}
template <typename Dtype>
-void ACLLocalConnectLayer<Dtype>::SetupACLLayer(const vector<Blob<Dtype>*>& bottom,
+void ACLLocalConnectLayer<Dtype>::SetupACLOperator(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top){
- TensorShape input_shape((unsigned int)bottom[0]->width(), (unsigned int)bottom[0]->height(),(unsigned int)bottom[0]->channels(),(unsigned int)bottom[0]->num());
- checkreshape(input_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;
+ arm_compute::TensorShape input_shape((unsigned int)bottom[0]->width(), (unsigned int)bottom[0]->height(),(unsigned int)bottom[0]->channels(),(unsigned int)bottom[0]->num());
+ if (is_operator_init_done(input_shape)) return;
+ set_operator_init_done();
+
+ // Initialize ACL.
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
int stride_x =this->stride_;
int stride_y =this->stride_;
int pad_y=this->pad_;
unsigned int kernel_x=this->kernel_size_;
unsigned int kernel_y=this->kernel_size_;
- PadStrideInfo conv_info(stride_x,stride_y,pad_x,pad_y);
- TensorShape weights_shape(kernel_x,kernel_y,(unsigned int)this->channels_, (unsigned int)this->num_output_);
- TensorShape biases_shape ((unsigned int)this->num_output_);
- TensorShape output_shape((unsigned int)top[0]->width(), (unsigned int)top[0]->height(),(unsigned int)top[0]->channels(),(unsigned int)top[0]->num());
-
- if (Caffe::arm_gpu_mode()) {
- Dtype *top_data = top[0]->mutable_gpu_data();
- const Dtype* bottom_data = bottom[0]->gpu_data();
- //[kernel_x, kernel_y, IFM, OFM]
- new_tensor(this->gpu().weights,weights_shape,(void*)(this->blobs_[0].get()->mutable_gpu_data()));
- tensor_mem(this->gpu().weights,(void*)(this->blobs_[0].get()->mutable_gpu_data()));
- //[OFM]
- if (this->bias_term_) {
- new_tensor(this->gpu().biases,biases_shape,(void*)(this->blobs_[1].get()->mutable_gpu_data()));
- tensor_mem(this->gpu().biases,(void*)(this->blobs_[1].get()->mutable_gpu_data()));
- }
+ arm_compute::PadStrideInfo conv_info(stride_x,stride_y,pad_x,pad_y);
+ arm_compute::TensorShape weights_shape(kernel_x,kernel_y,(unsigned int)this->channels_, (unsigned int)this->num_output_);
+ arm_compute::TensorShape biases_shape ((unsigned int)this->num_output_);
+ arm_compute::TensorShape output_shape((unsigned int)top[0]->width(), (unsigned int)top[0]->height(),(unsigned int)top[0]->channels(),(unsigned int)top[0]->num());
- //[width, height, IFM]
- new_tensor(this->gpu().input,input_shape,(void*)bottom_data);
- //[width, height, OFM]
- new_tensor(this->gpu().output,output_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().weights,this->gpu().biases,this->gpu().output,conv_info);
-#ifdef USE_PROFILING
- }
-#endif //USE_PROFILING
-#ifdef USE_CONV_CACHE
- for(int i = 0; i < 16; ++i){
- fprintf(stderr, "<GPU>check cache[%d]\n", i);
- if(this->gpu().cache.layer[i] == nullptr){
- this->gpu().cache.layer[i] = this->gpu().layer;
- this->gpu().cache.input[i] = this->gpu().input;
- this->gpu().cache.output[i] = this->gpu().output;
- this->gpu().cache.weights[i] = this->gpu().weights;
- this->gpu().cache.biases[i] = this->gpu().biases;
- break;
- }
- }
-#endif //USE_CONV_CACHE
- }else{
- Dtype *top_data = top[0]->mutable_cpu_data();
- const Dtype* bottom_data = bottom[0]->cpu_data();
- //[kernel_x, kernel_y, IFM, OFM]
- new_tensor(this->cpu().weights,weights_shape,(void*)(this->blobs_[0].get()->mutable_cpu_data()));
