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Implementation of a cGAN to perform a data generation task on the CIFAR10 dataset. This generation can be used both to augment the original CIFAR10 dataset or to generate a new dataset, based on CIFAR10 classes, from scratch. It also has been implemented a pre-trained classifier in order to evaluate the performance of the cGAN model.

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Data Augmentation with cGAN

Requirements

  • Pytorch
  • Numpy
  • Pandas
  • Seaborn
  • Sklearn

CGAN

Training

To train the CGAN is necessary to create a configuration file and then execute:

python3 -m train_cgan --run_name <model name> --config <path/to/configuration/file.yaml>

NOTE: examples of configuration file are contained in config/cgan/ folder.

Testing

To test the CGAN execute:

python3 -m test_cgan --config <path/to/configuration/file.yaml>

NOTE: for each model specify the same configuration file used during its training.

Classificator

Training

To train and test the classificator it's required to create a configuration file and then execute:

python3 -m train_classifier --run_name <model name> --config <path/to/configuration/file.yaml>

NOTE: examples of configuration file are contained in config/classifier/ folder.

Testing

To test a classificator model execute:

python3 -m test_classifier --model_name <model name> --config <path/to/configuration/file.yaml>

NOTES:

  • For each model specify the same configuration file used during its training;
  • As model name specify only the file name and do not write the complete path.

Results

Graphs visualization

To visualize the obtained results, both for CGAN and classifier, execute:

tensorboard --logdir=<path/to/tensorboard/folder/>

FID scores registered

Each model has been used to generate a new CIFAR10 dataset, that has been compared with the original one class by class through the FID score. Here are the obtained values.

