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| from transformers import AutoTokenizer, CLIPProcessor, SiglipModel, AutoProcessor | |
| import requests | |
| from PIL import Image | |
| from modeling_nllb_clip import NLLBCLIPModel | |
| import torch.nn.functional as F | |
| from sentence_transformers import SentenceTransformer, util | |
| from PIL import Image, ImageFile | |
| import requests | |
| import torch | |
| import numpy as np | |
| import gradio as gr | |
| import spaces | |
| ## NLLB Inference | |
| nllb_clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| nllb_clip_processor = nllb_clip_processor.image_processor | |
| nllb_clip_tokenizer = AutoTokenizer.from_pretrained( | |
| "facebook/nllb-200-distilled-600M" | |
| ) | |
| def nllb_clip_inference(image,labels): | |
| labels = labels.split(",") | |
| image_inputs = nllb_clip_processor(images=image, return_tensors="pt") | |
| text_inputs = nllb_clip_tokenizer(labels, padding="longest", return_tensors="pt",) | |
| nllb_clip_model = NLLBCLIPModel.from_pretrained("visheratin/nllb-clip-base") | |
| outputs = nllb_clip_model(input_ids = text_inputs.input_ids, attention_mask = text_inputs.attention_mask, pixel_values=image_inputs.pixel_values) | |
| normalized_tensor = F.softmax(outputs["logits_per_text"], dim=0) | |
| normalized_tensor = normalized_tensor.detach().numpy() | |
| return {labels[i]: float(np.array(normalized_tensor)[i]) for i in range(len(labels))} | |
| # SentenceTransformers CLIP-ViT-B-32 | |
| img_model = SentenceTransformer('clip-ViT-B-32') | |
| text_model = SentenceTransformer('sentence-transformers/clip-ViT-B-32-multilingual-v1') | |
| def infer_st(image, texts): | |
| texts = texts.split(",") | |
| img_embeddings = img_model.encode(image) | |
| text_embeddings = text_model.encode(texts) | |
| cos_sim = util.cos_sim(text_embeddings, img_embeddings) | |
| return {texts[i]: float(np.array(cos_sim)[i]) for i in range(len(texts))} | |
| ### SigLIP Inference | |
| siglip_model = SiglipModel.from_pretrained("google/siglip-base-patch16-256-multilingual") | |
| siglip_processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256-multilingual") | |
| def postprocess_siglip(output, labels): | |
| return {labels[i]: float(np.array(output[0])[i]) for i in range(len(labels))} | |
| def siglip_detector(image, texts): | |
| inputs = siglip_processor(text=texts, images=image, return_tensors="pt", | |
| padding="max_length") | |
| with torch.no_grad(): | |
| outputs = siglip_model(**inputs) | |
| logits_per_image = outputs.logits_per_image | |
| probs = torch.sigmoid(logits_per_image) | |
| probs = normalize_tensor(probs) | |
| return probs | |
| def normalize_tensor(tensor): | |
| # no other normalization works well for visual purposes | |
| sum_tensor = torch.sum(tensor) | |
| normalized_tensor = tensor / sum_tensor | |
| return normalized_tensor | |
| def infer_siglip(image, candidate_labels): | |
| candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")] | |
| siglip_out = siglip_detector(image, candidate_labels) | |
| return postprocess_siglip(siglip_out, labels=candidate_labels) | |
| def infer(image, labels): | |
| st_out = infer_st(image, labels) | |
| nllb_out = nllb_clip_inference(image, labels) | |
| siglip_out = infer_siglip(image, labels) | |
| return st_out, siglip_out, nllb_out | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Compare Multilingual Zero-shot Image Classification") | |
| gr.Markdown("Compare the performance of SigLIP and other models on zero-shot classification in this Space.") | |
| gr.Markdown("Three models are compared: CLIP-ViT, NLLB-CLIP and SigLIP. Note that SigLIP outputs are normalized for visualization purposes.") | |
| gr.Markdown("NLLB-CLIP is a multilingual vision-language model that combines [NLLB](https://ai.meta.com/research/no-language-left-behind/) with [CLIP](https://openai.com/research/clip) to extend CLIP to 200+ languages.") | |
| gr.Markdown("CLIP-ViT is CLIP model extended to other languages using [multilingual knowledge distillation](https://arxiv.org/abs/2004.09813).") | |
| gr.Markdown("Finally, SigLIP is the state-of-the-art vision-language model released by Google. Multilingual checkpoint is pre-trained by Google.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(type="pil") | |
| text_input = gr.Textbox(label="Input a list of labels") | |
| run_button = gr.Button("Run", visible=True) | |
| with gr.Column(): | |
| st_output = gr.Label(label = "CLIP-ViT Multilingual Output", num_top_classes=3) | |
| siglip_output = gr.Label(label = "SigLIP Output", num_top_classes=3) | |
| nllb_output = gr.Label(label = "NLLB-CLIP Output", num_top_classes=3) | |
| examples = [["./cat.jpg", "eine Katze, köpek, un oiseau"]] | |
| gr.Examples( | |
| examples = examples, | |
| inputs=[image_input, text_input], | |
| outputs=[st_output, | |
| siglip_output, | |
| nllb_output], | |
| fn=infer, | |
| cache_examples=True | |
| ) | |
| run_button.click(fn=infer, | |
| inputs=[image_input, text_input], | |
| outputs=[st_output, | |
| siglip_output, | |
| nllb_output]) | |
| demo.launch() |