def main():
net = U_Net(img_ch=1, num_classes=3).to(device)
train_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(384),
joint_transforms.RandomRotate(10),
joint_transforms.RandomHorizontallyFlip()
])
center_crop = joint_transforms.CenterCrop(crop_size)
train_input_transform = extended_transforms.ImgToTensor()
target_transform = extended_transforms.MaskToTensor()
make_dataset_fn = bladder.make_dataset_v2
train_set = bladder.Bladder(data_path,
'train',
joint_transform=train_joint_transform,
center_crop=center_crop,
transform=train_input_transform,
target_transform=target_transform,
make_dataset_fn=make_dataset_fn)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
if loss_name == 'dice_':
criterion = SoftDiceLossV2(activation='sigmoid',
num_classes=3).to(device)
elif loss_name == 'bcew_':
criterion = nn.BCEWithLogitsLoss().to(device)
optimizer = optim.Adam(net.parameters(), lr=1e-4)
train(train_loader, net, criterion, optimizer, n_epoch, 0)
def main(args):
writer = SummaryWriter(log_dir=args.tensorboard_log_dir)
w, h = map(int, args.input_size.split(','))
joint_transform = joint_transforms.Compose([
joint_transforms.FreeScale((h, w)),
joint_transforms.RandomHorizontallyFlip(),
])
normalize = ((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
src_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
])
tgt_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*normalize),
])
val_input_transform = standard_transforms.Compose([
extended_transforms.FreeScale((h, w)),
standard_transforms.ToTensor(),
standard_transforms.Normalize(*normalize),
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.ToPILImage()
src_dataset = GTA5DataSetLMDB(
args.data_dir, args.data_list,
joint_transform=joint_transform,
transform=src_input_transform,
target_transform=target_transform,
)
src_loader = data.DataLoader(
src_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=True, drop_last=True
)
tgt_dataset = CityscapesDataSetLMDB(
args.data_dir_target, args.data_list_target,
joint_transform=joint_transform,
transform=tgt_input_transform,
target_transform=target_transform,
)
tgt_loader = data.DataLoader(
tgt_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=True, drop_last=True
)
val_dataset = CityscapesDataSetLMDB(
args.data_dir_val, args.data_list_val,
transform=val_input_transform,
target_transform=target_transform,
)
val_loader = data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, drop_last=False
)
style_trans = StyleTrans(args)
style_trans.train(src_loader, tgt_loader, val_loader, writer)
writer.close()
def __call__(self, image, boxes, labels):
if self.augment and self.mode == 'train':
transformer = t.Compose([
t.ConvertFromPIL(),
t.PhotometricDistort(),
t.Expand(IMAGENET_MEAN),
t.RandomSampleCrop(),
t.RandomMirror(),
t.ToPercentCoords(),
t.Resize(self.image_size),
t.Normalize(IMAGENET_MEAN, IMAGENET_STD),
t.ToTensor()
])
else:
transformer = t.Compose([
t.ConvertFromPIL(),
t.ToPercentCoords(),
t.Resize(self.image_size),
t.Normalize(IMAGENET_MEAN, IMAGENET_STD),
t.ToTensor()
])
return transformer(image, boxes, labels)
def transform_image_and_mask_tt(self, image, mask, angle=None, crop_size=None):
assert self.use_iaa == False
transforms = [JT.RandomHorizontallyFlip()]
if crop_size is not None:
transforms.append(JT.RandomCrop(size=crop_size))
if angle is not None:
transforms.append(JT.RandomRotate(degree=angle))
jt_random = JT.RandomOrderApply(transforms)
jt_transform = JT.Compose([
JT.ToPILImage(),
jt_random,
JT.ToNumpy(),
])
return jt_transform(image, mask)
def main(train_args):
net = PSPNet(num_classes=voc.num_classes).cuda()
if len(train_args['snapshot']) == 0:
curr_epoch = 1
train_args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else:
print('training resumes from ' + train_args['snapshot'])
net.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name, train_args['snapshot'])))
split_snapshot = train_args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
train_args['best_record'] = {
'epoch': int(split_snapshot[1]),
'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]),
'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]),
'fwavacc': float(split_snapshot[11])
}
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
input_transform = standard_transforms.Compose([
ToTensor(),
Normalize([.485, .456, .406], [.229, .224, .225]),
])
joint_transform = joint_transforms.Compose([
joint_transforms.CenterCrop(224),
# joint_transforms.Scale(2),
joint_transforms.RandomHorizontallyFlip(),
])
target_transform = standard_transforms.Compose([
extended_transforms.MaskToTensor(),
])
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage(),
])
visualize = standard_transforms.Compose([
standard_transforms.Scale(400),
standard_transforms.CenterCrop(400),
standard_transforms.ToTensor()
])
val_input_transform = standard_transforms.Compose([
CenterCrop(224),
ToTensor(),
Normalize([.485, .456, .406], [.229, .224, .225]),
])
val_target_transform = standard_transforms.Compose([
CenterCrop(224),
extended_transforms.MaskToTensor(),
])
train_set = voc.VOC('train',
transform=input_transform,
target_transform=target_transform,
joint_transform=joint_transform)
train_loader = DataLoader(train_set,
batch_size=4,
num_workers=4,
shuffle=True)
val_set = voc.VOC('val',
transform=val_input_transform,
target_transform=val_target_transform)
val_loader = DataLoader(val_set,
batch_size=4,
num_workers=4,
shuffle=False)
# criterion = CrossEntropyLoss2d(size_average=True, ignore_index=voc.ignore_label).cuda()
criterion = torch.nn.CrossEntropyLoss(ignore_index=voc.ignore_label).cuda()
optimizer = optim.SGD(net.parameters(),
lr=train_args['lr'],
momentum=train_args['momentum'],
weight_decay=train_args['weight_decay'])
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
# open(os.path.join(ckpt_path, exp_name, 'loss_001_aux_SGD_momentum_95_random_lr_001.txt'), 'w').write(str(train_args) + '\n\n')
for epoch in range(curr_epoch, train_args['epoch_num'] + 1):
# adjust_learning_rate(optimizer,epoch,net,train_args)
train(train_loader, net, criterion, optimizer, epoch, train_args)
validate(val_loader, net, criterion, optimizer, epoch, train_args,
restore_transform, visualize)
adjust_learning_rate(optimizer, epoch, net, train_args)
def train_with_correspondences(save_folder, startnet, args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
check_mkdir(save_folder)
writer = SummaryWriter(save_folder)
# Network and weight loading
model_config = model_configs.PspnetCityscapesConfig()
net = model_config.init_network().to(device)
if args['snapshot'] == 'latest':
args['snapshot'] = get_latest_network_name(save_folder)
if len(args['snapshot']) == 0: # If start from beginning
state_dict = torch.load(startnet)
# needed since we slightly changed the structure of the network in
# pspnet
state_dict = rename_keys_to_match(state_dict)
net.load_state_dict(state_dict) # load original weights
start_iter = 0
args['best_record'] = {
'iter': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else: # If continue training
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(os.path.join(save_folder,
args['snapshot']))) # load weights
split_snapshot = args['snapshot'].split('_')
start_iter = int(split_snapshot[1])
with open(os.path.join(save_folder, 'bestval.txt')) as f:
best_val_dict_str = f.read()
args['best_record'] = eval(best_val_dict_str.rstrip())
net.train()
freeze_bn(net)
# Data loading setup
if args['corr_set'] == 'rc':
corr_set_config = data_configs.RobotcarConfig()
elif args['corr_set'] == 'cmu':
corr_set_config = data_configs.CmuConfig()
sliding_crop_im = joint_transforms.SlidingCropImageOnly(
713, args['stride_rate'])
input_transform = model_config.input_transform
pre_validation_transform = model_config.pre_validation_transform
target_transform = extended_transforms.MaskToTensor()
train_joint_transform_seg = joint_transforms.Compose([
joint_transforms.Resize(1024),
joint_transforms.RandomRotate(10),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomCrop(713)
])
train_joint_transform_corr = corr_transforms.Compose([
corr_transforms.CorrResize(1024),
corr_transforms.CorrRandomCrop(713)
])
# keep list of segmentation loaders and validators
seg_loaders = list()
validators = list()
# Correspondences
corr_set = correspondences.Correspondences(
corr_set_config.correspondence_path,
corr_set_config.correspondence_im_path,
input_size=(713, 713),
mean_std=model_config.mean_std,
input_transform=input_transform,
joint_transform=train_joint_transform_corr)
