mirror of https://github.com/AlexeyAB/darknet.git
Joseph Redmon
10 years ago
parent
2774cd86d4
commit
e7d43fd65d
7 changed files with 66 additions and 250 deletions
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#include "cuda_runtime.h" |
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#include "curand.h" |
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#include "cublas_v2.h" |
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extern "C" { |
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#include "local_layer.h" |
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#include "gemm.h" |
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#include "blas.h" |
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#include "im2col.h" |
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#include "col2im.h" |
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#include "utils.h" |
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#include "cuda.h" |
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} |
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__global__ void scale_bias_kernel(float *output, float *biases, int n, int size) |
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{ |
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int offset = blockIdx.x * blockDim.x + threadIdx.x; |
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int filter = blockIdx.y; |
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int batch = blockIdx.z; |
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if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter]; |
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} |
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void scale_bias_gpu(float *output, float *biases, int batch, int n, int size) |
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{ |
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dim3 dimGrid((size-1)/BLOCK + 1, n, batch); |
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dim3 dimBlock(BLOCK, 1, 1); |
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scale_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size); |
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check_error(cudaPeekAtLastError()); |
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} |
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__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) |
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{ |
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__shared__ float part[BLOCK]; |
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int i,b; |
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int filter = blockIdx.x; |
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int p = threadIdx.x; |
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float sum = 0; |
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for(b = 0; b < batch; ++b){ |
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for(i = 0; i < size; i += BLOCK){ |
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int index = p + i + size*(filter + n*b); |
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sum += (p+i < size) ? delta[index]*x_norm[index] : 0; |
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} |
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} |
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part[p] = sum; |
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__syncthreads(); |
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if (p == 0) { |
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for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i]; |
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} |
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} |
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void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) |
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{ |
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backward_scale_kernel<<<n, BLOCK>>>(x_norm, delta, batch, n, size, scale_updates); |
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check_error(cudaPeekAtLastError()); |
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} |
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__global__ void add_bias_kernel(float *output, float *biases, int n, int size) |
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{ |
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int offset = blockIdx.x * blockDim.x + threadIdx.x; |
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int filter = blockIdx.y; |
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int batch = blockIdx.z; |
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if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter]; |
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} |
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void add_bias_gpu(float *output, float *biases, int batch, int n, int size) |
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{ |
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dim3 dimGrid((size-1)/BLOCK + 1, n, batch); |
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dim3 dimBlock(BLOCK, 1, 1); |
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add_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size); |
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check_error(cudaPeekAtLastError()); |
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} |
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__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) |
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{ |
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__shared__ float part[BLOCK]; |
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int i,b; |
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int filter = blockIdx.x; |
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int p = threadIdx.x; |
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float sum = 0; |
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for(b = 0; b < batch; ++b){ |
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for(i = 0; i < size; i += BLOCK){ |
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int index = p + i + size*(filter + n*b); |
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sum += (p+i < size) ? delta[index] : 0; |
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} |
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} |
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part[p] = sum; |
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__syncthreads(); |
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if (p == 0) { |
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for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i]; |
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} |
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} |
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void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) |
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{ |
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backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size); |
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check_error(cudaPeekAtLastError()); |
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} |
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void forward_local_layer_gpu(local_layer l, network_state state) |
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{ |
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int i; |
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int m = l.n; |
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int k = l.size*l.size*l.c; |
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int n = local_out_height(l)* |
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local_out_width(l); |
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fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); |
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for(i = 0; i < l.batch; ++i){ |
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im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu); |
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float * a = l.filters_gpu; |
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float * b = l.col_image_gpu; |
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float * c = l.output_gpu; |
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gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); |
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} |
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if(l.batch_normalize){ |
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if(state.train){ |
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fast_mean_gpu(l.output_gpu, l.batch, l.n, l.out_h*l.out_w, l.spatial_mean_gpu, l.mean_gpu); |
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fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.n, l.out_h*l.out_w, l.spatial_variance_gpu, l.variance_gpu); |
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scal_ongpu(l.n, .95, l.rolling_mean_gpu, 1); |
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axpy_ongpu(l.n, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1); |
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scal_ongpu(l.n, .95, l.rolling_variance_gpu, 1); |
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axpy_ongpu(l.n, .05, l.variance_gpu, 1, l.rolling_variance_gpu, 1); |
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// cuda_pull_array(l.variance_gpu, l.mean, l.n); |
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// printf("%f\n", l.mean[0]); |
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copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); |
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normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_h*l.out_w); |
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copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); |
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} else { |
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normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.n, l.out_h*l.out_w); |
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} |
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scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w); |
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} |
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n); |
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activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation); |
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} |
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void backward_local_layer_gpu(local_layer l, network_state state) |
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{ |
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int i; |
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int m = l.n; |
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int n = l.size*l.size*l.c; |
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int k = local_out_height(l)* |
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local_out_width(l); |
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gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu); |
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backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k); |
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if(l.batch_normalize){ |
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backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.scale_updates_gpu); |
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scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w); |
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fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.spatial_mean_delta_gpu, l.mean_delta_gpu); |
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fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.spatial_variance_delta_gpu, l.variance_delta_gpu); |
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normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.delta_gpu); |
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} |
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for(i = 0; i < l.batch; ++i){ |
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float * a = l.delta_gpu; |
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float * b = l.col_image_gpu; |
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float * c = l.filter_updates_gpu; |
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im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu); |
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gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n); |
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if(state.delta){ |
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float * a = l.filters_gpu; |
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float * b = l.delta_gpu; |
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float * c = l.col_image_gpu; |
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gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k); |
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col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w); |
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} |
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} |
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} |
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void pull_local_layer(local_layer layer) |
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{ |
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cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size); |
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cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); |
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cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size); |
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cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); |
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if (layer.batch_normalize){ |
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cuda_pull_array(layer.scales_gpu, layer.scales, layer.n); |
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cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); |
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cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); |
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} |
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} |
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void push_local_layer(local_layer layer) |
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{ |
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cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size); |
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cuda_push_array(layer.biases_gpu, layer.biases, layer.n); |
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cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size); |
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cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); |
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if (layer.batch_normalize){ |
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cuda_push_array(layer.scales_gpu, layer.scales, layer.n); |
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cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); |
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cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); |
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} |
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} |
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void update_local_layer_gpu(local_layer layer, int batch, float learning_rate, float momentum, float decay) |
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{ |
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int size = layer.size*layer.size*layer.c*layer.n; |
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axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); |
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scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); |
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axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1); |
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scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); |
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axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1); |
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axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1); |
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scal_ongpu(size, momentum, layer.filter_updates_gpu, 1); |
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} |
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