diff --git a/timm/models/gluon_resnet.py b/timm/models/gluon_resnet.py index a0bc4bb2..2c1b0fe9 100644 --- a/timm/models/gluon_resnet.py +++ b/timm/models/gluon_resnet.py @@ -5,10 +5,9 @@ by Ross Wightman """ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from .helpers import load_pretrained from .layers import SEModule from .registry import register_model -from .resnet import ResNet, Bottleneck, BasicBlock +from .resnet import _create_resnet_with_cfg, Bottleneck, BasicBlock def _cfg(url='', **kwargs): @@ -34,9 +33,6 @@ default_cfgs = { 'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'), 'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'), 'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'), - 'gluon_resnet50_v1e': _cfg(url=''), - 'gluon_resnet101_v1e': _cfg(url=''), - 'gluon_resnet152_v1e': _cfg(url=''), 'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'), 'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'), 'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'), @@ -50,318 +46,190 @@ default_cfgs = { } +def _create_resnet(variant, pretrained=False, **kwargs): + default_cfg = default_cfgs[variant] + return _create_resnet_with_cfg(variant, default_cfg, pretrained=pretrained, **kwargs) + + @register_model -def gluon_resnet18_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet18_v1b(pretrained=False, **kwargs): """Constructs a ResNet-18 model. """ - default_cfg = default_cfgs['gluon_resnet18_v1b'] - model = ResNet(BasicBlock, [2, 2, 2, 2], 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 + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs) + return _create_resnet('gluon_resnet18_v1b', pretrained, **model_args) @register_model -def gluon_resnet34_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet34_v1b(pretrained=False, **kwargs): """Constructs a ResNet-34 model. """ - default_cfg = default_cfgs['gluon_resnet34_v1b'] - model = ResNet(BasicBlock, [3, 4, 6, 3], 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 + model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs) + return _create_resnet('gluon_resnet34_v1b', pretrained, **model_args) @register_model -def gluon_resnet50_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet50_v1b(pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ - default_cfg = default_cfgs['gluon_resnet50_v1b'] - model = ResNet(Bottleneck, [3, 4, 6, 3], 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 + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs) + return _create_resnet('gluon_resnet50_v1b', pretrained, **model_args) @register_model -def gluon_resnet101_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet101_v1b(pretrained=False, **kwargs): """Constructs a ResNet-101 model. """ - default_cfg = default_cfgs['gluon_resnet101_v1b'] - model = ResNet(Bottleneck, [3, 4, 23, 3], 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 + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs) + return _create_resnet('gluon_resnet101_v1b', pretrained, **model_args) @register_model -def gluon_resnet152_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet152_v1b(pretrained=False, **kwargs): """Constructs a ResNet-152 model. """ - default_cfg = default_cfgs['gluon_resnet152_v1b'] - model = ResNet(Bottleneck, [3, 8, 36, 3], 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 + model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs) + return _create_resnet('gluon_resnet152_v1b', pretrained, **model_args) @register_model -def gluon_resnet50_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet50_v1c(pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ - default_cfg = default_cfgs['gluon_resnet50_v1c'] - model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=32, stem_type='deep', **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', **kwargs) + return _create_resnet('gluon_resnet50_v1c', pretrained, **model_args) @register_model -def gluon_resnet101_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet101_v1c(pretrained=False, **kwargs): """Constructs a ResNet-101 model. """ - default_cfg = default_cfgs['gluon_resnet101_v1c'] - model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=32, stem_type='deep', **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', **kwargs) + return _create_resnet('gluon_resnet101_v1c', pretrained, **model_args) @register_model -def gluon_resnet152_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet152_v1c(pretrained=False, **kwargs): """Constructs a ResNet-152 model. """ - default_cfg = default_cfgs['gluon_resnet152_v1c'] - model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=32, stem_type='deep', **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', **kwargs) + return _create_resnet('gluon_resnet152_v1c', pretrained, **model_args) @register_model -def gluon_resnet50_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet50_v1d(pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ - default_cfg = default_cfgs['gluon_resnet50_v1d'] - model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) + return _create_resnet('gluon_resnet50_v1d', pretrained, **model_args) @register_model -def gluon_resnet101_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet101_v1d(pretrained=False, **kwargs): """Constructs a ResNet-101 model. """ - default_cfg = default_cfgs['gluon_resnet101_v1d'] - model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) + return _create_resnet('gluon_resnet101_v1d', pretrained, **model_args) @register_model -def gluon_resnet152_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet152_v1d(pretrained=False, **kwargs): """Constructs a ResNet-152 model. """ - default_cfg = default_cfgs['gluon_resnet152_v1d'] - model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model - - -@register_model -def gluon_resnet50_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs): - """Constructs a ResNet-50-V1e model. No pretrained weights for any 'e' variants - """ - default_cfg = default_cfgs['gluon_resnet50_v1e'] - model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=64, stem_type='deep', avg_down=True, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) + return _create_resnet('gluon_resnet152_v1d', pretrained, **model_args) @register_model -def gluon_resnet101_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs): - """Constructs a ResNet-101 model. - """ - default_cfg = default_cfgs['gluon_resnet101_v1e'] - model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=64, stem_type='deep', avg_down=True, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model - - -@register_model -def gluon_resnet152_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs): - """Constructs a ResNet-152 model. - """ - default_cfg = default_cfgs['gluon_resnet152_v1e'] - model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=64, stem_type='deep', avg_down=True, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model - - -@register_model -def