corr_loader = DataLoader(corr_set,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
# Cityscapes Training
c_config = data_configs.CityscapesConfig()
seg_set_cs = cityscapes.CityScapes(
c_config.train_im_folder,
c_config.train_seg_folder,
c_config.im_file_ending,
c_config.seg_file_ending,
id_to_trainid=c_config.id_to_trainid,
joint_transform=train_joint_transform_seg,
sliding_crop=None,
transform=input_transform,
target_transform=target_transform)
seg_loader_cs = DataLoader(seg_set_cs,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
seg_loaders.append(seg_loader_cs)
# Cityscapes Validation
val_set_cs = cityscapes.CityScapes(
c_config.val_im_folder,
c_config.val_seg_folder,
c_config.im_file_ending,
c_config.seg_file_ending,
id_to_trainid=c_config.id_to_trainid,
sliding_crop=sliding_crop_im,
transform=input_transform,
target_transform=target_transform,
transform_before_sliding=pre_validation_transform)
val_loader_cs = DataLoader(val_set_cs,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
validator_cs = Validator(val_loader_cs,
n_classes=c_config.n_classes,
save_snapshot=False,
extra_name_str='Cityscapes')
validators.append(validator_cs)
# Vistas Training and Validation
if args['include_vistas']:
v_config = data_configs.VistasConfig(
use_subsampled_validation_set=True, use_cityscapes_classes=True)
seg_set_vis = cityscapes.CityScapes(
v_config.train_im_folder,
v_config.train_seg_folder,
v_config.im_file_ending,
v_config.seg_file_ending,
id_to_trainid=v_config.id_to_trainid,
joint_transform=train_joint_transform_seg,
sliding_crop=None,
transform=input_transform,
target_transform=target_transform)
seg_loader_vis = DataLoader(seg_set_vis,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
seg_loaders.append(seg_loader_vis)
val_set_vis = cityscapes.CityScapes(
v_config.val_im_folder,
v_config.val_seg_folder,
v_config.im_file_ending,
v_config.seg_file_ending,
id_to_trainid=v_config.id_to_trainid,
sliding_crop=sliding_crop_im,
transform=input_transform,
target_transform=target_transform,
transform_before_sliding=pre_validation_transform)
val_loader_vis = DataLoader(val_set_vis,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
validator_vis = Validator(val_loader_vis,
n_classes=v_config.n_classes,
save_snapshot=False,
extra_name_str='Vistas')
validators.append(validator_vis)
else:
seg_loader_vis = None
map_validator = None
# Extra Training
extra_seg_set = cityscapes.CityScapes(
corr_set_config.train_im_folder,
corr_set_config.train_seg_folder,
corr_set_config.im_file_ending,
corr_set_config.seg_file_ending,
id_to_trainid=corr_set_config.id_to_trainid,
joint_transform=train_joint_transform_seg,
sliding_crop=None,
transform=input_transform,
target_transform=target_transform)
extra_seg_loader = DataLoader(extra_seg_set,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
seg_loaders.append(extra_seg_loader)
# Extra Validation
extra_val_set = cityscapes.CityScapes(
corr_set_config.val_im_folder,
corr_set_config.val_seg_folder,
corr_set_config.im_file_ending,
corr_set_config.seg_file_ending,
id_to_trainid=corr_set_config.id_to_trainid,
sliding_crop=sliding_crop_im,
transform=input_transform,
target_transform=target_transform,
transform_before_sliding=pre_validation_transform)
extra_val_loader = DataLoader(extra_val_set,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
extra_validator = Validator(extra_val_loader,
n_classes=corr_set_config.n_classes,
save_snapshot=True,
extra_name_str='Extra')
validators.append(extra_validator)
# Loss setup
if args['corr_loss_type'] == 'class':
corr_loss_fct = CorrClassLoss(input_size=[713, 713])
else:
corr_loss_fct = FeatureLoss(
input_size=[713, 713],
loss_type=args['corr_loss_type'],
feat_dist_threshold_match=args['feat_dist_threshold_match'],
feat_dist_threshold_nomatch=args['feat_dist_threshold_nomatch'],
n_not_matching=0)
seg_loss_fct = torch.nn.CrossEntropyLoss(
reduction='elementwise_mean',
ignore_index=cityscapes.ignore_label).to(device)
# Optimizer setup
optimizer = optim.SGD([{
'params': [
param for name, param in net.named_parameters()
if name[-4:] == 'bias' and param.requires_grad
],
'lr':
2 * args['lr']
}, {
'params': [
param for name, param in net.named_parameters()
if name[-4:] != 'bias' and param.requires_grad
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'],
nesterov=True)
if len(args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(os.path.join(save_folder, 'opt_' + args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
open(os.path.join(save_folder,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
if len(args['snapshot']) == 0:
f_handle = open(os.path.join(save_folder, 'log.log'), 'w', buffering=1)
else:
clean_log_before_continuing(os.path.join(save_folder, 'log.log'),
start_iter)
f_handle = open(os.path.join(save_folder, 'log.log'), 'a', buffering=1)
##########################################################################
#
# MAIN TRAINING CONSISTS OF ALL SEGMENTATION LOSSES AND A CORRESPONDENCE LOSS
#
##########################################################################
softm = torch.nn.Softmax2d()
val_iter = 0
train_corr_loss = AverageMeter()
train_seg_cs_loss = AverageMeter()
train_seg_extra_loss = AverageMeter()
train_seg_vis_loss = AverageMeter()
seg_loss_meters = list()
seg_loss_meters.append(train_seg_cs_loss)
if args['include_vistas']:
seg_loss_meters.append(train_seg_vis_loss)
seg_loss_meters.append(train_seg_extra_loss)
curr_iter = start_iter
for i in range(args['max_iter']):
optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
optimizer.param_groups[1]['lr'] = args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
#######################################################################
# SEGMENTATION UPDATE STEP
#######################################################################
#
for si, seg_loader in enumerate(seg_loaders):
# get segmentation training sample
inputs, gts = next(iter(seg_loader))
slice_batch_pixel_size = inputs.size(0) * inputs.size(
2) * inputs.size(3)
inputs = inputs.to(device)
gts = gts.to(device)
optimizer.zero_grad()
outputs, aux = net(inputs)
main_loss = args['seg_loss_weight'] * seg_loss_fct(outputs, gts)
aux_loss = args['seg_loss_weight'] * seg_loss_fct(aux, gts)
loss = main_loss + 0.4 * aux_loss
loss.backward()
optimizer.step()
seg_loss_meters[si].update(main_loss.item(),
slice_batch_pixel_size)
#######################################################################
# CORRESPONDENCE UPDATE STEP
#######################################################################
if args['corr_loss_weight'] > 0 and args[
'n_iterations_before_corr_loss'] < curr_iter:
img_ref, img_other, pts_ref, pts_other, weights = next(
iter(corr_loader))
# Transfer data to device
# img_ref is from the "good" sequence with generally better
# segmentation results
img_ref = img_ref.to(device)
img_other = img_other.to(device)
pts_ref = [p.to(device) for p in pts_ref]
pts_other = [p.to(device) for p in pts_other]
weights = [w.to(device) for w in weights]
# Forward pass
if args['corr_loss_type'] == 'hingeF': # Works on features
net.output_all = True
with torch.no_grad():
output_feat_ref, aux_feat_ref, output_ref, aux_ref = net(
img_ref)
output_feat_other, aux_feat_other, output_other, aux_other = net(
img_other
) # output1 must be last to backpropagate derivative correctly
net.output_all = False
else: # Works on class probs
with torch.no_grad():
output_ref, aux_ref = net(img_ref)
if args['corr_loss_type'] != 'hingeF' and args[
'corr_loss_type'] != 'hingeC':
output_ref = softm(output_ref)
aux_ref = softm(aux_ref)
# output1 must be last to backpropagate derivative correctly
output_other, aux_other = net(img_other)
if args['corr_loss_type'] != 'hingeF' and args[
'corr_loss_type'] != 'hingeC':
output_other = softm(output_other)
aux_other = softm(aux_other)
# Correspondence filtering
pts_ref_orig, pts_other_orig, weights_orig, batch_inds_to_keep_orig = correspondences.refine_correspondence_sample(
output_ref,
output_other,
pts_ref,
pts_other,
weights,
remove_same_class=args['remove_same_class'],
remove_classes=args['classes_to_ignore'])
pts_ref_orig = [
p for b, p in zip(batch_inds_to_keep_orig, pts_ref_orig)
if b.item() > 0
]
pts_other_orig = [
p for b, p in zip(batch_inds_to_keep_orig, pts_other_orig)
if b.item() > 0
]
weights_orig = [
p for b, p in zip(batch_inds_to_keep_orig, weights_orig)
if b.item() > 0
]
if args['corr_loss_type'] == 'hingeF':
# remove entire samples if needed
output_vals_ref = output_feat_ref[batch_inds_to_keep_orig]
