wehub-resource-sync
115 lines
3.4 KiB
Python
Executable File
115 lines
3.4 KiB
Python
Executable File
# Copyright (c) 2022 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 json
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import os
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import sys
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import tempfile
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import unittest
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import warnings
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.io import DataLoader, 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([10]).astype('float32')
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label = np.random.randint(0, 10 - 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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class SimpleNet(nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc = nn.Linear(10, 10)
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def forward(self, image):
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return self.fc(image)
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class TestAutoTune(unittest.TestCase):
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def setUp(self):
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self.batch_size = 1
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self.dataset = RandomDataset(10)
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def test_dataloader_use_autotune(self):
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paddle.incubate.autotune.set_config(
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config={
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"dataloader": {
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"enable": True,
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"tuning_steps": 1,
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}
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}
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)
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loader = DataLoader(
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self.dataset, batch_size=self.batch_size, num_workers=0
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)
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def test_dataloader_disable_autotune(self):
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config = {"dataloader": {"enable": False, "tuning_steps": 1}}
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tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
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json.dump(config, tfile)
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tfile.close()
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paddle.incubate.autotune.set_config(tfile.name)
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os.remove(tfile.name)
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loader = DataLoader(
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self.dataset, batch_size=self.batch_size, num_workers=2
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)
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if sys.platform == 'darwin' or sys.platform == 'win32':
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self.assertEqual(loader.num_workers, 0)
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else:
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self.assertEqual(loader.num_workers, 2)
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def test_distributor_batch_sampler_autotune(self):
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paddle.incubate.autotune.set_config(
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config={
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"dataloader": {
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"enable": True,
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"tuning_steps": 1,
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}
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}
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)
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batch_sampler = paddle.io.DistributedBatchSampler(
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self.dataset, batch_size=self.batch_size
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)
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loader = DataLoader(
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self.dataset, batch_sampler=batch_sampler, num_workers=2
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)
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class TestAutoTuneAPI(unittest.TestCase):
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def test_set_config_warnings(self):
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with warnings.catch_warnings(record=True) as w:
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config = {"kernel": {"enable": 1, "tuning_range": True}}
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tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
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json.dump(config, tfile)
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tfile.close()
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paddle.incubate.autotune.set_config(tfile.name)
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os.remove(tfile.name)
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self.assertTrue(len(w) == 2)
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if __name__ == '__main__':
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unittest.main()
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