import torch import torch.nn.functional as F import numpy as np import os from contextlib import nullcontext from src.segment_anything import sam_model_registry import torch.nn as nn # 延迟导入 diffusion_model，避免在不需要时触发 diffusers 依赖 # from diffusion_model.stable_diffusion import diffusion from open_clip.eva_clip.eva_vit_model import Attention def context_adapter(clip, args): last_block_attn=clip.visual.blocks[-1].attn attn_config=extract_attention_config(last_block_attn) new_attn=CustomAttention(args.mode, **attn_config) device = next(last_block_attn.parameters()).device new_attn = new_attn.to(device) with torch.no_grad(): for param_name, param_value in last_block_attn.named_parameters(): if param_name in new_attn.state_dict(): new_attn.state_dict()[param_name].copy_(param_value) clip.visual.blocks[-1].attn = new_attn class CustomAttention(Attention): def __init__(self, mode, *args, **kwargs): super().__init__(*args, **kwargs) self.mode=mode if mode=="csa_vfm_distill": self.q_adapter = nn.Sequential( nn.Linear(self.proj.in_features, 1024), nn.SiLU(), nn.Linear(1024, 1024), nn.SiLU(), nn.Linear(1024, self.proj.in_features)) self.k_adapter = nn.Sequential( nn.Linear(self.proj.in_features, 1024), nn.SiLU(), nn.Linear(1024, 1024), nn.SiLU(), nn.Linear(1024, self.proj.in_features)) else: self.context_adapter = nn.Sequential( nn.Linear(self.proj.in_features, 1024), nn.SiLU(), nn.Linear(1024, 1024), nn.SiLU(), nn.Linear(1024, self.proj.in_features)) self.alpha=0.7 self._reset_mlp_parameters() def _reset_mlp_parameters(self): if self.mode == "csa_vfm_distill": for layer in self.q_adapter: if isinstance(layer, nn.Linear): nn.init.xavier_uniform_(layer.weight) if layer.bias is not None: nn.init.zeros_(layer.bias) for layer in self.k_adapter: if isinstance(layer, nn.Linear): nn.init.xavier_uniform_(layer.weight) if layer.bias is not None: nn.init.zeros_(layer.bias) else: for layer in self.context_adapter: if isinstance(layer, nn.Linear): nn.init.xavier_uniform_(layer.weight) if layer.bias is not None: nn.init.zeros_(layer.bias) def ss_attn(self, x, mode): B, N, C = x.shape if self.subln: q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) k = F.linear(input=x, weight=self.k_proj.weight, bias=None) v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) if self.mode=="csa_vfm_distill": q_distill = self.q_adapter(q) k_distill = self.k_adapter(k) q_distill=q + q_distill*(self.alpha) k_distill=k + k_distill*(self.alpha) q_distill=q_distill.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) k_distill=k_distill.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) q_distill = q_distill.contiguous().view(B*self.num_heads, N, -1) k_distill = k_distill.contiguous().view(B*self.num_heads, N, -1) elif self.mode=="qq_vfm_distill": q_distill=self.context_adapter(q) q_distill=q_distill.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) q_distill = q_distill.contiguous().view(B*self.num_heads, N, -1) else: raise NotImplementedError q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) else: qkv_bias = None if self.q_bias is not None: qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C q, k, v = qkv[0], qkv[1], qkv[2] q = q.contiguous().view(B*self.num_heads, N, -1) k = k.contiguous().view(B*self.num_heads, N, -1) v = v.contiguous().view(B*self.num_heads, N, -1) if 'qq' in mode: q_attn = torch.bmm(q, q.transpose(1, 2)) # self.scale attn_weights = F.softmax(q_attn, dim=-1) elif 'csa' in mode: q_attn = torch.bmm(q, q.transpose(1, 2)) # self.scale k_attn = torch.bmm(k, k.transpose(1, 2)) # self.scale attn_weights = F.softmax(q_attn + k_attn, dim=-1) elif 'vv' in mode: v_attn = torch.bmm(v, v.transpose(1, 2)) # self.scale attn_weights = F.softmax(v_attn, dim=-1) elif 'kk' in mode: k_attn = torch.bmm(k, k.transpose(1, 2)) # self.scale attn_weights = F.softmax(k_attn, dim=-1) elif 'all' in mode: q_attn = torch.bmm(q, q.transpose(1, 2)) # self.scale k_attn = torch.bmm(k, k.transpose(1, 2)) # self.scale v_attn = torch.bmm(v, v.transpose(1, 2)) # self.scale _attn = (q_attn+k_attn+v_attn)/3.0 attn_weights = F.softmax(_attn, dim=-1) else: raise NotImplementedError(f"Mode '{mode}' is not implemented.") attn_output = torch.bmm(attn_weights, v) attn_output = attn_output.transpose(0, 1).contiguous().view(N, B, C).transpose(0, 1) # B,N,C attn_output = self.inner_attn_ln(attn_output) attn_output = self.proj(attn_output) attn_output = self.proj_drop(attn_output) if mode=="qq_vfm_distill": return attn_output, q_distill[:,1:] elif mode=="kk_vfm_distill": return attn_output, k[:,1:] elif mode=="csa_vfm_distill": return attn_output, (q_distill[:,1:], k_distill[:,1:]) elif mode=="vv_vfm_distill": return attn_output, v[:,1:] elif mode=="all_vfm_distill": return attn_output, (q[:,1:], k[:,1:], v[:,1:]) else: return attn_output def extract_attention_config(attn_module): config = { "dim": attn_module.proj.in_features, # 输入维度 "num_heads": attn_module.num_heads, # 多头注意力的头数 "qkv_bias": attn_module.q_bias is not None, # QKV 是否使用偏置 "qk_scale": attn_module.scale, # QK 的缩放因子 "attn_drop": attn_module.attn_drop.p, # Dropout 概率 "proj_drop": attn_module.proj_drop.p, # 输出投影的 Dropout 概率 "subln": attn_module.subln, # 是否使用 SubLayerNorm "rope": attn_module.rope, "norm_layer": type(attn_module.inner_attn_ln), # 使用的归一化层类型 "xattn": getattr(attn_module, "xattn", False), # 是否使用 xattn 模式 } return config def get_autocast(precision): if precision == "bf16": return lambda: torch.autocast("cuda", dtype=torch.bfloat16) elif precision == "amp": return lambda: torch.cuda.amp.autocast() else: return lambda: nullcontext() def mask2box(mask): ys, xs = np.where(mask) y0, y1 = ys.min(), ys.max() x0, x1 = xs.min(), xs.max() return x0, y0, x1, y1 def build_vfm(args): name=args.use_vfm sam_ckpts = { "sam-B": "/opt/tiger/xiaomoguhzz/sam/sam_vit_b_01ec64.pth", "sam-L": "/opt/tiger/xiaomoguhzz/sam/sam_vit_l_0b3195.pth", } dinov2_ckpts = { "dinov2-L": "dinov2_vitl14_reg", "dinov2-B": "dinov2_vitb14_reg", "dinov2-B-noreg": "dinov2_vitb14", "dinov2-L-noreg": "dinov2_vitl14", } dino_ckpts = { "dino-B-8": "dino_vitb8", "dino-B-16": "dino_vitb16", } vfm = None if name.startswith("dinov2"): if name in dinov2_ckpts: model_name = dinov2_ckpts[name] try: vfm = torch.hub.load('facebookresearch/dinov2', model_name).half() except Exception as e: raise RuntimeError(f"Failed to load DINOv2 model '{name}': {e}") else: raise NotImplementedError(f"VLM model '{name}' not supported under DINOv2 category.") elif name.startswith("dino"): if name in dino_ckpts: model_name = dino_ckpts[name] try: vfm = torch.hub.load('facebookresearch/dino:main', model_name).half() except Exception as e: raise RuntimeError(f"Failed to load DINO model '{name}': {e}") else: raise NotImplementedError(f"VLM model '{name}' not supported under DINO category.") elif name.startswith("sd_dino"): model_name = dinov2_ckpts['dinov2-B'] try: dinov2 = torch.hub.load('facebookresearch/dinov2', model_name).half().eval() except Exception as e: raise RuntimeError(f"Failed to load DINOv2 model '{name}': {e}") try: # 延迟导入：只在 sd_dino 模式下才导入 diffusers 相关依赖 from diffusion_model.stable_diffusion import diffusion sd=diffusion(attention_layers_to_use=[-4, -6],model='v2.1', time_step=45, device=args.device, dtype=torch.float16).eval() except Exception as e: raise RuntimeError(f"Failed to load diffusion model") for p in dinov2.parameters(): p.requires_grad = False for p in sd.parameters(): p.requires_grad = False return [dinov2, sd] elif name.startswith("sam_dino"): name='dinov2-B' model_name = dinov2_ckpts[name] try: dinov2 = torch.hub.load('facebookresearch/dinov2', model_name).half() except Exception as e: raise RuntimeError(f"Failed to load DINOv2 model '{name}': {e}") sam = sam_model_registry['vit_l'](checkpoint="/opt/tiger/xiaomoguhzz/sam/sam_vit_l_0b3195.pth").half() for p in dinov2.parameters(): p.requires_grad = False for p in sam.parameters(): p.requires_grad = False return [dinov2, sam] elif name.startswith("sam"): if name in sam_ckpts: vit_type = "vit_b" if "B" in name else "vit_l" checkpoint_path = sam_ckpts[name] try: vfm = sam_model_registry[vit_type](checkpoint=checkpoint_path).half() except Exception as