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image_generator.py
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import yaml
import sys
import torch
import torch.nn as nn
import torch.optim as optim
from data.image_preprocessing import dataLoader
from utils.utils import to_gpu, loss_plot, image_grid
from utils.metrics import compute_metrics
from models.dcgan import weights_init, Generator, Generator_128, Generator_256
from models.dcgan import Discriminator, Discriminator_128, Discriminator_SN_128, Discriminator_256, Discriminator_SN_256, Discriminator_SN, training_loop
def parseyaml():
with open(sys.argv[1]) as ctrl_file:
params = yaml.load(ctrl_file, Loader=yaml.FullLoader)
return params
def main():
params = parseyaml()
if params['arch'] == 'Generator':
device = to_gpu(ngpu=params['n_gpu'])
if params['image_size'] == 64:
netG = Generator(ngpu=0, nz=256,
ngf=64, nc=64).to(device)
elif params['image_size'] == 128:
netG = Generator_128(ngpu=0, nz=256,
ngf=64, nc=64).to(device)
elif params['image_size'] == 256:
netG = Generator_256(ngpu=0, nz=256,
ngf=64, nc=64).to(device)
netG.apply(weights_init)
netG.load_state_dict(torch.load(params['path']))
for i in range(params['quantity']):
fixed_noise = torch.randn(64, 256, 1, 1, device=device)
fakes = netG(fixed_noise)
for j in range(len(fakes)):
save_image(fakes[j], params['out'] + params['run'] +
'_' + str(i) + '_' + str(j) + '_img.png')
else:
dataloader = dataLoader(
path=params['path'], image_size=params['image_size'], batch_size=params['batch_size'],
workers=params['loader_workers'])
device = to_gpu(ngpu=params['n_gpu'])
if params['arch'] == 'DCGAN':
if params['image_size'] == 64:
netG = Generator(ngpu=params['n_gpu'], nz=params['latent_vector'],
ngf=params['gen_feature_maps'], nc=params['number_channels']).to(device)
netD = Discriminator(params['n_gpu'], nc=params['number_channels'],
ndf=params['dis_feature_maps']).to(device)
elif params['image_size'] == 128:
netG = Generator_128(ngpu=params['n_gpu'], nz=params['latent_vector'],
ngf=params['gen_feature_maps'], nc=params['number_channels']).to(device)
netD = Discriminator_128(params['n_gpu'], nc=params['number_channels'],
ndf=params['dis_feature_maps']).to(device)
elif params['image_size'] == 256:
netG = Generator_256(ngpu=params['n_gpu'], nz=params['latent_vector'],
ngf=params['gen_feature_maps'], nc=params['number_channels']).to(device)
netD = Discriminator_256(params['n_gpu'], nc=params['number_channels'],
ndf=params['dis_feature_maps']).to(device)
elif params['arch'] == 'SNGAN':
if params['image_size'] == 64:
netG = Generator(ngpu=params['n_gpu'], nz=params['latent_vector'],
ngf=params['gen_feature_maps'], nc=params['number_channels']).to(device)
netD = Discriminator_SN(params['n_gpu'], nc=params['number_channels'],
ndf=params['dis_feature_maps']).to(device)
elif params['image_size'] == 128:
netG = Generator_128(ngpu=params['n_gpu'], nz=params['latent_vector'],
ngf=params['gen_feature_maps'], nc=params['number_channels']).to(device)
netD = Discriminator_SN_128(params['n_gpu'], nc=params['number_channels'],
ndf=params['dis_feature_maps']).to(device)
elif params['image_size'] == 256:
netG = Generator_256(ngpu=params['n_gpu'], nz=params['latent_vector'],
ngf=params['gen_feature_maps'], nc=params['number_channels']).to(device)
netD = Discriminator_SN_256(params['n_gpu'], nc=params['number_channels'],
ndf=params['dis_feature_maps']).to(device)
if (device.type == 'cuda') and (params['n_gpu'] > 1):
netG = nn.DataParallel(netG, list(range(params['n_gpu'])))
if (device.type == 'cuda') and (params['n_gpu'] > 1):
netD = nn.DataParallel(netD, list(range(params['n_gpu'])))
netG.apply(weights_init)
netD.apply(weights_init)
print(netG)
print(netD)
criterion = nn.BCELoss()
fixed_noise = torch.randn(params['image_size'],
params['latent_vector'], 1, 1, device=device)
if params['learning_rate'] >= 1:
optimizerD = optim.Adam(netD.parameters(), lr=0.0002 * params['learning_rate'], betas=(
params['beta_adam'], 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=0.0002, betas=(
params['beta_adam'], 0.999))
else:
optimizerD = optim.Adam(netD.parameters(), lr=params['learning_rate'], betas=(
params['beta_adam'], 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=params['learning_rate'], betas=(
params['beta_adam'], 0.999))
G_losses, D_losses, img_list, img_list_only = training_loop(num_epochs=params['num_epochs'], dataloader=dataloader,
netG=netG, netD=netD, device=device, criterion=criterion, nz=params[
'latent_vector'],
optimizerG=optimizerG, optimizerD=optimizerD, fixed_noise=fixed_noise, out=params['out'] + params['run'] + '_')
loss_plot(G_losses=G_losses, D_losses=D_losses, out=params['out'] + params['run'] + '_')
image_grid(dataloader=dataloader, img_list=img_list,
device=device, out=params['out'] + params['run'] + '_')
compute_metrics(real=next(iter(dataloader)), fakes=img_list_only,
size=params['image_size'], out=params['out'] + params['run'] + '_')
if __name__ == "__main__":
main()