Visu transfo

.gitignore

@@ -71,4 +71,5 @@ target/
 *.swo
 
 # dwi_ml stuff
-.ipynb_config/
+.ipynb_config/
+.ipynb_checkpoints/

dwi_ml/data/dataset/multi_subject_containers.py

@@ -208,6 +208,16 @@ def get_mri_data(self, subj_idx: int, group_idx: int,
 
         Contrary to get_volume_verify_cache, this does not send data to
         cache for later use.
+
+        Parameters
+        ----------
+        subj_idx: int
+            The subject id.
+        group_idx: int
+            The volume group idx.
+        load_it: bool
+            If data is lazy, get the volume as a LazyMRIData (False) or load it
+            as non-lazy (if True).
         """
         if self.subjs_data_list.is_lazy:
             if load_it:
@@ -484,6 +494,7 @@ def load_data(self, load_training=True, load_validation=True,
                 self.streamline_groups = poss_strea_groups
                 self.streamlines_contain_connectivity = contains_connectivity
 
+            self.streamline_groups = list(self.streamline_groups)
             group_info = (self.volume_groups, self.nb_features,
                           self.streamline_groups,
                           self.streamlines_contain_connectivity)

dwi_ml/data/processing/dwi/dwi.py

@@ -5,8 +5,9 @@
 from dipy.reconst.shm import sph_harm_lookup
 import nibabel as nib
 import numpy as np
+
 from scilpy.io.utils import validate_sh_basis_choice
-from scilpy.reconst.raw_signal import compute_sh_coefficients
+from scilpy.reconst.sh import compute_sh_coefficients
 
 eps = 1e-6
 

dwi_ml/data/processing/streamlines/data_augmentation.py

@@ -8,7 +8,8 @@
 from nibabel.streamlines.tractogram import (PerArrayDict, PerArraySequenceDict)
 import numpy as np
 
-from scilpy.tracking.tools import resample_streamlines_step_size
+from scilpy.tractograms.streamline_operations import \
+    resample_streamlines_step_size
 from scilpy.utils.streamlines import compress_sft
 
 

dwi_ml/data/processing/streamlines/post_processing.py

@@ -6,7 +6,7 @@
 
 from scilpy.tractanalysis.tools import \
     extract_longest_segments_from_profile as segmenting_func
-from scilpy.tractanalysis.uncompress import uncompress
+from scilpy.tractograms.uncompress import uncompress
 
 # We could try using nan instead of zeros for non-existing previous dirs...
 DEFAULT_UNEXISTING_VAL = torch.zeros((1, 3), dtype=torch.float32)

dwi_ml/io_utils.py

@@ -43,7 +43,7 @@ def add_memory_args(p: ArgumentParser, add_lazy_options=False,
         add_processes_arg(ram_options)
         ram_options.add_argument(
             '--use_gpu', action='store_true',
-            help="If set, use GPU for processing. Cannot be used \ntogether "
+            help="If set, use GPU for processing. Cannot be used together "
                  "with --processes.")
     else:
         p.add_argument('--use_gpu', action='store_true',

dwi_ml/models/direction_getter_models.py

@@ -209,6 +209,26 @@ def _prepare_dirs_for_loss(self, target_dirs: List[Tensor]):
 
     def compute_loss(self, outputs: List[Tensor],
                      target_streamlines: List[Tensor], average_results=True):
+        """
+        Parameters
+        ----------
+        outputs: List[Tensor]
+            Your model's outputs
+        target_streamlines: List[Tensor]
+            The streamlines. Directions will be computed and formatted based
+            on child class requirements.
+        average_results: bool
+            If true, returns the average over all values.
+
+        Returns
+        -------
+        If compress_loss or average_results:
+            Tuple(tensor, n)
+                The average loss and the n points averaged.
+        Else:
+            List[Tensor]
+                The loss for each point in each streamline.
+        """
         if self.compress_loss and not average_results:
             raise ValueError("Current implementation of compress_loss does not "
                              "allow returning non-averaged loss.")

dwi_ml/models/main_models.py

@@ -207,6 +207,22 @@ def forward(self, inputs, streamlines):
     def compute_loss(self, model_outputs, target_streamlines):
         raise NotImplementedError
 
