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Copy patheltwise_layer.cu
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134 lines (126 loc) · 4.42 KB
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#include <cfloat>
#include <vector>
#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
__global__ void MaxForward(const int nthreads, const Dtype* bottom_data_a,
const Dtype* bottom_data_b, const int blob_idx, Dtype* top_data,
int* mask) {
CUDA_KERNEL_LOOP(index, nthreads) {
Dtype maxval = -FLT_MAX;
int maxidx = -1;
if (bottom_data_a[index] > bottom_data_b[index]) {
// only update for very first bottom_data blob (blob_idx == 0)
if (blob_idx == 0) {
maxval = bottom_data_a[index];
top_data[index] = maxval;
maxidx = blob_idx;
mask[index] = maxidx;
}
} else {
maxval = bottom_data_b[index];
top_data[index] = maxval;
maxidx = blob_idx + 1;
mask[index] = maxidx;
}
}
}
template <typename Dtype>
void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
int* mask = NULL;
const int count = top[0]->count();
Dtype* top_data = top[0]->mutable_gpu_data();
switch (op_) {
case EltwiseParameter_EltwiseOp_PROD:
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);
}
break;
case EltwiseParameter_EltwiseOp_SUM:
caffe_gpu_set(count, Dtype(0.), top_data);
// TODO(shelhamer) does cuBLAS optimize to sum for coeff = 1?
for (int i = 0; i < bottom.size(); ++i) {
caffe_gpu_axpy(count, coeffs_[i], bottom[i]->gpu_data(), top_data);
}
break;
case EltwiseParameter_EltwiseOp_MAX:
mask = max_idx_.mutable_gpu_data();
// NOLINT_NEXT_LINE(whitespace/operators)
MaxForward<Dtype> <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), 0, top_data, mask);
for (int i = 2; i < bottom.size(); ++i) {
// NOLINT_NEXT_LINE(whitespace/operators)
MaxForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, top_data, bottom[i]->gpu_data(), i-1, top_data, mask);
}
break;
default:
LOG(FATAL) << "Unknown elementwise operation.";
}
}
template <typename Dtype>
__global__ void MaxBackward(const int nthreads, const Dtype* top_diff,
const int blob_idx, const int* mask, Dtype* bottom_diff) {
CUDA_KERNEL_LOOP(index, nthreads) {
Dtype gradient = 0;
if (mask[index] == blob_idx) {
gradient += top_diff[index];
}
bottom_diff[index] = gradient;
}
}
template <typename Dtype>
void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const int* mask = NULL;
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) {
if (propagate_down[i]) {
const Dtype* bottom_data = bottom[i]->gpu_data();
Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
switch (op_) {
case EltwiseParameter_EltwiseOp_PROD:
if (stable_prod_grad_) {
bool initialized = false;
for (int j = 0; j < bottom.size(); ++j) {
if (i == j) { continue; }
if (!initialized) {
caffe_copy(count, bottom[j]->gpu_data(), bottom_diff);
initialized = true;
} else {
caffe_gpu_mul(count, bottom[j]->gpu_data(), bottom_diff,
bottom_diff);
}
}
} else {
caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
}
caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
break;
case EltwiseParameter_EltwiseOp_SUM:
if (coeffs_[i] == Dtype(1.)) {
caffe_copy(count, top_diff, bottom_diff);
} else {
caffe_gpu_scale(count, coeffs_[i], top_diff, bottom_diff);
}
break;
case EltwiseParameter_EltwiseOp_MAX:
mask = max_idx_.gpu_data();
MaxBackward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
<<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, top_diff, i, mask, bottom_diff);
break;
default:
LOG(FATAL) << "Unknown elementwise operation.";
}
}
}
}
INSTANTIATE_LAYER_GPU_FUNCS(EltwiseLayer);
} // namespace caffe