app.py

import torch


model = torch.hub.load("facebookresearch/swag", model="vit_h14_in1k")

# we also convert the model to eval mode
model.eval()

resolution = 518

import os
os.system("wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json -O in_cls_idx.json")

import gradio as gr

from PIL import Image
from torchvision import transforms

import json


with open("in_cls_idx.json", "r") as f:
    imagenet_id_to_name = {int(cls_id): name for cls_id, (label, name) in json.load(f).items()}




def load_image(image_path):
    return Image.open(image_path).convert("RGB")



def transform_image(image, resolution):
    transform = transforms.Compose([
        transforms.Resize(
            resolution,
            interpolation=transforms.InterpolationMode.BICUBIC,
        ),
        transforms.CenterCrop(resolution),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        ),
    ])
    image = transform(image)
    # we also add a batch dimension to the image since that is what the model expects
    image = image[None, :]
    return image
    
def visualize_and_predict(model, resolution, image_path):
    image = load_image(image_path)
    image = transform_image(image, resolution)
    
    # we do not need to track gradients for inference
    with torch.no_grad():
        _, preds = model(image).topk(5)
    # convert preds to a Python list and remove the batch dimension
    preds = preds.tolist()[0]

    return preds
    
os.system("wget https://github.com/pytorch/hub/raw/master/images/dog.jpg -O dog.jpg")



def inference(img):
    preds = visualize_and_predict(model, resolution, img)
    return [imagenet_id_to_name[cls_id] for cls_id in preds]

inputs = gr.inputs.Image(type='filepath')
outputs = gr.outputs.Textbox(label="Output")

title = "SWAG"

description = "Gradio demo for Revisiting Weakly Supervised Pre-Training of Visual Perception Models. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.08371' target='_blank'>Revisiting Weakly Supervised Pre-Training of Visual Perception Models</a> | <a href='https://github.com/facebookresearch/SWAG' target='_blank'>Github Repo</a></p>"


gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['dog.jpg']]).launch(enable_queue=True,cache_examples=True)