Load the tracking mask correctly

dwi_ml/training/projects/learn2track_trainer.py

@@ -2,11 +2,13 @@
 import logging
 from typing import List
 
+import h5py
 import numpy as np
 import torch
 
 from dwi_ml.models.projects.learn2track_model import Learn2TrackModel
 from dwi_ml.tracking.propagation import propagate_multiple_lines
+from dwi_ml.tracking.utils import prepare_tracking_mask
 from dwi_ml.training.with_generation.trainer import \
     DWIMLTrainerForTrackingOneInput
 
@@ -92,10 +94,24 @@ def get_dirs_at_last_pos(_lines: List[torch.Tensor], n_last_pos):
 
         theta = 2 * np.pi  # theta = 360 degrees
         max_nbr_pts = int(200 / self.model.step_size)
-        results = propagate_multiple_lines(
-            lines, update_memory_after_removing_lines, get_dirs_at_last_pos,
-            theta=theta, step_size=self.model.step_size,
-            verify_opposite_direction=False, mask=self.tracking_mask,
-            max_nbr_pts=max_nbr_pts, append_last_point=False,
-            normalize_directions=True)
-        return results
+
+        final_lines = []
+        for subj_idx, line_idx in ids_per_subj.items():
+
+            with h5py.File(self.batch_loader.dataset.hdf5_file, 'r') as hdf_handle:
+                subj_id = self.batch_loader.context_subset.subjects[subj_idx]
+                logging.debug("Loading subj {} ({})'s tracking mask."
+                              .format(subj_idx, subj_id))
+                tracking_mask, _ = prepare_tracking_mask(
+                    hdf_handle, self.tracking_mask_group, subj_id=subj_id,
+                    mask_interp='nearest')
+                tracking_mask.move_to(self.device)
+
+            final_lines.extend(propagate_multiple_lines(
+                lines[line_idx], update_memory_after_removing_lines,
+                get_dirs_at_last_pos, theta=theta,
+                step_size=self.model.step_size, verify_opposite_direction=False,
+                mask=tracking_mask, max_nbr_pts=max_nbr_pts,
+                append_last_point=False, normalize_directions=True))
+
+        return final_lines

dwi_ml/training/projects/transformer_trainer.py

@@ -1,9 +1,12 @@
 # -*- coding: utf-8 -*-
+import logging
 from typing import List
 
+import h5py
 import numpy as np
 import torch
 from dwi_ml.tracking.propagation import propagate_multiple_lines
+from dwi_ml.tracking.utils import prepare_tracking_mask
 
 from dwi_ml.training.with_generation.trainer import \
     DWIMLTrainerForTrackingOneInput
@@ -45,9 +48,24 @@ def get_dirs_at_last_pos(_lines: List[torch.Tensor], n_last_pos):
 
         theta = 2 * np.pi  # theta = 360 degrees
         max_nbr_pts = int(200 / self.model.step_size)
-        return propagate_multiple_lines(
-            lines, update_memory_after_removing_lines, get_dirs_at_last_pos,
-            theta=theta, step_size=self.model.step_size,
-            verify_opposite_direction=False, mask=self.tracking_mask,
-            max_nbr_pts=max_nbr_pts, append_last_point=False,
-            normalize_directions=True)
+
+        final_lines = []
+        for subj_idx, line_idx in ids_per_subj.items():
+
+            with h5py.File(self.batch_loader.dataset.hdf5_file, 'r') as hdf_handle:
+                subj_id = self.batch_loader.context_subset.subjects[subj_idx]
+                logging.debug("Loading subj {} ({})'s tracking mask."
+                              .format(subj_idx, subj_id))
+                tracking_mask, _ = prepare_tracking_mask(
+                    hdf_handle, self.tracking_mask_group, subj_id=subj_id,
+                    mask_interp='nearest')
+                tracking_mask.move_to(self.device)
+
+            final_lines.extend(propagate_multiple_lines(
+                lines[line_idx], update_memory_after_removing_lines,
+                get_dirs_at_last_pos, theta=theta,
+                step_size=self.model.step_size, verify_opposite_direction=False,
+                mask=tracking_mask, max_nbr_pts=max_nbr_pts,
+                append_last_point=False, normalize_directions=True))
+
+        return final_lines

dwi_ml/training/with_generation/trainer.py

@@ -100,26 +100,7 @@ def __init__(self, add_a_tracking_validation_phase: bool = False,
         self.tracking_phase_nb_steps_init = tracking_phase_nb_steps_init
         self.tracking_mask_group = tracking_phase_mask_group
 
-        self.tracking_mask = None
-        if add_a_tracking_validation_phase:
-            # Right now, using any subject's, and supposing that they are all
-            # in the same space. Else, code would need refactoring to allow
-            # tracking on multiple subjects. Or we can loop on each subject.
-            logging.warning("***************\n"
-                            "CODE NEEDS REFACTORING. USING THE SAME TRACKING "
-                            "MASK FOR ALL SUBJECTS.\n"
-                            "***************\n")
-            any_subj = self.batch_loader.dataset.training_set.subjects[0]
-            if tracking_phase_mask_group is not None:
-                with h5py.File(self.batch_loader.dataset.hdf5_file, 'r') \
-                        as hdf_handle:
-                    logging.info("Loading tracking mask.")
-                    self.tracking_mask, _ = prepare_tracking_mask(
-                        hdf_handle, tracking_phase_mask_group, subj_id=any_subj,
-                        mask_interp='nearest')
-                    self.tracking_mask.move_to(self.device)
-
-            self.compute_connectivity = self.batch_loader.data_contains_connectivity
+        self.compute_connectivity = self.batch_loader.data_contains_connectivity
 
         # -------- Monitors
         # At training time: only the one metric used for training.
@@ -387,9 +368,28 @@ def get_dirs_at_last_pos(_lines: List[torch.Tensor], n_last_pos):
 
         theta = 2 * np.pi  # theta = 360 degrees
         max_nbr_pts = int(200 / self.model.step_size)
-        return propagate_multiple_lines(
-            lines, update_memory_after_removing_lines, get_dirs_at_last_pos,
-            theta=theta, step_size=self.model.step_size,
-            verify_opposite_direction=False, mask=self.tracking_mask,
-            max_nbr_pts=max_nbr_pts, append_last_point=False,
-            normalize_directions=True)
+
+        # Looping on subjects because current implementation requires a single
+        # tracking mask. But all the rest (get_dirs_at_last_pos, particularly)
+        # work on multiple subjects because the batch loader loads input
+        # according to subject id. Could refactor "propagate_multiple_line" to
+        # accept multiple masks or manage it differently.
+        final_lines = []
+        for subj_idx, line_idx in ids_per_subj.items():
+            with h5py.File(self.batch_loader.dataset.hdf5_file, 'r') as hdf_handle:
+                subj_id = self.batch_loader.context_subset.subjects[subj_idx]
+                logging.debug("Loading subj {} ({})'s tracking mask."
+                              .format(subj_idx, subj_id))
+                tracking_mask, _ = prepare_tracking_mask(
+                    hdf_handle, self.tracking_mask_group, subj_id=subj_id,
+                    mask_interp='nearest')
+                tracking_mask.move_to(self.device)
+
+            final_lines.extend(propagate_multiple_lines(
+                lines[line_idx], update_memory_after_removing_lines,
+                get_dirs_at_last_pos, theta=theta,
+                step_size=self.model.step_size, verify_opposite_direction=False,
+                mask=tracking_mask, max_nbr_pts=max_nbr_pts,
+                append_last_point=False, normalize_directions=True))
+
+        return final_lines