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117 lines
3.8 KiB
117 lines
3.8 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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#include "paddle/fluid/framework/reader.h"
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namespace paddle {
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namespace framework {
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DDim ReaderBase::shape(size_t idx) const {
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PADDLE_ENFORCE_LT(
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idx, shapes_.size(),
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"Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx,
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shapes_.size());
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return shapes_[idx];
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}
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void ShuffleReader::ReadNext(std::vector<LoDTensor>* out) {
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if (iteration_pos_ >= buffer_.size()) {
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// Reload buffer with new data
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buffer_.clear();
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buffer_.reserve(buffer_size_);
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for (int i = 0; i < buffer_size_; ++i) {
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if (reader_->HasNext()) {
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buffer_.push_back(std::vector<LoDTensor>());
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reader_->ReadNext(&buffer_.back());
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} else {
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break;
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}
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}
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// TODO(fengjiayi): 'std::random_shuffle' can be very slow. It needs to be
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// optimize.
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std::random_shuffle(buffer_.begin(), buffer_.end());
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iteration_pos_ = 0;
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}
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out->clear();
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if (!buffer_.empty()) {
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std::swap(*out, buffer_[iteration_pos_++]);
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}
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// if buffer_ is empty, the 'out' will return as an empty vector.
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}
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void BatchReader::ReadNext(std::vector<LoDTensor>* out) {
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buffer_.clear();
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buffer_.reserve(batch_size_);
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for (int i = 0; i < batch_size_; ++i) {
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if (reader_->HasNext()) {
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buffer_.push_back(std::vector<LoDTensor>());
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reader_->ReadNext(&buffer_.back());
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} else {
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break;
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}
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}
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// Concat instances
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out->clear();
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if (buffer_.empty()) {
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// if buffer_ is empty, the 'out' will return as an empty vector.
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return;
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}
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int out_num = buffer_[0].size();
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out->reserve(out_num);
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for (int j = 0; j < out_num; ++j) {
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// Merge shape and check date type
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std::type_index batch_type = buffer_[0][j].type();
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DDim batch_shape = buffer_[0][j].dims();
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for (size_t i = 1; i < buffer_.size(); ++i) {
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std::type_index ins_type = buffer_[i][j].type();
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DDim ins_shape = buffer_[i][j].dims();
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PADDLE_ENFORCE_EQ(batch_type, ins_type);
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PADDLE_ENFORCE_EQ(slice_ddim(batch_shape, 1, batch_shape.size()),
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slice_ddim(ins_shape, 1, ins_shape.size()));
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PADDLE_ENFORCE_GT(ins_shape[0], 0);
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batch_shape[0] += ins_shape[0];
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}
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LoDTensor out_tensor;
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out_tensor.Resize(batch_shape);
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out_tensor.mutable_data(platform::CPUPlace(), batch_type);
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int64_t dst_offset = 0;
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// Merge lod and data
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LoD batch_lod;
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for (size_t i = 0; i < buffer_.size(); ++i) {
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DDim ins_shape = buffer_[i][j].dims();
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LoD ins_lod = buffer_[i][j].lod();
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if (i == 0) {
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batch_lod = ins_lod;
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} else {
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PADDLE_ENFORCE_EQ(batch_lod.size(), ins_lod.size());
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for (size_t level_idx = 0; level_idx < batch_lod.size(); ++level_idx) {
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auto& lod_level = batch_lod[level_idx];
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for (size_t k = 1; k < ins_lod[level_idx].size(); ++k) {
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lod_level.push_back(ins_lod[level_idx][k] + lod_level.back());
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}
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}
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}
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Tensor dst = out_tensor.Slice(dst_offset, dst_offset + ins_shape[0]);
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Copy(buffer_[i][j], platform::CPUPlace(), &dst);
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dst_offset += ins_shape[0];
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}
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out_tensor.set_lod(batch_lod);
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out->push_back(out_tensor);
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}
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}
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} // namespace framework
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} // namespace paddle
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