• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
1# Copyright 2020-2021 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 HWC2CHW op in DE
17"""
18import numpy as np
19import pytest
20import mindspore.dataset as ds
21import mindspore.dataset.transforms.py_transforms
22import mindspore.dataset.vision.c_transforms as c_vision
23import mindspore.dataset.vision.py_transforms as py_vision
24from mindspore import log as logger
25from util import diff_mse, visualize_list, save_and_check_md5
26
27GENERATE_GOLDEN = False
28
29DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
30SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
31
32
33def test_HWC2CHW_callable():
34    """
35    Test HWC2CHW is callable
36    """
37    logger.info("Test HWC2CHW callable")
38    img = np.zeros([50, 50, 3])
39    assert img.shape == (50, 50, 3)
40
41    # test one tensor
42    img1 = c_vision.HWC2CHW()(img)
43    assert img1.shape == (3, 50, 50)
44
45    # test input multiple tensors
46    with pytest.raises(RuntimeError) as info:
47        imgs = [img, img]
48        _ = c_vision.HWC2CHW()(*imgs)
49    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)
50
51    with pytest.raises(RuntimeError) as info:
52        _ = c_vision.HWC2CHW()(img, img)
53    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)
54
55
56def test_HWC2CHW(plot=False):
57    """
58    Test HWC2CHW
59    """
60    logger.info("Test HWC2CHW")
61
62    # First dataset
63    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
64    decode_op = c_vision.Decode()
65    hwc2chw_op = c_vision.HWC2CHW()
66    data1 = data1.map(operations=decode_op, input_columns=["image"])
67    data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])
68
69    # Second dataset
70    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
71    data2 = data2.map(operations=decode_op, input_columns=["image"])
72
73    image_transposed = []
74    image = []
75    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
76                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
77        transposed_item = item1["image"].copy()
78        original_item = item2["image"].copy()
79        image_transposed.append(transposed_item.transpose(1, 2, 0))
80        image.append(original_item)
81
82        # check if the shape of data is transposed correctly
83        # transpose the original image from shape (H,W,C) to (C,H,W)
84        mse = diff_mse(transposed_item, original_item.transpose(2, 0, 1))
85        assert mse == 0
86    if plot:
87        visualize_list(image, image_transposed)
88
89
90def test_HWC2CHW_md5():
91    """
92    Test HWC2CHW(md5)
93    """
94    logger.info("Test HWC2CHW with md5 comparison")
95
96    # First dataset
97    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
98    decode_op = c_vision.Decode()
99    hwc2chw_op = c_vision.HWC2CHW()
100    data1 = data1.map(operations=decode_op, input_columns=["image"])
101    data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])
102
103    # Compare with expected md5 from images
104    filename = "HWC2CHW_01_result.npz"
105    save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
106
107
108def test_HWC2CHW_comp(plot=False):
109    """
110    Test HWC2CHW between python and c image augmentation
111    """
112    logger.info("Test HWC2CHW with c_transform and py_transform comparison")
113
114    # First dataset
115    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
116    decode_op = c_vision.Decode()
117    hwc2chw_op = c_vision.HWC2CHW()
118    data1 = data1.map(operations=decode_op, input_columns=["image"])
119    data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])
120
121    # Second dataset
122    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
123    transforms = [
124        py_vision.Decode(),
125        py_vision.ToTensor(),
126        py_vision.HWC2CHW()
127    ]
128    transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
129    data2 = data2.map(operations=transform, input_columns=["image"])
130
131    image_c_transposed = []
132    image_py_transposed = []
133    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
134                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
135        c_image = item1["image"]
136        py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
137
138        # Compare images between that applying c_transform and py_transform
139        mse = diff_mse(py_image, c_image)
140        # Note: The images aren't exactly the same due to rounding error
141        assert mse < 0.001
142        image_c_transposed.append(c_image.transpose(1, 2, 0))
143        image_py_transposed.append(py_image.transpose(1, 2, 0))
144    if plot:
145        visualize_list(image_c_transposed, image_py_transposed, visualize_mode=2)
146
147
148if __name__ == '__main__':
149    test_HWC2CHW_callable()
150    test_HWC2CHW(True)
151    test_HWC2CHW_md5()
152    test_HWC2CHW_comp(True)
153