def main():
global args, best_error, n_iter
args = parser.parse_args()
normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
train_transform = custom_transforms.Compose([
custom_transforms.RandomRotate(),
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=4,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
print(len(train_loader))
def get_train_transforms(normalize): train_transforms = [] train_transforms.append(transforms.Scale(160)) train_transforms.append(transforms.RandomHorizontalFlip()) train_transforms.append(transforms.RandomColor(0.15)) train_transforms.append(transforms.RandomRotate(15)) train_transforms.append(transforms.RandomSizedCrop(128)) train_transforms.append(transforms.ToTensor()) train_transforms.append(normalize) train_transforms = transforms.Compose(train_transforms) return train_transforms
return sample if __name__ == '__main__': print(os.getcwd()) # from dataloaders import custom_transforms as tr import custom_transforms as tr from torch.utils.data import DataLoader from torchvision import transforms import matplotlib.pyplot as plt composed_transforms_tr = transforms.Compose([ tr.RandomHorizontalFlip(), tr.RandomScale((0.5, 0.75)), tr.RandomCrop((512, 1024)), tr.RandomRotate(5), tr.ToTensor()]) msrab_train= MSRAB(split='train',transform=composed_transforms_tr) dataloader = DataLoader(msrab_train, batch_size=2, shuffle=True, num_workers=2) for ii, sample in enumerate(dataloader): print(ii, sample["image"].size(), sample["label"].size(), type(sample["image"]), type(sample["label"])) for jj in range(sample["image"].size()[0]): img = sample['image'].numpy() gt = sample['label'].numpy() tmp = np.array(gt[jj]*255.0).astype(np.uint8) tmp = np.squeeze(tmp, axis=0) tmp = np.expand_dims(tmp, axis=2) segmap = np.concatenate((tmp,tmp,tmp), axis=2)
return _img, _target
def __str__(self):
return 'ISPRS(split=' + str(self.split) + ')'
if __name__ == '__main__':
import custom_transforms as tr
from utils import decode_segmap
from torch.utils.data import DataLoader
from torchvision import transforms
import matplotlib.pyplot as plt
composed_transforms_tr = transforms.Compose([tr.RandomHorizontalFlip(),
tr.RandomCrop(512),
tr.RandomRotate(15),
tr.ToTensor()]
)
isprs_train = ISPRSSegmentation(split='train', transform=composed_transforms_tr)
dataloader = DataLoader(isprs_train, batch_size=5, shuffle=True, num_workers=2)
for ii, sample in enumerate(dataloader):
for jj in range(sample["image"].size()[0]):
img = sample['image'].numpy()
gt = sample['label'].numpy()
tmp = np.array(gt[jj]).astype(np.uint8)
tmp = np.squeeze(tmp, axis=0)
segmap = decode_segmap(tmp, dataset='ISPRS')
img_tmp = np.transpose(img[jj], axes=[1, 2, 0]).astype(np.uint8)
def main(args):
# parse args
best_acc1 = 0.0
if args.gpu >= 0:
print("Use GPU: {}".format(args.gpu))
else:
print('You are using CPU for computing!',
'Yet we assume you are using a GPU.',
'You will NOT be able to switch between CPU and GPU training!')
# set up the model + loss
if args.use_custom_conv:
print("Using custom convolutions in the network")
model = default_model(conv_op=CustomConv2d, num_classes=100)
else:
model = default_model(num_classes=100)
model_arch = "simplenet"
# model_arch = "simplenet_batchnorm2d"
# model_arch = "resnet18"
# model_arch = "resnet34"
criterion = nn.CrossEntropyLoss()
# put everthing to gpu
if args.gpu >= 0:
model = model.cuda(args.gpu)
criterion = criterion.cuda(args.gpu)
# setup the optimizer
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# 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_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
if args.gpu < 0:
model = model.cpu()
else:
model = model.cuda(args.gpu)
# only load the optimizer if necessary
if (not args.evaluate) and (not args.attack):
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {}, acc1 {})"
.format(args.resume, checkpoint['epoch'], best_acc1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# set up transforms for data augmentation
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transforms = []
train_transforms.append(transforms.Scale(160))
train_transforms.append(transforms.RandomHorizontalFlip())
train_transforms.append(transforms.RandomColor(0.15))
train_transforms.append(transforms.RandomRotate(15))
train_transforms.append(transforms.RandomSizedCrop(128))
train_transforms.append(transforms.ToTensor())
train_transforms.append(normalize)
train_transforms = transforms.Compose(train_transforms)
# val transofrms
val_transforms=[]
val_transforms.append(transforms.Scale(160, interpolations=None))
val_transforms.append(transforms.ToTensor())
val_transforms.append(normalize)
val_transforms = transforms.Compose(val_transforms)
if (not args.evaluate) and (not args.attack):
print("Training time data augmentations:")
print(train_transforms)
# setup dataset and dataloader
train_dataset = MiniPlacesLoader(args.data_folder,
split='train', transforms=train_transforms)
val_dataset = MiniPlacesLoader(args.data_folder,
split='val', transforms=val_transforms)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, sampler=None, drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=100, shuffle=False,
num_workers=args.workers, pin_memory=True, sampler=None, drop_last=False)
# testing only
if (args.evaluate==args.attack) and args.evaluate:
print("Cann't set evaluate and attack to True at the same time!")
