void forward_network(network net, network_state state) { int i; for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; if(l.type == CONVOLUTIONAL){ forward_convolutional_layer(l, state); } else if(l.type == DECONVOLUTIONAL){ forward_deconvolutional_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 == 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 == DROPOUT){ forward_dropout_layer(l, state); } else if(l.type == ROUTE){ forward_route_layer(l, net); } state.input = l.output; } }
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_network(network net, network_state state) { int i; for(i = 0; i < net.n; ++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 == NORMALIZATION){ forward_normalization_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 == 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); } state.input = l.output; } }