Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_tensorrt_pooling_converter
nhzlx
7 years ago
commit
0dcbeda2e6
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# 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 paddle.fluid as fluid
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import numpy
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import sys
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TRAIN_FILES = ['train.recordio']
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TEST_FILES = ['test.recordio']
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DICT_DIM = 5147
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# embedding dim
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emb_dim = 128
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# hidden dim
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hid_dim = 128
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# class num
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class_dim = 2
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# epoch num
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epoch_num = 10
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def build_program(is_train):
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file_obj_handle = fluid.layers.io.open_files(
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filenames=TRAIN_FILES if is_train else TEST_FILES,
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shapes=[[-1, 1], [-1, 1]],
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lod_levels=[1, 0],
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dtypes=['int64', 'int64'])
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file_obj = fluid.layers.io.double_buffer(file_obj_handle)
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with fluid.unique_name.guard():
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data, label = fluid.layers.read_file(file_obj)
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emb = fluid.layers.embedding(input=data, size=[DICT_DIM, emb_dim])
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conv_3 = fluid.nets.sequence_conv_pool(
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input=emb,
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num_filters=hid_dim,
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filter_size=3,
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act="tanh",
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pool_type="sqrt")
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conv_4 = fluid.nets.sequence_conv_pool(
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input=emb,
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num_filters=hid_dim,
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filter_size=4,
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act="tanh",
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pool_type="sqrt")
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prediction = fluid.layers.fc(input=[conv_3, conv_4],
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size=class_dim,
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act="softmax")
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# cross entropy loss
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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# mean loss
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avg_cost = fluid.layers.mean(x=cost)
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acc = fluid.layers.accuracy(input=prediction, label=label)
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if is_train:
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# SGD optimizer
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sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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return {'loss': avg_cost, 'log': [avg_cost, acc], 'file': file_obj_handle}
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def main():
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train = fluid.Program()
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startup = fluid.Program()
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test = fluid.Program()
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with fluid.program_guard(train, startup):
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train_args = build_program(is_train=True)
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with fluid.program_guard(test, startup):
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test_args = build_program(is_train=False)
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use_cuda = fluid.core.is_compiled_with_cuda()
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# startup
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place=place)
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exe.run(startup)
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train_exe = fluid.ParallelExecutor(
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use_cuda=use_cuda,
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loss_name=train_args['loss'].name,
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main_program=train)
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test_exe = fluid.ParallelExecutor(
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use_cuda=use_cuda, main_program=test, share_vars_from=train_exe)
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fetch_var_list = [var.name for var in train_args['log']]
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for epoch_id in range(epoch_num):
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# train
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try:
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batch_id = 0
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while True:
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loss, acc = map(numpy.array,
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train_exe.run(fetch_list=fetch_var_list))
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print 'Train epoch', epoch_id, 'batch', batch_id, 'loss:', loss, 'acc:', acc
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batch_id += 1
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except fluid.core.EOFException:
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print 'End of epoch', epoch_id
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train_args['file'].reset()
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# test
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loss = []
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acc = []
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try:
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while True:
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loss_np, acc_np = map(numpy.array,
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test_exe.run(fetch_list=fetch_var_list))
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loss.append(loss_np[0])
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acc.append(acc_np[0])
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except:
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test_args['file'].reset()
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print 'Test loss:', numpy.mean(loss), 'acc:', numpy.mean(acc)
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if __name__ == '__main__':
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main()
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@ -1,146 +0,0 @@
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# 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 paddle.fluid as fluid
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import numpy
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import sys
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TRAIN_FILES = ['train.recordio']
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TEST_FILES = ['test.recordio']
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DICT_DIM = 89528
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# embedding dim
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emb_dim = 128
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# hidden dim
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hid_dim = 128
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# hidden dim2
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hid_dim2 = 96
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# class num
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class_dim = 2
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def network_cfg(is_train, pass_num=100):
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with fluid.unique_name.guard():
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train_file_obj = fluid.layers.open_files(
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filenames=TRAIN_FILES,
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pass_num=pass_num,
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shapes=[[-1, 1], [-1, 1]],
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lod_levels=[1, 0],
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dtypes=['int64', 'int64'])
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test_file_obj = fluid.layers.open_files(
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filenames=TEST_FILES,
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pass_num=1,
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shapes=[[-1, 1], [-1, 1]],
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lod_levels=[1, 0],
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dtypes=['int64', 'int64'])
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if is_train:
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file_obj = fluid.layers.shuffle(train_file_obj, buffer_size=1000)
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else:
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file_obj = test_file_obj
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file_obj = fluid.layers.double_buffer(
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file_obj,
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name="train_double_buffer" if is_train else 'test_double_buffer')
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data, label = fluid.layers.read_file(file_obj)
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emb = fluid.layers.embedding(input=data, size=[DICT_DIM, emb_dim])
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# sequence conv with window size = 3
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win_size = 3
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conv_3 = fluid.nets.sequence_conv_pool(
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input=emb,
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num_filters=hid_dim,
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filter_size=win_size,
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act="tanh",
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pool_type="max")
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# fc layer after conv
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fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
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# probability of each class
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prediction = fluid.layers.fc(input=[fc_1],
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size=class_dim,
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act="softmax")
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# cross entropy loss
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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# mean loss
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avg_cost = fluid.layers.mean(x=cost)
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acc = fluid.layers.accuracy(input=prediction, label=label)
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if is_train:
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# SGD optimizer
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sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.01)
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sgd_optimizer.minimize(avg_cost)
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return {
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'loss': avg_cost,
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'log': [avg_cost, acc],
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'file': train_file_obj if is_train else test_file_obj
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}
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def main():
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train = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(train, startup):
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train_args = network_cfg(is_train=True)
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test = fluid.Program()
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with fluid.program_guard(test, fluid.Program()):
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test_args = network_cfg(is_train=False)
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# startup
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place = fluid.CUDAPlace(0)
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exe = fluid.Executor(place=place)
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exe.run(startup)
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train_exe = fluid.ParallelExecutor(
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use_cuda=True, loss_name=train_args['loss'].name, main_program=train)
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fetch_var_list = [var.name for var in train_args['log']]
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for i in xrange(sys.maxint):
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result = map(numpy.array,
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train_exe.run(fetch_list=fetch_var_list
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if i % 1000 == 0 else []))
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if len(result) != 0:
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print 'Train: ', result
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if i % 1000 == 0:
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test_exe = fluid.ParallelExecutor(
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use_cuda=True, main_program=test, share_vars_from=train_exe)
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loss = []
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acc = []
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try:
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while True:
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loss_np, acc_np = map(
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numpy.array, test_exe.run(fetch_list=fetch_var_list))
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loss.append(loss_np[0])
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acc.append(acc_np[0])
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except:
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test_args['file'].reset()
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print 'TEST: ', numpy.mean(loss), numpy.mean(acc)
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if __name__ == '__main__':
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main()
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