, 41.6, 29.6, 12.8, -7.9, -29.5, -51.0, -72.9, -94.2, -101.3, -101.1, -95.9, -88.8, -86.4, -90.5, -92.0, -88.9, -80.5, -70.9, -58.7, -47.2, -36.1, -28.2, -25.5, -23.2, -24.4, -26.0, -75.5, -78.7, -77.1, -70.6, -69.1, -70.2, -67.1, -71.9, -71.0, -66.5, -63.1, -63.7, -3.0, -8.4, -10.6, -8.6, -9.7, -6.2, -0.4, 6.4, 8.1, 8.1, 7.3, 4.4, -2.8, -4.5, -3.8, -3.8, -0.5, 0.1, 0.2, -0.7, 297.3, 297.5, 297.1, 295.3, 289.6, 281.0, 269.0, 254.4, 248.4, 252.4, 262.8, 271.4, 275.5, 275.9, 276.2, 276.1, 277.5, 263.8, 256.5, 248.4, 240.7, 235.2, 231.9, 235.1, 239.6, 243.9, 248.8, 232.6, 224.6, 216.5, 208.7, 226.3, 222.9, 220.4, 221.5, 223.6, 254.8, 249.9, 247.2, 245.5, 246.5, 249.1, 242.2, 241.6, 241.9, 244.7, 241.0, 240.6, 242.6, 229.5, 223.1, 221.9, 222.1, 227.5, 237.9, 227.3, 221.9, 221.5, 222.9, 229.4, 239.7, 224.8, 223.4, 224.0, 235.3, 223.6, 222.9, 224.6, 0.835, 0.018, 0.114, 0.060, 109.024, 85.151, -8.880, 7.084, -11.781, 10.370, -6.909, -4.882, -0.845, -7.403, -1.630, -4.368, -5.367, 0.925, -1.645, -4.888, 1.159, -3.330, -1.646, -4.081, -1.647, 0.525, 0.100, 0.196, -0.484, 0.958, 0.046, -0.296, 0.321, 0.003, -0.181, -0.084, -0.053, -0.041, -0.033, -0.001, 0.24, 0.26, 0.00, 0.00, 0.00, 0.11, 0.02, 0.00, 0.00, 0.00, 0.00, 0.35, 0.11, 0.13, 0.00, 1.07, 0.00, 0.00, 1.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00 2, 0, 0.040, 0.98, 1, 0.092699, 0.229108, -0.968977, -0.150172, 0.219173, -0.964060, -0.030, 0.228, 79.9, 82.0, 86.2, 90.1, 91.3, 89.5, 85.1, 81.2, 74.7, 77.2, 80.7, 85.0, 89.4, 92.8, 95.0, 92.1, 88.1, 84.0, 80.1, 76.9, 83.8, 85.5, 87.3, 88.2, 87.6, 85.9, 84.1, 83.2, 131.0, 132.9, 137.1, 141.2, 142.7, 140.8, 136.6, 132.2, 126.8, 129.6, 133.4, 138.0, 142.3, 145.6, 147.7, 145.3, 141.9, 138.0, 133.7, 129.7, 134.8, 136.6, 138.5, 139.3, 138.6, 136.9, 135.0, 134.2, 49.5, 45.6, 44.2, 46.1, 50.3, 54.7, 55.7, 53.7, 51.2, 49.0, 47.6, 47.1, 48.2, 50.4, 53.2, 54.4, 54.5, 54.5, 53.9, 53.0, 51.8, 52.6, 52.0, 50.3, 48.4, 47.6, 48.2, 49.9, 53.3, 49.1, 47.7, 49.8, 54.1, 58.2, 59.7, 57.9, 55.5, 53.1, 51.5, 51.1, 52.2, 54.1, 56.6, 58.0, 58.4, 58.4, 57.8, 57.1, 55.6, 56.5, 55.9, 54.1, 52.2, 51.2, 51.9, 53.7, -33.0, -31.0, -26.6, -22.6, -21.1, -22.9, -27.5, -31.6, -39.2, -36.2, -32.1, -27.5, -23.0, -19.7, -17.4, -20.3, -24.3, -28.5, -32.7, -36.4, -29.1, -27.3, -25.4, -24.5, -25.1, -26.9, -28.8, -29.7, 18.9, 20.9, 25.2, 29.4, 30.8, 28.8, 24.4, 20.0, 14.8, 17.5, 21.2, 25.8, 30.3, 34.0, 36.6, 33.6, 29.8, 25.7, 21.4, 17.5, 22.8, 24.6, 26.5, 27.4, 26.8, 25.0, 23.1, 22.2, -64.3, -68.6, -70.0, -67.7, -63.1, -58.5, -57.5, -59.7, -64.0, -65.5, -66.1, -66.1, -65.0, -63.0, -60.5, -58.9, -58.4, -58.5, -59.5, -61.3, -62.0, -61.0, -61.6, -63.5, -65.5, -66.5, -65.9, -64.0, -58.4, -62.8, -64.6, -62.6, -58.0, -53.6, -51.9, -53.6, -56.5, -58.6, -59.9, -60.4, -59.8, -58.7, -56.8, -54.6, -53.4, -53.0, -53.5, -54.5, -56.4, -55.5, -56.3, -58.2, -60.2, -61.1, -60.3, -58.4, 210.2, 210.8, 210.6, 209.8, 208.9, 208.5, 208.4, 209.3, 214.9, 212.2, 209.6, 208.0, 208.0, 208.9, 210.1, 208.6, 207.4, 207.6, 209.2, 211.8, 210.2, 209.9, 209.8, 210.0, 210.5, 210.8, 210.9, 210.6, 203.0, 204.1, 205.0, 205.3, 204.7, 203.7, 202.8, 202.6, 204.0, 202.9, 202.2, 202.3, 204.0, 206.7, 209.5, 206.4, 203.6, 201.9, 201.6, 202.7, 204.1, 204.2, 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eye_lmk_x_1, eye_lmk_x_2, eye_lmk_x_3, eye_lmk_x_4, eye_lmk_x_5, eye_lmk_x_6, eye_lmk_x_7, eye_lmk_x_8, eye_lmk_x_9, eye_lmk_x_10, eye_lmk_x_11, eye_lmk_x_12, eye_lmk_x_13, eye_lmk_x_14, eye_lmk_x_15, eye_lmk_x_16, eye_lmk_x_17, eye_lmk_x_18, eye_lmk_x_19, eye_lmk_x_20, eye_lmk_x_21, eye_lmk_x_22, eye_lmk_x_23, eye_lmk_x_24, eye_lmk_x_25, eye_lmk_x_26, eye_lmk_x_27, eye_lmk_x_28, eye_lmk_x_29, eye_lmk_x_30, eye_lmk_x_31, eye_lmk_x_32, eye_lmk_x_33, eye_lmk_x_34, eye_lmk_x_35, eye_lmk_x_36, eye_lmk_x_37, eye_lmk_x_38, eye_lmk_x_39, eye_lmk_x_40, eye_lmk_x_41, eye_lmk_x_42, eye_lmk_x_43, eye_lmk_x_44, eye_lmk_x_45, eye_lmk_x_46, eye_lmk_x_47, eye_lmk_x_48, eye_lmk_x_49, eye_lmk_x_50, eye_lmk_x_51, eye_lmk_x_52, eye_lmk_x_53, eye_lmk_x_54, eye_lmk_x_55, eye_lmk_y_0, eye_lmk_y_1, eye_lmk_y_2, eye_lmk_y_3, eye_lmk_y_4, eye_lmk_y_5, eye_lmk_y_6, eye_lmk_y_7, eye_lmk_y_8, eye_lmk_y_9, eye_lmk_y_10, eye_lmk_y_11, eye_lmk_y_12, eye_lmk_y_13, eye_lmk_y_14, eye_lmk_y_15, eye_lmk_y_16, eye_lmk_y_17, eye_lmk_y_18, eye_lmk_y_19, eye_lmk_y_20, eye_lmk_y_21, eye_lmk_y_22, eye_lmk_y_23, eye_lmk_y_24, eye_lmk_y_25, eye_lmk_y_26, eye_lmk_y_27, eye_lmk_y_28, eye_lmk_y_29, eye_lmk_y_30, eye_lmk_y_31, eye_lmk_y_32, eye_lmk_y_33, eye_lmk_y_34, eye_lmk_y_35, eye_lmk_y_36, eye_lmk_y_37, eye_lmk_y_38, eye_lmk_y_39, eye_lmk_y_40, eye_lmk_y_41, eye_lmk_y_42, eye_lmk_y_43, eye_lmk_y_44, eye_lmk_y_45, eye_lmk_y_46, eye_lmk_y_47, eye_lmk_y_48, eye_lmk_y_49, eye_lmk_y_50, eye_lmk_y_51, eye_lmk_y_52, eye_lmk_y_53, eye_lmk_y_54, eye_lmk_y_55, eye_lmk_X_0, eye_lmk_X_1, eye_lmk_X_2, eye_lmk_X_3, eye_lmk_X_4, eye_lmk_X_5, eye_lmk_X_6, eye_lmk_X_7, eye_lmk_X_8, eye_lmk_X_9, eye_lmk_X_10, eye_lmk_X_11, eye_lmk_X_12, eye_lmk_X_13, eye_lmk_X_14, eye_lmk_X_15, eye_lmk_X_16, eye_lmk_X_17, eye_lmk_X_18, eye_lmk_X_19, eye_lmk_X_20, eye_lmk_X_21, eye_lmk_X_22, eye_lmk_X_23, eye_lmk_X_24, eye_lmk_X_25, eye_lmk_X_26, eye_lmk_X_27, eye_lmk_X_28, eye_lmk_X_29, eye_lmk_X_30, eye_lmk_X_31, eye_lmk_X_32, eye_lmk_X_33, eye_lmk_X_34, eye_lmk_X_35, eye_lmk_X_36, eye_lmk_X_37, eye_lmk_X_38, eye_lmk_X_39, eye_lmk_X_40, eye_lmk_X_41, eye_lmk_X_42, eye_lmk_X_43, eye_lmk_X_44, eye_lmk_X_45, eye_lmk_X_46, eye_lmk_X_47, eye_lmk_X_48, eye_lmk_X_49, eye_lmk_X_50, eye_lmk_X_51, eye_lmk_X_52, eye_lmk_X_53, eye_lmk_X_54, eye_lmk_X_55, eye_lmk_Y_0, eye_lmk_Y_1, eye_lmk_Y_2, eye_lmk_Y_3, eye_lmk_Y_4, eye_lmk_Y_5, eye_lmk_Y_6, eye_lmk_Y_7, eye_lmk_Y_8, eye_lmk_Y_9, eye_lmk_Y_10, eye_lmk_Y_11, eye_lmk_Y_12, eye_lmk_Y_13, eye_lmk_Y_14, eye_lmk_Y_15, eye_lmk_Y_16, eye_lmk_Y_17, eye_lmk_Y_18, eye_lmk_Y_19, eye_lmk_Y_20, eye_lmk_Y_21, eye_lmk_Y_22, eye_lmk_Y_23, eye_lmk_Y_24, eye_lmk_Y_25, eye_lmk_Y_26, eye_lmk_Y_27, eye_lmk_Y_28, eye_lmk_Y_29, eye_lmk_Y_30, eye_lmk_Y_31, eye_lmk_Y_32, eye_lmk_Y_33, eye_lmk_Y_34, eye_lmk_Y_35, eye_lmk_Y_36, eye_lmk_Y_37, eye_lmk_Y_38, eye_lmk_Y_39, eye_lmk_Y_40, eye_lmk_Y_41, eye_lmk_Y_42, eye_lmk_Y_43, eye_lmk_Y_44, eye_lmk_Y_45, eye_lmk_Y_46, eye_lmk_Y_47, eye_lmk_Y_48, eye_lmk_Y_49, eye_lmk_Y_50, eye_lmk_Y_51, eye_lmk_Y_52, eye_lmk_Y_53, eye_lmk_Y_54, eye_lmk_Y_55, eye_lmk_Z_0, eye_lmk_Z_1, eye_lmk_Z_2, eye_lmk_Z_3, eye_lmk_Z_4, eye_lmk_Z_5, eye_lmk_Z_6, eye_lmk_Z_7, eye_lmk_Z_8, eye_lmk_Z_9, eye_lmk_Z_10, eye_lmk_Z_11, eye_lmk_Z_12, eye_lmk_Z_13, eye_lmk_Z_14, eye_lmk_Z_15, eye_lmk_Z_16, eye_lmk_Z_17, eye_lmk_Z_18, eye_lmk_Z_19, eye_lmk_Z_20, eye_lmk_Z_21, eye_lmk_Z_22, eye_lmk_Z_23, eye_lmk_Z_24, eye_lmk_Z_25, eye_lmk_Z_26, eye_lmk_Z_27, eye_lmk_Z_28, eye_lmk_Z_29, eye_lmk_Z_30, eye_lmk_Z_31, eye_lmk_Z_32, eye_lmk_Z_33, eye_lmk_Z_34, eye_lmk_Z_35, eye_lmk_Z_36, eye_lmk_Z_37, eye_lmk_Z_38, eye_lmk_Z_39, eye_lmk_Z_40, eye_lmk_Z_41, eye_lmk_Z_42, eye_lmk_Z_43, eye_lmk_Z_44, eye_lmk_Z_45, eye_lmk_Z_46, eye_lmk_Z_47, eye_lmk_Z_48, eye_lmk_Z_49, eye_lmk_Z_50, eye_lmk_Z_51, eye_lmk_Z_52, eye_lmk_Z_53, eye_lmk_Z_54, eye_lmk_Z_55, pose_Tx, pose_Ty, pose_Tz, pose_Rx, pose_Ry, pose_Rz, x_0, x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8, x_9, x_10, x_11, x_12, x_13, 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================================================ FILE: test_code_released_old/checkpoints/gpubatch_resnet/opt.txt ================================================ ------------ Options ------------- aspect_ratio: 1.0 batchSize: 1 checkpoints_dir: ./checkpoints csv_path: /home/esa/Downloads/higher_resolution/test_code_released/00116.csv dataroot: ./ dataset_mode: aligned display_id: 0 display_port: 8097 display_winsize: 256 fineSize: 128 gpu_ids: [1] how_many: 1 init_type: normal input_nc: 3 isTrain: False loadSize: 128 max_dataset_size: inf model: pix2pix nThreads: 8 n_layers_D: 3 name: gpubatch_resnet ndf: 256 ngf: 128 no_dropout: False no_flip: True norm: batch ntest: inf output_nc: 3 phase: test resize_or_crop: resize_and_crop results_dir: results_video serial_batches: False which_direction: AtoB which_epoch: latest which_model_netD: basic which_model_netG: resnet_6blocks which_ref: /home/esa/Downloads/higher_resolution/test_code_released/new_crop/1235.png which_ref_d1: path/to/reference/image which_ref_d2: path/to/reference/image -------------- End ---------------- ================================================ FILE: test_code_released_old/data/__init__.py ================================================ ================================================ FILE: test_code_released_old/data/aligned_dataset.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Mar 13 14:30:12 2019 @author:Soumya """ import os.path import random import torchvision.transforms as transforms import torch from data.base_dataset import BaseDataset #from data.image_folder import make_dataset from PIL import Image import pandas #from natsort import natsorted, ns #import glob #import numpy as np #import sys #sys.path.append('/home/esa/Downloads/face-alignment') class AlignedDataset(BaseDataset): def initialize(self, opt): self.opt = opt self.root = opt.dataroot self.dir_AB = os.path.join(opt.dataroot) self.ref_path=opt.which_ref self.csv_path=opt.csv_path def __getitem__(self, index): SI_path= self.ref_path df2 = pandas.read_csv(self.csv_path) a=list(range(296,299))+ list(range(679,696))#+ list(range(299,435)) refdf = df2[df2.columns[a]] f=0 A1 = Image.open(SI_path).convert('RGB') A1 = A1.resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC) A1 = transforms.ToTensor()(A1) A = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(A1) for i in range(0,len(refdf)): paramA=torch.tensor(refdf.values[i]).view(1,-1) paramA[0,0:3]=(paramA[0,0:3]-(-0.70))/1.4 paramA[0,3:20]=paramA[0,3:20]/5 if f==0: P1=paramA else: P1=torch.cat([P1,paramA],dim=0) f=f+1 if (not self.opt.no_flip) and random.random() < 0.5: idx = [i for i in range(A.size(2) - 1, -1, -1)] idx = torch.LongTensor(idx) A = A.index_select(2, idx) return {'A': A, 'PB': P1, 'A_paths': SI_path}#SAB_path[0] def __len__(self): return len(self.ref_path) def name(self): return 'AlignedDataset' ================================================ FILE: test_code_released_old/data/aligned_dataset.py~ ================================================ import os.path import random import torchvision.transforms as transforms import torch from data.base_dataset import BaseDataset from data.image_folder import make_dataset, make_grouped_dataset from PIL import Image import pandas from natsort import natsorted, ns import glob import numpy as np class AlignedDataset(BaseDataset): def initialize(self, opt): self.opt = opt self.root = opt.dataroot self.dir_AB = os.path.join(opt.dataroot) self.AB_paths = sorted(make_dataset(self.dir_AB)) self.all_iden=np.loadtxt('/home/esa/Downloads/files/train_vox1.txt',dtype=str) assert(opt.resize_or_crop == 'resize_and_crop') # pdb.set_trace() def __getitem__(self, index): # print(index) IM_path=self.AB_paths[index] ind=os.path.basename(os.path.dirname(os.path.dirname((os.path.dirname(IM_path))))) identity=torch.tensor(np.where(self.all_iden == ind)[0]).unsqueeze(0)#torch.tensor(self.all_iden.index(ind)).unsqueeze(0) identity_code=identity#torch.zeros(64) # identity_code[identity]=1 all_images_infolder=glob.glob(os.path.dirname(IM_path)+'/*') AB_path = natsorted(all_images_infolder, alg=ns.IGNORECASE) current_index_number=AB_path.index(IM_path)#int(os.path.basename(IM_path)[:-4]) # last_fra_number = int(os.path.basename(AB_path[len(AB_path)])[:-4]) if(len(AB_path)>1): new_list=AB_path[0:current_index_number] + AB_path[current_index_number+1:len(AB_path)] # if current_index_number= self.opt.max_dataset_size: break yield data ================================================ FILE: test_code_released_old/data/data_loader.py ================================================ def CreateDataLoader(opt): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt) return data_loader ================================================ FILE: test_code_released_old/data/image_folder.py ================================================ ############################################################################### # Code from # https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py # Modified the original code so that it also loads images from the current # directory as well as the subdirectories ############################################################################### import torch.utils.data as data from PIL import Image import os import os.path import numpy as np IMG_EXTENSIONS = [ '.