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| from functools import partial | |
| import timm.models.vision_transformer | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from timm.models.layers import trunc_normal_ | |
| class VisionTransformer(timm.models.vision_transformer.VisionTransformer): | |
| """ Vision Transformer with support for global average pooling | |
| """ | |
| def __init__(self, global_pool=False, **kwargs): | |
| super(VisionTransformer, self).__init__(**kwargs) | |
| self.global_pool = global_pool | |
| if self.global_pool: | |
| norm_layer = kwargs['norm_layer'] | |
| embed_dim = kwargs['embed_dim'] | |
| self.fc_norm = norm_layer(embed_dim) | |
| del self.norm # remove the original norm | |
| def forward_features(self, x): | |
| B = x.shape[0] | |
| x = self.patch_embed(x) | |
| cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks | |
| x = torch.cat((cls_tokens, x), dim=1) | |
| x = x + self.pos_embed | |
| x = self.pos_drop(x) | |
| for blk in self.blocks: | |
| x = blk(x) | |
| if self.global_pool: | |
| x = x[:, 1:, :].mean(dim=1,keepdim=True) # global pool without cls token | |
| outcome = self.fc_norm(x) | |
| else: | |
| x = self.norm(x) | |
| outcome = x[:, 0] | |
| return outcome | |
| def RETFound_mae(**kwargs): | |
| model = VisionTransformer( | |
| patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) | |
| return model | |
| def Dinov2(args, **kwargs): | |
| if args.model_arch == 'dinov2_vits14': | |
| arch = 'vit_small_patch14_dinov2.lvd142m' | |
| elif args.model_arch == 'dinov2_vitb14': | |
| arch = 'vit_base_patch14_dinov2.lvd142m' | |
| elif args.model_arch == 'dinov2_vitl14': | |
| arch = 'vit_large_patch14_dinov2.lvd142m' | |
| elif args.model_arch == 'dinov2_vitg14': | |
| arch = 'vit_giant_patch14_dinov2.lvd142m' | |
| else: | |
| raise ValueError(f"Unknown model_arch '{args.model_arch}'. " | |
| f"Expected one of: dinov2_vits14, dinov2_vitb14, dinov2_vitl14, dinov2_vitg14") | |
| model = timm.create_model( | |
| arch, | |
| pretrained=True, | |
| img_size=224, | |
| **kwargs | |
| ) | |
| return model | |
| def RETFound_dinov2(args, **kwargs): | |
| model = timm.create_model( | |
| 'vit_large_patch14_dinov2.lvd142m', | |
| pretrained=True, | |
| img_size=224, | |
| **kwargs | |
| ) | |
| return model | |
| def Dinov3(args, **kwargs): | |
| # Load ViT-L/16 backbone (hub model has `head = Identity` by default) | |
| model = torch.hub.load( | |
| repo_or_dir="facebookresearch/dinov3", | |
| model=args.model_arch, | |
| pretrained=False, # main() will load your checkpoint | |
| trust_repo=True, | |
| ) | |
| # Figure out feature dimension for the probe | |
| feat_dim = getattr(model, "embed_dim", None) or getattr(model, "num_features", None) | |
| model.head = nn.Linear(feat_dim, args.nb_classes) | |
| trunc_normal_(model.head.weight, std=2e-5) | |
| if model.head.bias is not None: | |
| nn.init.zeros_(model.head.bias) | |
| return model | |