repair vgg precision problem

model_zoo/official/cv/vgg16/eval.py

@@ -158,7 +158,7 @@ def test(cloud_args=None):
             args.models = [args.pre_trained,]
 
         for model in args.models:
-            dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size)
+            dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size, mode='eval')
             eval_dataloader = dataset.create_tuple_iterator()
             network = vgg16(args.num_classes, args, phase="test")
 

model_zoo/official/cv/vgg16/src/config.py

@@ -64,7 +64,7 @@ imagenet_cfg = edict({
     "image_size": '224,224',
     "pad_mode": 'pad',
     "padding": 1,
-    "has_bias": True,
+    "has_bias": False,
     "batch_norm": False,
     "keep_checkpoint_max": 10,
     "initialize_mode": "KaimingNormal",

model_zoo/official/cv/vgg16/src/vgg.py

@@ -31,10 +31,11 @@ def _make_layer(base, args, batch_norm):
         if v == 'M':
             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
         else:
-            weight_shape = (v, in_channels, 3, 3)
-            weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
-            if args.initialize_mode == "KaimingNormal":
-                weight = 'normal'
+            weight = 'ones'
+            if args.initialize_mode == "XavierUniform":
+                weight_shape = (v, in_channels, 3, 3)
+                weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
+
             conv2d = nn.Conv2d(in_channels=in_channels,
                                out_channels=v,
                                kernel_size=3,

model_zoo/official/cv/vgg16/train.py

@@ -127,7 +127,7 @@ def parse_args(cloud_args=None):
     # logging and checkpoint related
     parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
     parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
-    parser.add_argument('--ckpt_interval', type=int, default=2, help='ckpt_interval')
+    parser.add_argument('--ckpt_interval', type=int, default=5, help='ckpt_interval')
     parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
 
     # distributed related
@@ -200,12 +200,12 @@ if __name__ == '__main__':
         device_num = args.group_size
         context.reset_auto_parallel_context()
         context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                          mirror_mean=True)
+                                          parameter_broadcast=True, mirror_mean=True)
     else:
         context.set_context(device_id=args.device_id)
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
-    # select for master rank save ckpt or all rank save, compatiable for model parallel
+    # select for master rank save ckpt or all rank save, compatible for model parallel
     args.rank_save_ckpt_flag = 0
     if args.is_save_on_master:
         if args.rank == 0: