Permalink
Fetching contributors…
Cannot retrieve contributors at this time
151 lines (141 sloc) 5.42 KB
#include <vector>
#include "caffe/filler.hpp"
#include "caffe/layers/inner_product_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const int num_output = this->layer_param_.inner_product_param().num_output();
bias_term_ = this->layer_param_.inner_product_param().bias_term();
transpose_ = this->layer_param_.inner_product_param().transpose();
N_ = num_output;
const int axis = bottom[0]->CanonicalAxisIndex(
this->layer_param_.inner_product_param().axis());
// Dimensions starting from "axis" are "flattened" into a single
// length K_ vector. For example, if bottom[0]'s shape is (N, C, H, W),
// and axis == 1, N inner products with dimension CHW are performed.
K_ = bottom[0]->count(axis);
// Check if we need to set up the weights
if (this->blobs_.size() > 0) {
LOG(INFO) << "Skipping parameter initialization";
} else {
if (bias_term_) {
this->blobs_.resize(2);
} else {
this->blobs_.resize(1);
}
// Initialize the weights
vector<int> weight_shape(2);
if (transpose_) {
weight_shape[0] = K_;
weight_shape[1] = N_;
} else {
weight_shape[0] = N_;
weight_shape[1] = K_;
}
this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
// fill the weights
shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
this->layer_param_.inner_product_param().weight_filler()));
weight_filler->Fill(this->blobs_[0].get());
// If necessary, initialize and fill the bias term
if (bias_term_) {
vector<int> bias_shape(1, N_);
this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
this->layer_param_.inner_product_param().bias_filler()));
bias_filler->Fill(this->blobs_[1].get());
}
} // parameter initialization
this->param_propagate_down_.resize(this->blobs_.size(), true);
}
template <typename Dtype>
void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// Figure out the dimensions
const int axis = bottom[0]->CanonicalAxisIndex(
this->layer_param_.inner_product_param().axis());
const int new_K = bottom[0]->count(axis);
CHECK_EQ(K_, new_K)
<< "Input size incompatible with inner product parameters.";
// The first "axis" dimensions are independent inner products; the total
// number of these is M_, the product over these dimensions.
M_ = bottom[0]->count(0, axis);
// The top shape will be the bottom shape with the flattened axes dropped,
// and replaced by a single axis with dimension num_output (N_).
vector<int> top_shape = bottom[0]->shape();
top_shape.resize(axis + 1);
top_shape[axis] = N_;
top[0]->Reshape(top_shape);
// Set up the bias multiplier
if (bias_term_) {
vector<int> bias_shape(1, M_);
bias_multiplier_.Reshape(bias_shape);
caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
}
}
template <typename Dtype>
void InnerProductLayer<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* weight = this->blobs_[0]->cpu_data();
caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans,
M_, N_, K_, (Dtype)1.,
bottom_data, weight, (Dtype)0., top_data);
if (bias_term_) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
bias_multiplier_.cpu_data(),
this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
}
}
template <typename Dtype>
void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) {
if (this->param_propagate_down_[0]) {
const Dtype* top_diff = top[0]->cpu_diff();
const Dtype* bottom_data = bottom[0]->cpu_data();
// Gradient with respect to weight
if (transpose_) {
caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
K_, N_, M_,
(Dtype)1., bottom_data, top_diff,
(Dtype)1., this->blobs_[0]->mutable_cpu_diff());
} else {
caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
N_, K_, M_,
(Dtype)1., top_diff, bottom_data,
(Dtype)1., this->blobs_[0]->mutable_cpu_diff());
}
}
if (bias_term_ && this->param_propagate_down_[1]) {
const Dtype* top_diff = top[0]->cpu_diff();
// Gradient with respect to bias
caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
bias_multiplier_.cpu_data(), (Dtype)1.,
this->blobs_[1]->mutable_cpu_diff());
}
if (propagate_down[0]) {
const Dtype* top_diff = top[0]->cpu_diff();
// Gradient with respect to bottom data
if (transpose_) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
M_, K_, N_,
(Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
(Dtype)0., bottom[0]->mutable_cpu_diff());
} else {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
M_, K_, N_,
(Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
(Dtype)0., bottom[0]->mutable_cpu_diff());
}
}
}
#ifdef CPU_ONLY
STUB_GPU(InnerProductLayer);
#endif
INSTANTIATE_CLASS(InnerProductLayer);
REGISTER_LAYER_CLASS(InnerProduct);
} // namespace caffe