!1544 Python Unit Test for TenCrop

Merge pull request !1544 from MahdiRahmaniHanzaki/I1IHNG-ten-crop

tests/ut/python/dataset/test_five_crop.py

@@ -21,29 +21,13 @@ import pytest
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.py_transforms as vision
 from mindspore import log as logger
+from util import visualize
 
 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 visualize(image_1, image_2):
-    """
-    visualizes the image using FiveCrop
-    """
-    plt.subplot(161)
-    plt.imshow(image_1)
-    plt.title("Original")
-
-    for i, image in enumerate(image_2):
-        image = (image.transpose(1, 2, 0) * 255).astype(np.uint8)
-        plt.subplot(162 + i)
-        plt.imshow(image)
-        plt.title("image {} in FiveCrop".format(i + 1))
-
-    plt.show()
-
-
-def test_five_crop_op():
+def test_five_crop_op(plot=False):
     """
     Test FiveCrop
     """
@@ -79,8 +63,8 @@ def test_five_crop_op():
 
         logger.info("dtype of image_1: {}".format(image_1.dtype))
         logger.info("dtype of image_2: {}".format(image_2.dtype))
-
-        # visualize(image_1, image_2)
+        if plot:
+            visualize(np.array([image_1]*10), (image_2 * 255).astype(np.uint8).transpose(0, 2, 3, 1))
 
         # The output data should be of a 4D tensor shape, a stack of 5 images.
         assert len(image_2.shape) == 4
@@ -111,5 +95,5 @@ def test_five_crop_error_msg():
 
 
 if __name__ == "__main__":
-    test_five_crop_op()
+    test_five_crop_op(plot=True)
     test_five_crop_error_msg()

tests/ut/python/dataset/test_ten_crop.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 TenCrop in DE
+"""
+import pytest
+import numpy as np
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.py_transforms as vision
+from util import visualize, save_and_check_md5
+from mindspore import log as logger
+
+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 util_test_ten_crop(crop_size, vertical_flip=False, plot=False):
+    """
+    Utility function for testing TenCrop. Input arguments are given by other tests
+    """
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    transforms_1 = [
+        vision.Decode(),
+        vision.ToTensor(),
+    ]
+    transform_1 = vision.ComposeOp(transforms_1)
+    data1 = data1.map(input_columns=["image"], operations=transform_1())
+
+    # Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    transforms_2 = [
+        vision.Decode(),
+        vision.TenCrop(crop_size, use_vertical_flip=vertical_flip),
+        lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
+    ]
+    transform_2 = vision.ComposeOp(transforms_2)
+    data2 = data2.map(input_columns=["image"], operations=transform_2())
+    num_iter = 0
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        num_iter += 1
+        image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        image_2 = item2["image"]
+
+        logger.info("shape of image_1: {}".format(image_1.shape))
+        logger.info("shape of image_2: {}".format(image_2.shape))
+
+        logger.info("dtype of image_1: {}".format(image_1.dtype))
+        logger.info("dtype of image_2: {}".format(image_2.dtype))
+
+        if plot:
+            visualize(np.array([image_1]*10), (image_2 * 255).astype(np.uint8).transpose(0, 2, 3, 1))
+
+        # The output data should be of a 4D tensor shape, a stack of 10 images.
+        assert len(image_2.shape) == 4
+        assert image_2.shape[0] == 10
+
+
+def test_ten_crop_op_square(plot=False):
+    """
+    Tests TenCrop for a square crop
+    """
+
+    logger.info("test_ten_crop_op_square")
+    util_test_ten_crop(200, plot=plot)
+
+
+def test_ten_crop_op_rectangle(plot=False):
+    """
+    Tests TenCrop for a rectangle crop
+    """
+
+    logger.info("test_ten_crop_op_rectangle")
+    util_test_ten_crop((200, 150), plot=plot)
+
+
+def test_ten_crop_op_vertical_flip(plot=False):
+    """
+    Tests TenCrop with vertical flip set to True
+    """
+
+    logger.info("test_ten_crop_op_vertical_flip")
+    util_test_ten_crop(200, vertical_flip=True, plot=plot)
+
+
+def test_ten_crop_md5():
+    """
+    Tests TenCrops for giving the same results in multiple runs.
+    Since TenCrop is a deterministic function, we expect it to return the same result for a specific input every time
+    """
+    logger.info("test_ten_crop_md5")
+
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    transforms_2 = [
+        vision.Decode(),
+        vision.TenCrop((200, 100), use_vertical_flip=True),
+        lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
+    ]
+    transform_2 = vision.ComposeOp(transforms_2)
+    data2 = data2.map(input_columns=["image"], operations=transform_2())
+    # Compare with expected md5 from images
+    filename = "ten_crop_01_result.npz"
+    save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN)
+
+
+def test_ten_crop_list_size_error_msg():
+    """
+    Tests TenCrop error message when the size arg has more than 2 elements
+    """
+    logger.info("test_ten_crop_list_size_error_msg")
+
+    with pytest.raises(TypeError) as info:
+        transforms = [
+            vision.Decode(),
+            vision.TenCrop([200, 200, 200]),
+            lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
+        ]
+    error_msg = "Size should be a single integer or a list/tuple (h, w) of length 2."
+    assert error_msg == str(info.value)
+
+
+def test_ten_crop_invalid_size_error_msg():
+    """
+    Tests TenCrop error message when the size arg is not positive
+    """
+    logger.info("test_ten_crop_invalid_size_error_msg")
+
+    with pytest.raises(ValueError) as info:
+        transforms = [
+            vision.Decode(),
+            vision.TenCrop(0),
+            lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
+        ]
+    error_msg = "Input is not within the required range"
+    assert error_msg == str(info.value)
+
+    with pytest.raises(ValueError) as info:
+        transforms = [
+            vision.Decode(),
+            vision.TenCrop(-10),
+            lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
+        ]
+
+    assert error_msg == str(info.value)
+
+
+def test_ten_crop_wrong_img_error_msg():
+    """
+    Tests TenCrop error message when the image is not in the correct format.
+    """
+    logger.info("test_ten_crop_wrong_img_error_msg")
+
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    transforms = [
+        vision.Decode(),
+        vision.TenCrop(200),
+        vision.ToTensor()
+    ]
+    transform = vision.ComposeOp(transforms)
+    data = data.map(input_columns=["image"], operations=transform())
+
+    with pytest.raises(RuntimeError) as info:
+        data.create_tuple_iterator().get_next()
+    error_msg = "TypeError: img should be PIL Image or Numpy array. Got <class 'tuple'>"
+
+    # error msg comes from ToTensor()
+    assert error_msg in str(info.value)
+
+
+if __name__ == "__main__":
+    test_ten_crop_op_square(plot=True)
+    test_ten_crop_op_rectangle(plot=True)
+    test_ten_crop_op_vertical_flip(plot=True)
+    test_ten_crop_md5()
+    test_ten_crop_list_size_error_msg()
+    test_ten_crop_invalid_size_error_msg()
+    test_ten_crop_wrong_img_error_msg()