refactor: optimized split corr runtime

src/neuron_proofreader/merge_proofreading/merge_inference.py

@@ -103,7 +103,7 @@ def predict(self, x_nodes):
         numpy.ndarray
             Predicted merge site likelihoods.
         """
-        with torch.no_grad():
+        with torch.inference_mode():
             x_nodes = x_nodes.to(self.device)
             y_nodes = sigmoid(self.model(x_nodes))
             return np.squeeze(ml_util.to_cpu(y_nodes, to_numpy=True), axis=1)

src/neuron_proofreader/proposal_graph.py

@@ -645,12 +645,11 @@ def edge_attr(self, i, key="xyz", ignore=False):
         return attrs
 
     def edge_length(self, edge):
-        length = 0
-        for i in range(1, len(self.edges[edge]["xyz"])):
-            length += geometry.dist(
-                self.edges[edge]["xyz"][i], self.edges[edge]["xyz"][i - 1]
-            )
-        return length
+        xyz = self.edges[edge]["xyz"]
+        if len(xyz) < 2:
+            return 0.0
+        else:
+            return np.linalg.norm(xyz[1:] - xyz[:-1], axis=1).sum()
 
     def find_fragments_near_xyz(self, query_xyz, max_dist):
         hits = dict()

src/neuron_proofreader/split_proofreading/split_feature_extraction.py

@@ -10,6 +10,8 @@
 """
 
 from concurrent.futures import ThreadPoolExecutor, as_completed
+from skimage.transform import resize
+from time import time
 from torch_geometric.data import HeteroData
 
 import numpy as np
@@ -264,6 +266,7 @@ def __call__(self, subgraph, features):
             for thread in as_completed(pending.keys()):
                 proposal = pending.pop(thread)
                 extractor = thread.result()
+
                 profiles[proposal] = extractor.get_intensity_profile()
                 patches[proposal] = extractor.get_input_patch()
 
@@ -413,7 +416,7 @@ def get_input_patch(self):
             raw image data and channel 1 contains segmentation data.
         """
         img = img_util.resize(self.img, self.patch_shape)
-        mask = img_util.resize(self.mask, self.patch_shape, True)
+        mask = resize_segmentation(self.mask, self.patch_shape)
         return np.stack([img, mask], axis=0)
 
     def get_intensity_profile(self):
@@ -954,3 +957,29 @@ def get_feature_dict():
         proposals.
     """
     return {"branch": 2, "proposal": 70}
+
+
+def resize_segmentation(mask, new_shape):
+    """
+    Resizes a segmentation mask to the given new shape.
+
+    Parameters
+    ----------
+    mask : numpy.ndarray
+        Segmentation mask to be resized.
+    new_shape : Tuple[int]
+        New shape of segmentation mask.
+
+    Returns
+    -------
+    mask : numpy.ndarray
+        Resized segmentation mask.
+    """
+    mask = resize(
+        mask,
+        new_shape,
+        order=0,
+        preserve_range=True,
+        anti_aliasing=False,
+    ).astype(mask.dtype)
+    return mask

src/neuron_proofreader/split_proofreading/split_inference.py

@@ -29,7 +29,6 @@
 """
 
 from time import time
-from torch.nn.functional import sigmoid
 from tqdm import tqdm
 
 import networkx as nx
@@ -327,10 +326,11 @@ def predict(self, data):
             Dictionary that maps proposal IDs to model predictions.
         """
         # Generate predictions
-        with torch.no_grad():
+        with torch.inference_mode():
             device = self.config.ml.device
             x = data.get_inputs().to(device)
-            hat_y = sigmoid(self.model(x))
+            with torch.cuda.amp.autocast(enabled=True):
+                hat_y = torch.sigmoid(self.model(x))
 
         # Reformat predictions
         idx_to_id = data.idxs_proposals.idx_to_id

src/neuron_proofreader/utils/img_util.py

@@ -730,7 +730,7 @@ def remove_small_segments(segmentation, min_size):
     return segmentation
 
 
-def resize(img, new_shape, is_segmentation=False):
+def resize(img, new_shape):
     """
     Resize a 3D image to the specified new shape using linear interpolation.
 
@@ -740,22 +740,14 @@ def resize(img, new_shape, is_segmentation=False):
         Input 3D image array with shape (depth, height, width).
     new_shape : Tuple[int]
         Desired output shape as (new_depth, new_height, new_width).
-    is_segmentation : bool, optional
-        Indication of whether the image represents a segmentation mask.
 
     Returns
     -------
     numpy.ndarray
         Resized 3D image with shape equal to "new_shape".
     """
-    # Set parameters
-    order = 0 if is_segmentation else 3
-    multiplier = 4 if is_segmentation else 1
     zoom_factors = np.array(new_shape) / np.array(img.shape)
-
-    # Resize image
-    img = zoom(multiplier * img, zoom_factors, order=order)
-    return img / multiplier
+    return zoom(img, zoom_factors, order=1, prefilter=False)
 
 
 def to_physical(voxel, anisotropy, offset=(0, 0, 0)):