def __init__(self, image_size, latent_dim = 512, style_depth = 8, network_capacity = 16, transparent = False, fp16 = False, cl_reg = False, steps = 1, lr = 1e-4, fq_layers = [], fq_dict_size = 256, attn_layers = []):
super().__init__()
self.lr = lr
self.steps = steps
self.ema_updater = EMA(0.995)
self.S = StyleVectorizer(latent_dim, style_depth)
self.G = Generator(image_size, latent_dim, network_capacity, transparent = transparent)
self.D = Discriminator(image_size, network_capacity, fq_layers = fq_layers, fq_dict_size = fq_dict_size, attn_layers = attn_layers, transparent = transparent)
self.SE = StyleVectorizer(latent_dim, style_depth)
self.GE = Generator(image_size, latent_dim, network_capacity, transparent = transparent)
# experimental contrastive loss discriminator regularization
self.D_cl = ContrastiveLearner(self.D, image_size, hidden_layer='flatten') if cl_reg else None
set_requires_grad(self.SE, False)
set_requires_grad(self.GE, False)
generator_params = list(self.G.parameters()) + list(self.S.parameters())
self.G_opt = DiffGrad(generator_params, lr = self.lr, betas=(0.5, 0.9))
self.D_opt = DiffGrad(self.D.parameters(), lr = self.lr, betas=(0.5, 0.9))
self._init_weights()
self.reset_parameter_averaging()
self.cuda()
if fp16:
(self.S, self.G, self.D, self.SE, self.GE), (self.G_opt, self.D_opt) = amp.initialize([self.S, self.G, self.D, self.SE, self.GE], [self.G_opt, self.D_opt], opt_level='O2')
def __init__(self, image_size, latent_dim = 512, fmap_max = 512, style_depth = 8, network_capacity = 16, transparent = False, fp16 = False, cl_reg = False, steps = 1, lr = 1e-4, fq_layers = [], fq_dict_size = 256, attn_layers = [], no_const = False):
super().__init__()
self.lr = lr
self.steps = steps
self.ema_updater = EMA(0.995)
self.S = StyleVectorizer(latent_dim, style_depth)
self.G = Generator(image_size, latent_dim, network_capacity, transparent = transparent, attn_layers = attn_layers, no_const = no_const, fmap_max = fmap_max)
self.D = Discriminator(image_size, network_capacity, fq_layers = fq_layers, fq_dict_size = fq_dict_size, attn_layers = attn_layers, transparent = transparent, fmap_max = fmap_max)
self.SE = StyleVectorizer(latent_dim, style_depth)
self.GE = Generator(image_size, latent_dim, network_capacity, transparent = transparent, attn_layers = attn_layers, no_const = no_const)
# experimental contrastive loss discriminator regularization
assert not (transparent and cl_reg), 'contrastive loss regularization does not work with transparent images yet'
self.D_cl = ContrastiveLearner(self.D, image_size, hidden_layer='flatten') if cl_reg else None
# wrapper for augmenting all images going into the discriminator
self.D_aug = AugWrapper(self.D, image_size)
set_requires_grad(self.SE, False)
set_requires_grad(self.GE, False)
generator_params = list(self.G.parameters()) + list(self.S.parameters())
self.G_opt = AdamP(generator_params, lr = self.lr, betas=(0.5, 0.9))
self.D_opt = AdamP(self.D.parameters(), lr = self.lr, betas=(0.5, 0.9))
self._init_weights()
self.reset_parameter_averaging()
self.cuda()
if fp16:
(self.S, self.G, self.D, self.SE, self.GE), (self.G_opt, self.D_opt) = amp.initialize([self.S, self.G, self.D, self.SE, self.GE], [self.G_opt, self.D_opt], opt_level='O2')
