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 dataflow_test():
from DataFlow.sequence_folders import SequenceFolder
import custom_transforms
from torch.utils.data import DataLoader
from DataFlow.validation_folders import ValidationSet
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize])
valid_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize])
datapath = 'G:/data/KITTI/KittiRaw_formatted'
seed = 8964
train_set = SequenceFolder(datapath, transform=train_transform, seed=seed, train=True,
sequence_length=3)
train_loader = DataLoader(train_set, batch_size=4, shuffle=True, num_workers=4,
pin_memory=True)
val_set = ValidationSet(datapath, transform=valid_transform)
print("length of train loader is %d" % len(train_loader))
val_loader = DataLoader(val_set, batch_size=4, shuffle=False, num_workers=4, pin_memory=True)
print("length of val loader is %d" % len(val_loader))
dataiter = iter(train_loader)
imgs, intrinsics = next(dataiter)
print(len(imgs))
print(intrinsics.shape)
pass
def transform_pair_train(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.HorizontalFlip(),
tr.GaussianBlur(),
tr.Normalize(mean=self.source_dist['mean'],
std=self.source_dist['std'],
if_pair=True),
tr.ToTensor(if_pair=True),
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(), #随机水平翻转
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size), #随机尺寸裁剪
tr.RandomGaussianBlur(), #随机高斯模糊
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)), #归一化
tr.ToTensor()
])
return composed_transforms(sample)
def transform(self, sample):
if self.train:
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomVerticalFlip(),
tr.RandomScaleCrop(),
tr.ToTensor()
])
else:
composed_transforms = transforms.Compose([
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
#tr.RandomHorizontalFlip(),
# to make the image in the same batch has same shape
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size),
#tr.RandomGaussianBlur(),
#tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
if not self.random_match:
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
#tr.Remap(self.building_table, self.nonbuilding_table, self.channels)
tr.RandomGaussianBlur(),
#tr.ConvertFromInts(),
#tr.PhotometricDistort(),
tr.Normalize(mean=self.source_dist['mean'],
std=self.source_dist['std']),
tr.ToTensor(),
])
else:
composed_transforms = transforms.Compose([
tr.HistogramMatching(),
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
tr.Normalize(mean=self.source_dist['mean'],
std=self.source_dist['std']),
tr.ToTensor(),
])
return composed_transforms(sample)
num_workers=4)
for item in dataloader:
train = item['image']
# train = np.array(train) #?
print(train.shape)
print('sample {} images to calculate'.format(train.shape[0]))
mean = np.mean(train.numpy(), axis=(0, 2, 3))
std = np.std(train.numpy(), axis=(0, 2, 3))
return mean, std
if __name__ == '__main__':
trs = tf.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=512, crop_size=512),
tr.RandomGaussianBlur(), #高斯模糊
tr.Normalize(mean=LungDataset.mean, std=LungDataset.std),
tr.ToTensor()
])
dataset = LungDataset(root_dir=r'D:\code\U-net',
transforms=trs,
train=True)
# dataset = LungDataset(root_dir=r'D:\code\U-net', transforms = False , train=True)
# print(dataset.get_mean_std())
for item in dataset:
# print(item['label'].shape)
# plt.imshow(image, cmap='gray')
# print(item['image'])
print(item['image'].min(), item['image'].max(), item['label'].min(),
item['label'].max())
def main():
global best_error, n_iter, device
args = parser.parse_args()
save_path = save_path_formatter(args, parser)
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)
if args.evaluate:
args.epochs = 0
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(SummaryWriter(args.save_path/'valid'/str(i)))
# Data loading code
train_transform = custom_transforms.Compose([
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor()
])
valid_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = KITTIDataset(
root_dir,
sequences,
max_distance=args.max_distance,
transform=None
)
val_set = KITTIDataset(
root_dir,
sequences,
max_distance=args.max_distance,
transform=None
)
print('{} samples found in {} train scenes'.format(len(train_set), len(train_set)))
print('{} samples found in {} valid scenes'.format(len(val_set), len(val_set)))
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_set, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
config_file = "./configs/e2e_mask_rcnn_R_50_FPN_1x.yaml"
cfg.merge_from_file(config_file)
cfg.freeze()
pretrained_model_path = "./e2e_mask_rcnn_R_50_FPN_1x.pth"
disvo = DISVO(cfg, pretrained_model_path).cuda()
if args.pretrained_disvo:
print("=> using pre-trained weights for Dispnet")
weights = torch.load(args.pretrained_disvo)
disvo.load_state_dict(weights['state_dict'])
else:
disvo.init_weights()
cudnn.benchmark = True
print('=> setting adam solver')
optim_params = [
{'params': disvo.parameters(), 'lr': args.lr}
]
optimizer = torch.optim.Adam(optim_params,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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'])
logger = TermLogger(n_epochs=args.epochs, train_size=min(len(train_loader), args.epoch_size), valid_size=len(val_loader))
logger.epoch_bar.start()
if args.pretrained_disvo or args.evaluate:
logger.reset_valid_bar()
errors, error_names = validate(args, val_loader, disvo, 0, logger, output_writers)
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, 0)
error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names[2:9], errors[2:9]))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for epoch in range(args.epochs):
logger.epoch_bar.update(epoch)
# train for one epoch
logger.reset_train_bar()
train_loss = train(args, train_loader, disvo, optimizer, args.epoch_size, logger, training_writer)
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
# evaluate on validation set
logger.reset_valid_bar()
errors, error_names = validate(args, val_loader, disvo, 0, logger, output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# 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)
decisive_error = errors[1]
if best_error < 0:
best_error = decisive_error
# remember lowest error and save checkpoint
is_best = decisive_error < best_error
best_error = min(best_error, decisive_error)
save_checkpoint(
args.save_path, {
'epoch': epoch + 1,
'state_dict': disvo.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])
logger.epoch_bar.finish()
def main():
global args, best_error, n_iter, device
args = parser.parse_args()
save_path = save_path_formatter(args, parser)
args.save_path = 'checkpoints_shifted' / save_path
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(
SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = ShiftedSequenceFolder(
args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length,
target_displacement=args.target_displacement)
# if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping
if args.with_gt:
from datasets.validation_folders import ValidationSet
val_set = ValidationSet(args.data, transform=valid_transform)
else:
val_set = SequenceFolder(
args.data,
transform=valid_transform,
seed=args.seed,
train=False,
sequence_length=args.sequence_length,
)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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)
adjust_loader = torch.utils.data.DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True
) # workers is set to 0 to avoid multiple instances to be modified at the same time
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
train.args = args
