def __init__(self, config):
super(Trainer, self).__init__()
self.config = config
self.use_cuda = self.config['cuda']
self.device_ids = self.config['gpu_ids']
self.netG = Generator(self.config['netG'], self.use_cuda,
self.device_ids)
self.localD = LocalDis(self.config['netD'], self.use_cuda,
self.device_ids)
self.globalD = GlobalDis(self.config['netD'], self.use_cuda,
self.device_ids)
self.optimizer_g = torch.optim.Adam(self.netG.parameters(),
lr=self.config['lr'],
betas=(self.config['beta1'],
self.config['beta2']))
d_params = list(self.localD.parameters()) + list(
self.globalD.parameters())
self.optimizer_d = torch.optim.Adam(d_params,
lr=config['lr'],
betas=(self.config['beta1'],
self.config['beta2']))
if self.use_cuda:
self.netG.to(self.device_ids[0])
self.localD.to(self.device_ids[0])
self.globalD.to(self.device_ids[0])
class Trainer(nn.Module):
def __init__(self, config):
super(Trainer, self).__init__()
self.config = config
self.use_cuda = self.config['cuda']
self.device_ids = self.config['gpu_ids']
self.netG = Generator(self.config['netG'], self.use_cuda,
self.device_ids)
self.localD = LocalDis(self.config['netD'], self.use_cuda,
self.device_ids)
self.globalD = GlobalDis(self.config['netD'], self.use_cuda,
self.device_ids)
self.optimizer_g = torch.optim.Adam(self.netG.parameters(),
lr=self.config['lr'],
betas=(self.config['beta1'],
self.config['beta2']))
d_params = list(self.localD.parameters()) + list(
self.globalD.parameters())
self.optimizer_d = torch.optim.Adam(d_params,
lr=config['lr'],
betas=(self.config['beta1'],
self.config['beta2']))
if self.use_cuda:
self.netG.to(self.device_ids[0])
self.localD.to(self.device_ids[0])
self.globalD.to(self.device_ids[0])
def forward(self, x, bboxes, masks, ground_truth, compute_loss_g=False):
self.train()
l1_loss = nn.L1Loss()
losses = {}
x1, x2, offset_flow = self.netG(x, masks)
local_patch_gt = local_patch(ground_truth, bboxes)
x1_inpaint = x1 * masks + x * (1. - masks)
x2_inpaint = x2 * masks + x * (1. - masks)
local_patch_x1_inpaint = local_patch(x1_inpaint, bboxes)
local_patch_x2_inpaint = local_patch(x2_inpaint, bboxes)
# D part
# wgan d loss
local_patch_real_pred, local_patch_fake_pred = self.dis_forward(
self.localD, local_patch_gt, local_patch_x2_inpaint.detach())
global_real_pred, global_fake_pred = self.dis_forward(
self.globalD, ground_truth, x2_inpaint.detach())
losses['wgan_d'] = torch.mean(local_patch_fake_pred - local_patch_real_pred) + \
torch.mean(global_fake_pred - global_real_pred) * self.config['global_wgan_loss_alpha']
# gradients penalty loss
local_penalty = self.calc_gradient_penalty(
self.localD, local_patch_gt, local_patch_x2_inpaint.detach())
global_penalty = self.calc_gradient_penalty(self.globalD, ground_truth,
x2_inpaint.detach())
losses['wgan_gp'] = local_penalty + global_penalty
# G part
if compute_loss_g:
sd_mask = spatial_discounting_mask(self.config)
losses['l1'] = l1_loss(local_patch_x1_inpaint * sd_mask, local_patch_gt * sd_mask) * \
self.config['coarse_l1_alpha'] + \
l1_loss(local_patch_x2_inpaint * sd_mask, local_patch_gt * sd_mask)
losses['ae'] = l1_loss(x1 * (1. - masks), ground_truth * (1. - masks)) * \
self.config['coarse_l1_alpha'] + \
l1_loss(x2 * (1. - masks), ground_truth * (1. - masks))