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', ] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def make_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir train=np.loadtxt('/home/esa/Downloads/files/test_vox1.txt',dtype=str) for root, _, fnames in sorted(os.walk(dir)): identity=os.path.basename(os.path.dirname(os.path.dirname(root))) if np.isin(identity,train): for fname in fnames: if is_image_file(fname): path = os.path.join(root, fname) images.append(path) return images def make_grouped_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir fnames = sorted(os.walk(dir)) for fname in sorted(fnames): paths = [] root = fname[0] for f in sorted(fname[2]): if is_image_file(f): paths.append(os.path.join(root, f)) if len(paths) > 0: images.append(paths) return images def default_loader(path): return Image.open(path).convert('RGB') class ImageFolder(data.Dataset): def __init__(self, root, transform=None, return_paths=False, loader=default_loader): imgs = make_dataset(root) if len(imgs) == 0: raise(RuntimeError("Found 0 images in: " + root + "\n" "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) self.root = root self.imgs = imgs self.transform = transform self.return_paths = return_paths self.loader = loader def __getitem__(self, index): path = self.imgs[index] img = self.loader(path) if self.transform is not None: img = self.transform(img) if self.return_paths: return img, path else: return img def __len__(self): return len(self.imgs) ================================================ FILE: test_code_released_old/data/image_folder.py~ ================================================ ############################################################################### # Code from # https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py # Modified the original code so that it also loads images from the current # directory as well as the subdirectories ############################################################################### import torch.utils.data as data from PIL import Image import os import os.path import numpy as np IMG_EXTENSIONS = [ '.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', ] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def make_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir train=np.loadtxt('/home/esa/Downloads/files/train_vox1.txt',dtype=str) for root, _, fnames in sorted(os.walk(dir)): identity=os.path.basename(os.path.dirname(os.path.dirname(root))) if np.isin(identity,train): for fname in fnames: if is_image_file(fname): path = os.path.join(root, fname) images.append(path) return images def make_grouped_dataset(dir): images = [] assert os.path.isdir(dir), '%s is not a valid directory' % dir fnames = sorted(os.walk(dir)) for fname in sorted(fnames): paths = [] root = fname[0] for f in sorted(fname[2]): if is_image_file(f): paths.append(os.path.join(root, f)) if len(paths) > 0: images.append(paths) return images def default_loader(path): return Image.open(path).convert('RGB') class ImageFolder(data.Dataset): def __init__(self, root, transform=None, return_paths=False, loader=default_loader): imgs = make_dataset(root) if len(imgs) == 0: raise(RuntimeError("Found 0 images in: " + root + "\n" "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) self.root = root self.imgs = imgs self.transform = transform self.return_paths = return_paths self.loader = loader def __getitem__(self, index): path = self.imgs[index] img = self.loader(path) if self.transform is not None: img = self.transform(img) if self.return_paths: return img, path else: return img def __len__(self): return len(self.imgs) ================================================ FILE: test_code_released_old/image_crop.py ================================================ #from imutils.face_utils import FaceAligner from imutils.face_utils import rect_to_bb import imutils import dlib import cv2 #from imutils import face_utils import numpy as np from PIL import Image #import pdb #def pre_process(path): for i in range(2): detector = dlib.get_frontal_face_detector() cap = cv2.imread('/home/esa/Pictures/some.jpg') # add your image here image= cv2.resize(cap, (400, 400)) RGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) c=1235 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) rects = detector(RGB, 1) faces = dlib.full_object_detections() for rect in rects: c1=rect.dcenter() (x, y, w, h) = rect_to_bb(rect) w=np.int(w*1.6) h=np.int(h*1.6) x=c1.x-np.int(w/2.0) y=c1.y-np.int(h/2.0) if y<0: y=0 if x<0: x=0 faceOrig = imutils.resize(RGB[y:y+h, x:x+w],height=256) #y=10,h+60,W+40 d_num = np.asarray(faceOrig) f_im = Image.fromarray(d_num) f_im.save('./new_crop/'+str(c)+'.png') ================================================ FILE: test_code_released_old/models/__init__.py ================================================ ================================================ FILE: test_code_released_old/models/base_model.py ================================================ import os import torch class BaseModel(): def name(self): return 'BaseModel' def initialize(self, opt): self.opt = opt self.gpu_ids = opt.gpu_ids self.isTrain = opt.isTrain self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) def set_input(self, input): self.input = input def forward(self): pass # used in test time, no backprop def test(self): pass def get_image_paths(self): pass def optimize_parameters(self): pass def get_current_visuals(self): return self.input def get_current_errors(self): return {} def save(self, label): pass # helper saving function that can be used by subclasses def save_network(self, network, network_label, epoch_label, gpu_ids): save_filename = '%s_net_%s.pth' % (epoch_label, network_label) save_path = os.path.join(self.save_dir, save_filename) torch.save(network.cpu().state_dict(), save_path) if len(gpu_ids) and torch.cuda.is_available(): network.cuda(gpu_ids[0]) # helper loading function that can be used by subclasses def load_network(self, network, network_label, epoch_label): save_filename = '%s_net_%s.pth' % (epoch_label, network_label) save_path = os.path.join(self.save_dir, save_filename) network.load_state_dict(torch.load(save_path)) # update learning rate (called once every epoch) def update_learning_rate(self): for scheduler in self.schedulers: scheduler.step() lr = self.optimizers[0].param_groups[0]['lr'] print('learning rate = %.7f' % lr) ================================================ FILE: test_code_released_old/models/models.py ================================================ def create_model(opt): model = None print(opt.model) if opt.model == 'cycle_gan': assert(opt.dataset_mode == 'unaligned') from .cycle_gan_model import CycleGANModel model = CycleGANModel() elif opt.model == 'pix2pix': assert(opt.dataset_mode == 'aligned') from .pix2pix_model import Pix2PixModel model = Pix2PixModel() elif opt.model == 'test': assert(opt.dataset_mode == 'single') from .test_model import TestModel model = TestModel() else: raise ValueError("Model [%s] not recognized." % opt.model) model.initialize(opt) print("model [%s] was created" % (model.name())) return model ================================================ FILE: test_code_released_old/models/networks.py ================================================ import torch import torch.nn as nn from torch.nn import init import functools from torch.autograd import Variable from torch.optim import lr_scheduler import torch.nn.functional as F import pdb import numpy as np ############################################################################### # Functions ############################################################################### def weights_init_normal(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('Linear') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_xavier(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('Linear') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_kaiming(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('Linear') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_orthogonal(m): classname = m.__class__.__name__ print(classname) if classname.find('Conv') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('Linear') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def init_weights(net, init_type='normal'): print('initialization method [%s]' % init_type) if init_type == 'normal': net.apply(weights_init_normal) elif init_type == 'xavier': net.apply(weights_init_xavier) elif init_type == 'kaiming': net.apply(weights_init_kaiming) elif init_type == 'orthogonal': net.apply(weights_init_orthogonal) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) def get_norm_layer(norm_type='instance'): if norm_type == 'batch': norm_layer = functools.partial(nn.BatchNorm2d, affine=True) elif norm_type == 'instance': norm_layer = functools.partial(nn.InstanceNorm2d, affine=False) elif norm_type == 'none': norm_layer = None else: raise NotImplementedError('normalization layer [%s] is not found' % norm_type) return norm_layer def get_scheduler(optimizer, opt): if opt.lr_policy == 'lambda': def lambda_rule(epoch): lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) return lr_l scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) elif opt.lr_policy == 'step': # scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) scheduler = lr_scheduler.StepLR(optimizer, step_size=150000, gamma=0.1) elif opt.lr_policy == 'plateau': scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) else: return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) return scheduler def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', gpu_ids=[]): netG = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netG == 'resnet_9blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, gpu_ids=gpu_ids) elif which_model_netG == 'resnet_6blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, gpu_ids=gpu_ids) else: raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG) if len(gpu_ids) > 0: netG.cuda(gpu_ids[0]) init_weights(netG, init_type=init_type) return netG def define_D(input_nc, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', gpu_ids=[]): netD = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netD == 'basic': netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'n_layers': netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'pixel': netD = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) else: raise NotImplementedError('Discriminator model name [%s] is not recognized' % which_model_netD) if use_gpu: netD.cuda(gpu_ids[0]) init_weights(netD, init_type=init_type) return netD def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print(net) print('Total number of parameters: %d' % num_params) ############################################################################## # Classes ############################################################################## # Defines the GAN loss which uses either LSGAN or the regular GAN. # When LSGAN is used, it is basically same as MSELoss, # but it abstracts away the need to create the target label tensor # that has the same size as the input class GANLoss(nn.Module): def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor): super(GANLoss, self).