diff --git a/model_zoo/research/cv/cycle_gan/README.md b/model_zoo/research/cv/cycle_gan/README.md index b6ec907d30c..22995a70ba6 100644 --- a/model_zoo/research/cv/cycle_gan/README.md +++ b/model_zoo/research/cv/cycle_gan/README.md @@ -228,7 +228,7 @@ python export.py --platform [PLATFORM] --G_A_ckpt [G_A_CKPT] --G_B_ckpt [G_B_CKP # [Description of Random Situation](#contents) -In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py. +If you set --use_random=False, there are no random when training. # [ModelZoo Homepage](#contents) diff --git a/model_zoo/research/cv/cycle_gan/src/dataset/cyclegan_dataset.py b/model_zoo/research/cv/cycle_gan/src/dataset/cyclegan_dataset.py index fd3f19e76e4..ba20e42c317 100644 --- a/model_zoo/research/cv/cycle_gan/src/dataset/cyclegan_dataset.py +++ b/model_zoo/research/cv/cycle_gan/src/dataset/cyclegan_dataset.py @@ -21,29 +21,38 @@ import mindspore.dataset.vision.c_transforms as C from .distributed_sampler import DistributedSampler from .datasets import UnalignedDataset, ImageFolderDataset -def create_dataset(args, shuffle=True, max_dataset_size=float("inf")): +def create_dataset(args): """Create dataset""" dataroot = args.dataroot phase = args.phase batch_size = args.batch_size device_num = args.device_num rank = args.rank + shuffle = args.use_random + max_dataset_size = args.max_dataset_size cores = multiprocessing.cpu_count() num_parallel_workers = min(8, int(cores / device_num)) image_size = args.image_size mean = [0.5 * 255] * 3 std = [0.5 * 255] * 3 if phase == "train": - dataset = UnalignedDataset(dataroot, phase, max_dataset_size=max_dataset_size) + dataset = UnalignedDataset(dataroot, phase, max_dataset_size=max_dataset_size, use_random=args.use_random) distributed_sampler = DistributedSampler(len(dataset), device_num, rank, shuffle=shuffle) ds = de.GeneratorDataset(dataset, column_names=["image_A", "image_B"], sampler=distributed_sampler, num_parallel_workers=num_parallel_workers) - trans = [ - C.RandomResizedCrop(image_size, scale=(0.5, 1.0), ratio=(0.75, 1.333)), - C.RandomHorizontalFlip(prob=0.5), - C.Normalize(mean=mean, std=std), - C.HWC2CHW() - ] + if args.use_random: + trans = [ + C.RandomResizedCrop(image_size, scale=(0.5, 1.0), ratio=(0.75, 1.333)), + C.RandomHorizontalFlip(prob=0.5), + C.Normalize(mean=mean, std=std), + C.HWC2CHW() + ] + else: + trans = [ + C.Resize((image_size, image_size)), + C.Normalize(mean=mean, std=std), + C.HWC2CHW() + ] ds = ds.map(operations=trans, input_columns=["image_A"], num_parallel_workers=num_parallel_workers) ds = ds.map(operations=trans, input_columns=["image_B"], num_parallel_workers=num_parallel_workers) ds = ds.batch(batch_size, drop_remainder=True) diff --git a/model_zoo/research/cv/cycle_gan/src/dataset/datasets.py b/model_zoo/research/cv/cycle_gan/src/dataset/datasets.py index d12b5621a3e..00dbdb2f9e5 100644 --- a/model_zoo/research/cv/cycle_gan/src/dataset/datasets.py +++ b/model_zoo/research/cv/cycle_gan/src/dataset/datasets.py @@ -52,7 +52,7 @@ class UnalignedDataset: Two domain image path list. """ - def __init__(self, dataroot, phase, max_dataset_size=float("inf")): + def __init__(self, dataroot, phase, max_dataset_size=float("inf"), use_random=True): self.dir_A = os.path.join(dataroot, phase + 'A') self.dir_B = os.path.join(dataroot, phase + 'B') @@ -60,12 +60,14 @@ class UnalignedDataset: self.B_paths = sorted(make_dataset(self.dir_B, max_dataset_size)) # load