Cleanup, refactoring of Feature extraction code, add tests, fix tests, non hook feature extraction working with torchscript

tests/test_models.py

@@ -106,3 +106,26 @@ def test_model_forward_torchscript(model_name, batch_size):
 
     assert outputs.shape[0] == batch_size
     assert not torch.isnan(outputs).any(), 'Output included NaNs'
+
+
+EXCLUDE_FEAT_FILTERS = [
+    'hrnet*',  '*pruned*',  # hopefully fix at some point
+    'legacy*',  # not going to bother
+]
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_forward_features(model_name, batch_size):
+    """Run a single forward pass with each model in feature extraction mode"""
+    model = create_model(model_name, pretrained=False, features_only=True)
+    model.eval()
+    expected_channels = model.feature_info.channels()
+    assert len(expected_channels) >= 4  # all models here should have at least 4 feature levels by default, some 5 or 6
+    input_size = (3, 128, 128)  # jit compile is already a bit slow and we've tested normal res already...
+    outputs = model(torch.randn((batch_size, *input_size)))
+    assert len(expected_channels) == len(outputs)
+    for e, o in zip(expected_channels, outputs):
+        assert e == o.shape[1]
+        assert o.shape[0] == batch_size
+        assert not torch.isnan(o).any()

timm/models/features.py

@@ -75,8 +75,14 @@ class FeatureInfo:
 
 
 class FeatureHooks:
+    """ Feature Hook Helper
 
-    def __init__(self, hooks, named_modules, out_as_dict=False, out_map=None, default_hook_type='forward'):
+    This module helps with the setup and extraction of hooks for extracting features from
+    internal nodes in a model by node name. This works quite well in eager Python but needs
+    redesign for torcscript.
+    """
+
+    def __init__(self, hooks, named_modules, 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):
@@ -92,7 +98,6 @@ class FeatureHooks:
             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
@@ -100,11 +105,8 @@ class FeatureHooks:
             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())
+    def get_output(self, device) -> Dict[str, torch.tensor]:
+        output = self._feature_outputs[device]
         self._feature_outputs[device] = OrderedDict()  # clear after reading
         return output
 
@@ -123,160 +125,8 @@ def _module_list(module, flatten_sequential=False):
     return ml
 
