def load_training_set(args_dict, train_list_path, train_on_f=True, train_on_g=True): """needed to get the threshold value for prediction at onsets""" train_dataset = TurnPredictionDataset(args_dict['feature_dict_list'], annotations_dir_train, train_list_path, args_dict['sequence_length'], prediction_length, 'train', data_select=data_set_select, train_on_f=train_on_f, train_on_g=train_on_g) train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=False, num_workers=0, drop_last=True, pin_memory=p_memory) return train_dataset, train_dataloader
def load_test_set(args_dict, test_list_path, test_on_g=True, test_on_f=True): test_dataset = TurnPredictionDataset(args_dict['feature_dict_list'], annotations_dir_test, test_list_path, args_dict['sequence_length'], prediction_length, 'test', data_select=data_set_select, test_on_f=test_on_f, test_on_g=test_on_g) test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0, drop_last=False, pin_memory=p_memory) return test_dataset, test_dataloader
p_memory = True # %% Data loaders # ============================================================================= ''' build dataloader to load data ''' # ============================================================================= t1 = t.time() # training set data loader print('feature dict list:', feature_dict_list) train_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir, train_list_path, sequence_length, prediction_length, 'train', data_select=data_set_select) train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=shuffle, num_workers=0, drop_last=True, pin_memory=p_memory) feature_size_dict = train_dataset.get_feature_size_dict() if slow_test: # slow test loader test_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir,
# torch.cuda.device(randint(0,1)) dtype = torch.cuda.FloatTensor dtype_long = torch.cuda.LongTensor p_memory = True else: dtype = torch.FloatTensor dtype_long = torch.LongTensor p_memory = True # %% Data loaders t1 = t.time() # training set data loader print('feature dict list:', feature_dict_list) train_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir, train_list_path, sequence_length, prediction_length, 'train', data_select=data_set_select, train_on_f=train_on_f, train_on_g=train_on_g) train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=shuffle, num_workers=0, drop_last=True, pin_memory=p_memory) feature_size_dict = train_dataset.get_feature_size_dict() if slow_test: # slow test loader test_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir, test_list_path, sequence_length, prediction_length, 'test', data_select=data_set_select, test_on_f=test_on_f, test_on_g=test_on_g) test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0, drop_last=False, pin_memory=p_memory) else: # quick test loader
print('Use CUDA: ' + str(use_cuda)) if use_cuda: # torch.cuda.device(randint(0,1)) dtype = torch.cuda.FloatTensor dtype_long = torch.cuda.LongTensor p_memory = True else: dtype = torch.FloatTensor dtype_long = torch.LongTensor p_memory = True # %% Data loaders t1 = t.time() complete_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir, complete_path, sequence_length, prediction_length, 'test', data_select=data_set_select) complete_dataloader = DataLoader(complete_dataset, batch_size=1, shuffle=False, num_workers=0, # previously shuffle = shuffle drop_last=False, pin_memory=p_memory) feature_size_dict = complete_dataset.get_feature_size_dict() print('time taken to load data: ' + str(t.time() - t1)) complete_file_list = list(pd.read_csv(complete_path, header=None, dtype=str)[0]) lstm = torch.load('lstm_models/ling_50ms.p') ffnn = torch.load('smol_from_big.p') s = nn.Sigmoid()
p_memory = True else: dtype = torch.FloatTensor dtype_long = torch.LongTensor p_memory = True # %% Data loaders t1 = t.time() # training set data loader print('feature dict list:', feature_dict_list) #How do I load the true values? How is b[4] the true values? train_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir, train_list_path, sequence_length, prediction_length, 'train', data_select=data_set_select, annotations_key=annotations_role) train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=shuffle, num_workers=0, drop_last=True, pin_memory=p_memory) feature_size_dict = train_dataset.get_feature_size_dict() if slow_test: # slow test loader test_dataset = TurnPredictionDataset(feature_dict_list, annotations_dir,