__init__() self.real_label = target_real_label self.fake_label = target_fake_label # self.real_label =torch.empty(1).uniform_(0.8, 1)[0]# target_real_label # # self.fake_label = torch.empty(1).uniform_(0, 0.2)[0]#target_fake_label # self.real_label_var = None self.fake_label_var = None self.Tensor = tensor if use_lsgan: self.loss = nn.MSELoss() else: self.loss = nn.BCEWithLogitsLoss()#nn.BCELoss() def get_target_tensor(self, input, target_is_real): target_tensor = None if target_is_real: create_label = ((self.real_label_var is None) or (self.real_label_var.numel() != input.numel())) if create_label: real_tensor = self.Tensor(input.size()).fill_(self.real_label) self.real_label_var = Variable(real_tensor, requires_grad=False) target_tensor = self.real_label_var else: create_label = ((self.fake_label_var is None) or (self.fake_label_var.numel() != input.numel())) if create_label: fake_tensor = self.Tensor(input.size()).fill_(self.fake_label) self.fake_label_var = Variable(fake_tensor, requires_grad=False) target_tensor = self.fake_label_var return target_tensor def __call__(self, input, target_is_real): target_tensor = self.get_target_tensor(input, target_is_real) return self.loss(input, target_tensor) class VGGLoss(nn.Module): def __init__(self, gpu_ids): super(VGGLoss, self).__init__() self.vgg = Vgg19().cuda() self.criterion = nn.L1Loss() self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] def forward(self, x, y): x_vgg, y_vgg = self.vgg(x), self.vgg(y) loss = 0 for i in range(len(x_vgg)): loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) return loss # Defines the generator that consists of Resnet blocks between a few # downsampling/upsampling operations. # Code and idea originally from Justin Johnson's architecture. # https://github.com/jcjohnson/fast-neural-style/ class ResnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, gpu_ids=[], padding_type='reflect'): assert(n_blocks >= 0) super(ResnetGenerator, self).__init__() self.input_nc = input_nc self.output_nc = output_nc self.ngf = ngf self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), norm_layer(ngf), nn.ReLU(True)] n_downsampling = 2 for i in range(n_downsampling): mult = 2**i model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), norm_layer(ngf * mult * 2), nn.ReLU(True)] mult = 2**n_downsampling for i in range(n_blocks): model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] for i in range(n_downsampling): mult = 2**(n_downsampling - i) model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1, bias=use_bias), norm_layer(int(ngf * mult / 2)), nn.ReLU(True)] model += [nn.ReflectionPad2d(3)] model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] model += [nn.Tanh()] self.model = nn.Sequential(*model) def forward(self, input): # if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): # return nn.parallel.data_parallel(self.model, input, self.gpu_ids) # else: return self.model(input) # Define a resnet block class ResnetBlock(nn.Module): def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): super(ResnetBlock, self).__init__() self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): conv_block = [] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)] if use_dropout: conv_block += [nn.Dropout(0.5)] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)] return nn.Sequential(*conv_block) def forward(self, x): out = x + self.conv_block(x) return out # Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(NLayerDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 3 padw = 1 sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.Conv2d(ndf, ndf, kernel_size=kw, stride=2, padding=padw), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2**n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), nn.Conv2d(ndf * nf_mult, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), # nn.LayerNorm([ndf * nf_mult,np.int(ndf/nf_mult),np.int(ndf/nf_mult)]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult_prev = nf_mult nf_mult = min(2**n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), # nn.LayerNorm([ndf*nf_mult,15,15]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] sequence += [nn.Conv2d(ndf * nf_mult,ndf * nf_mult, kernel_size=kw, stride=1, padding=padw)] sequence += [nn.Conv2d(ndf * nf_mult, 128, kernel_size=kw, stride=2, padding=padw)] # if use_sigmoid: # sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) self.conv1=nn.Conv2d(128, 1, kernel_size=4, stride=1, padding=1, bias=True) self.conv2=nn.Linear(128*8**2,20)#nn.Conv2d(128, 20, kernel_size=4, stride=1, padding=0, bias=True) self.aux_layer = nn.Sequential(nn.Linear(128*8**2, 939), nn.Softmax()) def forward(self, input): out1=self.model(input)#nn.parallel.data_parallel(self.model, input, self.gpu_ids)# # out=F.sigmoid(self.conv1(out1)) out=self.conv1(out1) cond=F.sigmoid(self.conv2(out1.view(out1.shape[0],-1))) class_pred=self.aux_layer(out1.view(out1.shape[0], -1)) return out,cond,class_pred # self.model = nn.Sequential(*sequence) # class PixelDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(PixelDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.net = [ nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), norm_layer(ndf * 2), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] if use_sigmoid: self.net.append(nn.Sigmoid()) self.net = nn.Sequential(*self.net) def forward(self, input): if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.net, input, self.gpu_ids) else: return self.net(input) from torchvision import models class Vgg19(torch.nn.Module): def __init__(self, requires_grad=False): super(Vgg19, self).__init__() vgg_pretrained_features = models.vgg19(pretrained=True).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() for x in range(2): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(2, 7): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(7, 12): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(12, 21): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(21, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h_relu1 = self.slice1(X) h_relu2 = self.slice2(h_relu1) h_relu3 = self.slice3(h_relu2) h_relu4 = self.slice4(h_relu3) h_relu5 = self.slice5(h_relu4) out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] return out ================================================ FILE: test_code_released_old/models/networks.py~ ================================================ import torch import torch.nn as nn from torch.nn import init import functools from torch.autograd import Variable from torch.optim import lr_scheduler import torch.nn.functional as F import pdb import numpy as np ############################################################################### # Functions ############################################################################### def normalize_to_distribution(x): x = x.view(x.size(0), -1) x_max, idx = torch.max(x, dim=1, keepdim=True) x = x / (x_max.expand_as(x) + 1e-8) x_sum = torch.sum(x, dim=1) x = x / x_sum.unsqueeze(1).expand_as(x) return x def normalize_scale(x): x = x.view(x.size(0), -1) x_max, idx = torch.max(x, dim=1, keepdim=True) x = x / (x_max.expand_as(x) + 1e-8) return x class KLLoss(nn.Module): def __init__(self): super(KLLoss, self).__init__() def forward(self, x0, y0): x0 = normalize_to_distribution(x0) y0 = normalize_to_distribution(y0) # print(x0,y0) # pdb.set_trace() x_log = torch.log(x0 + 1e-8) y_log = torch.log(y0 + 1e-8) loss = torch.sum(torch.mul(y0, (y_log - x_log)), dim=1) return loss.sum() class ACCLoss(nn.Module): def __init__(self): super(ACCLoss, self).__init__() def forward(self, x0, y0): print(x0.size(),y0.size()) x0 = normalize_to_distribution(x0) y0 = normalize_to_distribution(y0) msx = x0 - x0.mean(dim=1, keepdim=True).expand_as(x0) msy = y0 - y0.mean(dim=1, keepdim=True).expand_as(y0) denom = torch.sqrt(torch.sum(torch.pow(msx, 2), dim=1)) * torch.sqrt(torch.sum(torch.pow(msy, 2), dim=1)) # we opimize 1 - rho which means our cost function is at the range of [0-2] where 2 is total disagreement loss = 1 - (torch.sum(msx * msy, dim=1) / (denom + 1e-8) ) return loss.sum() class ENEGNSSLoss(nn.Module): def __init__(self): super(ENEGNSSLoss, self).__init__() def forward(self, pred, gt_fix): pred = normalize_scale(pred) gt_fix[gt_fix > 0.0] = 1.0 gt_fix = gt_fix.view(gt_fix.size(0), -1) p_mean = pred.mean(dim=1, keepdim=True).expand_as(pred) p_std = pred.std(dim=1, keepdim=True).expand_as(pred) pred = (pred - p_mean) / (p_std + 1e-8) nss = torch.sum(gt_fix * pred, dim=1, keepdim=True) / gt_fix.sum(dim=1, keepdim=True) nss_e = torch.exp(-nss) return nss_e.sum() def weights_init_normal(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('Linear') != -1: init.normal(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_xavier(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('Linear') != -1: init.xavier_normal(m.weight.data, gain=0.02) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_kaiming(m): classname = m.__class__.__name__ # print(classname) if classname.find('Conv') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('Linear') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_in') elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def weights_init_orthogonal(m): classname = m.__class__.__name__ print(classname) if classname.find('Conv') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('Linear') != -1: init.orthogonal(m.weight.data, gain=1) elif classname.find('BatchNorm2d') != -1: init.normal(m.weight.data, 1.0, 0.02) init.constant(m.bias.data, 0.0) def init_weights(net, init_type='normal'): print('initialization method [%s]' % init_type) if init_type == 'normal': net.apply(weights_init_normal) elif init_type == 'xavier': net.apply(weights_init_xavier) elif init_type == 'kaiming': net.apply(weights_init_kaiming) elif init_type == 'orthogonal': net.apply(weights_init_orthogonal) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) def get_norm_layer(norm_type='instance'): if norm_type == 'batch': norm_layer = functools.partial(nn.BatchNorm2d, affine=True) elif norm_type == 'instance': norm_layer = functools.partial(nn.InstanceNorm2d, affine=False) elif norm_type == 'none': norm_layer = None else: raise NotImplementedError('normalization layer [%s] is not found' % norm_type) return norm_layer def get_scheduler(optimizer, opt): if opt.lr_policy == 'lambda': def lambda_rule(epoch): lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) return lr_l scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) elif opt.lr_policy == 'step': # scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) scheduler = lr_scheduler.StepLR(optimizer, step_size=150000, gamma=0.1) elif opt.lr_policy == 'plateau': scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) else: return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) return scheduler def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', gpu_ids=[]): netG = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netG == 'resnet_9blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, gpu_ids=gpu_ids) elif which_model_netG == 'resnet_6blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, gpu_ids=gpu_ids) elif which_model_netG == 'unet_128': netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) elif which_model_netG == 'unet_256': netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) else: raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG) if len(gpu_ids) > 0: netG.cuda(gpu_ids[0]) init_weights(netG, init_type=init_type) return netG def define_D(input_nc, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', gpu_ids=[]): netD = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netD == 'basic': netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'n_layers': netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'pixel': netD = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) else: raise NotImplementedError('Discriminator model name [%s] is not recognized' % which_model_netD) if use_gpu: netD.cuda(gpu_ids[0]) init_weights(netD, init_type=init_type) return netD def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print(net) print('Total number of parameters: %d' % num_params) ############################################################################## # Classes ############################################################################## # Defines the GAN loss which uses either LSGAN or the regular GAN. # When LSGAN is used, it is basically same as MSELoss, # but it abstracts away the need to create the target label tensor # that has the same size as the input class GANLoss(nn.Module): def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor): super(GANLoss, self).