|
|
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 |
|
|
|
|
|
|
|
|
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 |
|
|
} |
|
|
|
|
|
|
|
|
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') |
|
|
} |
|
|
|
|
|
|
|
|
class Residual(nn.Module): |
|
|
def __init__(self, fn): |
|
|
super().__init__() |
|
|
self.fn = fn |
|
|
|
|
|
def forward(self, x): |
|
|
return self.fn(x) + x |
|
|
|
|
|
|
|
|
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() |
|
|
) |
|
|
|
|
|
def get_classifier(self): |
|
|
return self.head |
|
|
|
|
|
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() |
|
|
|
|
|
def forward_features(self, x): |
|
|
x = self.stem(x) |
|
|
x = self.blocks(x) |
|
|
x = self.pooling(x) |
|
|
return x |
|
|
|
|
|
def forward(self, x): |
|
|
x = self.forward_features(x) |
|
|
x = self.head(x) |
|
|
|
|
|
return x |
|
|
|
|
|
|
|
|
def _create_convmixer(variant, pretrained=False, **kwargs): |
|
|
return build_model_with_cfg(ConvMixer, variant, pretrained, default_cfg=default_cfgs[variant], **kwargs) |
|
|
|
|
|
|
|
|
@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) |
|
|
|
|
|
|
|
|
@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) |
|
|
|
|
|
|
|
|
@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) |
