Add option to concat coords.

src/dwi_ml/cli/l2t_train_model.py

@@ -71,7 +71,8 @@ def init_from_args(args, sub_loggers_level):
     dg_args = check_args_direction_getter(args)
     # (Nb features)
     input_group_idx = dataset.volume_groups.index(args.input_group_name)
-    args.nb_features = dataset.nb_features[input_group_idx]
+
+    nb_features = dataset.nb_features[input_group_idx]
     # Final model
     with Timer("\n\nPreparing model", newline=True, color='yellow'):
         # INPUTS: verifying args
@@ -84,10 +85,13 @@ def init_from_args(args, sub_loggers_level):
             nb_previous_dirs=args.nb_previous_dirs,
             normalize_prev_dirs=args.normalize_prev_dirs,
             # INPUTS
+            nb_features_per_point=nb_features,
             input_embedding_key=args.input_embedding_key,
-            input_embedded_size=args.input_embedded_size,
-            nb_features=args.nb_features, kernel_size=args.kernel_size,
-            nb_cnn_filters=args.nb_cnn_filters,
+            input_embedding_nn_out_size=args.input_embedded_size,
+            input_embedding_cnn_kernel_size=args.kernel_size,
+            input_embedding_cnn_nb_filters=args.nb_cnn_filters,
+            add_raw_coords_to_input=args.add_raw_coords_to_input,
+            add_relative_coords_to_input=args.add_relative_coords_to_input,
             # RNN
             rnn_key=args.rnn_key, rnn_layer_sizes=args.rnn_layer_sizes,
             dropout=args.dropout,

src/dwi_ml/cli/tests/test_all_steps_l2t.py

@@ -48,6 +48,9 @@ def test_training(script_runner, experiments_path):
     ret = script_runner.run('l2t_train_model',
                             experiments_path, experiment_name, hdf5_file,
                             input_group_name, streamline_group_name,
+                            '--add_relative_coords_to_input',
+                            '--input_embedding_key', 'nn_embedding',
+                            '--input_embedded_size', '8',
                             '--max_epochs', '1', '--batch_size_training', '5',
                             '--batch_size_validation', '5',
                             '--dg_key', 'gaussian',

src/dwi_ml/cli/tt_train_model.py

@@ -107,19 +107,23 @@ def init_from_args(args, sub_loggers_level):
 
     # (nb features)
     input_group_idx = dataset.volume_groups.index(args.input_group_name)
-    args.nb_features = dataset.nb_features[input_group_idx]
+    nb_features = dataset.nb_features[input_group_idx]
 
     # Final model
     with Timer("\n\nPreparing model", newline=True, color='yellow'):
         model = cls(
             experiment_name=args.experiment_name,
             step_size=args.step_size, compress_lines=args.compress_th,
             # Concerning inputs:
-            max_len=args.max_len, nb_features=args.nb_features,
+            max_len=args.max_len,
+            nb_features_per_point=nb_features,
+            add_raw_coords_to_input=args.add_raw_coords_to_input,
+            add_relative_coords_to_input=args.add_relative_coords_to_input,
             positional_encoding_key=args.position_encoding,
             input_embedding_key=args.input_embedding_key,
-            input_embedded_size=args.input_embedded_size,
-            nb_cnn_filters=args.nb_cnn_filters, kernel_size=args.kernel_size,
+            input_embedding_nn_out_size=args.input_embedded_size,
+            input_embedding_cnn_nb_filters=args.nb_cnn_filters,
+            input_embedding_cnn_kernel_size=args.kernel_size,
             # Torch's transformer parameters
             ffnn_hidden_size=args.ffnn_hidden_size,
             nheads=args.nheads, dropout_rate=args.dropout_rate,

src/dwi_ml/data/dataset/multi_subject_containers.py

@@ -15,7 +15,7 @@
 from dwi_ml.data.dataset.checks_for_groups import prepare_groups_info
 from dwi_ml.data.dataset.mri_data_containers import MRIDataAbstract
 from dwi_ml.data.dataset.subjectdata_list_containers import (
-    LazySubjectsDataList, SubjectsDataList)
+    SubjectsDataListAbstract, LazySubjectsDataList, SubjectsDataList)
 from dwi_ml.data.dataset.single_subject_containers import (LazySubjectData,
                                                            SubjectData)
 
@@ -50,7 +50,7 @@ def __init__(self, set_name: str, hdf5_file: str, lazy: bool,
 
         # The subjects data list will be either a SubjectsDataList or a
         # LazySubjectsDataList depending on MultisubjectDataset.is_lazy.
-        self.subjs_data_list = None
+        self.subjs_data_list = None # type:SubjectsDataListAbstract
         self.subjects = []  # type:List[str]
         self.nb_subjects = 0
 

src/dwi_ml/data/dataset/subjectdata_list_containers.py

@@ -21,7 +21,7 @@ def __init__(self, hdf5_path: str, log):
 
         # Do not access it directly. Use get_subj.
         # Will be a list of SubjectData or LazySubjectData
-        self._subjects_data_list = []
+        self._subjects_data_list = [] #type:List[SubjectDataAbstract]
 
     def add_subject(self, subject_data: SubjectDataAbstract):
         """