diff --git a/ppdet/data/data_feed.py b/ppdet/data/data_feed.py
index 110fd68524263a2bd80f0d02b47d1f9ea5e1903c..26bd297a00926ad6e0ff852b3f1fb0de5c6fec3a 100644
--- a/ppdet/data/data_feed.py
+++ b/ppdet/data/data_feed.py
@@ -453,7 +453,7 @@ class FasterRCNNTrainFeed(DataFeed):
                      'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
                      'is_crowd'
                  ],
-                 image_shape=[None, 3, None, None],
+                 image_shape=[3, None, None],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      RandomFlipImage(prob=0.5),
@@ -505,7 +505,7 @@ class FasterRCNNEvalFeed(DataFeed):
                                      COCO_VAL_IMAGE_DIR).__dict__,
                  fields=['image', 'im_info', 'im_id', 'im_shape', 'gt_box',
                          'gt_label', 'is_difficult'],
-                 image_shape=[None, 3, None, None],
+                 image_shape=[3, None, None],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      NormalizeImage(mean=[0.485, 0.456, 0.406],
@@ -552,7 +552,7 @@ class FasterRCNNTestFeed(DataFeed):
                  dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
                                        COCO_VAL_IMAGE_DIR).__dict__,
                  fields=['image', 'im_info', 'im_id', 'im_shape'],
-                 image_shape=[None, 3, None, None],
+                 image_shape=[3, None, None],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      NormalizeImage(mean=[0.485, 0.456, 0.406],
@@ -600,7 +600,7 @@ class MaskRCNNTrainFeed(DataFeed):
                      'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
                      'is_crowd', 'gt_mask'
                  ],
-                 image_shape=[None, 3, None, None],
+                 image_shape=[3, None, None],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      RandomFlipImage(prob=0.5, is_mask_flip=True),
@@ -646,7 +646,7 @@ class MaskRCNNEvalFeed(DataFeed):
                  dataset=CocoDataSet(COCO_VAL_ANNOTATION,
                                      COCO_VAL_IMAGE_DIR).__dict__,
                  fields=['image', 'im_info', 'im_id', 'im_shape'],
-                 image_shape=[None, 3, None, None],
+                 image_shape=[3, None, None],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      NormalizeImage(mean=[0.485, 0.456, 0.406],
@@ -698,7 +698,7 @@ class MaskRCNNTestFeed(DataFeed):
                  dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
                                        COCO_VAL_IMAGE_DIR).__dict__,
                  fields=['image', 'im_info', 'im_id', 'im_shape'],
-                 image_shape=[None, 3, None, None],
+                 image_shape=[3, None, None],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      NormalizeImage(
@@ -743,7 +743,7 @@ class SSDTrainFeed(DataFeed):
     def __init__(self,
                  dataset=VocDataSet().__dict__,
                  fields=['image', 'gt_box', 'gt_label'],
-                 image_shape=[None, 3, 300, 300],
+                 image_shape=[3, 300, 300],
                  sample_transforms=[
                      DecodeImage(to_rgb=True, with_mixup=False),
                      NormalizeBox(),
@@ -802,7 +802,7 @@ class SSDEvalFeed(DataFeed):
             dataset=VocDataSet(VOC_VAL_ANNOTATION).__dict__,
             fields=['image', 'im_shape', 'im_id', 'gt_box',
                          'gt_label', 'is_difficult'],
-            image_shape=[None, 3, 300, 300],
+            image_shape=[3, 300, 300],
             sample_transforms=[
                 DecodeImage(to_rgb=True, with_mixup=False),
                 NormalizeBox(),
@@ -847,7 +847,7 @@ class SSDTestFeed(DataFeed):
     def __init__(self,
                  dataset=SimpleDataSet(VOC_VAL_ANNOTATION).__dict__,
                  fields=['image', 'im_id', 'im_shape'],
-                 image_shape=[None, 3, 300, 300],
+                 image_shape=[3, 300, 300],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      ResizeImage(target_size=300, use_cv2=False, interp=1),
@@ -893,7 +893,7 @@ class YoloTrainFeed(DataFeed):
     def __init__(self,
                  dataset=CocoDataSet().__dict__,
                  fields=['image', 'gt_box', 'gt_label', 'gt_score'],
-                 image_shape=[None, 3, 608, 608],
+                 image_shape=[3, 608, 608],
                  sample_transforms=[
                      DecodeImage(to_rgb=True, with_mixup=True),
                      MixupImage(alpha=1.5, beta=1.5),
@@ -955,7 +955,7 @@ class YoloEvalFeed(DataFeed):
                                      COCO_VAL_IMAGE_DIR).__dict__,
                  fields=['image', 'im_size', 'im_id', 'gt_box',
                          'gt_label', 'is_difficult'],
-                 image_shape=[None, 3, 608, 608],
+                 image_shape=[3, 608, 608],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      ResizeImage(target_size=608, interp=2),
@@ -1013,7 +1013,7 @@ class YoloTestFeed(DataFeed):
                  dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
                                        COCO_VAL_IMAGE_DIR).__dict__,
                  fields=['image', 'im_size', 'im_id'],
-                 image_shape=[None, 3, 608, 608],
+                 image_shape=[3, 608, 608],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
                      ResizeImage(target_size=608, interp=2),
diff --git a/ppdet/modeling/model_input.py b/ppdet/modeling/model_input.py
index 376438963a1d53cf0fe2126592af0421cd82a508..1ded8b20e785097a26be93d782ac7aa2cd64c8a6 100644
--- a/ppdet/modeling/model_input.py
+++ b/ppdet/modeling/model_input.py
@@ -40,7 +40,7 @@ feed_var_def = [
 
 
 def create_feed(feed, iterable=False, sub_prog_feed=False):
-    image_shape = feed.image_shape
+    image_shape = [None] + feed.image_shape
     feed_var_map = {var['name']: var for var in feed_var_def}
     feed_var_map['image'] = {
         'name': 'image',
@@ -98,14 +98,14 @@ def create_feed(feed, iterable=False, sub_prog_feed=False):
                     'lod_level': 0
                 }
                 image_name_list.append(name)
-        feed_var_map['im_info']['shape'] = [feed.num_scale * 3]
+        feed_var_map['im_info']['shape'] = [None, feed.num_scale * 3]
         feed.fields = image_name_list + feed.fields[1:]
     if sub_prog_feed:
         box_names = ['bbox', 'bbox_flip']
         for box_name in box_names:
             sub_prog_feed = {
                 'name': box_name,
-                'shape': [6],
+                'shape': [None, 6],
                 'dtype': 'float32',
                 'lod_level': 1
             }