Add the necessary rescale op and the option of not shuffling validation data

model_zoo/googlenet/src/dataset.py

@@ -32,7 +32,10 @@ def create_dataset(data_home, repeat_num=1, training=True):
         data_dir = os.path.join(data_home, "cifar-10-verify-bin")
 
     rank_size, rank_id = _get_rank_info()
-    data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
+    if training:
+        data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=True)
+    else:
+        data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=False)
 
     resize_height = cfg.image_height
     resize_width = cfg.image_width
@@ -41,6 +44,7 @@ def create_dataset(data_home, repeat_num=1, training=True):
     random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4))  # padding_mode default CONSTANT
     random_horizontal_op = vision.RandomHorizontalFlip()
     resize_op = vision.Resize((resize_height, resize_width))  # interpolation default BILINEAR
+    rescale_op = vision.Rescale(1.0/255.0, 0.0)
     normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
     changeswap_op = vision.HWC2CHW()
     type_cast_op = C.TypeCast(mstype.int32)
@@ -48,21 +52,18 @@ def create_dataset(data_home, repeat_num=1, training=True):
     c_trans = []
     if training:
         c_trans = [random_crop_op, random_horizontal_op]
-    c_trans += [resize_op, normalize_op, changeswap_op]
+    c_trans += [resize_op, rescale_op, normalize_op, changeswap_op]
 
     # apply map operations on images
     data_set = data_set.map(input_columns="label", operations=type_cast_op)
     data_set = data_set.map(input_columns="image", operations=c_trans)
 
-    # apply repeat operations
-    data_set = data_set.repeat(repeat_num)
-
-    # apply shuffle operations
-    data_set = data_set.shuffle(buffer_size=10)
-
     # apply batch operations
     data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
 
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
     return data_set