__init__() self.real_label = target_real_label self.fake_label = target_fake_label # self.real_label =torch.empty(1).uniform_(0.8, 1)[0]# target_real_label # # self.fake_label = torch.empty(1).uniform_(0, 0.2)[0]#target_fake_label # self.real_label_var = None self.fake_label_var = None self.Tensor = tensor if use_lsgan: self.loss = nn.MSELoss() else: self.loss = nn.BCEWithLogitsLoss()#nn.BCELoss() def get_target_tensor(self, input, target_is_real): target_tensor = None if target_is_real: create_label = ((self.real_label_var is None) or (self.real_label_var.numel() != input.numel())) if create_label: real_tensor = self.Tensor(input.size()).fill_(self.real_label) self.real_label_var = Variable(real_tensor, requires_grad=False) target_tensor = self.real_label_var else: create_label = ((self.fake_label_var is None) or (self.fake_label_var.numel() != input.numel())) if create_label: fake_tensor = self.Tensor(input.size()).fill_(self.fake_label) self.fake_label_var = Variable(fake_tensor, requires_grad=False) target_tensor = self.fake_label_var return target_tensor def __call__(self, input, target_is_real): target_tensor = self.get_target_tensor(input, target_is_real) return self.loss(input, target_tensor) class VGGLoss(nn.Module): def __init__(self, gpu_ids): super(VGGLoss, self).__init__() self.vgg = Vgg19().cuda() self.criterion = nn.L1Loss() self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] def forward(self, x, y): x_vgg, y_vgg = self.vgg(x), self.vgg(y) loss = 0 for i in range(len(x_vgg)): loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) return loss # Defines the generator that consists of Resnet blocks between a few # downsampling/upsampling operations. # Code and idea originally from Justin Johnson's architecture. # https://github.com/jcjohnson/fast-neural-style/ class ResnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, gpu_ids=[], padding_type='reflect'): assert(n_blocks >= 0) super(ResnetGenerator, self).__init__() self.input_nc = input_nc self.output_nc = output_nc self.ngf = ngf self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), norm_layer(ngf), nn.ReLU(True)] n_downsampling = 2 for i in range(n_downsampling): mult = 2**i model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), norm_layer(ngf * mult * 2), nn.ReLU(True)] mult = 2**n_downsampling for i in range(n_blocks): model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] for i in range(n_downsampling): mult = 2**(n_downsampling - i) model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1, bias=use_bias), norm_layer(int(ngf * mult / 2)), nn.ReLU(True)] model += [nn.ReflectionPad2d(3)] model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] model += [nn.Tanh()] self.model = nn.Sequential(*model) def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) # Define a resnet block class ResnetBlock(nn.Module): def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): super(ResnetBlock, self).__init__() self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): conv_block = [] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)] if use_dropout: conv_block += [nn.Dropout(0.5)] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)] return nn.Sequential(*conv_block) def forward(self, x): out = x + self.conv_block(x) return out # Defines the Unet generator. # |num_downs|: number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck class UnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(UnetGenerator, self).__init__() self.gpu_ids = gpu_ids # Unet encoder self.conv1 = nn.Conv2d(input_nc, ngf, 4, 2, 1) self.conv2 = nn.Conv2d(ngf, ngf * 2, 4, 2, 1) self.conv2_bn = nn.BatchNorm2d(ngf * 2) #nn.InstanceNorm2d(ngf*2, affine=True)#)# self.conv3 = nn.Conv2d(ngf * 2, ngf * 4, 4, 2, 1) self.conv3_bn = nn.BatchNorm2d(ngf * 4) #nn.InstanceNorm2d(ngf*4, affine=True)#)# self.conv4 = nn.Conv2d(ngf * 4, ngf * 8, 4, 2, 1) self.conv4_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)#)# self.conv5 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) self.conv5_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# self.conv6 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) self.conv6_bn = nn.BatchNorm2d(ngf * 8) # self.conv7 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) self.conv7_bn =nn.BatchNorm2d(ngf * 8) # nn.InstanceNorm2d(ngf*8, affine=True)# # self.conv8 = nn.Conv2d(ngf * 8, ngf * 8, 4, 2, 1) # self.conv8_bn = nn.BatchNorm2d(d * 8) # Unet decoder # self.deconv1 = nn.ConvTranspose2d(ngf * 8, ngf * 8, 4, 2, 1) self.deconv1=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8, ngf*8, kernel_size=3, stride=1, padding=1)) self.deconv1_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# # self.deconv2 = nn.ConvTranspose2d(ngf * 8 * 2, ngf * 8, 4, 2, 1) self.deconv2=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8 * 2, ngf*8, kernel_size=3, stride=1, padding=1)) self.deconv2_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# # self.deconv3 = nn.ConvTranspose2d(ngf * 8 * 2, ngf * 8, 4, 2, 1) self.deconv1=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8*2, ngf*8, kernel_size=3, stride=1, padding=1)) self.deconv3_bn = nn.BatchNorm2d(ngf * 8) #nn.InstanceNorm2d(ngf*8, affine=True)# # self.deconv4 = nn.ConvTranspose2d(ngf * 8 * 2, ngf * 4, 4, 2, 1) self.deconv4=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 8*2, ngf*4, kernel_size=3, stride=1, padding=1)) self.deconv4_bn = nn.BatchNorm2d(ngf * 4) #nn.InstanceNorm2d(ngf*4, affine=True)# # self.deconv5 = nn.ConvTranspose2d(ngf * 4 * 2, ngf * 2, 4, 2, 1) self.deconv5=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 4 * 2, ngf*2, kernel_size=3, stride=1, padding=1)) self.deconv5_bn = nn.BatchNorm2d(ngf * 2) #nn.InstanceNorm2d(ngf*2, affine=True)# # self.deconv6 = nn.ConvTranspose2d(ngf * 2 * 2, ngf, 4, 2, 1) self.deconv6=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 2 * 2, ngf, kernel_size=3, stride=1, padding=1)) self.deconv6_bn = nn.BatchNorm2d(ngf) #nn.InstanceNorm2d(ngf, affine=True)# # self.deconv7 = nn.ConvTranspose2d(ngf * 2, output_nc, 4, 2, 1) self.deconv7=nn.Sequential( nn.Upsample(scale_factor = 2, mode='nearest'), # nn.ReflectionPad2d(1), nn.Conv2d(ngf * 2, output_nc, kernel_size=3, stride=1, padding=1)) # self.deconv7_bn = nn.BatchNorm2d(ngf) # self.deconv8 = nn.ConvTranspose2d(ngf * 2, output_nc, 4, 2, 1) # weight_init # def weight_init(self, mean, std): # for m in self._modules: # normal_init(self._modules[m], mean, std) # forward method # def forward(self, input): # e1 = self.conv1(input) # e2 = self.conv2_bn(self.conv2(F.relu(e1))) # e3 = self.conv3_bn(self.conv3(F.relu(e2))) # e4 = self.conv4_bn(self.conv4(F.relu(e3))) # e5 = self.conv5_bn(self.conv5(F.relu(e4))) # e6 = self.conv6_bn(self.conv6(F.relu(e5))) # e7 = self.conv7_bn(self.conv7(F.leaky_relu(e6, 0.2))) # e7 = self.conv7(F.leaky_relu(e6)) # e8 = self.conv8(F.leaky_relu(e7, 0.2)) # e8=e88[:,0:512,:,:] # e_return=e88[:,512:1024,:,:] # e8 = self.conv8_bn(self.conv8(F.leaky_relu(e7, 0.2))) # d1 = self.deconv1_bn(self.deconv1(F.relu(e7))) # d1 = torch.cat([d1, e6], 1) # d2 = self.deconv2_bn(self.deconv2(F.relu(d1))) # d2 = torch.cat([d2, e5], 1) # d3 = self.deconv3_bn(self.deconv3(F.relu(d2))) # d3 = torch.cat([d3, e4], 1) # d4 = self.deconv4_bn(self.deconv4(F.relu(d3))) # d4 = F.dropout(self.deconv4_bn(self.deconv4(F.relu(d3))), 0.5) # d4 = torch.cat([d4, e3], 1) # d44=self.deconv5(F.relu(d4)) # d5 = self.deconv5_bn(d44) # d5 = torch.cat([d5, e2], 1) # d6 = self.deconv6_bn(self.deconv6(F.relu(d5))) # d6 = torch.cat([d6, e1], 1) # d7 = self.deconv7(d6) # d7 = torch.cat([d7, e1], 1) # d8 = self.deconv8(F.relu(d7)) # o = F.tanh(d7) # return o#,e_return def forward(self, input): e1 = self.conv1(input) e2 = self.conv2(F.leaky_relu(e1, 0.2)) e3 = self.conv3(F.leaky_relu(e2, 0.2)) e4 = self.conv4(F.leaky_relu(e3, 0.2)) e5 = self.conv5(F.leaky_relu(e4, 0.2)) e6 = self.conv6(F.leaky_relu(e5, 0.2)) # e7 = self.conv7_bn(self.conv7(F.leaky_relu(e6, 0.2))) e7 = self.conv7(F.leaky_relu(e6, 0.2)) # e8 = self.conv8(F.leaky_relu(e7, 0.2)) # e8=e88[:,0:512,:,:] # e_return=e88[:,512:1024,:,:] # e8 = self.conv8_bn(self.conv8(F.leaky_relu(e7, 0.2))) d1 = self.deconv1(F.relu(e7)) d1 = torch.cat([d1, e6], 1) d2 = self.deconv2(F.relu(d1)) d2 = torch.cat([d2, e5], 1) d3 = self.deconv3(F.relu(d2)) d3 = torch.cat([d3, e4], 1) d4 = self.deconv4(F.relu(d3)) # d4 = F.dropout(self.deconv4_bn(self.deconv4(F.relu(d3))), 0.5) d4 = torch.cat([d4, e3], 1) d44=self.deconv5(F.relu(d4)) d5 = self.deconv5_bn(d44) d5 = torch.cat([d5, e2], 1) d6 = self.deconv6(F.relu(d5)) d6 = torch.cat([d6, e1], 1) d7 = self.deconv7(d6) # d7 = torch.cat([d7, e1], 1) # d8 = self.deconv8(F.relu(d7)) o = F.tanh(d7) return o#,e_return #class UnetGenerator(nn.Module): # def __init__(self, input_nc, output_nc, num_downs, ngf=64, # norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): # super(UnetGenerator, self).__init__() # self.gpu_ids = gpu_ids # # # construct unet structure # unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # for i in range(num_downs - 5): # unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) # unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) # unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) # unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) # unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # # self.model = unet_block # # def forward(self, input): # if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): # return nn.parallel.data_parallel(self.model, input, self.gpu_ids) # else: # return self.model(input) # # ## Defines the submodule with skip connection. ## X -------------------identity---------------------- X ## |-- downsampling -- |submodule| -- upsampling --| #class UnetSkipConnectionBlock(nn.Module): # def __init__(self, outer_nc, inner_nc, input_nc=None, # submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): # super(UnetSkipConnectionBlock, self).__init__() # self.outermost = outermost # if type(norm_layer) == functools.partial: # use_bias = norm_layer.func == nn.InstanceNorm2d # else: # use_bias = norm_layer == nn.InstanceNorm2d # if input_nc is None: # input_nc = outer_nc # downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, # stride=2, padding=1, bias=use_bias) # downrelu = nn.LeakyReLU(0.2, True) # downnorm = norm_layer(inner_nc) # uprelu = nn.ReLU(True) # upnorm = norm_layer(outer_nc) # # if outermost: # upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, # kernel_size=4, stride=2, # padding=1) # down = [downconv] # up = [uprelu, upconv, nn.Tanh()] # model = down + [submodule] + up # elif innermost: # upconv = nn.ConvTranspose2d(inner_nc, outer_nc, # kernel_size=4, stride=2, # padding=1, bias=use_bias) # down = [downrelu, downconv] # up = [uprelu, upconv, upnorm] # model = down + up # else: # upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, # kernel_size=4, stride=2, # padding=1, bias=use_bias) # down = [downrelu, downconv, downnorm] # up = [uprelu, upconv, upnorm] # # if use_dropout: # model = down + [submodule] + up + [nn.Dropout(0.5)] # else: # model = down + [submodule] + up # # self.model = nn.Sequential(*model) # # def forward(self, x): # if self.outermost: # return self.model(x) # else: # return torch.cat([x, self.model(x)], 1) # Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(NLayerDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 3 padw = 1 sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.Conv2d(ndf, ndf, kernel_size=kw, stride=2, padding=padw), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2**n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), nn.Conv2d(ndf * nf_mult, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), # nn.LayerNorm([ndf * nf_mult,np.int(ndf/nf_mult),np.int(ndf/nf_mult)]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] nf_mult_prev = nf_mult nf_mult = min(2**n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), # nn.LayerNorm([ndf*nf_mult,15,15]), # norm_layer(ndf * nf_mult), # nn.LeakyReLU(0.2, True) nn.ReLU() ] sequence += [nn.Conv2d(ndf * nf_mult,ndf * nf_mult, kernel_size=kw, stride=1, padding=padw)] sequence += [nn.Conv2d(ndf * nf_mult, 128, kernel_size=kw, stride=2, padding=padw)] # if use_sigmoid: # sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) self.conv1=nn.Conv2d(128, 1, kernel_size=4, stride=1, padding=1, bias=True) self.conv2=nn.Linear(128*8**2,20)#nn.Conv2d(128, 20, kernel_size=4, stride=1, padding=0, bias=True) self.aux_layer = nn.Sequential(nn.Linear(128*8**2, 939), nn.Softmax()) def forward(self, input): out1=nn.parallel.data_parallel(self.model, input, self.gpu_ids)# self.model(input) # out=F.sigmoid(self.conv1(out1)) out=self.conv1(out1) cond=F.sigmoid(self.conv2(out1.view(out1.shape[0],-1))) class_pred=self.aux_layer(out1.view(out1.shape[0], -1)) return out,cond,class_pred # self.model = nn.Sequential(*sequence) # def forward(self, input): # if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): # return nn.parallel.data_parallel(self.model, input, self.gpu_ids) # else: # return self.model(input) class PixelDiscriminator(nn.Module): def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]): super(PixelDiscriminator, self).