def __init__(self, image_size, latent_dim = 512, fmap_max = 512, style_depth = 8, network_capacity = 16, transparent = False, fp16 = False, cl_reg = False, steps = 1, lr = 1e-4, ttur_mult = 2, fq_layers = [], fq_dict_size = 256, attn_layers = [], no_const = False, lr_mlp = 0.1, rank = 0):
super().__init__()
self.lr = lr
self.steps = steps
self.ema_updater = EMA(0.995)
self.S = StyleVectorizer(latent_dim, style_depth, lr_mul = lr_mlp)
self.G = Generator(image_size, latent_dim, network_capacity, transparent = transparent, attn_layers = attn_layers, no_const = no_const, fmap_max = fmap_max)
self.D = Discriminator(image_size, network_capacity, fq_layers = fq_layers, fq_dict_size = fq_dict_size, attn_layers = attn_layers, transparent = transparent, fmap_max = fmap_max)
self.SE = StyleVectorizer(latent_dim, style_depth, lr_mul = lr_mlp)
self.GE = Generator(image_size, latent_dim, network_capacity, transparent = transparent, attn_layers = attn_layers, no_const = no_const)
self.D_cl = None
if cl_reg:
from contrastive_learner import ContrastiveLearner
# experimental contrastive loss discriminator regularization
assert not transparent, 'contrastive loss regularization does not work with transparent images yet'
self.D_cl = ContrastiveLearner(self.D, image_size, hidden_layer='flatten')
# wrapper for augmenting all images going into the discriminator
self.D_aug = AugWrapper(self.D, image_size)
# turn off grad for exponential moving averages
set_requires_grad(self.SE, False)
set_requires_grad(self.GE, False)
# init optimizers
generator_params = list(self.G.parameters()) + list(self.S.parameters())
self.G_opt = Adam(generator_params, lr = self.lr, betas=(0.5, 0.9))
self.D_opt = Adam(self.D.parameters(), lr = self.lr * ttur_mult, betas=(0.5, 0.9))
# init weights
self._init_weights()
self.reset_parameter_averaging()
self.cuda(rank)
# startup apex mixed precision
self.fp16 = fp16
if fp16:
(self.S, self.G, self.D, self.SE, self.GE), (self.G_opt, self.D_opt) = amp.initialize([self.S, self.G, self.D, self.SE, self.GE], [self.G_opt, self.D_opt], opt_level='O1', num_losses=3)
def Contrastive(num_classes, loss='triplet', pretrained=True, **kwargs):
ConEncoder = torchreid.models.build_model(name='vit_timm_diet',
num_classes=num_classes,
loss=loss,
pretrained=pretrained)
#torchreid.utils.load_pretrained_weights(ConEncoder, '/home/danish/deep-person-reid/scripts/log/model/model.pth.tar-112')
learner = ContrastiveLearner(
ConEncoder,
image_size=224,
hidden_layer='head',
# layer name where output is hidden dimension. this can also be an integer specifying the index of the child
project_hidden=True, # use projection head
project_dim=128, # projection head dimensions, 128 from paper
use_nt_xent_loss=False, # the above mentioned loss, abbreviated
temperature=0.1, # temperature
augment_both=True # augment both query and key
)
return learner
).to(device)
g_ema.requires_grad_(False)
g_ema.eval()
accumulate(g_ema, generator, 0)
augment_fn = nn.Sequential(
nn.ReflectionPad2d(int((math.sqrt(2) - 1) * args.size / 4)), # zoom out
augs.RandomHorizontalFlip(),
RandomApply(augs.RandomAffine(degrees=0, translate=(0.25, 0.25), shear=(15, 15)), p=0.2),
RandomApply(augs.RandomRotation(180), p=0.2),
