adding new TCs to six ops and skip md5 case in RandomPerspective

tests/ut/python/dataset/test_center_crop.py

@@ -141,9 +141,9 @@ def test_crop_grayscale(height=375, width=375):
 
 
 if __name__ == "__main__":
-    test_center_crop_op(600, 600, True)
+    test_center_crop_op(600, 600, plot=True)
     test_center_crop_op(300, 600)
     test_center_crop_op(600, 300)
     test_center_crop_md5()
-    test_center_crop_comp(True)
+    test_center_crop_comp(plot=True)
     test_crop_grayscale()

tests/ut/python/dataset/test_normalizeOp.py

@@ -12,26 +12,54 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
+"""
+Testing Normalize op in DE
+"""
 import numpy as np
-
+import matplotlib.pyplot as plt
 import mindspore.dataset as ds
-import mindspore.dataset.transforms.vision.c_transforms as vision
+import mindspore.dataset.transforms.vision.c_transforms as c_vision
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
 from mindspore import log as logger
+from util import diff_mse, save_and_check_md5
 
 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"
 
+GENERATE_GOLDEN = False
+
+
+def visualize_mse(image_de_normalized, image_np_normalized, mse, image_original):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    plt.subplot(141)
+    plt.imshow(image_original)
+    plt.title("Original image")
+
+    plt.subplot(142)
+    plt.imshow(image_de_normalized)
+    plt.title("DE normalized image")
+
+    plt.subplot(143)
+    plt.imshow(image_np_normalized)
+    plt.title("Numpy normalized image")
+
+    plt.subplot(144)
+    plt.imshow(image_de_normalized - image_np_normalized)
+    plt.title("Difference image, mse : {}".format(mse))
+    plt.show()
+
 
-def normalize_np(image):
+def normalize_np(image, mean, std):
     """
     Apply the normalization
     """
     #  DE decodes the image in RGB by deafult, hence
     #  the values here are in RGB
     image = np.array(image, np.float32)
-    image = image - np.array([121.0, 115.0, 100.0])
-    image = image * (1.0 / np.array([70.0, 68.0, 71.0]))
+    image = image - np.array(mean)
+    image = image * (1.0 / np.array(std))
     return image
 
 
@@ -41,7 +69,7 @@ def get_normalized(image_id):
     Reads the image using DE ops and then normalizes using Numpy
     """
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    decode_op = vision.Decode()
+    decode_op = c_vision.Decode()
     data1 = data1.map(input_columns=["image"], operations=decode_op)
     num_iter = 0
     for item in data1.create_dict_iterator():
@@ -52,15 +80,61 @@ def get_normalized(image_id):
     return None
 
 
-def test_normalize_op():
+def util_test_normalize(mean, std, op_type):
     """
-    Test Normalize
+    Utility function for testing Normalize. Input arguments are given by other tests
     """
-    logger.info("Test Normalize")
+    if op_type == "cpp":
+        # define map operations
+        decode_op = c_vision.Decode()
+        normalize_op = c_vision.Normalize(mean, std)
+        # Generate dataset
+        data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+        data = data.map(input_columns=["image"], operations=decode_op)
+        data = data.map(input_columns=["image"], operations=normalize_op)
+    elif op_type == "python":
+        # define map operations
+        transforms = [
+            py_vision.Decode(),
+            py_vision.ToTensor(),
+            py_vision.Normalize(mean, std)
+        ]
+        transform = py_vision.ComposeOp(transforms)
+        # Generate dataset
+        data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+        data = data.map(input_columns=["image"], operations=transform())
+    else:
+        raise ValueError("Wrong parameter value")
+    return data
 
