def __init__(self, config, data_loader):
"""Set parameters of neural network and its training."""
self.ssim_loss = SSIM()
self.generator = None
self.discriminator = None
self.distance_based_loss = None
self.g_optimizer = None
self.d_optimizer = None
self.g_conv_dim = 128
self.beta1 = 0.9
self.beta2 = 0.999
self.learning_rate = 0.0001
self.image_size = config.image_size
self.num_epochs = config.num_epochs
self.distance_weight = config.distance_weight
self.noise = config.noise
self.residual = config.residual
self.data_loader = data_loader
self.generate_path = config.generate_path
self.model_path = config.model_path
self.tensorboard = config.tensorboard
if self.tensorboard:
self.tb_writer = tensorboardX.SummaryWriter(
filename_suffix='_%s_%s' %
(config.distance_weight, config.dataset))
self.tb_graph_added = False
self.build_model()
def train(epoch, loader, model, optimizer, scheduler, device):
loader = tqdm(loader)
criterion = SSIM()#nn.MSELoss()
latent_loss_weight = 0.25
sample_size = 25
mse_sum = 0
mse_n = 0
for i, (img, label) in enumerate(loader):
model.zero_grad()
img = img.to(device)
out, latent_loss = model(img)
recon_loss = criterion((out*0.5 + 0.5), (img*0.5 + 0.5))
latent_loss = latent_loss.mean()
loss = recon_loss + latent_loss_weight * latent_loss
loss.backward()
if scheduler is not None:
scheduler.step()
optimizer.step()
mse_sum += recon_loss.item() * img.shape[0]
mse_n += img.shape[0]
print(recon_loss)
lr = optimizer.param_groups[0]['lr']
loader.set_description(
(
f'epoch: {epoch + 1}; mse: {recon_loss.item():.5f}; '
f'latent: {latent_loss.item():.3f}; avg mse: {mse_sum / mse_n:.5f}; '
f'lr: {lr:.5f}'
)
)
if i % 100 == 0:
model.eval()
sample = img[:sample_size]
with torch.no_grad():
out, _ = model(sample)
utils.save_image(
torch.cat([sample, out], 0),
f'sample/{str(epoch + 1).zfill(5)}_{str(i).zfill(5)}.png',
nrow=sample_size,
normalize=True,
range=(-1, 1),
)
model.train()
def __init__(self):
super(Model, self).__init__()
self.cross_entropy = nn.CrossEntropyLoss()
self.mse = nn.MSELoss(reduce=True, size_average=True)
self.l1 = nn.L1Loss()
self.SL1 = nn.SmoothL1Loss()
self.ssim = SSIM(window_size=11)
self.avg = nn.AdaptiveAvgPool2d(1)
self.predict_net = PredictNet(64)
self.device = next(self.predict_net.parameters()).device
self.resnet = resnet50_backbone(pretrained=True)
self.regression = nn.Sequential(
nn.Conv2d(1 * 2, 1, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(inplace=True),
)
self.res_out = nn.Sequential(
nn.Conv2d(2048, 1024, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(inplace=True),
nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(inplace=True),
nn.Conv2d(512, 64, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(inplace=True),
)
init_nets = [self.regression, self.predict_net, self.res_out]
for net in init_nets:
net.apply(weights_init_xavier)
def get_log_diff_fn(eps=0.2):
log_255_sq = np.float32(2 * np.log(255.0))
log_255_sq = log_255_sq.item() # int
max_val = np.float32(log_255_sq - np.log(eps))
max_val = max_val.item() # int
log_255_sq = torch.from_numpy(np.array(log_255_sq)).float().to(
self.device)
max_val = torch.from_numpy(np.array(max_val)).float().to(
self.device)
def log_diff_fn(in_a, in_b):
diff = 255.0 * (in_a - in_b)
val = log_255_sq - torch.log(diff**2 + eps)
return val / max_val
return log_diff_fn
self.log_diff_fn = get_log_diff_fn(1)
self.downsample_filter = DownSampleFilter()
self.upsample_filter = UpSampleFilter()
def compute_loss_ssim(self, img_pyramid, img_warped_pyramid,
occ_mask_list):
loss_list = []
for scale in range(self.num_scales):
img, img_warped, occ_mask = img_pyramid[scale], img_warped_pyramid[
scale], occ_mask_list[scale]
divider = occ_mask.mean((1, 2, 3))
occ_mask_pad = occ_mask.repeat(1, 3, 1, 1)
ssim = SSIM(img * occ_mask_pad, img_warped * occ_mask_pad)
loss_ssim = torch.clamp((1.0 - ssim) / 2.0, 0, 1).mean((1, 2, 3))
loss_ssim = loss_ssim / (divider + 1e-12)
loss_list.append(loss_ssim[:, None])
