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| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| import torch, open_clip | |
| from PIL import Image | |
| from IPython.display import display | |
| for model_name in ['RN50', 'ViT-B-32', 'ViT-L-14']: | |
| checkpoint_path = hf_hub_download("chendelong/RemoteCLIP", f"RemoteCLIP-{model_name}.pt", cache_dir='checkpoints') | |
| print(f'{model_name} is downloaded to {checkpoint_path}.') | |
| model_name = 'RN50' # 'RN50' or 'ViT-B-32' or 'ViT-L-14' | |
| model, _, preprocess = open_clip.create_model_and_transforms(model_name) | |
| tokenizer = open_clip.get_tokenizer(model_name) | |
| path_to_your_checkpoints = 'checkpoints/models--chendelong--RemoteCLIP/snapshots/bf1d8a3ccf2ddbf7c875705e46373bfe542bce38' | |
| ckpt = torch.load(f"{path_to_your_checkpoints}/RemoteCLIP-{model_name}.pt", map_location="cpu") | |
| def remote_clip(input_image,input_text): | |
| text_queries = [input_text] | |
| text = tokenizer(text_queries) | |
| image = Image.open(input_image) | |
| image = preprocess(image).unsqueeze(0) | |
| with torch.no_grad(), torch.cuda.amp.autocast(): | |
| image_features = model.encode_image(image.cuda()) | |
| text_features = model.encode_text(text.cuda()) | |
| image_features /= image_features.norm(dim=-1, keepdim=True) | |
| text_features /= text_features.norm(dim=-1, keepdim=True) | |
| text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1).cpu().numpy()[0] | |
| print(f'Predictions of {model_name}:') | |
| for query, prob in zip(text_queries, text_probs): | |
| print(f"{query:<40} {prob * 100:5.1f}%") | |
| demo = gr.Interface(fn=greet, inputs=[gr.Image(type="pil"), gr.Text(type="text")], outputs="text") | |
| demo.launch() |