186 lines
7.4 KiB
Python
186 lines
7.4 KiB
Python
# Copyright 2020-2022 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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Test RandomApply op in Dataset
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"""
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import numpy as np
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.transforms as data_trans
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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from util import visualize_list, config_get_set_seed, \
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config_get_set_num_parallel_workers, save_and_check_md5_pil
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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_apply_c():
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"""
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Feature: RandomApply Op
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Description: Test C++ implementation, both valid and invalid input
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Expectation: Dataset pipeline runs successfully and results are verified for valid input.
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Invalid input is detected.
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"""
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original_seed = config_get_set_seed(0)
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def test_config(arr, op_list, prob=0.5):
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try:
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data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
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data = data.map(operations=data_trans.RandomApply(op_list, prob), input_columns=["col"])
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res = []
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for i in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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res.append(i["col"].tolist())
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return res
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except (TypeError, ValueError) as e:
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return str(e)
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res1 = test_config([[0, 1]], [data_trans.Duplicate(), data_trans.Concatenate()])
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assert res1 in [[[0, 1]], [[0, 1, 0, 1]]]
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# test single nested compose
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assert test_config([[0, 1, 2]], [
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data_trans.Compose([data_trans.Duplicate(), data_trans.Concatenate(), data_trans.Slice([0, 1, 2])])]) == \
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[[0, 1, 2]]
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assert test_config([[0, 1, 2]], [
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data_trans.Compose(
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[data_trans.Duplicate(), data_trans.Concatenate(), lambda x: x, data_trans.Slice([0, 1, 2])])]) == \
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[[0, 1, 2]]
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# test exception
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assert "is not of type [<class 'list'>]" in test_config([1, 0], data_trans.TypeCast(mstype.int32))
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assert "Input prob is not within the required interval" in test_config([0, 1], [data_trans.Slice([0, 1])], 1.1)
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assert "is not of type [<class 'float'>, <class 'int'>]" in test_config([1, 0], [data_trans.TypeCast(mstype.int32)],
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None)
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assert "transforms list with value None is not of type [<class 'list'>]" in test_config([1, 0], None)
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assert "is neither a transforms op (TensorOperation) nor a callable pyfunc" in \
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test_config([[0, 1, 2]], [data_trans.Duplicate(), data_trans.Concatenate(), "zyx"])
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# Restore configuration
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ds.config.set_seed(original_seed)
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def test_random_apply_op(plot=False):
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"""
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Feature: RandomApply op
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Description: Test RandomApply in Python transformations
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Expectation: The dataset is processed as expected
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"""
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logger.info("test_random_apply_op")
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# define map operations
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transforms_list = [vision.CenterCrop(64), vision.RandomRotation(30)]
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transforms1 = [
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vision.Decode(True),
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data_trans.RandomApply(transforms_list, prob=0.6),
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vision.ToTensor()
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]
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transform1 = data_trans.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 = data_trans.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_apply = []
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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_apply.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_apply)
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def test_random_apply_md5():
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"""
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Feature: RandomApply op
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Description: Test RandomApply op with md5 check
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Expectation: Passes the md5 check test
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"""
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logger.info("test_random_apply_md5")
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original_seed = config_get_set_seed(10)
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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_list = [vision.CenterCrop(64), vision.RandomRotation(30)]
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transforms = [
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vision.Decode(True),
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# Note: using default value "prob=0.5"
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data_trans.RandomApply(transforms_list),
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vision.ToTensor()
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]
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transform = data_trans.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_apply_01_result.npz"
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save_and_check_md5_pil(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_apply_exception_random_crop_badinput():
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"""
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Feature: RandomApply op
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Description: Test RandomApply with invalid input for one of the transform functions
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Expectation: Correct error is raised as expected
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"""
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logger.info("test_random_apply_exception_random_crop_badinput")
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original_seed = config_get_set_seed(200)
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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_list = [vision.Resize([32, 32]),
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vision.RandomCrop(100), # crop size > image size
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vision.RandomRotation(30)]
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transforms = [
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vision.Decode(True),
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data_trans.RandomApply(transforms_list, prob=0.6),
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vision.ToTensor()
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]
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transform = data_trans.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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try:
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_ = data.create_dict_iterator(num_epochs=1).__next__()
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Crop size" in str(e)
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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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if __name__ == '__main__':
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test_random_apply_c()
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test_random_apply_op(plot=True)
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test_random_apply_md5()
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test_random_apply_exception_random_crop_badinput()
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