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add DeconvolutionLayer, using BaseConvolutionLayer
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,85 @@ | ||
| #include <vector> | ||
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| #include "caffe/filler.hpp" | ||
| #include "caffe/layer.hpp" | ||
| #include "caffe/util/im2col.hpp" | ||
| #include "caffe/util/math_functions.hpp" | ||
| #include "caffe/vision_layers.hpp" | ||
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| namespace caffe { | ||
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| template <typename Dtype> | ||
| void DeconvolutionLayer<Dtype>::compute_output_shape() { | ||
| this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_ | ||
| - 2 * this->pad_h_; | ||
| this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_ | ||
| - 2 * this->pad_w_; | ||
| } | ||
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| template <typename Dtype> | ||
| void DeconvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, | ||
| const vector<Blob<Dtype>*>& top) { | ||
| const Dtype* weight = this->blobs_[0]->cpu_data(); | ||
| for (int i = 0; i < bottom.size(); ++i) { | ||
| const Dtype* bottom_data = bottom[i]->cpu_data(); | ||
| Dtype* top_data = top[i]->mutable_cpu_data(); | ||
| for (int n = 0; n < this->num_; ++n) { | ||
| this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight, | ||
| top_data + top[i]->offset(n)); | ||
| if (this->bias_term_) { | ||
| const Dtype* bias = this->blobs_[1]->cpu_data(); | ||
| this->forward_cpu_bias(top_data + top[i]->offset(n), bias); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| template <typename Dtype> | ||
| void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, | ||
| const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { | ||
| const Dtype* weight = this->blobs_[0]->cpu_data(); | ||
| Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff(); | ||
| if (this->param_propagate_down_[0]) { | ||
| caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff); | ||
| } | ||
| if (this->bias_term_ && this->param_propagate_down_[1]) { | ||
| caffe_set(this->blobs_[1]->count(), Dtype(0), | ||
| this->blobs_[1]->mutable_cpu_diff()); | ||
| } | ||
| for (int i = 0; i < top.size(); ++i) { | ||
| const Dtype* top_diff = top[i]->cpu_diff(); | ||
| const Dtype* bottom_data = bottom[i]->cpu_data(); | ||
| Dtype* bottom_diff = bottom[i]->mutable_cpu_diff(); | ||
| // Bias gradient, if necessary. | ||
| if (this->bias_term_ && this->param_propagate_down_[1]) { | ||
| Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff(); | ||
| for (int n = 0; n < this->num_; ++n) { | ||
| this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n)); | ||
| } | ||
| } | ||
| if (this->param_propagate_down_[0] || propagate_down[i]) { | ||
| for (int n = 0; n < this->num_; ++n) { | ||
| // Gradient w.r.t. weight. Note that we will accumulate diffs. | ||
| if (this->param_propagate_down_[0]) { | ||
| this->weight_cpu_gemm(top_diff + top[i]->offset(n), | ||
| bottom_data + bottom[i]->offset(n), weight_diff); | ||
| } | ||
| // Gradient w.r.t. bottom data, if necessary, reusing the column buffer | ||
| // we might have just computed above. | ||
| if (propagate_down[i]) { | ||
| this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight, | ||
| bottom_diff + bottom[i]->offset(n), | ||
| this->param_propagate_down_[0]); | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| #ifdef CPU_ONLY | ||
| STUB_GPU(DeconvolutionLayer); | ||
| #endif | ||
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| INSTANTIATE_CLASS(DeconvolutionLayer); | ||
| REGISTER_LAYER_CLASS(DECONVOLUTION, DeconvolutionLayer); | ||
| } // namespace caffe |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,71 @@ | ||
| #include <vector> | ||
|
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| #include "caffe/filler.hpp" | ||
| #include "caffe/layer.hpp" | ||
| #include "caffe/util/im2col.hpp" | ||
| #include "caffe/util/math_functions.hpp" | ||
| #include "caffe/vision_layers.hpp" | ||
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| namespace caffe { | ||
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| template <typename Dtype> | ||
| void DeconvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, | ||
| const vector<Blob<Dtype>*>& top) { | ||
| const Dtype* weight = this->blobs_[0]->gpu_data(); | ||
| for (int i = 0; i < bottom.size(); ++i) { | ||
| const Dtype* bottom_data = bottom[i]->gpu_data(); | ||
| Dtype* top_data = top[i]->mutable_gpu_data(); | ||
| for (int n = 0; n < this->num_; ++n) { | ||
| this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight, | ||
| top_data + top[i]->offset(n)); | ||
| if (this->bias_term_) { | ||
| const Dtype* bias = this->blobs_[1]->gpu_data(); | ||
| this->forward_gpu_bias(top_data + top[i]->offset(n), bias); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| template <typename Dtype> | ||
| void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, | ||
| const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { | ||
| const Dtype* weight = this->blobs_[0]->gpu_data(); | ||
| Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff(); | ||
| if (this->param_propagate_down_[0]) { | ||
| caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff); | ||
| } | ||
| if (this->bias_term_ && this->param_propagate_down_[1]) { | ||
| caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), | ||
| this->blobs_[1]->mutable_gpu_diff()); | ||
| } | ||
| for (int i = 0; i < top.size(); ++i) { | ||
| const Dtype* top_diff = top[i]->gpu_diff(); | ||
| const Dtype* bottom_data = bottom[i]->gpu_data(); | ||
| Dtype* bottom_diff = bottom[i]->mutable_gpu_diff(); | ||
| // Bias gradient, if necessary. | ||
| if (this->bias_term_ && this->param_propagate_down_[1]) { | ||
| Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff(); | ||
| for (int n = 0; n < this->num_; ++n) { | ||
| this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n)); | ||
| } | ||
| } | ||
| if (this->param_propagate_down_[0] || propagate_down[i]) { | ||
| for (int n = 0; n < this->num_; ++n) { | ||
| // gradient w.r.t. weight. Note that we will accumulate diffs. | ||
| if (this->param_propagate_down_[0]) { | ||
| this->weight_gpu_gemm(top_diff + top[i]->offset(n), | ||
| bottom_data + bottom[i]->offset(n), weight_diff); | ||
| } | ||
| // gradient w.r.t. bottom data, if necessary. | ||
| if (propagate_down[i]) { | ||
| this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight, | ||
| bottom_diff + bottom[i]->offset(n)); | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| INSTANTIATE_LAYER_GPU_FUNCS(DeconvolutionLayer); | ||
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| } // namespace caffe |
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