# create model
print("=> creating model")
disp_net = models.DispNetS().cuda()
output_exp = args.mask_loss_weight > 0
if not output_exp:
print("=> no mask loss, PoseExpnet will only output pose")
pose_exp_net = models.PoseExpNet(
nb_ref_imgs=args.sequence_length - 1,
output_exp=args.mask_loss_weight > 0).to(device)
if args.pretrained_exp_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_exp_pose)
pose_exp_net.load_state_dict(weights['state_dict'], strict=False)
else:
pose_exp_net.init_weights()
if args.pretrained_disp:
print("=> using pre-trained weights for Dispnet")
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'])
else:
disp_net.init_weights()
cudnn.benchmark = True
disp_net = torch.nn.DataParallel(disp_net)
pose_exp_net = torch.nn.DataParallel(pose_exp_net)
print('=> setting adam solver')
parameters = chain(disp_net.parameters(), pose_exp_net.parameters())
optimizer = torch.optim.Adam(parameters,
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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_loss', 'explainability_loss', 'smooth_loss'])
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader))
logger.epoch_bar.start()
for epoch in range(args.epochs):
logger.epoch_bar.update(epoch)
# train for one epoch
logger.reset_train_bar()
train_loss = train(args, train_loader, disp_net, pose_exp_net,
optimizer, args.epoch_size, logger, training_writer)
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
if (epoch + 1) % 5 == 0:
train_set.adjust = True
logger.reset_train_bar(len(adjust_loader))
average_shifts = adjust_shifts(args, train_set, adjust_loader,
pose_exp_net, epoch, logger,
training_writer)
shifts_string = ' '.join(
['{:.3f}'.format(s) for s in average_shifts])
logger.train_writer.write(
' * adjusted shifts, average shifts are now : {}'.format(
shifts_string))
for i, shift in enumerate(average_shifts):
training_writer.add_scalar('shifts{}'.format(i), shift, epoch)
train_set.adjust = False
# evaluate on validation set
logger.reset_valid_bar()
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net,
epoch, logger,
output_writers)
else:
errors, error_names = validate_without_gt(args, val_loader,
disp_net, pose_exp_net,
epoch, logger,
output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# 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)
decisive_error = errors[0]
if best_error < 0:
best_error = decisive_error
# remember lowest error and save checkpoint
is_best = decisive_error < best_error
best_error = min(best_error, decisive_error)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_exp_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])
logger.epoch_bar.finish()
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
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 and transpose
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()
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data_dir))
train_transform = custom_transforms.Compose([
#custom_transforms.RandomRotate(),
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(),
normalize
])
#train set, loader only建立一个
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,
depth_format='png')
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 args.val_with_depth_gt:
from datasets.validation_folders2 import ValidationSet
val_set_with_depth_gt = ValidationSet(args.data_dir,
transform=valid_transform,
depth_format='png')
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)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.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 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.resume:
print("=> resuming from checkpoint")
dispnet_weights = torch.load(args.save_path /
'dispnet_checkpoint.pth.tar')
disp_net.load_state_dict(dispnet_weights['state_dict'])
cudnn.benchmark = True
disp_net = torch.nn.DataParallel(disp_net)
print('=> setting adam solver')
parameters = chain(disp_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'])
#
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.reset_epoch_bar()
else:
logger = None
#预先评估下
criterion_train = MaskedL1Loss().to(device) # l1LOSS 容易优化
criterion_val = ComputeErrors().to(device)
#depth_error_names,depth_errors = validate_depth_with_gt(val_loader_depth, disp_net,criterion=criterion_val, epoch=0, logger=logger,tb_writer=tb_writer,global_vars_dict=global_vars_dict)
#logger.reset_epoch_bar()
# logger.epoch_logger_update(epoch=0,time=0,names=depth_error_names,values=depth_errors)
epoch_time = AverageMeter()
end = time.time()
#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.
logger.reset_train_bar()
logger.reset_valid_bar()
errors = [0]
error_names = ['no error names depth']
#3.2 train for one epoch---------
loss_names, losses = train_depth_gt(train_loader=train_loader,
disp_net=disp_net,
optimizer=optimizer,
criterion=criterion_train,
logger=logger,
train_writer=tb_writer,
global_vars_dict=global_vars_dict)
#3.3 evaluate on validation set-----
depth_error_names, depth_errors = validate_depth_with_gt(
val_loader=val_loader_depth,
disp_net=disp_net,
criterion=criterion_val,
epoch=epoch,
logger=logger,
tb_writer=tb_writer,
global_vars_dict=global_vars_dict)
epoch_time.update(time.time() - end)
end = time.time()
#3.5 log_terminal
#if args.log_terminal:
if args.log_terminal:
logger.epoch_logger_update(epoch=epoch,
time=epoch_time,
names=depth_error_names,
values=depth_errors)
# tensorboard scaler
#train loss
for loss_name, loss in zip(loss_names, losses.avg):
tb_writer.add_scalar('train/' + loss_name, loss, epoch)
#val_with_gt loss
for name, error in zip(depth_error_names, depth_errors.avg):
tb_writer.add_scalar('val/' + name, error, epoch)
#3.6 save model and remember lowest error and save checkpoint
total_loss = losses.avg[0]
if best_error < 0:
best_error = total_loss
is_best = total_loss <= best_error
best_error = min(best_error, total_loss)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': None
}, {
'epoch': epoch + 1,
'state_dict': None
}, {
'epoch': epoch + 1,
'state_dict': None
}, is_best)
if args.log_terminal:
logger.epoch_bar.finish()
def main():
global best_error, n_iter, device
args = parser.parse_args()
timestamp = datetime.datetime.now().strftime("%m-%d-%H:%M")
save_path = Path(args.name)
args.save_path = 'checkpoints' / save_path / timestamp
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
cudnn.deterministic = True
cudnn.benchmark = True
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(
SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
normalize = custom_transforms.Normalize(mean=[0.45, 0.45, 0.45],
std=[0.225, 0.225, 0.225])
train_transform = custom_transforms.Compose([
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length)
# if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping
if args.with_gt:
from datasets.validation_folders import ValidationSet
val_set = ValidationSet(args.data, transform=valid_transform)
else:
val_set = SequenceFolder(args.data,
transform=valid_transform,
seed=args.seed,
train=False,
sequence_length=args.sequence_length)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
disp_net = models.DispResNet(args.resnet_layers,
args.with_pretrain).to(device)
pose_net = models.PoseResNet(18, args.with_pretrain).to(device)
# load parameters
if args.pretrained_disp:
print("=> using pre-trained weights for DispResNet")
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'], strict=False)
if args.pretrained_pose:
print("=> using pre-trained weights for PoseResNet")
weights = torch.load(args.pretrained_pose)
pose_net.load_state_dict(weights['state_dict'], strict=False)
