| from transformers import AutoImageProcessor, AutoModel | |
| from PIL import Image | |
| import requests | |
| import torch | |
| import torch.nn as nn | |
| class ViT_Adapter(nn.Module): | |
| def __init__(self, input_dim=3, output_dim=768, attention=False, pool=False, nheads=8, dropout=0.1): | |
| super(ViT_Adapter, self).__init__() | |
| self.model = AutoModel.from_pretrained('autoregressive/models/vit-small') | |
| def forward(self, x): | |
| x = self.model(x,interpolate_pos_encoding=True) | |
| return x.last_hidden_state[:, 1:] | |
| if __name__ == '__main__': | |
| model = ViT_Adapter().cuda() | |
| import pdb;pdb.set_trace() | |
| print(sum(p.numel() for p in model.parameters())) | |
| inputs = torch.randn(4,3,512,512).cuda() | |
| outputs = model(inputs) | |
| print(outputs.shape) |