Add ConvMixer

timm/models/__init__.py

@@ -4,6 +4,7 @@ from .byobnet import *
 from .cait import *
 from .coat import *
 from .convit import *
+from .convmixer import *
 from .crossvit import *
 from .cspnet import *
 from .densenet import *

timm/models/convmixer.py

@@ -0,0 +1,101 @@
+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)