debug load_ckpt,rename the network parameters;move the raiseerror of dataset size from dataset.py to train.py, give the control switch to user

model_zoo/official/cv/mobilenetv2/eval.py

@@ -28,7 +28,7 @@ from src.utils import switch_precision, set_context
 if __name__ == '__main__':
     args_opt = eval_parse_args()
     config = set_config(args_opt)
-    backbone_net, head_net, net = define_net(args_opt, config)
+    backbone_net, head_net, net = define_net(config)
 
     #load the trained checkpoint file to the net for evaluation
     if args_opt.head_ckpt:
@@ -42,6 +42,10 @@ if __name__ == '__main__':
 
     dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, config=config)
     step_size = dataset.get_dataset_size()
+    if step_size == 0:
+        raise ValueError("The step_size of dataset is zero. Check if the images count of train dataset is more \
+            than batch_size in config.py")
+
     net.set_train(False)
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')

model_zoo/official/cv/mobilenetv2/scripts/run_eval.sh

@@ -103,9 +103,9 @@ run_cpu()
 if [ $# -gt 4 ] || [ $# -lt 3 ]
 then
     echo "Usage:
-          Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT]
+          Ascend: sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT]
-          GPU: sh run_infer.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT]
+          GPU: sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT]
-          CPU: sh run_infer.sh [PLATFORM] [DATASET_PATH] [BACKBONE_CKPT] [HEAD_CKPT]"
+          CPU: sh run_eval.sh [PLATFORM] [DATASET_PATH] [BACKBONE_CKPT] [HEAD_CKPT]"
 exit 1
 fi
 

model_zoo/official/cv/mobilenetv2/src/dataset.py

@@ -36,7 +36,6 @@ def create_dataset(dataset_path, do_train, config, repeat_num=1):
         config(struct): the config of train and eval in diffirent platform.
         repeat_num(int): the repeat times of dataset. Default: 1.
 
-
     Returns:
         dataset
     """
@@ -96,11 +95,7 @@ def create_dataset(dataset_path, do_train, config, repeat_num=1):
     # apply dataset repeat operation
     ds = ds.repeat(repeat_num)
 
-    step_size = ds.get_dataset_size()
+    return ds
-    if step_size == 0:
-        raise ValueError("The step_size of dataset is zero. Check if the images of train dataset is more than batch_\
-            size in config.py")
-    return ds, step_size
 
 
 def extract_features(net, dataset_path, config):
@@ -112,12 +107,16 @@ def extract_features(net, dataset_path, config):
                              config=config,
                              repeat_num=1)
     step_size = dataset.get_dataset_size()
+    if step_size == 0:
+        raise ValueError("The step_size of dataset is zero. Check if the images count of train dataset is more \
+            than batch_size in config.py")
+
     model = Model(net)
 
     for i, data in enumerate(dataset.create_dict_iterator(output_numpy=True)):
         features_path = os.path.join(features_folder, f"feature_{i}.npy")
         label_path = os.path.join(features_folder, f"label_{i}.npy")
-        if not os.path.exists(features_path or not os.path.exists(label_path)):
+        if not os.path.exists(features_path) or not os.path.exists(label_path):
             image = data["image"]
             label = data["label"]
             features = model.predict(Tensor(image))

model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py

@@ -284,8 +284,11 @@ class MobileNetV2(nn.Cell):
     MobileNetV2 architecture.
 
     Args:
-        backbone(nn.Cell):
+        class_num (Cell): number of classes.
-        head(nn.Cell):
+        width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
+        has_dropout (bool): Is dropout used. Default is false
+        inverted_residual_setting (list): Inverted residual settings. Default is None
+        round_nearest (list): Channel round to . Default is 8
     Returns:
         Tensor, output tensor.
 
@@ -310,14 +313,11 @@ class MobileNetV2(nn.Cell):
 
 class MobileNetV2Combine(nn.Cell):
     """
-    MobileNetV2 architecture.
+    MobileNetV2Combine architecture.
 
     Args:
-        class_num (Cell): number of classes.
+        backbone (Cell): the features extract layers.
-        width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
+        head (Cell):  the fully connected layers.
-        has_dropout (bool): Is dropout used. Default is false
-        inverted_residual_setting (list): Inverted residual settings. Default is None
-        round_nearest (list): Channel round to . Default is 8
     Returns:
         Tensor, output tensor.
 
@@ -326,7 +326,7 @@ class MobileNetV2Combine(nn.Cell):
     """
 
     def __init__(self, backbone, head):
-        super(MobileNetV2Combine, self).__init__()
+        super(MobileNetV2Combine, self).__init__(auto_prefix=False)
         self.backbone = backbone
         self.head = head
 

model_zoo/official/cv/mobilenetv2/src/models.py

@@ -119,20 +119,9 @@ def load_ckpt(network, pretrain_ckpt_path, trainable=True):
         for param in network.get_parameters():
             param.requires_grad = False
 
-def define_net(args, config):
+def define_net(config):
     backbone_net = MobileNetV2Backbone()
     head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes)
     net = mobilenet_v2(backbone_net, head_net)
 
-    # load the ckpt file to the network for fine tune or incremental leaning
-    if args.pretrain_ckpt:
-        if args.train_method == "fine_tune":
-            load_ckpt(net, args.pretrain_ckpt)
-        elif args.train_method == "incremental_learn":
-            load_ckpt(backbone_net, args.pretrain_ckpt, trainable=False)
-        elif args.train_method == "train":
-            pass
-        else:
-            raise ValueError("must input the usage of pretrain_ckpt when the pretrain_ckpt isn't None")
-
     return backbone_net, head_net, net

model_zoo/official/cv/mobilenetv2/train.py

@@ -35,7 +35,7 @@ from src.config import set_config
 
 from src.args import train_parse_args
 from src.utils import context_device_init, switch_precision, config_ckpoint
-from src.models import CrossEntropyWithLabelSmooth, define_net
+from src.models import CrossEntropyWithLabelSmooth, define_net, load_ckpt
 
 set_seed(1)
 
@@ -50,7 +50,18 @@ if __name__ == '__main__':
     context_device_init(config)
 
     # define network
-    backbone_net, head_net, net = define_net(args_opt, config)
+    backbone_net, head_net, net = define_net(config)
+
+    # load the ckpt file to the network for fine tune or incremental leaning
+    if args_opt.pretrain_ckpt:
+        if args_opt.train_method == "fine_tune":
+            load_ckpt(net, args_opt.pretrain_ckpt)
+        elif args_opt.train_method == "incremental_learn":
+            load_ckpt(backbone_net, args_opt.pretrain_ckpt, trainable=False)
+        elif args_opt.train_method == "train":
+            pass
+        else:
+            raise ValueError("must input the usage of pretrain_ckpt when the pretrain_ckpt isn't None")
 
     # CPU only support "incremental_learn"
     if args_opt.train_method == "incremental_learn":
@@ -60,7 +71,11 @@ if __name__ == '__main__':
     elif args_opt.train_method in ("train", "fine_tune"):
         if args_opt.platform == "CPU":
             raise ValueError("Currently, CPU only support \"incremental_learn\", not \"fine_tune\" or \"train\".")
-        dataset, step_size = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, config=config)
+        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, config=config)
+        step_size = dataset.get_dataset_size()
+        if step_size == 0:
+            raise ValueError("The step_size of dataset is zero. Check if the images count of train dataset is more \
+                than batch_size in config.py")
 
     # Currently, only Ascend support switch precision.
     switch_precision(net, mstype.float16, config)