forked from mindspore-Ecosystem/mindspore
497 lines
16 KiB
Python
497 lines
16 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 mindspore.dataset as ds
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testPK/data"
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def test_imagefolder_basic():
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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.ImageFolderDatasetV2(DATA_DIR)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 44
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def test_imagefolder_numsamples():
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logger.info("Test Case numSamples")
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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.ImageFolderDatasetV2(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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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 10
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random_sampler = ds.RandomSampler(num_samples=3, replacement=True)
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data1 = ds.ImageFolderDatasetV2(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():
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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.ImageFolderDatasetV2(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():
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num_iter += 1
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assert num_iter == 3
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def test_imagefolder_numshards():
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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.ImageFolderDatasetV2(DATA_DIR, num_shards=4, shard_id=3)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 11
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def test_imagefolder_shardid():
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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.ImageFolderDatasetV2(DATA_DIR, num_shards=4, shard_id=1)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 11
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def test_imagefolder_noshuffle():
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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.ImageFolderDatasetV2(DATA_DIR, shuffle=False)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 44
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def test_imagefolder_extrashuffle():
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logger.info("Test Case extraShuffle")
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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.ImageFolderDatasetV2(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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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 88
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def test_imagefolder_classindex():
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logger.info("Test Case classIndex")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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class_index = {"class3": 333, "class1": 111}
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, class_indexing=class_index, shuffle=False)
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data1 = data1.repeat(repeat_count)
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golden = [111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111,
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333, 333, 333, 333, 333, 333, 333, 333, 333, 333, 333]
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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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assert item["label"] == golden[num_iter]
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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 == 22
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def test_imagefolder_negative_classindex():
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logger.info("Test Case negative classIndex")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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class_index = {"class3": -333, "class1": 111}
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, class_indexing=class_index, shuffle=False)
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data1 = data1.repeat(repeat_count)
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golden = [111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111,
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-333, -333, -333, -333, -333, -333, -333, -333, -333, -333, -333]
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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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assert item["label"] == golden[num_iter]
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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 == 22
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def test_imagefolder_extensions():
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logger.info("Test Case extensions")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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ext = [".jpg", ".JPEG"]
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, extensions=ext)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 44
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def test_imagefolder_decode():
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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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ext = [".jpg", ".JPEG"]
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, extensions=ext, decode=True)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 44
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def test_sequential_sampler():
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logger.info("Test Case SequentialSampler")
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golden = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
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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()
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, 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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for item in data1.create_dict_iterator(): # each data is a dictionary
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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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logger.info("Result: {}".format(result))
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assert result == golden
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def test_random_sampler():
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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.ImageFolderDatasetV2(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 44
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def test_distributed_sampler():
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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(10, 1)
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 5
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def test_pk_sampler():
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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(3)
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 12
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def test_subset_random_sampler():
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logger.info("Test Case SubsetRandomSampler")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
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sampler = ds.SubsetRandomSampler(indices)
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 12
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def test_weighted_random_sampler():
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logger.info("Test Case WeightedRandomSampler")
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# define parameters
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repeat_count = 1
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# apply dataset operations
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weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 1.1]
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sampler = ds.WeightedRandomSampler(weights, 11)
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data1 = ds.ImageFolderDatasetV2(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 11
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def test_imagefolder_rename():
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logger.info("Test Case rename")
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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.ImageFolderDatasetV2(DATA_DIR, num_samples=10)
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data1 = data1.repeat(repeat_count)
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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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 == 10
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data1 = data1.rename(input_columns=["image"], output_columns="image2")
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num_iter = 0
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for item in data1.create_dict_iterator(): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image2"]))
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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 == 10
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def test_imagefolder_zip():
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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.ImageFolderDatasetV2(DATA_DIR, num_samples=10)
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data2 = ds.ImageFolderDatasetV2(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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for item in data3.create_dict_iterator(): # each data is a dictionary
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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 == 10
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if __name__ == '__main__':
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test_imagefolder_basic()
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logger.info('test_imagefolder_basic Ended.\n')
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test_imagefolder_numsamples()
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logger.info('test_imagefolder_numsamples Ended.\n')
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test_sequential_sampler()
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logger.info('test_sequential_sampler Ended.\n')
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test_random_sampler()
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logger.info('test_random_sampler Ended.\n')
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test_distributed_sampler()
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logger.info('test_distributed_sampler Ended.\n')
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test_pk_sampler()
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logger.info('test_pk_sampler Ended.\n')
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test_subset_random_sampler()
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logger.info('test_subset_random_sampler Ended.\n')
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test_weighted_random_sampler()
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logger.info('test_weighted_random_sampler Ended.\n')
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test_imagefolder_numshards()
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logger.info('test_imagefolder_numshards Ended.\n')
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test_imagefolder_shardid()
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logger.info('test_imagefolder_shardid Ended.\n')
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test_imagefolder_noshuffle()
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logger.info('test_imagefolder_noshuffle Ended.\n')
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test_imagefolder_extrashuffle()
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logger.info('test_imagefolder_extrashuffle Ended.\n')
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test_imagefolder_classindex()
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logger.info('test_imagefolder_classindex Ended.\n')
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test_imagefolder_negative_classindex()
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logger.info('test_imagefolder_negative_classindex Ended.\n')
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test_imagefolder_extensions()
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logger.info('test_imagefolder_extensions Ended.\n')
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test_imagefolder_decode()
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logger.info('test_imagefolder_decode Ended.\n')
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test_imagefolder_rename()
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logger.info('test_imagefolder_rename Ended.\n')
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test_imagefolder_zip()
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logger.info('test_imagefolder_zip Ended.\n')
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