__init__() self.gpu_ids = gpu_ids if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.net = [ nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), norm_layer(ndf * 2), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] if use_sigmoid: self.net.append(nn.Sigmoid()) self.net = nn.Sequential(*self.net) def forward(self, input): if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.net, input, self.gpu_ids) else: return self.net(input) from torchvision import models class Vgg19(torch.nn.Module): def __init__(self, requires_grad=False): super(Vgg19, self).__init__() vgg_pretrained_features = models.vgg19(pretrained=True).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() for x in range(2): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(2, 7): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(7, 12): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(12, 21): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(21, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h_relu1 = self.slice1(X) h_relu2 = self.slice2(h_relu1) h_relu3 = self.slice3(h_relu2) h_relu4 = self.slice4(h_relu3) h_relu5 = self.slice5(h_relu4) out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] return out ================================================ FILE: test_code_released_old/models/pix2pix_model.py ================================================ import torch from collections import OrderedDict #from torch.autograd import Variable import util.util as util from util.image_pool import ImagePool from .base_model import BaseModel from . import networks #import itertools import torch.nn as nn import torch.nn.functional as F import pdb import torchvision import os from torch import autograd import copy from natsort import natsorted, ns class Estimate(nn.Module): def __init__(self): super(Estimate, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, 3, stride=1, padding=1), nn.Conv2d(64, 64, 3, stride=1, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.Conv2d(64, 128, 3, stride=1, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.Conv2d(128, 3, 3, stride=1, padding=1) ) def forward(self, x): x = F.tanh(self.features(x)) return x class Pix2PixModel(BaseModel): def name(self): return 'Pix2PixModel' def _compute_loss_smooth(self, mat): return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \ torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :])) def initialize(self, opt): BaseModel.initialize(self, opt) self.isTrain = opt.isTrain self.B=opt.batchSize self.netG = torch.nn.DataParallel(networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids),device_ids=self.gpu_ids) self.netGN = torch.nn.DataParallel(networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids),device_ids=self.gpu_ids) self.I_E=Estimate().cuda() if self.isTrain: use_sigmoid = opt.no_lsgan self.netDA = torch.nn.DataParallel(networks.define_D(opt.input_nc, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids),device_ids=self.gpu_ids) if not self.isTrain or opt.continue_train: self.load_network(self.netG, 'G', opt.which_epoch) self.load_network(self.netGN, 'GN', opt.which_epoch) self.load_network(self.I_E, 'I_E', opt.which_epoch) if self.isTrain: self.load_network(self.netDA, 'DA', opt.which_epoch) # self.generator_test = copy.deepcopy(self.netG) # self.generator_testN = copy.deepcopy(self.netGN) if self.isTrain: # self.fake_AB_pool = ImagePool(opt.pool_size) # define loss functions self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor) self.criterionL1_F = torch.nn.L1Loss(size_average=False) self.criterionL1 = torch.nn.L1Loss() self.criterionL2 = torch.nn.MSELoss() self.criterionVGG = networks.VGGLoss(self.gpu_ids) self.criterionCE = torch.nn.CrossEntropyLoss() # initialize optimizers self.schedulers = [] self.optimizers = [] self.optimizer_I = torch.optim.Adam(self.I_E.parameters(),lr=opt.lr, betas=(opt.beta1, 0.999)) #0.1 in other program self.optimizer_G = torch.optim.RMSprop(self.netG.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netG.parameters(), # lr=opt.lr, betas=(0, 0.999),amsgrad=False,weight_decay=0) self.optimizer_GN =torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)# torch.optim.Adam(self.netGN.parameters(), #torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr)# self.optimizer_DA = torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netDA.parameters(), #torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr)# self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_GN) self.optimizers.append(self.optimizer_DA) self.optimizers.append(self.optimizer_I) for optimizer in self.optimizers: self.schedulers.append(networks.get_scheduler(optimizer, opt)) print('---------- Networks initialized -------------') networks.print_network(self.netG) if self.isTrain: networks.print_network(self.netDA) print('-----------------------------------------------') def set_input(self, input): input_A = input['A'] P_B = input['PB'] if len(self.gpu_ids) > 0: input_A = input_A.cuda(self.gpu_ids[0], async=True) P_B = P_B.cuda(self.gpu_ids[0], async=True) self.input_A = input_A self.P_B = P_B self.image_paths = input['A_paths']# if AtoB else 'B_paths'] def test(self): self.netG.eval() self.netGN.eval() self.I_E.eval() # pdb.set_trace() desti=os.path.dirname(self.image_paths[0])+'/'+self.opt.results_dir if not os.path.isdir(desti): os.makedirs(desti) for i in range(0,self.P_B.size(1)): self.param_B = self.P_B[0,i,:].unsqueeze(0) self.param_B=self.param_B.view(-1,20).float() self.real_A=self.input_A[:,0:3,:,:] self.real_A.requires_grad=True I_p=self.I_E(self.real_A) # self.param_A=self.param_A.view(-1,20).float() self.AUN = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128)/100000000 self.AUN[:,0:3] = 0.5 # self.fake_B = self.netGN(torch.cat([I_p,self.AUN],dim=1)) #####################ORIGINAL###################################################### AUR = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128) ################################################################################### I_f=self.I_E(self.fake_B) self.fake_B_recon=self.netG(torch.cat([I_f.data,AUR],dim=1)) torchvision.utils.save_image((self.fake_B_recon*0.5)+0.5,desti+'/'+str(i)+'_re'+'.png') # #put your video_path os.system("ffmpeg -r 25 -i ./new_crop/results_video/%01d_re.png -vcodec mpeg4 -y movie.mp4") os.system('rm -r ./new_crop/results_video/') def get_image_paths(self): return self.image_paths # return self.ref_path def _compute_loss_D(self, estim, is_real): return -torch.mean(estim) if is_real else torch.mean(estim) ================================================ FILE: test_code_released_old/models/pix2pix_model.py~ ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Oct 25 17:51:34 2018 @author: esa """ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 23 17:42:29 2018 @author: esa """ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Oct 22 19:08:31 2018 @author: esa """ import torch from collections import OrderedDict #from torch.autograd import Variable import util.util as util from util.image_pool import ImagePool from .base_model import BaseModel from . import networks #import itertools import torch.nn as nn import torch.nn.functional as F import pdb import torchvision import os from torch import autograd import copy class Estimate(nn.Module): def __init__(self): super(Estimate, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, 4, stride=2, padding=1), nn.Conv2d(64, 64, 4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 128, kernel_size=4, stride=2, padding=1, bias=True), # nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True), # nn.Conv2d(64,128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 3, kernel_size=4, stride=2, padding=1, bias=True), # nn.BatchNorm2d(64), # nn.ReLU(inplace=True), ) # self.classifier = nn.Sequential( # nn.Linear(576, 236) # ) def forward(self, x): x = F.tanh(self.features(x)) # y=F.tanh(self.classifier(x.view(x.size(0),576))) # o = F.tanh(d8) # x6 = self.down4(x5) return x #class Estimate(nn.Module): # def __init__(self): # super(Estimate, self).__init__() ## self.features = nn.Sequential( # self.conv1=nn.Conv2d(3, 64, 4, stride=2, padding=1) # self.conv2 = nn.Conv2d(64, 64, 4, stride=2, padding=1) # self.conv2_bn=nn.BatchNorm2d(64) ## nn.ReLU(inplace=True), # self.deconv1=nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1, bias=True) ## nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True), ## nn.Conv2d(64,128, kernel_size=4, stride=2, padding=1), # self.deconv1_bn=nn.BatchNorm2d(64) ## nn.ReLU(inplace=True), # self.deconv2=nn.ConvTranspose2d(64*2, 3, kernel_size=4, stride=2, padding=1, bias=True) ## nn.BatchNorm2d(64), ## nn.ReLU(inplace=True), ## ) ## s ## self.classifier = nn.Sequential( ## nn.Linear(576, 236) ## ) # def forward(self, input): # # e1 = self.conv1(input) # e2 = self.conv2_bn(self.conv2(F.relu(e1))) # d1 = self.deconv1_bn(self.deconv1(F.relu(e2))) # d1 = torch.cat([d1, e1], 1) # d2 = self.deconv2(d1) # o = F.tanh(d2) ## y=F.tanh(self.classifier(x.view(x.size(0),576))) # ## o = F.tanh(d8) ## x6 = self.down4(x5) # return o class Pix2PixModel(BaseModel): def name(self): return 'Pix2PixModel' def _compute_loss_smooth(self, mat): return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \ torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :])) def initialize(self, opt): BaseModel.initialize(self, opt) self.isTrain = opt.isTrain self.B=opt.batchSize # load/define networks self.netG = networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) self.netGN = networks.define_G(opt.input_nc+20, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) # self.netGR = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, # opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) self.I_E=Estimate().cuda() # pdb.set_trace() # self.I_D=Decoder().cuda() #setting up the saliency model for loss calculations # self.u_sal=saliency_encoder().cuda() if self.isTrain: use_sigmoid = opt.no_lsgan self.netDA = networks.define_D(opt.input_nc, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids) # self.netDB = networks.define_D(opt.input_nc, opt.ndf, # opt.which_model_netD, # opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids) if not self.isTrain or opt.continue_train: self.load_network(self.netG, 'G', opt.which_epoch) self.load_network(self.netGN, 'GN', opt.which_epoch) self.load_network(self.I_E, 'I_E', opt.which_epoch) # self.load_network(self.I_D, 'I_D', opt.which_epoch) if self.isTrain: self.load_network(self.netDA, 'DA', opt.which_epoch) # self.load_network(self.netDB, 'DB', opt.which_epoch) self.generator_test = copy.deepcopy(self.netG) self.generator_testN = copy.deepcopy(self.netGN) if self.isTrain: self.fake_AB_pool = ImagePool(opt.pool_size) # define loss functions self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor) self.criterionL1_F = torch.nn.L1Loss(size_average=False) self.criterionL1 = torch.nn.L1Loss() self.criterionL2 = torch.nn.MSELoss() self.criterionVGG = networks.VGGLoss(self.gpu_ids) self.criterionCE = torch.nn.CrossEntropyLoss() # initialize optimizers self.schedulers = [] self.optimizers = [] # params = list(self.netG.parameters()) # params += list(self.I_E.parameters()) self.optimizer_I = torch.optim.Adam(self.I_E.parameters(),lr=opt.lr, betas=(opt.beta1, 0.999)) #0.1 in other program # self.optimizer_D = torch.optim.Adam(self.I_D.parameters(), # lr=0.0002, betas=(opt.beta1, 0.999),weight_decay=0) self.optimizer_G = torch.optim.RMSprop(self.netG.