output_vals_other = output_feat_other[batch_inds_to_keep_orig]
else:
# remove entire samples if needed
output_vals_ref = output_ref[batch_inds_to_keep_orig]
output_vals_other = output_other[batch_inds_to_keep_orig]
pts_ref_aux, pts_other_aux, weights_aux, batch_inds_to_keep_aux = correspondences.refine_correspondence_sample(
aux_ref,
aux_other,
pts_ref,
pts_other,
weights,
remove_same_class=args['remove_same_class'],
remove_classes=args['classes_to_ignore'])
pts_ref_aux = [
p for b, p in zip(batch_inds_to_keep_aux, pts_ref_aux)
if b.item() > 0
]
pts_other_aux = [
p for b, p in zip(batch_inds_to_keep_aux, pts_other_aux)
if b.item() > 0
]
weights_aux = [
p for b, p in zip(batch_inds_to_keep_aux, weights_aux)
if b.item() > 0
]
if args['corr_loss_type'] == 'hingeF':
# remove entire samples if needed
aux_vals_ref = aux_feat_ref[batch_inds_to_keep_orig]
aux_vals_other = aux_feat_other[batch_inds_to_keep_orig]
else:
# remove entire samples if needed
aux_vals_ref = aux_ref[batch_inds_to_keep_aux]
aux_vals_other = aux_other[batch_inds_to_keep_aux]
optimizer.zero_grad()
# correspondence loss
if output_vals_ref.size(0) > 0:
loss_corr_hr = corr_loss_fct(output_vals_ref,
output_vals_other, pts_ref_orig,
pts_other_orig, weights_orig)
else:
loss_corr_hr = 0 * output_vals_other.sum()
if aux_vals_ref.size(0) > 0:
loss_corr_aux = corr_loss_fct(
aux_vals_ref, aux_vals_other, pts_ref_aux, pts_other_aux,
weights_aux) # use output from img1 as "reference"
else:
loss_corr_aux = 0 * aux_vals_other.sum()
loss_corr = args['corr_loss_weight'] * \
(loss_corr_hr + 0.4 * loss_corr_aux)
loss_corr.backward()
optimizer.step()
train_corr_loss.update(loss_corr.item())
#######################################################################
# LOGGING ETC
#######################################################################
curr_iter += 1
val_iter += 1
writer.add_scalar('train_seg_loss_cs', train_seg_cs_loss.avg,
curr_iter)
writer.add_scalar('train_seg_loss_extra', train_seg_extra_loss.avg,
curr_iter)
writer.add_scalar('train_seg_loss_vis', train_seg_vis_loss.avg,
curr_iter)
writer.add_scalar('train_corr_loss', train_corr_loss.avg, curr_iter)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'], curr_iter)
if (i + 1) % args['print_freq'] == 0:
str2write = '[iter %d / %d], [train corr loss %.5f] , [seg cs loss %.5f], [seg vis loss %.5f], [seg extra loss %.5f]. [lr %.10f]' % (
curr_iter, len(corr_loader), train_corr_loss.avg,
train_seg_cs_loss.avg, train_seg_vis_loss.avg,
train_seg_extra_loss.avg, optimizer.param_groups[1]['lr'])
print(str2write)
f_handle.write(str2write + "\n")
if val_iter >= args['val_interval']:
val_iter = 0
for validator in validators:
validator.run(net,
optimizer,
args,
curr_iter,
save_folder,
f_handle,
writer=writer)
# Post training
f_handle.close()
writer.close()
def main():
"""Create the model and start the training."""
args = get_arguments()
w, h = map(int, args.input_size.split(','))
w_target, h_target = map(int, args.input_size_target.split(','))
# Create network
student_net = FCN8s(args.num_classes, args.model_path_prefix)
student_net = torch.nn.DataParallel(student_net)
student_net = student_net.cuda()
mean_std = ([103.939, 116.779, 123.68], [1.0, 1.0, 1.0])
train_joint_transform = joint_transforms.Compose([
joint_transforms.FreeScale((h, w)),
])
input_transform = standard_transforms.Compose([
extended_transforms.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std),
])
val_input_transform = standard_transforms.Compose([
extended_transforms.FreeScale((h, w)),
extended_transforms.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std),
])
target_transform = extended_transforms.MaskToTensor()
# show img
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.Lambda(lambda x: x.div_(255)),
standard_transforms.ToPILImage(),
extended_transforms.FlipChannels(),
])
visualize = standard_transforms.ToTensor()
if '5' in args.data_dir:
src_dataset = GTA5DataSetLMDB(
args.data_dir,
args.data_list,
joint_transform=train_joint_transform,
transform=input_transform,
target_transform=target_transform,
)
else:
src_dataset = CityscapesDataSetLMDB(
args.data_dir,
args.data_list,
joint_transform=train_joint_transform,
transform=input_transform,
target_transform=target_transform,
)
src_loader = data.DataLoader(src_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
tgt_val_dataset = CityscapesDataSetLMDB(
args.data_dir_target,
args.data_list_target,
# no val resize
# joint_transform=val_joint_transform,
transform=val_input_transform,
target_transform=target_transform,
)
tgt_val_loader = data.DataLoader(
tgt_val_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
)
optimizer = optim.SGD(student_net.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer = optim.Adam(
# student_net.parameters(), lr=args.learning_rate,
# weight_decay=args.weight_decay
# )
student_params = list(student_net.parameters())
# interp = partial(
# nn.functional.interpolate,
# size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True
# )
# interp_tgt = partial(
# nn.functional.interpolate,
# size=(h_target, w_target), mode='bilinear', align_corners=True
# )
upsample = nn.Upsample(size=(h_target, w_target), mode='bilinear')
n_class = args.num_classes
# src_criterion = torch.nn.CrossEntropyLoss(
# ignore_index=255, reduction='sum')
src_criterion = torch.nn.CrossEntropyLoss(ignore_index=255,
size_average=False)
num_batches = len(src_loader)
highest = 0
for epoch in range(args.num_epoch):
cls_loss_rec = AverageMeter()
aug_loss_rec = AverageMeter()
mask_rec = AverageMeter()
confidence_rec = AverageMeter()
miu_rec = AverageMeter()
data_time_rec = AverageMeter()
batch_time_rec = AverageMeter()
# load_time_rec = AverageMeter()
# trans_time_rec = AverageMeter()
tem_time = time.time()
for batch_index, src_data in enumerate(src_loader):
student_net.train()
optimizer.zero_grad()
# train with source
# src_images, src_label, src_img_name, (load_time, trans_time) = src_data
src_images, src_label, src_img_name = src_data
src_images = src_images.cuda()
src_label = src_label.cuda()
data_time_rec.update(time.time() - tem_time)
src_output = student_net(src_images)
# src_output = interp(src_output)
# Segmentation Loss
cls_loss_value = src_criterion(src_output, src_label)
cls_loss_value /= src_images.shape[0]
total_loss = cls_loss_value
total_loss.backward()
optimizer.step()
_, predict_labels = torch.max(src_output, 1)
lbl_pred = predict_labels.detach().cpu().numpy()
lbl_true = src_label.detach().cpu().numpy()
_, _, _, mean_iu, _ = _evaluate(lbl_pred, lbl_true, 19)
cls_loss_rec.update(cls_loss_value.detach_().item())
miu_rec.update(mean_iu)
# load_time_rec.update(torch.mean(load_time).item())
# trans_time_rec.update(torch.mean(trans_time).item())
batch_time_rec.update(time.time() - tem_time)
tem_time = time.time()
if (batch_index + 1) % args.print_freq == 0:
print(
f'Epoch [{epoch+1:d}/{args.num_epoch:d}][{batch_index+1:d}/{num_batches:d}]\t'
f'Time: {batch_time_rec.avg:.2f} '
f'Data: {data_time_rec.avg:.2f} '
# f'Load: {load_time_rec.avg:.2f} '
# f'Trans: {trans_time_rec.avg:.2f} '
f'Mean iu: {miu_rec.avg*100:.1f} '
f'CLS: {cls_loss_rec.avg:.2f}')
miu = test_miou(student_net, tgt_val_loader, upsample,
'./dataset/info.json')
if miu > highest:
torch.save(student_net.module.state_dict(),
osp.join(args.snapshot_dir, f'final_fcn.pth'))
highest = miu
print('>' * 50 + f'save highest with {miu:.2%}')
def load_dataset(self):
# set mean and std value from ImageNet dataset
if self.args.input_normalize:
rgb_mean, rgb_std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
else:
rgb_mean, rgb_std = [122.675, 116.669,
104.008], [58.395, 57.12, 57.375]
# train joint transforms
train_jts = joint_transforms.Compose(
[ # joint_transforms.ElasticTransform(),
joint_transforms.Resize(self.args.max_size),
joint_transforms.Apply(tv_transforms.Lambda(
lambda img: img.astype(np.float) / 255),
th=[False, True]),
joint_transforms.RandomCrop(self.args.crop_size,
ignore_label=255),
joint_transforms.RandomHorizontallyFlip(),
])
# train source transforms
train_sts = tv_transforms.Compose(
[ # extend_transforms.RandomGaussianBlur(blur_prob=0.1),
# extend_transforms.RandomBright(),
extend_transforms.ImageToTensor(self.args.input_normalize),
tv_transforms.Normalize(mean=rgb_mean, std=rgb_std)
])
# train target transforms
# train_tts = extend_transforms.MapToTensor()
train_tts = extend_transforms.GrayImageToTensor(False)
train_dataset = saliency.SaliencyMergedData(
root=self.args.dataset_dir,
phase='train',
