forked from mindspore-Ecosystem/mindspore
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
# Copyright 2019 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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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 import log as logger
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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_exception_01():
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"""
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Test single exception with invalid input
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"""
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logger.info("test_exception_01")
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ds.config.set_num_parallel_workers(1)
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data = ds.TFRecordDataset(DATA_DIR, columns_list=["image"])
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with pytest.raises(ValueError) as info:
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data = data.map(input_columns=["image"], operations=vision.Resize(100, 100))
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assert "Invalid interpolation mode." in str(info.value)
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def test_exception_02():
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"""
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Test multiple exceptions with invalid input
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"""
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logger.info("test_exception_02")
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num_samples = 0
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with pytest.raises(ValueError) as info:
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
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assert "num_samples must be greater than 0" in str(info.value)
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num_samples = -1
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with pytest.raises(ValueError) as info:
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
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assert "num_samples must be greater than 0" in str(info.value)
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num_samples = 1
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
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data = data.map(input_columns=["image"], operations=vision.Decode())
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data = data.map(input_columns=["image"], operations=vision.Resize((100, 100)))
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# Confirm 1 sample in dataset
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assert sum([1 for _ in data]) == 1
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num_iters = 0
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for _ in data.create_dict_iterator():
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num_iters += 1
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assert num_iters == 1
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if __name__ == '__main__':
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test_exception_01()
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test_exception_02()
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