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import torch | |
import gradio as gr | |
import json | |
from torchvision import transforms | |
import torch.nn.functional as F | |
TORCHSCRIPT_PATH = "res/screensim-resnet-uda+web350k.torchscript" | |
IMG_SIZE = (256, 128) | |
model = torch.jit.load(TORCHSCRIPT_PATH) | |
img_transforms = transforms.Compose([ | |
transforms.Resize(IMG_SIZE), | |
transforms.ToTensor(), | |
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) | |
]) | |
def predict(img1, img2, thresh=0.35): | |
img_input1 = img_transforms(img1).unsqueeze(0) | |
img_input2 = img_transforms(img2).unsqueeze(0) | |
diff = torch.linalg.norm(model(img_input1) - model(img_input2)) | |
return "{:.3f}".format(diff), "same screen" if float(diff) < thresh else "different screens" | |
example_imgs = [ | |
["res/example_pair1.jpg", "res/example_pair2.jpg", 0.35], | |
["res/example_pair1.jpg", "res/example.jpg", 0.35] | |
] | |
interface = gr.Interface(fn=predict, inputs=[gr.Image(type="pil"), gr.Image(type="pil"), gr.Slider(0.2, 0.5, step=0.05, value=0.35)], outputs=["text", "text"], examples=example_imgs) | |
interface.launch() | |