return
# set up visualizer
if args.vis:
visualizer = default_attention(criterion)
else:
visualizer = None
# evaluation
if args.resume and args.evaluate:
print("Testing the model ...")
cudnn.deterministic = True
validate(val_loader, model, -1, args, visualizer=visualizer)
return
# attack
if args.resume and args.attack:
print("Generating adversarial samples for the model ..")
cudnn.deterministic = True
validate(val_loader, model, -1, args,
attacker=default_attack(criterion),
visualizer=visualizer)
return
# enable cudnn benchmark
cudnn.enabled = True
cudnn.benchmark = True
# warmup the training
if (args.start_epoch == 0) and (args.warmup_epochs > 0):
print("Warmup the training ...")
for epoch in range(0, args.warmup_epochs):
train(train_loader, model, criterion, optimizer, epoch, "warmup", args)
# start the training
print("Training the model ...")
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, "train", args)
# evaluate on validation set
acc1 = validate(val_loader, model, epoch, args)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint({
'epoch': epoch + 1,
'model_arch': model_arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best)
def main():
global args, best_error, n_iter
args = parser.parse_args()
save_path = Path(args.name)
args.save_path = 'checkpoints' / save_path
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
train_transform = custom_transforms.Compose([
custom_transforms.RandomRotate(),
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
training_writer = SummaryWriter(args.save_path)
intrinsics = np.array(
[542.822841, 0, 315.593520, 0, 542.576870, 237.756098, 0, 0,
1]).astype(np.float32).reshape((3, 3))
train_set = SequenceFolder(root=args.dataset_dir,
intrinsics=intrinsics,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
print("=> creating model")
mask_net = MaskNet6.MaskNet6().cuda()
flow_net = back2future.Model(nlevels=args.nlevels).cuda()
pose_net = PoseNetB6.PoseNetB6().cuda()
if args.pretrained_mask:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_mask)
mask_net.load_state_dict(weights['state_dict'])
else:
mask_net.init_weights()
mask_net = torch.nn.DataParallel(mask_net)
if args.pretrained_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_pose)
pose_net.load_state_dict(weights['state_dict'])
else:
pose_net.init_weights()
if args.pretrained_flow:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_flow)
flow_net.load_state_dict(weights['state_dict'])
else:
flow_net.init_weights()
print('=> setting adam solver')
parameters = chain(mask_net.parameters(), pose_net.parameters(),
flow_net.parameters())
optimizer = torch.optim.Adam(parameters,
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
# training
best_error = 0
train_loss = 0
for epoch in tqdm(range(args.epochs)):
if args.fix_flownet:
for fparams in flow_net.parameters():
fparams.requires_grad = False
if args.fix_masknet:
for fparams in mask_net.parameters():
fparams.requires_grad = False
if args.fix_posenet:
for fparams in pose_net.parameters():
fparams.requires_grad = False
is_best = train_loss < best_error
best_error = min(best_error, train_loss)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': mask_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_net.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': flow_net.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': optimizer.state_dict()
}, is_best)
train_loss = train(train_loader, mask_net, pose_net, flow_net,
optimizer, args.epoch_size, training_writer)
def main():
global args, best_error, n_iter
n_iter = 0
best_error = 0
args = parser.parse_args()
'''
args = ['--name', 'deemo', '--FCCMnet', 'PatchWiseNetwork',
'--dataset_dir', '/notebooks/FCCM/pre-process/pre-process', '--label_dir', '/notebooks/FCCM',
'--batch-size', '4', '--epochs','100', '--lr', '1e-4' ]
'''
save_path = Path(args.name)
args.save_path = 'checkpoints'/save_path
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
train_writer = SummaryWriter(args.save_path)
torch.manual_seed(args.seed)
train_transform = custom_transforms.Compose([
custom_transforms.RandomRotate(),
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor() ])
train_set = Generate_train_set(
root = args.dataset_dir,
label_root = args.label_dir,
transform=train_transform,
seed=args.seed,
train=True
)
val_set = Generate_val_set(
root = args.dataset_dir,
label_root = args.label_dir,
seed=args.seed
)
print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes)))
