-
Notifications
You must be signed in to change notification settings - Fork 18.6k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Added batchnorm layer with tests #1891
Closed
Closed
Changes from all commits
Commits
File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,283 @@ | ||
| #include <algorithm> | ||
| #include <vector> | ||
|
|
||
| #include "caffe/common_layers.hpp" | ||
| #include "caffe/filler.hpp" | ||
| #include "caffe/layer.hpp" | ||
| #include "caffe/util/math_functions.hpp" | ||
|
|
||
| namespace caffe { | ||
| template <typename Dtype> | ||
| void BNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom, | ||
| const vector<Blob<Dtype>*>& top) { | ||
| top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), | ||
| bottom[0]->height(), bottom[0]->width()); | ||
|
|
||
| x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(), | ||
| bottom[0]->height(), bottom[0]->width()); | ||
|
|
||
| // Figure out the dimensions | ||
| N_ = bottom[0]->num(); | ||
| C_ = bottom[0]->channels(); | ||
| H_ = bottom[0]->height(); | ||
| W_ = bottom[0]->width(); | ||
|
|
||
| // mean | ||
| spatial_mean_.Reshape(N_, C_, 1, 1); | ||
| batch_mean_.Reshape(1, C_, 1, 1); | ||
| // variance | ||
| spatial_variance_.Reshape(N_, C_, 1, 1); | ||
| batch_variance_.Reshape(1, C_, 1, 1); | ||
| // buffer blod | ||
| buffer_blob_.Reshape(N_, C_, H_, W_); | ||
|
|
||
| // fill spatial multiplier | ||
| spatial_sum_multiplier_.Reshape(1, 1, H_, W_); | ||
| Dtype* spatial_multiplier_data = spatial_sum_multiplier_.mutable_cpu_data(); | ||
| caffe_set(spatial_sum_multiplier_.count(), Dtype(1), spatial_multiplier_data); | ||
| caffe_set(spatial_sum_multiplier_.count(), Dtype(0), spatial_sum_multiplier_.mutable_cpu_diff()); | ||
| // fill batch multiplier | ||
| batch_sum_multiplier_.Reshape(N_, 1, 1, 1); | ||
| Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data(); | ||
| caffe_set(batch_sum_multiplier_.count(), Dtype(1), batch_multiplier_data); | ||
| caffe_set(batch_sum_multiplier_.count(), Dtype(0), batch_sum_multiplier_.mutable_cpu_diff()); | ||
| this->param_propagate_down_.resize(this->blobs_.size(), true); | ||
| } | ||
| template <typename Dtype> | ||
| void BNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom, | ||
| const vector<Blob<Dtype>*>& top) { | ||
| top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), | ||
| bottom[0]->height(), bottom[0]->width()); | ||
|
|
||
| x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(), | ||
| bottom[0]->height(), bottom[0]->width()); | ||
| // Figure out the dimensions | ||
| N_ = bottom[0]->num(); | ||
| C_ = bottom[0]->channels(); | ||
| H_ = bottom[0]->height(); | ||
| W_ = bottom[0]->width(); | ||
| var_eps_ = 1e-9; | ||
|
|
||
| // mean | ||
| spatial_mean_.Reshape(N_, C_, 1, 1); | ||
| batch_mean_.Reshape(1, C_, 1, 1); | ||
| // variance | ||
| spatial_variance_.Reshape(N_, C_, 1, 1); | ||
| batch_variance_.Reshape(1, C_, 1, 1); | ||
| // buffer blod | ||
| buffer_blob_.Reshape(N_, C_, H_, W_); | ||
|
|
||
| // fill spatial multiplier | ||
| spatial_sum_multiplier_.Reshape(1, 1, H_, W_); | ||
| Dtype* spatial_multiplier_data = spatial_sum_multiplier_.mutable_cpu_data(); | ||
| caffe_set(spatial_sum_multiplier_.count(), Dtype(1), spatial_multiplier_data); | ||
| caffe_set(spatial_sum_multiplier_.count(), Dtype(0), spatial_sum_multiplier_.mutable_cpu_diff()); | ||
|
|
||
| // fill batch multiplier | ||
| batch_sum_multiplier_.Reshape(N_, 1, 1, 1); | ||
| Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data(); | ||
| caffe_set(batch_sum_multiplier_.count(), Dtype(1), batch_multiplier_data); | ||
| caffe_set(batch_sum_multiplier_.count(), Dtype(0), batch_sum_multiplier_.mutable_cpu_diff()); | ||
|
|
||
| // Check if we need to set up the weights | ||
| if (this->blobs_.size() > 0) { | ||
| LOG(INFO) << "Skipping parameter initialization"; | ||
| } else { | ||
| this->blobs_.resize(2); | ||
|
|
||
| // fill scale with scale_filler | ||
| this->blobs_[0].reset(new Blob<Dtype>(1, C_, 1, 1)); | ||
| shared_ptr<Filler<Dtype> > scale_filler(GetFiller<Dtype>( | ||
| this->layer_param_.bn_param().scale_filler())); | ||
| scale_filler->Fill(this->blobs_[0].get()); | ||
|
|
||
| // fill shift with shift_filler | ||
| this->blobs_[1].reset(new Blob<Dtype>(1, C_, 1, 1)); | ||
| shared_ptr<Filler<Dtype> > shift_filler(GetFiller<Dtype>( | ||
| this->layer_param_.bn_param().shift_filler())); | ||
| shift_filler->Fill(this->blobs_[1].get()); | ||
|
|
||
| } // parameter initialization | ||
| this->param_propagate_down_.resize(this->blobs_.size(), true); | ||
