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1# Copyright 2019 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""
16Testing RandomErasing op in DE
17"""
18import numpy as np
19
20import mindspore.dataset as ds
21import mindspore.dataset.transforms.py_transforms
22import mindspore.dataset.vision.py_transforms as vision
23from mindspore import log as logger
24from util import diff_mse, visualize_image, save_and_check_md5, \
25    config_get_set_seed, config_get_set_num_parallel_workers
26
27DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
28SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
29
30GENERATE_GOLDEN = False
31
32
33def test_random_erasing_op(plot=False):
34    """
35    Test RandomErasing and Cutout
36    """
37    logger.info("test_random_erasing")
38
39    # First dataset
40    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
41    transforms_1 = [
42        vision.Decode(),
43        vision.ToTensor(),
44        vision.RandomErasing(value='random')
45    ]
46    transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
47    data1 = data1.map(operations=transform_1, input_columns=["image"])
48
49    # Second dataset
50    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
51    transforms_2 = [
52        vision.Decode(),
53        vision.ToTensor(),
54        vision.Cutout(80)
55    ]
56    transform_2 = mindspore.dataset.transforms.py_transforms.Compose(transforms_2)
57    data2 = data2.map(operations=transform_2, input_columns=["image"])
58
59    num_iter = 0
60    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
61                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
62        num_iter += 1
63        image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
64        image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
65
66        logger.info("shape of image_1: {}".format(image_1.shape))
67        logger.info("shape of image_2: {}".format(image_2.shape))
68
69        logger.info("dtype of image_1: {}".format(image_1.dtype))
70        logger.info("dtype of image_2: {}".format(image_2.dtype))
71
72        mse = diff_mse(image_1, image_2)
73        if plot:
74            visualize_image(image_1, image_2, mse)
75
76
77def test_random_erasing_md5():
78    """
79    Test RandomErasing with md5 check
80    """
81    logger.info("Test RandomErasing with md5 check")
82    original_seed = config_get_set_seed(5)
83    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
84
85    # Generate dataset
86    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
87    transforms_1 = [
88        vision.Decode(),
89        vision.ToTensor(),
90        vision.RandomErasing(value='random')
91    ]
92    transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
93    data = data.map(operations=transform_1, input_columns=["image"])
94    # Compare with expected md5 from images
95    filename = "random_erasing_01_result.npz"
96    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
97
98    # Restore configuration
99    ds.config.set_seed(original_seed)
100    ds.config.set_num_parallel_workers((original_num_parallel_workers))
101
102
103if __name__ == "__main__":
104    test_random_erasing_op(plot=True)
105    test_random_erasing_md5()
106