repair problem in vgg16 cifar10 version

model_zoo/official/cv/vgg16/README.md

@@ -38,9 +38,11 @@ epcoh: 2 step: 781, loss is 1.827582
 
 ### Evaluation
 
+- Do eval as follows, need to specify dataset type as "cifar10" or "imagenet2012"
 ```
-python eval.py --data_path=your_data_path --device_id=6 --checkpoint_path=./train_vgg_cifar10-70-781.ckpt > out.eval.log 2>&1 & 
+python eval.py --data_path=your_data_path --dataset="cifar10" --pre_trained=./train_vgg_cifar10-70-781.ckpt > out.eval.log 2>&1 & 
 ```
+- If the using dataset is
 The above python command will run in the background, you can view the results through the file `out.eval.log`.
 
 You will get the accuracy as following:
@@ -93,7 +95,7 @@ parameters/options:
   --device_target       the evaluation backend type, default is Ascend.
   --data_path           the storage path of datasetd 
   --device_id           the device which used to evaluate model.
-  --checkpoint_path     the checkpoint file path used to evaluate model.
+  --pre_trained     the checkpoint file path used to evaluate model.
 ```
 
 ### Distribute Training

model_zoo/official/cv/vgg16/eval.py

@@ -52,15 +52,15 @@ class ParameterReduce(nn.Cell):
 def parse_args(cloud_args=None):
     """parse_args"""
     parser = argparse.ArgumentParser('mindspore classification test')
-    parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'],
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
                         help='device where the code will be implemented. (Default: Ascend)')
     # dataset related
-    parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="imagenet2012")
+    parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
     parser.add_argument('--data_path', type=str, default='', help='eval data dir')
     parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
     # network related
     parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
-    parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load. '
+    parser.add_argument('--pre_trained', default='', type=str, help='fully path of pretrained model to load. '
                         'If it is a direction, it will test all ckpt')
 
     # logging related
@@ -68,9 +68,6 @@ def parse_args(cloud_args=None):
     parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
     parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
 
-    # roma obs
-    parser.add_argument('--train_url', type=str, default="", help='train url')
-
     args_opt = parser.parse_args()
     args_opt = merge_args(args_opt, cloud_args)
 
@@ -82,6 +79,8 @@ def parse_args(cloud_args=None):
     args_opt.image_size = cfg.image_size
     args_opt.num_classes = cfg.num_classes
     args_opt.per_batch_size = cfg.batch_size
+    args_opt.momentum = cfg.momentum
+    args_opt.weight_decay = cfg.weight_decay
     args_opt.buffer_size = cfg.buffer_size
     args_opt.pad_mode = cfg.pad_mode
     args_opt.padding = cfg.padding
@@ -130,23 +129,23 @@ def test(cloud_args=None):
     args.logger.save_args(args)
 
     if args.dataset == "cifar10":
-        net = vgg16(num_classes=args.num_classes)
-        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
+        net = vgg16(num_classes=args.num_classes, args=args)
+        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, args.momentum,
                        weight_decay=args.weight_decay)
         loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
         model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
 
-        param_dict = load_checkpoint(args.checkpoint_path)
+        param_dict = load_checkpoint(args.pre_trained)
         load_param_into_net(net, param_dict)
         net.set_train(False)
-        dataset = vgg_create_dataset(args.data_path, 1, False)
+        dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, training=False)
         res = model.eval(dataset)
         print("result: ", res)
     else:
         # network
         args.logger.important_info('start create network')
-        if os.path.isdir(args.pretrained):
-            models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
+        if os.path.isdir(args.pre_trained):
+            models = list(glob.glob(os.path.join(args.pre_trained, '*.ckpt')))
             print(models)
             if args.graph_ckpt:
                 f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
@@ -154,14 +153,10 @@ def test(cloud_args=None):
                 f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
             args.models = sorted(models, key=f)
         else:
-            args.models = [args.pretrained,]
+            args.models = [args.pre_trained,]
 
         for model in args.models:
-            if args.dataset == "cifar10":
-                dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, training=False)
-            else:
-                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)
             eval_dataloader = dataset.create_tuple_iterator()
             network = vgg16(args.num_classes, args, phase="test")
 

model_zoo/official/cv/vgg16/scripts/run_distribute_train.sh

@@ -47,6 +47,6 @@ do
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
-    python train.py --data_path=$2 --device_id=$i &> log &
+    python train.py --data_path=$2 --device_target="Ascend" --device_id=$i &> log &
     cd ..
 done
\ No newline at end of file

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

@@ -138,5 +138,5 @@ def vgg16(num_classes=1000, args=None, phase="train"):
         >>> vgg16(num_classes=1000)
     """
 
-    net = Vgg(cfg['16'], num_classes=num_classes, args=args, batch_norm=True, phase=phase)
+    net = Vgg(cfg['16'], num_classes=num_classes, args=args, batch_norm=args.batch_norm, phase=phase)
     return net

model_zoo/official/cv/vgg16/train.py

@@ -109,7 +109,7 @@ class ProgressMonitor(Callback):
 def parse_args(cloud_args=None):
     """parameters"""
     parser = argparse.ArgumentParser('mindspore classification training')
-    parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'],
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
                         help='device where the code will be implemented. (Default: Ascend)')
     parser.add_argument('--device_id', type=int, default=1, help='device id of GPU or Ascend. (Default: None)')
 
@@ -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=5000, help='ckpt_interval')
+    parser.add_argument('--ckpt_interval', type=int, default=2, help='ckpt_interval')
     parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
 
     # distributed related