images from '/path/to/data/trainB' self.A_size = len(self.A_paths) # get the size of dataset A self.B_size = len(self.B_paths) # get the size of dataset B + self.use_random = use_random def __getitem__(self, index): - if index % max(self.A_size, self.B_size) == 0: + index_B = index % self.B_size + if index % max(self.A_size, self.B_size) == 0 and self.use_random: random.shuffle(self.A_paths) + index_B = random.randint(0, self.B_size - 1) A_path = self.A_paths[index % self.A_size] - index_B = random.randint(0, self.B_size - 1) B_path = self.B_paths[index_B] A_img = np.array(Image.open(A_path).convert('RGB')) B_img = np.array(Image.open(B_path).convert('RGB')) diff --git a/model_zoo/research/cv/cycle_gan/src/models/networks.py b/model_zoo/research/cv/cycle_gan/src/models/networks.py index a557e9dc2c8..7ad5e9fc70d 100644 --- a/model_zoo/research/cv/cycle_gan/src/models/networks.py +++ b/model_zoo/research/cv/cycle_gan/src/models/networks.py @@ -15,7 +15,7 @@ """Cycle GAN network.""" import mindspore.nn as nn - +from mindspore.common import initializer as init def init_weights(net, init_type='normal', init_gain=0.02): """ @@ -27,12 +27,14 @@ def init_weights(net, init_type='normal', init_gain=0.02): init_gain (float): Gain factor for normal and xavier. """ - for cell in net.cells_and_names(): - if isinstance(cell, nn.Conv2d): + for _, cell in net.cells_and_names(): + if isinstance(cell, (nn.Conv2d, nn.Conv2dTranspose)): if init_type == 'normal': - cell.weight.set_data(init.initializer(init.Normal(init_gain))) + cell.weight.set_data(init.initializer(init.Normal(init_gain), cell.weight.shape)) elif init_type == 'xavier': - cell.weight.set_data(init.initializer(init.XavierUniform(init_gain))) + cell.weight.set_data(init.initializer(init.XavierUniform(init_gain), cell.weight.shape)) + elif init_type == 'constant': + cell.weight.set_data(init.initializer(0.001, cell.weight.shape)) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) elif isinstance(cell, nn.BatchNorm2d): diff --git a/model_zoo/research/cv/cycle_gan/src/utils/args.py b/model_zoo/research/cv/cycle_gan/src/utils/args.py index 9bd8f9a7e2e..6790be7fbe6 100644 --- a/model_zoo/research/cv/cycle_gan/src/utils/args.py +++ b/model_zoo/research/cv/cycle_gan/src/utils/args.py @@ -105,6 +105,9 @@ def get_args(phase): parser.add_argument('--save_imgs', type=ast.literal_eval, default=True, \ help='whether save imgs when epoch end, if True result images will generate in ' '`outputs_dir/imgs`, default is True.') + parser.add_argument('--use_random', type=ast.literal_eval, default=True, \ + help='whether use random when training, default is True.') + parser.add_argument('--max_dataset_size', type=int, default=None, help='max images pre epoch, default is None.') if phase == "export": parser.add_argument("--file_name", type=str, default="cyclegan", help="output file name prefix.") parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', \ @@ -140,6 +143,14 @@ def get_args(phase): if args.dataroot is None and (phase in ["train", "predict"]): raise ValueError('Must set dataroot!') + if not args.use_random: + args.need_dropout = False + args.init_type = "constant" + + if args.max_dataset_size is None: + args.max_dataset_size = float("inf") + + args.n_epochs = min(args.max_epoch, args.n_epochs) args.n_epochs_decay = args.max_epoch - args.n_epochs args.phase = phase return args