+    def merge_batches_outputs(self, all_outputs, new_batch):
+        """
+        To be used at testing time. At training or validation time, outputs are
+        discarded after each batch; only the loss is measured. At testing time,
+        it may be necessary to merge batches. The way to do it will depend on
+        your model's format.
+
+        Parameters
+        ----------
+        all_outputs: Any or None
+            All previous outputs from previous batches, already combined.
+        new_batch:
+            The batch to merge
+        """
+        raise NotImplementedError
+
 
 class ModelWithNeighborhood(MainModelAbstract):
     """
@@ -752,3 +768,27 @@ def compute_loss(self, model_outputs: List[Tensor], target_streamlines,
     def move_to(self, device):
         super().move_to(device)
         self.direction_getter.move_to(device)
+
+    def merge_batches_outputs(self, all_outputs, new_batch, device=None):
+        # 1. Send new batch to device
+        if 'gaussian' in self.direction_getter.key:
+            # all_outputs = (means, sigmas)
+            new_batch = ([m.to(device=device) for m in new_batch[0]],
+                         [s.to(device=device) for s in new_batch[1]])
+        else:
+            new_batch = [a.to(device) for a in new_batch]
+
+        # 2. Concat
+        if all_outputs is None:
+            return new_batch
+        else:
+            if 'gaussian' in self.direction_getter.key:
+                # all_outputs = (means, sigmas)
+                all_outputs[0].extend(new_batch[0])
+                all_outputs[1].extend(new_batch[1])
+            elif 'fisher' in self.direction_getter.key:
+                raise NotImplementedError
+            else:
+                # all_outputs = a list of tensors per streamline.
+                all_outputs.extend(new_batch)
+            return all_outputs

dwi_ml/models/projects/copy_previous_dirs.py

@@ -52,8 +52,5 @@ def compute_loss(self, model_outputs: List[torch.Tensor],
         if self.skip_first_point:
             target_streamlines = [t[1:] for t in target_streamlines]
 
-        if self._context == 'visu':
-            return self.direction_getter.compute_loss(
-                model_outputs, target_streamlines, average_results)
-        else:
-            raise NotImplementedError
+        return self.direction_getter.compute_loss(
+            model_outputs, target_streamlines, average_results)

dwi_ml/models/projects/learn2track_model.py

@@ -189,6 +189,11 @@ def __init__(self, experiment_name,
         self.instantiate_direction_getter(self.rnn_model.output_size)
 
     def set_context(self, context):
+        # Training, validation: Used by trainer. Nothing special.
+        # Tracking: Used by tracker. Returns only the last point.
+        #     Preparing_backward: Used by tracker. Nothing special, but does
+        #     not return only the last point.
+        # Visu: Nothing special. Used by tester.
         assert context in ['training', 'validation', 'tracking', 'visu',
                            'preparing_backward']
         self._context = context
@@ -253,7 +258,7 @@ def forward(self, x: List[torch.tensor],
         """
         # Reminder.
         # Correct interpolation and management of points should be done before.
-        if self._context is None:
+        if self.context is None:
             raise ValueError("Please set context before usage.")
 
         # Right now input is always flattened (interpolation is implemented
@@ -268,7 +273,7 @@ def forward(self, x: List[torch.tensor],
         # Making sure we can use default 'enforce_sorted=True' with packed
         # sequences.
         unsorted_indices = None
-        if not self._context == 'tracking':
+        if not self.context == 'tracking':
             # Ordering streamlines per length.
             lengths = torch.as_tensor([len(s) for s in x])
             _, sorted_indices = torch.sort(lengths, descending=True)
@@ -351,7 +356,7 @@ def forward(self, x: List[torch.tensor],
             x = x + copy_prev_dir
 
         # Unpacking.
-        if not self._context == 'tracking':
+        if not self.context == 'tracking':
             # (during tracking: keeping as one single tensor.)
             if 'gaussian' in self.dg_key or 'fisher' in self.dg_key:
                 # Separating mean, sigmas (gaussian) or mean, kappa (fisher)
@@ -370,7 +375,7 @@ def forward(self, x: List[torch.tensor],
         if return_hidden:
             # Return the hidden states too. Necessary for the generative
             # (tracking) part, done step by step.
-            if not self._context == 'tracking':
+            if not self.context == 'tracking':
                 # (ex: when preparing backward tracking.
                 #  Must also re-sort hidden states.)
                 if self.rnn_model.rnn_torch_key == 'lstm':
@@ -402,7 +407,7 @@ def copy_prev_dir(self, dirs):
             # Converting the input directions into classes the same way as
             # during loss, but convert to one-hot.
             # The first previous dir (0) converts to index 0.
-            if self._context == 'tracking':
+            if self.context == 'tracking':
                 if dirs[0].shape[0] == 0:
                     copy_prev_dir = torch.zeros(
                         len(dirs),