disp_net = torch.nn.DataParallel(disp_net)
pose_net = torch.nn.DataParallel(pose_net)
print('=> setting adam solver')
optim_params = [{
'params': disp_net.parameters(),
'lr': args.lr
}, {
'params': pose_net.parameters(),
'lr': args.lr
}]
optimizer = torch.optim.Adam(optim_params,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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_loss', 'smooth_loss',
'geometry_consistency_loss'
])
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader))
logger.epoch_bar.start()
for epoch in range(args.epochs):
logger.epoch_bar.update(epoch)
# train for one epoch
logger.reset_train_bar()
train_loss = train(args, train_loader, disp_net, pose_net, optimizer,
args.epoch_size, logger, training_writer)
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
# evaluate on validation set
logger.reset_valid_bar()
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net,
epoch, logger,
output_writers)
else:
errors, error_names = validate_without_gt(args, val_loader,
disp_net, pose_net,
epoch, logger,
output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# 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)
decisive_error = errors[1]
if best_error < 0:
best_error = decisive_error
# remember lowest error and save checkpoint
is_best = decisive_error < best_error
best_error = min(best_error, decisive_error)
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()
}, 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])
logger.epoch_bar.finish()
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 = MaskResNet6.MaskResNet6().cuda()
pose_net = PoseNetB6.PoseNetB6().cuda()
mask_net = torch.nn.DataParallel(mask_net)
pose_net = torch.nn.DataParallel(pose_net)
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'], False)
else:
mask_net.init_weights()
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'], False)
else:
pose_net.init_weights()
print('=> setting adam solver')
parameters = chain(mask_net.parameters(), pose_net.parameters())
optimizer = torch.optim.Adam(parameters,
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
# training
best_error = 100001
train_loss = 100000
for epoch in tqdm(range(args.epochs)):
train_loss = train(train_loader, mask_net, pose_net, optimizer,
args.epoch_size, training_writer)
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.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': optimizer.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_net.state_dict()
}, is_best)
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)
print('===> Start from scratch')
if cuda:
model.cuda()
cudnn.benchmark = True
#%%
parser = argparse.ArgumentParser()
args = parser.parse_args()
args.base_size = 513
args.crop_size = 513
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(), #随机水平翻转
tr.RandomScaleCrop(base_size=args.base_size,
crop_size=args.crop_size), #随机尺寸裁剪
tr.RandomGaussianBlur(), #随机高斯模糊
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), #归一化
tr.ToTensor()
])
#%%
import matplotlib.pyplot as plt
import numpy as np
# dataset.utils import decode_segmap
tbar = tqdm(dataloader)
num_img_tr = len(dataloader)
for epoch in range(0, 10):
for i, sample in enumerate(tbar):
image, target = sample['image'], sample['label']
def main():
global best_error, n_iter, device
args = parser.parse_args()
if args.dataset_format == 'stacked':
from datasets.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from datasets.sequence_folders import SequenceFolder
save_path = save_path_formatter(args, parser)
args.save_path = 'checkpoints' / save_path
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p() #如果没有，则建立，有则啥都不干 in Path.py小工具
torch.manual_seed(args.seed)
if args.evaluate:
args.epochs = 0
#tensorboard SummaryWriter
training_writer = SummaryWriter(args.save_path) #for tensorboard
output_writers = [] #list
if args.log_output:
for i in range(3):
output_writers.append(
SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
'''transform'''
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(
args.data, #processed_data_train_sets
transform=train_transform, #把几种变换函数输入进去
seed=args.seed,
train=True,
sequence_length=args.sequence_length)
# if no Groundtruth is avalaible, Validation set is
# the same type as training set to measure photometric loss from warping
if args.with_gt:
from datasets.validation_folders import ValidationSet
val_set = ValidationSet(args.data, transform=valid_transform)
else:
val_set = SequenceFolder(
args.data,
transform=valid_transform,
seed=args.seed,
train=False,
sequence_length=args.sequence_length,
)
print('{} samples found in {} train scenes'.format(
len(train_set), len(train_set.scenes))) #训练集都是序列,不用左右
print('{} samples found in {} valid scenes'.format(
len(val_set), len(val_set.scenes))) #测试集也是序列,不需要左右
train_loader = torch.utils.data.DataLoader( #data(list): [tensor(B,3,H,W),list(B),(B,H,W),(b,h,w)]
dataset=train_set, #sequenceFolder
batch_size=args.batch_size,
shuffle=True, #打乱
num_workers=args.workers, #多线程读取数据
pin_memory=True)
val_loader = torch.utils.data.DataLoader(
dataset=val_set,
batch_size=args.batch_size,
shuffle=False, #不打乱
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
#disp
disp_net = models.DispNetS().to(device)
output_exp = args.mask_loss_weight > 0
if not output_exp:
print("=> no mask loss, PoseExpnet will only output pose")
#pose
pose_exp_net = models.PoseExpNet(
nb_ref_imgs=args.sequence_length - 1,
output_exp=args.mask_loss_weight > 0).to(device)
#init posenet
if args.pretrained_exp_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_exp_pose)
pose_exp_net.load_state_dict(weights['state_dict'], strict=False)
else:
pose_exp_net.init_weights()
#init dispNet
if args.pretrained_disp:
print("=> using pre-trained weights for Dispnet")
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'])
else:
disp_net.init_weights()
cudnn.benchmark = True
disp_net = torch.nn.DataParallel(disp_net)
pose_exp_net = torch.nn.DataParallel(pose_exp_net)
print('=> setting adam solver')
#可以看到两个一起训练
optim_params = [{
'params': disp_net.parameters(),
'lr': args.lr
}, {
'params': pose_exp_net.parameters(),
'lr': args.lr
}]
optimizer = torch.optim.Adam(optim_params,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
#训练结果写入csv
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_loss', 'explainability_loss', 'smooth_loss'])
n_epochs = args.epochs
train_size = min(len(train_loader), args.epoch_size)
valid_size = len(val_loader)
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader))
logger.epoch_bar.start()
if args.pretrained_disp or args.evaluate:
logger.reset_valid_bar()
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net,
0, logger, output_writers)
else:
errors, error_names = validate_without_gt(args, val_loader,
disp_net, pose_exp_net,
0, logger,
output_writers)
for error, name in zip(
errors, error_names
): #validation时，对['Total loss', 'Photo loss', 'Exp loss']三个 epoch-record 指标添加记录值
training_writer.add_scalar(name, error, 0)
error_string = ', '.join(
'{} : {:.3f}'.format(name, error)
for name, error in zip(error_names[2:9], errors[2:9]))
logger.valid_writer.write(' * Avg {}'.format(error_string))
#main cycle
for epoch in range(args.epochs):
logger.epoch_bar.update(epoch)
logger.reset_train_bar()
#1. train for one epoch
train_loss = train(args, train_loader, disp_net, pose_exp_net,
optimizer, args.epoch_size, logger, training_writer)
#其他参数都好解释， logger: SelfDefined class,
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
logger.reset_valid_bar()
