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| from transformers import CLIPVisionModel | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from dataclasses import 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): | |
| if self.args.grid_size == -1: # no grid pooling | |
| return image_features | |
| if self.args.grid_size == 0: # take cls token | |
| return image_features[:, 0:1, :] | |
| if self.args.grid_size == 1: # global avg pooling | |
| return image_features.mean(dim=1, keepdim=True) | |
| cls_features = image_features[:, 0:1, :] | |
| image_features = image_features[:, 1:, :] #drop cls token | |
| 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((cls_features, image_features), dim=1) |