def main(exp_const, data_const, model_const): np.random.seed(exp_const.seed) torch.manual_seed(exp_const.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False print('Creating network ...') model = Constants() model.const = model_const model.object_encoder = ObjectEncoder(model.const.object_encoder) model.cap_encoder = CapEncoder(model.const.cap_encoder) o_dim = model.object_encoder.const.object_feature_dim if exp_const.contextualize == True: o_dim = model.object_encoder.const.context_layer.hidden_size model.lang_sup_criterion = create_cap_info_nce_criterion( o_dim, model.object_encoder.const.object_feature_dim, model.cap_encoder.model.config.hidden_size, model.cap_encoder.model.config.hidden_size // 2, model.const.cap_info_nce_layers) if model.const.model_num != -1: loaded_object_encoder = torch.load(model.const.object_encoder_path) print('Loaded model number:', loaded_object_encoder['step']) model.object_encoder.load_state_dict( loaded_object_encoder['state_dict']) model.lang_sup_criterion.load_state_dict( torch.load(model.const.lang_sup_criterion_path)['state_dict']) if exp_const.random_lang is True: model.cap_encoder.load_state_dict( torch.load(model.const.cap_encoder_path)['state_dict']) model.object_encoder.cuda() model.cap_encoder.cuda() model.lang_sup_criterion.cuda() print('Creating dataloader ...') dataset = FlickrDataset(data_const) with torch.no_grad(): results = eval_model(model, dataset, exp_const) filename = os.path.join( exp_const.exp_dir, f'results_{data_const.subset}_{model_const.model_num}.json') io.dump_json_object(results, filename)
def main(exp_const, data_const, model_const): np.random.seed(exp_const.seed) torch.manual_seed(exp_const.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False io.mkdir_if_not_exists(exp_const.exp_dir, recursive=True) io.mkdir_if_not_exists(exp_const.log_dir) io.mkdir_if_not_exists(exp_const.model_dir) io.mkdir_if_not_exists(exp_const.vis_dir) print('Creating network ...') model = Constants() model.const = model_const model.object_encoder = ObjectEncoder(model.const.object_encoder) model.cap_encoder = CapEncoder(model.const.cap_encoder) o_dim = model.object_encoder.const.object_feature_dim if exp_const.contextualize == True: o_dim = model.object_encoder.const.context_layer.hidden_size model.lang_sup_criterion = create_cap_info_nce_criterion( o_dim, model.object_encoder.const.object_feature_dim, model.cap_encoder.model.config.hidden_size, model.cap_encoder.model.config.hidden_size // 2) if model.const.model_num != -1: print('Loading model num', model.const.model_num, '...') loaded_object_encoder = torch.load(model.const.object_encoder_path) print(loaded_object_encoder['step']) model.object_encoder.load_state_dict( loaded_object_encoder['state_dict']) model.lang_sup_criterion.load_state_dict( torch.load(model.const.lang_sup_criterion_path)['state_dict']) model.object_encoder.cuda() model.cap_encoder.cuda() model.lang_sup_criterion.cuda() print('Creating dataloader ...') dataset = CocoDataset(data_const) dataloader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=1) eval_model(model, dataloader, exp_const)
def main(exp_const,data_const,model_const): np.random.seed(exp_const.seed) torch.manual_seed(exp_const.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False io.mkdir_if_not_exists(exp_const.exp_dir,recursive=True) io.mkdir_if_not_exists(exp_const.log_dir) io.mkdir_if_not_exists(exp_const.model_dir) io.mkdir_if_not_exists(exp_const.vis_dir) tb_writer = SummaryWriter(log_dir=exp_const.log_dir) model_num = model_const.model_num save_constants({ f'exp_{model_num}': exp_const, f'data_train_{model_num}': data_const['train'], f'data_val_{model_num}': data_const['val'], f'model_{model_num}': model_const}, exp_const.exp_dir) print('Creating network ...') model = Constants() model.const = model_const model.object_encoder = ObjectEncoder(model.const.object_encoder) model.cap_encoder = CapEncoder(model.const.cap_encoder) if exp_const.random_lang is True: model.cap_encoder.random_init() c_dim = model.object_encoder.const.object_feature_dim if exp_const.contextualize==True: c_dim = model.object_encoder.const.context_layer.hidden_size model.self_sup_criterion = create_info_nce_criterion( model.object_encoder.const.object_feature_dim, c_dim, model.object_encoder.const.context_layer.hidden_size) o_dim = model.object_encoder.const.object_feature_dim if exp_const.contextualize==True: o_dim = model.object_encoder.const.context_layer.hidden_size model.lang_sup_criterion = create_cap_info_nce_criterion( o_dim, model.object_encoder.const.object_feature_dim, model.cap_encoder.model.config.hidden_size, model.cap_encoder.model.config.hidden_size//2, model.const.cap_info_nce_layers) if model.const.model_num != -1: model.object_encoder.load_state_dict( torch.load(model.const.object_encoder_path)['state_dict']) model.self_sup_criterion.load_state_dict( torch.load(model.const.self_sup_criterion_path)['state_dict']) model.lang_sup_criterion.load_state_dict( torch.load(model.const.lang_sup_criterion_path)['state_dict']) model.object_encoder.cuda() model.cap_encoder.cuda() model.self_sup_criterion.cuda() model.lang_sup_criterion.cuda() model.object_encoder.to_file( os.path.join(exp_const.exp_dir,'object_encoder.txt')) model.self_sup_criterion.to_file( os.path.join(exp_const.exp_dir,'self_supervised_criterion.txt')) model.lang_sup_criterion.to_file( os.path.join(exp_const.exp_dir,'lang_supervised_criterion.txt')) print('Creating dataloader ...') dataloaders = {} if exp_const.dataset=='coco': Dataset = CocoDataset elif exp_const.dataset=='flickr': Dataset = FlickrDataset else: msg = f'{exp_const.dataset} not implemented' raise NotImplementedError(msg) for mode, const in data_const.items(): dataset = Dataset(const) if mode=='train': shuffle=True batch_size=exp_const.train_batch_size else: shuffle=True batch_size=exp_const.val_batch_size dataloaders[mode] = DataLoader( dataset, batch_size=batch_size, shuffle=shuffle, num_workers=exp_const.num_workers) train_model(model,dataloaders,exp_const,tb_writer)