wehub-resource-sync
252 lines
8.4 KiB
Python
252 lines
8.4 KiB
Python
# Copyright (c) 2024 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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# [AUTO-GENERATED] Unit test for paddle.io (Dataset, DataLoader, Sampler)
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# 自动生成的单测，覆盖 paddle.io 模块中未覆盖的代码路径
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# Target: cover uncovered lines in paddle/python/paddle/io/
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# 目标：覆盖 DataLoader、Dataset、Sampler 的初始化和基本功能
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"""
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This test covers the following modules and code paths:
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这个测试覆盖以下模块和代码路径：
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1. TensorDataset - 单张量数据集初始化和索引 (returns tuple of scalars per index)
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2. ComposeDataset - 组合数据集
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3. SequenceSampler - 顺序采样器
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4. RandomSampler - 随机采样器 (有/无放回)
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5. BatchSampler - 批量采样器 (drop_last)
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"""
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import unittest
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import paddle
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from paddle.io import (
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BatchSampler,
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ComposeDataset,
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DataLoader,
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Dataset,
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RandomSampler,
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SequenceSampler,
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Subset,
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TensorDataset,
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)
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class SimpleDataset(Dataset):
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"""Simple dataset for testing."""
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def __init__(self, data):
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self.data = data
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def __getitem__(self, idx):
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return self.data[idx]
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def __len__(self):
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return len(self.data)
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class TestTensorDataset(unittest.TestCase):
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"""Test TensorDataset.
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TensorDataset in Paddle takes a list of tensors.
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__getitem__ returns a tuple of indexed rows (one per input tensor).
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"""
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def setUp(self):
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paddle.disable_static()
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def test_tensor_dataset_basic(self):
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"""Basic TensorDataset usage - returns tuple of indexed rows."""
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data1 = paddle.randn([10, 5])
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data2 = paddle.randn([10, 3])
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dataset = TensorDataset([data1, data2])
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self.assertEqual(len(dataset), 10)
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item = dataset[0]
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self.assertIsInstance(item, tuple)
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self.assertEqual(len(item), 2)
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# Each element is the 0th row of the corresponding input tensor
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self.assertEqual(item[0].shape, [5])
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self.assertEqual(item[1].shape, [3])
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def test_tensor_dataset_1d(self):
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"""TensorDataset with 1D tensors."""
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data1 = paddle.randn([10])
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data2 = paddle.randn([10])
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dataset = TensorDataset([data1, data2])
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self.assertEqual(len(dataset), 10)
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item = dataset[0]
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self.assertIsInstance(item, tuple)
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self.assertEqual(len(item), 2)
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def test_tensor_dataset_iter(self):
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"""Iterate over TensorDataset."""
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data = paddle.randn([5, 3])
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data2 = paddle.randn([5, 2])
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dataset = TensorDataset([data, data2])
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items = [dataset[i] for i in range(5)]
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self.assertEqual(len(items), 5)
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def test_tensor_dataset_varargs(self):
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"""TensorDataset with multiple tensors in list."""
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data1 = paddle.randn([10, 5])
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data2 = paddle.randn([10, 3])
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data3 = paddle.randn([10, 2])
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dataset = TensorDataset([data1, data2, data3])
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self.assertEqual(len(dataset), 10)
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item = dataset[0]
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self.assertIsInstance(item, tuple)
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self.assertEqual(len(item), 3)
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self.assertEqual(item[0].shape, [5])
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self.assertEqual(item[1].shape, [3])
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self.assertEqual(item[2].shape, [2])
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def test_tensor_dataset_varargs_single(self):
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"""TensorDataset with single tensor in list."""
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data = paddle.randn([8, 4])
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dataset = TensorDataset([data])
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self.assertEqual(len(dataset), 8)
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item = dataset[0]
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self.assertIsInstance(item, tuple)
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self.assertEqual(len(item), 1)
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self.assertEqual(item[0].shape, [4])
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class TestComposeDataset(unittest.TestCase):
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"""Test ComposeDataset."""
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def setUp(self):
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paddle.disable_static()
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def test_compose_dataset_basic(self):
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"""ComposeDataset combines multiple datasets."""