+
+def util_test_normalize_grayscale(num_output_channels, mean, std):
+    """
+    Utility function for testing Normalize. Input arguments are given by other tests
+    """
+    transforms = [
+        py_vision.Decode(),
+        py_vision.Grayscale(num_output_channels),
+        py_vision.ToTensor(),
+        py_vision.Normalize(mean, std)
+    ]
+    transform = py_vision.ComposeOp(transforms)
+    # Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+    return data
+
+
+def test_normalize_op_c(plot=False):
+    """
+    Test Normalize in cpp transformations
+    """
+    logger.info("Test Normalize in cpp")
+    mean = [121.0, 115.0, 100.0]
+    std = [70.0, 68.0, 71.0]
     # define map operations
-    decode_op = vision.Decode()
-    normalize_op = vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
+    decode_op = c_vision.Decode()
+    normalize_op = c_vision.Normalize(mean, std)
 
     #  First dataset
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
@@ -74,36 +148,64 @@ def test_normalize_op():
     num_iter = 0
     for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
         image_de_normalized = item1["image"]
-        image_np_normalized = normalize_np(item2["image"])
-        diff = image_de_normalized - image_np_normalized
-        mse = np.sum(np.power(diff, 2))
+        image_original = item2["image"]
+        image_np_normalized = normalize_np(image_original, mean, std)
+        mse = diff_mse(image_de_normalized, image_np_normalized)
         logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
         assert mse < 0.01
-        # Uncomment these blocks to see visual results
-        # plt.subplot(131)
-        # plt.imshow(image_de_normalized)
-        # plt.title("DE normalize image")
-        #
-        # plt.subplot(132)
-        # plt.imshow(image_np_normalized)
-        # plt.title("Numpy normalized image")
-        #
-        # plt.subplot(133)
-        # plt.imshow(diff)
-        # plt.title("Difference image, mse : {}".format(mse))
-        #
-        # plt.show()
+        if plot:
+            visualize_mse(image_de_normalized, image_np_normalized, mse, image_original)
+        num_iter += 1
+
+
+def test_normalize_op_py(plot=False):
+    """
+    Test Normalize in python transformations
+    """
+    logger.info("Test Normalize in python")
+    mean = [0.475, 0.45, 0.392]
+    std = [0.275, 0.267, 0.278]
+    # define map operations
+    transforms = [
+        py_vision.Decode(),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+    normalize_op = py_vision.Normalize(mean, std)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform())
+    data1 = data1.map(input_columns=["image"], operations=normalize_op)
+
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform())
+
+    num_iter = 0
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_np_normalized = (normalize_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8)
+        image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        mse = diff_mse(image_de_normalized, image_np_normalized)
+        logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
+        assert mse < 0.01
+        if plot:
+            visualize_mse(image_de_normalized, image_np_normalized, mse, image_original)
         num_iter += 1
 
 
 def test_decode_op():
+    """
+    Test Decode op
+    """
     logger.info("Test Decode")
 
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
                                shuffle=False)
 
     # define map operations
-    decode_op = vision.Decode()
+    decode_op = c_vision.Decode()
 
     # apply map operations on images
     data1 = data1.map(input_columns=["image"], operations=decode_op)
@@ -112,22 +214,21 @@ def test_decode_op():
     for item in data1.create_dict_iterator():
         logger.info("Looping inside iterator {}".format(num_iter))
         _ = item["image"]
-        # plt.subplot(131)
-        # plt.imshow(image)
-        # plt.title("DE image")
-        # plt.show()
         num_iter += 1
 
 
 def test_decode_normalize_op():
+    """
+    Test Decode op followed by Normalize op
+    """
     logger.info("Test [Decode, Normalize] in one Map")
 
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
                                shuffle=False)
 
     # define map operations
-    decode_op = vision.Decode()
-    normalize_op = vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
+    decode_op = c_vision.Decode()
+    normalize_op = c_vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
 
     # apply map operations on images
     data1 = data1.map(input_columns=["image"], operations=[decode_op, normalize_op])
@@ -136,14 +237,139 @@ def test_decode_normalize_op():
     for item in data1.create_dict_iterator():
         logger.info("Looping inside iterator {}".format(num_iter))
         _ = item["image"]
-        # plt.subplot(131)
-        # plt.imshow(image)
-        # plt.title("DE image")
-        # plt.show()
         num_iter += 1
 