augs.RandomResizedCrop(size=(args.size, args.size), scale=(1, 1), ratio=(1, 1)),
RandomApply(augs.RandomResizedCrop(size=(args.size, args.size), scale=(0.5, 0.9)), p=0.1), # zoom in
RandomApply(augs.RandomErasing(), p=0.1),
)
contrast_learner = (
ContrastiveLearner(discriminator, args.size, augment_fn=augment_fn, hidden_layer=(-1, 0))
if args.contrastive > 0
else None
)
g_reg_ratio = args.g_reg_every / (args.g_reg_every + 1)
d_reg_ratio = args.d_reg_every / (args.d_reg_every + 1)
g_optim = th.optim.Adam(
generator.parameters(), lr=args.lr * g_reg_ratio, betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio),
)
d_optim = th.optim.Adam(
discriminator.parameters(), lr=args.lr * d_reg_ratio, betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio),
)
if args.checkpoint is not None:
def __init__(
self,
image_size,
latent_dim=512,
style_depth=8,
network_capacity=16,
transparent=False,
fp16=False,
cl_reg=False,
augment_fn=None,
steps=1,
lr=1e-4,
fq_layers=[],
fq_dict_size=256,
attn_layers=[],
):
super().__init__()
self.lr = lr
self.steps = steps
self.ema_updater = EMA(0.995)
self.S = StyleVectorizer(latent_dim, style_depth)
self.G = Generator(image_size,
latent_dim,
network_capacity,
transparent=transparent,
attn_layers=attn_layers)
self.D = Discriminator(
image_size,
network_capacity,
fq_layers=fq_layers,
fq_dict_size=fq_dict_size,
attn_layers=attn_layers,
transparent=transparent,
)
self.SE = StyleVectorizer(latent_dim, style_depth)
self.GE = Generator(image_size,
latent_dim,
network_capacity,
transparent=transparent,
attn_layers=attn_layers)
set_requires_grad(self.SE, False)
set_requires_grad(self.GE, False)
generator_params = list(self.G.parameters()) + list(
self.S.parameters())
self.G_opt = DiffGrad(generator_params, lr=self.lr, betas=(0.5, 0.9))
self.D_opt = DiffGrad(self.D.parameters(),
lr=self.lr,
betas=(0.5, 0.9))
self._init_weights()
self.reset_parameter_averaging()
self.cuda()
if fp16:
(self.S, self.G, self.D, self.SE,
self.GE), (self.G_opt, self.D_opt) = amp.initialize(
[self.S, self.G, self.D, self.SE, self.GE],
[self.G_opt, self.D_opt],
opt_level="O2")
# experimental contrastive loss discriminator regularization
if augment_fn is not None:
self.augment_fn = augment_fn
else:
self.augment_fn = nn.Sequential(
nn.ReflectionPad2d(int((sqrt(2) - 1) * image_size / 4)),
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.7),
augs.RandomGrayscale(p=0.2),
augs.RandomHorizontalFlip(),
RandomApply(augs.RandomAffine(degrees=0,
translate=(0.25, 0.25),
shear=(15, 15)),
p=0.3),
RandomApply(nn.Sequential(
augs.RandomRotation(180),
augs.CenterCrop(size=(image_size, image_size))),
p=0.2),
augs.RandomResizedCrop(size=(image_size, image_size)),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
RandomApply(augs.RandomErasing(), p=0.1),
)
self.D_cl = (ContrastiveLearner(self.D,
image_size,
augment_fn=self.augment_fn,
fp16=fp16,
hidden_layer="flatten")
if cl_reg else None)
g_ema.eval()
accumulate(g_ema, generator, 0)
if args.contrastive > 0:
contrast_learner = ContrastiveLearner(
discriminator,
args.size,
augment_fn=nn.Sequential(
nn.ReflectionPad2d(
int((math.sqrt(2) - 1) * args.size /
4)), # zoom out
augs.RandomHorizontalFlip(),
RandomApply(augs.RandomAffine(degrees=0,