# 2. validate on validation set
if args.with_gt: #<class 'list'>: ['Total loss', 'Photo loss', 'Exp loss']
errors, error_names = validate_with_gt(args, val_loader, disp_net,
epoch, logger,
output_writers)
else:
errors, error_names = validate_without_gt(args, val_loader,
disp_net, pose_exp_net,
epoch, logger,
output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error,
epoch) #损失函数中记录epoch-record指标
# 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)
# 3. remember lowest error and save checkpoint
decisive_error = errors[1]
if best_error < 0:
best_error = decisive_error
is_best = decisive_error < best_error
best_error = min(best_error, decisive_error)
#模型保存
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_exp_net.module.state_dict()
}, is_best)
with open(args.save_path / args.log_summary,
'a') as csvfile: #每个epoch留下结果
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([train_loss,
decisive_error]) #第二个就是validataion 中的epoch-record
# loss<class 'list'>: ['Total loss', 'Photo loss', 'Exp loss']
logger.epoch_bar.finish()
def main():
global n_iter
args = parser.parse_args()
save_path = save_path_formatter(args, parser)
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)
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(
SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
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.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
ttype=args.ttype)
val_set = SequenceFolder(args.data,
transform=valid_transform,
seed=args.seed,
ttype=args.ttype2)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
dpsnet = PSNet(args.nlabel, args.mindepth).cuda()
if args.pretrained_dps:
print("=> using pre-trained weights for DPSNet")
weights = torch.load(args.pretrained_dps)
dpsnet.load_state_dict(weights['state_dict'])
else:
dpsnet.init_weights()
cudnn.benchmark = True
dpsnet = torch.nn.DataParallel(dpsnet)
print('=> setting adam solver')
parameters = chain(dpsnet.parameters())
optimizer = torch.optim.Adam(parameters,
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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'])
for epoch in range(args.epochs):
adjust_learning_rate(args, optimizer, epoch)
# train for one epoch
train_loss = train(args, train_loader, dpsnet, optimizer,
args.epoch_size, training_writer)
errors, error_names = validate_with_gt(args, val_loader, dpsnet, epoch,
output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# 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)
decisive_error = errors[0]
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': dpsnet.module.state_dict()
}, epoch)
with open(args.save_path / args.log_summary, 'a') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([train_loss, decisive_error])
def main():
global best_error, n_iter, device
args = parser.parse_args()
if args.dataset_format == 'stacked':
from datasets.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from datasets.sequence_folders import SequenceFolder
save_path = save_path_formatter(args, parser)
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)
if args.evaluate:
args.epochs = 0
tb_writer = SummaryWriter(args.save_path)
# Data loading code
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length)
# if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping
if args.with_gt:
from datasets.validation_folders import ValidationSet
val_set = ValidationSet(args.data, transform=valid_transform)
else:
val_set = SequenceFolder(
args.data,
transform=valid_transform,
seed=args.seed,
train=False,
sequence_length=args.sequence_length,
)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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=False,
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
disp_net = models.DispNetS().to(device)
seg_net = DeepLab(num_classes=args.nclass,
backbone=args.backbone,
output_stride=args.out_stride,
sync_bn=args.sync_bn,
freeze_bn=args.freeze_bn).to(device)
if args.pretrained_seg:
print("=> using pre-trained weights for seg net")
weights = torch.load(args.pretrained_seg)
seg_net.load_state_dict(weights, strict=False)
output_exp = args.mask_loss_weight > 0
if not output_exp:
print("=> no mask loss, PoseExpnet will only output pose")
pose_exp_net = models.PoseExpNet(
nb_ref_imgs=args.sequence_length - 1,
output_exp=args.mask_loss_weight > 0).to(device)
if args.pretrained_exp_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_exp_pose)
pose_exp_net.load_state_dict(weights['state_dict'], strict=False)
else:
pose_exp_net.init_weights()
if args.pretrained_disp:
print("=> using pre-trained weights for Dispnet")
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'])
else:
disp_net.init_weights()
cudnn.benchmark = True
disp_net = torch.nn.DataParallel(disp_net)
pose_exp_net = torch.nn.DataParallel(pose_exp_net)
seg_net = torch.nn.DataParallel(seg_net)
print('=> setting adam solver')
optim_params = [{
'params': disp_net.parameters(),
'lr': args.lr
}, {
'params': pose_exp_net.parameters(),
'lr': args.lr
}]
optimizer = torch.optim.Adam(optim_params,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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_loss', 'explainability_loss', 'smooth_loss'])
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader))
logger.epoch_bar.start()
if args.pretrained_disp or args.evaluate:
logger.reset_valid_bar()
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net,
0, logger, tb_writer)
else:
errors, error_names = validate_without_gt(args, val_loader,
disp_net, pose_exp_net,
0, logger, tb_writer)
for error, name in zip(errors, error_names):
tb_writer.add_scalar(name, error, 0)
error_string = ', '.join(
'{} : {:.3f}'.format(name, error)
for name, error in zip(error_names[2:9], errors[2:9]))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for epoch in range(args.epochs):
logger.epoch_bar.update(epoch)
# train for one epoch
logger.reset_train_bar()
train_loss = train(args, train_loader, disp_net, pose_exp_net, seg_net,
optimizer, args.epoch_size, logger, tb_writer)
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
# evaluate on validation set
logger.reset_valid_bar()
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net,
seg_net, epoch, logger,
tb_writer)
else:
errors, error_names = validate_without_gt(args, val_loader,
disp_net, pose_exp_net,
epoch, logger, tb_writer)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
logger.valid_writer.write(' * Avg {}'.format(error_string))
for error, name in zip(errors, error_names):
tb_writer.add_scalar(name, error, epoch)
# 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)
decisive_error = errors[1]
if best_error < 0:
best_error = decisive_error
# remember lowest error and save checkpoint
is_best = decisive_error < best_error
best_error = min(best_error, decisive_error)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_exp_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])
logger.epoch_bar.finish()
def main():
global args, best_error, n_iter
args = parser.parse_args()
if args.dataset_format == 'stacked':
from datasets.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from datasets.sequence_folders import SequenceFolder
save_path = Path(args.name)
args.save_path = 'checkpoints'/save_path #/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])
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(SummaryWriter(args.save_path/'valid'/str(i)))
# 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()
train_transform = custom_transforms.Compose([
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))
train_set = SequenceFolder(