 
+def test_normalize_md5_01():
+    """
+    Test Normalize with md5 check: valid mean and std
+    expected to pass
+    """
+    logger.info("test_normalize_md5_01")
+    data_c = util_test_normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0], "cpp")
+    data_py = util_test_normalize([0.475, 0.45, 0.392], [0.275, 0.267, 0.278], "python")
+
+    # check results with md5 comparison
+    filename1 = "normalize_01_c_result.npz"
+    filename2 = "normalize_01_py_result.npz"
+    save_and_check_md5(data_c, filename1, generate_golden=GENERATE_GOLDEN)
+    save_and_check_md5(data_py, filename2, generate_golden=GENERATE_GOLDEN)
+
+
+def test_normalize_md5_02():
+    """
+    Test Normalize with md5 check: len(mean)=len(std)=1 with RGB images
+    expected to pass
+    """
+    logger.info("test_normalize_md5_02")
+    data_py = util_test_normalize([0.475], [0.275], "python")
+
+    # check results with md5 comparison
+    filename2 = "normalize_02_py_result.npz"
+    save_and_check_md5(data_py, filename2, generate_golden=GENERATE_GOLDEN)
+
+
+def test_normalize_exception_unequal_size_c():
+    """
+    Test Normalize in c transformation: len(mean) != len(std)
+    expected to raise ValueError
+    """
+    logger.info("test_normalize_exception_unequal_size_c")
+    try:
+        _ = c_vision.Normalize([100, 250, 125], [50, 50, 75, 75])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Length of mean and std must be equal"
+
+
+def test_normalize_exception_unequal_size_py():
+    """
+    Test Normalize in python transformation: len(mean) != len(std)
+    expected to raise ValueError
+    """
+    logger.info("test_normalize_exception_unequal_size_py")
+    try:
+        _ = py_vision.Normalize([0.50, 0.30, 0.75], [0.18, 0.32, 0.71, 0.72])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Length of mean and std must be equal"
+
+
+def test_normalize_exception_invalid_size_py():
+    """
+    Test Normalize in python transformation: len(mean)=len(std)=2
+    expected to raise RuntimeError
+    """
+    logger.info("test_normalize_exception_invalid_size_py")
+    data = util_test_normalize([0.75, 0.25], [0.18, 0.32], "python")
+    try:
+        _ = data.create_dict_iterator().get_next()
+    except RuntimeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Length of mean and std must both be 1 or" in str(e)
+
+
+def test_normalize_exception_invalid_range_py():
+    """
+    Test Normalize in python transformation: value is not in range [0,1]
+    expected to raise ValueError
+    """
+    logger.info("test_normalize_exception_invalid_range_py")
+    try:
+        _ = py_vision.Normalize([0.75, 1.25, 0.5], [0.1, 0.18, 1.32])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Input is not within the required range" in str(e)
+
+
+def test_normalize_grayscale_md5_01():
+    """
+    Test Normalize with md5 check: len(mean)=len(std)=1 with 1 channel grayscale images
+    expected to pass
+    """
+    logger.info("test_normalize_grayscale_md5_01")
+    data = util_test_normalize_grayscale(1, [0.5], [0.175])
+    # check results with md5 comparison
+    filename = "normalize_03_py_result.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+
+def test_normalize_grayscale_md5_02():
+    """
+    Test Normalize with md5 check: len(mean)=len(std)=3 with 3 channel grayscale images
+    expected to pass
+    """
+    logger.info("test_normalize_grayscale_md5_02")
+    data = util_test_normalize_grayscale(3, [0.5, 0.5, 0.5], [0.175, 0.235, 0.512])
+    # check results with md5 comparison
+    filename = "normalize_04_py_result.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+
+def test_normalize_grayscale_exception():
+    """
+    Test Normalize: len(mean)=len(std)=3 with 1 channel grayscale images
+    expected to raise RuntimeError
+    """
+    logger.info("test_normalize_grayscale_exception")
+    try:
+        _ = util_test_normalize_grayscale(1, [0.5, 0.5, 0.5], [0.175, 0.235, 0.512])
+    except RuntimeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Input is not within the required range" in str(e)
+
+
 if __name__ == "__main__":
     test_decode_op()
     test_decode_normalize_op()
-    test_normalize_op()
+    test_normalize_op_c(plot=True)
+    test_normalize_op_py(plot=True)
+    test_normalize_md5_01()
+    test_normalize_md5_02()
+    test_normalize_exception_unequal_size_c()
+    test_normalize_exception_unequal_size_py()
+    test_normalize_exception_invalid_size_py()
+    test_normalize_exception_invalid_range_py()
+    test_normalize_grayscale_md5_01()
+    test_normalize_grayscale_md5_02()
+    test_normalize_grayscale_exception()

