!33263 [MD] UT Updates - more eager tests, minor updates

Merge pull request !33263 from cathwong/ckw_ut_misc_fixes

tests/ut/python/dataset/test_adjustgamma.py

@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -42,6 +42,11 @@ def generate_numpy_random_rgb(shape):
 
 
 def test_adjust_gamma_c_eager():
+    """
+    Feature: AdjustGamma op
+    Description: Test eager support for AdjustGamma C++ op
+    Expectation: Receive non-None output image from op
+    """
     # Eager 3-channel
     rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
     img_in = rgb_flat.reshape((8, 8, 3))
@@ -50,8 +55,19 @@ def test_adjust_gamma_c_eager():
     img_out = adjustgamma_op(img_in)
     assert img_out is not None
 
+    img_in2 = PIL.Image.open("../data/dataset/apple.jpg").convert("RGB")
+
+    adjustgamma_op2 = C.AdjustGamma(10, 1)
+    img_out2 = adjustgamma_op2(img_in2)
+    assert img_out2 is not None
+
 
 def test_adjust_gamma_py_eager():
+    """
+    Feature: AdjustGamma op
+    Description: Test eager support for AdjustGamma Python op
+    Expectation: Receive non-None output image from op
+    """
     # Eager 3-channel
     rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.uint8)
     img_in = PIL.Image.fromarray(rgb_flat.reshape((8, 8, 3)))
@@ -60,6 +76,12 @@ def test_adjust_gamma_py_eager():
     img_out = adjustgamma_op(img_in)
     assert img_out is not None
 
+    img_in2 = PIL.Image.open("../data/dataset/apple.jpg").convert("RGB")
+
+    adjustgamma_op2 = F.AdjustGamma(10, 1)
+    img_out2 = adjustgamma_op2(img_in2)
+    assert img_out2 is not None
+
 
 def test_adjust_gamma_c_eager_gray():
     # Eager 3-channel

tests/ut/python/dataset/test_eager_vision.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,12 +14,19 @@
 # ==============================================================================
 import cv2
 import numpy as np
+import pytest
 from PIL import Image
 import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision.py_transforms as PY
 from mindspore import log as logger
 
 
-def test_eager_decode():
+def test_eager_decode_c():
+    """
+    Feature: Decode op
+    Description: Test eager support for Decode C++ op
+    Expectation: Output image size from op is correct
+    """
     img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
 
@@ -35,6 +42,27 @@ def test_eager_decode():
     assert img2.shape == (2268, 4032, 3)
 
 
+def test_eager_decode_py():
+    """
+    Feature: Decode op
+    Description: Test eager support for Decode Python op
+    Expectation: Output image size from op is correct
+    """
+    img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = PY.Decode()(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    assert img.size == (4032, 2268)
+
+    fp = open("../data/dataset/apple.jpg", "rb")
+    img2 = fp.read()
+
+    img2 = PY.Decode()(img2)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img2), img2.size))
+    assert img2.size == (4032, 2268)
+
+
 def test_eager_resize():
     img = cv2.imread("../data/dataset/apple.jpg")
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
@@ -44,6 +72,7 @@ def test_eager_resize():
 
     assert img.shape == (32, 32, 3)
 
+
 def test_eager_rescale():
     img = cv2.imread("../data/dataset/apple.jpg")
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
@@ -54,9 +83,15 @@ def test_eager_rescale():
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
     pixel_rescaled = img[0][0][0]
 
-    assert pixel*rescale_factor == pixel_rescaled
+    assert pixel * rescale_factor == pixel_rescaled
 