translate=(0.25, 0.25),
shear=(15, 15)),
p=0.1),
RandomApply(augs.RandomRotation(180), p=0.1),
augs.RandomResizedCrop(size=(args.size, args.size),
scale=(1, 1),
ratio=(1, 1)),
RandomApply(augs.RandomResizedCrop(size=(args.size,
args.size),
scale=(0.5, 0.9)),
p=0.1), # zoom in
RandomApply(augs.RandomErasing(), p=0.1),
),
hidden_layer=(-1, 0),
)
else:
contrast_learner = None
g_reg_ratio = args.g_reg_every / (args.g_reg_every + 1)
def __init__(self, args=args):
super().__init__()
self.args = args
# random_seed setting
random_seed = args.randomseed
np.random.seed(random_seed)
torch.manual_seed(random_seed)
if torch.cuda.device_count() > 1:
torch.cuda.manual_seed_all(random_seed)
else:
torch.cuda.manual_seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
self.pretrain_stage = self.args.pretrainstage
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.slomofc = model.Slomofc(
self.args.data_h, self.args.data_w, self.device, self.pretrain_stage
)
self.slomofc.to(self.device)
if self.pretrain_stage:
self.learner = ContrastiveLearner(
self.slomofc,
image_size=128,
hidden_layer="avgpool",
use_momentum=True, # use momentum for key encoder
momentum_value=0.999,
project_hidden=False, # no projection heads
use_bilinear=True, # in paper, logits is bilinear product of query / key
use_nt_xent_loss=False, # use regular contrastive loss
augment_both=False, # in curl, only the key is augmented
)
if self.args.init_type != "":
init_net(self.slomofc, self.args.init_type)
print(self.args.init_type + " initializing slomo done!")
if self.args.train_continue:
if not self.args.nocomet and self.args.cometid != "":
self.comet_exp = ExistingExperiment(
previous_experiment=self.args.cometid
)
elif not self.args.nocomet and self.args.cometid == "":
self.comet_exp = Experiment(
workspace=self.args.workspace, project_name=self.args.projectname
)
else:
self.comet_exp = None
self.ckpt_dict = torch.load(self.args.checkpoint)
self.slomofc.load_state_dict(self.ckpt_dict["model_state_dict"])
self.args.init_learning_rate = self.ckpt_dict["learningRate"]
if not self.pretrain_stage:
self.optimizer = optim.Adam(
self.slomofc.parameters(), lr=self.args.init_learning_rate
)
else:
self.optimizer = optim.Adam(self.learner.parameters(), lr=3e-4)
self.optimizer.load_state_dict(self.ckpt_dict["opt_state_dict"])
print("Pretrained model loaded!")
else:
# start logging info in comet-ml
if not self.args.nocomet:
self.comet_exp = Experiment(
workspace=self.args.workspace, project_name=self.args.projectname
)
# self.comet_exp.log_parameters(flatten_opts(self.args))
else:
self.comet_exp = None
if not self.pretrain_stage:
self.ckpt_dict = {
"trainLoss": {},
"valLoss": {},
"valPSNR": {},
"valSSIM": {},
"learningRate": {},
"epoch": -1,
"detail": "End to end Super SloMo.",
"trainBatchSz": self.args.train_batch_size,
"validationBatchSz": self.args.validation_batch_size,
}
else:
self.ckpt_dict = {
"conLoss": {},
"learningRate": {},
"epoch": -1,
"detail": "Pretrain_stage of Super SloMo.",
"trainBatchSz": self.args.train_batch_size,
}
if not self.pretrain_stage:
self.optimizer = optim.Adam(
self.slomofc.parameters(), lr=self.args.init_learning_rate
)
else:
self.optimizer = optim.Adam(self.learner.parameters(), lr=3e-4)
