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CPU Upgrade
Running
on
CPU Upgrade
| import torch | |
| from transformers import pipeline, SiglipModel, AutoProcessor | |
| import numpy as np | |
| import gradio as gr | |
| siglip_checkpoint = "nielsr/siglip-base-patch16-224" | |
| clip_checkpoint = "openai/clip-vit-base-patch16" | |
| clip_detector = pipeline(model=clip_checkpoint, task="zero-shot-image-classification") | |
| siglip_model = SiglipModel.from_pretrained("google/siglip-base-patch16-224") | |
| siglip_processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") | |
| def postprocess(output): | |
| return {out["label"]: float(out["score"]) for out in output} | |
| 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) | |
| return probs | |
| def infer(image, candidate_labels): | |
| candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")] | |
| siglip_out = siglip_detector(image, candidate_labels) | |
| clip_out = clip_detector(image, candidate_labels=candidate_labels) | |
| return postprocess(clip_out), postprocess_siglip(siglip_out, labels=candidate_labels) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Compare CLIP and SigLIP") | |
| gr.Markdown("Compare the performance of CLIP and SigLIP on zero-shot classification in this Space 👇") | |
| 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(): | |
| clip_output = gr.Label(label = "CLIP Output", num_top_classes=3) | |
| siglip_output = gr.Label(label = "SigLIP Output", num_top_classes=3) | |
| examples = [["./baklava.jpg", "baklava, souffle, tiramisu"]] | |
| gr.Examples( | |
| examples = examples, | |
| inputs=[image_input, text_input], | |
| outputs=[clip_output, | |
| siglip_output | |
| ], | |
| fn=infer, | |
| cache_examples=True | |
| ) | |
| run_button.click(fn=infer, | |
| inputs=[image_input, text_input], | |
| outputs=[clip_output, | |
| siglip_output | |
| ]) | |
| demo.launch() |