 
-class LayerGetterHooks(nn.ModuleDict):
-    """ LayerGetterHooks
-    TODO
-    """
-
-    def __init__(self, model, feature_info, flatten_sequential=False, out_as_dict=False, out_map=None,
-                 default_hook_type='forward'):
-        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 feature_info}
-        layers = OrderedDict()
-        hooks = []
-        for new_name, old_name, module in modules:
-            layers[new_name] = module
-            for fn, fm in module.named_modules(prefix=old_name):
-                if fn in remaining:
-                    hooks.append(dict(module=fn, hook_type=remaining[fn]))
-                    del remaining[fn]
-            if not remaining:
-                break
-        assert not remaining, f'Return layers ({remaining}) are not present in model'
-        super(LayerGetterHooks, self).__init__(layers)
-        self.hooks = FeatureHooks(hooks, model.named_modules(), out_as_dict=out_as_dict, out_map=out_map)
-
-    def forward(self, x) -> Dict[Any, torch.Tensor]:
-        for name, module in self.items():
-            x = module(x)
-        return self.hooks.get_output(x.device)
-
-
-class LayerGetterDict(nn.ModuleDict):
-    """
-    Module wrapper that returns intermediate layers from a model as a dictionary
-
-    Originally based on concepts from IntermediateLayerGetter at
-    https://github.com/pytorch/vision/blob/d88d8961ae51507d0cb680329d985b1488b1b76b/torchvision/models/_utils.py
-
-    It has a strong assumption that the modules have been registered into the model in the same
-    order as they are used. This means that one should **not** reuse the same nn.Module twice
-    in the forward if you want this to work.
-
-    Additionally, it is only able to query submodules that are directly assigned to the model
-    class (`model.feature1`) or at most one Sequential container deep (`model.features.1`, so
-    long as `features` is a sequential container assigned to the model).
-
-    All Sequential containers that are directly assigned to the original model will have their
-    modules assigned to this module with the name `model.features.1` being changed to `model.features_1`
-
-    Arguments:
-        model (nn.Module): model on which we will extract the features
-        return_layers (Dict[name, new_name]): a dict containing the names
-            of the modules for which the activations will be returned as
-            the key of the dict, and the value of the dict is the name
-            of the returned activation (which the user can specify).
-        concat (bool): whether to concatenate intermediate features that are lists or tuples
-            vs select element [0]
-        flatten_sequential (bool): whether to flatten sequential modules assigned to model
-
-    """
-
-    def __init__(self, model, return_layers, concat=False, flatten_sequential=False):
-        self.return_layers = {}
-        self.concat = concat
-        modules = _module_list(model, flatten_sequential=flatten_sequential)
-        remaining = set(return_layers.keys())
-        layers = OrderedDict()
-        for new_name, old_name, module in modules:
-            layers[new_name] = module
-            if old_name in remaining:
-                self.return_layers[new_name] = return_layers[old_name]
-                remaining.remove(old_name)
-            if not remaining:
-                break
-        assert not remaining and len(self.return_layers) == len(return_layers), \
-            f'Return layers ({remaining}) are not present in model'
-        super(LayerGetterDict, self).__init__(layers)
-
-    def forward(self, x) -> Dict[Any, torch.Tensor]:
-        out = OrderedDict()
-        for name, module in self.items():
-            x = module(x)
-            if name in self.return_layers:
-                out_id = self.return_layers[name]
-                if isinstance(x, (tuple, list)):
-                    # If model tap is a tuple or list, concat or select first element
-                    # FIXME this may need to be more generic / flexible for some nets
-                    out[out_id] = torch.cat(x, 1) if self.concat else x[0]
-                else:
-                    out[out_id] = x
-        return out
-
-
-class LayerGetterList(nn.Sequential):
-    """
-    Module wrapper that returns intermediate layers from a model as a list
-
-    Originally based on concepts from IntermediateLayerGetter at
-    https://github.com/pytorch/vision/blob/d88d8961ae51507d0cb680329d985b1488b1b76b/torchvision/models/_utils.py
-
-    It has a strong assumption that the modules have been registered into the model in the same
-    order as they are used. This means that one should **not** reuse the same nn.Module twice
-    in the forward if you want this to work.
-
-    Additionally, it is only able to query submodules that are directly assigned to the model
-    class (`model.feature1`) or at most one Sequential container deep (`model.features.1`) so
-    long as `features` is a sequential container assigned to the model and flatten_sequent=True.
-
-    All Sequential containers that are directly assigned to the original model will have their
-    modules assigned to this module with the name `model.features.1` being changed to `model.features_1`
-
-    Arguments:
-        model (nn.Module): model on which we will extract the features
-        return_layers (Dict[name, new_name]): a dict containing the names
-            of the modules for which the activations will be returned as
-            the key of the dict, and the value of the dict is the name
-            of the returned activation (which the user can specify).
-        concat (bool): whether to concatenate intermediate features that are lists or tuples
-            vs select element [0]
-        flatten_sequential (bool): whether to flatten sequential modules assigned to model
-
-    """
-
-    def __init__(self, model, return_layers, concat=False, flatten_sequential=False):
-        super(LayerGetterList, self).__init__()
-        self.return_layers = {}
-        self.concat = concat
-        modules = _module_list(model, flatten_sequential=flatten_sequential)
-        remaining = set(return_layers.keys())
-        for new_name, orig_name, module in modules:
-            self.add_module(new_name, module)
-            if orig_name in remaining:
-                self.return_layers[new_name] = return_layers[orig_name]
-                remaining.remove(orig_name)
-            if not remaining:
-                break
-        assert not remaining and len(self.return_layers) == len(return_layers), \
-            f'Return layers ({remaining}) are not present in model'
-
-    def forward(self, x) -> List[torch.Tensor]:
-        out = []
-        for name, module in self.named_children():
-            x = module(x)
-            if name in self.return_layers:
-                if isinstance(x, (tuple, list)):
-                    # If model tap is a tuple or list, concat or select first element
-                    # FIXME this may need to be more generic / flexible for some nets
-                    out.append(torch.cat(x, 1) if self.concat else x[0])
-                else:
-                    out.append(x)
-        return out
-
-
-def _resolve_feature_info(net, out_indices, feature_info=None):
-    if feature_info is None:
-        feature_info = getattr(net, 'feature_info')
+def _get_feature_info(net, out_indices):
+    feature_info = getattr(net, 'feature_info')
     if isinstance(feature_info, FeatureInfo):
         return feature_info.from_other(out_indices)
     elif isinstance(feature_info, (list, tuple)):
@@ -293,51 +143,135 @@ def _get_return_layers(feature_info, out_map):
     return return_layers
 
 
-class FeatureNet(nn.Module):
-    """ FeatureNet
+class FeatureDictNet(nn.ModuleDict):
+    """ Feature extractor with OrderedDict return
 
-    Wrap a model and extract features as specified by the out indices, the network
-    is partially re-built from contained modules using the LayerGetters.
+    Wrap a model and extract features as specified by the out indices, the network is
+    partially re-built from contained modules.
 
-    Please read the docstrings of the LayerGetter classes, they will not work on all models.
+    There is a strong assumption that the modules have been registered into the model in the same
+    order as they are used. There should be no reuse of the same nn.Module more than once, including
+    trivial modules like `self.relu = nn.ReLU`.
+
+    Only submodules that are directly assigned to the model class (`model.feature1`) or at most
+    one Sequential container deep (`model.features.1`, with flatten_sequent=True) can be captured.
+    All Sequential containers that are directly assigned to the original model will have their
+    modules assigned to this module with the name `model.features.1` being changed to `model.features_1`
+
+    Arguments:
+        model (nn.Module): model from which we will extract the features
+        out_indices (tuple[int]): model output indices to extract features for
+        out_map (sequence): list or tuple specifying desired return id for each out index,
+            otherwise str(index) is used
+        feature_concat (bool): whether to concatenate intermediate features that are lists or tuples
+            vs select element [0]
+        flatten_sequential (bool): whether to flatten sequential modules assigned to model
     """
     def __init__(
-            self, net,
-            out_indices=(0, 1, 2, 3, 4), out_map=None, out_as_dict=False, use_hooks=False,
-            feature_info=None, feature_concat=False, flatten_sequential=False):
-        super(FeatureNet, self).__init__()
-        self.feature_info = _resolve_feature_info(net, out_indices, feature_info)
-        if use_hooks:
-            self.body = LayerGetterHooks(net, self.feature_info, out_as_dict=out_as_dict, out_map=out_map)
-        else:
-            return_layers = _get_return_layers(self.feature_info, out_map)
-            lg_args = dict(return_layers=return_layers, concat=feature_concat, flatten_sequential=flatten_sequential)
-            self.body = LayerGetterDict(net, **lg_args) if out_as_dict else LayerGetterList(net, **lg_args)
+            self, model,
+            out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
+        super(FeatureDictNet, self).__init__()
+        self.feature_info = _get_feature_info(model, out_indices)
+        self.concat = feature_concat
+        self.return_layers = {}
+        return_layers = _get_return_layers(self.feature_info, out_map)
+        modules = _module_list(model, flatten_sequential=flatten_sequential)
+        remaining = set(return_layers.keys())
+        layers = OrderedDict()
+        for new_name, old_name, module in modules:
+            layers[new_name] = module
+            if old_name in remaining:
+                # return id has to be consistently str type for torchscript
+                self.return_layers[new_name] = str(return_layers[old_name])
+                remaining.remove(old_name)
+            if not remaining:
+                break
+        assert not remaining and len(self.return_layers) == len(return_layers), \
+            f'Return layers ({remaining}) are not present in model'
+        self.update(layers)
 
-    def forward(self, x):
-        output = self.body(x)
-        return output
+    def _collect(self, x) -> (Dict[str, torch.Tensor]):
+        out = OrderedDict()
+        for name, module in self.items():
+            x = module(x)
+            if name in self.return_layers:
+                out_id = self.return_layers[name]
+                if isinstance(x, (tuple, list)):
+                    # If model tap is a tuple or list, concat or select first element
+                    # FIXME this may need to be more generic / flexible for some nets
+                    out[out_id] = torch.cat(x, 1) if self.concat else x[0]
+                else:
+                    out[out_id] = x
+        return out
+
+    def forward(self, x) -> Dict[str, torch.Tensor]:
+        return self._collect(x)
 
 
-class FeatureHookNet(nn.Module):
+class FeatureListNet(FeatureDictNet):
+    """ Feature extractor with list return
+
+    See docstring for FeatureDictNet above, this class exists only to appease Torchscript typing constraints.
+    In eager Python we could have returned List[Tensor] vs Dict[id, Tensor] based on a member bool.
+    """
+    def __init__(
+            self, model,
+            out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
+        super(FeatureListNet, self).__init__(
+            model, out_indices=out_indices, out_map=out_map, feature_concat=feature_concat,
+            flatten_sequential=flatten_sequential)
+
+    def forward(self, x) -> (List[torch.Tensor]):
+        return list(self._collect(x).values())
+
+
+class FeatureHookNet(nn.ModuleDict):
     """ FeatureHookNet
 
-    Wrap a model and extract features specified by the out indices.
+    Wrap a model and extract features specified by the out indices using forward/forward-pre hooks.
 
-    Features are extracted via hooks without modifying the underlying network in any way. If only
-    part of the model is used it is up to the caller to remove unneeded layers as this wrapper
-    does not rewrite and remove unused top-level modules like FeatureNet with LayerGetter.
+    If `no_rewrite` is True, features are extracted via hooks without modifying the underlying
+    network in any way.
+
+    If `no_rewrite` is False, the model will be re-written as in the
+    FeatureList/FeatureDict case by folding first to second (Sequential only) level modules into this one.
+
+    FIXME this does not currently work with Torchscript, see FeatureHooks class
     """
     def __init__(
-            self, net,
-            out_indices=(0, 1, 2, 3, 4), out_as_dict=False, out_map=None,
-            feature_info=None, feature_concat=False):
+            self, model,
+            out_indices=(0, 1, 2, 3, 4), out_map=None, out_as_dict=False, no_rewrite=False,
+            feature_concat=False, flatten_sequential=False, default_hook_type='forward'):
         super(FeatureHookNet, self).__init__()
-        self.feature_info = _resolve_feature_info(net, out_indices, feature_info)
-        self.body = net
-        self.hooks = FeatureHooks(
-            self.feature_info, self.body.named_modules(), out_as_dict=out_as_dict, out_map=out_map)
+        assert not torch.jit.is_scripting()
+        self.feature_info = _get_feature_info(model, out_indices)
+        self.out_as_dict = out_as_dict
+        layers = OrderedDict()
+        hooks = []
+        if no_rewrite:
+            assert not flatten_sequential
+            if hasattr(model, 'reset_classifier'):  # make sure classifier is removed?
+                model.reset_classifier(0)
+            layers['body'] = model
+            hooks.extend(self.feature_info)
+        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 new_name, old_name, module in modules:
+                layers[new_name] = module
+                for fn, fm in module.named_modules(prefix=old_name):
+                    if fn in remaining:
+                        hooks.append(dict(module=fn, hook_type=remaining[fn]))
+                        del remaining[fn]
+                if not remaining:
+                    break
+            assert not remaining, f'Return layers ({remaining}) are not present in model'
+        self.update(layers)
+        self.hooks = FeatureHooks(hooks, model.named_modules(), out_map=out_map)
 
     def forward(self, x):
-        self.body(x)
-        return self.hooks.get_output(x.device)
+        for name, module in self.items():
+            x = module(x)
+        out = self.hooks.get_output(x.device)
+        return out if self.out_as_dict else list(out.values())

timm/models/gluon_xception.py

@@ -252,7 +252,7 @@ class Xception65(nn.Module):
 def _create_gluon_xception(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         Xception65, variant, pretrained, default_cfg=default_cfgs[variant],
-        feature_cfg=dict(use_hooks=True), **kwargs)
+        feature_cfg=dict(feature_cls='hook'), **kwargs)
 
 
 @register_model

timm/models/helpers.py

@@ -8,7 +8,7 @@ import torch
 import torch.nn as nn
 import torch.utils.model_zoo as model_zoo
 