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ds1 = SimpleDataset(paddle.randn([10, 5]))
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ds2 = SimpleDataset(paddle.randn([10, 3]))
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composed = ComposeDataset([ds1, ds2])
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self.assertEqual(len(composed), 10)
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item = composed[0]
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self.assertEqual(len(item), 2)
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def test_compose_dataset_single(self):
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"""ComposeDataset with single dataset."""
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ds = SimpleDataset(paddle.randn([5, 2]))
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composed = ComposeDataset([ds])
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self.assertEqual(len(composed), 5)
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class TestSamplers(unittest.TestCase):
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"""Test various samplers."""
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def setUp(self):
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paddle.disable_static()
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def test_sequence_sampler(self):
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"""SequenceSampler yields indices in order."""
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sampler = SequenceSampler(data_source=list(range(10)))
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indices = list(sampler)
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self.assertEqual(indices, list(range(10)))
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def test_random_sampler_no_replacement(self):
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"""RandomSampler without replacement."""
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sampler = RandomSampler(data_source=list(range(10)))
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indices = list(sampler)
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self.assertEqual(len(indices), 10)
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self.assertEqual(sorted(indices), list(range(10)))
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def test_random_sampler_with_replacement(self):
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"""RandomSampler with replacement."""
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sampler = RandomSampler(
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data_source=list(range(5)), replacement=True, num_samples=20
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)
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indices = list(sampler)
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self.assertEqual(len(indices), 20)
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def test_batch_sampler_no_drop_last(self):
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"""BatchSampler without drop_last."""
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sampler = SequenceSampler(data_source=list(range(10)))
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bs = BatchSampler(sampler=sampler, batch_size=3, drop_last=False)
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batches = list(bs)
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self.assertEqual(len(batches), 4)
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self.assertEqual(batches[0], [0, 1, 2])
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def test_batch_sampler_drop_last(self):
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"""BatchSampler with drop_last."""
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sampler = SequenceSampler(data_source=list(range(10)))
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bs = BatchSampler(sampler=sampler, batch_size=3, drop_last=True)
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batches = list(bs)
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self.assertEqual(len(batches), 3)
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class TestSubset(unittest.TestCase):
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"""Test Subset."""
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def setUp(self):
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paddle.disable_static()
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def test_subset_basic(self):
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"""Subset selects a subset of a dataset."""
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ds = SimpleDataset(paddle.randn([20, 5]))
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subset = Subset(ds, indices=[0, 2, 5, 10])
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self.assertEqual(len(subset), 4)
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item = subset[0]
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self.assertEqual(item.shape, [5])
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class TestDataLoader(unittest.TestCase):
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"""Test DataLoader with SimpleDataset."""
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def setUp(self):
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paddle.disable_static()
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def test_dataloader_basic(self):
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"""DataLoader with SimpleDataset."""
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ds = SimpleDataset(paddle.randn([20, 5]))
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loader = DataLoader(ds, batch_size=4)
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batches = list(loader)
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self.assertEqual(len(batches), 5)
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# Each batch is a single stacked tensor
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self.assertIsInstance(batches[0], paddle.Tensor)
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self.assertEqual(batches[0].shape, [4, 5])
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def test_dataloader_batch_size_one(self):
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"""DataLoader with batch_size=1."""
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ds = SimpleDataset(paddle.randn([5, 3]))
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loader = DataLoader(ds, batch_size=1)
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batches = list(loader)
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self.assertEqual(len(batches), 5)
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def test_dataloader_drop_last(self):
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"""DataLoader with drop_last."""
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ds = SimpleDataset(paddle.randn([10, 3]))
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loader = DataLoader(ds, batch_size=4, drop_last=True)
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batches = list(loader)
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self.assertEqual(len(batches), 2)
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def test_dataloader_num_workers_zero(self):
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"""DataLoader with num_workers=0."""
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ds = SimpleDataset(paddle.randn([8, 3]))
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loader = DataLoader(ds, batch_size=4, num_workers=0)
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batches = list(loader)
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self.assertEqual(len(batches), 2)
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def test_dataloader_return_list(self):
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"""DataLoader with return_list=False returns numpy arrays."""
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ds = SimpleDataset(paddle.randn([8, 3]))
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loader = DataLoader(ds, batch_size=4, return_list=False)
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batches = list(loader)
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# With return_list=False, each batch is a list of numpy arrays
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self.assertEqual(len(batches), 2)
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if __name__ == '__main__':
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unittest.main()
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