self.scheduler = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=self.args.milestones, gamma=0.1
)
# Channel wise mean calculated on adobe240-fps training dataset
if not self.pretrain_stage:
mean = [0.5, 0.5, 0.5]
std = [1, 1, 1]
self.normalize = transforms.Normalize(mean=mean, std=std)
self.transform = transforms.Compose([transforms.ToTensor(), self.normalize])
else:
self.transform = transforms.Compose([transforms.ToTensor()])
trainset = dataloader.SuperSloMo(
root=self.args.dataset_root + "/train", transform=self.transform, train=True
)
self.trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=self.args.train_batch_size,
num_workers=self.args.num_workers,
shuffle=True,
)
if not self.pretrain_stage:
validationset = dataloader.SuperSloMo(
root=self.args.dataset_root + "/validation",
transform=self.transform,
# randomCropSize=(128, 128),
train=False,
)
self.validationloader = torch.utils.data.DataLoader(
validationset,
batch_size=self.args.validation_batch_size,
num_workers=self.args.num_workers,
shuffle=False,
)
### loss
if not self.pretrain_stage:
self.supervisedloss = supervisedLoss()
self.best = {
"valLoss": 99999999,
"valPSNR": -1,
"valSSIM": -1,
}
else:
self.best = {
"conLoss": 99999999,
}
self.checkpoint_counter = int(
(self.ckpt_dict["epoch"] + 1) / self.args.checkpoint_epoch
)
class Trainer:
def __init__(self, args=args):
super().__init__()
self.args = args
# random_seed setting
random_seed = args.randomseed
np.random.seed(random_seed)
torch.manual_seed(random_seed)
if torch.cuda.device_count() > 1:
torch.cuda.manual_seed_all(random_seed)
else:
torch.cuda.manual_seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
self.pretrain_stage = self.args.pretrainstage
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.slomofc = model.Slomofc(
self.args.data_h, self.args.data_w, self.device, self.pretrain_stage
)
self.slomofc.to(self.device)
if self.pretrain_stage:
self.learner = ContrastiveLearner(
self.slomofc,
image_size=128,
hidden_layer="avgpool",
use_momentum=True, # use momentum for key encoder
momentum_value=0.999,
project_hidden=False, # no projection heads
use_bilinear=True, # in paper, logits is bilinear product of query / key
use_nt_xent_loss=False, # use regular contrastive loss
augment_both=False, # in curl, only the key is augmented
)
if self.args.init_type != "":
init_net(self.slomofc, self.args.init_type)
print(self.args.init_type + " initializing slomo done!")
if self.args.train_continue:
if not self.args.nocomet and self.args.cometid != "":
self.comet_exp = ExistingExperiment(
previous_experiment=self.args.cometid
)
elif not self.args.nocomet and self.args.cometid == "":
self.comet_exp = Experiment(
workspace=self.args.workspace, project_name=self.args.projectname
)
else:
self.comet_exp = None
self.ckpt_dict = torch.load(self.args.checkpoint)
self.slomofc.load_state_dict(self.ckpt_dict["model_state_dict"])
self.args.init_learning_rate = self.ckpt_dict["learningRate"]
if not self.pretrain_stage:
self.optimizer = optim.Adam(
self.slomofc.parameters(), lr=self.args.init_learning_rate
)
else:
self.optimizer = optim.Adam(self.learner.parameters(), lr=3e-4)
self.optimizer.load_state_dict(self.ckpt_dict["opt_state_dict"])
print("Pretrained model loaded!")