dataset_list=self.args.datasets,
joint_transform=train_jts,
source_transform=train_sts,
target_transform=train_tts,
)
self.train_sampler = None
if self.args.weighted_sampler:
self.train_sampler = sampler.DatasetWeightedSampler(
train_dataset.weight_list, self.args.train_num_sampler)
self.train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=self.args.batch_size,
drop_last=True,
num_workers=self.args.workers,
shuffle=(self.train_sampler is None),
pin_memory=True,
sampler=self.train_sampler,
)
# valid joint transforms
valid_jts = joint_transforms.Compose([
joint_transforms.Resize(self.args.crop_size),
# joint_transforms.RandomCrop(args.crop_size),
])
# valid source transforms
valid_sts = tv_transforms.Compose([
extend_transforms.ImageToTensor(self.args.input_normalize),
tv_transforms.Normalize(mean=rgb_mean, std=rgb_std)
])
# valid target transforms
valid_tts = extend_transforms.GrayImageToTensor()
valid_dataset = saliency.SaliencyMergedData(
root=self.args.dataset_dir,
phase='valid',
dataset_list=self.args.datasets,
joint_transform=valid_jts,
source_transform=valid_sts,
target_transform=valid_tts,
)
self.valid_sampler = None
if self.args.weighted_sampler:
self.valid_sampler = sampler.DatasetWeightedSampler(
valid_dataset.weight_list, self.args.valid_num_sampler)
self.valid_loader = torch.utils.data.DataLoader(
valid_dataset,
drop_last=True,
batch_size=self.args.test_batch_size,
num_workers=self.args.workers,
shuffle=False,
pin_memory=True,
sampler=self.valid_sampler,
)
def train_with_clustering(save_folder, tmp_seg_folder, startnet, args):
print(save_folder.split('/')[-1])
skip_clustering = False
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
check_mkdir(save_folder)
writer = SummaryWriter(save_folder)
check_mkdir(tmp_seg_folder)
# Network and weight loading
model_config = model_configs.PspnetCityscapesConfig()
net = model_config.init_network(
n_classes=args['n_clusters'],
for_clustering=True,
output_features=True,
use_original_base=args['use_original_base']).to(device)
state_dict = torch.load(startnet)
if 'resnet101' in startnet:
load_resnet101_weights(net, state_dict)
else:
# needed since we slightly changed the structure of the network in pspnet
state_dict = rename_keys_to_match(state_dict)
# different amount of classes
init_last_layers(state_dict, args['n_clusters'])
net.load_state_dict(state_dict) # load original weights
start_iter = 0
args['best_record'] = {
'iter': 0,
'val_loss_feat': 1e10,
'val_loss_out': 1e10,
'val_loss_cluster': 1e10
}
# Data loading setup
if args['corr_set'] == 'rc':
corr_set_config = data_configs.RobotcarConfig()
elif args['corr_set'] == 'pola':
corr_set_config = data_configs.PolaConfig()
elif args['corr_set'] == 'cmu':
corr_set_config = data_configs.CmuConfig()
elif args['corr_set'] == 'both':
corr_set_config1 = data_configs.CmuConfig()
corr_set_config2 = data_configs.RobotcarConfig()
ref_image_lists = corr_set_config.reference_image_list
# ref_image_lists = glob.glob("/media/HDD1/datasets/Creusot_Jan15/Creusot_3/*.jpg", recursive=True)
# print(f'ici on print ref image list ---------------------------------------------------- {ref_image_lists}')
# print(corr_set_config)
# corr_im_paths = [corr_set_config.correspondence_im_path]
# ref_featurs_pos = [corr_set_config.reference_feature_poitions]
input_transform = model_config.input_transform
#corr_set_train = correspondences.Correspondences(corr_set_config.correspondence_path,
# corr_set_config.correspondence_im_path,
# input_size=(713, 713),
# input_transform=input_transform,
# joint_transform=train_joint_transform_corr,
# listfile=corr_set_config.correspondence_train_list_file)
scales = [0, 1, 2, 3]
# corr_set_train = Poladata.MonoDataset(corr_set_config,
# seg_folder = "media/HDD1/NsemSEG/Result_fold/" ,
# im_file_ending = ".jpg" )
train_joint_transform = joint_transforms.Compose([
# train_joint_transform_corr = corr_transforms.Compose([
# corr_transforms.CorrResize(1024),
# corr_transforms.CorrRandomCrop(713)
joint_transforms.Resize(1024),
joint_transforms.RandomCrop(713)
])
sliding_crop = joint_transforms.SlidingCrop(713, 2 / 3., 255)
# corr_set_train = correspondences.Correspondences(corr_set_config.train_im_folder,
# corr_set_config.train_im_folder,
# input_size=(713, 713),
# input_transform=input_transform,
# joint_transform=train_joint_transform,
# listfile=None)
corr_set_train = Poladata.MonoDataset(
corr_set_config.train_im_folder,
corr_set_config.train_seg_folder,
im_file_ending=".jpg",
id_to_trainid=None,
joint_transform=train_joint_transform,
sliding_crop=sliding_crop,
transform=input_transform,
target_transform=None, #train_joint_transform,
transform_before_sliding=None #sliding_crop
)
#print (corr_set_train)
# print(corr_set_train.mask)
corr_loader_train = DataLoader(corr_set_train,
batch_size=1,
num_workers=args['n_workers'],
shuffle=True)
# corr_loader_train = input_transform(corr_loader_train)
# print(corr_loader_train)
seg_loss_fct = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
# Optimizer setup
optimizer = optim.SGD([{
'params': [
param for name, param in net.named_parameters()
if name[-4:] == 'bias' and param.requires_grad
],
'lr':
2 * args['lr']
}, {
'params': [
param for name, param in net.named_parameters()
if name[-4:] != 'bias' and param.requires_grad
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'],
nesterov=True)
# Clustering
deepcluster = clustering.Kmeans(args['n_clusters'])
if skip_clustering:
deepcluster.set_index(cluster_centroids)
open(os.path.join(save_folder,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
f_handle = open(os.path.join(save_folder, 'log.log'), 'w', buffering=1)
# clean_log_before_continuing(os.path.join(save_folder, 'log.log'), start_iter)
# f_handle = open(os.path.join(save_folder, 'log.log'), 'a', buffering=1)
val_iter = 0
curr_iter = start_iter
while curr_iter <= args['max_iter']:
net.eval()
net.output_features = True
# max_num_features_per_image = args['max_features_per_image']
# print('-----------------------------------------------------------------')
# print (f'ref_image_lists est: {ref_image_lists},model_config es : {model_config} , net es: {net} , max feature par image es : {max_num_features_per_image} ')
# print('-----------------------------------------------------------------')
# print('le next du loader es : ---------------')
# print(next(iter(corr_loader_train)))
# features, _ = extract_features_for_reference(net, model_config, ref_image_lists,
# corr_im_paths, ref_featurs_pos,
# max_num_features_per_image=args['max_features_per_image'],
# fraction_correspondeces=0.5)
print(
'ici on a la len de la ref im list --------------------------------------------------------'
)
print(len(ref_image_lists))
features = extract_features_for_reference_nocorr(
net,
model_config,
corr_set_train,
10,
max_num_features_per_image=args['max_features_per_image'])
cluster_features = np.vstack(features)
del features
# cluster the features
cluster_indices, clustering_loss, cluster_centroids, pca_info = deepcluster.cluster_imfeatures(
cluster_features, verbose=True, use_gpu=False)
# save cluster centroids
h5f = h5py.File(
os.path.join(save_folder, 'centroids_%d.h5' % curr_iter), 'w')
h5f.create_dataset('cluster_centroids', data=cluster_centroids)
h5f.create_dataset('pca_transform_Amat', data=pca_info[0])
h5f.create_dataset('pca_transform_bvec', data=pca_info[1])
h5f.close()
# Print distribution of clusters
cluster_distribution, _ = np.histogram(
cluster_indices,
bins=np.arange(args['n_clusters'] + 1),
density=True)
str2write = 'cluster distribution ' + \
np.array2string(cluster_distribution, formatter={
'float_kind': '{0:.8f}'.format}).replace('\n', ' ')
print(str2write)
f_handle.write(str2write + "\n")
# set last layer weight to a normal distribution
reinit_last_layers(net)
# make a copy of current network state to do cluster assignment
net_for_clustering = copy.deepcopy(net)
optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
optimizer.param_groups[1]['lr'] = args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
net.train()
freeze_bn(net)
net.output_features = False
cluster_training_count = 0
# Train using the training correspondence set
corr_train_loss = AverageMeter()
seg_train_loss = AverageMeter()
feature_train_loss = AverageMeter()
while cluster_training_count < args[
'cluster_interval'] and curr_iter <= args['max_iter']:
# First extract cluster labels using saved network checkpoint
print(
'on rentre dans la boucle extract cluster_______________________________________________'
