refine warning in image classification (#4018)

* refine warning

PaddleCV/image_classification/README.md

@@ -261,8 +261,6 @@ export FLAGS_fraction_of_gpu_memory_to_use=0.80
 python -m paddle.distributed.launch train.py \
        --model=ShuffleNetV2_x0_25 \
        --batch_size=2048 \
-       --class_dim=1000 \
-       --image_shape=3,224,224 \
        --lr_strategy=cosine_decay_warmup \
        --num_epochs=240 \
        --lr=0.5 \

PaddleCV/image_classification/README_en.md

@@ -256,8 +256,6 @@ export FLAGS_fraction_of_gpu_memory_to_use=0.80
 python -m paddle.distributed.launch train.py \
        --model=ShuffleNetV2_x0_25 \
        --batch_size=2048 \
-       --class_dim=1000 \
-       --image_shape=3,224,224 \
        --lr_strategy=cosine_decay_warmup \
        --num_epochs=240 \
        --lr=0.5 \

PaddleCV/image_classification/eval.py

@@ -52,8 +52,6 @@ add_arg('use_se',           bool, True,                 "Whether to use Squeeze-
 
 
 def eval(args):
-    image_shape = args.image_shape
-
     model_list = [m for m in dir(models) if "__" not in m]
     assert args.model in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
@@ -62,8 +60,11 @@ def eval(args):
     ), "{} doesn't exist, please load right pretrained model path for eval".format(
         args.pretrained_model)
 
+    assert args.image_shape[
+        1] <= args.resize_short_size, "Please check the args:image_shape and args:resize_short_size, The croped size(image_shape[1]) must smaller than or equal to the resized length(resize_short_size) "
+
     image = fluid.data(
-        name='image', shape=[None] + image_shape, dtype='float32')
+        name='image', shape=[None] + args.image_shape, dtype='float32')
     label = fluid.data(name='label', shape=[None, 1], dtype='int64')
 
     # model definition

PaddleCV/image_classification/infer.py

@@ -54,14 +54,17 @@ add_arg('use_se',           bool, True,                 "Whether to use Squeeze-
 
 
 def infer(args):
-    image_shape = args.image_shape
     model_list = [m for m in dir(models) if "__" not in m]
     assert args.model in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
     assert os.path.isdir(args.pretrained_model
                          ), "please load right pretrained model path for infer"
+
+    assert args.image_shape[
+        1] <= args.resize_short_size, "Please check the args:image_shape and args:resize_short_size, The croped size(image_shape[1]) must smaller than or equal to the resized length(resize_short_size) "
+
     image = fluid.data(
-        name='image', shape=[None] + image_shape, dtype='float32')
+        name='image', shape=[None] + args.image_shape, dtype='float32')
 
     if args.model.startswith('EfficientNet'):
         model = models.__dict__[args.model](is_test=True,