Feat mlp comparison

src/neuron_proofreader/machine_learning/gnn_models.py

@@ -34,7 +34,14 @@ class VisionHGAT(torch.nn.Module):
         str(("proposal", "to", "branch")),
     ]
 
-    def __init__(self, patch_shape, heads=2, hidden_dim=128, n_layers=2):
+    def __init__(
+        self,
+        patch_shape,
+        disable_msg_passing=False,
+        heads=2,
+        hidden_dim=128,
+        n_layers=2,
+    ):
         # Call parent class
         super().__init__()
 
@@ -43,9 +50,15 @@ def __init__(self, patch_shape, heads=2, hidden_dim=128, n_layers=2):
         self.patch_embedding = init_patch_embedding(patch_shape, hidden_dim // 2)
 
         # Message passing layers
-        self.gat1 = self.init_gat(hidden_dim, hidden_dim, heads)
-        self.gat2 = self.init_gat(hidden_dim * heads, hidden_dim, heads)
-        self.output = nn.Linear(hidden_dim * heads ** 2, 1)
+        self.disable_msg_passing = disable_msg_passing
+        if self.disable_msg_passing:
+            self.gat1 = self.init_mlp_layers(hidden_dim, n_layers)
+            self.gat2 = self.init_mlp_layers(hidden_dim, n_layers)
+            self.output = nn.Linear(hidden_dim, 1)
+        else:
+            self.gat1 = self.init_gat(hidden_dim, hidden_dim, heads)
+            self.gat2 = self.init_gat(hidden_dim * heads, hidden_dim, heads)
+            self.output = nn.Linear(hidden_dim * heads ** 2, 1)
 
         # Initialize weights
         self.init_weights()
@@ -63,6 +76,18 @@ def init_gat(self, hidden_dim, edge_dim, heads):
             gat_dict[relation] = init_gat(hidden_dim, edge_dim, heads)
         return nn_geometric.HeteroConv(gat_dict)
 
+    def init_mlp_layers(self, hidden_dim, n_layers=2):
+        layers = nn.ModuleList()
+        for _ in range(n_layers):
+            layers.append(
+                nn_geometric.HeteroDictLinear(
+                    hidden_dim,
+                    hidden_dim,
+                    types=("branch", "proposal")
+                )
+            )
+        return layers
+
     def init_weights(self):
         """
         Initializes linear layers.
@@ -91,8 +116,14 @@ def forward(self, input_dict):
         x_dict["proposal"] = torch.cat((x_dict["proposal"], x_img), dim=1)
 
         # Message passing
-        x_dict = self.gat1(x_dict, edge_index_dict)
-        x_dict = self.gat2(x_dict, edge_index_dict)
+        if self.disable_msg_passing:
+            for layer in self.gat1:
+                x_dict = layer(x_dict)
+            for layer in self.gat2:
+                x_dict = layer(x_dict)
+        else:
+            x_dict = self.gat1(x_dict, edge_index_dict)
+            x_dict = self.gat2(x_dict, edge_index_dict)
         return self.output(x_dict["proposal"])
 
 

src/neuron_proofreader/machine_learning/subgraph_sampler.py

@@ -202,7 +202,7 @@ class SeededSubgraphSampler(SubgraphSampler):
     def __init__(self, graph, gnn_depth=2, max_proposals=64):
         # Call parent class
         super(SeededSubgraphSampler, self).__init__(
-            graph, gnn_depth, max_proposals
+            graph, gnn_depth=gnn_depth, max_proposals=max_proposals
         )
 
     # --- Batch Generation ---

src/neuron_proofreader/split_proofreading/split_feature_extraction.py

@@ -11,7 +11,6 @@
 
 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
@@ -402,7 +401,7 @@ def __init__(
         node1, node2 = tuple(self.proposal)
         self.annotate_edge(node1)
         self.annotate_edge(node2)
-        self.annotate_proposal()        
+        self.annotate_proposal()
 
     # --- Core Routines ---
     def get_input_patch(self):