)
net.to("cpu")
net_for_clustering.to(device)
net_for_clustering.eval()
net_for_clustering.output_features = True
data_samples = []
extract_label_count = 0
while (extract_label_count < args['chunk_size']) and (
cluster_training_count + extract_label_count <
args['cluster_interval']
) and (val_iter + extract_label_count < args['val_interval']) and (
extract_label_count + curr_iter <= args['max_iter']):
# img_ref, img_other, pts_ref, pts_other, _ = next(iter(corr_set_train))
corr_loader_train = input_transform(corr_loader_train)
print(
f'la valeur de corr loader train es de {corr_loader_train} lors de l iteration : {curr_iter}'
)
img_ref, img_other, pts_ref, pts_other, _ = next(
iter(corr_loader_train))
# print('le next du loader es : ---------------')
# print(next(iter(corr_loader_train)))
# print(img_ref)
# Transfer data to device
img_ref = img_ref.to(device)
with torch.no_grad():
features = net_for_clustering(img_ref)
# assign feature to clusters for entire patch
output = features.cpu().numpy()
output_flat = output.reshape(
(output.shape[0], output.shape[1], -1))
cluster_image = np.zeros(
(output.shape[0], output.shape[2], output.shape[3]),
dtype=np.int64)
for b in range(output_flat.shape[0]):
out_f = output_flat[b]
out_f2, _ = preprocess_features(np.swapaxes(out_f, 0, 1),
pca_info=pca_info)
cluster_labels = deepcluster.assign(out_f2)
cluster_image[b] = cluster_labels.reshape(
(output.shape[2], output.shape[3]))
cluster_image = torch.from_numpy(cluster_image).to(device)
# assign cluster to correspondence positions
cluster_labels = assign_cluster_ids_to_correspondence_points(
features,
pts_ref, (deepcluster, pca_info),
inds_other=pts_other,
orig_im_size=(713, 713))
# Transfer data to cpu
img_ref = img_ref.cpu()
cluster_labels = [p.cpu() for p in cluster_labels]
cluster_image = cluster_image.cpu()
data_samples.append((img_ref, cluster_labels, cluster_image))
extract_label_count += 1
net_for_clustering.to("cpu")
net.to(device)
for data_sample in data_samples:
img_ref, cluster_labels, cluster_image = data_sample
# Transfer data to device
img_ref = img_ref.to(device)
cluster_labels = [p.to(device) for p in cluster_labels]
cluster_image = cluster_image.to(device)
optimizer.zero_grad()
outputs_ref, aux_ref = net(img_ref)
seg_main_loss = seg_loss_fct(outputs_ref, cluster_image)
seg_aux_loss = seg_loss_fct(aux_ref, cluster_image)
loss = args['seg_loss_weight'] * \
(seg_main_loss + 0.4 * seg_aux_loss)
loss.backward()
optimizer.step()
cluster_training_count += 1
if type(seg_main_loss) == torch.Tensor:
seg_train_loss.update(seg_main_loss.item(), 1)
####################################################################################################
# LOGGING ETC
####################################################################################################
curr_iter += 1
val_iter += 1
writer.add_scalar('train_seg_loss', seg_train_loss.avg,
curr_iter)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'],
curr_iter)
if (curr_iter + 1) % args['print_freq'] == 0:
str2write = '[iter %d / %d], [train seg loss %.5f], [train corr loss %.5f], [train feature loss %.5f]. [lr %.10f]' % (
curr_iter + 1, args['max_iter'], seg_train_loss.avg,
optimizer.param_groups[1]['lr'])
print(str2write)
f_handle.write(str2write + "\n")
if curr_iter > args['max_iter']:
break
# Post training
f_handle.close()
writer.close()
def main(args):
writer = SummaryWriter(log_dir=args.tensorboard_log_dir)
w, h = map(int, args.input_size.split(','))
w_target, h_target = map(int, args.input_size_target.split(','))
joint_transform = joint_transforms.Compose([
joint_transforms.FreeScale((h, w)),
joint_transforms.RandomHorizontallyFlip(),
])
normalize = ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*normalize),
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.ToPILImage()
if '5' in args.data_dir:
dataset = GTA5DataSetLMDB(
args.data_dir,
args.data_list,
joint_transform=joint_transform,
transform=input_transform,
target_transform=target_transform,
)
else:
dataset = CityscapesDataSetLMDB(
args.data_dir,
args.data_list,
joint_transform=joint_transform,
transform=input_transform,
target_transform=target_transform,
)
loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
val_dataset = CityscapesDataSetLMDB(
args.data_dir_target,
args.data_list_target,
# joint_transform=joint_transform,
transform=input_transform,
target_transform=target_transform)
val_loader = data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
upsample = nn.Upsample(size=(h_target, w_target),
mode='bilinear',
align_corners=True)
net = resnet101_ibn_a_deeplab(args.model_path_prefix,
n_classes=args.n_classes)
# optimizer = get_seg_optimizer(net, args)
optimizer = torch.optim.SGD(net.parameters(), args.learning_rate,
args.momentum)
net = torch.nn.DataParallel(net)
criterion = torch.nn.CrossEntropyLoss(size_average=False,
ignore_index=args.ignore_index)
num_batches = len(loader)
for epoch in range(args.num_epoch):
loss_rec = AverageMeter()
data_time_rec = AverageMeter()
batch_time_rec = AverageMeter()
tem_time = time.time()
for batch_index, batch_data in enumerate(loader):
show_fig = (batch_index + 1) % args.show_img_freq == 0
iteration = batch_index + 1 + epoch * num_batches
# poly_lr_scheduler(
# optimizer=optimizer,
# init_lr=args.learning_rate,
# iter=iteration - 1,
# lr_decay_iter=args.lr_decay,
# max_iter=args.num_epoch*num_batches,
# power=args.poly_power,
# )
net.train()
# net.module.freeze_bn()
img, label, name = batch_data
img = img.cuda()
label_cuda = label.cuda()
data_time_rec.update(time.time() - tem_time)
output = net(img)
loss = criterion(output, label_cuda)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_rec.update(loss.item())
writer.add_scalar('A_seg_loss', loss.item(), iteration)
batch_time_rec.update(time.time() - tem_time)
tem_time = time.time()
if (batch_index + 1) % args.print_freq == 0:
print(
f'Epoch [{epoch+1:d}/{args.num_epoch:d}][{batch_index+1:d}/{num_batches:d}]\t'
f'Time: {batch_time_rec.avg:.2f} '
f'Data: {data_time_rec.avg:.2f} '
f'Loss: {loss_rec.avg:.2f}')
if show_fig:
base_lr = optimizer.param_groups[0]["lr"]
output = torch.argmax(output, dim=1).detach()[0, ...].cpu()
fig, axes = plt.subplots(2, 1, figsize=(12, 14))
axes = axes.flat
axes[0].imshow(colorize_mask(output.numpy()))
axes[0].set_title(name[0])
axes[1].imshow(colorize_mask(label[0, ...].numpy()))
axes[1].set_title(f'seg_true_{base_lr:.6f}')
writer.add_figure('A_seg', fig, iteration)
mean_iu = test_miou(net, val_loader, upsample,
'./ae_seg/dataset/info.json')
torch.save(
net.module.state_dict(),
os.path.join(args.save_path_prefix,
f'{epoch:d}_{mean_iu*100:.0f}.pth'))
writer.close()
def train_with_ignite(networks, dataset, data_dir, batch_size, img_size,
epochs, lr, momentum, num_workers, optimizer, logger):
from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
from ignite.metrics import Loss
from utils.metrics import MultiThresholdMeasures, Accuracy, IoU, F1score
# device
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# build model
model = get_network(networks)
# log model summary
input_size = (3, img_size, img_size)
summarize_model(model.to(device), input_size, logger, batch_size, device)
# build loss
loss = torch.nn.BCEWithLogitsLoss()
# build optimizer and scheduler
model_optimizer = get_optimizer(optimizer, model, lr, momentum)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(model_optimizer)
# transforms on both image and mask
train_joint_transforms = jnt_trnsf.Compose([
jnt_trnsf.RandomCrop(img_size),
jnt_trnsf.RandomRotate(5),
jnt_trnsf.RandomHorizontallyFlip()
])
# transforms only on images
train_image_transforms = std_trnsf.Compose([
std_trnsf.ColorJitter(0.05, 0.05, 0.05, 0.05),
std_trnsf.ToTensor(),
std_trnsf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_joint_transforms = jnt_trnsf.Compose([jnt_trnsf.Safe32Padding()])
test_image_transforms = std_trnsf.Compose([
std_trnsf.ToTensor(),
std_trnsf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# transforms only on mask
mask_transforms = std_trnsf.Compose([std_trnsf.ToTensor()])
# build train / test loader
train_loader = get_loader(dataset=dataset,
data_dir=data_dir,
train=True,
joint_transforms=train_joint_transforms,
image_transforms=train_image_transforms,
mask_transforms=mask_transforms,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
test_loader = get_loader(dataset=dataset,
data_dir=data_dir,
train=False,
joint_transforms=test_joint_transforms,
image_transforms=test_image_transforms,
