fix bug for mobilenet in model_zoo

mindspore/model_zoo/mobilenetv2/src/dataset.py

@@ -21,7 +21,6 @@ import mindspore.dataset.engine as de
 import mindspore.dataset.transforms.vision.c_transforms as C
 import mindspore.dataset.transforms.c_transforms as C2
 
-
 def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
     """
     create a train or eval dataset
@@ -44,7 +43,9 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
             ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
                                          num_shards=rank_size, shard_id=rank_id)
     elif platform == "GPU":
-        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
+        from mindspore.communication.management import get_rank, get_group_size
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
+                                     num_shards=get_group_size(), shard_id=get_rank())
     else:
         raise ValueError("Unsupport platform.")
 

mindspore/model_zoo/mobilenetv2/train.py

@@ -32,7 +32,7 @@ from mindspore.train.model import Model, ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.communication.management import init
+from mindspore.communication.management import init, get_group_size
 import mindspore.dataset.engine as de
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
@@ -157,6 +157,11 @@ if __name__ == '__main__':
         # train on gpu
         print("train args: ", args_opt, "\ncfg: ", config_gpu)
 
+        init('nccl')
+        context.set_auto_parallel_context(parallel_mode="data_parallel",
+                                          mirror_mean=True,
+                                          device_num=get_group_size())
+
         # define net
         net = mobilenet_v2(num_classes=config_gpu.num_classes, platform="GPU")
         # define loss
@@ -223,7 +228,7 @@ if __name__ == '__main__':
                 cell.to_float(mstype.float32)
         if config_ascend.label_smooth > 0:
             loss = CrossEntropyWithLabelSmooth(
-                smooth_factor=config_ascend.label_smooth, num_classes=config.num_classes)
+                smooth_factor=config_ascend.label_smooth, num_classes=config_ascend.num_classes)
         else:
             loss = SoftmaxCrossEntropyWithLogits(
                 is_grad=False, sparse=True, reduction='mean')

mindspore/model_zoo/mobilenetv3/eval.py

@@ -24,7 +24,8 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import dtype as mstype
 from src.dataset import create_dataset
 from src.config import config_ascend, config_gpu
-from src.mobilenetV2 import mobilenet_v2
+from src.mobilenetV3 import mobilenet_v3_large
+
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
@@ -49,7 +50,7 @@ if __name__ == '__main__':
 
     loss = nn.SoftmaxCrossEntropyWithLogits(
         is_grad=False, sparse=True, reduction='mean')
-    net = mobilenet_v2(num_classes=config_platform.num_classes)
+    net = mobilenet_v3_large(num_classes=config_platform.num_classes)
 
     if args_opt.platform == "Ascend":
         net.to_float(mstype.float16)

mindspore/model_zoo/mobilenetv3/src/dataset.py

@@ -44,7 +44,9 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
             ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
                                          num_shards=rank_size, shard_id=rank_id)
     elif platform == "GPU":
-        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
+        from mindspore.communication.management import get_rank, get_group_size
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
+                                     num_shards=get_group_size(), shard_id=get_rank())
     else:
         raise ValueError("Unsupport platform.")
 

mindspore/model_zoo/mobilenetv3/train.py

@@ -33,7 +33,7 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 import mindspore.dataset.engine as de
-from mindspore.communication.management import init
+from mindspore.communication.management import init, get_group_size
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import config_gpu, config_ascend
@@ -157,6 +157,11 @@ if __name__ == '__main__':
         # train on gpu
         print("train args: ", args_opt, "\ncfg: ", config_gpu)
 
+        init('nccl')
+        context.set_auto_parallel_context(parallel_mode="data_parallel",
+                                          mirror_mean=True,
+                                          device_num=get_group_size())
+
         # define net
         net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
         # define loss