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import os
import torch
import gradio as gr
from PIL import Image
from torchvision import transforms
torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
model = torch.hub.load('pytorch/vision:v0.9.0', 'alexnet', pretrained=True)
model.eval()
# Download ImageNet labels
os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
def inference(input_image):
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
# move the input and model to GPU for speed if available
if torch.cuda.is_available():
input_batch = input_batch.to('cuda')
model.to('cuda')
with torch.no_grad():
output = model(input_batch)
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Read the categories
with open("imagenet_classes.txt", "r") as f:
categories = [s.strip() for s in f.readlines()]
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
result = {}
for i in range(top5_prob.size(0)):
result[categories[top5_catid[i]]] = top5_prob[i].item()
return result
inputs = gr.components.Image(type='pil')
outputs = gr.components.Label(num_top_classes=5)
title = "ALEXNET"
description = "Gradio demo for Alexnet, the 2012 ImageNet winner achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. 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/1404.5997'>One weird trick for parallelizing convolutional neural networks</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py'>Github Repo</a></p>"
examples = [
['dog.jpg']
]
gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch() |