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bn_layer.cpp
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bn_layer.cpp
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#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>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// set phase
phase_ = Caffe::phase();
test_initialized_ = false;
// Figure out the dimensions
N_ = bottom[0]->num();
C_ = bottom[0]->channels();
H_ = bottom[0]->height();
W_ = bottom[0]->width();
var_eps_ = 1e-10;
// reshape blob
top[0]->Reshape(N_, C_, H_, W_);
x_norm_.Reshape(N_, C_, H_, W_);
// 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);
batch_variance_sqrt_.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);
// 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);
// 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());
/*
// fill scale and shift with data mean and variance
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = top[0]->mutable_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());
// fill shift
caffe_copy(batch_mean_.count(), batch_mean_.cpu_data(), this->blobs_[1]->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
// normalize variance
caffe_powx(batch_variance_.count(), batch_variance_.cpu_data(), Dtype(0.5),
batch_variance_.mutable_cpu_data());
caffe_add_scalar(batch_variance_.count(), var_eps_, batch_variance_.mutable_cpu_data());
// fill scale
caffe_copy(batch_variance_.count(), batch_variance_.cpu_data(), this->blobs_[0]->mutable_cpu_data());
*/
} // 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* const_bottom_data = bottom[0]->cpu_data();
const Dtype* const_top_data = top[0]->cpu_data();
Dtype* top_data = top[0]->mutable_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(), const_bottom_data, Dtype(2),
buffer_blob_.mutable_cpu_data());
if (phase_ == Caffe::TEST && test_initialized_ == true) {
// do nothing;
} else {
// 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_)), const_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(), const_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_copy(batch_variance_.count(), batch_variance_.cpu_data(), batch_variance_sqrt_.mutable_cpu_data());
caffe_add_scalar(batch_variance_sqrt_.count(), var_eps_, batch_variance_sqrt_.mutable_cpu_data());
caffe_powx(batch_variance_sqrt_.count(), batch_variance_sqrt_.cpu_data(), Dtype(0.5),
batch_variance_sqrt_.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_, 1, Dtype(1),
batch_sum_multiplier_.cpu_data(), batch_variance_sqrt_.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);
// save 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(), const_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* const_bottom_diff = bottom[0]->cpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
const Dtype* const_top_diff = top[0]->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();
// gradient w.r.t. scale
caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), const_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_data());
// EX across batch
caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1), spatial_variance_.cpu_data(),
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), const_top_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), shift_diff);
// 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(), const_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), const_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(), const_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);
// variance normalization
//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_, 1, Dtype(1),
batch_sum_multiplier_.cpu_data(), batch_variance_sqrt_.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_bottom_diff, buffer_blob_.cpu_data(), bottom_diff);
}
#ifdef CPU_ONLY
STUB_GPU(BNLayer);
#endif
INSTANTIATE_CLASS(BNLayer);
REGISTER_LAYER_CLASS(BN, BNLayer);
} // namespace caffe