'imsize' : 128,# Spatial size of training images. All images will be resized to this size during preprocessing.
'nc' : 3,# Number of channles in the training images. For coloured images this is 3.
'nz' : 200,# Size of the Z latent vector (the input to the generator).
'ngf' : 128,# Size of feature maps in the generator. The depth will be multiples of this.
'ndf' : 128, # Size of features maps in the discriminator. The depth will be multiples of this.
'nepochs' : 1,# Number of training epochs.
'lr' : 0.0002,# Learning rate for optimizers
'beta1' : 0.5,# Beta1 hyperparam for Adam optimizer
'save_epoch' : 2}# Save step.
# Use GPU is available else use CPU.
device = torch.device("cuda:0" if(torch.cuda.is_available()) else "cpu")
print(device, " will be used.\n")
# Get the data.
dataloader = get_celeba(params)
# Plot the training images.
sample_batch = next(iter(dataloader))
plt.figure(figsize=(8, 8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(vutils.make_grid(
sample_batch[0].to(device)[ : 64], padding=2, normalize=True).cpu(), (1, 2, 0)))
plt.show()
checkpoint =torch.load('./model/model_final.pth')
# Create the generator.
netG = Generator(params).to(device)
'ngf':
64, # Size of feature maps in the generator. The depth will be multiples of this.
'ndf':
64, # Size of features maps in the discriminator. The depth will be multiples of this.
'nepochs': 10, # Number of training epochs.
'lr': 0.0002, # Learning rate for optimizers
'beta1': 0.5, # Beta1 hyperparam for Adam optimizer
'save_epoch': 2
} # Save step.
# Use GPU is available else use CPU.
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
print(device, " will be used.\n")
# Get the data.
true_dataloader, masked_dataloader = get_celeba(params)
'''
# Plot the training images.
sample_batch = next(iter(dataloader))
plt.figure(figsize=(8, 8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(vutils.make_grid(
sample_batch[0].to(device)[ : 64], padding=2, normalize=True).cpu(), (1, 2, 0)))
plt.show()
'''
# Create the generator.
netG = Generator(params)