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import torch.nn as nn | ||
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD | ||
from timm.models.registry import register_model | ||
from .helpers import build_model_with_cfg | ||
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def _cfg(url='', **kwargs): | ||
return { | ||
'url': url, | ||
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, | ||
'crop_pct': .96, 'interpolation': 'bicubic', | ||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head', | ||
'first_conv': 'stem.0', | ||
**kwargs | ||
} | ||
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default_cfgs = { | ||
'convmixer_1536_20': _cfg(url='https://github.com/tmp-iclr/convmixer/releases/download/timm-v1.0/convmixer_1536_20_ks9_p7.pth.tar'), | ||
'convmixer_768_32': _cfg(url='https://github.com/tmp-iclr/convmixer/releases/download/timm-v1.0/convmixer_768_32_ks7_p7_relu.pth.tar'), | ||
'convmixer_1024_20_ks9_p14': _cfg(url='https://github.com/tmp-iclr/convmixer/releases/download/timm-v1.0/convmixer_1024_20_ks9_p14.pth.tar') | ||
} | ||
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class Residual(nn.Module): | ||
def __init__(self, fn): | ||
super().__init__() | ||
self.fn = fn | ||
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def forward(self, x): | ||
return self.fn(x) + x | ||
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class ConvMixer(nn.Module): | ||
def __init__(self, dim, depth, kernel_size=9, patch_size=7, in_chans=3, num_classes=1000, activation=nn.GELU, **kwargs): | ||
super().__init__() | ||
self.num_classes = num_classes | ||
self.num_features = dim | ||
self.head = nn.Linear(dim, num_classes) if num_classes > 0 else nn.Identity() | ||
self.stem = nn.Sequential( | ||
nn.Conv2d(in_chans, dim, kernel_size=patch_size, stride=patch_size), | ||
activation(), | ||
nn.BatchNorm2d(dim) | ||
) | ||
self.blocks = nn.Sequential( | ||
*[nn.Sequential( | ||
Residual(nn.Sequential( | ||
nn.Conv2d(dim, dim, kernel_size, groups=dim, padding="same"), | ||
activation(), | ||
nn.BatchNorm2d(dim) | ||
)), | ||
nn.Conv2d(dim, dim, kernel_size=1), | ||
activation(), | ||
nn.BatchNorm2d(dim) | ||
) for i in range(depth)] | ||
) | ||
self.pooling = nn.Sequential( | ||
nn.AdaptiveAvgPool2d((1, 1)), | ||
nn.Flatten() | ||
) | ||
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def get_classifier(self): | ||
return self.head | ||
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def reset_classifier(self, num_classes, global_pool=''): | ||
self.num_classes = num_classes | ||
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() | ||
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def forward_features(self, x): | ||
x = self.stem(x) | ||
x = self.blocks(x) | ||
x = self.pooling(x) | ||
return x | ||
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def forward(self, x): | ||
x = self.forward_features(x) | ||
x = self.head(x) | ||
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return x | ||
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def _create_convmixer(variant, pretrained=False, **kwargs): | ||
return build_model_with_cfg(ConvMixer, variant, pretrained, default_cfg=default_cfgs[variant], **kwargs) | ||
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@register_model | ||
def convmixer_1536_20(pretrained=False, **kwargs): | ||
model_args = dict(dim=1536, depth=20, kernel_size=9, patch_size=7, **kwargs) | ||
return _create_convmixer('convmixer_1536_20', pretrained, **model_args) | ||
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@register_model | ||
def convmixer_768_32(pretrained=False, **kwargs): | ||
model_args = dict(dim=768, depth=32, kernel_size=7, patch_size=7, activation=nn.ReLU, **kwargs) | ||
return _create_convmixer('convmixer_768_32', pretrained, **model_args) | ||
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@register_model | ||
def convmixer_1024_20_ks9_p14(pretrained=False, **kwargs): | ||
model_args = dict(dim=1024, depth=20, kernel_size=9, patch_size=14, **kwargs) | ||
return _create_convmixer('convmixer_1024_20_ks9_p14', pretrained, **model_args) |