-from .features import FeatureNet, FeatureHookNet
+from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
 from .layers import Conv2dSame
 
 
@@ -234,15 +234,15 @@ def build_model_with_cfg(
             filter_fn=pretrained_filter_fn, strict=pretrained_strict)
     
     if features:
-        feature_cls = feature_cfg.pop('feature_cls', FeatureNet)
-        if isinstance(feature_cls, str):
-            feature_cls = feature_cls.lower()
-            if feature_cls == 'hook' or feature_cls == 'featurehooknet':
-                feature_cls = FeatureHookNet
-            else:
-                assert False, f'Unknown feature class {feature_cls}'
-        if feature_cls == FeatureHookNet and hasattr(model, 'reset_classifier'):
-            model.reset_classifier(0)
+        feature_cls = FeatureListNet
+        if 'feature_cls' in feature_cfg:
+            feature_cls = feature_cfg.pop('feature_cls')
+            if isinstance(feature_cls, str):
+                feature_cls = feature_cls.lower()
+                if 'hook' in feature_cls:
+                    feature_cls = FeatureHookNet
+                else:
+                    assert False, f'Unknown feature class {feature_cls}'
         model = feature_cls(model, **feature_cfg)
     
     return model

timm/models/mobilenetv3.py

@@ -211,7 +211,8 @@ class MobileNetV3Features(nn.Module):
             return features
         else:
             self.blocks(x)
-            return self.feature_hooks.get_output(x.device)
+            out = self.feature_hooks.get_output(x.device)
+            return list(out.values())
 
 
 def _create_mnv3(model_kwargs, variant, pretrained=False):
@@ -220,6 +221,7 @@ def _create_mnv3(model_kwargs, variant, pretrained=False):
         model_kwargs.pop('num_classes', 0)
         model_kwargs.pop('num_features', 0)
         model_kwargs.pop('head_conv', None)
+        model_kwargs.pop('head_bias', None)
         model_cls = MobileNetV3Features
     else:
         load_strict = True

timm/models/nasnet.py

@@ -554,7 +554,7 @@ class NASNetALarge(nn.Module):
 def _create_nasnet(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         NASNetALarge, variant, pretrained, default_cfg=default_cfgs[variant],
-        feature_cfg=dict(feature_cls='hook'),  # not possible to re-write this model, must use FeatureHookNet
+        feature_cfg=dict(feature_cls='hook', no_rewrite=True),  # not possible to re-write this model
         **kwargs)
 
 

timm/models/pnasnet.py

@@ -337,7 +337,7 @@ class PNASNet5Large(nn.Module):
 def _create_pnasnet(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         PNASNet5Large, variant, pretrained, default_cfg=default_cfgs[variant],
-        feature_cfg=dict(feature_cls='hook'),  # not possible to re-write this model, must use FeatureHookNet
+        feature_cfg=dict(feature_cls='hook', no_rewrite=True),  # not possible to re-write this model
         **kwargs)
 
 

timm/models/selecsls.py

@@ -74,6 +74,29 @@ class SequentialList(nn.Sequential):
         return x
 
 
+class SelectSeq(nn.Module):
+    def __init__(self, mode='index', index=0):
+        super(SelectSeq, self).__init__()
+        self.mode = mode
+        self.index = index
+
+    @torch.jit._overload_method  # noqa: F811
+    def forward(self, x):
+        # type: (List[torch.Tensor]) -> (torch.Tensor)
+        pass
+
+    @torch.jit._overload_method  # noqa: F811
+    def forward(self, x):
+        # type: (Tuple[torch.Tensor]) -> (torch.Tensor)
+        pass
+
+    def forward(self, x) -> torch.Tensor:
+        if self.mode == 'index':
+            return x[self.index]
+        else:
+            return torch.cat(x, dim=1)
+
+
 def conv_bn(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1):
     if padding is None:
         padding = ((stride - 1) + dilation * (k - 1)) // 2
@@ -137,8 +160,10 @@ class SelecSLS(nn.Module):
 