args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length
)
# if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping
val_set = SequenceFolder(
args.data,
transform=valid_transform,
seed=args.seed,
train=False,
sequence_length=args.sequence_length,
)
if args.with_flow_gt:
from datasets.validation_flow import ValidationFlow
val_flow_set = ValidationFlow(root=args.kitti_dir,
sequence_length=args.sequence_length, transform=valid_flow_transform)
if args.DEBUG:
train_set.__len__ = 32
train_set.samples = train_set.samples[:32]
print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes)))
print('{} samples found in {} valid 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=False,
num_workers=args.workers, pin_memory=True, drop_last=True)
if args.with_flow_gt:
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)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
if args.flownet=='SpyNet':
flow_net = getattr(models, args.flownet)(nlevels=args.nlevels, pre_normalization=normalize).cuda()
else:
flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda()
# load pre-trained 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")
flownet_weights = torch.load(args.save_path/'flownet_checkpoint.pth.tar')
flow_net.load_state_dict(flownet_weights['state_dict'])
# import ipdb; ipdb.set_trace()
cudnn.benchmark = True
flow_net = torch.nn.DataParallel(flow_net)
print('=> setting adam solver')
parameters = chain(flow_net.parameters())
optimizer = torch.optim.Adam(parameters, args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
milestones = [300]
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1)
if args.min:
print("using min method")
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))
logger.epoch_bar.start()
else:
logger=None
for epoch in range(args.epochs):
scheduler.step()
if args.fix_flownet:
for fparams in flow_net.parameters():
fparams.requires_grad = False
if args.log_terminal:
logger.epoch_bar.update(epoch)
logger.reset_train_bar()
# train for one epoch
train_loss = train(train_loader, flow_net, optimizer, args.epoch_size, logger, training_writer)
if args.log_terminal:
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
logger.reset_valid_bar()
if args.with_flow_gt:
flow_errors, flow_error_names = validate_flow_with_gt(val_flow_loader, flow_net, epoch, logger, output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(flow_error_names, flow_errors))
if args.log_terminal:
logger.valid_writer.write(' * Avg {}'.format(error_string))
else:
print('Epoch {} completed'.format(epoch))
for error, name in zip(flow_errors, flow_error_names):
training_writer.add_scalar(name, error, epoch)
decisive_error = flow_errors[0]
if best_error < 0:
best_error = decisive_error
# remember lowest error and save checkpoint
is_best = decisive_error <= best_error
best_error = min(best_error, decisive_error)
save_checkpoint(
args.save_path, {
'epoch': epoch + 1,
'state_dict': flow_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': optimizer.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()
def main():
global opt, best_prec1
opt = parser.parse_args()
print(opt)
# Data loading
train_transform = custom_transforms.Compose([
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(),
custom_transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
valid_transform = custom_transforms.Compose([
custom_transforms.ArrayToTensor(),
custom_transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
print('Loading scenes in', opt.data_dir)
train_set = SequenceFolder(opt.data_dir,
transform=train_transform,
seed=opt.seed,
train=True,
sequence_length=opt.sequence_length)
val_set = ValidationSet(opt.data_dir, transform=valid_transform)
print(len(train_set), 'samples found')
print(len(val_set), 'samples found')
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.workers,
pin_memory=True)
# val_loader = torch.utils.data.DataLoader(val_set, batch_size=opt.batch_size,
# shuffle=False, num_workers=opt.workers,
# pin_memory=True)
if opt.epoch == 0:
opt.epoch_size = len(train_loader)
# Done loading
disp_model = dispnet.DispNet().cuda()
pose_model = posenet.PoseNet().cuda()
disp_model, pose_model, optimizer = init.setup(disp_model, pose_model, opt)
print(disp_model, pose_model)
trainer = train.Trainer(disp_model, pose_model, optimizer, opt)
if opt.resume:
if os.path.isfile(opt.resume):
# disp_model, pose_model, optimizer, opt, best_prec1 = init.resumer(opt, disp_model, pose_model, optimizer)
disp_model, pose_model, optimizer, opt = init.resumer(
opt, disp_model, pose_model, optimizer)
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
cudnn.benchmark = True
for epoch in range(opt.start_epoch, opt.epochs):
utils.adjust_learning_rate(opt, optimizer, epoch)
print("Starting epoch number:", epoch + 1, "Learning rate:",
optimizer.param_groups[0]["lr"])
if opt.testOnly == False:
trainer.train(train_loader, epoch, opt)
# init.save_checkpoint(opt, disp_model, pose_model, optimizer, best_prec1, epoch)
init.save_checkpoint(opt, disp_model, pose_model, optimizer, epoch)
def train(self):
global n_iter
if not self.train_flow:
self.pose_net.train()
self.disp_net.train()
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
self.train_set = SequenceFolder(
self.config['data'],
transform=train_transform,
split='train',
seed=self.config['seed'],
img_height=self.config['img_height'],
img_width=self.config['img_width'],
sequence_length=self.config['sequence_length'])
self.val_set = SequenceFolder(
self.config['data'],
transform=valid_transform,
split='val',
seed=self.config['seed'],
img_height=self.config['img_height'],
img_width=self.config['img_width'],
sequence_length=self.config['sequence_length'])
self.train_loader = torch.utils.data.DataLoader(
self.train_set,
shuffle=True,
drop_last=True,
num_workers=self.config['data_workers'],
batch_size=self.config['batch_size'],
pin_memory=False)
self.val_loader = torch.utils.data.DataLoader(
self.val_set,
shuffle=True,
batch_size=self.config['batch_size'],
drop_last=True,
num_workers=self.config['data_workers'],
pin_memory=False)
optim_params = [{
'params': v.parameters(),
'lr': self.config['learning_rate']
} for v in self.nets.values()]
self.optimizer = torch.optim.Adam(
optim_params,
betas=(self.config['momentum'], self.config['beta']),
weight_decay=self.config['weight_decay'])
self.logger = TermLogger(n_epochs=self.config['epoch'],
train_size=min(len(self.train_loader),
self.config['epoch_size']),
valid_size=len(self.val_loader))
self.logger.epoch_bar.start()
for epoch in range(self.epochs):
self.logger.epoch_bar.update(epoch)
self.logger.reset_train_bar()
epoch_train_loss = self.training_inside_epoch()
self.logger.train_writer.write(
' training * Avg Loss : {:.3f}'.format(epoch_train_loss))
self.logger.reset_valid_bar()
epoch_val_loss = self.validate_inside_epoch_without_gt()
self.logger.valid_writer.write(
' validation * Avg Loss : {:.3f}'.format(epoch_val_loss))
def main():
global args, best_photo_loss, n_iter
args = parser.parse_args()
if args.dataset_format == 'stacked':
from datasets.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from datasets.sequence_folders import SequenceFolder
save_path = Path('{}epochs{},seq{},b{},lr{},p{},m{},s{}'.format(
args.epochs,
',epochSize' + str(args.epoch_size) if args.epoch_size > 0 else '',
args.sequence_length, args.batch_size, args.lr, args.photo_loss_weight,
args.mask_loss_weight, args.smooth_loss_weight))
timestamp = datetime.datetime.now().strftime("%m-%d-%H:%M")
args.save_path = 'checkpoints' / save_path / timestamp
