void backward_convolutional_layer(convolutional_layer l, network_state state) { int i; int m = l.n; int n = l.size*l.size*l.c; int k = convolutional_out_height(l)* convolutional_out_width(l); gradient_array(l.output, m*k*l.batch, l.activation, l.delta); backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); for(i = 0; i < l.batch; ++i){ float *a = l.delta + i*m*k; float *b = l.col_image; float *c = l.filter_updates; float *im = state.input+i*l.c*l.h*l.w; im2col_cpu(im, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); if(state.delta){ a = l.filters; b = l.delta + i*m*k; c = l.col_image; gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); } } }
void resize_convolutional_layer(convolutional_layer *l, int w, int h) { l->w = w; l->h = h; int out_w = convolutional_out_width(*l); int out_h = convolutional_out_height(*l); l->out_w = out_w; l->out_h = out_h; l->outputs = l->out_h * l->out_w * l->out_c; l->inputs = l->w * l->h * l->c; l->col_image = realloc(l->col_image, out_h*out_w*l->size*l->size*l->c*sizeof(float)); l->output = realloc(l->output, l->batch*out_h * out_w * l->n*sizeof(float)); l->delta = realloc(l->delta, l->batch*out_h * out_w * l->n*sizeof(float)); #ifdef GPU cuda_free(l->col_image_gpu); cuda_free(l->delta_gpu); cuda_free(l->output_gpu); l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c); l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n); l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n); #endif }
void forward_convolutional_layer(convolutional_layer l, network_state state) { int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); int i; fill_cpu(l.outputs*l.batch, 0, l.output, 1); /* if(l.binary){ binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters); binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales); swap_binary(&l); } */ if(l.binary){ int m = l.n; int k = l.size*l.size*l.c; int n = out_h*out_w; char *a = l.cfilters; float *b = state.workspace; float *c = l.output; for(i = 0; i < l.batch; ++i){ im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm_bin(m,n,k,1,a,k,b,n,c,n); c += n*m; state.input += l.c*l.h*l.w; } scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w); add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); activate_array(l.output, m*n*l.batch, l.activation); return; } int m = l.n; int k = l.size*l.size*l.c; int n = out_h*out_w; float *a = l.filters; float *b = state.workspace; float *c = l.output; for(i = 0; i < l.batch; ++i){ im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); c += n*m; state.input += l.c*l.h*l.w; } if(l.batch_normalize){ forward_batchnorm_layer(l, state); } add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); activate_array(l.output, m*n*l.batch, l.activation); }
void forward_convolutional_layer(const convolutional_layer l, network_state state) { int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); int i; bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w); int m = l.n; int k = l.size*l.size*l.c; int n = out_h*out_w; float *a = l.filters; float *b = l.col_image; float *c = l.output; for(i = 0; i < l.batch; ++i){ im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); c += n*m; state.input += l.c*l.h*l.w; } activate_array(l.output, m*n*l.batch, l.activation); }
image get_convolutional_delta(convolutional_layer l) { int h,w,c; h = convolutional_out_height(l); w = convolutional_out_width(l); c = l.n; return float_to_image(w,h,c,l.delta); }
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation) { int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; l.h = h; l.w = w; l.c = c; l.n = n; l.batch = batch; l.stride = stride; l.size = size; l.pad = pad; l.filters = calloc(c*n*size*size, sizeof(float)); l.filter_updates = calloc(c*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); //float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c)); printf("%f\n", scale); for(i = 0; i < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale; for(i = 0; i < n; ++i){ l.biases[i] = scale; } int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); l.out_h = out_h; l.out_w = out_w; l.out_c = n; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float)); l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); #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); l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c); 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); #endif l.activation = activation; fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n); return l; }
layer make_xnor_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize) { int i; layer l = {0}; l.type = XNOR; l.h = h; l.w = w; l.c = c; l.n = n; l.batch = batch; l.stride = stride; l.size = size; l.pad = pad; l.batch_normalize = batch_normalize; l.filters = calloc(c*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); l.out_h = out_h; l.out_w = out_w; l.out_c = n; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); if(batch_normalize){ l.scales = calloc(n, sizeof(float)); for(i = 0; i < n; ++i){ l.scales[i] = 1; } l.mean = calloc(n, sizeof(float)); l.variance = calloc(n, sizeof(float)); l.rolling_mean = calloc(n, sizeof(float)); l.rolling_variance = calloc(n, sizeof(float)); } l.activation = activation; fprintf(stderr, "XNOR Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n); return l; }
