forked from mindspore-Ecosystem/mindspore
131 lines
4.9 KiB
Python
131 lines
4.9 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing RandomPerspective op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.transforms
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import mindspore.dataset.vision.transforms as vision
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from mindspore.dataset.vision.utils import Inter
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from mindspore import log as logger
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from util import visualize_list, save_and_check_md5, \
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config_get_set_seed, config_get_set_num_parallel_workers
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GENERATE_GOLDEN = False
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def test_random_perspective_op(plot=False):
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"""
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Test RandomPerspective in python transformations
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"""
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logger.info("test_random_perspective_op")
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# define map operations
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transforms1 = [
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vision.Decode(True),
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vision.RandomPerspective(),
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vision.ToTensor()
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]
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transform1 = mindspore.dataset.transforms.transforms.Compose(transforms1)
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transforms2 = [
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vision.Decode(True),
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vision.ToTensor()
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]
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transform2 = mindspore.dataset.transforms.transforms.Compose(transforms2)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(operations=transform1, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(operations=transform2, input_columns=["image"])
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image_perspective = []
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image_original = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_perspective.append(image1)
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image_original.append(image2)
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if plot:
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visualize_list(image_original, image_perspective)
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def skip_test_random_perspective_md5():
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"""
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Test RandomPerspective with md5 comparison
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"""
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logger.info("test_random_perspective_md5")
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original_seed = config_get_set_seed(5)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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vision.Decode(True),
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vision.RandomPerspective(distortion_scale=0.3, prob=0.7,
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interpolation=Inter.BILINEAR),
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vision.Resize(1450), # resize to a smaller size to prevent round-off error
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vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.transforms.Compose(transforms)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data = data.map(operations=transform, input_columns=["image"])
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# check results with md5 comparison
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filename = "random_perspective_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers((original_num_parallel_workers))
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def test_random_perspective_exception_distortion_scale_range():
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"""
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Test RandomPerspective: distortion_scale is not in [0, 1], expected to raise ValueError
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"""
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logger.info("test_random_perspective_exception_distortion_scale_range")
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try:
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_ = vision.RandomPerspective(distortion_scale=1.5)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input distortion_scale is not within the required interval of [0.0, 1.0]."
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def test_random_perspective_exception_prob_range():
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"""
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Test RandomPerspective: prob is not in [0, 1], expected to raise ValueError
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"""
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logger.info("test_random_perspective_exception_prob_range")
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try:
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_ = vision.RandomPerspective(prob=1.2)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input prob is not within the required interval of [0.0, 1.0]."
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if __name__ == "__main__":
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test_random_perspective_op(plot=True)
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skip_test_random_perspective_md5()
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test_random_perspective_exception_distortion_scale_range()
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test_random_perspective_exception_prob_range()
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