loss = torch.cat(loss_list, 1).sum(1)
return loss
def train(self):
self.scheduler.step()
self.loss.step()
epoch = self.scheduler.last_epoch + 1
lr = self.scheduler.get_lr()[0]
self.args.Noisy = True
self.ckp.write_log(
'[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr))
)
self.loss.start_log()
self.model.train()
criterion_ssim = SSIM(window_size=11, size_average=False)
criterion_ssim = criterion_ssim.cuda()
timer_data, timer_model = utility.timer(), utility.timer()
for batch, (lr, hr, _, idx_scale) in enumerate(self.loader_train):
lr, hr = self.prepare(lr, hr)
timer_data.hold()
timer_model.tic()
self.optimizer.zero_grad()
sr = self.model(lr, idx_scale)
loss = self.loss(sr, hr) #+ self.ssim*criterion_ssim(sr, hr)
loss.backward()
if self.args.gclip > 0:
utils.clip_grad_value_(
self.model.parameters(),
self.args.gclip
)
self.optimizer.step()
timer_model.hold()
if (batch + 1) % self.args.print_every == 0:
self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format(
(batch + 1) * self.args.batch_size,
len(self.loader_train.dataset),
self.loss.display_loss(batch),
timer_model.release(),
timer_data.release()))
timer_data.tic()
self.loss.end_log(len(self.loader_train))
self.error_last = self.loss.log[-1, -1]
def __init__(self,
model,
lr=1e-1,
n_epochs=10,
verbose=True,
dir_base='./output/checkpoints'):
self.model = model
self.lr = lr
self.n_epochs = n_epochs
self.verbose = verbose
# Initializations
self.dir_base = dir_base
if not os.path.exists(self.dir_base):
os.makedirs(self.dir_base)
self.dir_log = f'{dir_base}/log.txt'
self.best_summary_loss = 10**5
self.epoch = 0
self.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
self.model.to(self.device)
# Define the optimizer
self.params = [p for p in self.model.parameters() if p.requires_grad]
self.optimizer = torch.optim.AdamW(self.params, lr=self.lr)
self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer=self.optimizer,
mode='min',
factor=0.5,
patience=1,
verbose=True,
threshold=0.00005,
threshold_mode='abs',
cooldown=0,
min_lr=1e-8,
eps=1e-8)
# Define the loss
self.criterion = SSIM()
self.log(f'====================================================')
self.log(
f'Fitter prepared | Time: {datetime.utcnow().isoformat()} | Device: {self.device}'
)
def compute_pairwise_loss(self, tgt_img, ref_img, tgt_depth, ref_depth,
pose, intrinsic):
ref_img_warped, valid_mask, projected_depth, computed_depth = inverse_warp2(
ref_img, tgt_depth, ref_depth, pose, intrinsic, 'zeros')
diff_img = (tgt_img - ref_img_warped).abs()
diff_depth = ((computed_depth - projected_depth).abs() /
(computed_depth + projected_depth).abs()).clamp(0, 1)
ssim_map = (0.5 * (1 - SSIM(tgt_img, ref_img_warped))).clamp(0, 1)
diff_img = (0.15 * diff_img + 0.85 * ssim_map)
# Modified in 01.19.2020
#weight_mask = (1 - diff_depth)
#diff_img = diff_img * weight_mask
# compute loss
reconstruction_loss = diff_img.mean()
geometry_consistency_loss = diff_depth.mean()
#reconstruction_loss = mean_on_mask(diff_img, valid_mask)
#geometry_consistency_loss = mean_on_mask(diff_depth, valid_mask)
return reconstruction_loss, geometry_consistency_loss
def main():
global args, best_loss
global logger
global device, kwargs
if args.model_type == 'fcn':
filter_list = [
1,
int(args.model_multiplier * 4),
int(args.model_multiplier * 8),
int(args.model_multiplier * 16),
int(args.model_multiplier * 32),
int(args.model_multiplier * 64), 10
]
print('Model filter sizes list is {}'.format(filter_list))
model = VAE(filters=filter_list,
dilations=[1, 1, 1, 1, 1, 1],
paddings=[0, 0, 0, 0, 0, 0],
strides=[1, 1, 2, 1, 2, 2],
decoder_kernels=[3, 4, 4, 4, 4, 4],
decoder_paddings=[1, 0, 0, 0, 0, 0],
decoder_strides=[1, 1, 1, 2, 2, 1],
split_filter=args.split_filter).to(device)
print(model)
elif args.model_type == 'fcns_1n':
filter_list = [
1,
int(args.model_multiplier * 4),
int(args.model_multiplier * 8),
int(args.model_multiplier * 16),
]
print('Model filter sizes list is {}'.format(filter_list))