mask_transforms=mask_transforms,
batch_size=1,
shuffle=False,
num_workers=num_workers)
# build trainer / evaluator with ignite
trainer = create_supervised_trainer(model,
model_optimizer,
loss,
device=device)
measure = MultiThresholdMeasures()
evaluator = create_supervised_evaluator(model,
metrics={
'': measure,
'pix-acc': Accuracy(measure),
'iou': IoU(measure),
'loss': Loss(loss),
'f1': F1score(measure),
},
device=device)
# initialize state variable for checkpoint
state = update_state(model.state_dict(), 0, 0, 0, 0, 0)
# make ckpt path
ckpt_root = './ckpt/'
filename = '{network}_{optimizer}_lr_{lr}_epoch_{epoch}.pth'
ckpt_path = os.path.join(ckpt_root, filename)
# execution after every training iteration
@trainer.on(Events.ITERATION_COMPLETED)
def log_training_loss(trainer):
num_iter = (trainer.state.iteration - 1) % len(train_loader) + 1
if num_iter % 20 == 0:
logger.info("Epoch[{}] Iter[{:03d}] Loss: {:.2f}".format(
trainer.state.epoch, num_iter, trainer.state.output))
# execution after every training epoch
@trainer.on(Events.EPOCH_COMPLETED)
def log_training_results(trainer):
# evaluate on training set
evaluator.run(train_loader)
metrics = evaluator.state.metrics
logger.info(
"Training Results - Epoch: {} Avg-loss: {:.3f}\n Pix-acc: {}\n IoU: {}\n F1: {}\n"
.format(trainer.state.epoch, metrics['loss'],
str(metrics['pix-acc']), str(metrics['iou']),
str(metrics['f1'])))
# update state
update_state(weight=model.state_dict(),
train_loss=metrics['loss'],
val_loss=state['val_loss'],
val_pix_acc=state['val_pix_acc'],
val_iou=state['val_iou'],
val_f1=state['val_f1'])
# execution after every epoch
@trainer.on(Events.EPOCH_COMPLETED)
def log_validation_results(trainer):
# evaluate test(validation) set
evaluator.run(test_loader)
metrics = evaluator.state.metrics
logger.info(
"Validation Results - Epoch: {} Avg-loss: {:.3f}\n Pix-acc: {}\n IoU: {}\n F1: {}\n"
.format(trainer.state.epoch, metrics['loss'],
str(metrics['pix-acc']), str(metrics['iou']),
str(metrics['f1'])))
# update scheduler
lr_scheduler.step(metrics['loss'])
# update and save state
update_state(weight=model.state_dict(),
train_loss=state['train_loss'],
val_loss=metrics['loss'],
val_pix_acc=metrics['pix-acc'],
val_iou=metrics['iou'],
val_f1=metrics['f1'])
path = ckpt_path.format(network=networks,
optimizer=optimizer,
lr=lr,
epoch=trainer.state.epoch)
save_ckpt_file(path, state)
trainer.run(train_loader, max_epochs=epochs)
def main():
net = FCN32VGG(num_classes=mapillary.num_classes).cuda()
if len(args['snapshot']) == 0:
curr_epoch = 1
args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else:
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'])))
split_snapshot = args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
args['best_record'] = {
'epoch': int(split_snapshot[1]),
'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]),
'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]),
'fwavacc': float(split_snapshot[11])
}
net.train()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
short_size = int(min(args['input_size']) / 0.875)
train_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.RandomCrop(args['input_size']),
joint_transforms.RandomHorizontallyFlip()
])
val_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.CenterCrop(args['input_size'])
])
input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage()
])
visualize = standard_transforms.ToTensor()
train_set = mapillary.Mapillary('semantic',
'training',
joint_transform=train_joint_transform,
transform=input_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=8,
shuffle=True,
pin_memory=True)
val_set = mapillary.Mapillary('semantic',
'validation',
joint_transform=val_joint_transform,
transform=input_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set,
batch_size=args['val_batch_size'],
num_workers=8,
shuffle=False,
pin_memory=True)
criterion = CrossEntropyLoss2d(size_average=False).cuda()
optimizer = optim.SGD([{
'params': [
param
for name, param in net.named_parameters() if name[-4:] == 'bias'
],
'lr':
2 * args['lr']
}, {
'params': [
param
for name, param in net.named_parameters() if name[-4:] != 'bias'
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'])
if len(args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name, 'opt_' + args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(
os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()).replace(':', '-') + '.txt'),
'w').write(str(args) + '\n\n')
scheduler = ReduceLROnPlateau(optimizer,
'min',
patience=args['lr_patience'],
min_lr=1e-10)
for epoch in range(curr_epoch, args['epoch_num'] + 1):
train(train_loader, net, criterion, optimizer, epoch, args)
val_loss = validate(val_loader, net, criterion, optimizer, epoch, args,
restore_transform, visualize)
scheduler.step(val_loss)
torch.save(net.state_dict(), PATH)
def main(train_args):
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal(m.weight.data, mean=0, std=0.01)
torch.nn.init.constant(m.bias.data, 0)
net = VGG(num_classes=VOC.num_classes)
net.apply(weights_init)
net_dict = net.state_dict()
pretrain = torch.load('./vgg16_20M.pkl')
pretrain_dict = pretrain.state_dict()
pretrain_dict = {
'features.' + k: v
for k, v in pretrain_dict.items() if 'features.' + k in net_dict
}
net_dict.update(pretrain_dict)
net.load_state_dict(net_dict)
net = nn.DataParallel(net)
net = net.cuda()
if len(train_args['snapshot']) == 0:
curr_epoch = 1
train_args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else:
print('training resumes from ' + train_args['snapshot'])
net.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name, train_args['snapshot'])))
split_snapshot = train_args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
train_args['best_record'] = {
'epoch': int(split_snapshot[1]),
'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]),
'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]),
'fwavacc': float(split_snapshot[11])
}
net.train()
mean_std = ([0.408, 0.457, 0.481], [1, 1, 1])
joint_transform_train = joint_transforms.Compose(
[joint_transforms.RandomCrop((321, 321))])
joint_transform_test = joint_transforms.Compose(
[joint_transforms.RandomCrop((512, 512))])
input_transform = standard_transforms.Compose([
#standard_transforms.Resize((321,321)),
#standard_transforms.RandomCrop(224),
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = standard_transforms.Compose([
#standard_transforms.Resize((224,224)),
extended_transforms.MaskToTensor()
])
#target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage(),
])
visualize = standard_transforms.Compose([
standard_transforms.Resize(400),
standard_transforms.CenterCrop(400),
standard_transforms.ToTensor()
])
train_set = VOC.VOC('train',
joint_transform=joint_transform_train,
transform=input_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set,
batch_size=20,
num_workers=4,
shuffle=True)
val_set = VOC.VOC('val',
joint_transform=joint_transform_test,
transform=input_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set,
batch_size=1,
num_workers=4,
shuffle=False)
criterion = CrossEntropyLoss2d(size_average=False,
ignore_index=VOC.ignore_label).cuda()
#optimizer = optim.SGD(net.parameters(), lr = train_args['lr'], momentum=0.9,weight_decay=train_args['weight_decay'])
optimizer = optim.SGD(
[{
'params': [
param for name, param in net.named_parameters()
if name[-4:] == 'bias'
],
'lr':
2 * train_args['lr'],
'momentum':
train_args['momentum'],
'weight_decay':
0
}, {
'params': [
param for name, param in net.named_parameters()
if name[-4:] != 'bias'
],
'lr':
train_args['lr'],
'momentum':
train_args['momentum'],
'weight_decay':
train_args['weight_decay']
}], {
'params': [
param for name, param in net.named_parameters()
if name[-8:] == 'voc.bias'
],
'lr':
20 * train_args['lr'],
'momentum':
train_args['momentum'],
'weight_decay':
0
}, {
'params': [
param for name, param in net.named_parameters()
if name[-10:] != 'voc.weight'
],
'lr':
10 * train_args['lr'],
'momentum':
train_args['momentum'],
'weight_decay':
train_args['weight_decay']
})
if len(train_args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name,
'opt_' + train_args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * train_args['lr']
optimizer.param_groups[1]['lr'] = train_args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(
os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(train_args) + '\n\n')
#scheduler = ReduceLROnPlateau(optimizer, 'min', patience=train_args['lr_patience'], min_lr=1e-10, verbose=True)
scheduler = StepLR(optimizer, step_size=13, gamma=0.1)