-def test_eager_normalize():
+
+def test_eager_normalize_c():
+    """
+    Feature: Normalize op
+    Description: Test eager support for Normalize C++ op
+    Expectation: Output image info from op is correct
+    """
     img = Image.open("../data/dataset/apple.jpg").convert("RGB")
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
     pixel = img.getpixel((0, 0))[0]
@@ -69,6 +104,27 @@ def test_eager_normalize():
 
     assert (pixel - mean_vec[0]) / std_vec[0] == pixel_normalized
 
+
+def test_eager_normalize_py():
+    """
+    Feature: Normalize op
+    Description: Test eager support for Normalize Python op
+    Expectation: Output image info from op is correct
+    """
+    img = Image.open("../data/dataset/apple.jpg").convert("RGB")
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    pixel = img.getpixel((0, 0))[0]
+
+    img = PY.ToTensor()(img)
+
+    mean_vec = [.100, .100, .100]
+    std_vec = [.2, .2, .2]
+    img = PY.Normalize(mean=mean_vec, std=std_vec)(img)
+    pixel_normalized = img[0][0][0]
+
+    assert (pixel / 255 - mean_vec[0]) / std_vec[0] == pytest.approx(pixel_normalized, 0.0001)
+
+
 def test_eager_HWC2CHW():
     img = cv2.imread("../data/dataset/apple.jpg")
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
@@ -80,7 +136,13 @@ def test_eager_HWC2CHW():
 
     assert channel == (channel_swaped[1], channel_swaped[2], channel_swaped[0])
 
-def test_eager_pad():
+
+def test_eager_pad_c():
+    """
+    Feature: Pad op
+    Description: Test eager support for Pad C++ op
+    Expectation: Output image size info from op is correct
+    """
     img = Image.open("../data/dataset/apple.jpg").convert("RGB")
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
 
@@ -95,6 +157,89 @@ def test_eager_pad():
 
     assert size_padded == (size[0] + 2 * pad, size[1] + 2 * pad, size[2])
 
+
+def test_eager_pad_py():
+    """
+    Feature: Pad op
+    Description: Test eager support for Pad Python op
+    Expectation: Output image size info from op is correct
+    """
+    img = Image.open("../data/dataset/apple.jpg").convert("RGB")
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = PY.Resize(size=(32, 32))(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    size = img.size
+
+    pad = 4
+    img = PY.Pad(padding=pad)(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    size_padded = img.size
+
+    assert size_padded == (size[0] + 2 * pad, size[1] + 2 * pad)
+
+
+def test_eager_cutout_pil_c():
+    """
+    Feature: CutOut op
+    Description: Test eager support for CutOut C++ op with PIL input
+    Expectation: Output image size info from op is correct
+    """
+    img = Image.open("../data/dataset/apple.jpg").convert("RGB")
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = C.Resize(size=(32, 32))(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    size = img.shape
+
+    img = C.CutOut(2, 4)(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    size_cutout = img.shape
+
+    assert size_cutout == size
+
+
+def test_eager_cutout_pil_py():
+    """
+    Feature: CutOut op
+    Description: Test eager support for CutOut Python op with PIL input
+    Expectation: Receive non-None output image from op
+    """
+    img = Image.open("../data/dataset/apple.jpg").convert("RGB")
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = PY.Resize(size=(32, 32))(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = PY.ToTensor()(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = PY.Cutout(2, 4)(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    assert img is not None
+
+
+def test_eager_cutout_cv_c():
+    """
+    Feature: CutOut op
+    Description: Test eager support for CutOut C++ op with CV input
+    Expectation: Output image size info from op is correct
+    """
+    img = cv2.imread("../data/dataset/apple.jpg")
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+
+    img = C.Resize(size=(32, 32))(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    size = img.shape
+
+    img = C.CutOut(2, 4)(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
+    size_cutout = img.shape
+
+    assert size_cutout == size
+
+
 def test_eager_exceptions():
     try:
         img = "../data/dataset/apple.jpg"
@@ -125,12 +270,49 @@ def test_eager_exceptions():
         assert "Input should be NumPy or PIL image" in str(e)
 
