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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
@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) |