help='batch size') parser.add_argument('--load_from', type=str, default=None, help='directory to load from') parser.add_argument('--loss', type=str, default='ls', help='loss function: bce or ls') parser.add_argument('--mode', type=str, default='train', help='mode train or evaluate') args = parser.parse_args() lr = args.lr noise_dim = args.noise_dim base_dim = args.base_dim loss = args.loss opt = VanillaGANOptimizer() runner = TrainingRunner('dcgan', create_dogs_dataset(), dogs.Generator(base_dim=base_dim, noise_dim=noise_dim), dogs.Discriminator(base_dim=base_dim), VanillaGANOptimizer(dsc_lr=lr, gen_lr=lr, loss=loss), args=args) runner.execute(args)
Copyright 2018 JetBrains, s.r.o Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from dogsgan.data.dogs import create_dogs_dataset from dogsgan.training.optimizers import VanillaGANOptimizer from dogsgan.training.runner import TrainingRunner import dogsgan.models.dogs as dogs if __name__ == '__main__': lr = 2e-4 noise_dim = 1024 for base_dim in [16, 32, 64, 96, 128, 160, 196, 256, 320]: opt = VanillaGANOptimizer() runner = TrainingRunner( f'dcgan-{base_dim}', create_dogs_dataset(), dogs.Generator(base_dim=base_dim, noise_dim=noise_dim), dogs.Discriminator(base_dim=base_dim), VanillaGANOptimizer(dsc_lr=lr, gen_lr=lr)) runner.train(epochs=100)
import argparse from dogsgan.data.dogs import create_dogs_dataset from dogsgan.training.optimizers import WGANGPOptimizer from dogsgan.training.runner import TrainingRunner import dogsgan.models.dogs as dogs if __name__ == '__main__': parser = argparse.ArgumentParser(description='run dcgan training') parser.add_argument('--lr', type=float, default=1e-4 ,help='learning rate') parser.add_argument('--l', type=float, default=10.0, help='gradient penalty coefficent') parser.add_argument('--noise_dim', type=int, default=1024, help='noise dimension') parser.add_argument('--base_dim', type=int, default=128, help='base dimension') parser.add_argument('--batch_size', type=int, default=64, help='batch size') parser.add_argument('--load_from', type=str, default=None, help='directory to load from') parser.add_argument('--mode', type=str, default='train', help='mode train or evaluate') args = parser.parse_args() lr = args.lr l = args.l noise_dim = args.noise_dim base_dim = args.base_dim runner = TrainingRunner('wgan_gp', create_dogs_dataset(), dogs.Generator(noise_dim=noise_dim, base_dim=base_dim), dogs.Discriminator(base_dim=base_dim, batch_norm=False), WGANGPOptimizer(lr=lr, l=l), args=args) runner.execute(args)
""" Copyright 2018 JetBrains, s.r.o Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from torch.utils.data import DataLoader from dogsgan.data.dogs import create_dogs_dataset from torchvision.utils import make_grid, save_image if __name__ == '__main__': loader = DataLoader(create_dogs_dataset(), batch_size=104, shuffle=True) for X, _ in loader: save_image(make_grid(X, normalize=True), 'dogs.png') break
def compute_marginal(dists): sums = torch.sum(dists, 0) return sums / torch.sum(sums) def inception_score(images): inception = inception_v3(pretrained=True) inception.train(False) has_cuda = torch.cuda.device_count() > 0 if has_cuda: inception = inception.cuda() scores = apply_classifier(inception, images) mdist = compute_marginal(scores) divs = [] for i in range(0, scores.shape[0]): divs.append(kl_divergence(scores[i, :], mdist)) sd = torch.Tensor(divs).std().exp().item() return torch.Tensor(divs).mean().exp().item(), sd / (scores.shape[0]**0.5) if __name__ == '__main__': dogs_dataset = create_dogs_dataset( normalize=False, image_transforms=[transforms.Resize((299, 299))]) print(inception_score(dogs_dataset)) #14.634567260742188, 0.02150835660283012
""" Copyright 2018 JetBrains, s.r.o Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from dogsgan.data.dogs import create_dogs_dataset from dogsgan.training.optimizers import WGANGPOptimizer from dogsgan.training.runner import TrainingRunner import dogsgan.models.dogs as dogs if __name__ == '__main__': for l in [1.0, 2.0, 4.0, 8.0, 10.0, 16.0]: runner = TrainingRunner( f'wgan_gp-{l}', create_dogs_dataset(), dogs.Generator(base_dim=224), dogs.Discriminator(batch_norm=False, base_dim=224), WGANGPOptimizer(l=l)) runner.train(epochs=100, batch_size=64)