 
+def test_eager_exceptions_normalize():
+    """
+    Feature: Normalize op
+    Description: Exception eager support test for Normalize Python op
+    Expectation: Error input image is detected
+    """
+    try:
+        img = Image.open("../data/dataset/apple.jpg").convert("RGB")
+        mean_vec = [.100, .100, .100]
+        std_vec = [.2, .2, .2]
+        _ = PY.Normalize(mean=mean_vec, std=std_vec)(img)
+        assert False
+    except TypeError as e:
+        assert "img should be NumPy image" in str(e)
+
+
+def test_eager_exceptions_pad():
+    """
+    Feature: Pad op
+    Description: Exception eager support test for Pad Python op
+    Expectation: Error input image is detected
+    """
+    try:
+        img = "../data/dataset/apple.jpg"
+        _ = PY.Pad(padding=4)(img)
+        assert False
+    except TypeError as e:
+        assert "img should be PIL image" in str(e)
+
+
 if __name__ == '__main__':
-    test_eager_decode()
+    test_eager_decode_c()
+    test_eager_decode_py()
     test_eager_resize()
     test_eager_rescale()
-    test_eager_normalize()
+    test_eager_normalize_c()
+    test_eager_normalize_py()
     test_eager_HWC2CHW()
-    test_eager_pad()
+    test_eager_pad_c()
+    test_eager_pad_py()
+    test_eager_cutout_pil_c()
+    test_eager_cutout_pil_py()
+    test_eager_cutout_cv_c()
     test_eager_exceptions()
-  
+    test_eager_exceptions_normalize()
+    test_eager_exceptions_pad()

tests/ut/python/dataset/test_hwc2chw.py

@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -30,9 +30,11 @@ DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
 SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 
-def test_HWC2CHW_callable():
+def test_hwc2chw_callable():
     """
-    Test HWC2CHW is callable
+    Feature: HWC2CHW op
+    Description: Test HWC2CHW op is callable.
+    Expectation: Valid input succeeds. Invalid input fails.
     """
     logger.info("Test HWC2CHW callable")
     img = np.zeros([50, 50, 3])
@@ -59,7 +61,7 @@ def test_HWC2CHW_callable():
     assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)
 
 
-def test_HWC2CHW_multi_channels():
+def test_hwc2chw_multi_channels():
     """
     Feature: Test HWC2CHW feature
     Description: The input is a HWC format array with 5 channels
@@ -81,9 +83,11 @@ def test_HWC2CHW_multi_channels():
         assert np.allclose(item[0], expect_output)
 
 
-def test_HWC2CHW(plot=False):
+def test_hwc2chw(plot=False):
     """
-    Test HWC2CHW
+    Feature: HWC2CHW op
+    Description: Test HWC2CHW op in pipeline
+    Expectation: Pipelines succeed with comparison mse=0
     """
     logger.info("Test HWC2CHW")
 
@@ -115,9 +119,11 @@ def test_HWC2CHW(plot=False):
         visualize_list(image, image_transposed)
 
 
-def test_HWC2CHW_md5():
+def test_hwc2chw_md5():
     """
-    Test HWC2CHW(md5)
+    Feature: HWC2CHW op
+    Description: Test HWC2CHW op with md5 check.
+    Expectation: Pipeline results match in md5 comparison
     """
     logger.info("Test HWC2CHW with md5 comparison")
 
@@ -133,9 +139,11 @@ def test_HWC2CHW_md5():
     save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def test_HWC2CHW_comp(plot=False):
+def test_hwc2chw_comp(plot=False):
     """
-    Test HWC2CHW between python and c image augmentation
+    Feature: HWC2CHW op
+    Description: Test HWC2CHW between Python and C image augmentation
+    Expectation: Image augmentations should be almost the same with mse < 0.001
     """
     logger.info("Test HWC2CHW with c_transform and py_transform comparison")
 
@@ -150,9 +158,7 @@ def test_HWC2CHW_comp(plot=False):
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     transforms = [
         py_vision.Decode(),
-        py_vision.ToTensor(),
+        py_vision.ToTensor()]
-        py_vision.HWC2CHW()
-    ]
     transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
     data2 = data2.map(operations=transform, input_columns=["image"])
 
@@ -161,7 +167,7 @@ def test_HWC2CHW_comp(plot=False):
     for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                             data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
         c_image = item1["image"]
-        py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        py_image = (item2["image"] * 255).astype(np.uint8)
 