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
train_writer = SummaryWriter(args.save_path / 'train')
valid_writer = SummaryWriter(args.save_path / 'valid')
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(
SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
input_transform = custom_transforms.Compose([
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=input_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length)
val_set = SequenceFolder(args.data,
transform=custom_transforms.Compose([
custom_transforms.ArrayToTensor(), normalize
]),
seed=args.seed,
train=False,
sequence_length=args.sequence_length)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
disp_net = models.DispNetS().cuda()
output_exp = args.mask_loss_weight > 0
if not output_exp:
print("=> no mask loss, PoseExpnet will only output pose")
pose_exp_net = models.PoseExpNet(
nb_ref_imgs=args.sequence_length - 1,
output_exp=args.mask_loss_weight > 0).cuda()
if args.pretrained_exp_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_exp_pose)
pose_exp_net.load_state_dict(weights['state_dict'], strict=False)
else:
pose_exp_net.init_weights()
if args.pretrained_disp:
print("=> using pre-trained weights for Dispnet")
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'])
else:
disp_net.init_weights()
cudnn.benchmark = True
disp_net = torch.nn.DataParallel(disp_net)
pose_exp_net = torch.nn.DataParallel(pose_exp_net)
print('=> setting adam solver')
parameters = chain(disp_net.parameters(), pose_exp_net.parameters())
optimizer = torch.optim.Adam(parameters,
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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_loss', 'explainability_loss', 'smooth_loss'])
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader))
logger.epoch_bar.start()
for epoch in range(args.epochs):
logger.epoch_bar.update(epoch)
# train for one epoch
logger.reset_train_bar()
train_loss = train(train_loader, disp_net, pose_exp_net, optimizer,
args.epoch_size, logger, train_writer)
logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss))
# evaluate on validation set
logger.reset_valid_bar()
valid_photo_loss, valid_exp_loss, valid_total_loss = validate(
val_loader, disp_net, pose_exp_net, epoch, logger, output_writers)
logger.valid_writer.write(
' * Avg Photo Loss : {:.3f}, Valid Loss : {:.3f}, Total Loss : {:.3f}'
.format(valid_photo_loss, valid_exp_loss, valid_total_loss))
valid_writer.add_scalar(
'photometric_error', valid_photo_loss * 4, n_iter
) # Loss is multiplied by 4 because it's only one scale, instead of 4 during training
valid_writer.add_scalar('explanability_loss', valid_exp_loss * 4,
n_iter)
valid_writer.add_scalar('total_loss', valid_total_loss * 4, n_iter)
if best_photo_loss < 0:
best_photo_loss = valid_photo_loss
# remember lowest error and save checkpoint
is_best = valid_photo_loss < best_photo_loss
best_photo_loss = min(valid_photo_loss, best_photo_loss)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_exp_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, valid_total_loss])
logger.epoch_bar.finish()
def main():
global best_error, n_iter, device
args = parser.parse_args()
if args.dataset_format == 'stacked':
from datasets.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from datasets.sequence_folders import SequenceFolder, StereoSequenceFolder
save_path = save_path_formatter(args, parser)
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)
if args.evaluate:
args.epochs = 0
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(SummaryWriter(args.save_path/'valid'/str(i)))
# Data loading code
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(),
normalize
])
valid_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = StereoSequenceFolder(
args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.sequence_length
)
# if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping
if args.with_gt:
from datasets.validation_folders import ValidationSet
val_set = ValidationSet(
args.data,
transform=valid_transform
)
else:
val_set = StereoSequenceFolder(
args.data,
transform=valid_transform,
seed=args.seed,
train=False,
sequence_length=args.sequence_length,
)
print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes)))
print('{} samples found in {} valid 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)
val_loader = torch.utils.data.DataLoader(
val_set, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# 没有epoch_size的时候（=0），每个epoch训练train_set中所有的samples
# 有epoch_size的时候，每个epoch只训练一部分train_set
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
# 初始化网络结构
print("=> creating model")
# disp_net = models.DispNetS().to(device)
disp_net = models.DispResNet(3).to(device)
output_exp = args.mask_loss_weight > 0
if not output_exp:
print("=> no mask loss, PoseExpnet will only output pose")
# 如果有mask loss，PoseExpNet 要输出mask和pose estimation，因为两个输出共享encoder网络
# pose_exp_net = PoseExpNet(nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device)
pose_exp_net = models.PoseExpNet(nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device)
if args.pretrained_exp_pose:
print("=> using pre-trained weights for explainabilty and pose net")
weights = torch.load(args.pretrained_exp_pose)
pose_exp_net.load_state_dict(weights['state_dict'], strict=False)
else:
pose_exp_net.init_weights()
if args.pretrained_disp:
print("=> using pre-trained weights for Dispnet")
weights = torch.load(args.pretrained_disp)
disp_net.load_state_dict(weights['state_dict'])
else:
disp_net.init_weights()
cudnn.benchmark = True
# 并行化
disp_net = torch.nn.DataParallel(disp_net)
pose_exp_net = torch.nn.DataParallel(pose_exp_net)
# 训练方式：Adam
print('=> setting adam solver')
# 两个网络一起
optim_params = [
{'params': disp_net.parameters(), 'lr': args.lr},
{'params': pose_exp_net.parameters(), 'lr': args.lr}
]
optimizer = torch.optim.Adam(optim_params,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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_loss', 'explainability_loss', 'smooth_loss'])
# 对pretrained模型先做评估
if args.pretrained_disp or args.evaluate:
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net, 0, output_writers)
else:
errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, 0, output_writers)
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, 0)
error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names[2:9], errors[2:9]))
# 正式训练
for epoch in range(args.epochs):
# train for one epoch 训练一个周期
print('\n')
train_loss = train(args, train_loader, disp_net, pose_exp_net, optimizer, args.epoch_size, training_writer, epoch)
# evaluate on validation set
print('\n')
if args.with_gt:
errors, error_names = validate_with_gt(args, val_loader, disp_net, epoch, output_writers)
else:
errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, epoch, output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# 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)
# 验证输出四个loss：总体final loss，warping loss以及mask正则化loss
# 可自选以哪一种loss作为best model的标准
decisive_error = errors[0]
if best_error < 0:
best_error = decisive_error
# remember lowest error and save checkpoint
# 保存validation最佳model
is_best = decisive_error < best_error
best_error = min(best_error, decisive_error)
save_checkpoint(
args.save_path, {
'epoch': epoch + 1,
'state_dict': disp_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': pose_exp_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])
def main():
global n_iter
args = parser.parse_args()
save_path = save_path_formatter(args, parser)
args.save_path = 'checkpoints' / (args.exp + '_' + save_path)
