[data.augmentations] Remove elastic deformation parallelisation

src/mednet/data/augmentations.py

@@ -14,7 +14,6 @@ input image.
 
 import functools
 import logging
-import multiprocessing.pool
 import random
 import typing
 
@@ -131,7 +130,6 @@ def _elastic_deformation_on_batch(
     spline_order: int = 1,
     mode: str = "nearest",
     p: float = 1.0,
-    pool: multiprocessing.pool.Pool | None = None,
 ) -> torch.Tensor:
     """Perform elastic deformation on a batch of images.
 
@@ -157,8 +155,6 @@ def _elastic_deformation_on_batch(
     p
         Probability that this transformation will be applied.  Meaningful when
         using it as a data augmentation technique.
-    pool
-        The multiprocessing pool to use.
 
     Returns
     -------
@@ -178,11 +174,7 @@ def _elastic_deformation_on_batch(
 
     # if a mp pool is available, do it in parallel
     augmented_images: typing.Any
-    if pool is None:
-        augmented_images = map(partial, batch.cpu())
-    else:
-        augmented_images = pool.imap(partial, batch.cpu())
-
+    augmented_images = map(partial, batch.cpu())
     return torch.stack(list(augmented_images))
 
 
@@ -217,13 +209,6 @@ class ElasticDeformation:
     p
         Probability that this transformation will be applied.  Meaningful when
         using it as a data augmentation technique.
-    parallel
-        Use multiprocessing for processing batches of data: if set to -1
-        (default), disables multiprocessing.  If set to -2, then enable
-        auto-tune (use the minimum value between the first batch size and total
-        number of processing cores).  Set to 0 to enable as many processes as
-        processing cores available in the system. Set to >= 1 to enable that
-        many processes.
     """
 
     def __init__(
@@ -233,14 +218,12 @@ class ElasticDeformation:
         spline_order: int = 1,
         mode: str = "nearest",
         p: float = 1.0,
-        parallel: int = -2,
     ):
         self.alpha: float = alpha
         self.sigma: float = sigma
         self.spline_order: int = spline_order
         self.mode: str = mode
         self.p: float = p
-        self.parallel = parallel
 
     def __str__(self) -> str:
         parameters = [
@@ -249,50 +232,11 @@ class ElasticDeformation:
             f"spline_order={self.spline_order}",
             f"mode={self.mode}",
             f"p={self.p}",
-            f"parallel={self.parallel}",
         ]
         return f"{type(self).__name__}({', '.join(parameters)})"
 
-    @property
-    def parallel(self) -> int:
-        """Use multiprocessing for data augmentation.
-
-        If set to -1 (default), disables multiprocessing.  If set to -2,
-        then enable auto-tune (use the minimum value between the first
-        batch size and total number of processing cores).  Set to 0 to
-        enable as many processes as processing cores available in the
-        system. Set to >= 1 to enable that many processes.
-
-        Returns
-        -------
-        int
-            The multiprocessing type.
-        """
-
-        return self._parallel
-
-    @parallel.setter
-    def parallel(self, value):
-        self._parallel = value
-
-        if value >= 0:
-            instances = value or multiprocessing.cpu_count()
-            logger.info(
-                f"Applying data-augmentation using {instances} processes...",
-            )
-            self._mp_pool = multiprocessing.get_context("spawn").Pool(instances)
-        else:
-            self._mp_pool = None
-
     def __call__(self, img: torch.Tensor) -> torch.Tensor:
         if len(img.shape) == 4:
-            if self._mp_pool is None and self._parallel == -2:
-                # auto-tunning on first batch
-                instances = min(img.shape[0], multiprocessing.cpu_count())
-                self._mp_pool = multiprocessing.get_context("spawn").Pool(
-                    instances,
-                )
-
             return _elastic_deformation_on_batch(
                 img,
                 self.alpha,
@@ -300,7 +244,6 @@ class ElasticDeformation:
                 self.spline_order,
                 self.mode,
                 self.p,
-                self._mp_pool,
             ).to(img.device)
 
         if len(img.shape) == 3: