Move FeatureHooks into features.py, switch EfficientNet, MobileNetV3 to use build model helper

timm/models/efficientnet.py

@@ -33,9 +33,8 @@ from typing import List
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
 from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
-from .feature_hooks import FeatureHooks
-from .features import FeatureInfo
-from .helpers import load_pretrained, adapt_model_from_file
+from .features import FeatureInfo, FeatureHooks
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d, create_conv2d
 from .registry import register_model
 
@@ -471,29 +470,19 @@ class EfficientNetFeatures(nn.Module):
             return self.feature_hooks.get_output(x.device)
 
 
-def _create_effnet(model_kwargs, default_cfg, pretrained=False):
+def _create_effnet(model_kwargs, variant, pretrained=False):
     if model_kwargs.pop('features_only', False):
         load_strict = False
         model_kwargs.pop('num_classes', 0)
         model_kwargs.pop('num_features', 0)
         model_kwargs.pop('head_conv', None)
-        model_class = EfficientNetFeatures
+        model_cls = EfficientNetFeatures
     else:
         load_strict = True
-        model_class = EfficientNet
-    variant = model_kwargs.pop('variant', '')
-    model = model_class(**model_kwargs)
-    model.default_cfg = default_cfg
-    if '_pruned' in variant:
-        model = adapt_model_from_file(model, variant)
-    if pretrained:
-        load_pretrained(
-            model,
-            default_cfg,
-            num_classes=model_kwargs.get('num_classes', 0),
-            in_chans=model_kwargs.get('in_chans', 3),
-            strict=load_strict)
-    return model
+        model_cls = EfficientNet
+    return build_model_with_cfg(
+        model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
+        pretrained_strict=load_strict, **model_kwargs)
 
 
 def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
@@ -528,7 +517,7 @@ def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs)
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -564,7 +553,7 @@ def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs)
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -593,7 +582,7 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs,variant, pretrained)
     return model
 
 
@@ -622,7 +611,7 @@ def _gen_mobilenet_v2(
         act_layer=resolve_act_layer(kwargs, 'relu6'),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -652,7 +641,7 @@ def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -687,7 +676,7 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -731,10 +720,9 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
         channel_multiplier=channel_multiplier,
         act_layer=resolve_act_layer(kwargs, 'swish'),
         norm_kwargs=resolve_bn_args(kwargs),
-        variant=variant,
         **kwargs,
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -763,7 +751,7 @@ def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0
         act_layer=resolve_act_layer(kwargs, 'relu'),
         **kwargs,
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -793,7 +781,7 @@ def _gen_efficientnet_condconv(
         act_layer=resolve_act_layer(kwargs, 'swish'),
         **kwargs,
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -834,7 +822,7 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs,
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -867,7 +855,7 @@ def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -900,7 +888,7 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
         norm_kwargs=resolve_bn_args(kwargs),
         **kwargs
     )
-    model = _create_effnet(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_effnet(model_kwargs, variant, pretrained)
     return model
 
 
@@ -1239,7 +1227,7 @@ def efficientnet_b1_pruned(pretrained=False, **kwargs):
     kwargs['pad_type'] = 'same'
     variant = 'efficientnet_b1_pruned'
     model = _gen_efficientnet(
-        variant, channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
+        variant, channel_multiplier=1.0, depth_multiplier=1.1, pruned=True, pretrained=pretrained, **kwargs)
     return model
 
 
@@ -1249,7 +1237,8 @@ def efficientnet_b2_pruned(pretrained=False, **kwargs):
     kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
     kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
-        'efficientnet_b2_pruned', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
+        'efficientnet_b2_pruned', channel_multiplier=1.1, depth_multiplier=1.2, pruned=True,
+        pretrained=pretrained, **kwargs)
     return model
 
 
@@ -1259,7 +1248,8 @@ def efficientnet_b3_pruned(pretrained=False, **kwargs):
     kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
     kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
-        'efficientnet_b3_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
+        'efficientnet_b3_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pruned=True,
+        pretrained=pretrained, **kwargs)
     return model
 
 

timm/models/feature_hooks.py

@@ -1,46 +0,0 @@
-""" PyTorch Feature Hook Helper
-
-This class helps gather features from a network via hooks specified on the module name.
-
-Hacked together by Ross Wightman
-"""
-import torch
-
-from collections import defaultdict, OrderedDict
-from functools import partial, partialmethod
-from typing import List
-
-
-class FeatureHooks:
-
-    def __init__(self, hooks, named_modules, out_as_dict=False, out_map=None, default_hook_type='forward'):
-        # setup feature hooks
-        modules = {k: v for k, v in named_modules}
-        for i, h in enumerate(hooks):
-            hook_name = h['module']
-            m = modules[hook_name]
-            hook_id = out_map[i] if out_map else hook_name
-            hook_fn = partial(self._collect_output_hook, hook_id)
-            hook_type = h['hook_type'] if 'hook_type' in h else default_hook_type
-            if hook_type == 'forward_pre':
-                m.register_forward_pre_hook(hook_fn)
-            elif hook_type == 'forward':
-                m.register_forward_hook(hook_fn)
-            else:
-                assert False, "Unsupported hook type"
-        self._feature_outputs = defaultdict(OrderedDict)
-        self.out_as_dict = out_as_dict
-
-    def _collect_output_hook(self, hook_id, *args):
-        x = args[-1]  # tensor we want is last argument, output for fwd, input for fwd_pre
-        if isinstance(x, tuple):
-            x = x[0]  # unwrap input tuple
-        self._feature_outputs[x.device][hook_id] = x
-
-    def get_output(self, device) -> List[torch.tensor]:
-        if self.out_as_dict:
-            output = self._feature_outputs[device]
-        else:
-            output = list(self._feature_outputs[device].values())
-        self._feature_outputs[device] = OrderedDict()  # clear after reading
-        return output

