add_depthwiseConv_op_gpu
Yang Yu
7 years ago
commit
e5e206e2b6
@ -0,0 +1,114 @@
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# Copyright (c) 2016 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 sys
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import argparse
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import matplotlib.pyplot as plt
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def parse_args():
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parser = argparse.ArgumentParser('Parse Log')
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parser.add_argument(
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'--file_path', '-f', type=str, help='the path of the log file')
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parser.add_argument(
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'--sample_rate',
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'-s',
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type=float,
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default=1.0,
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help='the rate to take samples from log')
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parser.add_argument(
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'--log_period', '-p', type=int, default=1, help='the period of log')
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args = parser.parse_args()
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return args
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def parse_file(file_name):
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loss = []
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error = []
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with open(file_name) as f:
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for i, line in enumerate(f):
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line = line.strip()
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if not line.startswith('pass'):
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continue
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line_split = line.split(' ')
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if len(line_split) != 5:
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continue
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loss_str = line_split[2][:-1]
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cur_loss = float(loss_str.split('=')[-1])
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loss.append(cur_loss)
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err_str = line_split[3][:-1]
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cur_err = float(err_str.split('=')[-1])
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error.append(cur_err)
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accuracy = [1.0 - err for err in error]
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return loss, accuracy
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def sample(metric, sample_rate):
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interval = int(1.0 / sample_rate)
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if interval > len(metric):
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return metric[:1]
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num = len(metric) / interval
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idx = [interval * i for i in range(num)]
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metric_sample = [metric[id] for id in idx]
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return metric_sample
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def plot_metric(metric,
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batch_id,
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graph_title,
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line_style='b-',
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line_label='y',
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line_num=1):
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plt.figure()
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plt.title(graph_title)
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if line_num == 1:
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plt.plot(batch_id, metric, line_style, label=line_label)
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else:
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for i in range(line_num):
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plt.plot(batch_id, metric[i], line_style[i], label=line_label[i])
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plt.xlabel('batch')
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plt.ylabel(graph_title)
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plt.legend()
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plt.savefig(graph_title + '.jpg')
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plt.close()
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def main():
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args = parse_args()
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assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
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loss, accuracy = parse_file(args.file_path)
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batch = [args.log_period * i for i in range(len(loss))]
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batch_sample = sample(batch, args.sample_rate)
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loss_sample = sample(loss, args.sample_rate)
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accuracy_sample = sample(accuracy, args.sample_rate)
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plot_metric(loss_sample, batch_sample, 'loss', line_label='loss')
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plot_metric(
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accuracy_sample,
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batch_sample,
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'accuracy',
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line_style='g-',
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line_label='accuracy')
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if __name__ == '__main__':
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main()
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@ -0,0 +1,144 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/operators/math/sequence_padding.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <typename T>
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class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
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public:
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void operator()(const platform::CPUDeviceContext& context,
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const framework::LoDTensor& seq, framework::Tensor& padding,
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bool norm_by_times) {
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auto lod = seq.lod();
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PADDLE_ENFORCE_GT(lod.size(), 0UL,
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"The LoD of LoDTensor seq should not be null.");
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const size_t level = 0;
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framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
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auto seq_dims = seq.dims();
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PADDLE_ENFORCE_EQ(seq_dims[0], abs_offset_lod[level].back(),
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"The first dimension of LoDTensor seq should be "
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"equal to the sum of all sequences's length.");
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auto padding_dims = padding.dims();
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PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
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"The input padding should be a 3-D Tensor of shape "
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"[max_sequence_length, num_sequences, sequence_width].");
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const size_t max_sequence_length = MaximumSequenceLength(lod, level);
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PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
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"The first dimension of Tensor padding should be the "
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"maximum length of all sequences in LoDTensor seq.");
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const size_t num_sequences = abs_offset_lod[level].size() - 1;
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PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
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"The second dimension of Tensor padding should be the "
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"number of sequences in LoDTensor seq.");
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const size_t sequence_width = seq.numel() / seq_dims[0];
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PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
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"The third dimension of Tensor padding should be the "
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"width of sequence in LoDTensor seq.");
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const T* seq_data = seq.data<T>();
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T* padding_data = padding.data<T>();
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for (size_t i = 0; i < max_sequence_length; ++i) {
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for (size_t j = 0; j < num_sequences; ++j) {
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size_t start_pos = abs_offset_lod[level][j];
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size_t sequence_length = abs_offset_lod[level][j + 1] - start_pos;
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if (i < sequence_length) {
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// i > 0 => sequence_length > 0
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T scale =
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norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
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for (size_t k = 0; k < sequence_width; ++k) {
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padding_data[(i * num_sequences + j) * sequence_width + k] =
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seq_data[(start_pos + i) * sequence_width + k] * scale;
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}
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} else {
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memset(padding_data + (i * num_sequences + j) * sequence_width, 0,
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sequence_width * sizeof(T));
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}
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}
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}
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}
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};
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template <typename T>
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class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
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public:
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void operator()(const platform::CPUDeviceContext& context,
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framework::LoDTensor& seq, const framework::Tensor& padding,
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bool norm_by_times) {
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auto lod = seq.lod();
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PADDLE_ENFORCE_GT(lod.size(), 0UL,
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"The LoD of LoDTensor seq should not be null.");
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const size_t level = 0;
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framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
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auto seq_dims = seq.dims();
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PADDLE_ENFORCE_EQ(seq_dims[0], abs_offset_lod[level].back(),
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"The first dimension of LoDTensor seq should be "
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"equal to the sum of all sequences's length.");
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auto padding_dims = padding.dims();
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PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
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"The input padding should be a 3-D Tensor of shape "
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"[max_sequnece_length, num_sequences, sequence_width].");
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const size_t max_sequence_length = MaximumSequenceLength(lod, level);
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PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
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"The first dimension of Tensor padding should be "
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"the maximum length of all sequences in LoDTensor seq.");
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const size_t num_sequences = abs_offset_lod[level].size() - 1;
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PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
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"The second dimension of Tensor padding should be "
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"the number of sequences in LoDTensor seq.");
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const size_t sequence_width = seq.numel() / seq_dims[0];
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PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
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"The third dimension of Tensor padding should be the "
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"width of sequence in LoDTensor seq.");
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const T* padding_data = padding.data<T>();
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T* seq_data = seq.data<T>();
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for (size_t i = 0; i < num_sequences; ++i) {
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size_t start_pos = abs_offset_lod[level][i];
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size_t sequence_length = abs_offset_lod[level][i + 1] - start_pos;
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for (size_t j = 0; j < sequence_length; ++j) {
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// sequence_width > j > 0
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T scale =
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norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
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for (size_t k = 0; k < sequence_width; ++k) {
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seq_data[(start_pos + j) * sequence_width + k] =
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padding_data[(j * num_sequences + i) * sequence_width + k] *
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scale;
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}
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}
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}
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}
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};
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template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
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template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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