void resize_convolutional_layer(convolutional_layer *l, int w, int h) { l->w = w; l->h = h; int out_w = convolutional_out_width(*l); int out_h = convolutional_out_height(*l); l->out_w = out_w; l->out_h = out_h; l->outputs = l->out_h * l->out_w * l->out_c; l->inputs = l->w * l->h * l->c; l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); if(l->batch_normalize){ l->x = realloc(l->x, l->batch*l->outputs*sizeof(float)); l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); } #ifdef GPU cuda_free(l->delta_gpu); cuda_free(l->output_gpu); l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); if(l->batch_normalize){ cuda_free(l->x_gpu); cuda_free(l->x_norm_gpu); l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); } #ifdef CUDNN cudnn_convolutional_setup(l); #endif #endif l->workspace_size = get_workspace_size(*l); }
void forward_convolutional_layer(const convolutional_layer l, network_state state) { int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); int i; fill_cpu(l.outputs*l.batch, 0, l.output, 1); int m = l.n; int k = l.size*l.size*l.c; int n = out_h*out_w; float *a = l.filters; float *b = l.col_image; float *c = l.output; // printf("the l.size is %i \n", l.size); ///* //printf("the m,k,n is %i,%i,%i \n", m,k,n); for(i = 0; i < l.batch; ++i){ im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); c += n*m; state.input += l.c*l.h*l.w; } //*/ //add by fanghao /* int ii,jj,kk,mm,pp,tt; int lcc = l.c; int lhh = l.h; int lww = l.w; int kernel = l.size; int pad; if(l.pad) pad = l.size/2; else pad = l.pad; lhh += 2*pad; lww += 2*pad; float *dataP; dataP = (float *)calloc(lcc*lhh*lww, sizeof(float)); //printf("the l.h is %i \n", l.h); //printf("the l.w is %i \n", l.w); //printf("the lhh is %i \n", lhh); //printf("the lww is %i \n", lww); //printf("the pad is %i \n", pad); for(ii=0; ii < lcc; ii++) for(jj=pad; jj<lhh-pad; jj++) for(kk=pad; kk<lww-pad; kk++) dataP[ii*lhh*lww + jj*lww + kk] = state.input[ii*(lhh - 2*pad)*(lww-2*pad) + (jj - pad)*(lww - 2*pad) + kk-pad]; for(ii=0; ii<m; ii++) for(jj=0; jj<out_h; jj++) for(kk=0; kk<out_w; kk++) { float tempAcc = 0.0; for(mm=0; mm<lcc; mm++) for(pp=0; pp<kernel; pp++) for(tt=0; tt<kernel; tt++) tempAcc += a[ii*lcc*kernel*kernel+mm*kernel*kernel+pp*kernel+tt]*dataP[mm*lhh*lww+(l.stride*jj+pp)*lww+l.stride*kk+tt]; c[ii*out_h*out_w+jj*out_w+kk] = tempAcc; } // c += n*m; //state.input += l.c*l.h*l.w; // */ if(l.batch_normalize){ if(state.train){ mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean); variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance); normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w); } else { normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w); } scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w); } add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); activate_array(l.output, m*n*l.batch, l.activation); }
void resize_convolutional_layer(convolutional_layer *l, int w, int h) { l->w = w; l->h = h; int out_w = convolutional_out_width(*l); int out_h = convolutional_out_height(*l); l->out_w = out_w; l->out_h = out_h; l->outputs = l->out_h * l->out_w * l->out_c; l->inputs = l->w * l->h * l->c; l->output = realloc(l->output, l->batch*out_h * out_w * l->n*sizeof(float)); l->delta = realloc(l->delta, l->batch*out_h * out_w * l->n*sizeof(float)); #ifdef GPU cuda_free(l->delta_gpu); cuda_free(l->output_gpu); l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n); l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n); #ifdef CUDNN cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); cudnnSetFilter4dDescriptor(l->dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); cudnnSetFilter4dDescriptor(l->filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); int padding = l->pad ? l->size/2 : 0; cudnnSetConvolution2dDescriptor(l->convDesc, padding, padding, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, l->filterDesc, l->convDesc, l->dstTensorDesc, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &l->fw_algo); cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), l->filterDesc, l->ddstTensorDesc, l->convDesc, l->dsrcTensorDesc, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &l->bd_algo); cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), l->srcTensorDesc, l->ddstTensorDesc, l->convDesc, l->dfilterDesc, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &l->bf_algo); #endif #endif l->workspace_size = get_workspace_size(*l); }
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor) { int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; l.h = h; l.w = w; l.c = c; l.n = n; l.binary = binary; l.xnor = xnor; l.batch = batch; l.stride = stride; l.size = size; l.pad = pad; l.batch_normalize = batch_normalize; l.filters = calloc(c*n*size*size, sizeof(float)); l.filter_updates = calloc(c*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c)); for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1); int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); l.out_h = out_h; l.out_w = out_w; l.out_c = n; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); if(binary){ l.binary_filters = calloc(c*n*size*size, sizeof(float)); l.cfilters = calloc(c*n*size*size, sizeof(char)); l.scales = calloc(n, sizeof(float)); } if(xnor){ l.binary_filters = calloc(c*n*size*size, sizeof(float)); l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); } if(batch_normalize){ l.scales = calloc(n, sizeof(float)); l.scale_updates = calloc(n, sizeof(float)); for(i = 0; i < n; ++i){ l.scales[i] = 1; } l.mean = calloc(n, sizeof(float)); l.variance = calloc(n, sizeof(float)); l.rolling_mean = calloc(n, sizeof(float)); l.rolling_variance = calloc(n, sizeof(float)); } #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); 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.