e: raise RuntimeError(f"Failed to load SAM model '{name}' with checkpoint '{checkpoint_path}': {e}") else: raise NotImplementedError(f"VLM model '{name}' not supported under SAM category.") else: raise NotImplementedError(f"VLM model '{name}' not supported.") for p in vfm.parameters(): p.requires_grad = False return vfm def freeze_parameters(model, args): freeze_keys = get_freeze_keys(args) for name, param in model.named_parameters(): if name in freeze_keys: param.requires_grad = False return model def get_freeze_keys(args): if args.model=="ViT-B-16": return ViTB_16_freeze_keys elif args.model=="ViT-L-14" or args.model=="ViT-L-14-336": return ViTL_14_freeze_keys elif args.model=="EVA02-CLIP-B-16": if args.custom_freeze_para: return custom_EVA_ViTB_16_freeze_keys if args.mode=="qq_vfm_distill": return ViTB_EVA_16_qq_Distill_keys elif args.mode=="kk_vfm_distill": return ViTB_EVA_16_kk_Distill_keys elif args.mode=="sanity_check": return sanity_check_freeze_keys else: return BASE_EVA_ViTB_16_freeze_keys elif args.model=="EVA02-CLIP-L-14-336": if args.custom_freeze_para: return custom_EVA_ViTL_14_freeze_keys if args.mode=="qq_vfm_distill": return ViTL_EVA_14_qq_Distill_keys elif args.mode=="kk_vfm_distill": return ViTL_EVA_14_kk_Distill_keys elif args.mode=="sanity_check": return sanity_check_freeze_keys else: return BASE_EVA_ViTL_14_freeze_keys elif args.model=="siglip-so400m-patch14-384": return siglip_384_Distill_Freeze_keys elif "TinyCLIP" in args.model: # TinyCLIP-auto-ViT-63M-32-Text-31M 有12层，最后一个block索引为11 return TinyCLIP_63M_freeze_keys TinyCLIP_63M_freeze_keys=[ '_image_encoder.visual.transformer.resblocks.11.ln_2.weight', '_image_encoder.visual.transformer.resblocks.11.ln_2.bias', '_image_encoder.visual.transformer.resblocks.11.mlp.c_fc.weight', '_image_encoder.visual.transformer.resblocks.11.mlp.c_fc.bias', '_image_encoder.visual.transformer.resblocks.11.mlp.c_proj.weight', '_image_encoder.visual.transformer.resblocks.11.mlp.c_proj.bias', ] ViTB_16_freeze_keys=[ 'visual.transformer.resblocks.11.ln_2.weight', 'visual.transformer.resblocks.11.ln_2.bias', 'visual.transformer.resblocks.11.mlp.c_fc.weight', 'visual.transformer.resblocks.11.mlp.c_fc.bias', 'visual.transformer.resblocks.11.mlp.c_proj.weight', 'visual.transformer.resblocks.11.mlp.c_proj.bias'] ViTL_14_freeze_keys=[ 'visual.transformer.resblocks.23.ln_2.weight', 'visual.transformer.resblocks.23.ln_2.bias', 'visual.transformer.resblocks.23.mlp.c_fc.weight', 'visual.transformer.resblocks.23.mlp.c_fc.bias', 'visual.transformer.resblocks.23.mlp.c_proj.weight', 'visual.transformer.resblocks.23.mlp.c_proj.bias'] BASE_EVA_ViTB_16_freeze_keys=[ 'logit_scale', 'visual.blocks.11.norm2.weight', 'visual.blocks.11.norm2.bias', 'visual.blocks.11.mlp.w1.weight', 'visual.blocks.11.mlp.w1.bias', 'visual.blocks.11.mlp.w2.weight', 'visual.blocks.11.mlp.w2.bias', 'visual.blocks.11.mlp.w3.weight', 'visual.blocks.11.mlp.w3.bias', 'visual.blocks.11.mlp.ffn_ln.weight', 'visual.blocks.11.mlp.ffn_ln.bias'] BASE_EVA_ViTL_14_freeze_keys=[ 'logit_scale', 'visual.blocks.23.norm2.weight', 'visual.blocks.23.norm2.bias', 'visual.blocks.23.mlp.w1.weight', 'visual.blocks.23.mlp.w1.bias', 'visual.blocks.23.mlp.w2.weight', 'visual.blocks.23.mlp.w2.bias', 'visual.blocks.23.mlp.w3.weight', 'visual.blocks.23.mlp.w3.bias', 'visual.blocks.23.mlp.ffn_ln.weight', 'visual.blocks.23.mlp.ffn_ln.bias'] custom_EVA_ViTB_16_freeze_keys=['logit_scale'] custom_EVA_ViTL_14_freeze_keys=['logit_scale'] sanity_check_freeze_keys=['logit_scale'] ViTB_EVA_16_qq_Distill_keys=['visual.blocks.11.attn.k_proj.weight', ] + BASE_EVA_ViTB_16_freeze_keys ViTL_EVA_14_qq_Distill_keys=['visual.blocks.23.attn.k_proj.weight', ] + BASE_EVA_ViTL_14_freeze_keys ViTB_EVA_16_kk_Distill_keys=['visual.blocks.11.attn.q_proj.weight','visual.blocks.11.attn.q_bias'] + BASE_EVA_ViTB_16_freeze_keys ViTL_EVA_14_kk_Distill_keys=['visual.blocks.23.attn.q_proj.weight','visual.blocks.23.attn.q_bias'] + BASE_EVA_ViTL_14_freeze_keys siglip_384_Distill_Freeze_keys=['logit_scale', 'logit_bias', 'vision_model.head.probe', ]