RunName AllDataset Airplane Automobile Bird Cat Deer Dog Frog Horse Ship Truck
basic_cGAN - epoch30 25.0814 43.8033 51.2340 50.1187 35.8533 38.5345 50.2043 51.5834 50.0480 48.3488 51.9783
basic_cGAN - epoch100 19.7900 39.9388 43.6565 45.3907 32.5473 33.6034 51.6767 35.6730 39.1974 36.5174 40.2165
basic_cGAN_BCEWithLogits - epoch30 26.1962 46.6924 59.9631 53.5438 37.7538 47.6463 56.2596 41.2485 49.8263 43.2729 59.8866
basic_cGAN_BCEWithLogits - epoch100 18.3587 39.3256 47.1962 41.2120 28.7295 28.8009 42.4902 35.0227 35.2195 39.2759 43.8909
basic_cGAN_NOINIT - epoch30 28.2961 49.6503 55.5690 52.5145 41.0350 48.4470 57.8165 49.0617 66.2571 42.2449 53.7113
basic_cGAN_NOINIT - epoch100 20.6019 42.3565 48.9903 43.9808 35.1413 32.7063 50.8310 37.9150 46.3769 38.1915 45.6485
basic_cGAN_NONORM - epoch30 54.6617 85.8362 83.3916 93.3328 65.6713 74.7721 80.4029 82.7151 92.3878 75.4047 77.9362
basic_cGAN_NONORM - epoch100 33.9906 60.6132 62.9891 64.2217 47.2884 46.7887 61.3682 49.3307 62.6472 49.1161 57.0115
basic_cGAN_SGD - epoch30 230.954 354.929 272.712 335.822 365.622 351.331 403.674 324.052 325.617 336.270 342.140
basic_cGAN_SGD - epoch100 214.799 365.356 308.137 356.645 316.402 296.593 317.441 316.219 306.465 302.288 367.789
basic_cGAN_SmoothL1loss - epoch30 25.2347 47.6879 47.8939 51.5360 38.2146 45.1380 56.9448 40.9715 56.9873 44.1011 51.9278
basic_cGAN_SmoothL1loss - epoch100 17.5960 36.6824 44.6527 39.5682 27.6457 33.0219 43.7501 31.2807 35.8994 35.7174 44.7159
cGAN_SmoothL1loss_bs128 - epoch30 28.8892 50.3035 65.5648 53.7833 37.6564 45.9779 58.8280 45.3261 62.7498 51.2253 93.3664
cGAN_SmoothL1loss_bs128 - epoch100 18.4161 37.6183 52.4211 39.3867 30.0114 30.3955 42.6866 30.4660 39.6675 36.7077 84.9438
cGAN_SmoothL1loss_Adamax - epoch30 56.9044 75.2484 105.268 85.8292 85.4688 86.5913 103.241 86.7148 128.169 64.7571 86.9369
cGAN_SmoothL1loss_Adamax - epoch100 29.5539 47.2033 83.6270 53.9002 45.3708 44.6316 57.7528 60.4991 59.0338 62.2606 66.2551
cGAN_SmoothL1loss_NAdam - epoch30 26.9565 43.4741 53.5850 56.4188 41.4548 46.8531 64.0583 68.0111 59.6425 34.2241 53.8282
cGAN_SmoothL1loss_NAdam - epoch100 20.2487 39.7137 44.5671 44.1415 32.1569 34.8685 47.4983 72.6950 40.1842 39.2248 43.1007
basic_wGAN - epoch30 48.2562 68.4371 75.0996 71.9104 54.4870 83.9637 81.9094 84.2123 83.1763 54.3745 72.1841
basic_wGAN - epoch100 28.1914 54.6635 58.3065 49.8910 38.9040 37.1997 52.3032 38.4660 42.6299 41.8378 53.9019
cGAN_64_bs128 - epoch30 204.184 222.695 228.378 349.715 283.556 274.376 255.650 265.553 235.954 249.086 310.055
cGAN_64_bs128 - epoch100 152.912 217.718 224.458 231.542 219.200 198.162 230.474 222.847 218.450 222.499 238.959
custom_cGAN - epoch30 35.8927 58.0080 68.7365 69.9182 50.5328 72.1439 70.0219 68.9697 88.6795 62.6273 80.5999
custom_cGAN - epoch100 26.6001 46.4214 52.4751 59.7820 50.5737 49.3311 83.9058 71.9584 51.5222 56.8851 52.5101
custom_wGAN - epoch30 69.4120 93.0382 102.035 104.180 98.7037 92.2057 129.500 137.826 119.934 66.3337 94.8316
custom_wGAN - epoch100 42.3130 62.3584 80.7449 79.4685 59.9742 69.1960 85.8635 61.7054 68.5446 54.6184 72.8860
custom_cGAN_SmoothL1loss - epoch30 45.7718 61.5156 98.6816 68.1409 69.3301 61.7120 81.4490 69.4798 92.9643 56.3719 94.6059
custom_cGAN_SmoothL1loss - epoch100 18.4845 37.1320 40.4285 38.8271 31.3358 32.3157 47.3368 43.6842 35.2573 34.4679 40.3287
custom_cGAN_2 - epoch30 104.208 159.158 150.377 174.049 154.885 177.240 186.384 210.636 175.558 136.752 133.861
custom_cGAN_2 - epoch100 73.0790 111.615 184.046 175.774 139.551 151.019 128.772 141.181 174.255 126.151 210.390

Accuracy registered

Each generated dataset has been used to perform a classification task. So, it has been performed a fine tuning last 5 epochs over a pretrained AlexNet. The training has been performed using the generated datasets both as training set and as augmentation of the original CIFAR10 training set, for which has been used the 70% of images. The reference value for the accuracy over the original model was 9.1 %.

Here are the obtained values.

