ObMenu* menu_new(const gchar *name, const gchar *title, gboolean allow_shortcut_selection, gpointer data) { ObMenu *self; self = g_slice_new0(ObMenu); self->name = g_strdup(name); self->data = data; self->shortcut = parse_shortcut(title, allow_shortcut_selection, &self->title, &self->shortcut_position, &self->shortcut_always_show); g_hash_table_replace(menu_hash, self->name, self); /* Each menu has a single more_menu. When the menu spills past what can fit on the screen, a new menu frame entry is created from this more_menu, and a new menu frame for the submenu is created for this menu, also pointing to the more_menu. This can be done multiple times using the same more_menu. more_menu->more_menu will always be NULL, since there is only 1 for each menu. */ self->more_menu = g_slice_new0(ObMenu); self->more_menu->name = _("More..."); self->more_menu->title = _("More..."); self->more_menu->data = data; self->more_menu->shortcut = g_unichar_tolower(g_utf8_get_char("M")); return self; }
void menu_entry_set_label(ObMenuEntry *self, const gchar *label, gboolean allow_shortcut) { switch (self->type) { case OB_MENU_ENTRY_TYPE_SEPARATOR: g_free(self->data.separator.label); self->data.separator.label = g_strdup(label); break; case OB_MENU_ENTRY_TYPE_NORMAL: g_free(self->data.normal.label); self->data.normal.shortcut = parse_shortcut(label, allow_shortcut, &self->data.normal.label, &self->data.normal.shortcut_position, &self->data.normal.shortcut_always_show); break; default: g_assert_not_reached(); } }
void add_plugin_to_menu (gpointer data, gpointer user_data) { static int i=1; GtkPluginManagerMenu *menu=(GtkPluginManagerMenu *) user_data; GtkWidget *item; Plugin *plugin; plugin = PLUGIN(data); /* * Syntax plugins are automatically incorporate to the syntax check system so don't show that plugins here. * TODO: maybe configure this in preferences?? */ if (get_plugin_syntax_type(plugin)==-1){ item = gtk_menu_item_new_image_item(GTK_STOCK_EXECUTE, get_plugin_name(plugin)); gtk_widget_show(item); install_menu_hint(item, (gchar *)get_plugin_description(plugin), &main_window); g_signal_connect(G_OBJECT(item), "activate", G_CALLBACK(plugin_exec), (gpointer) menu); gtk_menu_shell_append(GTK_MENU_SHELL(menu), item); if (i<10) gtk_widget_add_accelerator(item, "activate", menu->priv->accel_group, parse_shortcut(i), GDK_CONTROL_MASK, GTK_ACCEL_VISIBLE); i++; } }
network parse_network_cfg(char *filename) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); network net = make_network(sections->size - 1); net.gpu_index = gpu_index; size_params params; section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); parse_net_options(options, &net); params.h = net.h; params.w = net.w; params.c = net.c; params.inputs = net.inputs; params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; size_t workspace_size = 0; n = n->next; int count = 0; free_section(s); fprintf(stderr, "layer filters size input output\n"); while(n){ params.index = count; fprintf(stderr, "%5d ", count); s = (section *)n->val; options = s->options; layer l = {0}; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ l = parse_activation(options, params); }else if(lt == RNN){ l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ l = parse_connected(options, params); }else if(lt == CROP){ l = parse_crop(options, params); }else if(lt == COST){ l = parse_cost(options, params); }else if(lt == REGION){ l = parse_region(options, params); }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); net.hierarchy = l.softmax_tree; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ l = parse_batchnorm(options, params); }else if(lt == MAXPOOL){ l = parse_maxpool(options, params); }else if(lt == REORG){ l = parse_reorg(options, params); }else if(lt == AVGPOOL){ l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; l.delta = net.layers[count-1].delta; #ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); option_unused(options); net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; free_section(s); n = n->next; ++count; if(n){ params.h = l.out_h; params.w = l.out_w; params.c = l.out_c; params.inputs = l.outputs; } } free_list(sections); net.outputs = get_network_output_size(net); net.output = get_network_output(net); if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU if(gpu_index >= 0){ net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); }else { net.workspace = calloc(1, workspace_size); } #else net.workspace = calloc(1, workspace_size); #endif } return net; }