timm/models/features.py

@@ -5,15 +5,14 @@ and provide a common interface for describing them.
 
 Hacked together by Ross Wightman
 """
-from collections import OrderedDict
-from typing import Dict, List, Tuple, Any
+from collections import OrderedDict, defaultdict
 from copy import deepcopy
+from functools import partial
+from typing import Dict, List, Tuple, Any
 
 import torch
 import torch.nn as nn
 
-from .feature_hooks import FeatureHooks
-
 
 class FeatureInfo:
 
@@ -75,6 +74,41 @@ class FeatureInfo:
         return len(self.info)
 
 
+class FeatureHooks:
+
+    def __init__(self, hooks, named_modules, out_as_dict=False, out_map=None, default_hook_type='forward'):
+        # setup feature hooks
+        modules = {k: v for k, v in named_modules}
+        for i, h in enumerate(hooks):
+            hook_name = h['module']
+            m = modules[hook_name]
+            hook_id = out_map[i] if out_map else hook_name
+            hook_fn = partial(self._collect_output_hook, hook_id)
+            hook_type = h['hook_type'] if 'hook_type' in h else default_hook_type
+            if hook_type == 'forward_pre':
+                m.register_forward_pre_hook(hook_fn)
+            elif hook_type == 'forward':
+                m.register_forward_hook(hook_fn)
+            else:
+                assert False, "Unsupported hook type"
+        self._feature_outputs = defaultdict(OrderedDict)
+        self.out_as_dict = out_as_dict
+
+    def _collect_output_hook(self, hook_id, *args):
+        x = args[-1]  # tensor we want is last argument, output for fwd, input for fwd_pre
+        if isinstance(x, tuple):
+            x = x[0]  # unwrap input tuple
+        self._feature_outputs[x.device][hook_id] = x
+
+    def get_output(self, device) -> List[torch.tensor]:   # FIXME deal with diff return types for torchscript?
+        if self.out_as_dict:
+            output = self._feature_outputs[device]
+        else:
+            output = list(self._feature_outputs[device].values())
+        self._feature_outputs[device] = OrderedDict()  # clear after reading
+        return output
+
+
 def _module_list(module, flatten_sequential=False):
     # a yield/iter would be better for this but wouldn't be compatible with torchscript
     ml = []

timm/models/mobilenetv3.py

@@ -16,9 +16,8 @@ from typing import List
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
 from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
-from .feature_hooks import FeatureHooks
-from .features import FeatureInfo
-from .helpers import load_pretrained
+from .features import FeatureInfo, FeatureHooks
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d, create_conv2d, get_act_fn, hard_sigmoid
 from .registry import register_model
 
@@ -215,27 +214,19 @@ class MobileNetV3Features(nn.Module):
             return self.feature_hooks.get_output(x.device)
 
 
-def _create_mnv3(model_kwargs, default_cfg, pretrained=False):
+def _create_mnv3(model_kwargs, variant, pretrained=False):
     if model_kwargs.pop('features_only', False):
         load_strict = False
         model_kwargs.pop('num_classes', 0)
         model_kwargs.pop('num_features', 0)
         model_kwargs.pop('head_conv', None)
-        model_class = MobileNetV3Features
+        model_cls = MobileNetV3Features
     else:
         load_strict = True
-        model_class = MobileNetV3
-
-    model = model_class(**model_kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(
-            model,
-            default_cfg,
-            num_classes=model_kwargs.get('num_classes', 0),
-            in_chans=model_kwargs.get('in_chans', 3),
-            strict=load_strict)
-    return model
+        model_cls = MobileNetV3
+    return build_model_with_cfg(
+        model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
+        pretrained_strict=load_strict, **model_kwargs)
 
 
 def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
@@ -272,7 +263,7 @@ def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kw
         se_kwargs=dict(gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True, divisor=1),
         **kwargs,
     )
-    model = _create_mnv3(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_mnv3(model_kwargs, variant, pretrained)
     return model
 
 
@@ -368,7 +359,7 @@ def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwarg
         se_kwargs=dict(act_layer=nn.ReLU, gate_fn=hard_sigmoid, reduce_mid=True, divisor=8),
         **kwargs,
     )
-    model = _create_mnv3(model_kwargs, default_cfgs[variant], pretrained)
+    model = _create_mnv3(model_kwargs, variant, pretrained)
     return model