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netG.parameters(), # lr=opt.lr, betas=(0, 0.999),amsgrad=False,weight_decay=0) self.optimizer_GN =torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)# torch.optim.Adam(self.netGN.parameters(), #torch.optim.RMSprop(self.netGN.parameters(), lr=opt.lr)# # lr=opt.lr, betas=(0, 0.999),amsgrad=False) # pdb.set_trace() self.optimizer_DA = torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr, alpha=0.99, eps=1e-8)#torch.optim.Adam(self.netDA.parameters(), #torch.optim.RMSprop(self.netDA.parameters(), lr=opt.lr)# # lr=opt.lr, betas=(0, 0.999),amsgrad=False,weight_decay=0) # self.optimizer_DB = torch.optim.Adam(self.netDB.parameters(), # lr=opt.lrD, betas=(opt.beta1, 0.999),amsgrad=True) self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_GN) # self.optimizers.append(self.optimizer_DB) self.optimizers.append(self.optimizer_DA) self.optimizers.append(self.optimizer_I) # self.optimizers.append(self.optimizer_D) for optimizer in self.optimizers: self.schedulers.append(networks.get_scheduler(optimizer, opt)) print('---------- Networks initialized -------------') networks.print_network(self.netG) if self.isTrain: networks.print_network(self.netDA) print('-----------------------------------------------') def set_input(self, input): # AtoB = self.opt.which_direction == 'AtoB' input_A = input['A'] #if AtoB else 'B'] input_B = input['B'] P_A = input['PA'] P_B = input['PB'] I = input['I'] C=input['C'] #if AtoB elself.criterionL1 = torch.nn.L1Loss(size_average=False)se 'A'] # pdb.set_trace() # input_B[:,0,0:3]=(input_B[:,0,0:3]-(-0.50))/1 # input_B[:,0,3:20]=input_B[:,0,3:20]/5 # pdb.set_trace() # print(input_B) # input_S = input['S'] if len(self.gpu_ids) > 0: input_A = input_A.cuda(self.gpu_ids[0], async=True) input_B = input_B.cuda(self.gpu_ids[0], async=True) P_A = P_A.cuda(self.gpu_ids[0], async=True) P_B = P_B.cuda(self.gpu_ids[0], async=True) I = I.cuda(self.gpu_ids[0], async=True) C = C.cuda(self.gpu_ids[0], async=True) # input_S = input_S.cuda(self.gpu_ids[0], async=True) self.input_A = input_A self.input_B = input_B self.P_A = P_A self.P_B = P_B self.I = I/939 self.C = C.long().view(-1) # self.input_S = input_S # print(self.input_A.size()) self.image_paths = input['A_paths']# if AtoB else 'B_paths'] self.ref_paths = input['B_paths'] # pdb.set_trace() # def test(self): # # self.real_A = self.input_A # self.real_A.requires_grad=True # # self.real_B = self.input_B # self.real_B.requires_grad=True # # self.param_A = self.P_A.view(self.B,20) # self.param_B = self.P_B.view(self.B,20) # self.netG.eval() # self.netGN.eval() # self.I_E.eval() ## self.param_A.requires_grad=True # # with torch.no_grad(): # I_p=self.I_E(self.real_A) ## pdb.set_trace() # self.AUN=self.param_A.float()/100000 # self.AUN[:,0:3] = 0.5 # ## self.AUN=self.real_B.float()+torch.randn(self.B,20).cuda()/10 ## self.AUN=torch.clamp(self.AUN,0,1) # I_pd=I_p.detach() ## pdb.set_trace() ## I_pd.requires_grad=True # self.fake_B = self.netGN(torch.cat([I_pd.data,self.AUN],dim=1)) # AUR=self.param_B.view(-1,20).float() # # with torch.no_grad(): # I_f=self.I_E(self.fake_B) # I_fd=I_f.detach() ## I_fd.requires_grad=True # ## pdb.set_trace() ## self.new_z2=z.view(z.size(0), z.size(1), 1, 1).expand(z.size(0), z.size(1), self.fake_B.size(2), self.fake_B.size(3)) ## with torch.no_grad(): # self.fake_B_recon=self.netG(torch.cat([I_fd.data,AUR],dim=1)) def test(self): # self.real_A = self.input_A # self.real_A.requires_grad=True # self.real_B = self.input_B # self.real_B.requires_grad=True self.netG.eval() self.netGN.eval() self.I_E.eval() # self.up1.eval() # self.param_A.requires_grad=True pdb.set_trace() desti=os.path.dirname(self.image_paths[0].replace('/home/esa/Downloads/unzippedFaces','./results')) if not os.path.isdir(desti): os.makedirs(desti) self.U=nn.Upsample(size=(256,256), scale_factor=None, mode='bilinear', align_corners=None) for i in range(0,self.P_A.size(1)): # pdb.set_trace() self.param_A = self.P_A[0,0,:].unsqueeze(0)#.view(self.B,i,20) self.param_B = self.P_B[0,i,:].unsqueeze(0)#.view(self.B,i,20) self.real_B=self.input_B[:,i*3:(i*3)+3,:,:] self.real_A=self.input_A[:,0:3,:,:] self.real_A.requires_grad=True # pdb.set_trace() # with torch.no_grad(): I_p=self.I_E(self.real_A) # pdb.set_trace() self.AUN=self.param_A.float()/100000 self.AUN[:,0:3] = 0.5 # # self.AUN=self.real_B.float()+torch.randn(self.B,20).cuda()/10 # self.AUN=torch.clamp(self.AUN,0,1) # I_pd=I_p.detach() # pdb.set_trace() # I_pd.requires_grad=True self.fake_B = self.netGN(torch.cat([I_p,self.AUN],dim=1)) AUR=self.param_B.view(-1,20).float() # AUR[:,2]=i/100 # AUR[:,3:20]=self.param_A.float()[:,3:20] # AUR[:,0]=0.75 # AUR[:,1]=0.5 # # AUR[:,2]=0.5 # AUR[:,0]=torch.rand(1)[0] # AUR[:,1]=torch.rand(1)[0] # AUR[:,2]=torch.rand(1)[0] # AUR[:,2]=(i+1)/10 # AUR[:,3]=0.2 # AUR[:,5]=0.8 # AUR[:,3:19]=torch.rand(16)/10 # AUR[:,3]=0.7 # AUR[:,16]=0 # AUR[:,18]=0.8 # with torch.no_grad(): I_f=self.I_E(self.fake_B) # I_fd=I_f.detach() # I_fd.requires_grad=True # pdb.set_trace() # self.new_z2=z.view(z.size(0), z.size(1), 1, 1).expand(z.size(0), z.size(1), self.fake_B.size(2), self.fake_B.size(3)) # with torch.no_grad(): self.real_A_up=self.U(self.real_A) self.fake_B_recon=self.netG(torch.cat([I_f.data,AUR],dim=1)) # self.fake_B_256=self.up1(torch.cat([self.real_A_up,self.U(self.fake_B_recon)],dim=1)) # pdb.set_trace() torchvision.utils.save_image((self.fake_B_recon*0.5)+0.5,desti+'/'+str(i)+'_re'+'.png') torchvision.utils.save_image((self.fake_B*0.5)+0.5,desti+'/'+str(i)+'_neut'+'.png') torchvision.utils.save_image((self.real_B*0.5)+0.5,desti+'/'+str(i)+'_gt'+'.png') # torchvision.utils.save_image((self.fake_B_256*0.5)+0.5,desti+'/'+str(i)+'_256'+'.png') # torchvision.utils.save_image((self.real_A_up*0.5)+0.5,desti+'/'+str(i)+'_A_up'+'.png') torchvision.utils.save_image((self.real_A*0.5)+0.5,desti+'/'+str(i)+'_ref'+'.png') # pdb.set_trace() torch.__version__ # pdb.set_trace() # os.system('export PATH="/home/esa/anaconda3/bin:$PATH"') # os.system('source activate x2face') # os.system('/home/esa/anaconda3/envs/x2face/bin/python /home/esa/Downloads/X2Face-master/UnwrapMosaic/Face2Face_UnwrapMosaic_run.py --driver_path %(language)s --source_path %(sp)s' %{'language': os.path.dirname(self.image_paths[0])+'/','sp': os.path.dirname(self.ref_paths[0])+'/'}) # os.system('source deactivate') # pdb.set_trace() def forward(self): self.real_A = self.input_A self.real_A.requires_grad=True self.real_B = self.input_B self.real_B.requires_grad=True self.param_A = self.P_A.view(self.B,20) self.param_B = self.P_B.view(self.B,20) # self.param_A.requires_grad=True # with torch.no_grad(): I_p=self.I_E(self.real_A) # pdb.set_trace() self.param_A=self.param_A.view(-1,20).float() self.AUN = self.param_A.view(self.param_A.size(0), self.param_A.size(1), 1, 1).expand( self.param_A.size(0), self.param_A.size(1), 128, 128)/100000000 # self.AUN=self.param_A.float()/100000 self.AUN[:,0:3] = 0.5 # # self.AUN=self.real_B.float()+torch.randn(self.B,20).cuda()/10 # self.AUN=torch.clamp(self.AUN,0,1) # I_pd=I_p.detach() # pdb.set_trace() # I_pd.requires_grad=True self.fake_B = self.netGN(torch.cat([I_p,self.AUN],dim=1)) self.param_B=self.param_B.view(-1,20).float() AUR = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128) # with torch.no_grad(): I_f=self.I_E(self.fake_B) # I_fd=I_f.detach() # I_fd.requires_grad=True # pdb.set_trace() # self.new_z2=z.view(z.size(0), z.size(1), 1, 1).expand(z.size(0), z.size(1), self.fake_B.size(2), self.fake_B.size(3)) # with torch.no_grad(): self.fake_B_recon=self.netG(torch.cat([I_f,AUR],dim=1)) # pdb.set_trace() # self.gar_con=self.u_sal(self.garbage) # pdb.set_trace() # get image paths def get_image_paths(self): return self.image_paths def _compute_loss_D(self, estim, is_real): return -torch.mean(estim) if is_real else torch.mean(estim) def backward_DA(self): def compute_grad2(d_out, x_in): batch_size = x_in.size(0) grad_dout = autograd.grad( outputs=d_out.sum(), inputs=x_in, create_graph=True, retain_graph=True, only_inputs=True )[0] grad_dout2 = grad_dout.pow(2) assert(grad_dout2.size() == x_in.size()) reg = grad_dout2.view(batch_size, -1).sum(1) return reg # Fake # stop backprop to the generator by detaching fake_B pred_real, cond_real,class_pred = self.netDA(self.real_A) # self.D_loss1 = -(torch.mean(pred_real) - torch.mean(pred_fake)) # self.D_loss2 = -(torch.mean(pred_real) - torch.mean(pred_fake2)) self.AUX_loss=self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C # pdb.set_trace() self.pred_loss_B=self.criterionL1(cond_real,self.param_A.float().detach())/self.B *self.opt.lambda_A self.loss_D_realB = self.criterionGAN(pred_real, True) + self.AUX_loss + self.pred_loss_B self.loss_D_realB.backward(retain_graph=True) self.reg = 0.05*compute_grad2(pred_real, self.real_A).mean() self.reg.backward() fake_AB = self.fake_B_recon.detach() fake_B=self.fake_B.detach() fake_AB.requires_grad=True fake_B.requires_gard=True # pdb.set_trace() pred_fake, cond_fake, class_pred1 = self.netDA(fake_AB) pred_fake2, cond_fake2,class_pred2 = self.netDA(fake_B) # pred_real, cond_real,class_pred = self.netDA(self.real_A) self.loss_D_fakeB = self.criterionGAN(pred_fake, False) self.loss_D_fakeB2 = self.criterionGAN(pred_fake2, False) loss_fake=self.loss_D_fakeB + self.loss_D_fakeB2 loss_fake.backward()#(retain_graph=True) # pdb.set_trace() # Real # real_AB = self.real_A#self.real_A # real_pred=self.real_B.float() + (torch.rand(self.B,20)/torch.randint(10, 100, (1,))).cuda() # pdb.set_trace() # self.AUX_loss=self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C ## pdb.set_trace() # self.pred_loss_B=self.criterionL1(cond_real,self.param_A.float().detach())/self.B *self.opt.lambda_A # m = nn.LogSoftmax() # Combined loss # self.loss_DB = ((self.loss_D_fakeB + self.loss_D_realB)*0.5) + self.pred_loss_B + self.loss_D_fakeB2 + self.AUX_loss # self.loss_DB = self.D_loss1 + self.D_loss2 + self.AUX_loss + self.pred_loss_B #+ self.loss_D_fakeB2 # pdb.set_trace() # self.loss_DB.backward() def backward_GR(self): # fake_B=self.fake_B # self.fake_B_re=self.netGR(torch.cat([fake_B.detach(),self.new_z2.detach()],dim=1)) # fake_BA = self.fake_B_re # pdb.set_trace() pred_fake, E_con, class_pred = self.netDA(self.fake_B) # gar_con.register_hook(print) self.loss_G_GANE = self.criterionGAN(pred_fake, True) #-torch.mean(pred_fake)# self.sal_loss2= self.criterionL1(E_con,self.param_A.detach()/1000000)/self.B*self.opt.lambda_A self.class_pred_loss =self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C # self.sal_loss_N1=self.criterionL1(gar_con,self.real_B.float().detach())/self.B*self.opt.lambda_A # self.recon_loss=self.criterionL1(self.fake_B_re,self.real_A.detach())*self.opt.lambda_B*0.1 with torch.no_grad(): I_f=self.I_E(self.fake_B) self.tar=self.netG(torch.cat([I_f.data,self.AUN],dim=1)) # pdb.set_trace() self.recon=self.criterionL1(self.fake_B,self.tar.detach())*self.opt.lambda_D self.recon_vgg=self.criterionVGG(self.fake_B,self.real_B.detach())*0.1 # AUR=self.param_B.view(-1,20).float() AUR = self.param_B.view(self.param_B.size(0), self.param_B.size(1), 1, 1).expand( self.param_B.size(0), self.param_B.size(1), 128, 128) I_f=self.I_E(self.fake_B) # pdb.set_trace() self.fake_B_re=self.netG(torch.cat([I_f,AUR],dim=1)) self.R=self.criterionL1(self.fake_B_re,self.real_B.detach())*self.opt.lambda_B self.loss_GR= self.loss_G_GANE + self.sal_loss2 +self.recon + self.R + self.class_pred_loss + self.recon_vgg # self.loss_GR= self.loss_G_GANE + self.sal_loss2 # pdb.set_trace() self.loss_GR.backward() def backward_G(self): def compute_loss_smooth(mat): return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \ torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :])) # First, G(A) should fake the discriminator # fake_B = self.netG(torch.cat([self.real_A,self.D_au],dim=1)) # fake_AB = fake_B # pred_fake, E_con = self.netDA(self.fake_B) pred_fake_recon, gar_con, class_pred = self.netDA(self.fake_B_recon) # pdb.set_trace() # self.loss_G_GANE = self.criterionGAN(pred_fake_recon, True) # pdb.set_trace() self.loss_G_GAN = self.criterionGAN(pred_fake_recon, True) #-torch.mean(pred_fake_recon)# # pdb.set_trace() # self.sal_loss2= self.criterionL1(E_con,self.AUN.detach())/self.B*self.opt.lambda_A self.sal_loss_N1=self.criterionL1(gar_con,self.param_B.float().detach())/self.B*self.opt.lambda_A self.recon_loss=self.criterionL1(self.fake_B_recon,self.real_B.detach())*self.opt.lambda_D self.class_pred_loss_recon=self.criterionCE(class_pred,self.C.detach())*self.opt.lambda_C # self.loss_smooth=compute_loss_smooth(self.fake_B)*self.opt.lambda_B # weight_regur=torch.mean(self.fake_B) self._loss_g_mask_1_smooth = self._compute_loss_smooth(self.fake_B_recon)*0.1 #* self.opt.lambda_A # print(weight_regur) #torch.mean(torch.pow(self.fake_B, 2))*self.opt.lambda_B self.loss_G = self.loss_G_GAN + self.recon_loss + self.sal_loss_N1 + self.class_pred_loss_recon #+ self._loss_g_mask_1_smooth #+weight_regur#+ self.loss_G_L1 # pdb.set_trace() self.loss_G.backward(retain_graph=True) def _gradinet_penalty_D(self): # interpolate sample alpha = torch.rand(self.B, 1, 1, 1).cuda().expand_as(self.real_B) interpolated = alpha * self.real_B.data + (1 - alpha) * self.fake_B_recon.data interpolated.requires_grad=True # p=self.fake_B.detach() # p.requires_grad=True # pdb.set_trace() interpolated_prob, cond_real,class_pred = self.netDA(interpolated) # compute gradients grad = torch.autograd.grad(outputs=interpolated_prob, inputs=interpolated, grad_outputs=torch.ones(interpolated_prob.size()).cuda(), retain_graph=True, create_graph=True, only_inputs=True)[0] # penalize gradients grad = grad.view(grad.size(0), -1) grad_l2norm = torch.sqrt(torch.sum(grad ** 2, dim=1)) self._loss_d_gp = torch.mean((grad_l2norm - 1) ** 2) * 1 self._loss_d_gp.backward() def optimize_parameters_D(self): self.forward() self.optimizer_DA.zero_grad() self.backward_DA() self.optimizer_DA.step() # for p in self.netDA.parameters(): # p.data.clamp_(-0.01, 0.01) # self.optimizer_DA.zero_grad() # self._gradinet_penalty_D() ## loss_D_gp.backward() # self.optimizer_DA.step() # self.optimizer_D.zero_grad() # self.backward_I() # for param in self.netG.parameters(): # print(param.grad.data.sum()) # pdb.set_trace() # self.optimizer_D.step() def optimize_parameters_G(self): def toogle_grad(model, requires_grad): for p in model.parameters(): p.requires_grad_(requires_grad) def update_average(model_tgt, model_src, beta): toogle_grad(model_src, False) toogle_grad(model_tgt, False) param_dict_src = dict(model_src.named_parameters()) for p_name, p_tgt in model_tgt.named_parameters(): p_src = param_dict_src[p_name] assert(p_src is not p_tgt) p_tgt.copy_(beta*p_tgt + (1. - beta)*p_src) # self.generator_test = copy.deepcopy(self.netG) # self.generator_testN = copy.deepcopy(self.netGN) self.optimizer_G.zero_grad() self.optimizer_I.zero_grad() self.backward_G() self.optimizer_G.step() # for param in self.netG.parameters(): # print(param.grad.data.sum()) # pdb.set_trace() # update_average(self.generator_test, self.netG, # beta=0.99) # self.optimizer_I.zero_grad() self.optimizer_I.step() # self.optimizer_GN.step() self.optimizer_GN.zero_grad() self.backward_GR() # for param in self.netG.parameters(): # print(param.grad.data.sum()) # pdb.set_trace() self.optimizer_GN.step() # update_average(self.generator_testN, self.netGN, # beta=0.99) def get_current_errors(self): return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]), ('G_GANE', self.loss_G_GANE.data[0]), # ('sal_loss', self.sal_loss.data[0]), ('sal_loss2', self.sal_loss2.data[0]), ('sal_lossN1', self.sal_loss_N1.data[0]), ('recon_loss', self.recon_loss.data[0]), # ('d_gp', self._loss_d_gp.data[0]), # ('recon_loss2', self.recon_loss2.data[0]), ('grad_pen', self.reg.data[0]), ('D_loss_fake', self.loss_D_fakeB.data[0]), ('D_loss_real', self.loss_D_realB.data[0]), ('vgg', self.recon_vgg.data[0]), ('class_loss',self.class_pred_loss.data[0]) # ('smooth', self._loss_g_mask_1_smooth.data[0]), # ('D_loss_fake_A',self.loss_D_fake.data[0]) ]) def get_current_visuals(self): torchvision.utils.save_image((self.fake_B_recon*0.5)+0.5,'recon_WGAN_dirac_steroid.png') torchvision.utils.save_image((self.fake_B*0.5)+0.5,'neut_WGAN_dirac_steroid.png') torchvision.utils.save_image((self.real_A*0.5)+0.5,'real_WGAN_dirac_steroid.png') # torchvision.utils.save_image((self.tar*0.5)+0.5,'tar.png') # real_A = util.tensor2im(self.input_A.data) fake_B = util.tensor2im(self.fake_B.data) fake_B_recon = util.tensor2im(self.fake_B_recon.data) return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('fake_B_recon', fake_B_recon)]) def save(self, label): self.save_network(self.netG, 'G', label, self.gpu_ids) self.save_network(self.netDA, 'DA', label, self.gpu_ids) self.save_network(self.I_E, 'I_E', label, self.gpu_ids) # self.save_network(self.I_D, 'I_D', label, self.gpu_ids) # self.save_network(self.netDB, 'DB', label, self.gpu_ids) self.save_network(self.netGN, 'GN', label, self.gpu_ids) ================================================ FILE: test_code_released_old/models/test_model.py ================================================ from torch.autograd import Variable from collections import OrderedDict import util.util as util from .base_model import BaseModel from . import networks class TestModel(BaseModel): def name(self): return 'TestModel' def initialize(self, opt): assert(not opt.isTrain) BaseModel.initialize(self, opt) self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids) which_epoch = opt.which_epoch self.load_network(self.netG, 'G', which_epoch) print('---------- Networks initialized -------------') networks.print_network(self.netG) print('-----------------------------------------------') def set_input(self, input): # we need to use single_dataset mode input_A = input['A'] if len(self.gpu_ids) > 0: input_A.cuda(self.gpu_ids[0], async=True) self.input_A = input_A self.image_paths = input['A_paths'] def test(self): self.real_A = Variable(self.input_A) self.fake_B = self.netG(self.real_A) # get image paths def get_image_paths(self): return self.image_paths def get_current_visuals(self): real_A = util.tensor2im(self.real_A.data) fake_B = util.tensor2im(self.fake_B.data) return OrderedDict([('real_A', real_A), ('fake_B', fake_B)]) ================================================ FILE: test_code_released_old/options/__init__.py ================================================ ================================================ FILE: test_code_released_old/options/base_options.py ================================================ import argparse import os from util import util import torch class BaseOptions(): def __init__(self): self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) self.initialized = False def initialize(self): self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size') self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size') self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size') self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') self.parser.add_argument('--which_model_netD', type=str, default='basic', help='selects model to use for netD') self.parser.add_argument('--which_model_netG', type=str, default='resnet_9blocks', help='selects model to use for netG') self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers') self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') self.parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') self.parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single]') self.parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. cycle_gan, pix2pix, test') self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA') self.parser.add_argument('--nThreads', default=8, type=int, help='# threads for loading data') self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size') self.parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') self.parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') self.parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]') self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') self.parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]') self.initialized = True def parse(self): if not self.initialized: self.initialize() self.opt = self.parser.parse_args() self.opt.isTrain = self.isTrain # train or test str_ids = self.opt.gpu_ids.split(',') self.opt.gpu_ids = [] for str_id in str_ids: id = int(str_id) if id >= 0: self.opt.gpu_ids.append(id) # set gpu ids if len(self.opt.gpu_ids) > 0: torch.cuda.set_device(self.opt.gpu_ids[0]) args = vars(self.opt) print('------------ Options -------------') for k, v in sorted(args.items()): print('%s: %s' % (str(k), str(v))) print('-------------- End ----------------') # save to the disk expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) util.mkdirs(expr_dir) file_name = os.path.join(expr_dir, 'opt.txt') with open(file_name, 'wt') as opt_file: opt_file.write('------------ Options -------------\n') for k, v in sorted(args.items()): opt_file.write('%s: %s\n' % (str(k), str(v))) opt_file.write('-------------- End ----------------\n') return self.opt ================================================ FILE: test_code_released_old/options/test_options.py ================================================ from .base_options import BaseOptions class TestOptions(BaseOptions): def initialize(self): BaseOptions.initialize(self) self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') self.parser.add_argument('--which_ref', type=str, default='path/to/reference/image', help='path to customized reference image') self.parser.add_argument('--csv_path', type=str, default='path/to/csv/driving', help='path to csv file containing AUs of driving video') self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run') self.parser.add_argument('--which_ref_d1', type=str, default='path/to/reference/image', help='path to customized driving_1 image') self.parser.add_argument('--which_ref_d2', type=str, default='path/to/reference/image', help='path to customized driving_2 image') self.isTrain = False ================================================ FILE: test_code_released_old/options/train_options.py ================================================ from .base_options import BaseOptions class TrainOptions(BaseOptions): def initialize(self): BaseOptions.initialize(self) self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen') self.parser.add_argument('--display_single_pane_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.') self.parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') self.parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') self.parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') self.parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') self.parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)') self.parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)') self.parser.add_argument('--lambda_C', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)') self.parser.add_argument('--lambda_D', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)') self.parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') self.parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau') self.parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') self.parser.add_argument('--identity', type=float, default=0.5, help='use identity mapping. Setting identity other than 1 has an effect of scaling the weight of the identity mapping loss.' 