device='cuda') model.load_weights('./snapshots/base.pth', load_opt=False) normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_datagen = DirectorySiameseLoader( impath, transforms.Compose([ transforms.Resize(256), transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.ToTensor(), normalize ])) train_generator = train_datagen.get_dset(8, 1) os.makedirs(f'./snapshots/pairs/{save_no}', exist_ok=True) try: h = model.fit_generator( train_generator, 20, schedule=[10, 15], tensorboard=f'logs/pair/{len(os.listdir("logs/pair"))}', epoch_end=model.checkpoint(f'./snapshots/pairs/{save_no}', 'ContrastiveLoss'), step=200) with open('siamese.json', 'w') as wr: json.dump(h, wr) finally: model.save_weights('./snapshots/pairs_temp.pth')
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) trainset = CIFAR10(root=root, train=True, download=True, transform=transform_train) trainloader = Loader(trainset, batch_size=64, shuffle=True, num_workers=0) testset = CIFAR10(root=root, train=False, download=True, transform=transform_test) testloader = Loader(testset, batch_size=100, shuffle=False, num_workers=0) schedule = LambdaLR(sgd, lrstep) history = model.fit_generator(trainloader, 300, validation_data=testloader, schedule=schedule) with open('logs/misc-01.json', 'w') as wr: json.dump(history, wr)