for epoch in range(curr_epoch, train_args['epoch_num'] + 1):
train(train_loader, net, criterion, optimizer, epoch, train_args)
val_loss = validate(val_loader, net, criterion, optimizer, epoch,
train_args, restore_transform, visualize)
#scheduler.step(val_loss)
scheduler.step()
def main():
torch.backends.cudnn.benchmark = True
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
vgg_model = VGGNet(requires_grad=True, remove_fc=True)
net = FCN8s(pretrained_net=vgg_model,
n_class=cityscapes.num_classes,
dropout_rate=0.4)
print('load model ' + args['snapshot'])
vgg_model = vgg_model.to(device)
net = net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
net.load_state_dict(
torch.load(os.path.join(ckpt_path, args['exp_name'],
args['snapshot'])))
net.eval()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
short_size = int(min(args['input_size']) / 0.875)
val_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.CenterCrop(args['input_size'])
])
test_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(*mean_std)])
target_transform = extended_transforms.MaskToTensor()
restore_transform = transforms.Compose(
[extended_transforms.DeNormalize(*mean_std),
transforms.ToPILImage()])
# test_set = cityscapes.CityScapes('test', transform=test_transform)
test_set = cityscapes.CityScapes('test',
joint_transform=val_joint_transform,
transform=test_transform,
target_transform=target_transform)
test_loader = DataLoader(test_set,
batch_size=1,
num_workers=8,
shuffle=False)
transform = transforms.ToPILImage()
check_mkdir(os.path.join(ckpt_path, args['exp_name'], 'test'))
gts_all, predictions_all = [], []
count = 0
for vi, data in enumerate(test_loader):
# img_name, img = data
img_name, img, gts = data
img_name = img_name[0]
# print(img_name)
img_name = img_name.split('/')[-1]
# img.save(os.path.join(ckpt_path, args['exp_name'], 'test', img_name))
img_transform = restore_transform(img[0])
# img_transform = img_transform.convert('RGB')
img_transform.save(
os.path.join(ckpt_path, args['exp_name'], 'test', img_name))
img_name = img_name.split('_leftImg8bit.png')[0]
# img = Variable(img, volatile=True).cuda()
img, gts = img.to(device), gts.to(device)
output = net(img)
prediction = output.data.max(1)[1].squeeze_(1).squeeze_(
0).cpu().numpy()
prediction_img = cityscapes.colorize_mask(prediction)
# print(type(prediction_img))
prediction_img.save(
os.path.join(ckpt_path, args['exp_name'], 'test',
img_name + '.png'))
# print(ckpt_path, args['exp_name'], 'test', img_name + '.png')
print('%d / %d' % (vi + 1, len(test_loader)))
gts_all.append(gts.data.cpu().numpy())
predictions_all.append(prediction)
# break
# if count == 1:
# break
# count += 1
gts_all = np.concatenate(gts_all)
predictions_all = np.concatenate(prediction)
acc, acc_cls, mean_iou, _ = evaluate(predictions_all, gts_all,
cityscapes.num_classes)
print(
'-----------------------------------------------------------------------------------------------------------'
)
print('[acc %.5f], [acc_cls %.5f], [mean_iu %.5f]' %
(acc, acc_cls, mean_iu))
def main(train_args):
net = PSPNet(num_classes=voc.num_classes).cuda()
if len(train_args['snapshot']) == 0:
curr_epoch = 1
train_args['best_record'] = {'epoch': 0, 'val_loss': 1e10, 'acc': 0, 'acc_cls': 0, 'mean_iu': 0, 'fwavacc': 0}
else:
print ('training resumes from ' + train_args['snapshot'])
net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, train_args['snapshot'])))
split_snapshot = train_args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
train_args['best_record'] = {'epoch': int(split_snapshot[1]), 'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]), 'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]), 'fwavacc': float(split_snapshot[11])}
net.train()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
train_simul_transform = simul_transforms.Compose([
simul_transforms.RandomSized(train_args['input_size']),
simul_transforms.RandomRotate(10),
simul_transforms.RandomHorizontallyFlip()
])
val_simul_transform = simul_transforms.Scale(train_args['input_size'])
train_input_transform = standard_transforms.Compose([
extended_transforms.RandomGaussianBlur(),
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage(),
])
visualize = standard_transforms.Compose([
standard_transforms.Scale(400),
standard_transforms.CenterCrop(400),
standard_transforms.ToTensor()
])
train_set = voc.VOC('train', joint_transform=train_simul_transform, transform=train_input_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set, batch_size=train_args['train_batch_size'], num_workers=8, shuffle=True)
val_set = voc.VOC('val', joint_transform=val_simul_transform, transform=val_input_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set, batch_size=1, num_workers=8, shuffle=False)
criterion = CrossEntropyLoss2d(size_average=True, ignore_index=voc.ignore_label).cuda()
optimizer = optim.SGD([
{'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
'lr': 2 * train_args['lr']},
{'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
'lr': train_args['lr'], 'weight_decay': train_args['weight_decay']}
], momentum=train_args['momentum'])
if len(train_args['snapshot']) > 0:
optimizer.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, 'opt_' + train_args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * train_args['lr']
optimizer.param_groups[1]['lr'] = train_args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt'), 'w').write(str(train_args) + '\n\n')
train(train_loader, net, criterion, optimizer, curr_epoch, train_args, val_loader, restore_transform, visualize)
def main(args):
writer = SummaryWriter(log_dir=args.tensorboard_log_dir)
w, h = map(int, args.input_size.split(','))
w_target, h_target = map(int, args.input_size_target.split(','))
joint_transform = joint_transforms.Compose([
joint_transforms.FreeScale((h, w)),
joint_transforms.RandomHorizontallyFlip(),
])
normalize = ((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
tgt_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*normalize),
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.ToPILImage()
if args.seg_net == 'fcn':
mean_std = ([103.939, 116.779, 123.68], [1.0, 1.0, 1.0])
val_input_transform = standard_transforms.Compose([
extended_transforms.FreeScale((h, w)),
extended_transforms.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std),
])
else:
normalize = ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
val_input_transform = standard_transforms.Compose([
extended_transforms.FreeScale((h, w)),
standard_transforms.ToTensor(),
standard_transforms.Normalize(*normalize),
])
tgt_dataset = Cityscapes16DataSetLMDB(
args.data_dir_target,
args.data_list_target,
joint_transform=joint_transform,
transform=tgt_input_transform,
target_transform=target_transform,
)
tgt_loader = data.DataLoader(tgt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
val_dataset = Cityscapes16DataSetLMDB(
args.data_dir_val,
args.data_list_val,
transform=val_input_transform,
target_transform=target_transform,
)
val_loader = data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
upsample = nn.Upsample(size=(h_target, w_target),
mode='bilinear',
align_corners=True)
if args.seg_net == 'fcn':
net = FCN8s(args.n_classes, pretrained=False)
net_static = FCN8s(args.n_classes, pretrained=False)
file_name = os.path.join(args.resume, args.fcn_name)
# for name, param in net.named_parameters():
# if 'feat' not in name:
# param.requires_grad = False
elif args.seg_net == 'deeplab_ibn':
deeplab = resnet101_ibn_a_deeplab()
file_name = os.path.join(args.resume, 'deeplab_ibn.pth')
net.load_state_dict(torch.load(file_name))
net_static.load_state_dict(torch.load(file_name))
for param in net_static.parameters():
param.requires_grad = False
optimizer = torch.optim.SGD(net.parameters(), args.learning_rate,
args.momentum)
net = torch.nn.DataParallel(net.cuda())
net_static = torch.nn.DataParallel(net_static.cuda())
# criterion = torch.nn.MSELoss()
# criterion = torch.nn.SmoothL1Loss()
criterion = torch.nn.CrossEntropyLoss(ignore_index=255)
gen_model = define_G()
gen_model.load_state_dict(
torch.load(os.path.join(args.resume, args.gen_name)))
gen_model.eval()
for param in gen_model.parameters():
param.requires_grad = False
gen_model = torch.nn.DataParallel(gen_model.cuda())
# for seg net
def normalize(x, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
if args.seg_net == 'fcn':
mean = [103.939, 116.779, 123.68]
flip_x = torch.cat(
[x[:, 2 - i, :, :].unsqueeze(1) for i in range(3)],
dim=1,
)