| } | ||
|
|
||
| template <typename Dtype> | ||
| void BNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, | ||
| const vector<Blob<Dtype>*>& top) { | ||
| const Dtype* bottom_data = bottom[0]->cpu_data(); | ||
| Dtype* top_data = top[0]->mutable_cpu_data(); | ||
| const Dtype* const_top_data = top[0]->cpu_data(); | ||
|
|
||
| const Dtype* scale_data = this->blobs_[0]->cpu_data(); | ||
| const Dtype* shift_data = this->blobs_[1]->cpu_data(); | ||
|
|
||
| // put the squares of bottom into buffer_blob_ | ||
| caffe_powx(bottom[0]->count(), bottom_data, Dtype(2), | ||
| buffer_blob_.mutable_cpu_data()); | ||
|
|
||
| // computes variance using var(X) = E(X^2) - (EX)^2 | ||
| // EX across spatial | ||
| caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1. / (H_ * W_)), bottom_data, | ||
| spatial_sum_multiplier_.cpu_data(), Dtype(0), spatial_mean_.mutable_cpu_data()); | ||
| // EX across batch | ||
| caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_), spatial_mean_.cpu_data(), | ||
| batch_sum_multiplier_.cpu_data(), Dtype(0), batch_mean_.mutable_cpu_data()); | ||
|
|
||
| // E(X^2) across spatial | ||
| caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1. / (H_ * W_)), buffer_blob_.cpu_data(), | ||
| spatial_sum_multiplier_.cpu_data(), Dtype(0), spatial_variance_.mutable_cpu_data()); | ||
| // E(X^2) across batch | ||
| caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_), spatial_variance_.cpu_data(), | ||
| batch_sum_multiplier_.cpu_data(), Dtype(0), batch_variance_.mutable_cpu_data()); | ||
|
|
||
| caffe_powx(batch_mean_.count(), batch_mean_.cpu_data(), Dtype(2), | ||
| buffer_blob_.mutable_cpu_data()); // (EX)^2 | ||
| caffe_sub(batch_mean_.count(), batch_variance_.cpu_data(), buffer_blob_.cpu_data(), | ||
| batch_variance_.mutable_cpu_data()); // variance | ||
|
|
||
| // do mean and variance normalization | ||
| // subtract mean | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), batch_mean_.cpu_data(), Dtype(0), | ||
| spatial_mean_.mutable_cpu_data()); | ||
|
|
||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(-1), | ||
| spatial_mean_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| buffer_blob_.mutable_cpu_data()); | ||
|
|
||
| caffe_add(buffer_blob_.count(), bottom_data, buffer_blob_.cpu_data(), top_data); | ||
|
|
||
| // normalize variance | ||
| caffe_add_scalar(batch_variance_.count(), var_eps_, batch_variance_.mutable_cpu_data()); | ||
| caffe_powx(batch_variance_.count(), batch_variance_.cpu_data(), Dtype(0.5), | ||
| batch_variance_.mutable_cpu_data()); | ||
|
|
||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), batch_variance_.cpu_data(), Dtype(0), | ||
| spatial_variance_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_variance_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| buffer_blob_.mutable_cpu_data()); | ||
|
|
||
| caffe_div(buffer_blob_.count(), const_top_data, buffer_blob_.cpu_data(), top_data); | ||
|
|
||
| //Saving x_norm | ||
| caffe_copy(buffer_blob_.count(),const_top_data,x_norm_.mutable_cpu_data()); | ||
| // scale | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0), | ||
| spatial_variance_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_variance_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| buffer_blob_.mutable_cpu_data()); | ||
| caffe_mul(buffer_blob_.count(), top_data, buffer_blob_.cpu_data(), top_data); | ||
|
|
||
| // shift | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), shift_data, Dtype(0), | ||
| spatial_mean_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_mean_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| buffer_blob_.mutable_cpu_data()); | ||
| caffe_add(buffer_blob_.count(), const_top_data, buffer_blob_.cpu_data(), top_data); | ||
|
|
||
| } | ||
|
|
||
| template <typename Dtype> | ||
| void BNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, | ||
| const vector<bool>& propagate_down, | ||
| const vector<Blob<Dtype>*>& bottom) { | ||
| const Dtype* top_diff = top[0]->cpu_diff(); | ||
| const Dtype* top_data = top[0]->cpu_data(); | ||
| const Dtype* bottom_data = bottom[0]->cpu_data(); | ||
| Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); | ||
|
|
||
| Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff(); | ||
| Dtype* shift_diff = this->blobs_[1]->mutable_cpu_diff(); | ||
| const Dtype* scale_data = this->blobs_[0]->cpu_data(); | ||
|
|
||
| // Propagate layer to parameters | ||
| // gradient w.r.t. scale | ||
| caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), top_diff, buffer_blob_.mutable_cpu_data()); | ||
