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Within-channel LRN layer #273
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42c9d66
add LRN within map layer and dependencies (eltwise product and power)
jeffdonahue f3e2fe6
add unit tests for new layer types
jeffdonahue 6e92f47
use average pool instead of conv
jeffdonahue bd756fe
add padding for average pooling
jeffdonahue d047bef
bug fix: average pooling already divides by N^2
jeffdonahue 6c844cc
use split layer in LRNMapLayer
jeffdonahue 2031663
use bvlc copyright
jeffdonahue 42b832c
add cifar example using LRN_MAP (just like the cuda-convnet layers-18pct
jeffdonahue 4c81ee5
fix some param bugs
jeffdonahue 824c344
merge LRNMapLayer into LRNLayer with norm_region proto field
jeffdonahue 24f7318
replace old cifar full with within channel LRN (per cuda-convnet
jeffdonahue 69ac4f4
minor polishing
jeffdonahue d61adb8
remove unnecessary local variables from EltwiseProductLayer
jeffdonahue 9fb7818
don't recompute pre_pad
jeffdonahue 45f8626
cleanup extra LRN method names
jeffdonahue 86db2a9
minor unit test cleanup
jeffdonahue bf511f7
cleanup power layer test suite
jeffdonahue 404f22d
update proto field IDs from placeholder values
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,62 @@ | ||
| // Copyright 2014 BVLC and contributors. | ||
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| #include <vector> | ||
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| #include "caffe/layer.hpp" | ||
| #include "caffe/vision_layers.hpp" | ||
| #include "caffe/util/math_functions.hpp" | ||
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| namespace caffe { | ||
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| template <typename Dtype> | ||
| void EltwiseProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom, | ||
| vector<Blob<Dtype>*>* top) { | ||
| CHECK_GE(bottom.size(), 2) << | ||
| "Eltwise Product Layer takes at least 2 blobs as input."; | ||
| CHECK_EQ(top->size(), 1) << | ||
| "Eltwise Product Layer takes a single blob as output."; | ||
| const int num = bottom[0]->num(); | ||
| const int channels = bottom[0]->channels(); | ||
| const int height = bottom[0]->height(); | ||
| const int width = bottom[0]->width(); | ||
| for (int i = 1; i < bottom.size(); ++i) { | ||
| CHECK_EQ(num, bottom[i]->num()); | ||
| CHECK_EQ(channels, bottom[i]->channels()); | ||
| CHECK_EQ(height, bottom[i]->height()); | ||
| CHECK_EQ(width, bottom[i]->width()); | ||
| } | ||
| (*top)[0]->Reshape(num, channels, height, width); | ||
| } | ||
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| template <typename Dtype> | ||
| Dtype EltwiseProductLayer<Dtype>::Forward_cpu( | ||
| const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) { | ||
| const int count = (*top)[0]->count(); | ||
| Dtype* top_data = (*top)[0]->mutable_cpu_data(); | ||
| caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data); | ||
| for (int i = 2; i < bottom.size(); ++i) { | ||
| caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data); | ||
| } | ||
| return Dtype(0.); | ||
| } | ||
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| template <typename Dtype> | ||
| void EltwiseProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, | ||
| const bool propagate_down, vector<Blob<Dtype>*>* bottom) { | ||
| if (propagate_down) { | ||
| const int count = top[0]->count(); | ||
| const Dtype* top_data = top[0]->cpu_data(); | ||
| const Dtype* top_diff = top[0]->cpu_diff(); | ||
| for (int i = 0; i < bottom->size(); ++i) { | ||
| const Dtype* bottom_data = (*bottom)[i]->cpu_data(); | ||
| Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff(); | ||
| caffe_div(count, top_data, bottom_data, bottom_diff); | ||
| caffe_mul(count, bottom_diff, top_diff, bottom_diff); | ||
| } | ||
| } | ||
| } | ||
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| INSTANTIATE_CLASS(EltwiseProductLayer); | ||
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| } // namespace caffe |
42 changes: 42 additions & 0 deletions
42
src/caffe/layers/eltwise_product_layer.cu
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
| // Copyright 2014 BVLC and contributors. | ||
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| #include <vector> | ||
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| #include "caffe/layer.hpp" | ||
| #include "caffe/vision_layers.hpp" | ||
| #include "caffe/util/math_functions.hpp" | ||
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| namespace caffe { | ||
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| template <typename Dtype> | ||
| Dtype EltwiseProductLayer<Dtype>::Forward_gpu( | ||
| const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) { | ||
| const int count = (*top)[0]->count(); | ||
| Dtype* top_data = (*top)[0]->mutable_gpu_data(); | ||
| caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data); | ||
| for (int i = 2; i < bottom.size(); ++i) { | ||
| caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data); | ||
| } | ||
| return Dtype(0.); | ||
| } | ||
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| template <typename Dtype> | ||
| void EltwiseProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, | ||
| const bool propagate_down, vector<Blob<Dtype>*>* bottom) { | ||
| if (propagate_down) { | ||
| const int count = top[0]->count(); | ||
| const Dtype* top_data = top[0]->gpu_data(); | ||
| const Dtype* top_diff = top[0]->gpu_diff(); | ||
| for (int i = 0; i < bottom->size(); ++i) { | ||
| const Dtype* bottom_data = (*bottom)[i]->gpu_data(); | ||
| Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff(); | ||
| caffe_gpu_div(count, top_data, bottom_data, bottom_diff); | ||
| caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff); | ||
| } | ||
| } | ||
| } | ||
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| INSTANTIATE_CLASS(EltwiseProductLayer); | ||
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| } // namespace caffe |
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It seems that both PowerLayer and EltwiseProductLayer are suitable to be refactored in #244.