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import clip | |
import torch | |
import gradio as gr | |
import torchvision.transforms as T | |
from PIL import Image | |
try: | |
from torchvision.transforms import InterpolationMode | |
BICUBIC = InterpolationMode.BICUBIC | |
except ImportError: | |
BICUBIC = Image.BICUBIC | |
import warnings | |
warnings.filterwarnings("ignore") | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model, preprocess = clip.load('ViT-L/14@336px') | |
model.to(device) | |
def zeroshot_detection(Press_Clear_Dont_Stack_Image): | |
inp = Press_Clear_Dont_Stack_Image | |
captions = "photo of a guardrail, no guardrail in the photo" #CHANGE THIS IF YOU WANT TO CHANGE THE PREDICTION: separate by commas | |
captions = captions.split(',') | |
caption = clip.tokenize(captions).to(device) | |
image = preprocess(inp).unsqueeze(0).to(device) | |
with torch.no_grad(): | |
image_features = model.encode_image(image) | |
text_features = model.encode_text(caption) | |
image_features /= image_features.norm(dim=-1, keepdim=True) | |
text_features /= text_features.norm(dim=-1, keepdim=True) | |
similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1) | |
values, indices = similarity[0].topk(len(captions)) | |
return {captions[indices[i].item()]: float(values[i].item()) for i in range(len(values))} | |
gr.Interface(fn=zeroshot_detection, | |
inputs=[gr.Image(type="pil")], | |
outputs=gr.Label(num_top_classes=1)).launch() | |