metadata
license: mit
Two steps only need.
First step. (git clone and install required packages)
git clone https://github.com/ByungKwanLee/TroL
bash install
Second step. (open, edit, and run demo.py)
import torch
from config import *
from PIL import Image
from utils.utils import *
import torch.nn.functional as F
from trol.load_trol import load_trol
from torchvision.transforms.functional import pil_to_tensor
# model selection
link = "TroL-7B" # [Select One] 'TroL-1.8B' | 'TroL-3.8B' | 'TroL-7B'
# User prompt
prompt_type="with_image" # Select one option "text_only", "with_image"
img_path='figures/demo.png'
question="What is the troll doing? Provide the detail in the image and imagine what the event happens."
# loading model
model, tokenizer = load_trol(link=link)
# cpu -> gpu
for param in model.parameters():
if not param.is_cuda:
param.data = param.to('cuda:0')
# prompt type -> input prompt
image_token_number = None
if prompt_type == 'with_image':
# Image Load
image = pil_to_tensor(Image.open(img_path).convert("RGB"))
if not "3.8B" in link:
image_token_number = 1225
image = F.interpolate(image.unsqueeze(0), size=(490, 490), mode='bicubic').squeeze(0)
inputs = [{'image': image, 'question': question}]
elif prompt_type=='text_only':
inputs = [{'question': question}]
# Generate
with torch.inference_mode():
_inputs = model.eval_process(inputs=inputs,
data='demo',
tokenizer=tokenizer,
device='cuda:0',
img_token_number=image_token_number)
generate_ids = model.generate(**_inputs, max_new_tokens=256, use_cache=True)
response = output_filtering(tokenizer.batch_decode(generate_ids, skip_special_tokens=False)[0], model)
print(response)
So easy Let's say TroL!