Fundamental Data Reading in C++ #8009
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| // Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #include "paddle/framework/reader.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
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| DDim ReaderBase::shape(size_t idx) const { | ||
| PADDLE_ENFORCE_LT( | ||
| idx, shapes_.size(), | ||
| "Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx, | ||
| shapes_.size()); | ||
| return shapes_[idx]; | ||
| } | ||
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| void ShuffleReader::ReadNext(std::vector<LoDTensor>* out) { | ||
| if (iteration_pos_ >= buffer_.size()) { | ||
| // Reload buffer with new data | ||
| buffer_.clear(); | ||
| buffer_.reserve(buffer_size_); | ||
| for (int i = 0; i < buffer_size_; ++i) { | ||
| if (reader_->HasNext()) { | ||
| buffer_.push_back(std::vector<LoDTensor>()); | ||
| reader_->ReadNext(&buffer_.back()); | ||
| } else { | ||
| break; | ||
| } | ||
| } | ||
| std::random_shuffle(buffer_.begin(), buffer_.end()); | ||
| iteration_pos_ = 0; | ||
| } | ||
| out->clear(); | ||
| if (!buffer_.empty()) { | ||
| std::swap(*out, buffer_[iteration_pos_++]); | ||
| } | ||
| // if buffer_ is empty, the 'out' will return as an empty vector. | ||
| } | ||
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| void BatchReader::ReadNext(std::vector<LoDTensor>* out) { | ||
| buffer_.clear(); | ||
| buffer_.reserve(batch_size_); | ||
| for (int i = 0; i < batch_size_; ++i) { | ||
| if (reader_->HasNext()) { | ||
| buffer_.push_back(std::vector<LoDTensor>()); | ||
| reader_->ReadNext(&buffer_.back()); | ||
| } else { | ||
| break; | ||
| } | ||
| } | ||
| // Concat instances | ||
| out->clear(); | ||
| if (buffer_.empty()) { | ||
| // if buffer_ is empty, the 'out' will return as an empty vector. | ||
| return; | ||
| } | ||
| int out_num = buffer_[0].size(); | ||
| out->reserve(out_num); | ||
| for (int j = 0; j < out_num; ++j) { | ||
| // Merge shape and check date type | ||
| std::type_index batch_type = buffer_[0][j].type(); | ||
| DDim batch_shape = buffer_[0][j].dims(); | ||
| for (size_t i = 1; i < buffer_.size(); ++i) { | ||
| std::type_index ins_type = buffer_[i][j].type(); | ||
| DDim ins_shape = buffer_[i][j].dims(); | ||
| PADDLE_ENFORCE_EQ(batch_type, ins_type); | ||
| PADDLE_ENFORCE_EQ(slice_ddim(batch_shape, 1, batch_shape.size()), | ||
| slice_ddim(ins_shape, 1, ins_shape.size())); | ||
| PADDLE_ENFORCE_GT(ins_shape[0], 0); | ||
| batch_shape[0] += ins_shape[0]; | ||
| } | ||
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| LoDTensor out_tensor; | ||
|
There was a problem hiding this comment. Maybe we can invoke There was a problem hiding this comment. The |
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| out_tensor.Resize(batch_shape); | ||
| out_tensor.mutable_data(platform::CPUPlace(), batch_type); | ||
| int64_t dst_offset = 0; | ||
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| // Merge lod and data | ||
| LoD batch_lod; | ||
| std::vector<size_t> top_level_lod({0}); | ||
| for (size_t i = 0; i < buffer_.size(); ++i) { | ||
| DDim ins_shape = buffer_[i][j].dims(); | ||
| LoD ins_lod = buffer_[i][j].lod(); | ||
| if (i == 0) { | ||
| batch_lod = ins_lod; | ||
| } else { | ||
| PADDLE_ENFORCE_EQ(batch_lod.size(), ins_lod.size()); | ||
| for (size_t level_idx = 0; level_idx < batch_lod.size(); ++level_idx) { | ||
| auto& lod_level = batch_lod[level_idx]; | ||
| for (size_t k = 1; k < ins_lod[level_idx].size(); ++k) { | ||
| lod_level.push_back(ins_lod[level_idx][k] + lod_level.back()); | ||
| } | ||
| } | ||
| } | ||
| top_level_lod.push_back( | ||
| top_level_lod.back() + | ||
| (ins_lod.empty() ? ins_shape[0] : (ins_lod[0].size() - 1))); | ||
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| Tensor dst = out_tensor.Slice(dst_offset, dst_offset + ins_shape[0]); | ||
| Copy(buffer_[i][j], platform::CPUPlace(), &dst); | ||
| dst_offset += ins_shape[0]; | ||
| } | ||
| batch_lod.insert(batch_lod.begin(), top_level_lod); | ||
| out_tensor.set_lod(batch_lod); | ||
| out->push_back(out_tensor); | ||
| } | ||
| } | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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This implementation could be very slow. However, we can optimize it later.
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Thanks, I didn't know that before. I will add a
TOODhere.