| | |
| | | |
| | | size_t get_workspace_size(layer l){ |
| | | #ifdef CUDNN |
| | | size_t most = 0; |
| | | size_t s = 0; |
| | | cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), |
| | | l.srcTensorDesc, |
| | | l.filterDesc, |
| | | l.convDesc, |
| | | l.dstTensorDesc, |
| | | l.fw_algo, |
| | | &s); |
| | | if (s > most) most = s; |
| | | cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), |
| | | l.srcTensorDesc, |
| | | l.ddstTensorDesc, |
| | | l.convDesc, |
| | | l.dfilterDesc, |
| | | l.bf_algo, |
| | | &s); |
| | | if (s > most) most = s; |
| | | cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), |
| | | l.filterDesc, |
| | | l.ddstTensorDesc, |
| | | l.convDesc, |
| | | l.dsrcTensorDesc, |
| | | l.bd_algo, |
| | | &s); |
| | | if (s > most) most = s; |
| | | return most; |
| | | #else |
| | | if(gpu_index >= 0){ |
| | | size_t most = 0; |
| | | size_t s = 0; |
| | | cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), |
| | | l.srcTensorDesc, |
| | | l.filterDesc, |
| | | l.convDesc, |
| | | l.dstTensorDesc, |
| | | l.fw_algo, |
| | | &s); |
| | | if (s > most) most = s; |
| | | cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), |
| | | l.srcTensorDesc, |
| | | l.ddstTensorDesc, |
| | | l.convDesc, |
| | | l.dfilterDesc, |
| | | l.bf_algo, |
| | | &s); |
| | | if (s > most) most = s; |
| | | cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), |
| | | l.filterDesc, |
| | | l.ddstTensorDesc, |
| | | l.convDesc, |
| | | l.dsrcTensorDesc, |
| | | l.bd_algo, |
| | | &s); |
| | | if (s > most) most = s; |
| | | return most; |
| | | } |
| | | #endif |
| | | return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); |
| | | #endif |
| | | } |
| | | |
| | | #ifdef GPU |
| | |
| | | } |
| | | |
| | | #ifdef GPU |
| | | l.filters_gpu = cuda_make_array(l.filters, c*n*size*size); |
| | | l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size); |
| | | if(gpu_index >= 0){ |
| | | l.filters_gpu = cuda_make_array(l.filters, c*n*size*size); |
| | | l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size); |
| | | |
| | | l.biases_gpu = cuda_make_array(l.biases, n); |
| | | l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); |
| | | l.biases_gpu = cuda_make_array(l.biases, n); |
| | | l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); |
| | | |
| | | l.scales_gpu = cuda_make_array(l.scales, n); |
| | | l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); |
| | | l.scales_gpu = cuda_make_array(l.scales, n); |
| | | l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); |
| | | |
| | | l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); |
| | | l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); |
| | | l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); |
| | | l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); |
| | | |
| | | if(binary){ |
| | | l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size); |
| | | } |
| | | if(xnor){ |
| | | l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size); |
| | | l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); |
| | | } |
| | | if(binary){ |
| | | l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size); |
| | | } |
| | | if(xnor){ |
| | | l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size); |
| | | l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); |
| | | } |
| | | |
| | | if(batch_normalize){ |
| | | l.mean_gpu = cuda_make_array(l.mean, n); |
| | | l.variance_gpu = cuda_make_array(l.variance, n); |
| | | if(batch_normalize){ |
| | | l.mean_gpu = cuda_make_array(l.mean, n); |
| | | l.variance_gpu = cuda_make_array(l.variance, n); |
| | | |
| | | l.rolling_mean_gpu = cuda_make_array(l.mean, n); |
| | | l.rolling_variance_gpu = cuda_make_array(l.variance, n); |
| | | l.rolling_mean_gpu = cuda_make_array(l.mean, n); |
| | | l.rolling_variance_gpu = cuda_make_array(l.variance, n); |
| | | |
| | | l.mean_delta_gpu = cuda_make_array(l.mean, n); |
| | | l.variance_delta_gpu = cuda_make_array(l.variance, n); |
| | | l.mean_delta_gpu = cuda_make_array(l.mean, n); |
| | | l.variance_delta_gpu = cuda_make_array(l.variance, n); |
| | | |
| | | l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); |
| | | l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); |
| | | } |
| | | l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); |
| | | l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); |
| | | } |
| | | #ifdef CUDNN |
| | | cudnnCreateTensorDescriptor(&l.srcTensorDesc); |
| | | cudnnCreateTensorDescriptor(&l.dstTensorDesc); |
| | | cudnnCreateFilterDescriptor(&l.filterDesc); |
| | | cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); |
| | | cudnnCreateTensorDescriptor(&l.ddstTensorDesc); |
| | | cudnnCreateFilterDescriptor(&l.dfilterDesc); |
| | | cudnnCreateConvolutionDescriptor(&l.convDesc); |
| | | cudnn_convolutional_setup(&l); |
| | | cudnnCreateTensorDescriptor(&l.srcTensorDesc); |
| | | cudnnCreateTensorDescriptor(&l.dstTensorDesc); |
| | | cudnnCreateFilterDescriptor(&l.filterDesc); |
| | | cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); |
| | | cudnnCreateTensorDescriptor(&l.ddstTensorDesc); |
| | | cudnnCreateFilterDescriptor(&l.dfilterDesc); |
| | | cudnnCreateConvolutionDescriptor(&l.convDesc); |
| | | cudnn_convolutional_setup(&l); |
| | | #endif |
| | | } |
| | | #endif |
| | | l.workspace_size = get_workspace_size(l); |
| | | l.activation = activation; |
| | |
| | | l.filters[i*l.c*l.size*l.size + j] *= scale; |
| | | } |
| | | l.biases[i] -= l.rolling_mean[i] * scale; |
| | | l.scales[i] = 1; |
| | | l.rolling_mean[i] = 0; |
| | | l.rolling_variance[i] = 1; |
| | | } |
| | | } |
| | | |
| | |
| | | } |
| | | */ |
| | | |
| | | if(l.xnor ){ |
| | | if(l.xnor){ |
| | | binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters); |
| | | swap_binary(&l); |
| | | binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input); |