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import gradio as gr | |
import torch | |
import torch.nn as nn | |
import numpy as np | |
class Generator(nn.Module): | |
def __init__(self): | |
super(Generator, self).__init__() | |
self.main = nn.Sequential( | |
nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False), | |
nn.BatchNorm2d(64 * 8), | |
nn.ReLU(True), | |
nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(64 * 4), | |
nn.ReLU(True), | |
nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(64 * 2), | |
nn.ReLU(True), | |
nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(64), | |
nn.ReLU(True), | |
nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False), | |
nn.Tanh() | |
) | |
def forward(self, input): | |
return self.main(input) | |
netG = Generator() | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model_file = "model.pth" | |
netG.load_state_dict(torch.load(model_file, map_location=device)) | |
netG.eval() | |
def generate_image(): | |
with torch.no_grad(): | |
noise = torch.randn(1, 100, 1, 1) | |
fake_image = netG(noise) | |
generated_image = fake_image.squeeze().cpu().numpy() | |
generated_image = np.transpose(generated_image, (1, 2, 0)) | |
generated_image = (generated_image + 1) / 2.0 | |
generated_image = (generated_image * 255).astype(np.uint8) | |
return generated_image | |
title = "DCGAN Image Generator ποΈπ¨" | |
description = "Generate non-existing images using DCGAN." | |
content = """ | |
## How to Generate π¨ | |
To generate an image, follow these steps: | |
1. Click \"Generate\" button to generate a image! | |
2. Once the image is generated, you can save it or share it to the community! | |
""" | |
iface = gr.Interface( | |
fn=generate_image, | |
inputs=None, | |
outputs="image", | |
title=title, | |
description=description, | |
article=content, | |
theme="soft", | |
api_name="generate" | |
) | |
iface.queue() | |
iface.launch() | |