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128 lines
4.2 KiB
128 lines
4.2 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 os
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import sys
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import six
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import time
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import unittest
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import multiprocessing
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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 Dataset, BatchSampler, DataLoader
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from paddle.fluid.dygraph.nn import Linear
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from paddle.fluid.dygraph.base import to_variable
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from test_multiprocess_dataloader_static import RandomDataset, prepare_places
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from test_multiprocess_dataloader_static import EPOCH_NUM, BATCH_SIZE, IMAGE_SIZE, SAMPLE_NUM, CLASS_NUM
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class SimpleFCNet(fluid.dygraph.Layer):
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def __init__(self):
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super(SimpleFCNet, self).__init__()
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param_attr = fluid.ParamAttr(initializer=fluid.initializer.Constant(
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value=0.8))
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bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Constant(
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value=0.5))
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self._fcs = []
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in_channel = IMAGE_SIZE
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for hidden_size in [10, 20, 30]:
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self._fcs.append(
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Linear(
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in_channel,
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hidden_size,
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act='tanh',
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param_attr=param_attr,
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bias_attr=bias_attr))
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in_channel = hidden_size
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self._fcs.append(
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Linear(
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in_channel,
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CLASS_NUM,
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act='softmax',
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param_attr=param_attr,
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bias_attr=bias_attr))
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def forward(self, image):
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out = image
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for fc in self._fcs:
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out = fc(out)
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return out
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class TestDygraphDataLoader(unittest.TestCase):
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def run_main(self, num_workers, places):
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fluid.default_startup_program().random_seed = 1
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fluid.default_main_program().random_seed = 1
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with fluid.dygraph.guard(places[0]):
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fc_net = SimpleFCNet()
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optimizer = fluid.optimizer.Adam(parameter_list=fc_net.parameters())
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dataset = RandomDataset(SAMPLE_NUM, CLASS_NUM)
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dataloader = DataLoader(
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dataset,
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places=places,
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num_workers=num_workers,
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batch_size=BATCH_SIZE,
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drop_last=True)
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assert len(dataloader) == int(SAMPLE_NUM / BATCH_SIZE)
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step_list = []
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loss_list = []
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start_t = time.time()
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for _ in six.moves.range(EPOCH_NUM):
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step = 0
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for image, label in dataloader():
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out = fc_net(image)
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loss = fluid.layers.cross_entropy(out, label)
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avg_loss = fluid.layers.reduce_mean(loss)
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avg_loss.backward()
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optimizer.minimize(avg_loss)
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fc_net.clear_gradients()
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loss_list.append(np.mean(avg_loss.numpy()))
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step += 1
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step_list.append(step)
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end_t = time.time()
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ret = {
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"time": end_t - start_t,
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"step": step_list,
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"loss": np.array(loss_list)
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}
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print("time cost", ret['time'], 'step_list', ret['step'])
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return ret
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def test_main(self):
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# dynamic graph do not run with_data_parallel
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for p in prepare_places(False):
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results = []
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for num_workers in [0, 2]:
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print(self.__class__.__name__, p, num_workers)
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sys.stdout.flush()
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ret = self.run_main(num_workers=num_workers, places=p)
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results.append(ret)
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diff = np.max(
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np.abs(results[0]['loss'] - results[1]['loss']) /
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np.abs(results[0]['loss']))
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self.assertLess(diff, 1e-2)
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if __name__ == '__main__':
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unittest.main()
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