| // EX across spatial | ||
| caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), buffer_blob_.cpu_data(), | ||
| spatial_sum_multiplier_.cpu_data(), Dtype(0), spatial_variance_.mutable_cpu_diff()); | ||
| // EX across batch | ||
| caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1), spatial_variance_.cpu_diff(), | ||
| batch_sum_multiplier_.cpu_data(), Dtype(0), scale_diff); | ||
|
|
||
| // gradient w.r.t. shift | ||
| // EX across spatial | ||
| caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), top_diff, | ||
| spatial_sum_multiplier_.cpu_data(), Dtype(0), spatial_mean_.mutable_cpu_diff()); | ||
| // EX across batch | ||
| caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1), spatial_mean_.cpu_diff(), | ||
| batch_sum_multiplier_.cpu_data(), Dtype(0), shift_diff); | ||
|
|
||
| // Propagate down | ||
|
|
||
| // put scale * top_diff to buffer_blob_ | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0), | ||
| spatial_variance_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_variance_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| buffer_blob_.mutable_cpu_data()); | ||
| caffe_mul(buffer_blob_.count(), top_diff, buffer_blob_.cpu_data(), buffer_blob_.mutable_cpu_data()); | ||
|
|
||
| // use new top diff for computation | ||
| caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), buffer_blob_.cpu_data(), bottom_diff); | ||
| // EX across spatial | ||
| caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), bottom_diff, | ||
| spatial_sum_multiplier_.cpu_data(), Dtype(0), spatial_mean_.mutable_cpu_data()); | ||
| // EX across batch | ||
| caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1), spatial_mean_.cpu_data(), | ||
| batch_sum_multiplier_.cpu_data(), Dtype(0), batch_mean_.mutable_cpu_data()); | ||
|
|
||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), batch_mean_.cpu_data(), Dtype(0), | ||
| spatial_mean_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_mean_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| bottom_diff); | ||
|
|
||
| caffe_mul(buffer_blob_.count(), x_norm_.cpu_data()/*top_data*/, bottom_diff, bottom_diff); | ||
|
|
||
| // EX across spatial | ||
| caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), buffer_blob_.cpu_data(), | ||
| spatial_sum_multiplier_.cpu_data(), Dtype(0), spatial_mean_.mutable_cpu_data()); | ||
| // EX across batch | ||
| caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1), spatial_mean_.cpu_data(), | ||
| batch_sum_multiplier_.cpu_data(), Dtype(0), batch_mean_.mutable_cpu_data()); | ||
|
|
||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), batch_mean_.cpu_data(), Dtype(0), | ||
| spatial_mean_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_mean_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(1), | ||
| bottom_diff); | ||
|
|
||
| caffe_cpu_axpby(buffer_blob_.count(), Dtype(1), buffer_blob_.cpu_data(), Dtype(-1. / (N_ * H_ * W_)), | ||
| bottom_diff); | ||
|
|
||
| // put the squares of bottom into buffer_blob_ | ||
| caffe_powx(buffer_blob_.count(), bottom_data, Dtype(2), | ||
| buffer_blob_.mutable_cpu_data()); | ||
|
|
||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), | ||
| batch_sum_multiplier_.cpu_data(), batch_variance_.cpu_data(), Dtype(0), | ||
| spatial_variance_.mutable_cpu_data()); | ||
| caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(1), | ||
| spatial_variance_.cpu_data(), spatial_sum_multiplier_.cpu_data(), Dtype(0), | ||
| buffer_blob_.mutable_cpu_data()); | ||
|
|
||
| caffe_div(buffer_blob_.count(), bottom_diff, buffer_blob_.cpu_data(), bottom_diff); | ||
|
|
||
| } | ||
| #ifdef CPU_ONLY | ||
| STUB_GPU(BNLayer); | ||
| #endif | ||
|
|
||
| INSTANTIATE_CLASS(BNLayer); | ||
| REGISTER_LAYER_CLASS(BN); | ||
| } // namespace caffe | ||
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
// I think we should add these following code between line 244 and 246. Correct me if I am wrong
// EX across spatial
caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), bottom_diff, spatial_multiplier_.cpu_data(), Dtype(0), spatial_mean_.mutable_cpu_data());
// EX across batch
caffe_cpu_gemv(CblasNoTrans, N_, C_, Dtype(1), spatial_mean_.cpu_data(), batch_sum_multiplier_.cpu_data(), Dtype(0), batch_mean_.mutable_cpu_data());
caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), batch_sum_multiplier_.cpu_data(), batch_mean_.cpu_data(), Dtype(0), spatial_mean_.mutable_cpu_data());
caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, Dtype(-1), spatial_mean_.cpu_data(), spatial_multiplier_.cpu_data(), Dtype(1), bottom_diff);