add use_random control options in cyclegan

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)
 

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)

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'))

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):

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