tests/ut/python/dataset/test_random_affine.py

+# Copyright 2020 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomAffine op in DE
+"""
+import numpy as np
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
+from mindspore import log as logger
+from util import visualize, save_and_check_md5, \
+    config_get_set_seed, config_get_set_num_parallel_workers
+
+GENERATE_GOLDEN = False
+
+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_random_affine_op(plot=False):
+    """
+    Test RandomAffine in python transformations
+    """
+    logger.info("test_random_affine_op")
+    # define map operations
+    transforms1 = [
+        py_vision.Decode(),
+        py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
+        py_vision.ToTensor()
+    ]
+    transform1 = py_vision.ComposeOp(transforms1)
+
+    transforms2 = [
+        py_vision.Decode(),
+        py_vision.ToTensor()
+    ]
+    transform2 = py_vision.ComposeOp(transforms2)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform1())
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform2())
+
+    image_affine = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_affine.append(image1)
+        image_original.append(image2)
+    if plot:
+        visualize(image_original, image_affine)
+
+
+def test_random_affine_md5():
+    """
+    Test RandomAffine with md5 comparison
+    """
+    logger.info("test_random_affine_md5")
+    original_seed = config_get_set_seed(55)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+    # define map operations
+    transforms = [
+        py_vision.Decode(),
+        py_vision.RandomAffine(degrees=(-5, 15), translate=(0.1, 0.3),
+                               scale=(0.9, 1.1), shear=(-10, 10, -5, 5)),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+
+    #  Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+
+    # check results with md5 comparison
+    filename = "random_affine_01_result.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers((original_num_parallel_workers))
+
+
+def test_random_affine_exception_negative_degrees():
+    """
+    Test RandomAffine: input degrees in negative, expected to raise ValueError
+    """
+    logger.info("test_random_affine_exception_negative_degrees")
+    try:
+        _ = py_vision.RandomAffine(degrees=-15)
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "If degrees is a single number, it cannot be negative."
+
+
+def test_random_affine_exception_translation_range():
+    """
+    Test RandomAffine: translation value is not in [0, 1], expected to raise ValueError
+    """
+    logger.info("test_random_affine_exception_translation_range")
+    try:
+        _ = py_vision.RandomAffine(degrees=15, translate=(0.1, 1.5))
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "translation values should be between 0 and 1"
+
+
+def test_random_affine_exception_scale_value():
+    """
+    Test RandomAffine: scale is not positive, expected to raise ValueError
+    """
+    logger.info("test_random_affine_exception_scale_value")
+    try:
+        _ = py_vision.RandomAffine(degrees=15, scale=(0.0, 1.1))
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "scale values should be positive"
+
+
+def test_random_affine_exception_shear_value():
+    """
+    Test RandomAffine: shear is a number but is not positive, expected to raise ValueError
+    """
+    logger.info("test_random_affine_exception_shear_value")
+    try:
+        _ = py_vision.RandomAffine(degrees=15, shear=-5)
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "If shear is a single number, it must be positive."
+
+
+def test_random_affine_exception_degrees_size():
+    """
+    Test RandomAffine: degrees is a list or tuple and its length is not 2,
+    expected to raise TypeError
+    """
+    logger.info("test_random_affine_exception_degrees_size")
+    try:
+        _ = py_vision.RandomAffine(degrees=[15])
+    except TypeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "If degrees is a sequence, the length must be 2."
+
+
+def test_random_affine_exception_translate_size():
+    """
+    Test RandomAffine: translate is not list or a tuple of length 2,
+    expected to raise TypeError
+    """
+    logger.info("test_random_affine_exception_translate_size")
+    try:
+        _ = py_vision.RandomAffine(degrees=15, translate=(0.1))
+    except TypeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "translate should be a list or tuple of length 2."
+
+
+def test_random_affine_exception_scale_size():
+    """
+    Test RandomAffine: scale is not a list or tuple of length 2,
+    expected to raise TypeError
+    """
+    logger.info("test_random_affine_exception_scale_size")
+    try:
+        _ = py_vision.RandomAffine(degrees=15, scale=(0.5))
+    except TypeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "scale should be a list or tuple of length 2."
+
+
+def test_random_affine_exception_shear_size():
+    """
+    Test RandomAffine: shear is not a list or tuple of length 2 or 4,
+    expected to raise TypeError
+    """
+    logger.info("test_random_affine_exception_shear_size")
+    try:
+        _ = py_vision.RandomAffine(degrees=15, shear=(-5, 5, 10))
+    except TypeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "shear should be a list or tuple and it must be of length 2 or 4."
+
+
+if __name__ == "__main__":
+    test_random_affine_op(plot=True)
+    test_random_affine_md5()
+    test_random_affine_exception_negative_degrees()
+    test_random_affine_exception_translation_range()
+    test_random_affine_exception_scale_value()
+    test_random_affine_exception_shear_value()
+    test_random_affine_exception_degrees_size()
+    test_random_affine_exception_translate_size()
+    test_random_affine_exception_scale_size()
+    test_random_affine_exception_shear_size()

