forked from mindspore-Ecosystem/mindspore
1101 lines
37 KiB
Python
1101 lines
37 KiB
Python
# Copyright 2019-2022 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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import os
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import shutil
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import numpy as np
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import pytest
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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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DATA_DIR = "../data/dataset/testPK/data"
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DATA_DIR_2 = "../data/dataset/testImageNetData2/train"
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DATA_DIR_3 = "../data/dataset/testImageNetData2/encrypt_train"
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def test_imagefolder_basic():
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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset basic read
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Expectation: The dataset is processed 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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with num_samples parameter
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1): # 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.ImageFolderDataset(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):
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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.ImageFolderDataset(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):
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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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with num_shards parameter
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Expectation: The dataset is processed 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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with shard_id parameter
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Expectation: The dataset is processed 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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with no shuffle
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Expectation: The dataset is processed 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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with extra shuffle
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with class_indexing parameter
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1, output_numpy=True): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with negative class_indexing parameter
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1, output_numpy=True): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with extensions parameter
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with decode parameter
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Expectation: The dataset is processed 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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ext = [".jpg", ".JPEG"]
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data1 = ds.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with SequentialSampler
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1, output_numpy=True): # 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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assert num_iter == 44
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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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with RandomSampler
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Expectation: The dataset is processed 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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with DistributedSampler
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Expectation: The dataset is processed 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(10, 1)
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data1 = ds.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with PKSampler
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Expectation: The dataset is processed 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(3)
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data1 = ds.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with SubsetRandomSampler
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1): # 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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"""
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Feature: ImageFolderDataset
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Description: Test ImageFolderDataset with WeightedRandomSampler
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Expectation: The dataset is processed as expected
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"""
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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.ImageFolderDataset(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(num_epochs=1): # 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_weighted_random_sampler_exception():
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"""
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Feature: ImageFolderDataset
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Description: Test error cases for ImageFolderDataset with WeightedRandomSampler
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Expectation: Correct error is thrown as expected
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"""
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logger.info("Test error cases for WeightedRandomSampler")
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error_msg_1 = "type of weights element must be number"
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with pytest.raises(TypeError, match=error_msg_1):
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weights = ""
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ds.WeightedRandomSampler(weights)
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error_msg_2 = "type of weights element must be number"
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with pytest.raises(TypeError, match=error_msg_2):
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weights = (0.9, 0.8, 1.1)
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ds.WeightedRandomSampler(weights)
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error_msg_3 = "WeightedRandomSampler: weights vector must not be empty"
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with pytest.raises(RuntimeError, match=error_msg_3):
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weights = []
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sampler = ds.WeightedRandomSampler(weights)
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sampler.parse()
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error_msg_4 = "WeightedRandomSampler: weights vector must not contain negative numbers, got: "
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with pytest.raises(RuntimeError, match=error_msg_4):
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weights = [1.0, 0.1, 0.02, 0.3, -0.4]
|
|
sampler = ds.WeightedRandomSampler(weights)
|
|
sampler.parse()
|
|
|
|
error_msg_5 = "WeightedRandomSampler: elements of weights vector must not be all zero"
|
|
with pytest.raises(RuntimeError, match=error_msg_5):
|
|
weights = [0, 0, 0, 0, 0]
|
|
sampler = ds.WeightedRandomSampler(weights)
|
|
sampler.parse()
|
|
|
|
|
|
def test_chained_sampler_01():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: Random and Sequential, with repeat
