Upload CLIP/adapter.py with huggingface_hub

CLIP/adapter.py

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+
+import torch
+from torch import nn
+from huggingface_hub import PyTorchModelHubMixin
+
+import torch.nn.functional as F
+
+# Residual CLIP Adapter
+class ClipAdapter(nn.Module):
+    def __init__(self, c_in, bottleneck=768):
+        super(ClipAdapter, self).__init__()
+        self.fc1 = nn.Sequential(
+            nn.Linear(c_in, bottleneck, bias=False),
+            nn.LeakyReLU(inplace=False)
+        )
+        self.fc2 = nn.Sequential(
+            nn.Linear(bottleneck, c_in, bias=False),
+            nn.LeakyReLU(inplace=False)
+        )
+
+    def forward(self, x):
+        x = self.fc1(x)
+        y = self.fc2(x)
+        return x, y
+
+
+class CLIPAD(nn.Module, 
+             PyTorchModelHubMixin,
+             repo_url="https://github.com/Continual-Mega/Continual-Mega",
+             paper_url="https://arxiv.org/abs/2506.00956"):
+    def __init__(self, clip_model, features):
+        super().__init__()
+        self.clipmodel = clip_model
+        self.image_encoder = clip_model.visual
+        self.features = features
+        self.adapters = nn.ModuleList( [ClipAdapter(1024, bottleneck=768) for i in range(len(features))] )
+
+    def forward(self, x):
+        x = self.image_encoder.conv1(x)
+        x = x.reshape(x.shape[0], x.shape[1], -1) 
+        x = x.permute(0, 2, 1) 
+
+        x = torch.cat(
+            [self.image_encoder.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+             x], dim=1)
+        x = x + self.image_encoder.positional_embedding.to(x.dtype)
+
+        x = self.image_encoder.patch_dropout(x)
+        x = self.image_encoder.ln_pre(x)
+
+        x = x.permute(1, 0, 2)
+
+        ada_patch_tokens = []
+        
+        for i, res in enumerate(self.image_encoder.transformer.resblocks):
+            x, _ = res(x, attn_mask=None)
+            if (i + 1) in self.features:
+                adapt_med, adapt_out = self.adapters[self.features.index(i+1)](x)
+
+                x = 0.9 * x + 0.1 * adapt_out
+                ada_patch_tokens.append(adapt_med)
+            
+        x = x.permute(1, 0, 2)
+
+        ada_patch_tokens = [ada_patch_tokens[t].permute(1, 0, 2) for t in range(len(ada_patch_tokens))]
+
+        pooled, tokens = self.image_encoder._global_pool(x)
+        pooled = self.image_encoder.ln_post(pooled)
+
+        if self.image_encoder.proj is not None:
+            pooled = pooled @ self.image_encoder.proj
+
+        return pooled, ada_patch_tokens
+
+
+