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Running
on
A10G
| import gradio as gr | |
| import sys | |
| import torch | |
| import torchvision.transforms as T | |
| import torchvision.transforms.functional as TF | |
| sys.path.append('src/blip') | |
| sys.path.append('src/clip') | |
| import clip | |
| from models.blip import blip_decoder | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| print("Loading BLIP model...") | |
| blip_image_eval_size = 384 | |
| blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' | |
| blip_model = blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='large', med_config='./src/blip/configs/med_config.json') | |
| blip_model.eval() | |
| blip_model = blip_model.to(device) | |
| print("Loading CLIP model...") | |
| clip_model_name = 'ViT-L/14' | |
| clip_model, clip_preprocess = clip.load(clip_model_name, device=device) | |
| clip_model.to(device).eval() | |
| def generate_caption(pil_image): | |
| gpu_image = T.Compose([ | |
| T.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=TF.InterpolationMode.BICUBIC), | |
| T.ToTensor(), | |
| T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ])(pil_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| caption = blip_model.generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5) | |
| return caption[0] | |
| def inference(image): | |
| return generate_caption(image) | |
| inputs = [gr.inputs.Image(type='pil')] | |
| outputs = gr.outputs.Textbox(label="Output") | |
| title = "CLIP Interrogator" | |
| description = "First test of CLIP Interrogator on HuggingSpace" | |
| article = """ | |
| <p style='text-align: center'> | |
| <a href="">Colab Notebook</a> / | |
| <a href="">Github repo</a> | |
| </p> | |
| """ | |
| gr.Interface( | |
| inference, | |
| inputs, | |
| outputs, | |
| title=title, description=description, | |
| article=article, | |
| examples=[['example.jpg']] | |
| ).launch(enable_queue=True) | |