Add regex matching support to AttentionExtract. Add return_dict support to graph extractors and use returned output in AttentionExtractor

timm/models/_features_fx.py

@@ -118,6 +118,7 @@ class FeatureGraphNet(nn.Module):
             out_indices: Tuple[int, ...],
             out_map: Optional[Dict] = None,
             output_fmt: str = 'NCHW',
+            return_dict: bool = False,
     ):
         super().__init__()
         assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
@@ -127,9 +128,13 @@ class FeatureGraphNet(nn.Module):
         self.output_fmt = Format(output_fmt)
         return_nodes = _get_return_layers(self.feature_info, out_map)
         self.graph_module = create_feature_extractor(model, return_nodes)
+        self.return_dict = return_dict
 
     def forward(self, x):
-        return list(self.graph_module(x).values())
+        out = self.graph_module(x)
+        if self.return_dict:
+            return out
+        return list(out.values())
 
 
 class GraphExtractNet(nn.Module):
@@ -144,19 +149,23 @@ class GraphExtractNet(nn.Module):
         model: model to extract features from
         return_nodes: node names to return features from (dict or list)
         squeeze_out: if only one output, and output in list format, flatten to single tensor
+        return_dict: return as dictionary from extractor with node names as keys, ignores squeeze_out arg
     """
     def __init__(
             self,
             model: nn.Module,
             return_nodes: Union[Dict[str, str], List[str]],
             squeeze_out: bool = True,
+            return_dict: bool = False,
     ):
         super().__init__()
         self.squeeze_out = squeeze_out
         self.graph_module = create_feature_extractor(model, return_nodes)
+        self.return_dict = return_dict
 
     def forward(self, x) -> Union[List[torch.Tensor], torch.Tensor]:
-        out = list(self.graph_module(x).values())
-        if self.squeeze_out and len(out) == 1:
-            return out[0]
-        return out
+        out = self.graph_module(x)
+        if self.return_dict:
+            return out
+        out = list(out.values())
+        return out[0] if self.squeeze_out and len(out) == 1 else out

timm/utils/attention_extract.py

@@ -1,4 +1,5 @@
 import fnmatch
+import re
 from collections import OrderedDict
 from typing import Union, Optional, List
 
@@ -17,6 +18,7 @@ class AttentionExtract(torch.nn.Module):
             mode: str = 'eval',
             method: str = 'fx',
             hook_type: str = 'forward',
+            use_regex: bool = False,
     ):
         """ Extract attention maps (or other activations) from a model by name.
 
@@ -26,6 +28,7 @@ class AttentionExtract(torch.nn.Module):
             mode: 'train' or 'eval' model mode.
             method: 'fx' or 'hook' extraction method.
             hook_type: 'forward' or 'forward_pre' hooks used.
+            use_regex: Use regex instead of fnmatch
         """
         super().__init__()
         assert mode in ('train', 'eval')
@@ -41,11 +44,15 @@ class AttentionExtract(torch.nn.Module):
 
             node_names = get_graph_node_names(model)[0 if mode == 'train' else 1]
             names = names or self.default_node_names
-            matched = [g for g in node_names if any([fnmatch.fnmatch(g, n) for n in names])]
+            if use_regex:
+                regexes = [re.compile(r) for r in names]
+                matched = [g for g in node_names if any([r.match(g) for r in regexes])]
+            else:
+                matched = [g for g in node_names if any([fnmatch.fnmatch(g, n) for n in names])]
             if not matched:
                 raise RuntimeError(f'No node names found matching {names}.')
 
-            self.model = GraphExtractNet(model, matched)
+            self.model = GraphExtractNet(model, matched, return_dict=True)
             self.hooks = None
         else:
             # names are module names
@@ -54,7 +61,11 @@ class AttentionExtract(torch.nn.Module):
 
             module_names = [n for n, m in model.named_modules()]
             names = names or self.default_module_names
-            matched = [m for m in module_names if any([fnmatch.fnmatch(m, n) for n in names])]
+            if use_regex:
+                regexes = [re.compile(r) for r in names]
+                matched = [m for m in module_names if any([r.match(m) for r in regexes])]
+            else:
+                matched = [m for m in module_names if any([fnmatch.fnmatch(m, n) for n in names])]
             if not matched:
                 raise RuntimeError(f'No module names found matching {names}.')
 
@@ -71,5 +82,4 @@ class AttentionExtract(torch.nn.Module):
             output = self.hooks.get_output(device=x.device)
         else:
             output = self.model(x)
-            output = OrderedDict(zip(self.names, output))
         return output