else:
# start logging info in comet-ml
if not self.args.nocomet:
self.comet_exp = Experiment(
workspace=self.args.workspace, project_name=self.args.projectname
)
# self.comet_exp.log_parameters(flatten_opts(self.args))
else:
self.comet_exp = None
if not self.pretrain_stage:
self.ckpt_dict = {
"trainLoss": {},
"valLoss": {},
"valPSNR": {},
"valSSIM": {},
"learningRate": {},
"epoch": -1,
"detail": "End to end Super SloMo.",
"trainBatchSz": self.args.train_batch_size,
"validationBatchSz": self.args.validation_batch_size,
}
else:
self.ckpt_dict = {
"conLoss": {},
"learningRate": {},
"epoch": -1,
"detail": "Pretrain_stage of Super SloMo.",
"trainBatchSz": self.args.train_batch_size,
}
if not self.pretrain_stage:
self.optimizer = optim.Adam(
self.slomofc.parameters(), lr=self.args.init_learning_rate
)
else:
self.optimizer = optim.Adam(self.learner.parameters(), lr=3e-4)
self.scheduler = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=self.args.milestones, gamma=0.1
)
# Channel wise mean calculated on adobe240-fps training dataset
if not self.pretrain_stage:
mean = [0.5, 0.5, 0.5]
std = [1, 1, 1]
self.normalize = transforms.Normalize(mean=mean, std=std)
self.transform = transforms.Compose([transforms.ToTensor(), self.normalize])
else:
self.transform = transforms.Compose([transforms.ToTensor()])
trainset = dataloader.SuperSloMo(
root=self.args.dataset_root + "/train", transform=self.transform, train=True
)
self.trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=self.args.train_batch_size,
num_workers=self.args.num_workers,
shuffle=True,
)
if not self.pretrain_stage:
validationset = dataloader.SuperSloMo(
root=self.args.dataset_root + "/validation",
transform=self.transform,
# randomCropSize=(128, 128),
train=False,
)
self.validationloader = torch.utils.data.DataLoader(
validationset,
batch_size=self.args.validation_batch_size,
num_workers=self.args.num_workers,
shuffle=False,
)
### loss
if not self.pretrain_stage:
self.supervisedloss = supervisedLoss()
self.best = {
"valLoss": 99999999,
"valPSNR": -1,
"valSSIM": -1,
}
else:
self.best = {
"conLoss": 99999999,
}
self.checkpoint_counter = int(
(self.ckpt_dict["epoch"] + 1) / self.args.checkpoint_epoch
)
def train(self):
for epoch in range(self.ckpt_dict["epoch"] + 1, self.args.epochs):
print("Epoch: ", epoch)
if not self.pretrain_stage:
print("Training downstream task")
print("Training epoch {}".format(epoch))
_, _, train_loss = self.run_epoch(
epoch, self.trainloader, logimage=False, isTrain=True,
)
with torch.no_grad():
print("Validating epoch {}".format(epoch))
val_psnr, val_ssim, val_loss = self.run_epoch(
epoch, self.validationloader, logimage=True, isTrain=False,
)
self.ckpt_dict["trainLoss"][str(epoch)] = train_loss
self.ckpt_dict["valLoss"][str(epoch)] = val_loss
self.ckpt_dict["valPSNR"][str(epoch)] = val_psnr
self.ckpt_dict["valSSIM"][str(epoch)] = val_ssim
else:
print("Training pretrain task")
conLoss = 0
for trainIndex, data in enumerate(self.trainloader, 0):
loss = self.learner(data)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.learner.update_moving_average() # update moving average of key encoder
conLoss += loss.item()
conLoss = conLoss / len(self.trainloader)
print(" Epoch: %4d conLoss: %0.4f " % (epoch, conLoss,))
self.ckpt_dict["conLoss"][str(epoch)] = conLoss
self.comet_exp.log_metric("conLoss", conLoss, epoch=epoch)
self.ckpt_dict["learningRate"][str(epoch)] = get_lr(self.optimizer)
self.ckpt_dict["epoch"] = epoch
self.best = self.save_best(self.ckpt_dict, self.best, epoch)
if (epoch % self.args.checkpoint_epoch) == self.args.checkpoint_epoch - 1:
self.save()
def save_best(self, current, best, epoch, pretrain_stage=True):
save_best_done = False
metrics = ["conLoss"] if pretrain_stage else ["valLoss", "valSSIM", "valPSNR"]
for metric_name in metrics:
if not save_best_done:
if "Loss" in metric_name:
if best[metric_name] > current[metric_name][str(epoch)]:
best[metric_name] = current[metric_name][str(epoch)]
self.save(metric_name)
print(
"New Best "
+ metric_name
+ ": "
+ str(best[metric_name])
+ "saved"
)
save_best_done = True
else:
if best[metric_name] < current[metric_name][str(epoch)]:
best[metric_name] = current[metric_name][str(epoch)]
self.save(metric_name)
print(
"New Best "
+ metric_name
+ ": "
+ str(best[metric_name])
+ "saved"
)
save_best_done = True
return best
@torch.no_grad()
def save(self, save_metric_name=""):
self.ckpt_dict["model_state_dict"] = self.slomofc.state_dict()
self.ckpt_dict["opt_state_dict"] = self.optimizer.state_dict()
file_name = (
str(self.checkpoint_counter) if save_metric_name == "" else save_metric_name
)
model_name = (
"/SuperSloMo" if not self.pretrain_stage else "/Pretrain_stage_SuperSloMo"
)
torch.save(
self.ckpt_dict, self.args.checkpoint_dir + model_name + file_name + ".ckpt",
)
if save_metric_name == "":
self.checkpoint_counter += 1
### Train and Valid
def run_epoch(self, epoch, dataloader, logimage=False, isTrain=True):
