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122 lines
4.7 KiB
122 lines
4.7 KiB
# Copyright (c) 2018 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 os
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import unittest
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import random
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import numpy as np
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import paddle.fluid as fluid
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import six
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import paddle
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os.environ["CPU_NUM"] = "2"
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class TestFetchUnmerged(unittest.TestCase):
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def conv_net(self, img, label):
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=img,
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filter_size=5,
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num_filters=8,
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pool_size=2,
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pool_stride=2,
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pool_type='max',
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act="relu")
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conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
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conv_pool_2 = fluid.nets.simple_img_conv_pool(
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input=conv_pool_1,
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filter_size=5,
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num_filters=16,
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pool_size=2,
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pool_stride=2,
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pool_type='avg',
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act="relu")
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hidden = fluid.layers.fc(input=conv_pool_2, size=32, act='relu')
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prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_loss = fluid.layers.mean(loss)
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return avg_loss, prediction
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def build_program(self, main, startup, is_test):
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with fluid.unique_name.guard():
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with fluid.program_guard(main, startup):
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img = fluid.layers.data(
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name='image', shape=[1, 28, 28], dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[1], dtype='int64')
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loss, prediction = self.conv_net(img, label)
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if not is_test:
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opt = fluid.optimizer.Adam(learning_rate=0.001)
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opt.minimize(loss)
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return [img, label], loss, prediction
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def fetch_unmerged(self, use_cuda=True):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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feeds, loss, prediction = self.build_program(main_program,
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startup_program, False)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_program)
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build_strategy = fluid.BuildStrategy()
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binary = fluid.CompiledProgram(main_program).with_data_parallel(
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loss_name=loss.name, build_strategy=build_strategy)
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iters = 2
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batch_size = 16
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=500),
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batch_size=batch_size)
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feeder = fluid.DataFeeder(feed_list=feeds, place=place)
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device_num = fluid.core.get_cuda_device_count() if use_cuda else 2
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for _ in range(iters):
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data = next(train_reader())
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loss_v, prediction_v = exe.run(binary,
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feed=feeder.feed(data),
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fetch_list=[loss, prediction],
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return_merged=False)
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self.assertEqual(np.array(loss_v).shape, (device_num, 1))
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self.assertEqual(
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np.array(prediction_v).shape,
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(device_num, batch_size / device_num, 10))
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for _ in range(iters):
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data = next(train_reader())
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loss_v, prediction_v = exe.run(binary,
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feed=feeder.feed(data),
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fetch_list=[loss, prediction],
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return_merged=True)
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self.assertEqual(np.array(loss_v).shape, (device_num, ))
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self.assertEqual(np.array(prediction_v).shape, (batch_size, 10))
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def test_fetch_unmerged(self):
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if fluid.core.is_compiled_with_cuda():
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self.fetch_unmerged(use_cuda=True)
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self.fetch_unmerged(use_cuda=False)
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def test_fetch_unmerged_parallel_graph(self):
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fluid.core.globals()['FLAGS_enable_parallel_graph'] = True
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if fluid.core.is_compiled_with_cuda():
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self.fetch_unmerged(use_cuda=True)
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self.fetch_unmerged(use_cuda=False)
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fluid.core.globals()['FLAGS_enable_parallel_graph'] = False
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if __name__ == '__main__':
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unittest.main()
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