         # Compare images between that applying c_transform and py_transform
         mse = diff_mse(py_image, c_image)
@@ -174,8 +180,8 @@ def test_HWC2CHW_comp(plot=False):
 
 
 if __name__ == '__main__':
-    test_HWC2CHW_callable()
+    test_hwc2chw_callable()
-    test_HWC2CHW_multi_channels()
+    test_hwc2chw_multi_channels()
-    test_HWC2CHW(True)
+    test_hwc2chw(True)
-    test_HWC2CHW_md5()
+    test_hwc2chw_md5()
-    test_HWC2CHW_comp(True)
+    test_hwc2chw_comp(True)

tests/ut/python/dataset/test_iterator.py

@@ -1,4 +1,4 @@
-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2019-2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -153,7 +153,10 @@ def test_iterator_exception():
 
 class MyDict(dict):
     def __getattr__(self, key):
-        return self[key]
+        try:
+            return self[key]
+        except KeyError:
+            raise AttributeError
 
     def __setattr__(self, key, value):
         self[key] = value
@@ -164,9 +167,10 @@ class MyDict(dict):
 
 def test_tree_copy():
     """
-    Testing copying the tree with a pyfunc that cannot be pickled
+    Feature: Iterators
+    Description: Test copying the tree with a pyfunc that cannot be pickled
+    Expectation: ids of iterator data are different than pre-iterator data
     """
-
     data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=COLUMNS)
     data1 = data.map(operations=[MyDict()])
 
@@ -174,7 +178,6 @@ def test_tree_copy():
 
     assert id(data1) != id(itr.dataset)
     assert id(data) != id(itr.dataset.children[0])
-    assert id(data1.operations[0]) == id(itr.dataset.operations[0])
 
     itr.release()
 

tests/ut/python/dataset/test_normalize_op.py

@@ -1,4 +1,4 @@
-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2019-2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,6 +16,7 @@
 Testing Normalize op in DE
 """
 import numpy as np
+from PIL import Image
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.py_transforms
 import mindspore.dataset.vision.c_transforms as c_vision
@@ -367,10 +368,38 @@ def test_multiple_channels():
     util_test(np.ones(shape=[20, 45, 4]) * 1.3, mean=[0.5, 0.6, 0.7, 0.8], std=[0.1, 0.2, 0.3, 0.4])
     util_test(np.ones(shape=[2, 2]), mean=[0.5], std=[0.1])
     util_test(np.ones(shape=[2, 2, 5]), mean=[0.5], std=[0.1])
-    util_test(np.ones(shape=[6, 6, 129]), mean=[0.5]*129, std=[0.1]*129)
+    util_test(np.ones(shape=[6, 6, 129]), mean=[0.5] * 129, std=[0.1] * 129)
     util_test(np.ones(shape=[6, 6, 129]), mean=[0.5], std=[0.1])
 
 
+def test_normalize_c_eager():
+    """
+    Feature: Normalize op
+    Description: Test eager support for Normalize C++ op
+    Expectation: Receive non-None output image from op
+    """
+    img_in = Image.open("../data/dataset/apple.jpg").convert("RGB")
+    mean_vec = [1, 100, 255]
+    std_vec = [1, 20, 255]
+    normalize_op = c_vision.Normalize(mean=mean_vec, std=std_vec)
+    img_out = normalize_op(img_in)
+    assert img_out is not None
+
+
+def test_normalize_py_eager():
+    """
+    Feature: Normalize op
+    Description: Test eager support for Normalize Python op
+    Expectation: Receive non-None output image from op
+    """
+    img_in = Image.open("../data/dataset/apple.jpg").convert("RGB")
+    img_in = py_vision.ToTensor()(img_in)
+    mean_vec = [0.1, 0.5, 1.0]
+    std_vec = [0.1, 0.4, 1.0]
+    normalize_op = py_vision.Normalize(mean=mean_vec, std=std_vec)
+    img_out = normalize_op(img_in)
+    assert img_out is not None
+
 
 if __name__ == "__main__":
     test_decode_op()
@@ -386,3 +415,5 @@ if __name__ == "__main__":
     test_normalize_grayscale_md5_01()
     test_normalize_grayscale_md5_02()
     test_normalize_grayscale_exception()
+    test_normalize_c_eager()
+    test_normalize_py_eager()

tests/ut/python/dataset/test_pyfunc_multiprocess.py

@@ -77,7 +77,6 @@ def create_dataset_pyop_multiproc(num_parallel_workers=None, max_rowsize=16, bat
 