         self.stem = conv_bn(in_chans, 32, stride=2)
         self.features = SequentialList(*[cfg['block'](*block_args) for block_args in cfg['features']])
+        self.from_seq = SelectSeq()  # from List[tensor] -> Tensor in module compatible way
         self.head = nn.Sequential(*[conv_bn(*conv_args) for conv_args in cfg['head']])
         self.num_features = cfg['num_features']
+        self.feature_info = cfg['feature_info']
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
@@ -165,7 +190,7 @@ class SelecSLS(nn.Module):
     def forward_features(self, x):
         x = self.stem(x)
         x = self.features(x)
-        x = self.head(x[0])
+        x = self.head(self.from_seq(x))
         return x
 
     def forward(self, x):
@@ -297,6 +322,7 @@ def _create_selecsls(variant, pretrained, model_kwargs):
         ])
     else:
         raise ValueError('Invalid net configuration ' + variant + ' !!!')
+    cfg['feature_info'] = feature_info
 
     # this model can do 6 feature levels by default, unlike most others, leave as 0-4 to avoid surprises?
     return build_model_with_cfg(

timm/models/tresnet.py

@@ -160,6 +160,9 @@ class Bottleneck(nn.Module):
                     conv2d_iabn(planes, planes, kernel_size=3, stride=1, act_layer=act_layer, act_param=1e-3),
                     aa_layer(channels=planes, filt_size=3, stride=2))
 
+        reduce_layer_planes = max(planes * self.expansion // 8, 64)
+        self.se = FastSEModule(planes, reduce_layer_planes) if use_se else None
+
         self.conv3 = conv2d_iabn(
             planes, planes * self.expansion, kernel_size=1, stride=1, act_layer="identity")
 
@@ -167,9 +170,6 @@ class Bottleneck(nn.Module):
         self.downsample = downsample
         self.stride = stride
 
-        reduce_layer_planes = max(planes * self.expansion // 8, 64)
-        self.se = FastSEModule(planes, reduce_layer_planes) if use_se else None
-
     def forward(self, x):
         if self.downsample is not None:
             residual = self.downsample(x)
@@ -225,8 +225,8 @@ class TResNet(nn.Module):
             dict(num_chs=self.planes, reduction=2, module=''),  # Not with S2D?
             dict(num_chs=self.planes, reduction=4, module='body.layer1'),
             dict(num_chs=self.planes * 2, reduction=8, module='body.layer2'),
-            dict(num_chs=self.planes * 4, reduction=16, module='body.layer3'),
-            dict(num_chs=self.planes * 8, reduction=32, module='body.layer4'),
+            dict(num_chs=self.planes * 4 * Bottleneck.expansion, reduction=16, module='body.layer3'),
+            dict(num_chs=self.planes * 8 * Bottleneck.expansion, reduction=32, module='body.layer4'),
         ]
 
         # head

timm/models/xception.py

@@ -228,7 +228,7 @@ class Xception(nn.Module):
 def _xception(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         Xception, variant, pretrained, default_cfg=default_cfgs[variant],
-        feature_cfg=dict(use_hooks=True), **kwargs)
+        feature_cfg=dict(feature_cls='hook'), **kwargs)
 
 
 @register_model

timm/models/xception_aligned.py

@@ -174,7 +174,7 @@ class XceptionAligned(nn.Module):
 def _xception(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         XceptionAligned, variant, pretrained, default_cfg=default_cfgs[variant],
-        feature_cfg=dict(flatten_sequential=True, use_hooks=True), **kwargs)
+        feature_cfg=dict(flatten_sequential=True, feature_cls='hook'), **kwargs)
 
 
 @register_model

validate.py

@@ -100,6 +100,8 @@ def validate(args):
     # might as well try to validate something
     args.pretrained = args.pretrained or not args.checkpoint
     args.prefetcher = not args.no_prefetcher
+    if args.legacy_jit:
+        set_jit_legacy()
 
     # create model
     model = create_model(
@@ -119,8 +121,6 @@ def validate(args):
     model, test_time_pool = apply_test_time_pool(model, data_config, args)
 
     if args.torchscript:
-        if args.legacy_jit:
-            set_jit_legacy()
         torch.jit.optimized_execution(True)
         model = torch.jit.script(model)