print('{} samples found in {} val scenes'.format(len(val_set), len(val_set.scenes)))
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_set, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, drop_last=True)
print("=> creating model")
if args.FCCMnet == 'VGG':
FCCM_net = models_inpytorch.vgg16(num_classes=19)
FCCM_net.features[0]=nn.Conv2d(1, 64, kernel_size=3, padding=1)
if args.FCCMnet == 'ResNet':
FCCM_net = models.resnet18(num_classes=19)
if args.FCCMnet == 'CatNet':
FCCM_net = models.catnet18(num_classes=19)
if args.FCCMnet == 'CatNet_FCCM':
FCCM_net = models.catnet1(num_classes=19)
if args.FCCMnet == 'ResNet_FCCM':
FCCM_net = models.resnet1(num_classes=19)
if args.FCCMnet == 'ImageWise':
FCCM_net = models.ImageWiseNetwork()
if args.FCCMnet == 'PatchWise':
FCCM_net = models.PatchWiseNetwork()
if args.FCCMnet == 'Baseline':
FCCM_net = models.Baseline()
FCCM_net = FCCM_net.cuda()
if args.pretrained_model:
print("=> using pre-trained weights for net")
weights = torch.load(args.pretrained_model)
FCCM_net.load_state_dict(weights['state_dict'])
cudnn.benchmark = True
FCCM_net = torch.nn.DataParallel(FCCM_net)
print('=> setting adam solver')
parameters = chain(FCCM_net.parameters())
optimizer = torch.optim.Adam(parameters, args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
is_best = False
best_error = float("inf")
FCCM_net.train()
loss = 0
for epoch in tqdm(range(args.epochs)):
is_best = loss <= best_error
best_error = min(best_error, loss)
save_checkpoint(
args.save_path, {
'epoch': epoch + 1,
'state_dict': FCCM_net.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': optimizer.state_dict()
}, is_best)
validation(val_loader, FCCM_net, epoch, train_writer)
loss = train(train_loader, FCCM_net, optimizer, args.epoch_size, train_writer)
def main():
global global_vars_dict
args = global_vars_dict['args']
best_error = -1 #best model choosing
#mkdir
timestamp = datetime.datetime.now().strftime("%m-%d-%H:%M")
args.save_path = Path('checkpoints') / Path(args.data_dir).stem / timestamp
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
if args.alternating:
args.alternating_flags = np.array([False, False, True])
#mk writers
tb_writer = SummaryWriter(args.save_path)
# Data loading code
flow_loader_h, flow_loader_w = 256, 832
if args.data_normalization == 'global':
normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
elif args.data_normalization == 'local':
normalize = custom_transforms.NormalizeLocally()
if args.fix_flownet:
train_transform = custom_transforms.Compose([
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
else:
train_transform = custom_transforms.Compose([
custom_transforms.RandomRotate(),
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
valid_flow_transform = custom_transforms.Compose([
custom_transforms.Scale(h=flow_loader_h, w=flow_loader_w),
custom_transforms.ArrayToTensor(), normalize
])
print("=> fetching scenes in '{}'".format(args.data_dir))
#train set, loader only建立一个
if args.dataset_format == 'stacked':
from datasets.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from datasets.sequence_folders import SequenceFolder
train_set = SequenceFolder( #mc data folder
args.data_dir,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length, #5
target_transform=None)
elif args.dataset_format == 'sequential_with_gt': # with all possible gt
from datasets.sequence_mc import SequenceFolder
train_set = SequenceFolder( # mc data folder
args.data_dir,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length, # 5
target_transform=None)
else:
return
if args.DEBUG:
train_set.__len__ = 32
train_set.samples = train_set.samples[:32]
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
#val set,loader 挨个建立
# if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping
if args.val_without_gt:
from datasets.sequence_folders2 import SequenceFolder #就多了一级文件夹
val_set_without_gt = SequenceFolder( #只有图
args.data_dir,
transform=valid_transform,
seed=None,
train=False,
sequence_length=args.sequence_length,
target_transform=None)
val_loader = torch.utils.data.DataLoader(val_set_without_gt,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
if args.val_with_depth_gt:
from datasets.validation_folders2 import ValidationSet
val_set_with_depth_gt = ValidationSet(args.data_dir,
transform=valid_transform)
val_loader_depth = torch.utils.data.DataLoader(
val_set_with_depth_gt,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
if args.val_with_flow_gt: #暂时没有
