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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
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