network *parse_network_cfg(char *filename) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); network *net = make_network(sections->size - 1); net->gpu_index = gpu_index; size_params params; section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); parse_net_options(options, net); params.h = net->h; params.w = net->w; params.c = net->c; params.inputs = net->inputs; params.batch = net->batch; params.time_steps = net->time_steps; params.net = net; size_t workspace_size = 0; n = n->next; int count = 0; free_section(s); fprintf(stderr, "layer filters size input output\n"); while(n){ params.index = count; fprintf(stderr, "%5d ", count); s = (section *)n->val; options = s->options; layer l = {0}; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); }else if(lt == DECONVOLUTIONAL){ l = parse_deconvolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ l = parse_activation(options, params); }else if(lt == LOGXENT){ l = parse_logistic(options, params); }else if(lt == L2NORM){ l = parse_l2norm(options, params); }else if(lt == RNN){ l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); }else if (lt == LSTM) { l = parse_lstm(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ l = parse_connected(options, params); }else if(lt == CROP){ l = parse_crop(options, params); }else if(lt == COST){ l = parse_cost(options, params); }else if(lt == REGION){ l = parse_region(options, params); }else if(lt == YOLO){ l = parse_yolo(options, params); }else if(lt == ISEG){ l = parse_iseg(options, params); }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); net->hierarchy = l.softmax_tree; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ l = parse_batchnorm(options, params); }else if(lt == MAXPOOL){ l = parse_maxpool(options, params); }else if(lt == REORG){ l = parse_reorg(options, params); }else if(lt == AVGPOOL){ l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params, net); }else if(lt == UPSAMPLE){ l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net->layers[count-1].output; l.delta = net->layers[count-1].delta; #ifdef GPU l.output_gpu = net->layers[count-1].output_gpu; l.delta_gpu = net->layers[count-1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } l.clip = net->clip; l.truth = option_find_int_quiet(options, "truth", 0); l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.dontsave = option_find_int_quiet(options, "dontsave", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); l.numload = option_find_int_quiet(options, "numload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); l.smooth = option_find_float_quiet(options, "smooth", 0); option_unused(options); net->layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; free_section(s); n = n->next; ++count; if(n){ params.h = l.out_h; params.w = l.out_w; params.c = l.out_c; params.inputs = l.outputs; } } free_list(sections); layer out = get_network_output_layer(net); net->outputs = out.outputs; net->truths = out.outputs; if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; net->output = out.output; net->input = calloc(net->inputs*net->batch, sizeof(float)); net->truth = calloc(net->truths*net->batch, sizeof(float)); #ifdef GPU net->output_gpu = out.output_gpu; net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); #endif if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU if(gpu_index >= 0){ net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); }else { net->workspace = calloc(1, workspace_size); } #else net->workspace = calloc(1, workspace_size); #endif } return net; }
network parse_network_cfg(char *filename) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); network net = make_network(sections->size - 1); size_params params; section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); parse_net_options(options, &net); params.h = net.h; params.w = net.w; params.c = net.c; params.inputs = net.inputs; params.batch = net.batch; params.time_steps = net.time_steps; size_t workspace_size = 0; n = n->next; int count = 0; free_section(s); while(n){ params.index = count; fprintf(stderr, "%d: ", count); s = (section *)n->val; options = s->options; layer l = {0}; if(is_convolutional(s)){ l = parse_convolutional(options, params); }else if(is_local(s)){ l = parse_local(options, params); }else if(is_activation(s)){ l = parse_activation(options, params); }else if(is_deconvolutional(s)){ l = parse_deconvolutional(options, params); }else if(is_rnn(s)){ l = parse_rnn(options, params); }else if(is_gru(s)){ l = parse_gru(options, params); }else if(is_crnn(s)){ l = parse_crnn(options, params); }else if(is_connected(s)){ l = parse_connected(options, params); }else if(is_crop(s)){ l = parse_crop(options, params); }else if(is_cost(s)){ l = parse_cost(options, params); }else if(is_detection(s)){ l = parse_detection(options, params); }else if(is_softmax(s)){ l = parse_softmax(options, params); }else if(is_normalization(s)){ l = parse_normalization(options, params); }else if(is_batchnorm(s)){ l = parse_batchnorm(options, params); }else if(is_maxpool(s)){ l = parse_maxpool(options, params); }else if(is_avgpool(s)){ l = parse_avgpool(options, params); }else if(is_route(s)){ l = parse_route(options, params, net); }else if(is_shortcut(s)){ l = parse_shortcut(options, params, net); }else if(is_dropout(s)){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; l.delta = net.layers[count-1].delta; #ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); option_unused(options); net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; free_section(s); n = n->next; ++count; if(n){ params.h = l.out_h; params.w = l.out_w; params.c = l.out_c; params.inputs = l.outputs; } } free_list(sections); net.outputs = get_network_output_size(net); net.output = get_network_output(net); if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); #else net.workspace = calloc(1, workspace_size); #endif } return net; }