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# pip install gradio
import gradio as gr
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.utils as vutils
# If `RuntimeError: Error(s) in loading state_dict for Generator` error occurs:
omit_module = True
# Spatial size of training images. All images will be resized to this
# size using a transformer.
image_size = 64
# Number of channels in the training images. For color images this is 3
nc = 1
# Size of z latent vector (i.e. size of generator input)
nz = 100
# Size of feature maps in generator
ngf = 64
# Size of feature maps in discriminator
ndf = 64
# Learning rate for optimizers
lr = 0.0002
# Beta1 hyperparam for Adam optimizers
beta1 = 0.5
# Number of GPUs available. Use 0 for CPU mode.
ngpu = 0
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
class Generator(nn.Module):
def __init__(self, ngpu):
super(Generator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. (ngf) x 32 x 32
nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 64 x 64
)
def forward(self, input):
return self.main(input)
# Create the generator
netG = Generator(ngpu).to(device)
# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.02.
netG.apply(weights_init)
checkpoint = torch.load("checkpoints/epoch1100.ckpt")
if omit_module:
for i in list(checkpoint['netG_state_dict'].keys()):
if (str(i).startswith('module.')):
checkpoint['netG_state_dict'][i[7:]] = checkpoint['netG_state_dict'].pop(i)
netG.load_state_dict(checkpoint['netG_state_dict'])
def genImg():
fixed_noise = torch.randn(64, nz, 1, 1, device=device)
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
fake_grid = vutils.make_grid(fake, padding=2, normalize=True)
return transforms.functional.to_pil_image(fake_grid)
demo = gr.Interface(fn=genImg, inputs=None, outputs="image")
demo.launch() |