Fix a few lines too long

dwi_ml/training/with_generation/trainer.py

@@ -25,8 +25,8 @@
 - Connectivity fit:
     Percentage of streamlines ending in a block of the volume indeed connected
     in the validation subject. Real connectivity matrices must be saved in the
-    hdf5. Right now, volumes are simply split into blocs (the same way as in the
-    hdf5, ex, to 10x10x10 volumes for a total of 1000 blocks), not based on
+    hdf5. Right now, volumes are simply split into blocs (the same way as in
+    the hdf5, ex, to 10x10x10 volumes for a total of 1000 blocks), not based on
     anatomical ROIs. It has the advantage that it does not rely on the quality
     of segmentation. It had the drawback that a generated streamline ending
     very close to the "true" streamline, but in another block, if the
@@ -175,9 +175,10 @@ def validate_one_batch(self, data, epoch):
                 logger.debug("Additional tracking-like generation validation "
                              "from batch.")
                 (gen_n, mean_final_dist, mean_clipped_final_dist,
-                 percent_IS_very_good, percent_IS_acceptable, percent_IS_very_far,
-                 diverging_pnt, connectivity) = self.validation_generation_one_batch(
-                    data, compute_all_scores=True)
+                 percent_IS_very_good, percent_IS_acceptable,
+                 percent_IS_very_far, diverging_pnt, connectivity) = \
+                    self.validation_generation_one_batch(
+                        data, compute_all_scores=True)
 
                 self.tracking_very_good_IS_monitor.update(
                     percent_IS_very_good, weight=gen_n)
@@ -196,8 +197,9 @@ def validate_one_batch(self, data, epoch):
                 self.tracking_connectivity_score_monitor.update(
                     connectivity, weight=gen_n)
             elif len(self.tracking_mean_final_distance_monitor.average_per_epoch) == 0:
-                logger.info("Skipping tracking-like generation validation from "
-                            "batch. No values yet: adding fake initial values.")
+                logger.info("Skipping tracking-like generation validation "
+                            "from batch. No values yet: adding fake initial "
+                            "values.")
                 # Fake values at the beginning
                 # Bad IS = 100%
                 self.tracking_very_good_IS_monitor.update(100.0)
@@ -216,8 +218,8 @@ def validate_one_batch(self, data, epoch):
 
                 self.tracking_connectivity_score_monitor.update(1)
             else:
-                logger.info("Skipping tracking-like generation validation from "
-                            "batch. Copying previous epoch's values.")
+                logger.info("Skipping tracking-like generation validation "
+                            "from batch. Copying previous epoch's values.")
                 # Copy previous value
                 for monitor in [self.tracking_very_good_IS_monitor,
                                 self.tracking_acceptable_IS_monitor,
@@ -238,7 +240,8 @@ def validation_generation_one_batch(self, data, compute_all_scores=False):
 
         # Possibly sending again to GPU even if done in the local loss
         # computation, but easier with current implementation.
-        real_lines = [line.to(self.device, non_blocking=True, dtype=torch.float)
+        real_lines = [line.to(self.device, non_blocking=True,
+                              dtype=torch.float)
                       for line in real_lines]
         last_pos = torch.vstack([line[-1, :] for line in real_lines])
 
@@ -267,7 +270,8 @@ def validation_generation_one_batch(self, data, compute_all_scores=False):
             final_dist_clipped = torch.mean(final_dist_clipped)
 
             # 2. Connectivity scores, if available (else None)
-            connectivity_score = self._compare_connectivity(lines, ids_per_subj)
+            connectivity_score = self._compare_connectivity(lines,
+                                                            ids_per_subj)
 
             # 3. "IS ratio", i.e. percentage of streamlines ending inside a
             # predefined radius.
@@ -280,9 +284,9 @@ def validation_generation_one_batch(self, data, compute_all_scores=False):
             final_dist = torch.mean(final_dist)
 
             # 4. Verify point where streamline starts diverging.
-            # abs(100 - score): 0 = good. 100 = bad (either abs(100) -> diverged
-            # at first point or abs(-100) = diverged after twice the expected
-            # length.
+            # abs(100 - score): 0 = good. 100 = bad (either
+            # abs(100) -> diverged at first point or
+            # abs(-100) = diverged after twice the expected length.
             total_point = 0
             for line, real_line in zip(lines, real_lines):
                 expected_nb = len(real_line)
@@ -321,7 +325,8 @@ def _compare_connectivity(self, lines, ids_per_subj):
             #  done in the loop for each subject.
             (connectivity_matrices, volume_sizes,
              connectivity_nb_blocs, connectivity_labels) = \
-                self.batch_loader.load_batch_connectivity_matrices(ids_per_subj)
+                self.batch_loader.load_batch_connectivity_matrices(
+                    ids_per_subj)
 
             score = 0.0
             for i, subj in enumerate(ids_per_subj.keys()):
@@ -365,7 +370,8 @@ def _compare_connectivity(self, lines, ids_per_subj):
             score = None
         return score
 
-    def propagate_multiple_lines(self, lines: List[torch.Tensor], ids_per_subj):
+    def propagate_multiple_lines(self, lines: List[torch.Tensor],
+                                 ids_per_subj):
         """
         Tractography propagation of 'lines'.
         """
@@ -396,7 +402,8 @@ def get_dirs_at_last_pos(_lines: List[torch.Tensor], n_last_pos):
         # 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:
+            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))
@@ -408,7 +415,8 @@ def get_dirs_at_last_pos(_lines: List[torch.Tensor], n_last_pos):
             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,
+                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))