SWAG / app.py
Ahsen Khaliq
Update app.py 1662beb
import torch
model = torch.hub.load("facebookresearch/swag", model="vit_h14_in1k")
# we also convert the model to eval mode
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([
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)