- tensor_mem(this->cpu().weights,(void*)(this->blobs_[0].get()->mutable_cpu_data()));
- //[OFM]
- if (this->bias_term_) {
- new_tensor(this->cpu().biases,biases_shape,(void*)(this->blobs_[1].get()->mutable_cpu_data()));
- tensor_mem(this->cpu().biases,(void*)(this->blobs_[1].get()->mutable_cpu_data()));
- }
-
- //[width, height, IFM]
- new_tensor(this->cpu().input,input_shape,(void*)bottom_data);
- //[width, height, OFM]
- new_tensor(this->cpu().output,output_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().weights,this->cpu().biases,this->cpu().output,conv_info);
-#ifdef USE_PROFILING
- }
-#endif //USE_PROFILING
+ //[kernel_x, kernel_y, IFM, OFM]
+ new_tensor(weights(),weights_shape,GetDataPtr(this,this->blobs_[0].get()));
+ //[OFM]
+ if (this->bias_term_) {
+ new_tensor(biases(),biases_shape,GetDataPtr(this,this->blobs_[1].get()));
}
+
+ //[width, height, IFM]
+ new_tensor(input(),input_shape,InputdataPtr(this,bottom));
+ //[width, height, OFM]
+ new_tensor(output(),output_shape,OutputdataPtr(this,top));
+ acl_configure(lc,this,conv_info);
}
template <typename Dtype>
void ACLLocalConnectLayer<Dtype>::Reshape(
}
template <typename Dtype>
+bool ACLLocalConnectLayer<Dtype>::Bypass_acl(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top){
+ bool bypass_acl=false;
+ if (this->force_bypass_acl_path_) {
+ bypass_acl=true;
+ }
+
+ ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+ if (conv_param.kernel_size_size()>2 ) {
+ bypass_acl=true;
+ }
+ return bypass_acl;
+}
+
+template <typename Dtype>
void ACLLocalConnectLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
- if(Caffe::arm_gpu_mode()){
+ if(isGPUMode()){
Forward_gpu(bottom, top);
return;
}
#ifdef USE_PROFILING
logtime_util log_time(ACL_LC_INFO);
#endif //USE_PROFILING
- if (this->force_bypass_acl_path_) {
- LocalConnectLayer<Dtype>::Forward_cpu(bottom,top);
- return;
- }
-
- ConvolutionParameter conv_param = this->layer_param_.convolution_param();
- if (conv_param.kernel_size_size()>2 ) {
+ if (Bypass_acl(bottom,top)) {
LocalConnectLayer<Dtype>::Forward_cpu(bottom,top);
return;
}
- SetupACLLayer(bottom,top);
- for (int i = 0; i < bottom.size(); ++i) {
- const Dtype* bottom_data = bottom[i]->cpu_data();
- Dtype* top_data = top[i]->mutable_cpu_data();
- tensor_mem(this->cpu().input,(void*)bottom_data);
- cpu_run();
- tensor_mem((void*)top_data,this->cpu().output);
- }
+ SetupACLOperator(bottom,top);
+ caffe::acl_run(this,bottom,top);
}
template <typename Dtype>
logtime_util log_time(ACL_LC_INFO);
#endif //USE_PROFILING
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
- if (this->force_bypass_acl_path_) {
- LocalConnectLayer<Dtype>::Forward_cpu(bottom,top);
- return;
- }
- if (conv_param.kernel_size_size()>2 ) {
+ if (Bypass_acl(bottom,top)) {
LocalConnectLayer<Dtype>::Forward_cpu(bottom,top);
return;
}
- SetupACLLayer(bottom,top);
- for (int i = 0; i < bottom.size(); ++i) {
- const Dtype* bottom_data = bottom[i]->gpu_data();
- Dtype* top_data = top[i]->mutable_gpu_data();
- tensor_mem(this->gpu().input,(void*)bottom_data);
- gpu_run();
- tensor_mem((void*)top_data,this->gpu().output);
- }
+ SetupACLOperator(bottom,top);
+ caffe::acl_run(this,bottom,top);
}
template <typename Dtype>
projects / platform / upstream / caffeonacl.git / blobdiff