void forward_deconvolutional_layer(const layer l, network_state state) { int i; int out_h = l.out_h; int out_w = l.out_w; int size = out_h*out_w; int m = l.size*l.size*l.n; int n = l.h*l.w; int k = l.c; fill_cpu(l.outputs*l.batch, 0, l.output, 1); for(i = 0; i < l.batch; ++i){ float *a = l.weights; float *b = state.input + i*l.c*l.h*l.w; float *c = state.workspace; gemm(1,0,m,n,k,1,a,m,b,n,0,c,n); col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size); } if(l.batch_normalize){ forward_batchnorm_layer(l, state); } else { add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); } activate_array(l.output, l.batch*l.n*size, l.activation); }
void forward_deconvolutional_layer(const 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_cpu(l.outputs * l.batch, 0, l.output, 1); for (i = 0; i < l.batch; ++i) { real_t *a = l.weights; real_t *b = net.input + i * l.c * l.h * l.w; real_t *c = net.workspace; gemm_cpu(1, 0, m, n, k, 1, a, m, b, n, 0, c, n); col2im_cpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output + i * l.outputs); } if (l.batch_normalize) { forward_batchnorm_layer(l, net); } else { add_bias(l.output, l.biases, l.batch, l.n, l.out_w * l.out_h); } activate_array(l.output, l.batch * l.n * l.out_w * l.out_h, l.activation); }
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_network(network net, network_state state) { state.workspace = net.workspace; int i; for(i = 0; i < net.n; ++i){ state.index = i; layer l = net.layers[i]; if(l.delta){ scal_cpu(l.outputs * l.batch, 0, l.delta, 1); } if(l.type == CONVOLUTIONAL){ forward_convolutional_layer(l, state); } else if(l.type == DECONVOLUTIONAL){ forward_deconvolutional_layer(l, state); } else if(l.type == ACTIVE){ forward_activation_layer(l, state); } else if(l.type == LOCAL){ forward_local_layer(l, state); } else if(l.type == NORMALIZATION){ forward_normalization_layer(l, state); } else if(l.type == BATCHNORM){ forward_batchnorm_layer(l, state); } else if(l.type == DETECTION){ forward_detection_layer(l, state); } else if(l.type == CONNECTED){ forward_connected_layer(l, state); } else if(l.type == RNN){ forward_rnn_layer(l, state); } else if(l.type == GRU){ forward_gru_layer(l, state); } else if(l.type == CRNN){ forward_crnn_layer(l, state); } else if(l.type == CROP){ forward_crop_layer(l, state); } else if(l.type == COST){ forward_cost_layer(l, state); } else if(l.type == SOFTMAX){ forward_softmax_layer(l, state); } else if(l.type == MAXPOOL){ forward_maxpool_layer(l, state); } else if(l.type == AVGPOOL){ forward_avgpool_layer(l, state); } else if(l.type == DROPOUT){ forward_dropout_layer(l, state); } else if(l.type == ROUTE){ forward_route_layer(l, net); } else if(l.type == SHORTCUT){ forward_shortcut_layer(l, state); } state.input = l.output; } }
void forward_convolutional_layer(convolutional_layer l, network net) { int i, j; fill_cpu(l.outputs*l.batch, 0, l.output, 1); if(l.xnor){ binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights); swap_binary(&l); binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input); net.input = l.binary_input; } int m = l.n/l.groups; int k = l.size*l.size*l.c/l.groups; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ for(j = 0; j < l.groups; ++j){ float *a = l.weights + j*l.nweights/l.groups; float *b = net.workspace; float *c = l.output + (i*l.groups + j)*n*m; im2col_cpu(net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w, l.c/l.groups, 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); } } if(l.batch_normalize){ forward_batchnorm_layer(l, net); } else { add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); } activate_array(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); }