tests/ut/python/dataset/test_random_apply.py

+# Copyright 2020 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomApply op in DE
+"""
+import numpy as np
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
+from mindspore import log as logger
+from util import visualize, config_get_set_seed, \
+    config_get_set_num_parallel_workers, save_and_check_md5
+
+GENERATE_GOLDEN = False
+
+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_random_apply_op(plot=False):
+    """
+    Test RandomApply in python transformations
+    """
+    logger.info("test_random_apply_op")
+    # define map operations
+    transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
+    transforms1 = [
+        py_vision.Decode(),
+        py_vision.RandomApply(transforms_list, prob=0.6),
+        py_vision.ToTensor()
+    ]
+    transform1 = py_vision.ComposeOp(transforms1)
+
+    transforms2 = [
+        py_vision.Decode(),
+        py_vision.ToTensor()
+    ]
+    transform2 = py_vision.ComposeOp(transforms2)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform1())
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform2())
+
+    image_apply = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_apply.append(image1)
+        image_original.append(image2)
+    if plot:
+        visualize(image_original, image_apply)
+
+
+def test_random_apply_md5():
+    """
+    Test RandomApply op with md5 check
+    """
+    logger.info("test_random_apply_md5")
+    original_seed = config_get_set_seed(10)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+    # define map operations
+    transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
+    transforms = [
+        py_vision.Decode(),
+        # Note: using default value "prob=0.5"
+        py_vision.RandomApply(transforms_list),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+
+    #  Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+
+    # check results with md5 comparison
+    filename = "random_apply_01_result.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers((original_num_parallel_workers))
+
+
+def test_random_apply_exception_random_crop_badinput():
+    """
+    Test RandomApply: test invalid input for one of the transform functions,
+    expected to raise error
+    """
+    logger.info("test_random_apply_exception_random_crop_badinput")
+    original_seed = config_get_set_seed(200)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+    # define map operations
+    transforms_list = [py_vision.Resize([32, 32]),
+                       py_vision.RandomCrop(100),  # crop size > image size
+                       py_vision.RandomRotation(30)]
+    transforms = [
+        py_vision.Decode(),
+        py_vision.RandomApply(transforms_list, prob=0.6),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+    #  Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+    try:
+        _ = data.create_dict_iterator().get_next()
+    except RuntimeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Crop size" in str(e)
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+if __name__ == '__main__':
+    test_random_apply_op(plot=True)
+    test_random_apply_md5()
+    test_random_apply_exception_random_crop_badinput()