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
training_writer = SummaryWriter(args.save_path)
output_writers = []
for i in range(3):
output_writers.append(SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
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.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
ttype=args.ttype,
dataset=args.dataset)
val_set = SequenceFolder(args.data,
transform=valid_transform,
seed=args.seed,
ttype=args.ttype2,
dataset=args.dataset)
train_set.samples = train_set.samples[:len(train_set) -
len(train_set) % args.batch_size]
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=1,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
mvdnet = MVDNet(args.nlabel, args.mindepth, no_pool=args.no_pool).cuda()
mvdnet.init_weights()
if args.pretrained_mvdn:
print("=> using pre-trained weights for MVDNet")
weights = torch.load(args.pretrained_mvdn)
mvdnet.load_state_dict(weights['state_dict'])
depth_cons = DepthCons().cuda()
depth_cons.init_weights()
if args.pretrained_cons:
print("=> using pre-trained weights for ConsNet")
weights = torch.load(args.pretrained_cons)
depth_cons.load_state_dict(weights['state_dict'])
cons_loss_ = ConsLoss().cuda()
print('=> setting adam solver')
if args.train_cons:
optimizer = torch.optim.Adam(depth_cons.parameters(),
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
mvdnet.eval()
else:
optimizer = torch.optim.Adam(mvdnet.parameters(),
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
cudnn.benchmark = True
mvdnet = torch.nn.DataParallel(mvdnet)
depth_cons = torch.nn.DataParallel(depth_cons)
print(' ==> setting log files')
with open(args.save_path / args.log_summary, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([
'train_loss', 'validation_abs_rel', 'validation_abs_diff',
'validation_sq_rel', 'validation_a1', 'validation_a2',
'validation_a3', 'mean_angle_error'
])
with open(args.save_path / args.log_full, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow(['train_loss'])
print(' ==> main Loop')
for epoch in range(args.epochs):
adjust_learning_rate(args, optimizer, epoch)
# train for one epoch
if args.evaluate:
train_loss = 0
else:
train_loss = train(args, train_loader, mvdnet, depth_cons,
cons_loss_, optimizer, args.epoch_size,
training_writer, epoch)
if not args.evaluate and (args.skip_v):
error_names = [
'abs_rel', 'abs_diff', 'sq_rel', 'a1', 'a2', 'a3', 'angle'
]
errors = [0] * 7
else:
errors, error_names = validate_with_gt(args, val_loader, mvdnet,
depth_cons, epoch,
output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# 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)
decisive_error = errors[0]
with open(args.save_path / args.log_summary, 'a') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([
train_loss, decisive_error, errors[1], errors[2], errors[3],
errors[4], errors[5], errors[6]
])
if args.evaluate:
break
if args.train_cons:
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': depth_cons.module.state_dict()
},
epoch,
file_prefixes=['cons'])
else:
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': mvdnet.module.state_dict()
},
epoch,
file_prefixes=['mvdnet'])
def main():
global n_iter
args = parser.parse_args()
save_path = save_path_formatter(args, parser)
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)
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(
SummaryWriter(args.save_path / 'valid' / str(i)))
# Data loading code
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.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(
args.data,
transform=train_transform,
seed=args.seed,
ttype=args.ttype,
add_geo=args.geo,
depth_source=args.depth_init,
pose_source='%s_poses.txt' %
args.pose_init if args.pose_init else 'poses.txt',
scale=False)
val_set = SequenceFolder(args.data,
transform=valid_transform,
seed=args.seed,
ttype=args.ttype2,
add_geo=args.geo,
depth_source=args.depth_init,
pose_source='%s_poses.txt' %
args.pose_init if args.pose_init else 'poses.txt',
scale=False)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid 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)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
depth_net = PSNet(args.nlabel,
args.mindepth,
add_geo_cost=args.geo,
depth_augment=False).cuda()
if args.pretrained_dps:
# for param in depth_net.feature_extraction.parameters():
# param.requires_grad = False
print("=> using pre-trained weights for DPSNet")
model_dict = depth_net.state_dict()
weights = torch.load(args.pretrained_dps)['state_dict']
pretrained_dict = {k: v for k, v in weights.items() if k in model_dict}
model_dict.update(pretrained_dict)
depth_net.load_state_dict(model_dict)
else:
depth_net.init_weights()
cudnn.benchmark = True
depth_net = torch.nn.DataParallel(depth_net)
print('=> setting adam solver')
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
depth_net.parameters()),
args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
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'])
for epoch in range(args.epochs):
adjust_learning_rate(args, optimizer, epoch)
# train for one epoch
train_loss = train(args, train_loader, depth_net, optimizer,
args.epoch_size, training_writer)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': depth_net.module.state_dict()
}, epoch)
with open(args.save_path / args.log_summary, 'a') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([train_loss])
from torchvision import datasets, transforms
import torch.utils.data as data
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image
import os
import custom_transforms as trans
img_transform = transforms.Compose([
trans.RandomHorizontalFlip(),
trans.RandomGaussianBlur(),
trans.RandomScaleCrop(700, 512),
trans.Normalize(),
trans.ToTensor()
])
class TrainImageFolder(data.Dataset):
def __init__(self, data_dir):
self.f = open(os.path.join(data_dir, 'train_id.txt'))
self.file_list = self.f.readlines()
self.data_dir = data_dir
def __getitem__(self, index):
img = Image.open(
os.path.join(self.data_dir, 'train_images',
self.file_list[index][:-1] + '.jpg')).convert('RGB')
parse = Image.open(
os.path.join(self.data_dir, 'train_segmentations',
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()
def main():
global args, best_error, n_iter
args = parser.parse_args()
save_path = Path(args.name)
args.save_path = 'checkpoints' / save_path #/timestamp
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
training_writer = SummaryWriter(args.save_path)
output_writer = SummaryWriter(args.save_path / 'valid')
# Data loading code
flow_loader_h, flow_loader_w = 384, 1280
train_transform = custom_transforms.Compose([
custom_transforms.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(h=256, w=256),
custom_transforms.ArrayToTensor(),
])
valid_transform = custom_transforms.Compose([
custom_transforms.Scale(h=flow_loader_h, w=flow_loader_w),
custom_transforms.ArrayToTensor()
])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=3)
if args.valset == "kitti2015":
from datasets.validation_flow import ValidationFlowKitti2015
val_set = ValidationFlowKitti2015(root=args.kitti_data,
transform=valid_transform)
elif args.valset == "kitti2012":
from datasets.validation_flow import ValidationFlowKitti2012
val_set = ValidationFlowKitti2012(root=args.kitti_data,
transform=valid_transform)
if args.DEBUG:
train_set.__len__ = 32
train_set.samples = train_set.samples[:32]
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in valid scenes'.format(len(val_set)))
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_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)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
