610 lines
20 KiB
Python
610 lines
20 KiB
Python
# Copyright 2021 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 LSUN dataset operators
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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.vision.c_transforms as vision
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testLSUN"
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def test_lsun_basic():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case basic")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 4
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def test_lsun_num_samples():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case num_samples")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, num_samples=10, num_parallel_workers=2)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 4
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random_sampler = ds.RandomSampler(num_samples=3, replacement=True)
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data1 = ds.LSUNDataset(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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assert num_iter == 3
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random_sampler = ds.RandomSampler(num_samples=3, replacement=False)
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data1 = ds.LSUNDataset(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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assert num_iter == 3
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def test_lsun_num_shards():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case numShards")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, num_shards=2, shard_id=1)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 2
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def test_lsun_shard_id():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case withShardID")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, num_shards=2, shard_id=0)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 2
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def test_lsun_no_shuffle():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case noShuffle")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, shuffle=False)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 4
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def test_lsun_extra_shuffle():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case extra_shuffle")
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# define parameters
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repeat_count = 2
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, shuffle=True)
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data1 = data1.shuffle(buffer_size=5)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 8
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def test_lsun_decode():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case decode")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, decode=True)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 4
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def test_sequential_sampler():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case SequentialSampler")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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sampler = ds.SequentialSampler(num_samples=10)
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", sampler=sampler)
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data1 = data1.repeat(repeat_count)
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result = []
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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result.append(item["label"])
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num_iter += 1
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assert num_iter == 2
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logger.info("Result: {}".format(result))
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def test_random_sampler():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case RandomSampler")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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sampler = ds.RandomSampler()
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 2
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def test_distributed_sampler():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case DistributedSampler")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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sampler = ds.DistributedSampler(2, 1)
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 1
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def test_pk_sampler():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case PKSampler")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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sampler = ds.PKSampler(1)
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 2
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def test_chained_sampler():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case Chained Sampler - Random and Sequential, with repeat")
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# Create chained sampler, random and sequential
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sampler = ds.RandomSampler()
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child_sampler = ds.SequentialSampler()
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sampler.add_child(child_sampler)
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# Create LSUNDataset with sampler
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", sampler=sampler)
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data1 = data1.repeat(count=3)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 6
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# Verify number of iterations
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 6
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def test_lsun_test_dataset():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case usage")
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, usage="test", num_samples=8)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 1
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def test_lsun_valid_dataset():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case usage")
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, usage="valid", num_samples=8)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 2
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def test_lsun_train_dataset():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case usage")
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", num_samples=8)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 2
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def test_lsun_all_dataset():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case usage")
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, usage="all", num_samples=8)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 4
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def test_lsun_classes():
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"""
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Feature: LSUN
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Description: test classes of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case usage")
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, usage="train", classes=["bedroom"], num_samples=8)
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num_iter = 0
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# each data is a dictionary
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 1
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def test_lsun_zip():
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"""
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Feature: LSUN
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Description: test basic usage of LSUN
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Expectation: the dataset is as expected
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"""
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logger.info("Test Case zip")
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# define parameters
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repeat_count = 2
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# apply dataset operations
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data1 = ds.LSUNDataset(DATA_DIR, num_samples=10)
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data2 = ds.LSUNDataset(DATA_DIR, num_samples=10)
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data1 = data1.repeat(repeat_count)
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# rename dataset2 for no conflict
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data2 = data2.rename(input_columns=["image", "label"], output_columns=["image1", "label1"])
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data3 = ds.zip((data1, data2))
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num_iter = 0
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# each data is a dictionary
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for item in data3.create_dict_iterator(num_epochs=1, output_numpy=True):
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 4
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def test_lsun_exception():
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"""
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Feature: LSUN
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Description: test error cases for LSUN
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Expectation: throw exception correctly
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"""
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logger.info("Test lsun exception")
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error_msg_1 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_1):
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ds.LSUNDataset(DATA_DIR, shuffle=False, sampler=ds.PKSampler(3))
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error_msg_2 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_2):
|
|
ds.LSUNDataset(DATA_DIR, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
|
|
|
|
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
|
|
with pytest.raises(RuntimeError, match=error_msg_3):
|
|
ds.LSUNDataset(DATA_DIR, num_shards=10)
|
|
|
|
error_msg_4 = "shard_id is specified but num_shards is not"
|
|
with pytest.raises(RuntimeError, match=error_msg_4):
|
|
ds.LSUNDataset(DATA_DIR, shard_id=0)
|
|
|
|
error_msg_5 = "Input shard_id is not within the required interval"
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.LSUNDataset(DATA_DIR, num_shards=5, shard_id=-1)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.LSUNDataset(DATA_DIR, num_shards=5, shard_id=5)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.LSUNDataset(DATA_DIR, num_shards=2, shard_id=5)
|
|
|
|
error_msg_6 = "num_parallel_workers exceeds"
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.LSUNDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.LSUNDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.LSUNDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
|
|
|
|
error_msg_7 = "Argument shard_id"
|
|
with pytest.raises(TypeError, match=error_msg_7):
|
|
ds.LSUNDataset(DATA_DIR, num_shards=2, shard_id="0")
|
|
|
|
|
|
|
|
def test_lsun_exception_map():
|
|
"""
|
|
Feature: LSUN
|
|
Description: test error cases for LSUN
|
|
Expectation: throw exception correctly
|
|
"""
|
|
logger.info("Test lsun exception map")
|
|
def exception_func(item):
|
|
raise Exception("Error occur!")
|
|
|
|
def exception_func2(image, label):
|
|
raise Exception("Error occur!")
|
|
|
|
try:
|
|
data = ds.LSUNDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
assert False
|
|
except RuntimeError as e:
|
|
assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)
|
|
|
|
try:
|
|
data = ds.LSUNDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func2,
|
|
input_columns=["image", "label"],
|
|
output_columns=["image", "label", "label1"],
|
|
column_order=["image", "label", "label1"],
|
|
num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
assert False
|
|
except RuntimeError as e:
|
|
assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)
|
|
|
|
try:
|
|
data = ds.LSUNDataset(DATA_DIR)
|
|
data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
|
|
data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
assert False
|
|
except RuntimeError as e:
|
|
assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_lsun_basic()
|
|
test_lsun_num_samples()
|
|
test_sequential_sampler()
|
|
test_random_sampler()
|
|
test_distributed_sampler()
|
|
test_pk_sampler()
|
|
test_lsun_num_shards()
|
|
test_lsun_shard_id()
|
|
test_lsun_no_shuffle()
|
|
test_lsun_extra_shuffle()
|
|
test_lsun_decode()
|
|
test_lsun_test_dataset()
|
|
test_lsun_valid_dataset()
|
|
test_lsun_train_dataset()
|
|
test_lsun_all_dataset()
|
|
test_lsun_classes()
|
|
test_lsun_zip()
|
|
test_lsun_exception()
|
|
test_lsun_exception_map()
|