model = VAE1N(filters=filter_list,
dilations=[1, 1, 1],
paddings=[1, 1, 1],
strides=[2, 2, 2],
decoder_kernels=[4, 4, 3],
decoder_paddings=[1, 1, 1],
decoder_strides=[2, 2, 2],
latent_space_size=10).to(device)
elif args.model_type == 'fcns':
model = VAESimplifiedFC().to(device)
elif args.model_type == 'fc':
model = VAEBaseline(
latent_space_size=args.latent_space_size).to(device)
elif args.model_type == 'fc_conv':
model = VAEBaselineConv(
latent_space_size=args.latent_space_size).to(device)
if args.optimizer.startswith('adam'):
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
# Only finetunable params
lr=args.lr)
elif args.optimizer.startswith('rmsprop'):
optimizer = torch.optim.RMSprop(
filter(lambda p: p.requires_grad, model.parameters()),
# Only finetunable params
lr=args.lr)
elif args.optimizer.startswith('sgd'):
optimizer = torch.optim.SGD(
filter(lambda p: p.requires_grad, model.parameters()),
# Only finetunable params
lr=args.lr)
else:
raise ValueError('Optimizer not supported')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint (epoch {})".format(
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.predict:
pass
elif args.evaluate:
pass
else:
if args.dataset_type == 'fmnist':
train_dataset = FMNISTDataset(mode='train',
random_state=args.seed,
use_augs=args.do_augs)
val_dataset = FMNISTDataset(mode='val',
random_state=args.seed,
use_augs=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
**kwargs)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
**kwargs)
elif args.dataset_type == 'mnist':
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data',
train=True,
download=True,
transform=transforms.ToTensor()),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data',
train=False,
transform=transforms.ToTensor()),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
criterion = VAELoss(
use_running_mean=args.do_running_mean,
image_loss_type=args.image_loss_type,
image_loss_weight=args.img_loss_weight,
kl_loss_weight=args.kl_loss_weight,
ssim_window_size=args.ssim_window_size,
latent_space_size=args.latent_space_size).to(device)
# criterion = loss_function
ssim = SSIM(window_size=args.ssim_window_size,
size_average=True).to(device)
scheduler = MultiStepLR(optimizer,
milestones=[args.m1, args.m2],
gamma=0.1)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train_loss, train_img_loss, train_kl_loss, train_ssim = train(
train_loader, model, criterion, ssim, optimizer, epoch)
# evaluate on validation set
val_loss, val_img_loss, val_kl_loss, val_ssim = validate(
val_loader, model, criterion, ssim)
scheduler.step()
#============ TensorBoard logging ============#
# Log the scalar values
if args.tensorboard:
info = {
'eph_tr_loss': train_loss,
'eph_tr_ssim': train_ssim,
'eph_val_loss': val_loss,
'eph_val_ssim': val_ssim,
'eph_tr_img_loss': train_img_loss,
'eph_tr_kl_loss': train_kl_loss,
'eph_val_img_loss': val_img_loss,
'eph_val_kl_loss': val_kl_loss,
}
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch + 1)
# remember best prec@1 and save checkpoint
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'best_loss': best_loss,
}, is_best,
'weights/{}_checkpoint.pth.tar'.format(str(args.lognumber)),
'weights/{}_best.pth.tar'.format(str(args.lognumber)))
G = model.VGG_VAE(5)
D.apply(weights_init)
G.apply(weights_init)
D.cuda()
G.cuda()
print(D)
print(G)
D_criterion = torch.nn.BCEWithLogitsLoss().cuda()
D_optimizer = torch.optim.SGD(D.parameters(), lr=1e-3)
G_criterion = torch.nn.BCEWithLogitsLoss().cuda()
G_l1 = torch.nn.L1Loss().cuda()
G_msssim = MSSSIM().cuda()
G_ssim = SSIM().cuda()
G_optimizer = torch.optim.Adam(G.parameters(), lr=1e-3)
pathlib.Path(sample_output).mkdir(parents=True, exist_ok=True)
pathlib.Path(os.path.join(sample_output, "images")).mkdir(parents=True, exist_ok=True)
d_loss = 0
g_loss = 0
d_to_g_threshold = 0.5
g_to_d_threshold = 0.3
train_d = True
train_g = True
conditional_training = False
_si = 1