from datasets.validation_flow import ValidationFlow
val_flow_set = ValidationFlow(root=args.kitti_dir,
sequence_length=args.sequence_length,
transform=valid_flow_transform)
val_flow_loader = torch.utils.data.DataLoader(
val_flow_set,
batch_size=1,
# batch size is 1 since images in kitti have different sizes
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
if args.val_without_gt:
print('{} samples found in {} valid scenes'.format(
len(val_set_without_gt), len(val_set_without_gt.scenes)))
#1 create model
print("=> creating model")
#1.1 disp_net
disp_net = getattr(models, args.dispnet)().cuda()
output_exp = True #args.mask_loss_weight > 0
if not output_exp:
print("=> no mask loss, PoseExpnet will only output pose")
#1.2 pose_net
pose_net = getattr(models, args.posenet)(nb_ref_imgs=args.sequence_length -
1).cuda()
#1.3.flow_net
if args.flownet == 'SpyNet':
flow_net = getattr(models,
args.flownet)(nlevels=args.nlevels,
pre_normalization=normalize).cuda()
elif args.flownet == 'FlowNetC6': #flonwtc6
flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda()
elif args.flownet == 'FlowNetS':
flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda()
elif args.flownet == 'Back2Future':
flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda()
# 1.4 mask_net
mask_net = getattr(models,
args.masknet)(nb_ref_imgs=args.sequence_length - 1,
output_exp=True).cuda()
#2 载入参数
#2.1 pose
if args.pretrained_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_pose)
pose_net.load_state_dict(weights['state_dict'])
else:
pose_net.init_weights()
if args.pretrained_mask:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_mask)
mask_net.load_state_dict(weights['state_dict'])
else:
mask_net.init_weights()
# import ipdb; ipdb.set_trace()
if args.pretrained_disp:
print("=> using pre-trained weights from {}".format(
args.pretrained_disp))
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'])
else:
disp_net.init_weights()
if args.pretrained_flow:
print("=> using pre-trained weights for FlowNet")
weights = torch.load(args.pretrained_flow)
flow_net.load_state_dict(weights['state_dict'])
else:
flow_net.init_weights()
if args.resume:
print("=> resuming from checkpoint")
dispnet_weights = torch.load(args.save_path /
'dispnet_checkpoint.pth.tar')
posenet_weights = torch.load(args.save_path /
'posenet_checkpoint.pth.tar')
masknet_weights = torch.load(args.save_path /
'masknet_checkpoint.pth.tar')
flownet_weights = torch.load(args.save_path /
'flownet_checkpoint.pth.tar')
disp_net.load_state_dict(dispnet_weights['state_dict'])
pose_net.load_state_dict(posenet_weights['state_dict'])
flow_net.load_state_dict(flownet_weights['state_dict'])
mask_net.load_state_dict(masknet_weights['state_dict'])
# import ipdb; ipdb.set_trace()
cudnn.benchmark = True
disp_net = torch.nn.DataParallel(disp_net)
pose_net = torch.nn.DataParallel(pose_net)
mask_net = torch.nn.DataParallel(mask_net)
flow_net = torch.nn.DataParallel(flow_net)
print('=> setting adam solver')
parameters = chain(disp_net.parameters(), pose_net.parameters(),
mask_net.parameters(), flow_net.parameters())
optimizer = torch.optim.Adam(parameters,
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
if args.resume and (args.save_path /
'optimizer_checkpoint.pth.tar').exists():
print("=> loading optimizer from checkpoint")
optimizer_weights = torch.load(args.save_path /
'optimizer_checkpoint.pth.tar')
optimizer.load_state_dict(optimizer_weights['state_dict'])
with open(args.save_path / args.log_summary, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow(['train_loss', 'validation_loss'])
with open(args.save_path / args.log_full, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([
'train_loss', 'photo_cam_loss', 'photo_flow_loss',
'explainability_loss', 'smooth_loss'
])
#
if args.log_terminal:
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader_depth))
logger.epoch_bar.start()
else:
logger = None
#预先评估下
if args.pretrained_disp or args.evaluate:
logger.reset_valid_bar()
if args.val_without_gt:
pass
#val_loss = validate_without_gt(val_loader,disp_net,pose_net,mask_net,flow_net,epoch=0, logger=logger, tb_writer=tb_writer,nb_writers=3,global_vars_dict = global_vars_dict)
#val_loss =0
if args.val_with_depth_gt:
pass
depth_errors, depth_error_names = validate_depth_with_gt(
val_loader_depth,
disp_net,
epoch=0,
logger=logger,
tb_writer=tb_writer,
global_vars_dict=global_vars_dict)
#3. main cycle
for epoch in range(1, args.epochs): #epoch 0 在第没入循环之前已经测试了.