# wgan g loss
local_patch_real_pred, local_patch_fake_pred = self.dis_forward(
self.localD, local_patch_gt, local_patch_x2_inpaint)
global_real_pred, global_fake_pred = self.dis_forward(
self.globalD, ground_truth, x2_inpaint)
losses['wgan_g'] = - torch.mean(local_patch_fake_pred) - \
torch.mean(global_fake_pred) * self.config['global_wgan_loss_alpha']
return losses, x2_inpaint, offset_flow
def dis_forward(self, netD, ground_truth, x_inpaint):
assert ground_truth.size() == x_inpaint.size()
batch_size = ground_truth.size(0)
batch_data = torch.cat([ground_truth, x_inpaint], dim=0)
batch_output = netD(batch_data)
real_pred, fake_pred = torch.split(batch_output, batch_size, dim=0)
return real_pred, fake_pred
# Calculate gradient penalty
def calc_gradient_penalty(self, netD, real_data, fake_data):
batch_size = real_data.size(0)
alpha = torch.rand(batch_size, 1, 1, 1)
alpha = alpha.expand_as(real_data)
if self.use_cuda:
alpha = alpha.cuda()
interpolates = alpha * real_data + (1 - alpha) * fake_data
interpolates = interpolates.requires_grad_().clone()
disc_interpolates = netD(interpolates)
grad_outputs = torch.ones(disc_interpolates.size())
if self.use_cuda:
grad_outputs = grad_outputs.cuda()
gradients = autograd.grad(outputs=disc_interpolates,
inputs=interpolates,
grad_outputs=grad_outputs,
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
gradients = gradients.view(batch_size, -1)
gradient_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()
return gradient_penalty
def inference(self, x, masks):
self.eval()
x1, x2, offset_flow = self.netG(x, masks)
# x1_inpaint = x1 * masks + x * (1. - masks)
x2_inpaint = x2 * masks + x * (1. - masks)
return x2_inpaint, offset_flow
def save_model(self, checkpoint_dir, iteration):
# Save generators, discriminators, and optimizers
gen_name = os.path.join(checkpoint_dir, 'gen_%08d.pt' % iteration)
dis_name = os.path.join(checkpoint_dir, 'dis_%08d.pt' % iteration)
opt_name = os.path.join(checkpoint_dir, 'optimizer.pt')
torch.save(self.netG.state_dict(), gen_name)
torch.save(
{
'localD': self.localD.state_dict(),
'globalD': self.globalD.state_dict()
}, dis_name)
torch.save(
{
'gen': self.optimizer_g.state_dict(),
'dis': self.optimizer_d.state_dict()
}, opt_name)
def resume(self, checkpoint_dir, iteration=0, test=False):
# Load generators
last_model_name = get_model_list(checkpoint_dir,
"gen",
iteration=iteration)
self.netG.load_state_dict(torch.load(last_model_name))
iteration = int(last_model_name[-11:-3])
if not test:
# Load discriminators
last_model_name = get_model_list(checkpoint_dir,
"dis",
iteration=iteration)
state_dict = torch.load(last_model_name)
self.localD.load_state_dict(state_dict['localD'])
self.globalD.load_state_dict(state_dict['globalD'])
# Load optimizers
state_dict = torch.load(
os.path.join(checkpoint_dir, 'optimizer.pt'))
self.optimizer_d.load_state_dict(state_dict['dis'])
self.optimizer_g.load_state_dict(state_dict['gen'])
print("Resume from {} at iteration {}".format(checkpoint_dir,
iteration))
logger.info("Resume from {} at iteration {}".format(
checkpoint_dir, iteration))
return iteration
def train_distributed(config, logger, writer, checkpoint_path):
dist.init_process_group(
backend='nccl',
# backend='gloo',
init_method='env://'
)
