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#ifdef USE_ACL
#include <vector>
#include "caffe/layers/acl_pooling_layer.hpp"
namespace caffe {
template <typename Dtype>
void ACLPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
PoolingLayer<Dtype>::LayerSetUp(bottom, top);
this->force_bypass_acl_path_= bypass_acl_class_layer & FLAGS_ENABLE_ACL_POOLING;
}
template <typename Dtype>
void ACLPoolingLayer<Dtype>::SetupACLLayer(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top){
TensorShape in_shape ((unsigned int)this->width_, (unsigned int)this->height_,(unsigned int)this->channels_);
TensorShape out_shape((unsigned int)this->pooled_width_, (unsigned int)this->pooled_height_,(unsigned int)this->channels_);
checkreshape(in_shape,Caffe::arm_gpu_mode());
if (!this->init_layer_) return;
this->init_layer_=false;
// Initialize ACL.
if (Caffe::arm_gpu_mode()) {
new_gpulayer();
}else{
new_cpulayer();
}
this->force_bypass_acl_path_=false;
PoolingLayerInfo *pool_info;
if(this->layer_param_.pooling_param().pool()==PoolingParameter_PoolMethod_MAX)
pool_info=new PoolingLayerInfo(PoolingType::MAX, this->kernel_w_, PadStrideInfo(this->stride_w_,this->stride_h_,this->pad_w_,this->pad_h_,DimensionRoundingType::CEIL));
else
pool_info=new PoolingLayerInfo(PoolingType::AVG, this->kernel_w_, PadStrideInfo(this->stride_w_,this->stride_h_,this->pad_w_,this->pad_h_,DimensionRoundingType::CEIL));
if (Caffe::arm_gpu_mode()) {
Dtype *top_data = top[0]->mutable_gpu_data();
const Dtype* bottom_data = bottom[0]->gpu_data();
new_tensor(this->gpu().input,in_shape,(void*)bottom_data);
new_tensor(this->gpu().output,out_shape,(void*)top_data);
#ifdef USE_PROFILING
logtime_util log_time(ACL_CONFIG_INFO);
#endif //USE_PROFILING
this->gpu().layer->configure(this->gpu().input,this->gpu().output,*pool_info);
}else{
Dtype *top_data = top[0]->mutable_cpu_data();
const Dtype* bottom_data = bottom[0]->cpu_data();
new_tensor(this->cpu().input,in_shape,(void*)bottom_data);
new_tensor(this->cpu().output,out_shape,(void*)top_data);
#ifdef USE_PROFILING
logtime_util log_time(ACL_CONFIG_INFO);
#endif //USE_PROFILING
this->cpu().layer->configure(this->cpu().input,this->cpu().output,*pool_info);
}
delete pool_info;
}
template <typename Dtype>
void ACLPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
PoolingLayer<Dtype>::Reshape(bottom, top);
}
template <typename Dtype>
void ACLPoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
if(Caffe::arm_gpu_mode()){
Forward_gpu(bottom, top);
return;
}
#ifdef USE_PROFILING
logtime_util log_time(ACL_POOLING_INFO);
#endif //USE_PROFILING
if (this->force_bypass_acl_path_|| this->layer_param_.pooling_param().global_pooling()) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return;
}
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = top[0]->mutable_cpu_data();
if (this->layer_param_.pooling_param().pool()!=PoolingParameter_PoolMethod_MAX &&
this->layer_param_.pooling_param().pool()!=PoolingParameter_PoolMethod_AVE) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return ;
}
if (this->kernel_h_!=this->kernel_w_ || top.size()>1) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return ;
}
if (this->kernel_h_!=2 && this->kernel_h_!=3) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return ;
}
SetupACLLayer(bottom,top);
for (int n = 0; n < bottom[0]->num(); ++n) {
tensor_mem(this->cpu().input,(void*)(bottom_data));
cpu_run();
tensor_mem((void*)(top_data),this->cpu().output);
bottom_data += bottom[0]->offset(1);
top_data += top[0]->offset(1);
}
}
template <typename Dtype>
void ACLPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
#ifdef USE_PROFILING
logtime_util log_time(ACL_POOLING_INFO);
#endif //USE_PROFILING
if (this->force_bypass_acl_path_|| this->layer_param_.pooling_param().global_pooling()) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return;
}
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
if (this->layer_param_.pooling_param().pool()!=PoolingParameter_PoolMethod_MAX &&
this->layer_param_.pooling_param().pool()!=PoolingParameter_PoolMethod_AVE) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return ;
}
if (this->kernel_h_!=this->kernel_w_) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return ;
}
if (this->kernel_h_!=2 && this->kernel_h_!=3) {
PoolingLayer<Dtype>::Forward_cpu(bottom,top);
return ;
}
SetupACLLayer(bottom,top);
for (int n = 0; n < bottom[0]->num(); ++n) {
tensor_mem(this->gpu().input,(void*)(bottom_data));
gpu_run();
tensor_mem((void*)(top_data),this->gpu().output);
bottom_data += bottom[0]->offset(1);
top_data += top[0]->offset(1);
}
}
template <typename Dtype>
ACLPoolingLayer<Dtype>::~ACLPoolingLayer() {
}
INSTANTIATE_CLASS(ACLPoolingLayer);
} // namespace caffe
#endif // USE_ACL
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