tests/ut/python/dataset/test_random_choice.py

+# Copyright 2020 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomChoice op in DE
+"""
+import numpy as np
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
+from mindspore import log as logger
+from util import visualize, diff_mse
+
+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_random_choice_op(plot=False):
+    """
+    Test RandomChoice in python transformations
+    """
+    logger.info("test_random_choice_op")
+    # define map operations
+    transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
+    transforms1 = [
+        py_vision.Decode(),
+        py_vision.RandomChoice(transforms_list),
+        py_vision.ToTensor()
+    ]
+    transform1 = py_vision.ComposeOp(transforms1)
+
+    transforms2 = [
+        py_vision.Decode(),
+        py_vision.ToTensor()
+    ]
+    transform2 = py_vision.ComposeOp(transforms2)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform1())
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform2())
+
+    image_choice = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_choice.append(image1)
+        image_original.append(image2)
+    if plot:
+        visualize(image_original, image_choice)
+
+
+def test_random_choice_comp(plot=False):
+    """
+    Test RandomChoice and compare with single CenterCrop results
+    """
+    logger.info("test_random_choice_comp")
+    # define map operations
+    transforms_list = [py_vision.CenterCrop(64)]
+    transforms1 = [
+        py_vision.Decode(),
+        py_vision.RandomChoice(transforms_list),
+        py_vision.ToTensor()
+    ]
+    transform1 = py_vision.ComposeOp(transforms1)
+
+    transforms2 = [
+        py_vision.Decode(),
+        py_vision.CenterCrop(64),
+        py_vision.ToTensor()
+    ]
+    transform2 = py_vision.ComposeOp(transforms2)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform1())
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform2())
+
+    image_choice = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_choice.append(image1)
+        image_original.append(image2)
+
+        mse = diff_mse(image1, image2)
+        assert mse == 0
+    if plot:
+        visualize(image_original, image_choice)
+
+
+def test_random_choice_exception_random_crop_badinput():
+    """
+    Test RandomChoice: hit error in RandomCrop with greater crop size,
+    expected to raise error
+    """
+    logger.info("test_random_choice_exception_random_crop_badinput")
+    # define map operations
+    # note: crop size[5000, 5000] > image size[4032, 2268]
+    transforms_list = [py_vision.RandomCrop(5000)]
+    transforms = [
+        py_vision.Decode(),
+        py_vision.RandomChoice(transforms_list),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+    #  Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+    try:
+        _ = data.create_dict_iterator().get_next()
+    except RuntimeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Crop size" in str(e)
+
+
+if __name__ == '__main__':
+    test_random_choice_op(plot=True)
+    test_random_choice_comp(plot=True)
+    test_random_choice_exception_random_crop_badinput()

