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246 lines
11 KiB
246 lines
11 KiB
"""Pytorch impl of MxNet Gluon ResNet/(SE)ResNeXt variants
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This file evolved from https://github.com/pytorch/vision 'resnet.py' with (SE)-ResNeXt additions
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and ports of Gluon variations (https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnet.py)
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by Ross Wightman
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"""
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg
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from .layers import SEModule
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from .registry import register_model
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from .resnet import ResNet, Bottleneck, BasicBlock
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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'gluon_resnet18_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
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'gluon_resnet34_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
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'gluon_resnet50_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
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'gluon_resnet101_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
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'gluon_resnet152_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
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'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth',
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first_conv='conv1.0'),
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'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth',
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first_conv='conv1.0'),
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'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth',
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first_conv='conv1.0'),
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'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth',
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first_conv='conv1.0'),
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'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth',
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first_conv='conv1.0'),
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'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth',
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first_conv='conv1.0'),
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'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth',
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first_conv='conv1.0'),
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'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth',
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first_conv='conv1.0'),
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'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth',
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first_conv='conv1.0'),
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'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
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'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
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'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
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'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
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'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
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'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
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'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth',
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first_conv='conv1.0'),
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}
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def _create_resnet(variant, pretrained=False, **kwargs):
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return build_model_with_cfg(ResNet, variant, default_cfg=default_cfgs[variant], pretrained=pretrained, **kwargs)
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@register_model
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def gluon_resnet18_v1b(pretrained=False, **kwargs):
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"""Constructs a ResNet-18 model.
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"""
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model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs)
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return _create_resnet('gluon_resnet18_v1b', pretrained, **model_args)
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@register_model
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def gluon_resnet34_v1b(pretrained=False, **kwargs):
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"""Constructs a ResNet-34 model.
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"""
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model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs)
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return _create_resnet('gluon_resnet34_v1b', pretrained, **model_args)
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@register_model
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def gluon_resnet50_v1b(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
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return _create_resnet('gluon_resnet50_v1b', pretrained, **model_args)
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@register_model
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def gluon_resnet101_v1b(pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs)
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return _create_resnet('gluon_resnet101_v1b', pretrained, **model_args)
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@register_model
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def gluon_resnet152_v1b(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs)
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return _create_resnet('gluon_resnet152_v1b', pretrained, **model_args)
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@register_model
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def gluon_resnet50_v1c(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', **kwargs)
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return _create_resnet('gluon_resnet50_v1c', pretrained, **model_args)
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@register_model
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def gluon_resnet101_v1c(pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', **kwargs)
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return _create_resnet('gluon_resnet101_v1c', pretrained, **model_args)
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@register_model
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def gluon_resnet152_v1c(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', **kwargs)
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return _create_resnet('gluon_resnet152_v1c', pretrained, **model_args)
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@register_model
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def gluon_resnet50_v1d(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('gluon_resnet50_v1d', pretrained, **model_args)
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@register_model
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def gluon_resnet101_v1d(pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('gluon_resnet101_v1d', pretrained, **model_args)
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@register_model
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def gluon_resnet152_v1d(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('gluon_resnet152_v1d', pretrained, **model_args)
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@register_model
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def gluon_resnet50_v1s(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], stem_width=64, stem_type='deep', **kwargs)
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return _create_resnet('gluon_resnet50_v1s', pretrained, **model_args)
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@register_model
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def gluon_resnet101_v1s(pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 23, 3], stem_width=64, stem_type='deep', **kwargs)
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return _create_resnet('gluon_resnet101_v1s', pretrained, **model_args)
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@register_model
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def gluon_resnet152_v1s(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], stem_width=64, stem_type='deep', **kwargs)
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return _create_resnet('gluon_resnet152_v1s', pretrained, **model_args)
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@register_model
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def gluon_resnext50_32x4d(pretrained=False, **kwargs):
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"""Constructs a ResNeXt50-32x4d model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
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return _create_resnet('gluon_resnext50_32x4d', pretrained, **model_args)
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@register_model
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def gluon_resnext101_32x4d(pretrained=False, **kwargs):
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"""Constructs a ResNeXt-101 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
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return _create_resnet('gluon_resnext101_32x4d', pretrained, **model_args)
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@register_model
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def gluon_resnext101_64x4d(pretrained=False, **kwargs):
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"""Constructs a ResNeXt-101 model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, **kwargs)
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return _create_resnet('gluon_resnext101_64x4d', pretrained, **model_args)
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@register_model
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def gluon_seresnext50_32x4d(pretrained=False, **kwargs):
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"""Constructs a SEResNeXt50-32x4d model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
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block_args=dict(attn_layer=SEModule), **kwargs)
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return _create_resnet('gluon_seresnext50_32x4d', pretrained, **model_args)
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@register_model
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def gluon_seresnext101_32x4d(pretrained=False, **kwargs):
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"""Constructs a SEResNeXt-101-32x4d model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4,
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block_args=dict(attn_layer=SEModule), **kwargs)
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return _create_resnet('gluon_seresnext101_32x4d', pretrained, **model_args)
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@register_model
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def gluon_seresnext101_64x4d(pretrained=False, **kwargs):
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"""Constructs a SEResNeXt-101-64x4d model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4,
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block_args=dict(attn_layer=SEModule), **kwargs)
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return _create_resnet('gluon_seresnext101_64x4d', pretrained, **model_args)
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@register_model
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def gluon_senet154(pretrained=False, **kwargs):
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"""Constructs an SENet-154 model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
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down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer=SEModule), **kwargs)
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return _create_resnet('gluon_senet154', pretrained, **model_args)
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