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import torch
# Download an example image from the pytorch website
import urllib
url, filename = ("https://pytorch.org/assets/images/deeplab1.png", "test.png")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
import gradio as gr
import matplotlib.pyplot as plt
model = torch.hub.load('pytorch/vision:v0.9.0', 'fcn_resnet101', pretrained=True)
def inference(input_image):
preprocess = transforms.Compose([
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)['out'][0]
output_predictions = output.argmax(0)
# create a color pallette, selecting a color for each class
palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
colors = torch.as_tensor([i for i in range(21)])[:, None] * palette
colors = (colors % 255).numpy().astype("uint8")
# plot the semantic segmentation predictions of 21 classes in each color
r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)
r.putpalette(colors)
plt.imshow(r)
plt.axis('off')
plt.savefig('out.png',bbox_inches='tight')
return 'out.png'
title = "FCN-RESNET101"
description = "Gradio demo for FCN-RESNET101, Fully-Convolutional Network model with a ResNet-101 backbone. 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/1605.06211'>Fully Convolutional Networks for Semantic Segmentation</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/fcn.py'>Github Repo</a></p>"
gr.Interface(
inference,
gr.inputs.Image(type="pil", label="Input"),
gr.outputs.Image(type="file", label="Output"),
title=title,
description=description,
article=article,
examples=[
["test.png"]
]).launch() |