tests/ut/python/dataset/test_random_order.py

+# Copyright 2020 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomOrder op in DE
+"""
+import numpy as np
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
+from mindspore import log as logger
+from util import visualize, diff_mse, config_get_set_seed, \
+    config_get_set_num_parallel_workers, save_and_check_md5
+
+GENERATE_GOLDEN = False
+
+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_random_order_op(plot=False):
+    """
+    Test RandomOrder in python transformations
+    """
+    logger.info("test_random_order_op")
+    # define map operations
+    transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
+    transforms1 = [
+        py_vision.Decode(),
+        py_vision.RandomOrder(transforms_list),
+        py_vision.ToTensor()
+    ]
+    transform1 = py_vision.ComposeOp(transforms1)
+
+    transforms2 = [
+        py_vision.Decode(),
+        py_vision.ToTensor()
+    ]
+    transform2 = py_vision.ComposeOp(transforms2)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform1())
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform2())
+
+    image_order = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_order.append(image1)
+        image_original.append(image2)
+    if plot:
+        visualize(image_original, image_order)
+
+
+def test_random_order_md5():
+    """
+    Test RandomOrder op with md5 check
+    """
+    logger.info("test_random_order_md5")
+    original_seed = config_get_set_seed(8)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+    # define map operations
+    transforms_list = [py_vision.RandomCrop(64), py_vision.RandomRotation(30)]
+    transforms = [
+        py_vision.Decode(),
+        py_vision.RandomOrder(transforms_list),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+
+    #  Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+
+    # check results with md5 comparison
+    filename = "random_order_01_result.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers((original_num_parallel_workers))
+
+
+if __name__ == '__main__':
+    test_random_order_op(plot=True)
+    test_random_order_md5()

tests/ut/python/dataset/test_random_perspective.py

+# Copyright 2020 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomPerspective op in DE
+"""
+import numpy as np
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
+from mindspore.dataset.transforms.vision.utils import Inter
+from mindspore import log as logger
+from util import visualize, save_and_check_md5, \
+    config_get_set_seed, config_get_set_num_parallel_workers
+
+
+GENERATE_GOLDEN = False
+
+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_random_perspective_op(plot=False):
+    """
+    Test RandomPerspective in python transformations
+    """
+    logger.info("test_random_perspective_op")
+    # define map operations
+    transforms1 = [
+        py_vision.Decode(),
+        py_vision.RandomPerspective(),
+        py_vision.ToTensor()
+    ]
+    transform1 = py_vision.ComposeOp(transforms1)
+
+    transforms2 = [
+        py_vision.Decode(),
+        py_vision.ToTensor()
+    ]
+    transform2 = py_vision.ComposeOp(transforms2)
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transform1())
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=transform2())
+
+    image_perspective = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_perspective.append(image1)
+        image_original.append(image2)
+    if plot:
+        visualize(image_original, image_perspective)
+
+
+def skip_test_random_perspective_md5():
+    """
+    Test RandomPerspective with md5 comparison
+    """
+    logger.info("test_random_perspective_md5")
+    original_seed = config_get_set_seed(5)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    # define map operations
+    transforms = [
+        py_vision.Decode(),
+        py_vision.RandomPerspective(distortion_scale=0.3, prob=0.7,
+                                    interpolation=Inter.BILINEAR),
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+
+    #  Generate dataset
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data = data.map(input_columns=["image"], operations=transform())
+
+    # check results with md5 comparison
+    filename = "random_perspective_01_result.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers((original_num_parallel_workers))
+
+
+def test_random_perspective_exception_distortion_scale_range():
+    """
+    Test RandomPerspective: distortion_scale is not in [0, 1], expected to raise ValueError
+    """
+    logger.info("test_random_perspective_exception_distortion_scale_range")
+    try:
+        _ = py_vision.RandomPerspective(distortion_scale=1.5)
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Input is not within the required range"
+
+
+def test_random_perspective_exception_prob_range():
+    """
+    Test RandomPerspective: prob is not in [0, 1], expected to raise ValueError
+    """
+    logger.info("test_random_perspective_exception_prob_range")
+    try:
+        _ = py_vision.RandomPerspective(prob=1.2)
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Input is not within the required range"
+
+
+if __name__ == "__main__":
+    test_random_perspective_op(plot=True)
+    skip_test_random_perspective_md5()
+    test_random_perspective_exception_distortion_scale_range()
+    test_random_perspective_exception_prob_range()