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - Random and Sequential, with repeat")
|
|
|
|
# Create chained sampler, random and sequential
|
|
sampler = ds.RandomSampler()
|
|
child_sampler = ds.SequentialSampler()
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
data1 = data1.repeat(count=3)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 132
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
assert num_iter == 132
|
|
|
|
|
|
def test_chained_sampler_02():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: Random and Sequential, with batch then repeat
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - Random and Sequential, with batch then repeat")
|
|
|
|
# Create chained sampler, random and sequential
|
|
sampler = ds.RandomSampler()
|
|
child_sampler = ds.SequentialSampler()
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
data1 = data1.batch(batch_size=5, drop_remainder=True)
|
|
data1 = data1.repeat(count=2)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 16
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
assert num_iter == 16
|
|
|
|
|
|
def test_chained_sampler_03():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: Random and Sequential, with repeat then batch
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - Random and Sequential, with repeat then batch")
|
|
|
|
# Create chained sampler, random and sequential
|
|
sampler = ds.RandomSampler()
|
|
child_sampler = ds.SequentialSampler()
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
data1 = data1.repeat(count=2)
|
|
data1 = data1.batch(batch_size=5, drop_remainder=False)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 18
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
assert num_iter == 18
|
|
|
|
|
|
def test_chained_sampler_04():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: Distributed and Random, with batch then repeat
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - Distributed and Random, with batch then repeat")
|
|
|
|
# Create chained sampler, distributed and random
|
|
sampler = ds.DistributedSampler(num_shards=4, shard_id=3)
|
|
child_sampler = ds.RandomSampler()
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
data1 = data1.batch(batch_size=5, drop_remainder=True)
|
|
data1 = data1.repeat(count=3)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 6
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
# Note: Each of the 4 shards has 44/4=11 samples
|
|
# Note: Number of iterations is (11/5 = 2) * 3 = 6
|
|
assert num_iter == 6
|
|
|
|
|
|
def test_chained_sampler_05():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: Distributed and WeightedRandom
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - Distributed and WeightedRandom")
|
|
|
|
# Create chained sampler, Distributed and WeightedRandom
|
|
sampler = ds.DistributedSampler(num_shards=2, shard_id=1)
|
|
weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
|
|
child_sampler = ds.WeightedRandomSampler(weights, num_samples=24)
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 12
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
# Note: Child WeightedRandomSampler produces 24 samples
|
|
# Note: DistributedSampler produces 24/2=12 samples
|
|
assert num_iter == 12
|
|
|
|
|
|
def test_chained_sampler_06():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: WeightedRandom and PKSampler
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - WeightedRandom and PKSampler")
|
|
|
|
# Create chained sampler, WeightedRandom and PKSampler
|
|
weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
|
|
sampler = ds.WeightedRandomSampler(weights=weights, num_samples=12)
|
|
child_sampler = ds.PKSampler(num_val=3) # Number of elements per class is 3 (and there are 4 classes)
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 12
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
# Note: WeightedRandomSampler produces 12 samples
|
|
# Note: Child PKSampler produces 12 samples
|
|
assert num_iter == 12
|
|
|
|
|
|
def test_chained_sampler_07():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: SubsetRandom and Distributed, 2 shards
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - SubsetRandom and Distributed, 2 shards")
|
|
|
|
# Create chained sampler, subset random and distributed
|
|
indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
|
|
sampler = ds.SubsetRandomSampler(indices, num_samples=12)
|
|
child_sampler = ds.DistributedSampler(num_shards=2, shard_id=1)
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 12
|
|
|
|
# Verify number of iterations
|
|
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
# Note: SubsetRandomSampler produces 12 samples
|
|
assert num_iter == 12
|
|
|
|
|
|
def test_chained_sampler_08():
|
|
"""
|
|
Feature: Chained Sampler
|
|
Description: Chained Samplers: SubsetRandom and Distributed, 4 shards
|
|
Expectation: Get correct number of data
|
|
"""
|
|
logger.info("Test Case Chained Sampler - SubsetRandom and Distributed, 4 shards")
|
|
|
|
# Create chained sampler, subset random and distributed
|
|
indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
|
sampler = ds.SubsetRandomSampler(indices, num_samples=11)
|
|
child_sampler = ds.DistributedSampler(num_shards=4, shard_id=1)
|
|
sampler.add_child(child_sampler)
|
|
# Create ImageFolderDataset with sampler
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
|
|
|
|
# Verify dataset size
|
|
data1_size = data1.get_dataset_size()
|
|
logger.info("dataset size is: {}".format(data1_size))
|
|
assert data1_size == 11
|
|
|
|
# Verify number of iterations
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
# Note: SubsetRandomSampler returns 11 samples
|
|
assert num_iter == 11
|
|
|
|
|
|
def test_imagefolder_rename():
|
|
"""
|
|
Feature: ImageFolderDataset
|
|
Description: Test rename on ImageFolderDataset
|
|
Expectation: The dataset is processed as expected
|
|
"""
|
|
logger.info("Test Case rename")
|
|
# define parameters
|
|
repeat_count = 1
|
|
|
|
# apply dataset operations
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, num_samples=10)
|
|
data1 = data1.repeat(repeat_count)
|
|
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
assert num_iter == 10
|
|
|
|
data1 = data1.rename(input_columns=["image"], output_columns="image2")
|
|
|
|
num_iter = 0
|
|
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image2"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
assert num_iter == 10
|
|
|
|
|
|
def test_imagefolder_zip():
|
|
"""
|
|
Feature: ImageFolderDataset
|
|
Description: Test zip on ImageFolderDataset
|
|
Expectation: The dataset is processed as expected
|
|
"""
|
|
logger.info("Test Case zip")
|
|
# define parameters
|
|
repeat_count = 2
|
|
|
|
# apply dataset operations
|
|
data1 = ds.ImageFolderDataset(DATA_DIR, num_samples=10)
|
|
data2 = ds.ImageFolderDataset(DATA_DIR, num_samples=10)
|
|
|
|
data1 = data1.repeat(repeat_count)
|
|
# rename dataset2 for no conflict
|
|
data2 = data2.rename(input_columns=["image", "label"], output_columns=["image1", "label1"])
|
|
data3 = ds.zip((data1, data2))
|
|
|
|
num_iter = 0
|
|
for item in data3.create_dict_iterator(num_epochs=1): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
logger.info("image is {}".format(item["image"]))
|
|
logger.info("label is {}".format(item["label"]))
|
|
num_iter += 1
|
|
|
|
logger.info("Number of data in data1: {}".format(num_iter))
|
|
assert num_iter == 10
|
|
|
|
|
|
def test_imagefolder_exception():
|
|
"""
|
|
Feature: ImageFolderDataset
|
|
Description: Test error cases on ImageFolderDataset
|
|
Expectation: Correct error is thrown as expected
|
|
"""
|
|
logger.info("Test imagefolder exception")
|
|
|
|
def exception_func(item):
|
|
raise Exception("Error occur!")