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(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.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); } #ifdef CUDNN cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnCreateFilterDescriptor(&l.filterDesc); cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); cudnnCreateTensorDescriptor(&l.ddstTensorDesc); cudnnCreateFilterDescriptor(&l.dfilterDesc); cudnnCreateConvolutionDescriptor(&l.convDesc); cudnnSetTensor4dDescriptor(l.dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w); cudnnSetTensor4dDescriptor(l.ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); cudnnSetFilter4dDescriptor(l.dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size); cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w); cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); cudnnSetFilter4dDescriptor(l.filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size); int padding = l.pad ? l.size/2 : 0; cudnnSetConvolution2dDescriptor(l.convDesc, padding, padding, l.stride, l.stride, 1, 1, CUDNN_CROSS_CORRELATION); cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l.srcTensorDesc, l.filterDesc, l.convDesc, l.dstTensorDesc, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &l.fw_algo); cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), l.filterDesc, l.ddstTensorDesc, l.convDesc, l.dsrcTensorDesc, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &l.bd_algo); cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), l.srcTensorDesc, l.ddstTensorDesc, l.convDesc, l.dfilterDesc, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &l.bf_algo); #endif #endif l.workspace_size = get_workspace_size(l); l.activation = activation; fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n); return l; }
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) { int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; l.groups = groups; l.h = h; l.w = w; l.c = c; l.n = n; l.binary = binary; l.xnor = xnor; l.batch = batch; l.stride = stride; l.size = size; l.pad = padding; l.batch_normalize = batch_normalize; l.weights = calloc(c/groups*n*size*size, sizeof(float)); l.weight_updates = calloc(c/groups*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); l.nweights = c/groups*n*size*size; l.nbiases = n; // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c/l.groups)); //scale = .02; //for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal(); int out_w = convolutional_out_width(l); int out_h = convolutional_out_height(l); l.out_h = out_h; l.out_w = out_w; l.out_c = n; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; l.output = calloc(l.batch*l.outputs, sizeof(float)); l.delta = calloc(l.batch*l.outputs, sizeof(float)); l.forward = forward_convolutional_layer; l.backward = backward_convolutional_layer; l.update = update_convolutional_layer; if(binary){ l.binary_weights = calloc(l.nweights, sizeof(float)); l.cweights = calloc(l.nweights, sizeof(char)); l.scales = calloc(n, sizeof(float)); } if(xnor){ l.binary_weights = calloc(l.nweights, sizeof(float)); l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); } if(batch_normalize){ l.scales = calloc(n, sizeof(float)); l.scale_updates = calloc(n, sizeof(float)); for(i = 0; i < n; ++i){ l.scales[i] = 1; } l.mean = calloc(n, sizeof(float)); l.variance = calloc(n, sizeof(float)); l.mean_delta = calloc(n, sizeof(float)); l.variance_delta = calloc(n, sizeof(float)); l.rolling_mean = calloc(n, sizeof(float)); l.rolling_variance = calloc(n, sizeof(float)); l.x = calloc(l.batch*l.outputs, sizeof(float)); l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); } if(adam){ l.m = calloc(l.nweights, sizeof(float)); l.v = calloc(l.nweights, sizeof(float)); l.bias_m = calloc(n, sizeof(float)); l.scale_m = calloc(n, sizeof(float)); l.bias_v = calloc(n, sizeof(float)); l.scale_v = calloc(n, sizeof(float)); } #ifdef GPU l.forward_gpu = forward_convolutional_layer_gpu; l.backward_gpu = backward_convolutional_layer_gpu; l.update_gpu = update_convolutional_layer_gpu; if(gpu_index >= 0){ if (adam) { l.m_gpu = cuda_make_array(l.m, l.nweights); l.v_gpu = cuda_make_array(l.v, l.nweights); l.bias_m_gpu = cuda_make_array(l.bias_m, n); l.bias_v_gpu = cuda_make_array(l.bias_v, n); l.scale_m_gpu = cuda_make_array(l.scale_m, n); l.scale_v_gpu = cuda_make_array(l.scale_v, n); } l.weights_gpu = cuda_make_array(l.weights, l.nweights); l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_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); if(binary){ l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); } if(xnor){ l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); 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); 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.scales_gpu = cuda_make_array(l.scales, n); l.scale_updates_gpu = cuda_make_array(l.scale_updates, 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.normTensorDesc); cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnCreateFilterDescriptor(&l.weightDesc); cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); cudnnCreateTensorDescriptor(&l.ddstTensorDesc); cudnnCreateFilterDescriptor(&l.dweightDesc); cudnnCreateConvolutionDescriptor(&l.convDesc); cudnn_convolutional_setup(&l); #endif } #endif l.workspace_size = get_workspace_size(l); l.activation = activation; //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); return l; }