wehub-resource-sync
120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
# Copyright (c) 2019 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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import unittest
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.io import DataLoader, Dataset
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BATCH_NUM = 4
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BATCH_SIZE = 8
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EPOCH_NUM = 2
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IMAGE_SIZE = 784
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CLASS_NUM = 10
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# define a random dataset
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, 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.num_samples
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dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
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class TestDygraphDataLoader(unittest.TestCase):
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def setUp(self):
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self.batch_size = BATCH_SIZE
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self.batch_num = BATCH_NUM
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self.epoch_num = EPOCH_NUM
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def iter_loader_data(self, loader):
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for _ in range(self.epoch_num):
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for image, label in loader():
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relu = F.relu(image)
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self.assertEqual(image.shape, [self.batch_size, IMAGE_SIZE])
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self.assertEqual(label.shape, [self.batch_size, 1])
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self.assertEqual(relu.shape, [self.batch_size, IMAGE_SIZE])
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def test_single_process_loader_filedescriptor(self):
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with base.dygraph.guard():
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loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=True,
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drop_last=True,
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use_shared_memory=True,
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num_workers=0,
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)
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self.iter_loader_data(loader)
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def test_multi_process_dataloader_filedescriptor(self):
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with base.dygraph.guard():
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loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=True,
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drop_last=True,
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use_shared_memory=True,
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num_workers=2,
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)
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self.iter_loader_data(loader)
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def test_single_process_loader_filename(self):
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paddle.base.core.globals()["FLAGS_dataloader_use_file_descriptor"] = (
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False
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)
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with base.dygraph.guard():
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loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=True,
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drop_last=True,
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use_shared_memory=True,
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num_workers=0,
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)
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self.iter_loader_data(loader)
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def test_multi_process_dataloader_filename(self):
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paddle.base.core.globals()["FLAGS_dataloader_use_file_descriptor"] = (
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False
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)
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with base.dygraph.guard():
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loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=True,
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drop_last=True,
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use_shared_memory=True,
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num_workers=2,
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)
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self.iter_loader_data(loader)
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if __name__ == '__main__':
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unittest.main()
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