if args.flownet == 'SpyNet':
flow_net = getattr(models, args.flownet)(nlevels=6, pretrained=True)
elif args.flownet == 'Back2Future':
flow_net = getattr(
models, args.flownet)(pretrained='pretrained/b2f_rm_hard.pth.tar')
elif args.flownet == 'PWCNet':
flow_net = models.pwc_dc_net(
'pretrained/pwc_net_chairs.pth.tar') # pwc_net.pth.tar')
else:
flow_net = getattr(models, args.flownet)()
if args.flownet in ['SpyNet', 'Back2Future', 'PWCNet']:
print("=> using pre-trained weights for " + args.flownet)
elif args.flownet in ['FlowNetC']:
print("=> using pre-trained weights for FlowNetC")
weights = torch.load('pretrained/FlowNet2-C_checkpoint.pth.tar')
flow_net.load_state_dict(weights['state_dict'])
elif args.flownet in ['FlowNetS']:
print("=> using pre-trained weights for FlowNetS")
weights = torch.load('pretrained/flownets.pth.tar')
flow_net.load_state_dict(weights['state_dict'])
elif args.flownet in ['FlowNet2']:
print("=> using pre-trained weights for FlowNet2")
weights = torch.load('pretrained/FlowNet2_checkpoint.pth.tar')
flow_net.load_state_dict(weights['state_dict'])
else:
flow_net.init_weights()
pytorch_total_params = sum(p.numel() for p in flow_net.parameters())
print("Number of model paramters: " + str(pytorch_total_params))
flow_net = flow_net.cuda()
cudnn.benchmark = True
if args.patch_type == 'circle':
patch, mask, patch_shape = init_patch_circle(args.image_size,
args.patch_size)
patch_init = patch.copy()
elif args.patch_type == 'square':
patch, patch_shape = init_patch_square(args.image_size,
args.patch_size)
patch_init = patch.copy()
mask = np.ones(patch_shape)
else:
sys.exit("Please choose a square or circle patch")
if args.patch_path:
patch, mask, patch_shape = init_patch_from_image(
args.patch_path, args.mask_path, args.image_size, args.patch_size)
patch_init = patch.copy()
if args.log_terminal:
logger = TermLogger(n_epochs=args.epochs,
train_size=min(len(train_loader), args.epoch_size),
valid_size=len(val_loader),
attack_size=args.max_count)
logger.epoch_bar.start()
else:
logger = None
for epoch in range(args.epochs):
if args.log_terminal:
logger.epoch_bar.update(epoch)
logger.reset_train_bar()
# train for one epoch
patch, mask, patch_init, patch_shape = train(patch, mask, patch_init,
patch_shape, train_loader,
flow_net, epoch, logger,
training_writer)
# Validate
errors, error_names = validate_flow_with_gt(patch, mask, patch_shape,
val_loader, flow_net,
epoch, logger,
output_writer)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
#
if args.log_terminal:
logger.valid_writer.write(' * Avg {}'.format(error_string))
else:
print('Epoch {} completed'.format(epoch))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
torch.save(patch, args.save_path / 'patch_epoch_{}'.format(str(epoch)))
if args.log_terminal:
logger.epoch_bar.finish()
def main():
global best_a3, n_iter, device
args = parser.parse_args()
torch.autograd.set_detect_anomaly(True) # 启动梯度侦测,用于查找梯度终断
"""====== step 1 : 根据使用的数据类型加载相应的数据流水线 ======"""
if args.dataset_format == 'stacked':
from DataFlow.stacked_sequence_folders import SequenceFolder
elif args.dataset_format == 'sequential':
from DataFlow.sequence_folders import SequenceFolder
"""====== step 2 : 准备存储目录 ======"""
save_path = save_path_formatter(args, parser)
if sys.platform is 'win32':
args.save_path = '.\checkpoints' / save_path
else: # linux
args.save_path = 'checkpoints' / save_path
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
tb_writer = SummaryWriter(args.save_path) # tensorboardx writer
"""====== step 3 : 指定随机数种子以便于实验复现 ======"""
torch.manual_seed(args.seed)
"""========= step 4 : 数据准备 =========="""
# 数据扩增
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.RandomHorizontalFlip(),
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(), normalize
])
valid_transform = custom_transforms.Compose(
[custom_transforms.ArrayToTensor(), normalize])
# 训练集
print("=> fetching data from '{}'".format(args.data))
train_set = SequenceFolder(args.data,
transform=train_transform,
seed=args.seed,
train=True,
sequence_length=args.SEQ_LENGTH)
# 验证集
val_set = ValidationSet(args.data, transform=valid_transform)
print('{} samples found in {} train scenes'.format(len(train_set),
len(train_set.scenes)))
print('{} samples found in {} valid scenes'.format(len(val_set),
len(val_set.scenes)))
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
"""========= step 5 : 加载模型 =========="""
print("=> creating models")
depth_net = models.DepthNet().to(device)
motion_net = models.MotionNet(intrinsic_pred=args.intri_pred).to(device)
if args.pretrained_depth:
print("=> using pre-trained weights for DepthNet")
weights = torch.load(args.pretrained_depth)
depth_net.load_state_dict(weights['state_dict'], strict=False)
if args.pretrained_motion:
print("=> using pre-trained weights for MotionNet")
weights = torch.load(args.pretrained_motion)
motion_net.load_state_dict(weights['state_dict'])
cudnn.benchmark = True
depth_net = torch.nn.DataParallel(depth_net)
motion_net = torch.nn.DataParallel(motion_net)
"""========= step 6 : 设置求解器 =========="""
print('=> setting adam solver')
optim_params = [{
'params': depth_net.parameters(),
'lr': args.lr
}, {
'params': motion_net.parameters(),
'lr': args.lr
}]
optimizer = torch.optim.Adam(optim_params,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
"""====== step 7 : 初始化损失函数计算器======="""
total_loss_calculator = LossFactory(
SEQ_LENGTH=args.SEQ_LENGTH,
rgb_weight=args.rgb_weight,
depth_smoothing_weight=args.depth_smoothing_weight,
ssim_weight=args.ssim_weight,
motion_smoothing_weight=args.motion_smoothing_weight,
rotation_consistency_weight=args.rotation_consistency_weight,
translation_consistency_weight=args.translation_consistency_weight,
depth_consistency_loss_weight=args.depth_consistency_loss_weight)
"""========= step 8 : 训练循环 =========="""
if args.epoch_size == 0:
args.epoch_size = len(
train_loader) # 如果不指定epoch_size,那么每一个epoch就把全部的训练数据过一遍
for epoch in range(args.epochs):
tqdm.write("\n===========TRAIN EPOCH [{}/{}]===========".format(
epoch + 1, args.epochs))
"""====== step 8.1 : 训练一个epoch ======"""
train_loss = train(args, train_loader, depth_net, motion_net,
optimizer, args.epoch_size, total_loss_calculator,
tb_writer)
tqdm.write('* Avg Loss : {:.3f}'.format(train_loss))
"""======= step 8.2 : 验证 ========"""
# 验证时要输出 : 深度指标abs_diff, abs_rel, sq_rel, a1, a2, a3
errors, error_names = validate_with_gt(args, val_loader, depth_net,
motion_net, epoch, tb_writer)
error_string = ', '.join('{} : {:.3f}'.format(name, error)
for name, error in zip(error_names, errors))
tqdm.write(error_string)
# TODO:输出验证集上的轨迹指标
# abs_rel, sq_rel, rms, log_rms, a1, a2, a3
tb_writer.add_scalar("Relative Errors/abs_rel", errors[0], epoch)
tb_writer.add_scalar("Relative Errors/sq_rel", errors[1], epoch)
tb_writer.add_scalar("Root Mean Squared Error/rms", errors[2], epoch)
tb_writer.add_scalar("Root Mean Squared Error/log_rms", errors[3],
epoch)
tb_writer.add_scalar("Thresholding accuracy/a1", errors[4], epoch)
tb_writer.add_scalar("Thresholding accuracy/a2", errors[5], epoch)
tb_writer.add_scalar("Thresholding accuracy/a3", errors[6], epoch)
"""======= step 8.3 : 保存验证效果最佳的模型状态 =========="""
decisive_a3 = errors[6] # 选取a3为关键指标
is_best = decisive_a3 > best_a3 # 如果当前的a3比之前记录的a3更大,那么模型的最佳状态就是现在的状态
best_a3 = max(best_a3, decisive_a3)
save_checkpoint(args.save_path, {
'epoch': epoch + 1,
'state_dict': depth_net.module.state_dict()
}, {
'epoch': epoch + 1,
'state_dict': motion_net.module.state_dict()
}, is_best)
pass # end of main
