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LIVE-T5
The LIVE-T5 model was proposed in Learning to Imagine: Visually-Augmented Natural Language Generation by Tianyi Tang, Yushuo Chen, Yifan Du, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found https://github.com/RUCAIBox/LIVE.
You should install the transformers
at https://github.com/RUCAIBox/LIVE.
import torch
import torch.nn as nn
from transformers import T5ForConditionalGeneration, AutoModel
class LiveModel(nn.Module):
def __init__(self):
super().__init__()
self.model = T5ForConditionalGeneration.from_pretrained('RUCAIBox/live-t5-base', image_fusion_encoder=True)
self.vision_model = AutoModel.from_pretrained('openai/clip-vit-base-patch32').vision_model
hidden_size = self.model.config.hidden_size
self.trans = nn.Sequential(
nn.Linear(self.vision_model.config.hidden_size, hidden_size * 4),
nn.ReLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
model = LiveModel()
trans = torch.load('trans.t5.pth')
model.trans.load_state_dict(trans)
# kwargs to model.forward() and model.generate()
# input_ids [batch_size, seq_len], same to hugging face
# attention_masks [batch_size, seq_len], same to hugging face
# labels [batch_size, seq_len], same to hugging face
# image_embeds [batch_size, image_num*patch_num, image_hidden_size], should be transfered using `trans`, image_num can be the sentence num of text, patch_num and image_hidden_size are 50 and 768 for openai/clip-vit-base-patch32, respectively
# images_mask [batch_size, seq_len, image_num], this is the mask in Figure 1, 1 represents the i-th word should attend to the j-th image
# images_mask_2d [batch_size, seq_len], 1 represents the i-th word should not be visually augmented, i.e., should not be attend to any image
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