gluon_resnet50_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet50_v1s(pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ - default_cfg = default_cfgs['gluon_resnet50_v1s'] - model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=64, stem_type='deep', **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 6, 3], stem_width=64, stem_type='deep', **kwargs) + return _create_resnet('gluon_resnet50_v1s', pretrained, **model_args) + @register_model -def gluon_resnet101_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet101_v1s(pretrained=False, **kwargs): """Constructs a ResNet-101 model. """ - default_cfg = default_cfgs['gluon_resnet101_v1s'] - model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=64, stem_type='deep', **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 23, 3], stem_width=64, stem_type='deep', **kwargs) + return _create_resnet('gluon_resnet101_v1s', pretrained, **model_args) @register_model -def gluon_resnet152_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnet152_v1s(pretrained=False, **kwargs): """Constructs a ResNet-152 model. """ - default_cfg = default_cfgs['gluon_resnet152_v1s'] - model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, - stem_width=64, stem_type='deep', **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 8, 36, 3], stem_width=64, stem_type='deep', **kwargs) + return _create_resnet('gluon_resnet152_v1s', pretrained, **model_args) + @register_model -def gluon_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnext50_32x4d(pretrained=False, **kwargs): """Constructs a ResNeXt50-32x4d model. """ - default_cfg = default_cfgs['gluon_resnext50_32x4d'] - model = ResNet( - 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: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) + return _create_resnet('gluon_resnext50_32x4d', pretrained, **model_args) @register_model -def gluon_resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnext101_32x4d(pretrained=False, **kwargs): """Constructs a ResNeXt-101 model. """ - default_cfg = default_cfgs['gluon_resnext101_32x4d'] - model = ResNet( - Bottleneck, [3, 4, 23, 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 + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs) + return _create_resnet('gluon_resnext101_32x4d', pretrained, **model_args) @register_model -def gluon_resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_resnext101_64x4d(pretrained=False, **kwargs): """Constructs a ResNeXt-101 model. """ - default_cfg = default_cfgs['gluon_resnext101_64x4d'] - model = ResNet( - Bottleneck, [3, 4, 23, 3], cardinality=64, 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 + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, **kwargs) + return _create_resnet('gluon_resnext101_64x4d', pretrained, **model_args) @register_model -def gluon_seresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_seresnext50_32x4d(pretrained=False, **kwargs): """Constructs a SEResNeXt50-32x4d model. """ - default_cfg = default_cfgs['gluon_seresnext50_32x4d'] - model = ResNet( - Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, - num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SEModule), **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, + block_args=dict(attn_layer=SEModule), **kwargs) + return _create_resnet('gluon_seresnext50_32x4d', pretrained, **model_args) @register_model -def gluon_seresnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_seresnext101_32x4d(pretrained=False, **kwargs): """Constructs a SEResNeXt-101-32x4d model. """ - default_cfg = default_cfgs['gluon_seresnext101_32x4d'] - model = ResNet( - Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, - num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SEModule), **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, + block_args=dict(attn_layer=SEModule), **kwargs) + return _create_resnet('gluon_seresnext101_32x4d', pretrained, **model_args) @register_model -def gluon_seresnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_seresnext101_64x4d(pretrained=False, **kwargs): """Constructs a SEResNeXt-101-64x4d model. """ - default_cfg = default_cfgs['gluon_seresnext101_64x4d'] - block_args = dict(attn_layer=SEModule) - model = ResNet( - Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4, - num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model + model_args = dict( + block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, + block_args=dict(attn_layer=SEModule), **kwargs) + return _create_resnet('gluon_seresnext101_64x4d', pretrained, **model_args) @register_model -def gluon_senet154(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def gluon_senet154(pretrained=False, **kwargs): """Constructs an SENet-154 model. """ - default_cfg = default_cfgs['gluon_senet154'] - block_args = dict(attn_layer=SEModule) - model = ResNet( - Bottleneck, [3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', down_kernel_size=3, - block_reduce_first=2, num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs) - model.default_cfg = default_cfg - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - return model - + model_args = dict( + block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', + down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer=SEModule), **kwargs) + return _create_resnet('gluon_senet154', pretrained, **model_args) diff --git a/timm/models/mobilenetv3.py b/timm/models/mobilenetv3.py index 850f7120..28af4a10 100644 --- a/timm/models/mobilenetv3.py +++ b/timm/models/mobilenetv3.py @@ -199,15 +199,6 @@ class MobileNetV3Features(nn.Module): hooks = self.feature_info.get_by_key(keys=('module', 'hook_type')) self.feature_hooks = FeatureHooks(hooks, self.named_modules()) - def feature_channels(self, idx=None): - """ Feature Channel Shortcut - Returns feature channel count for each output index if idx == None. If idx is an integer, will - return feature channel count for that feature block index (independent of out_indices setting). - """ - if isinstance(idx, int): - return self.feature_info[idx]['num_chs'] - return [self.feature_info[i]['num_chs'] for i in self.out_indices] - def forward(self, x) -> List[torch.Tensor]: x = self.conv_stem(x) x = self.bn1(x) diff --git a/timm/models/resnet.py b/timm/models/resnet.py index 1781faee..dfa864f2 100644 --- a/timm/models/resnet.py +++ b/timm/models/resnet.py @@ -525,7 +525,6 @@ def _create_resnet_with_cfg(variant, default_cfg, pretrained=False, **kwargs): out_indices = None if kwargs.pop('features_only', False): features = True - kwargs.pop('num_classes', 0) out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4)) pruned = kwargs.pop('pruned', False) @@ -910,7 +909,7 @@ def seresnext26tn_32x4d(pretrained=False, **kwargs): @register_model -def ecaresnext26tn_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def ecaresnext26tn_32x4d(pretrained=False, **kwargs): """Constructs an ECA-ResNeXt-26-TN model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant.