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README.md

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+## How to use UniCAS to extract features.
+
+The code below can be used to run inference; `UniCAS` expects images of size 224x224 that were extracted at 20× magnification.
+
+```python
+import functools
+import timm
+import torch
+from torchvision import transforms
+
+params = {
+    'patch_size': 16, 
+    'embed_dim': 1024, 
+    'depth': 24, 
+    'num_heads': 16, 
+    'init_values': 1e-05, 
+    'mlp_ratio': 2.671875 * 2, 
+    'mlp_layer': functools.partial(
+        timm.layers.mlp.GluMlp, gate_last=False
+    ), 
+    'act_layer': torch.nn.modules.activation.SiLU, 
+    'no_embed_class': False, 
+    'img_size': 224, 
+    'num_classes': 0, 
+    'in_chans': 3
+}
+
+model = timm.models.VisionTransformer(**params)
+print(model.load_state_dict(torch.load("UniCAS.pth"), strict=False))
+model = model.eval().to("cuda")
+
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=(0.485, 0.456, 0.406),  
+       std=(0.229, 0.224, 0.225),
+    ),
+])
+
+input = torch.rand(3, 224, 224)
+input = transforms.ToPILImage()(input)
+input = transform(input).unsqueeze(0)
+with torch.no_grad():
+    features = model(input.to("cuda"))
+print(features.shape)  # torch.Size([1, 1024])
+```

UniCAS.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:778e8da742d8b2775e70d934be9ac19536c6287d755ecdb429c097a78ee419e5
+size 1215229559