from transformers import CLIPVisionModel import torch import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass @dataclass class VisionEncoderConfig: n_embd: int = 2048 vision_tower_name: str = 'openai/clip-vit-large-patch14-336' grid_size: int = -1 # -1: no grid pooling, 0: take cls token, 1: global avg pooling, 2, 3, 4, ...: grid pooling class VisionEncoder(nn.Module): def __init__(self, args): super().__init__() self.args = args self.vit = CLIPVisionModel.from_pretrained(args.vision_tower_name) self.proj = nn.Linear(self.vit.config.hidden_size, args.n_embd, bias=False) def encode_images(self, images): B, N, C, H, W = images.shape images = images.view(B*N, C, H, W) image_features = self.vit(images).last_hidden_state L, D = image_features.shape[1], image_features.shape[2] # rerange [B*N, L, D] -> [B, N, L, D] image_features = image_features.view(B, N, L, D)[:, 0, :, :] image_features = self.grid_pooling(image_features) return self.proj(image_features) def grid_pooling(self, image_features): cls_features = image_features[:, 0:1, :] image_features = image_features[:, 1:, :] #drop cls token if self.args.grid_size == -1: # no grid pooling return torch.cat((image_features, cls_features), dim=1) if self.args.grid_size == 0: # take cls token return cls_features if self.args.grid_size == 1: # global avg pooling return torch.cat((image_features.mean(dim=1, keepdim=True), cls_features), dim=1) B, L, D = image_features.shape H_or_W = int(L**0.5) image_features = image_features.view(B, H_or_W, H_or_W, D) grid_stride = H_or_W // self.args.grid_size image_features = F.avg_pool2d(image_features.permute(0, 3, 1, 2), padding=0, kernel_size=grid_stride, stride=grid_stride) image_features = image_features.permute(0, 2, 3, 1).view(B, -1, D) return torch.cat((image_features, cls_features), dim=1)