Ahsen Khaliq
Update app.py
87368d2
import torch
import gradio as gr
import matplotlib.pyplot as plt
import torchvision
use_gpu = True if torch.cuda.is_available() else False
model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)
def dcgan(num_images):
noise, _ = model.buildNoiseData(int(num_images))
with torch.no_grad():
generated_images = model.test(noise)
plt.imshow(torchvision.utils.make_grid(generated_images).permute(1, 2, 0).cpu().numpy())
plt.axis("off")
return plt
inputs = gr.inputs.Number(label="number of images")
outputs = gr.outputs.Image(label="Output Image")
title = "DCGAN"
description = "demo for DCGAN. To use it, simply add the number of images to generate or click on the examples. Read more below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1511.06434'>Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a> | <a href='https://github.com/facebookresearch/pytorch_GAN_zoo/blob/master/models/DCGAN.py'>Github Repo</a></p>"
examples = [
[1],
[2],
[3],
[4],
[64]
]
gr.Interface(dcgan, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False, examples=examples).launch(debug=True)