# Find out what GPU on this compute node.
#
local_rank = torch.distributed.get_rank()
# this is the total # of GPUs across all nodes
# if using 2 nodes with 4 GPUs each, world size is 8
#
world_size = torch.distributed.get_world_size()
print("### global rank of curr node: {} of {}".format(local_rank, world_size))
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
#
print("local_rank: ", local_rank)
# dist.barrier()
torch.cuda.set_device(local_rank)
# Define the trainer
print("Creating models on device: ", local_rank)
input_dim = config['netG']['input_dim']
cnum = config['netG']['ngf']
use_cuda = True
gated = config['netG']['gated']
# Models
#
netG = Generator(config['netG'], use_cuda=True, device=local_rank).cuda()
netG = torch.nn.parallel.DistributedDataParallel(
netG,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True
)
localD = LocalDis(config['netD'], use_cuda=True, device_id=local_rank).cuda()
localD = torch.nn.parallel.DistributedDataParallel(
localD,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True
)
globalD = GlobalDis(config['netD'], use_cuda=True, device_id=local_rank).cuda()
globalD = torch.nn.parallel.DistributedDataParallel(
globalD,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True
)
if local_rank == 0:
logger.info("\n{}".format(netG))
logger.info("\n{}".format(localD))
logger.info("\n{}".format(globalD))
# Optimizers
#
optimizer_g = torch.optim.Adam(
netG.parameters(),
lr=config['lr'],
betas=(config['beta1'], config['beta2'])
)
d_params = list(localD.parameters()) + list(globalD.parameters())
optimizer_d = torch.optim.Adam(
d_params,
lr=config['lr'],
betas=(config['beta1'], config['beta2'])
)
# Data
#
sampler = None
train_dataset = Dataset(
data_path=config['train_data_path'],
with_subfolder=config['data_with_subfolder'],
image_shape=config['image_shape'],
random_crop=config['random_crop']
)
sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
# num_replicas=torch.cuda.device_count(),
num_replicas=len(config['gpu_ids']),
# rank = local_rank
)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=config['batch_size'],
shuffle=(sampler is None),
num_workers=config['num_workers'],
pin_memory=True,
sampler=sampler,
drop_last=True
)
# Get the resume iteration to restart training
#
# start_iteration = trainer.resume(config['resume']) if config['resume'] else 1
start_iteration = 1
print("\n\nStarting epoch: ", start_iteration)
iterable_train_loader = iter(train_loader)
if local_rank == 0:
time_count = time.time()
epochs = config['niter'] + 1
pbar = tqdm(range(start_iteration, epochs), dynamic_ncols=True, smoothing=0.01)
for iteration in pbar:
sampler.set_epoch(iteration)
try:
ground_truth = next(iterable_train_loader)
except StopIteration:
iterable_train_loader = iter(train_loader)
ground_truth = next(iterable_train_loader)
# Prepare the inputs
bboxes = random_bbox(config, batch_size=ground_truth.size(0))
x, mask = mask_image(ground_truth, bboxes, config)
# Move to proper device.
#
bboxes = bboxes.cuda(local_rank)
x = x.cuda(local_rank)
mask = mask.cuda(local_rank)
ground_truth = ground_truth.cuda(local_rank)
###### Forward pass ######
compute_g_loss = iteration % config['n_critic'] == 0
# losses, inpainted_result, offset_flow = forward(config, x, bboxes, mask, ground_truth,
# localD=localD, globalD=globalD,
# coarse_gen=coarse_generator, fine_gen=fine_generator,
# local_rank=local_rank, compute_loss_g=compute_g_loss)
losses, inpainted_result, offset_flow = forward(config, x, bboxes, mask, ground_truth,
netG=netG, localD=localD, globalD=globalD,
local_rank=local_rank, compute_loss_g=compute_g_loss)
# Scalars from different devices are gathered into vectors
#
for k in losses.keys():
if not losses[k].dim() == 0:
losses[k] = torch.mean(losses[k])
###### Backward pass ######
# Update D
if not compute_g_loss:
optimizer_d.zero_grad()
losses['d'] = losses['wgan_d'] + losses['wgan_gp'] * config['wgan_gp_lambda']
losses['d'].backward()
optimizer_d.step()
# Update G
if compute_g_loss:
optimizer_g.zero_grad()
losses['g'] = losses['ae'] * config['ae_loss_alpha']
losses['g'] += losses['l1'] * config['l1_loss_alpha']
losses['g'] += losses['wgan_g'] * config['gan_loss_alpha']
losses['g'].backward()
optimizer_g.step()
# Set tqdm description
#
if local_rank == 0:
log_losses = ['l1', 'ae', 'wgan_g', 'wgan_d', 'wgan_gp', 'g', 'd']
message = ' '
for k in log_losses:
v = losses.get(k, 0.)
writer.add_scalar(k, v, iteration)
message += '%s: %.4f ' % (k, v)
pbar.set_description(
(
f" {message}"
)
)
if local_rank == 0:
if iteration % (config['viz_iter']) == 0:
viz_max_out = config['viz_max_out']
if x.size(0) > viz_max_out:
viz_images = torch.stack([x[:viz_max_out], inpainted_result[:viz_max_out],
offset_flow[:viz_max_out]], dim=1)
else:
viz_images = torch.stack([x, inpainted_result, offset_flow], dim=1)
viz_images = viz_images.view(-1, *list(x.size())[1:])
vutils.save_image(viz_images,
'%s/niter_%08d.png' % (checkpoint_path, iteration),
nrow=3 * 4,
normalize=True)
# Save the model
if iteration % config['snapshot_save_iter'] == 0:
save_model(
netG, globalD, localD, optimizer_g, optimizer_d, checkpoint_path, iteration
)