Model updates. Add my best ResNet50 weights top-1=78.47. Add some other torchvision weights.

* Remove some models that don't exist as pretrained an likely never will (se)resnext152
* Add some torchvision weights as tv_ for models that I have added better weights for
* Add wide resnet recently added to torchvision along with resnext101-32x8d
* Add functionality to model registry to allow filtering on pretrained weight presence

README.md

@@ -67,6 +67,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
 |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling  |
 |---|---|---|---|---|
 | resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic |
+| resnet50 | 78.470 (21.530) | 94.266 (5.734) | 25.6M | bicubic |
 | seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
 | efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic |
 | mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic |

timm/models/dpn.py

@@ -35,9 +35,9 @@ def _cfg(url=''):
 default_cfgs = {
     'dpn68': _cfg(
         url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68-66bebafa7.pth'),
-    'dpn68b_extra': _cfg(
+    'dpn68b': _cfg(
         url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68b_extra-84854c156.pth'),
-    'dpn92_extra': _cfg(
+    'dpn92': _cfg(
         url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn92_extra-b040e4a9b.pth'),
     'dpn98': _cfg(
         url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn98-5b90dec4d.pth'),

timm/models/gluon_resnet.py

@@ -50,11 +50,9 @@ default_cfgs = {
     'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
     'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
     'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
-    'gluon_resnext152_32x4d': _cfg(url=''),
     'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
     'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
     'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
-    'gluon_seresnext152_32x4d': _cfg(url=''),
     'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth'),
 }
 
@@ -617,20 +615,6 @@ def gluon_resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwa
     return model
 
 
-@register_model
-def gluon_resnext152_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    """Constructs a ResNeXt152-32x4d model.
-    """
-    default_cfg = default_cfgs['gluon_resnext152_32x4d']
-    model = GluonResNet(
-        BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=4,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
-
-
 @register_model
 def gluon_seresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs a SEResNeXt50-32x4d model.
@@ -673,20 +657,6 @@ def gluon_seresnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **k
     return model
 
 
-@register_model
-def gluon_seresnext152_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    """Constructs a SEResNeXt152-32x4d model.
-    """
-    default_cfg = default_cfgs['gluon_seresnext152_32x4d']
-    model = GluonResNet(
-        BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=4, use_se=True,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    #if pretrained:
-    #    load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
-
-
 @register_model
 def gluon_senet154(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs an SENet-154 model.

timm/models/registry.py

@@ -5,22 +5,36 @@ from collections import defaultdict
 
 __all__ = ['list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules']
 
-_module_to_models = defaultdict(set)
-_model_to_module = {}
-_model_entrypoints = {}
+_module_to_models = defaultdict(set)  # dict of sets to check membership of model in module
+_model_to_module = {}  # mapping of model names to module names
+_model_entrypoints = {}  # mapping of model names to entrypoint fns
+_model_has_pretrained = set()  # set of model names that have pretrained weight url present
 
 
 def register_model(fn):
+    # lookup containing module
     mod = sys.modules[fn.__module__]
     module_name_split = fn.__module__.split('.')
     module_name = module_name_split[-1] if len(module_name_split) else ''
+
+    # add model to __all__ in module
+    model_name = fn.__name__
     if hasattr(mod, '__all__'):
-        mod.__all__.append(fn.__name__)
+        mod.__all__.append(model_name)
     else:
-        mod.__all__ = [fn.__name__]
-    _model_entrypoints[fn.__name__] = fn
-    _model_to_module[fn.__name__] = module_name
-    _module_to_models[module_name].add(fn.__name__)
+        mod.__all__ = [model_name]
+
+    # add entries to registry dict/sets
+    _model_entrypoints[model_name] = fn
+    _model_to_module[model_name] = module_name
+    _module_to_models[module_name].add(model_name)
+    has_pretrained = False  # check if model has a pretrained url to allow filtering on this
+    if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs:
+        # this will catch all models that have entrypoint matching cfg key, but miss any aliasing
+        # entrypoints or non-matching combos
+        has_pretrained = 'url' in mod.default_cfgs[model_name] and 'http' in mod.default_cfgs[model_name]['url']
+    if has_pretrained:
+        _model_has_pretrained.add(model_name)
     return fn
 