     # Setup transforms list which include Python ops
     transforms_list = [
-        py_vision.ToTensor(),
         lambda x: x,
         py_vision.HWC2CHW(),
         py_vision.RandomErasing(0.9, value='random'),

tests/ut/python/dataset/test_random_order.py

@@ -1,102 +1,106 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
-#
+#
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
+# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+# You may obtain a copy of the License at
-#
+#
-# http://www.apache.org/licenses/LICENSE-2.0
+# http://www.apache.org/licenses/LICENSE-2.0
-#
+#
-# Unless required by applicable law or agreed to in writing, software
+# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
+# See the License for the specific language governing permissions and
-# limitations under the License.
+# limitations under the License.
-# ==============================================================================
+# ==============================================================================
-"""
+"""
-Testing RandomOrder op in DE
+Testing RandomOrder op in DE
-"""
+"""
-import numpy as np
+import numpy as np
-import mindspore.dataset as ds
+import mindspore.dataset as ds
-import mindspore.dataset.transforms.py_transforms as py_transforms
+import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore import log as logger
+from mindspore import log as logger
-from util import visualize_list, config_get_set_seed, \
+from util import visualize_list, config_get_set_seed, \
-    config_get_set_num_parallel_workers, save_and_check_md5
+    config_get_set_num_parallel_workers, save_and_check_md5
-
+
-GENERATE_GOLDEN = False
+GENERATE_GOLDEN = False
-
+
-DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
-SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
+SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
-
+
-
+
-def test_random_order_op(plot=False):
+def test_random_order_op(plot=False):
-    """
+    """
-    Test RandomOrder in python transformations
+    Feature: RandomOrder op
-    """
+    Description: Test RandomOrder in python transformations
-    logger.info("test_random_order_op")
+    Expectation: Pipelines execute successfully
-    # define map operations
+    """
-    transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
+    logger.info("test_random_order_op")
-    transforms1 = [
+    # define map operations
-        py_vision.Decode(),
+    transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
-        py_transforms.RandomOrder(transforms_list),
+    transforms1 = [
-        py_vision.ToTensor()
+        py_vision.Decode(),
-    ]
+        py_transforms.RandomOrder(transforms_list),
-    transform1 = py_transforms.Compose(transforms1)
+        py_vision.ToTensor()
-
+    ]
-    transforms2 = [
+    transform1 = py_transforms.Compose(transforms1)
-        py_vision.Decode(),
+
-        py_vision.ToTensor()
+    transforms2 = [
-    ]
+        py_vision.Decode(),
-    transform2 = py_transforms.Compose(transforms2)
+        py_vision.ToTensor()
-
+    ]
-    #  First dataset
+    transform2 = py_transforms.Compose(transforms2)
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+
-    data1 = data1.map(operations=transform1, input_columns=["image"])
+    #  First dataset
-    #  Second dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(operations=transform1, input_columns=["image"])
-    data2 = data2.map(operations=transform2, input_columns=["image"])
+    #  Second dataset
-
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    image_order = []
+    data2 = data2.map(operations=transform2, input_columns=["image"])
-    image_original = []
+
-    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
+    image_order = []
-                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
+    image_original = []
-        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
-        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
-        image_order.append(image1)
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
-        image_original.append(image2)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
-    if plot:
+        image_order.append(image1)
-        visualize_list(image_original, image_order)
+        image_original.append(image2)
-
+    if plot:
-
+        visualize_list(image_original, image_order)
-def test_random_order_md5():
+
-    """
+
-    Test RandomOrder op with md5 check
+def test_random_order_md5():
-    """
+    """
-    logger.info("test_random_order_md5")
+    Feature: RandomOrder op
-    original_seed = config_get_set_seed(8)
+    Description: Test RandomOrder op with md5 check
-    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+    Expectation: Pipeline results match in md5 comparison
-    # define map operations
+    """
-    transforms_list = [py_vision.RandomCrop(64), py_vision.RandomRotation(30)]
+    logger.info("test_random_order_md5")
-    transforms = [
+    original_seed = config_get_set_seed(8)
-        py_vision.Decode(),
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
-        py_transforms.RandomOrder(transforms_list),
+    # define map operations
-        py_vision.ToTensor()
+    transforms_list = [py_vision.RandomCrop(64), py_vision.RandomRotation(30)]
-    ]
+    transforms = [
-    transform = py_transforms.Compose(transforms)
+        py_vision.Decode(),
-
+        py_transforms.RandomOrder(transforms_list),
-    #  Generate dataset
+        py_vision.ToTensor()
-    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    ]
-    data = data.map(operations=transform, input_columns=["image"])
+    transform = py_transforms.Compose(transforms)
-
+
-    # check results with md5 comparison
+    #  Generate dataset
-    filename = "random_order_01_result.npz"
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+    data = data.map(operations=transform, input_columns=["image"])
-
+
-    # Restore configuration
+    # check results with md5 comparison
-    ds.config.set_seed(original_seed)
+    filename = "random_order_01_result.npz"
-    ds.config.set_num_parallel_workers((original_num_parallel_workers))
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
-
+
-
+    # Restore configuration
-if __name__ == '__main__':
+    ds.config.set_seed(original_seed)
-    test_random_order_op(plot=True)
+    ds.config.set_num_parallel_workers((original_num_parallel_workers))
-    test_random_order_md5()
+
+
+if __name__ == '__main__':
+    test_random_order_op(plot=True)
+    test_random_order_md5()

