| /** |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| */ |
| #ifndef SRC_MODEL_LAYER_CONVOLUTION_H_ |
| #define SRC_MODEL_LAYER_CONVOLUTION_H_ |
| #include <stack> |
| #include <string> |
| #include <utility> |
| #include <vector> |
| #include "singa/model/layer.h" |
| |
| namespace singa { |
| class Convolution : public Layer { |
| public: |
| /// \copydoc Layer::layer_type() |
| // const std::string layer_type() const override { return "Convolution"; } |
| |
| /// \copydoc Layer::Setup(const LayerConf&); |
| void Setup(const vector<size_t>& in_shape, const LayerConf& conf) override; |
| const Shape GetOutputSampleShape() const override { |
| CHECK(out_sample_shape_.size()) << "You may haven't call Setup()"; |
| return out_sample_shape_; |
| } |
| |
| // void SetupParam(const Tensor &input); |
| /// \copydoc Layer::Forward(int flag, const Tensor&) |
| const Tensor Forward(int flag, const Tensor& input) override; |
| |
| /// \copydoc Layer::Backward(int, const Tensor&, const Tensor&); |
| const std::pair<Tensor, vector<Tensor>> Backward(int flag, |
| const Tensor& grad) override; |
| |
| void ToDevice(std::shared_ptr<Device> device) override; |
| |
| const std::vector<Tensor> param_values() override { |
| if (bias_term_) |
| return std::vector<Tensor>{weight_, bias_}; |
| else |
| return std::vector<Tensor>{weight_}; |
| } |
| |
| size_t kernel_w() const { return kernel_w_; } |
| size_t kernel_h() const { return kernel_h_; } |
| size_t pad_w() const { return pad_w_; } |
| size_t pad_h() const { return pad_h_; } |
| size_t stride_w() const { return stride_w_; } |
| size_t stride_h() const { return stride_h_; } |
| size_t num_filters() const { return num_filters_; } |
| size_t channels() const { return channels_; } |
| size_t height() const { return height_; } |
| size_t width() const { return width_; } |
| bool bias_term() const { return bias_term_; } |
| const Tensor& weight() const { return weight_; } |
| const Tensor& bias() const { return bias_; } |
| |
| void set_weight(const Tensor& w) { |
| weight_.ResetLike(w); |
| weight_.CopyData(w); |
| } |
| void set_bias(const Tensor& b) { |
| bias_.ResetLike(b); |
| bias_.CopyData(b); |
| } |
| |
| protected: |
| size_t kernel_w_, pad_w_, stride_w_; |
| size_t kernel_h_, pad_h_, stride_h_; |
| size_t channels_, height_, width_; |
| size_t col_height_, col_width_, conv_height_, conv_width_, num_filters_; |
| Tensor weight_, bias_; |
| // store intermediate data, i.e., input tensor |
| std::stack<Tensor> buf_; |
| bool bias_term_; |
| vector<size_t> out_sample_shape_; |
| }; |
| |
| void Im2col(const float* data_im, const int channels, const int height, |
| const int width, const int kernel_h, const int kernel_w, |
| const int pad_h, const int pad_w, const int stride_h, |
| const int stride_w, float* data_col); |
| |
| void Col2im(const float* data_col, const int channels, const int height, |
| const int width, const int kernel_h, const int kernel_w, |
| const int pad_h, const int pad_w, const int stride_h, |
| const int stride_w, float* data_im); |
| |
| } // namespace singa |
| #endif // SRC_MODEL_LAYER_CONVOLUTION_H_ |