!509 Fixup py Normalize op doc: takes input CHW

Merge pull request !509 from cathwong/ckw_dataset_py_ut2

mindspore/dataset/transforms/vision/py_transforms.py

@@ -220,7 +220,7 @@ class Decode:
 
 class Normalize:
     """
-    Normalize the input Numpy image array of shape (H, W, C) with the given mean and standard deviation.
+    Normalize the input Numpy image array of shape (C, H, W) with the given mean and standard deviation.
 
     The values of the array need to be in range [0.0, 1.0].
 

tests/ut/python/dataset/test_normalizeOp.py

@@ -15,7 +15,7 @@
 
 import mindspore.dataset.transforms.vision.c_transforms as vision
 import numpy as np
-
+import matplotlib.pyplot as plt
 import mindspore.dataset as ds
 from mindspore import log as logger
 
@@ -114,6 +114,7 @@ def test_decode_op():
         # plt.subplot(131)
         # plt.imshow(image)
         # plt.title("DE image")
+        # plt.show()
         num_iter += 1
 
 
@@ -138,8 +139,8 @@ def test_decode_normalize_op():
         # plt.subplot(131)
         # plt.imshow(image)
         # plt.title("DE image")
+        # plt.show()
         num_iter += 1
-        break
 
 
 if __name__ == "__main__":

tests/ut/python/dataset/test_random_color_adjust.py

@@ -182,8 +182,6 @@ def test_random_color_jitter_op_saturation():
     ]
     transform = py_vision.ComposeOp(transforms)
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    # data2 = data2.map(input_columns=["image"], operations=decode_op)
-    # data2 = data2.map(input_columns=["image"], operations=c_vision.Decode())
     data2 = data2.map(input_columns=["image"], operations=transform())
 
     num_iter = 0
@@ -220,8 +218,6 @@ def test_random_color_jitter_op_hue():
     # First dataset
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     decode_op = c_vision.Decode()
-    # channel_swap_op = c_vision.ChannelSwap()
-    # change_mode_op = c_vision.ChangeMode()
 
     random_jitter_op = c_vision.RandomColorAdjust((1, 1), (1, 1), (1, 1), (0.2, 0.2))