typo fix, get corpus for each trial

model_training/evaluate_model.py

@@ -21,6 +21,8 @@ parser.add_argument('--data_dir', type=str, default='../data/hdf5_data_final',
 parser.add_argument('--eval_type', type=str, default='test', choices=['val', 'test'],
                     help='Evaluation type: "val" for validation set, "test" for test set. '
                          'If "test", ground truth is not available.')
+parser.add_argument('--csv_path', type=str, default='../data/t15_copyTaskData_description.csv',
+                    help='Path to the CSV file with metadata about the dataset (relative to the current working directory).')
 parser.add_argument('--gpu_number', type=int, default=1,
                     help='GPU number to use for RNN model inference. Set to -1 to use CPU.')
 args = parser.parse_args()
@@ -33,6 +35,9 @@ data_dir = args.data_dir
 # define evaluation type
 eval_type = args.eval_type  # can be 'val' or 'test'. if 'test', ground truth is not available
 
+# load csv file
+b2txt_csv_df = pd.read_csv(args.csv_path)
+
 # load model args
 model_args = OmegaConf.load(os.path.join(model_path, 'checkpoint/args.yaml'))
 
@@ -85,7 +90,7 @@ for session in model_args['dataset']['sessions']:
     if f'data_{eval_type}.hdf5' in files:
         eval_file = os.path.join(data_dir, session, f'data_{eval_type}.hdf5')
 
-        data = load_h5py_file(eval_file)
+        data = load_h5py_file(eval_file, b2txt_csv_df)
         test_data[session] = data
 
         total_test_trials += len(test_data[session]["neural_features"])