diff --git a/cnn_e2e/ofrecord_util.py b/cnn_e2e/ofrecord_util.py
index f72efa7d20104475fe87e625c4a570ce493df7ec..6a2d9ca15a86ded1e60a18ec95e1657aa64ba837 100644
--- a/cnn_e2e/ofrecord_util.py
+++ b/cnn_e2e/ofrecord_util.py
@@ -6,6 +6,7 @@ import oneflow as flow
 
 def add_ofrecord_args(parser):
     parser.add_argument("--image_size", type=int, default=224, required=False, help="image size")
+    parser.add_argument("--resize_shorter", type=int, default=256, required=False, help="resize shorter for validation")
     parser.add_argument("--train_data_dir", type=str, default=None, help="train dataset directory")
     parser.add_argument("--train_data_part_num", type=int, default=256, help="train data part num")
     parser.add_argument("--val_data_dir", type=str, default=None, help="val dataset directory")
@@ -88,22 +89,21 @@ def load_imagenet_for_training2(args):
     total_device_num = args.num_nodes * args.gpu_num_per_node
     train_batch_size = total_device_num * args.batch_size_per_device
 
-    seed = 0
     color_space = 'RGB'
-    with flow.fixed_placement("cpu", "0:0"):
+    with flow.fixed_placement("cpu", "0:0-{}".format(args.gpu_num_per_node - 1)):
         ofrecord = flow.data.ofrecord_reader(args.train_data_dir,
                                              batch_size=train_batch_size,
                                              data_part_num=args.train_data_part_num,
                                              part_name_suffix_length=5,
+                                             random_shuffle = True,
                                              shuffle_after_epoch=True)
-        image = flow.data.OFRecordImageDecoderRandomCrop(ofrecord, "encoded", seed=seed,
+        image = flow.data.OFRecordImageDecoderRandomCrop(ofrecord, "encoded", #seed=seed,
                                                          color_space=color_space)
         label = flow.data.OFRecordRawDecoder(ofrecord, "class/label", shape=(), dtype=flow.int32)
-        rsz = flow.image.Resize(image, resize_x=args.image_size,
-                                resize_y=args.image_size,
+        rsz = flow.image.Resize(image, resize_x=args.image_size, resize_y=args.image_size,
                                 color_space=color_space)
 
-        rng = flow.random.CoinFlip(batch_size=train_batch_size, seed=seed)
+        rng = flow.random.CoinFlip(batch_size=train_batch_size)#, seed=seed)
         normal = flow.image.CropMirrorNormalize(rsz, mirror_blob=rng, color_space=color_space,
             mean=args.rgb_mean, std=args.rgb_std, output_dtype = flow.float)
         return label, normal
@@ -112,23 +112,19 @@ def load_imagenet_for_validation2(args):
     total_device_num = args.num_nodes * args.gpu_num_per_node
     val_batch_size = total_device_num * args.val_batch_size_per_device
 
-    seed = 0
     color_space = 'RGB'
-    with flow.fixed_placement("cpu", "0:0"):
+    with flow.fixed_placement("cpu", "0:0-{}".format(args.gpu_num_per_node - 1)):
         ofrecord = flow.data.ofrecord_reader(args.val_data_dir,
                                              batch_size=val_batch_size,
                                              data_part_num=args.val_data_part_num,
                                              part_name_suffix_length=5,
                                              shuffle_after_epoch=False)
-        image = flow.data.OFRecordImageDecoderRandomCrop(ofrecord, "encoded", seed=seed,
-                                                         color_space=color_space)
+        image = flow.data.OFRecordImageDecoder(ofrecord, "encoded", color_space=color_space)
         label = flow.data.OFRecordRawDecoder(ofrecord, "class/label", shape=(), dtype=flow.int32)
-        rsz = flow.image.Resize(image, resize_x=args.image_size,
-                                resize_y=args.image_size,
-                                color_space=color_space)
+        rsz = flow.image.Resize(image, resize_shorter=args.resize_shorter, color_space=color_space)
 
-        rng = flow.random.CoinFlip(batch_size=val_batch_size, seed=seed)
-        normal = flow.image.CropMirrorNormalize(rsz, mirror_blob=rng, color_space=color_space,
+        normal = flow.image.CropMirrorNormalize(rsz, color_space=color_space,
+            crop_h = args.image_size, crop_w = args.image_size, crop_pos_y = 0.5, crop_pos_x = 0.5,
             mean=args.rgb_mean, std=args.rgb_std, output_dtype = flow.float)
         return label, normal