#include "cuda_runtime.h" #include "curand.h" #include "cublas_v2.h" extern "C" { #include "convolutional_layer.h" #include "deconvolutional_layer.h" #include "batchnorm_layer.h" #include "gemm.h" #include "blas.h" #include "im2col.h" #include "col2im.h" #include "utils.h" #include "cuda.h" } extern "C" void forward_deconvolutional_layer_gpu(layer l, network net) { int i; int m = l.size*l.size*l.n; int n = l.h*l.w; int k = l.c; fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); for(i = 0; i < l.batch; ++i){ float *a = l.weights_gpu; float *b = net.input_gpu + i*l.c*l.h*l.w; float *c = net.workspace; gemm_gpu(1,0,m,n,k,1,a,m,b,n,0,c,n); col2im_gpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output_gpu+i*l.outputs); } if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, net); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } activate_array_gpu(l.output_gpu, l.batch*l.n*l.out_w*l.out_h, l.activation); } extern "C" void backward_deconvolutional_layer_gpu(layer l, network net) { int i; //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if(l.batch_normalize){ backward_batchnorm_layer_gpu(l, net); } else { backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } //if(net.delta_gpu) memset(net.delta_gpu, 0, l.batch*l.h*l.w*l.c*sizeof(float)); for(i = 0; i < l.batch; ++i){ int m = l.c; int n = l.size*l.size*l.n; int k = l.h*l.w; float *a = net.input_gpu + i*m*k; float *b = net.workspace; float *c = l.weight_updates_gpu; im2col_gpu(l.delta_gpu + i*l.outputs, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, b); gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); if(net.delta_gpu){ int m = l.c; int n = l.h*l.w; int k = l.size*l.size*l.n; float *a = l.weights_gpu; float *b = net.workspace; float *c = net.delta_gpu + i*n*m; gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } } } extern "C" void pull_deconvolutional_layer(layer l) { cuda_pull_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); cuda_pull_array(l.biases_gpu, l.biases, l.n); cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); if (l.batch_normalize){ cuda_pull_array(l.scales_gpu, l.scales, l.n); cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } } extern "C" void push_deconvolutional_layer(layer l) { cuda_push_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); cuda_push_array(l.biases_gpu, l.biases, l.n); cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); if (l.batch_normalize){ cuda_push_array(l.scales_gpu, l.scales, l.n); cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } } void update_deconvolutional_layer_gpu(layer l, update_args a) { float learning_rate = a.learning_rate*l.learning_rate_scale; float momentum = a.momentum; float decay = a.decay; int batch = a.batch; if(a.adam){ adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); if(l.scales_gpu){ adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); } }else{ axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); if(l.scales_gpu){ axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); } } }