tests/ut/python/dataset/test_random_rotation.py

@@ -1,4 +1,4 @@
-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2019-2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -68,6 +68,41 @@ def test_random_rotation_op_c(plot=False):
             visualize_image(original, rotation_de, mse, rotation_cv)
 
 
+def test_random_rotation_op_c_area():
+    """
+    Feature: RandomRotation op
+    Description: Test RandomRotation in C++ transformations op with Interpolation AREA
+    Expectation: Number of returned data rows is correct
+    """
+    logger.info("test_random_rotation_op_c_area")
+
+    # First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
+    decode_op = c_vision.Decode()
+    # Use [180, 180] to force rotate 180 degrees, expand is set to be True to match output size
+    # Use resample with Interpolation AREA
+    random_rotation_op = c_vision.RandomRotation((180, 180), expand=True, resample=Inter.AREA)
+    data1 = data1.map(operations=decode_op, input_columns=["image"])
+    data1 = data1.map(operations=random_rotation_op, input_columns=["image"])
+
+    # Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(operations=decode_op, input_columns=["image"])
+
+    num_iter = 0
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        rotation_de = item1["image"]
+        original = item2["image"]
+        logger.info("shape before rotate: {}".format(original.shape))
+        rotation_cv = cv2.rotate(original, cv2.ROTATE_180)
+        mse = diff_mse(rotation_de, rotation_cv)
+        logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse))
+        assert mse == 0
+        num_iter += 1
+    assert num_iter == 3
+
+
 def test_random_rotation_op_py(plot=False):
     """
     Test RandomRotation in python transformations op
@@ -104,6 +139,7 @@ def test_random_rotation_op_py(plot=False):
         if plot:
             visualize_image(original, rotation_de, mse, rotation_cv)
 
+
 def test_random_rotation_op_py_ANTIALIAS():
     """
     Test RandomRotation in python transformations op
@@ -125,6 +161,7 @@ def test_random_rotation_op_py_ANTIALIAS():
         num_iter += 1
     logger.info("use RandomRotation by Inter.ANTIALIAS process {} images.".format(num_iter))
 
+
 def test_random_rotation_expand():
     """
     Test RandomRotation op
@@ -238,6 +275,7 @@ def test_rotation_diff(plot=False):
 
 if __name__ == "__main__":
     test_random_rotation_op_c(plot=True)
+    test_random_rotation_op_c_area()
     test_random_rotation_op_py(plot=True)
     test_random_rotation_op_py_ANTIALIAS()
     test_random_rotation_expand()