forked from mindspore-Ecosystem/mindspore
118 lines
4.9 KiB
Python
118 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 Resize op in DE
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"""
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as vision
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from mindspore.dataset.transforms.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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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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GENERATE_GOLDEN = False
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def test_resize_op(plot=False):
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def test_resize_op_parameters(test_name, size, plot):
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"""
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Test resize_op
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"""
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logger.info("Test resize: {0}".format(test_name))
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# define map operations
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decode_op = vision.Decode()
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resize_op = vision.Resize(size)
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# apply map operations on images
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data1 = data1.map(input_columns=["image"], operations=decode_op)
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data2 = data1.map(input_columns=["image"], operations=resize_op)
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image_original = []
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image_resized = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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image_1 = item1["image"]
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image_2 = item2["image"]
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image_original.append(image_1)
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image_resized.append(image_2)
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if plot:
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visualize_list(image_original, image_resized)
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test_resize_op_parameters("Test single int for size", 10, plot=False)
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test_resize_op_parameters("Test tuple for size", (10, 15), plot=False)
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def test_resize_md5(plot=False):
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def test_resize_md5_parameters(test_name, size, filename, seed, plot):
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"""
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Test Resize with md5 check
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"""
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logger.info("Test Resize with md5 check: {0}".format(test_name))
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original_seed = config_get_set_seed(seed)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = vision.Decode()
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resize_op = vision.Resize(size)
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data1 = data1.map(input_columns=["image"], operations=decode_op)
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data2 = data1.map(input_columns=["image"], operations=resize_op)
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image_original = []
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image_resized = []
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# Compare with expected md5 from images
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save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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image_1 = item1["image"]
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image_2 = item2["image"]
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image_original.append(image_1)
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image_resized.append(image_2)
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if plot:
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visualize_list(image_original, image_resized)
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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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test_resize_md5_parameters("Test single int for size", 5, "resize_01_result.npz", 5, plot)
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test_resize_md5_parameters("Test tuple for size", (5, 7), "resize_02_result.npz", 7, plot)
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def test_resize_op_invalid_input():
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def test_invalid_input(test_name, size, interpolation, error, error_msg):
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logger.info("Test Resize with bad input: {0}".format(test_name))
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with pytest.raises(error) as error_info:
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vision.Resize(size, interpolation)
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assert error_msg in str(error_info.value)
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test_invalid_input("invalid size parameter type as a single number", 4.5, Inter.LINEAR, TypeError,
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"Size should be a single integer or a list/tuple (h, w) of length 2.")
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test_invalid_input("invalid size parameter shape", (2, 3, 4), Inter.LINEAR, TypeError,
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"Size should be a single integer or a list/tuple (h, w) of length 2.")
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test_invalid_input("invalid size parameter type in a tuple", (2.3, 3), Inter.LINEAR, TypeError,
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"incompatible constructor arguments.")
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test_invalid_input("invalid Interpolation value", (2.3, 3), None, KeyError, "None")
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if __name__ == "__main__":
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test_resize_op(plot=True)
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test_resize_md5(plot=True)
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test_resize_op_invalid_input()
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