#3.1 四个子网络，训练哪几个
if args.fix_flownet:
for fparams in flow_net.parameters():
fparams.requires_grad = False
if args.fix_masknet:
for fparams in mask_net.parameters():
fparams.requires_grad = False
if args.fix_posenet:
for fparams in pose_net.parameters():
fparams.requires_grad = False
if args.fix_dispnet:
for fparams in disp_net.parameters():
fparams.requires_grad = False
if args.log_terminal:
logger.epoch_bar.update(epoch)
logger.reset_train_bar()
#validation data
flow_error_names = ['no']
flow_errors = [0]
errors = [0]
error_names = ['no error names depth']
print('\nepoch [{}/{}]\n'.format(epoch + 1, args.epochs))
#3.2 train for one epoch---------
#train_loss=0
train_loss = train_gt(train_loader, disp_net, pose_net, mask_net,
flow_net, optimizer, logger, tb_writer,
global_vars_dict)
#3.3 evaluate on validation set-----
if args.val_without_gt:
val_loss = validate_without_gt(val_loader,
disp_net,
pose_net,
mask_net,
flow_net,
epoch=0,
logger=logger,
tb_writer=tb_writer,
nb_writers=3,
global_vars_dict=global_vars_dict)
if args.val_with_depth_gt:
depth_errors, depth_error_names = validate_depth_with_gt(
val_loader_depth,
disp_net,
epoch=epoch,
logger=logger,
tb_writer=tb_writer,
global_vars_dict=global_vars_dict)
if args.val_with_flow_gt:
pass
#flow_errors, flow_error_names = validate_flow_with_gt(val_flow_loader, disp_net, pose_net, mask_net, flow_net, epoch, logger, tb_writer)
#for error, name in zip(flow_errors, flow_error_names):
# training_writer.add_scalar(name, error, epoch)
#----------------------
#3.4 Up to you to chose the most relevant error to measure your model's performance, careful some measures are to maximize (such as a1,a2,a3)
if not args.fix_posenet:
decisive_error = 0 # flow_errors[-2] # epe_rigid_with_gt_mask
elif not args.fix_dispnet:
decisive_error = 0 # errors[0] #depth abs_diff
elif not args.fix_flownet:
decisive_error = 0 # flow_errors[-1] #epe_non_rigid_with_gt_mask
elif not args.fix_masknet:
decisive_error = 0 #flow_errors[3] # percent outliers
#3.5 log
if args.log_terminal:
logger.train_writer.write(
' * Avg Loss : {:.3f}'.format(train_loss))
logger.reset_valid_bar()
#eopch data log on tensorboard
#train loss
tb_writer.add_scalar('epoch/train_loss', train_loss, epoch)
#val_without_gt loss
if args.val_without_gt:
tb_writer.add_scalar('epoch/val_loss', val_loss, epoch)
if args.val_with_depth_gt:
#val with depth gt
for error, name in zip(depth_errors, depth_error_names):
tb_writer.add_scalar('epoch/' + name, error, epoch)
#3.6 save model and remember lowest error and save checkpoint
if best_error < 0:
best_error = train_loss
is_best = train_loss <= best_error
best_error = min(best_error, train_loss)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': mask_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': flow_net.module.state_dict()
}, is_best)
with open(args.save_path / args.log_summary, 'a') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([train_loss, decisive_error])
if args.log_terminal:
logger.epoch_bar.finish()