 
@@ -28,7 +42,7 @@ def _natural_key(string_):
     return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
 
 
-def list_models(filter='', module=''):
+def list_models(filter='', module='', pretrained=False):
     """ Return list of available model names, sorted alphabetically
 
     Args:
@@ -45,6 +59,8 @@ def list_models(filter='', module=''):
         models = _model_entrypoints.keys()
     if filter:
         models = fnmatch.filter(models, filter)
+    if pretrained:
+        models = _model_has_pretrained.intersection(models)
     return list(sorted(models, key=_natural_key))
 
 

timm/models/resnet.py

@@ -33,14 +33,22 @@ default_cfgs = {
     'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'),
     'resnet34': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'),
-    'resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
+    'resnet50': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/rw_resnet50-86acaeed.pth',
+        interpolation='bicubic'),
     'resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
     'resnet152': _cfg(url='https://download.pytorch.org/models/resnet152-b121ed2d.pth'),
-    'resnext50_32x4d': _cfg(url='https://www.dropbox.com/s/yxci33lfew51p6a/resnext50_32x4d-068914d1.pth?dl=1',
+    'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
+    'tv_resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
+    'wide_resnet50_2': _cfg(url='https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth'),
+    'wide_resnet101_2': _cfg(url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth'),
+    'resnext50_32x4d': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d-068914d1.pth',
         interpolation='bicubic'),
     'resnext101_32x4d': _cfg(url=''),
+    'resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth'),
     'resnext101_64x4d': _cfg(url=''),
-    'resnext152_32x4d': _cfg(url=''),
+    'tv_resnext50_32x4d': _cfg(url='https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth'),
     'ig_resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth'),
     'ig_resnext101_32x16d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth'),
     'ig_resnext101_32x32d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth'),
@@ -285,6 +293,61 @@ def resnet152(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     return model
 
 
+@register_model
+def tv_resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a ResNet-34 model with original Torchvision weights.
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfgs['tv_resnet34']
+    if pretrained:
+        load_pretrained(model, model.default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def tv_resnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a ResNet-50 model with original Torchvision weights.
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfgs['tv_resnet50']
+    if pretrained:
+        load_pretrained(model, model.default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def wide_resnet50_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a Wide ResNet-50-2 model.
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+    """
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], base_width=128,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfgs['wide_resnet50_2']
+    if pretrained:
+        load_pretrained(model, model.default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def wide_resnet101_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a Wide ResNet-100-2 model.
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same.
+    """
+    model = ResNet(
+        Bottleneck, [3, 4, 23, 3], base_width=128,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfgs['wide_resnet101_2']
+    if pretrained:
+        load_pretrained(model, model.default_cfg, num_classes, in_chans)
+    return model
+
+
 @register_model
 def resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs a ResNeXt50-32x4d model.
@@ -301,7 +364,7 @@ def resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
 
 @register_model
 def resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    """Constructs a ResNeXt-101 model.
+    """Constructs a ResNeXt-101 32x4d model.
     """
     default_cfg = default_cfgs['resnext101_32x4d']
     model = ResNet(
@@ -313,6 +376,20 @@ def resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     return model
 
 
+@register_model
+def resnext101_32x8d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a ResNeXt-101 32x8d model.
+    """
+    default_cfg = default_cfgs['resnext101_32x8d']
+    model = ResNet(
+        Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
 @register_model
 def resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs a ResNeXt101-64x4d model.
@@ -328,12 +405,12 @@ def resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
 
 
 @register_model
-def resnext152_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    """Constructs a ResNeXt152-32x4d model.
+def tv_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a ResNeXt50-32x4d model with original Torchvision weights.
     """
-    default_cfg = default_cfgs['resnext152_32x4d']
+    default_cfg = default_cfgs['tv_resnext50_32x4d']
     model = ResNet(
-        Bottleneck, [3, 8, 36, 3], cardinality=32, base_width=4,
+        Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg
     if pretrained: