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284 lines
8.3 KiB
284 lines
8.3 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import division
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.io import BatchSampler, Dataset, Sampler, SequenceSampler, \
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RandomSampler, WeightedRandomSampler
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from paddle.io import DistributedBatchSampler
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class RandomDataset(Dataset):
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def __init__(self, sample_num, class_num):
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self.sample_num = sample_num
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self.class_num = class_num
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def __getitem__(self, idx):
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np.random.seed(idx)
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image = np.random.random([IMAGE_SIZE]).astype('float32')
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label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
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return image, label
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def __len__(self):
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return self.sample_num
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class TestSampler(unittest.TestCase):
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def test_main(self):
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dataset = RandomDataset(100, 10)
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sampler = Sampler(dataset)
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try:
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iter(sampler)
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self.assertTrue(False)
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except NotImplementedError:
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pass
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class TestSequenceSampler(unittest.TestCase):
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def test_main(self):
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dataset = RandomDataset(100, 10)
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sampler = SequenceSampler(dataset)
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assert len(sampler) == 100
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for i, index in enumerate(iter(sampler)):
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assert i == index
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class TestRandomSampler(unittest.TestCase):
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def test_main(self):
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dataset = RandomDataset(100, 10)
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sampler = RandomSampler(dataset)
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assert len(sampler) == 100
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rets = []
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for i in iter(sampler):
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rets.append(i)
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assert tuple(sorted(rets)) == tuple(range(0, 100))
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def test_with_num_samples(self):
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dataset = RandomDataset(100, 10)
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sampler = RandomSampler(dataset, num_samples=50, replacement=True)
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assert len(sampler) == 50
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rets = []
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for i in iter(sampler):
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rets.append(i)
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assert i >= 0 and i < 100
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def test_with_generator(self):
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dataset = RandomDataset(100, 10)
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generator = iter(range(0, 60))
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sampler = RandomSampler(dataset, generator=generator)
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assert len(sampler) == 100
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rets = []
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for i in iter(sampler):
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rets.append(i)
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assert tuple(sorted(rets)) == tuple(range(0, 60))
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def test_with_generator_num_samples(self):
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dataset = RandomDataset(100, 10)
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generator = iter(range(0, 60))
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sampler = RandomSampler(
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dataset, generator=generator, num_samples=50, replacement=True)
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assert len(sampler) == 50
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rets = []
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for i in iter(sampler):
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rets.append(i)
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assert tuple(sorted(rets)) == tuple(range(0, 50))
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class TestBatchSampler(unittest.TestCase):
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def setUp(self):
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self.num_samples = 1000
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self.num_classes = 10
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self.batch_size = 32
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self.shuffle = False
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self.drop_last = False
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def init_batch_sampler(self):
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dataset = RandomDataset(self.num_samples, self.num_classes)
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bs = BatchSampler(
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dataset=dataset,
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batch_size=self.batch_size,
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shuffle=self.shuffle,
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drop_last=self.drop_last)
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return bs
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def test_main(self):
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bs = self.init_batch_sampler()
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# length check
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bs_len = (self.num_samples + int(not self.drop_last) \
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* (self.batch_size - 1)) // self.batch_size
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self.assertTrue(bs_len == len(bs))
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# output indices check
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if not self.shuffle:
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index = 0
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for indices in bs:
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for idx in indices:
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self.assertTrue(index == idx)
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index += 1
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class TestBatchSamplerDropLast(TestBatchSampler):
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def setUp(self):
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self.num_samples = 1000
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self.num_classes = 10
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self.batch_size = 32
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self.shuffle = False
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self.drop_last = True
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class TestBatchSamplerShuffle(TestBatchSampler):
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def setUp(self):
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self.num_samples = 1000
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self.num_classes = 10
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self.batch_size = 32
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self.shuffle = True
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self.drop_last = True
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class TestBatchSamplerWithSampler(TestBatchSampler):
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def init_batch_sampler(self):
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dataset = RandomDataset(1000, 10)
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sampler = SequenceSampler(dataset)
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bs = BatchSampler(
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sampler=sampler,
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batch_size=self.batch_size,
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drop_last=self.drop_last)
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return bs
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class TestBatchSamplerWithSamplerDropLast(unittest.TestCase):
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def setUp(self):
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self.num_samples = 1000
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self.num_classes = 10
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self.batch_size = 32
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self.shuffle = False
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self.drop_last = True
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class TestBatchSamplerWithSamplerShuffle(unittest.TestCase):
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def setUp(self):
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self.num_samples = 1000
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self.num_classes = 10
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self.batch_size = 32
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self.shuffle = True
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self.drop_last = True
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def test_main(self):
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try:
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dataset = RandomDataset(self.num_samples, self.num_classes)
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sampler = RandomSampler(dataset)
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bs = BatchSampler(
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sampler=sampler,
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shuffle=self.shuffle,
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batch_size=self.batch_size,
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drop_last=self.drop_last)
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self.assertTrue(False)
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except AssertionError:
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pass
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class TestWeightedRandomSampler(unittest.TestCase):
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def init_probs(self, total, pos):
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pos_probs = np.random.random((pos, )).astype('float32')
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probs = np.zeros((total, )).astype('float32')
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probs[:pos] = pos_probs
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np.random.shuffle(probs)
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return probs
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def test_replacement(self):
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probs = self.init_probs(20, 10)
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sampler = WeightedRandomSampler(probs, 30, True)
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assert len(sampler) == 30
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for idx in iter(sampler):
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assert probs[idx] > 0.
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def test_no_replacement(self):
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probs = self.init_probs(20, 10)
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sampler = WeightedRandomSampler(probs, 10, False)
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assert len(sampler) == 10
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idxs = []
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for idx in iter(sampler):
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assert probs[idx] > 0.
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idxs.append(idx)
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assert len(set(idxs)) == len(idxs)
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def test_assert(self):
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# all zeros
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probs = np.zeros((10, )).astype('float32')
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sampler = WeightedRandomSampler(probs, 10, True)
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try:
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for idx in iter(sampler):
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pass
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self.assertTrue(False)
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except AssertionError:
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self.assertTrue(True)
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# not enough pos
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probs = self.init_probs(10, 5)
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sampler = WeightedRandomSampler(probs, 10, False)
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try:
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for idx in iter(sampler):
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pass
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self.assertTrue(False)
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except AssertionError:
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self.assertTrue(True)
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# neg probs
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probs = -1.0 * np.ones((10, )).astype('float32')
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sampler = WeightedRandomSampler(probs, 10, True)
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try:
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for idx in iter(sampler):
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pass
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self.assertTrue(False)
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except AssertionError:
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self.assertTrue(True)
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def test_raise(self):
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# float num_samples
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probs = self.init_probs(10, 5)
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try:
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sampler = WeightedRandomSampler(probs, 2.3, True)
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self.assertTrue(False)
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except ValueError:
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self.assertTrue(True)
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# neg num_samples
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probs = self.init_probs(10, 5)
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try:
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sampler = WeightedRandomSampler(probs, -1, True)
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self.assertTrue(False)
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except ValueError:
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self.assertTrue(True)
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# no-bool replacement
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probs = self.init_probs(10, 5)
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try:
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sampler = WeightedRandomSampler(probs, 5, 5)
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self.assertTrue(False)
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except ValueError:
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self.assertTrue(True)
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if __name__ == '__main__':
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unittest.main()
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