'For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set optidentity = 0.1') self.isTrain = True ================================================ FILE: test_code_released_old/test.py ================================================ import os from options.test_options import TestOptions from data.data_loader import CreateDataLoader from models.models import create_model #from util.visualizer import Visualizer #from util import html import pdb opt = TestOptions().parse() opt.nThreads = 1 # test code only supports nThreads = 1 opt.batchSize = 1 # test code only supports batchSize = 1 opt.serial_batches = True # no shuffle opt.no_flip = True # no flip data_loader = CreateDataLoader(opt) dataset = data_loader.load_data() model = create_model(opt) #visualizer = Visualizer(opt) # create website web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch)) #webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch)) # test for i, data in enumerate(dataset): # pdb.set_trace() if i >= opt.how_many: break model.set_input(data) img_path = model.get_image_paths() model.test() img_path = model.get_image_paths() # print('%04d: process image... %s' % (i, img_path)) print("Done!") ================================================ FILE: test_code_released_old/util/__init__.py ================================================ ================================================ FILE: test_code_released_old/util/get_data.py ================================================ from __future__ import print_function import os import tarfile import requests from warnings import warn from zipfile import ZipFile from bs4 import BeautifulSoup from os.path import abspath, isdir, join, basename class GetData(object): """ Download CycleGAN or Pix2Pix Data. Args: technique : str One of: 'cyclegan' or 'pix2pix'. verbose : bool If True, print additional information. Examples: >>> from util.get_data import GetData >>> gd = GetData(technique='cyclegan') >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. """ def __init__(self, technique='cyclegan', verbose=True): url_dict = { 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets', 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' } self.url = url_dict.get(technique.lower()) self._verbose = verbose def _print(self, text): if self._verbose: print(text) @staticmethod def _get_options(r): soup = BeautifulSoup(r.text, 'lxml') options = [h.text for h in soup.find_all('a', href=True) if h.text.endswith(('.zip', 'tar.gz'))] return options def _present_options(self): r = requests.get(self.url) options = self._get_options(r) print('Options:\n') for i, o in enumerate(options): print("{0}: {1}".format(i, o)) choice = input("\nPlease enter the number of the " "dataset above you wish to download:") return options[int(choice)] def _download_data(self, dataset_url, save_path): if not isdir(save_path): os.makedirs(save_path) base = basename(dataset_url) temp_save_path = join(save_path, base) with open(temp_save_path, "wb") as f: r = requests.get(dataset_url) f.write(r.content) if base.endswith('.tar.gz'): obj = tarfile.open(temp_save_path) elif base.endswith('.zip'): obj = ZipFile(temp_save_path, 'r') else: raise ValueError("Unknown File Type: {0}.".format(base)) self._print("Unpacking Data...") obj.extractall(save_path) obj.close() os.remove(temp_save_path) def get(self, save_path, dataset=None): """ Download a dataset. Args: save_path : str A directory to save the data to. dataset : str, optional A specific dataset to download. Note: this must include the file extension. If None, options will be presented for you to choose from. Returns: save_path_full : str The absolute path to the downloaded data. """ if dataset is None: selected_dataset = self._present_options() else: selected_dataset = dataset save_path_full = join(save_path, selected_dataset.split('.')[0]) if isdir(save_path_full): warn("\n'{0}' already exists. Voiding Download.".format( save_path_full)) else: self._print('Downloading Data...') url = "{0}/{1}".format(self.url, selected_dataset) self._download_data(url, save_path=save_path) return abspath(save_path_full) ================================================ FILE: test_code_released_old/util/html.py ================================================ import dominate from dominate.tags import * import os class HTML: def __init__(self, web_dir, title, reflesh=0): self.title = title self.web_dir = web_dir self.img_dir = os.path.join(self.web_dir, 'images') if not os.path.exists(self.web_dir): os.makedirs(self.web_dir) if not os.path.exists(self.img_dir): os.makedirs(self.img_dir) # print(self.img_dir) self.doc = dominate.document(title=title) if reflesh > 0: with self.doc.head: meta(http_equiv="reflesh", content=str(reflesh)) def get_image_dir(self): return self.img_dir def add_header(self, str): with self.doc: h3(str) def add_table(self, border=1): self.t = table(border=border, style="table-layout: fixed;") self.doc.add(self.t) def add_images(self, ims, txts, links, width=400): self.add_table() with self.t: with tr(): for im, txt, link in zip(ims, txts, links): with td(style="word-wrap: break-word;", halign="center", valign="top"): with p(): with a(href=os.path.join('images', link)): img(style="width:%dpx" % width, src=os.path.join('images', im)) br() p(txt) def save(self): html_file = '%s/index.html' % self.web_dir f = open(html_file, 'wt') f.write(self.doc.render()) f.close() if __name__ == '__main__': html = HTML('web/', 'test_html') html.add_header('hello world') ims = [] txts = [] links = [] for n in range(4): ims.append('image_%d.png' % n) txts.append('text_%d' % n) links.append('image_%d.png' % n) html.add_images(ims, txts, links) html.save() ================================================ FILE: test_code_released_old/util/image_pool.py ================================================ import random import torch from torch.autograd import Variable class ImagePool(): def __init__(self, pool_size): self.pool_size = pool_size if self.pool_size > 0: self.num_imgs = 0 self.images = [] def query(self, images): if self.pool_size == 0: images.requires_grad=True return images return_images = [] for image in images: image = torch.unsqueeze(image, 0) if self.num_imgs < self.pool_size: self.num_imgs = self.num_imgs + 1 self.images.append(image) return_images.append(image) else: p = random.uniform(0, 1) if p > 0.5: random_id = random.randint(0, self.pool_size - 1) tmp = self.images[random_id].clone() self.images[random_id] = image return_images.append(tmp) else: return_images.append(image) return_images = Variable(torch.cat(return_images, 0)) return_images.requires_grad=True return return_images ================================================ FILE: test_code_released_old/util/util.py ================================================ from __future__ import print_function import torch import numpy as np from PIL import Image import os # Converts a Tensor into a Numpy array # |imtype|: the desired type of the converted numpy array def tensor2im(image_tensor, imtype=np.uint8): image_numpy = image_tensor[0].cpu().float().numpy() if image_numpy.shape[0] == 1: image_numpy = np.tile(image_numpy, (3, 1, 1)) image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 return image_numpy.astype(imtype) def diagnose_network(net, name='network'): mean = 0.0 count = 0 for param in net.parameters(): if param.grad is not None: mean += torch.mean(torch.abs(param.grad.data)) count += 1 if count > 0: mean = mean / count print(name) print(mean) def save_image(image_numpy, image_path): image_pil = Image.fromarray(image_numpy) image_pil.save(image_path) def print_numpy(x, val=True, shp=False): x = x.astype(np.float64) if shp: print('shape,', x.shape) if val: x = x.flatten() print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) def mkdirs(paths): if isinstance(paths, list) and not isinstance(paths, str): for path in paths: mkdir(path) else: mkdir(paths) def mkdir(path): if not os.path.exists(path): os.makedirs(path) ================================================ FILE: test_code_released_old/util/visualizer.py ================================================ import numpy as np import os import ntpath import time from . import util from . import html from scipy.misc import imresize class Visualizer(): def __init__(self, opt): # self.opt = opt self.display_id = opt.display_id self.use_html = opt.isTrain and not opt.no_html self.win_size = opt.display_winsize self.name = opt.name self.opt = opt self.saved = False if self.display_id > 0: import visdom self.vis = visdom.Visdom(port=opt.display_port) if self.use_html: self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') self.img_dir = os.path.join(self.web_dir, 'images') print('create web directory %s...' % self.web_dir) util.mkdirs([self.web_dir, self.img_dir]) self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') with open(self.log_name, "a") as log_file: now = time.strftime("%c") log_file.write('================ Training Loss (%s) ================\n' % now) def reset(self): self.saved = False # |visuals|: dictionary of images to display or save def display_current_results(self, visuals, epoch, save_result): if self.display_id > 0: # show images in the browser ncols = self.opt.display_single_pane_ncols if ncols > 0: h, w = next(iter(visuals.values())).shape[:2] table_css = """""" % (w, h) title = self.name label_html = '' label_html_row = '' nrows = int(np.ceil(len(visuals.items()) / ncols)) images = [] idx = 0 for label, image_numpy in visuals.items(): label_html_row += '%s' % label images.append(image_numpy.transpose([2, 0, 1])) idx += 1 if idx % ncols == 0: label_html += '%s' % label_html_row label_html_row = '' white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255 while idx % ncols != 0: images.append(white_image) label_html_row += '' idx += 1 if label_html_row != '': label_html += '%s' % label_html_row # pane col = image row self.vis.images(images, nrow=ncols, win=self.display_id + 1, padding=2, opts=dict(title=title + ' images')) label_html = '%s
' % label_html self.vis.text(table_css + label_html, win=self.display_id + 2, opts=dict(title=title + ' labels')) else: idx = 1 for label, image_numpy in visuals.items(): self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), win=self.display_id + idx) idx += 1 if self.use_html and (save_result or not self.saved): # save images to a html file self.saved = True for label, image_numpy in visuals.items(): img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) util.save_image(image_numpy, img_path) # update website webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) for n in range(epoch, 0, -1): webpage.add_header('epoch [%d]' % n) ims = [] txts = [] links = [] for label, image_numpy in visuals.items(): img_path = 'epoch%.3d_%s.png' % (n, label) ims.append(img_path) txts.append(label) links.append(img_path) webpage.add_images(ims, txts, links, width=self.win_size) webpage.save() # errors: dictionary of error labels and values def plot_current_errors(self, epoch, counter_ratio, opt, errors): if not hasattr(self, 'plot_data'): self.plot_data = {'X': [], 'Y': [], 'legend': list(errors.keys())} self.plot_data['X'].append(epoch + counter_ratio) self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']]) self.vis.line( X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), Y=np.array(self.plot_data['Y']), opts={ 'title': self.name + ' loss over time', 'legend': self.plot_data['legend'], 'xlabel': 'epoch', 'ylabel': 'loss'}, win=self.display_id) # errors: same format as |errors| of plotCurrentErrors def print_current_errors(self, epoch, i, errors, t): message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t) for k, v in errors.items(): message += '%s: %.3f ' % (k, v) print(message) with open(self.log_name, "a") as log_file: log_file.write('%s\n' % message) # save image to the disk def save_images(self, webpage, visuals, image_path, aspect_ratio=1.0): image_dir = webpage.get_image_dir() short_path = ntpath.basename(image_path[0]) name = os.path.splitext(short_path)[0] webpage.add_header(name) ims = [] txts = [] links = [] for label, im in visuals.items(): image_name = '%s_%s.png' % (name, label) save_path = os.path.join(image_dir, image_name) h, w, _ = im.shape if aspect_ratio > 1.0: im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') if aspect_ratio < 1.0: im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') util.save_image(im, save_path) ims.append(image_name) txts.append(label) links.append(image_name) webpage.add_images(ims, txts, links, width=self.win_size)