diff --git a/demo/dygraph/unstructured_pruning/README.md b/demo/dygraph/unstructured_pruning/README.md
index 3a861dffc742c66a5bf4193dcad6d74e30da394f..a1068542587da7f1131a2590380c20475b44f62f 100644
--- a/demo/dygraph/unstructured_pruning/README.md
+++ b/demo/dygraph/unstructured_pruning/README.md
@@ -89,9 +89,11 @@ python3.7 train.py --data imagenet --lr 0.05 --pruning_mode threshold --threshol
 
 ## 推理：
 ```bash
-python3.7 evalualte.py --pruned_model models/ --data imagenet
+python3.7 evaluate.py --pruned_model models/model-pruned.pdparams --data imagenet
 ```
 
+**注意**，上述`pruned_model` 参数应该指向pdparams文件。
+
 剪裁训练代码示例：
 ```python
 model = mobilenet_v1(num_classes=class_dim, pretrained=True)
diff --git a/demo/dygraph/unstructured_pruning/evaluate.py b/demo/dygraph/unstructured_pruning/evaluate.py
index d2c6aa56c13cc2e0cae84f00a8f71e66b6bff05b..e8827d1f5e7a6ec6ee20ff6134f996966064dd34 100644
--- a/demo/dygraph/unstructured_pruning/evaluate.py
+++ b/demo/dygraph/unstructured_pruning/evaluate.py
@@ -67,8 +67,6 @@ def compress(args):
             start_time = time.time()
             x_data = data[0]
             y_data = paddle.to_tensor(data[1])
-            if args.data == 'cifar10':
-                y_data = paddle.unsqueeze(y_data, 1)
 
             logits = model(x_data)
             loss = F.cross_entropy(logits, y_data)
diff --git a/demo/dygraph/unstructured_pruning/train.py b/demo/dygraph/unstructured_pruning/train.py
index 39b6804bfca609db4506d4df17e96040623b3233..5cf71789220da3e1e3672f9bb06ce8252aa716fc 100644
--- a/demo/dygraph/unstructured_pruning/train.py
+++ b/demo/dygraph/unstructured_pruning/train.py
@@ -145,8 +145,6 @@ def compress(args):
             start_time = time.time()
             x_data = data[0]
             y_data = paddle.to_tensor(data[1])
-            if args.data == 'cifar10':
-                y_data = paddle.unsqueeze(y_data, 1)
 
             logits = model(x_data)
             loss = F.cross_entropy(logits, y_data)
@@ -180,8 +178,6 @@ def compress(args):
             train_reader_cost += time.time() - reader_start
             x_data = data[0]
             y_data = paddle.to_tensor(data[1])
-            if args.data == 'cifar10':
-                y_data = paddle.unsqueeze(y_data, 1)
 
             train_start = time.time()
             logits = model(x_data)