print('Checkpoint {} saved !'.format(epoch + 1))
logger.image_summary(
'train_images',
[vis_image.cpu().numpy() for vis_image in vis_images],
step=tot_step_count)
if __name__ == '__main__':
if net_name == 'unet':
model = UNet(n_channels=3, n_classes=2)
else:
model = HNNNet(pretrained=True, class_number=2)
if config.D_MULTIPLY:
dnet = DNet(input_dim=3, output_dim=1, input_size=config.PATCH_SIZE)
else:
dnet = DNet(input_dim=4, output_dim=1, input_size=config.PATCH_SIZE)
g_optimizer = optim.SGD(model.parameters(),
lr=config.G_LEARNING_RATE,
momentum=0.9,
weight_decay=0.0005)
d_optimizer = optim.SGD(dnet.parameters(),
lr=config.D_LEARNING_RATE,
momentum=0.9,
weight_decay=0.0005)
resume = config.RESUME_MODEL
if resume:
if os.path.isfile(resume):
print("=> loading checkpoint '{}'".format(resume))
'train_images',
[vis_image.cpu().numpy() for vis_image in vis_images],
step=tot_step_count)
if __name__ == '__main__':
if net_name == 'unet':
model = UNet(n_channels=3, n_classes=2)
else:
model = HNNNet(pretrained=True, class_number=2)
if config.USE_DNET:
if config.D_MULTIPLY:
dnet = DNet(input_dim=3,
output_dim=1,
input_size=config.PATCH_SIZE)
else:
dnet = DNet(input_dim=4,
output_dim=1,
input_size=config.PATCH_SIZE)
else:
dnet = None
resume = config.RESUME_MODEL
if resume:
if os.path.isfile(resume):
print("=> loading checkpoint '{}'".format(resume))
checkpoint = torch.load(resume)
start_epoch = checkpoint['epoch'] + 1
start_step = checkpoint['step']