# For details see training.
psnr_value = 0
ssim_value = 0
loss_value = 0
if not isTrain:
valid_images = []
for index, all_data in enumerate(dataloader, 0):
self.optimizer.zero_grad()
(
Ft_p,
I0,
IFrame,
I1,
g_I0_F_t_0,
g_I1_F_t_1,
FlowBackWarp_I0_F_1_0,
FlowBackWarp_I1_F_0_1,
F_1_0,
F_0_1,
) = self.slomofc(all_data, pred_only=False, isTrain=isTrain)
if (not isTrain) and logimage:
if index % self.args.logimagefreq == 0:
valid_images.append(
255.0
* I0.cpu()[0]
.resize_(1, 1, self.args.data_h, self.args.data_w)
.repeat(1, 3, 1, 1)
)
valid_images.append(
255.0
* IFrame.cpu()[0]
.resize_(1, 1, self.args.data_h, self.args.data_w)
.repeat(1, 3, 1, 1)
)
valid_images.append(
255.0
* I1.cpu()[0]
.resize_(1, 1, self.args.data_h, self.args.data_w)
.repeat(1, 3, 1, 1)
)
valid_images.append(
255.0
* Ft_p.cpu()[0]
.resize_(1, 1, self.args.data_h, self.args.data_w)
.repeat(1, 3, 1, 1)
)
# loss
loss = self.supervisedloss(
Ft_p,
IFrame,
I0,
I1,
g_I0_F_t_0,
g_I1_F_t_1,
FlowBackWarp_I0_F_1_0,
FlowBackWarp_I1_F_0_1,
F_1_0,
F_0_1,
)
if isTrain:
loss.backward()
self.optimizer.step()
self.scheduler.step()
loss_value += loss.item()
# metrics
psnr_value += psnr(Ft_p, IFrame, outputTensor=False)
ssim_value += ssim(Ft_p, IFrame, outputTensor=False)
name_loss = "TrainLoss" if isTrain else "ValLoss"
itr = int(index + epoch * (len(dataloader)))
if self.comet_exp is not None:
self.comet_exp.log_metric(
"PSNR", psnr_value / len(dataloader), step=itr, epoch=epoch
)
self.comet_exp.log_metric(
"SSIM", ssim_value / len(dataloader), step=itr, epoch=epoch
)
self.comet_exp.log_metric(
name_loss, loss_value / len(dataloader), step=itr, epoch=epoch
)
if logimage:
upload_images(
valid_images,
epoch,
exp=self.comet_exp,
im_per_row=4,
rows_per_log=int(len(valid_images) / 4),
)
print(
" Loss: %0.6f Iterations: %4d/%4d ValPSNR: %0.4f ValSSIM: %0.4f "
% (
loss_value / len(dataloader),
index,
len(dataloader),
psnr_value / len(dataloader),
ssim_value / len(dataloader),
)
)
return (
(psnr_value / len(dataloader)),
(ssim_value / len(dataloader)),
(loss_value / len(dataloader)),
)
