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@ -13,7 +13,7 @@ def _cfg(url='', **kwargs):
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return {
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return {
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'url': url,
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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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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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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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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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'first_conv': 'conv1', 'classifier': 'fc',
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**kwargs
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**kwargs
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@ -21,11 +21,13 @@ def _cfg(url='', **kwargs):
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default_cfgs = {
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default_cfgs = {
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'skresnet18': _cfg(url=''),
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'skresnet18': _cfg(
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'skresnet26d': _cfg(),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth'),
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'skresnet34': _cfg(url=''),
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'skresnet50': _cfg(),
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'skresnet50': _cfg(),
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'skresnet50d': _cfg(),
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'skresnet50d': _cfg(),
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'skresnext50_32x4d': _cfg(),
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'skresnext50_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth'),
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}
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}
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@ -134,24 +136,10 @@ class SelectiveKernelBottleneck(nn.Module):
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@register_model
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@register_model
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def skresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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def skresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-18 model.
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"""Constructs a Selective Kernel ResNet-18 model.
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"""
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default_cfg = default_cfgs['skresnet18']
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sk_kwargs = dict(
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min_attn_channels=16,
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)
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model = ResNet(
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SelectiveKernelBasic, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
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block_args=dict(sk_kwargs=sk_kwargs), **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
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def sksresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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variation splits the input channels to the selective convolutions to keep param count down.
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"""Constructs a ResNet-18 model.
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"""
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"""
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default_cfg = default_cfgs['skresnet18']
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default_cfg = default_cfgs['skresnet18']
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sk_kwargs = dict(
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sk_kwargs = dict(
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@ -169,17 +157,21 @@ def sksresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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@register_model
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@register_model
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def skresnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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def skresnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-26 model.
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"""Constructs a Selective Kernel ResNet-34 model.
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Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
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variation splits the input channels to the selective convolutions to keep param count down.
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"""
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"""
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default_cfg = default_cfgs['skresnet26d']
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default_cfg = default_cfgs['skresnet34']
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sk_kwargs = dict(
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sk_kwargs = dict(
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keep_3x3=False,
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min_attn_channels=16,
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attn_reduction=8,
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split_input=True
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)
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)
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model = ResNet(
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model = ResNet(
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SelectiveKernelBottleneck, [2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True,
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SelectiveKernelBasic, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
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num_classes=num_classes, in_chans=in_chans, block_args=dict(sk_kwargs=sk_kwargs), zero_init_last_bn=False
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block_args=dict(sk_kwargs=sk_kwargs), zero_init_last_bn=False, **kwargs)
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**kwargs)
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model.default_cfg = default_cfg
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model.default_cfg = default_cfg
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if pretrained:
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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load_pretrained(model, default_cfg, num_classes, in_chans)
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@ -189,11 +181,12 @@ def skresnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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@register_model
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@register_model
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def skresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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def skresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Select Kernel ResNet-50 model.
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"""Constructs a Select Kernel ResNet-50 model.
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Based on config in "Compounding the Performance Improvements of Assembled Techniques in a
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Convolutional Neural Network"
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Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
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variation splits the input channels to the selective convolutions to keep param count down.
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"""
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"""
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sk_kwargs = dict(
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sk_kwargs = dict(
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attn_reduction=2,
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split_input=True,
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)
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)
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default_cfg = default_cfgs['skresnet50']
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default_cfg = default_cfgs['skresnet50']
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model = ResNet(
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model = ResNet(
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@ -208,11 +201,12 @@ def skresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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@register_model
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@register_model
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def skresnet50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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def skresnet50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a Select Kernel ResNet-50-D model.
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"""Constructs a Select Kernel ResNet-50-D model.
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Based on config in "Compounding the Performance Improvements of Assembled Techniques in a
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Convolutional Neural Network"
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Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
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variation splits the input channels to the selective convolutions to keep param count down.
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"""
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"""
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sk_kwargs = dict(
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sk_kwargs = dict(
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attn_reduction=2,
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split_input=True,
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)
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)
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default_cfg = default_cfgs['skresnet50d']
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default_cfg = default_cfgs['skresnet50d']
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model = ResNet(
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model = ResNet(
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