Cleanup FeatureInfo getters, add TF models sourced Xception41/65/71 weights

timm/models/efficientnet.py

@@ -441,7 +441,7 @@ class EfficientNetFeatures(nn.Module):
         # Register feature extraction hooks with FeatureHooks helper
         self.feature_hooks = None
         if feature_location != 'bottleneck':
-            hooks = self.feature_info.get_by_key(keys=('module', 'hook_type'))
+            hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
             self.feature_hooks = FeatureHooks(hooks, self.named_modules())
 
     def forward(self, x) -> List[torch.Tensor]:

timm/models/features.py

@@ -8,7 +8,7 @@ Hacked together by Ross Wightman
 from collections import OrderedDict, defaultdict
 from copy import deepcopy
 from functools import partial
-from typing import Dict, List, Tuple, Any
+from typing import Dict, List, Tuple
 
 import torch
 import torch.nn as nn
@@ -30,42 +30,46 @@ class FeatureInfo:
     def from_other(self, out_indices: Tuple[int]):
         return FeatureInfo(deepcopy(self.info), out_indices)
 
-    def channels(self, idx=None):
+    def get(self, key, idx=None):
-        """ feature channels accessor
+        """ Get value by key at specified index (indices)
-        if idx == None, returns feature channel count at each output index
+        if idx == None, returns value for key at each output index
-        if idx is an integer, return feature channel count for that feature module index
+        if idx is an integer, return value for that feature module index (ignoring output indices)
+        if idx is a list/tupple, return value for each module index (ignoring output indices)
         """
-        if isinstance(idx, int):
+        if idx is None:
-            return self.info[idx]['num_chs']
+            return [self.info[i][key] for i in self.out_indices]
-        return [self.info[i]['num_chs'] for i in self.out_indices]
+        if isinstance(idx, (tuple, list)):
+            return [self.info[i][key] for i in idx]
+        else:
+            return self.info[idx][key]
 
-    def reduction(self, idx=None):
+    def get_dicts(self, keys=None, idx=None):
-        """ feature reduction (output stride) accessor
+        """ return info dicts for specified keys (or all if None) at specified indices (or out_indices if None)
-        if idx == None, returns feature reduction factor at each output index
-        if idx is an integer, return feature channel count at that feature module index
         """
-        if isinstance(idx, int):
+        if idx is None:
-            return self.info[idx]['reduction']
-        return [self.info[i]['reduction'] for i in self.out_indices]
-
-    def module_name(self, idx=None):
-        """ feature module name accessor
-        if idx == None, returns feature module name at each output index
-        if idx is an integer, return feature module name at that feature module index
-        """
-        if isinstance(idx, int):
-            return self.info[idx]['module']
-        return [self.info[i]['module'] for i in self.out_indices]
-
-    def get_by_key(self, idx=None, keys=None):
-        """ return info dicts for specified keys (or all if None) at specified idx (or out_indices if None)
-        """
-        if isinstance(idx, int):
-            return self.info[idx] if keys is None else {k: self.info[idx][k] for k in keys}
             if keys is None:
                 return [self.info[i] for i in self.out_indices]
             else:
                 return [{k: self.info[i][k] for k in keys} for i in self.out_indices]
+        if isinstance(idx, (tuple, list)):
+            return [self.info[i] if keys is None else {k: self.info[i][k] for k in keys} for i in idx]
+        else:
+            return self.info[idx] if keys is None else {k: self.info[idx][k] for k in keys}
+
+    def channels(self, idx=None):
+        """ feature channels accessor
+        """
+        return self.get('num_chs', idx)
+
+    def reduction(self, idx=None):
+        """ feature reduction (output stride) accessor
+        """
+        return self.get('reduction', idx)
+
+    def module_name(self, idx=None):
+        """ feature module name accessor
+        """
+        return self.get('module', idx)
 
     def __getitem__(self, item):
         return self.info[item]
@@ -253,11 +257,11 @@ class FeatureHookNet(nn.ModuleDict):
             if hasattr(model, 'reset_classifier'):  # make sure classifier is removed?
                 model.reset_classifier(0)
             layers['body'] = model
-            hooks.extend(self.feature_info)
+            hooks.extend(self.feature_info.get_dicts())
         else:
             modules = _module_list(model, flatten_sequential=flatten_sequential)
             remaining = {f['module']: f['hook_type'] if 'hook_type' in f else default_hook_type
-                         for f in self.feature_info}
+                         for f in self.feature_info.get_dicts()}
             for new_name, old_name, module in modules:
                 layers[new_name] = module
                 for fn, fm in module.named_modules(prefix=old_name):

timm/models/mobilenetv3.py

@@ -186,7 +186,7 @@ class MobileNetV3Features(nn.Module):
         # Register feature extraction hooks with FeatureHooks helper
         self.feature_hooks = None
         if feature_location != 'bottleneck':
-            hooks = self.feature_info.get_by_key(keys=('module', 'hook_type'))
+            hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
             self.feature_hooks = FeatureHooks(hooks, self.named_modules())
 
     def forward(self, x) -> List[torch.Tensor]:

timm/models/xception_aligned.py

@@ -31,9 +31,12 @@ def _cfg(url='', **kwargs):
 
 
 default_cfgs = dict(
-    xception41=_cfg(url=''),
+    xception41=_cfg(
-    xception65=_cfg(url=''),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_41-e6439c97.pth'),
-    xception71=_cfg(url=''),
+    xception65=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_65-c9ae96e8.pth'),
+    xception71=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_71-8eec7df1.pth'),
 )
 
 
@@ -216,7 +219,6 @@ def xception65(pretrained=False, **kwargs):
     return _xception('xception65', pretrained=pretrained, **model_args)
 
 
-
 @register_model
 def xception71(pretrained=False, **kwargs):
     """ Modified Aligned Xception-71