|
|
|
|
def exception_func2(image, label):
|
|
raise Exception("Error occur!")
|
|
|
|
try:
|
|
data = ds.ImageFolderDataset(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.ImageFolderDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func2, input_columns=["image", "label"],
|
|
output_columns=["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.ImageFolderDataset(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)
|
|
|
|
data_dir_invalid = "../data/dataset/testPK"
|
|
try:
|
|
data = ds.ImageFolderDataset(data_dir_invalid)
|
|
for _ in data.__iter__():
|
|
pass
|
|
assert False
|
|
except RuntimeError as e:
|
|
assert "should be file, but got directory" in str(e)
|
|
|
|
|
|
def encrypt_func():
|
|
"""
|
|
Feature: Encrypt function
|
|
Description: Encrypt and save the image
|
|
Expectation: Success
|
|
"""
|
|
plain_dir = os.path.realpath(DATA_DIR_2)
|
|
cipher_dir = os.path.realpath(DATA_DIR_3)
|
|
|
|
for root, _, files in os.walk(plain_dir):
|
|
for f in files:
|
|
fn = os.path.join(root, f)
|
|
|
|
enc_file = os.path.join(cipher_dir, os.path.relpath(fn, plain_dir))
|
|
os.makedirs(os.path.dirname(enc_file), exist_ok=True)
|
|
|
|
with open(fn, 'rb')as f:
|
|
content = f.read()
|
|
|
|
new_content = b'helloworld' + content
|
|
|
|
with open(enc_file, 'wb')as f:
|
|
f.write(new_content)
|
|
|
|
|
|
def decrypt_func(cipher_file):
|
|
"""
|
|
Feature: Decrypt function
|
|
Description: Decrypt encrypted image data
|
|
Expectation: Decryption is successful, return bytes type data
|
|
"""
|
|
with open(cipher_file, 'rb')as f:
|
|
content = f.read()
|
|
new_content = content[10:]
|
|
return new_content
|
|
|
|
|
|
def test_imagefolder_decrypt():
|
|
"""
|
|
Feature: Test imagefolder decrypt
|
|
Description: Support decrypting encrypted image data
|
|
Expectation: Success
|
|
"""
|
|
logger.info("Test imagefolder decrypt")
|
|
|
|
encrypt_func()
|
|
|
|
resize_height = 224
|
|
resize_width = 224
|
|
|
|
# Create dataset and define map operations
|
|
ds1 = ds.ImageFolderDataset(DATA_DIR_3, decrypt=decrypt_func)
|
|
|
|
num_classes = 3
|
|
decode_op = vision.Decode()
|
|
resize_op = vision.Resize((resize_height, resize_width), vision.Inter.LINEAR)
|
|
one_hot_encode = data_trans.OneHot(num_classes) # num_classes is input argument
|
|
|
|
ds1 = ds1.map(operations=decode_op, input_columns=["image"])
|
|
ds1 = ds1.map(operations=resize_op, input_columns=["image"])
|
|
ds1 = ds1.map(operations=one_hot_encode, input_columns=["label"])
|
|
|
|
# apply batch operations
|
|
batch_size = 3
|
|
ds1 = ds1.batch(batch_size, drop_remainder=True)
|
|
|
|
ds2 = ds1
|
|
alpha = 0.2
|
|
transforms = [vision.MixUp(batch_size=batch_size, alpha=alpha, is_single=False)
|
|
]
|
|
ds1 = ds1.map(operations=transforms, input_columns=["image", "label"])
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num_iter = 0
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batch1_image1 = 0
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for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1, output_numpy=True),
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ds2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image1 = data1["image"]
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label1 = data1["label"]
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logger.info("label: {}".format(label1))
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|
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image2 = data2["image"]
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label2 = data2["label"]
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logger.info("label2: {}".format(label2))
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|
|
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if num_iter == 0:
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batch1_image1 = image1
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|
|
|
if num_iter == 1:
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|
lam = np.abs(label2 - label1)
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logger.info("lam value in multi: {}".format(lam))
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for index in range(batch_size):
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if np.square(lam[index]).mean() != 0:
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lam_value = 1 - np.sum(lam[index]) / 2
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img_golden = lam_value * image2[index] + (1 - lam_value) * batch1_image1[index]
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assert image1[index].all() == img_golden.all()
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logger.info("====test several batch mixup ok====")
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|
break
|
|
num_iter += 1
|
|
|
|
if os.path.exists(DATA_DIR_3):
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|
shutil.rmtree(DATA_DIR_3)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_imagefolder_basic()
|
|
logger.info('test_imagefolder_basic Ended.\n')
|
|
|
|
test_imagefolder_numsamples()
|
|
logger.info('test_imagefolder_numsamples Ended.\n')
|
|
|
|
test_sequential_sampler()
|
|
logger.info('test_sequential_sampler Ended.\n')
|
|
|
|
test_random_sampler()
|
|
logger.info('test_random_sampler Ended.\n')
|
|
|
|
test_distributed_sampler()
|
|
logger.info('test_distributed_sampler Ended.\n')
|
|
|
|
test_pk_sampler()
|
|
logger.info('test_pk_sampler Ended.\n')
|
|
|
|
test_subset_random_sampler()
|
|
logger.info('test_subset_random_sampler Ended.\n')
|
|
|
|
test_weighted_random_sampler()
|
|
logger.info('test_weighted_random_sampler Ended.\n')
|
|
|
|
test_weighted_random_sampler_exception()
|
|
logger.info('test_weighted_random_sampler_exception Ended.\n')
|
|
|
|
test_chained_sampler_01()
|
|
logger.info('test_chained_sampler_01 Ended.\n')
|
|
|
|
test_chained_sampler_02()
|
|
logger.info('test_chained_sampler_02 Ended.\n')
|
|
|
|
test_chained_sampler_03()
|
|
logger.info('test_chained_sampler_03 Ended.\n')
|
|
|
|
test_chained_sampler_04()
|
|
logger.info('test_chained_sampler_04 Ended.\n')
|
|
|
|
test_chained_sampler_05()
|
|
logger.info('test_chained_sampler_05 Ended.\n')
|
|
|
|
test_chained_sampler_06()
|
|
logger.info('test_chained_sampler_06 Ended.\n')
|
|
|
|
test_chained_sampler_07()
|
|
logger.info('test_chained_sampler_07 Ended.\n')
|
|
|
|
test_chained_sampler_08()
|
|
logger.info('test_chained_sampler_08 Ended.\n')
|
|
|
|
test_imagefolder_numshards()
|
|
logger.info('test_imagefolder_numshards Ended.\n')
|
|
|
|
test_imagefolder_shardid()
|
|
logger.info('test_imagefolder_shardid Ended.\n')
|
|
|
|
test_imagefolder_noshuffle()
|
|
logger.info('test_imagefolder_noshuffle Ended.\n')
|
|
|
|
test_imagefolder_extrashuffle()
|
|
logger.info('test_imagefolder_extrashuffle Ended.\n')
|
|
|
|
test_imagefolder_classindex()
|
|
logger.info('test_imagefolder_classindex Ended.\n')
|
|
|
|
test_imagefolder_negative_classindex()
|
|
logger.info('test_imagefolder_negative_classindex Ended.\n')
|
|
|
|
test_imagefolder_extensions()
|
|
logger.info('test_imagefolder_extensions Ended.\n')
|
|
|
|
test_imagefolder_decode()
|
|
logger.info('test_imagefolder_decode Ended.\n')
|
|
|
|
test_imagefolder_rename()
|
|
logger.info('test_imagefolder_rename Ended.\n')
|
|
|
|
test_imagefolder_zip()
|
|
logger.info('test_imagefolder_zip Ended.\n')
|
|
|
|
test_imagefolder_exception()
|
|
logger.info('test_imagefolder_exception Ended.\n')
|
|
|
|
test_imagefolder_decrypt()
|
|
logger.info('test_imagefolder_decrypt Ended.\n')
|