AntoineH/smart_augmentation
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Test WRN Brutus

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Harle, Antoine (Contracteur) 2019-12-04 16:34:02 -05:00
parent b26fbcd2a2
commit fa5bc72616
6 changed files with 72 additions and 20 deletions
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12
higher/test_dataug.py
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@ -109,12 +109,12 @@ if __name__ == "__main__":
t0 = time.process_time()
data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
data_train_aug.augement_data(aug_copy=10)
data_train_aug.augement_data(aug_copy=30)
print(data_train_aug)
dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
#xs, ys = next(iter(dl_train))
#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
xs, ys = next(iter(dl_train))
viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
model = LeNet(3,10).to(device)
#model = WideResNet(num_classes=10, wrn_size=16).to(device)
@ -149,9 +149,9 @@ if __name__ == "__main__":
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None)