condition_analysis = True # Substract one because the last one is the iterations one. for an_j in range(N_analyses - 1): condition_analysis &= List_f_analyses[an_j]( cf_a) == Standard_values_alyses[an_j] if (condition_analysis): pickle_results_path = pickle_results_path[i] model_file_path = models_path[i] """ ################################################################## LOAD THE CONFIGURATION FILE ################################################################## """ dtype = torch.float device = pytut.get_device_name(cuda_index=0) cf_a, training_logger = pkl.load_pickle(pickle_results_path) cf_a.dtype = dtype # Variable types cf_a.device = device ## Modify these parameters so that I am not f****d in memory in my litle servergb cf_a.datareader_lazy = True # Force lazyness for RAM optimization cf_a.batch_size_train = 30 cf_a.batch_size_validation = 30 cf_a.force_free_batch_memory = False max_instances_in_memory = 100 print_conf_params(cf_a) print("Expected EM: ", 100 * np.array(training_logger["validation"]["em"])) print("Expected F1: ", 100 * np.array(training_logger["validation"]["f1"])) """
#Nmodels = 3 for i in range(Nmodels): pickle_results_path,model_file_path = pytut.get_models_paths(list_model_ids[i], list_models_epoch_i[i],source_path = source_path) pickle_results_path_list.append(pickle_results_path) model_file_path_list.append(model_file_path) """ ################################################################## LOAD THE CONFIGURATION FILES ################################################################## """ dtype = torch.float device = pytut.get_device_name(cuda_index = 0) cf_a_list = [] training_logger_list = [] for i in range(Nmodels): [cf_a,training_logger] = pkl.load_pickle(pickle_results_path_list[i]) ## Set data and device parameters cf_a.dtype = dtype # Variable types cf_a.device = device cf_a.datareader_lazy = True # Force lazyness for RAM optimization cf_a.batch_size_train = 30 cf_a.batch_size_validation = 30 cf_a.max_instances_in_memory = 1000 ## Fix backwards compatibility cf_a.phrase_layer_hidden_size = cf_a.modeling_span_end_hidden_size cf_a.phrase_layer_hidden_size = cf_a.modeling_span_end_hidden_size