No Augmentation

RunName Accuracy
basic_cGAN - epoch30 57.75 %
basic_cGAN - epoch100 63.66 %
basic_cGAN_BCEWithLogits - epoch30 57.36 %
basic_cGAN_BCEWithLogits - epoch100 62.76 %
basic_cGAN_NOINIT - epoch30 55.90 %
basic_cGAN_NOINIT - epoch100 61.26 %
basic_cGAN_NONORM - epoch30 52.05 %
basic_cGAN_NONORM - epoch100 63.38 %
basic_cGAN_SGD - epoch30 9.86 %
basic_cGAN_SGD - epoch100 15.75 %
basic_cGAN_SmoothL1loss - epoch30 55.27 %
basic_cGAN_SmoothL1loss - epoch100 63.26 %
adamax_bs128_basic_cGAN_SmoothL1loss - epoch30 54.93 %
adamax_bs128_basic_cGAN_SmoothL1loss - epoch100 54.91 %
adamax_basic_cGAN_SmoothL1loss - epoch30 54.92 %
adamax_basic_cGAN_SmoothL1loss - epoch100 64.27 %
nadam_basic_cGAN_SmoothL1loss - epoch30 53.20 %
nadam_basic_cGAN_SmoothL1loss - epoch100 60.84 %
cGAN_SmoothL1loss_bs128 - epoch30 29.02 %
cGAN_SmoothL1loss_bs128 - epoch100 64.05 %
cGAN_SmoothL1loss_Adamax - epoch30 24.39 %
cGAN_SmoothL1loss_Adamax - epoch100 38.45 %
adamax_basic_cGAN_SmoothL1loss_NAdam - epoch30 54.75 %
adamax_basic_cGAN_SmoothL1loss_NAdam - epoch100 56.96 %
basic_wGAN - epoch30 52.54 %
basic_wGAN - epoch100 66.28 %
cGAN_64_bs128 - epoch30 16.97 %
cGAN_64_bs128 - epoch100 26.91 %
custom_cGAN - epoch30 42.74 %
custom_cGAN - epoch100 57.42 %
custom_wGAN - epoch30 35.85 %
custom_wGAN - epoch100 54.04 %
custom_cGAN_SmoothL1loss - epoch30 44.47 %
custom_cGAN_SmoothL1loss - epoch100 62.13 %
custom_cGAN_2 - epoch30 22.38 %
custom_cGAN_2 - epoch100 24.77 %

Augmentation

RunName Accuracy
basic_cGAN - epoch30 80.13 %
basic_cGAN - epoch100 78.99 %
basic_cGAN_BCEWithLogits - epoch30 79.29 %
basic_cGAN_BCEWithLogits - epoch100 78.66 %
basic_cGAN_NOINIT - epoch30 78.81 %
basic_cGAN_NOINIT - epoch100 79.85 %
basic_cGAN_NONORM - epoch30 79.77 %
basic_cGAN_NONORM - epoch100 80.48 %
basic_cGAN_SGD - epoch30 81.80 %
basic_cGAN_SGD - epoch100 81.39 %
basic_cGAN_SmoothL1loss - epoch30 78.98 %
basic_cGAN_SmoothL1loss - epoch100 80.08 %
adamax_basic_cGAN_SmoothL1loss - epoch30 81.38 %
adamax_basic_cGAN_SmoothL1loss - epoch100 81.50 %
nadam_basic_cGAN_SmoothL1loss - epoch30 79.81 %
nadam_basic_cGAN_SmoothL1loss - epoch100 78.83 %
adamax_bs128_basic_cGAN_SmoothL1loss - epoch30 81.47 %
adamax_bs128_basic_cGAN_SmoothL1loss - epoch100 81.59 %
cGAN_SmoothL1loss_bs128 - epoch30 80.35 %
cGAN_SmoothL1loss_bs128 - epoch100 79.46 %
cGAN_SmoothL1loss_Adamax - epoch30 80.12 %
cGAN_SmoothL1loss_Adamax - epoch100 79.78 %
adamax_basic_cGAN_SmoothL1loss_NAdam - epoch30 81.68 %
adamax_basic_cGAN_SmoothL1loss_NAdam - epoch100 81.91 %
basic_wGAN - epoch30 79.23 %
basic_wGAN - epoch100 80.30 %
cGAN_64_bs128 - epoch30 81.11 %
cGAN_64_bs128 - epoch100 80.87 %
custom_cGAN - epoch30 79.39 %
custom_cGAN - epoch100 79.91 %
custom_wGAN - epoch30 79.15 %
custom_wGAN - epoch100 79.86 %
custom_cGAN_SmoothL1loss - epoch30 78.92 %
custom_cGAN_SmoothL1loss - epoch100 79.61 %
custom_cGAN_2 - epoch30 80.04 %
custom_cGAN_2 - epoch100 80.91 %

About

Implementation of a cGAN to perform a data generation task on the CIFAR10 dataset. This generation can be used both to augment the original CIFAR10 dataset or to generate a new dataset, based on CIFAR10 classes, from scratch. It also has been implemented a pre-trained classifier in order to evaluate the performance of the cGAN model.

Topics

deep-neural-networks deep-learning torch pytorch generative-adversarial-network generative-model classification cifar10 data-augmentation cgan

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