new_x = []
for tem_x in flip_x:
tem_new_x = []
for c, m in zip(tem_x, mean):
tem_new_x.append(c.mul(255.0).sub(m).unsqueeze(0))
new_x.append(torch.cat(tem_new_x, dim=0).unsqueeze(0))
new_x = torch.cat(new_x, dim=0)
return new_x
else:
for tem_x in x:
for c, m, s in zip(tem_x, mean, std):
c = c.sub(m).div(s)
return x
def de_normalize(x, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
new_x = []
for tem_x in x:
tem_new_x = []
for c, m, s in zip(tem_x, mean, std):
tem_new_x.append(c.mul(s).add(s).unsqueeze(0))
new_x.append(torch.cat(tem_new_x, dim=0).unsqueeze(0))
new_x = torch.cat(new_x, dim=0)
return new_x
# ###################################################
# direct test with gen
# ###################################################
print('Direct Test')
mean_iu = test_miou(net, val_loader, upsample, './dataset/info.json')
direct_input_transform = standard_transforms.Compose([
extended_transforms.FreeScale((h, w)),
standard_transforms.ToTensor(),
standard_transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
val_dataset_direct = Cityscapes16DataSetLMDB(
args.data_dir_val,
args.data_list_val,
transform=direct_input_transform,
target_transform=target_transform,
)
val_loader_direct = data.DataLoader(val_dataset_direct,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False)
class NewModel(object):
def __init__(self, gen_net, val_net):
self.gen_net = gen_net
self.val_net = val_net
def __call__(self, x):
x = de_normalize(self.gen_net(x))
new_x = normalize(x)
out = self.val_net(new_x)
return out
def eval(self):
self.gen_net.eval()
self.val_net.eval()
new_model = NewModel(gen_model, net)
print('Test with Gen')
mean_iu = test_miou(new_model, val_loader_direct, upsample,
'./dataset/info.json')
# return
num_batches = len(tgt_loader)
highest = 0
for epoch in range(args.num_epoch):
loss_rec = AverageMeter()
data_time_rec = AverageMeter()
batch_time_rec = AverageMeter()
tem_time = time.time()
for batch_index, batch_data in enumerate(tgt_loader):
iteration = batch_index + 1 + epoch * num_batches
net.train()
net_static.eval() # fine-tune use eval
img, _, name = batch_data
img = img.cuda()
data_time_rec.update(time.time() - tem_time)
with torch.no_grad():
gen_output = gen_model(img)
gen_seg_output_logits = net_static(
normalize(de_normalize(gen_output)))
ori_seg_output_logits = net(normalize(de_normalize(img)))
prob = torch.nn.Softmax(dim=1)
max_value, label = torch.max(prob(gen_seg_output_logits), dim=1)
label_mask = torch.zeros(label.shape, dtype=torch.uint8).cuda()
for tem_label in range(19):
tem_mask = label == tem_label
if torch.sum(tem_mask) < 5:
continue
value_vec = max_value[tem_mask]
large_value = torch.topk(
value_vec, int(args.percent * value_vec.shape[0]))[0][0]
large_mask = max_value > large_value
label_mask = label_mask | (tem_mask & large_mask)
label[label_mask] = 255
# loss = criterion(ori_seg_output_logits, gen_seg_output_logits)
loss = criterion(ori_seg_output_logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_rec.update(loss.item())
writer.add_scalar('A_seg_loss', loss.item(), iteration)
batch_time_rec.update(time.time() - tem_time)
tem_time = time.time()
if (batch_index + 1) % args.print_freq == 0:
print(
f'Epoch [{epoch+1:d}/{args.num_epoch:d}][{batch_index+1:d}/{num_batches:d}]\t'
f'Time: {batch_time_rec.avg:.2f} '
f'Data: {data_time_rec.avg:.2f} '
f'Loss: {loss_rec.avg:.2f}')
if iteration % args.checkpoint_freq == 0:
mean_iu = test_miou(net,
val_loader,
upsample,
'./dataset/info.json',
print_results=False)
if mean_iu > highest:
torch.save(
net.module.state_dict(),
os.path.join(args.save_path_prefix,
'cityscapes_best_fcn.pth'))
highest = mean_iu
print(f'save fcn model with {mean_iu:.2%}')
print(('-' * 100 + '\n') * 3)
print('>' * 50 + 'Final Model')
net.module.load_state_dict(
torch.load(
os.path.join(args.save_path_prefix, 'cityscapes_best_fcn.pth')))
mean_iu = test_miou(net, val_loader, upsample, './dataset/info.json')
writer.close()
def main_worker(gpu, ngpus_per_node, args):
global best_mIoU
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
if args.pretrained:
print("=> using pre-trained model 'DFANet'")
model = DFANet(pretrained=True, pretrained_backbone=False)
else:
print("=> creating model 'DFANet'")
model = DFANet(pretrained=False, pretrained_backbone=True)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss(ignore_index=19).cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
metric = IoU(20, ignore_index=19)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_mIoU = checkpoint['best_mIoU']
if args.gpu is not None:
# best_mIoU may be from a checkpoint from a different GPU
best_mIoU = best_mIoU.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
train_dataset = Cityscapes(args.data,
split='train',
mode='fine',
target_type='semantic',
transform=joint_transforms.Compose([
joint_transforms.RandomHorizontalFlip(),
joint_transforms.RandomSized(1024),
joint_transforms.ToTensor(),
joint_transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(Cityscapes(
args.data,
split='val',
mode='fine',
target_type='semantic',
transform=joint_transforms.Compose([
joint_transforms.RandomHorizontalFlip(),
joint_transforms.RandomSized(1024),
joint_transforms.ToTensor(),
joint_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion, args)
return
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# evaluate on training data
train_mIoU, train_loss = validate(train_loader, model, criterion,
metric, args)
# evaluate on validation set
val_mIoU, val_loss = validate(val_loader, model, criterion, metric,
args)
print("Train mIoU: {}".format(train_mIoU))
print("Train Loss: {}".format(train_loss))
print("Val mIoU: {}".format(val_mIoU))
print("Val mIoU: {}".format(val_loss))
parser = argparse.ArgumentParser(description='P4-Celia Segmentation')
parser.add_argument('-p',
'--path',
dest='root',
type=str,
help='Root path for the data folder',
default='/media/data2TB/jeremyshi/data/cilia/')
# Specify the path for folder
args = parser.parse_args()
ROOT = args.root
# Joint_tranformation for training inputs and targets
train_joint_transformer = joint_transforms.Compose([
joint_transforms.RandomSizedCrop(256),
joint_transforms.RandomHorizontallyFlip()
])
# Tranformation for training inputs and targets (change them to tensors)
img_transform = transforms.Compose([transforms.ToTensor()])
cilia = getCilia.CiliaData(ROOT,
joint_transform=train_joint_transformer,
input_transform=img_transform,
target_transform=img_transform,
remove_cell=False)
# Loading the training data using native PyTorch loader (same for valiation and testing later)
train_loader = data.DataLoader(cilia, batch_size=1, shuffle=True)
print("Loaded training set!")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
num_filters = [16, 32]
batch_size = 32
lr = 1e-4
weight_decay = 1e-5
epochs = 600
# unet setting
num_conv_blocks = 1
# prob unet only
num_convs_per_block = num_conv_blocks
num_convs_fcomb = 1
partial_data = False
latent_dim = 6
beta = 10
# isotropic = False
# kaiming_normal and orthogonal
initializers = {'w': 'kaiming_normal', 'b': 'normal'}
# initializers = {'w':None, 'b':None}
# Transforms
joint_transfm = joint_transforms.Compose([joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomSizedCrop(128),
joint_transforms.RandomRotate(60)])
# joint_transfm = None
input_transfm = transforms.Compose([transforms.ToPILImage()])
target_transfm = transforms.Compose([transforms.ToTensor()])
# joint_transfm=None
# input_transfm=None
img_path = '2007_000033.jpg'
mask_path = '2007_000033.png'
img = Image.open(img_path).convert('RGB')
img = np.array(img)
tmp = img[:,:,0]
img[:,:,0] = img[:,:,2]
img[:,:,2] = tmp
image=Image.fromarray(np.uint8(img))
mask = Image.open(mask_path)
mean_std = ([0.408, 0.457, 0.481], [1, 1, 1])
joint_transform_train = joint_transforms.Compose([
joint_transforms.RandomCrop((321,321))
])
joint_transform_test = joint_transforms.Compose([
joint_transforms.RandomCrop((512,512))
])
input_transform = standard_transforms.Compose([
#standard_transforms.Resize((321,321)),
#standard_transforms.RandomCrop(224),
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = standard_transforms.Compose([
#standard_transforms.Resize((224,224)),
extended_transforms.MaskToTensor()
def get_transforms(scale_size, input_size, region_size, supervised, test,
al_algorithm, full_res, dataset):
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
if scale_size == 0:
print('(Data loading) Not scaling the data')
print('(Data loading) Random crops of ' + str(input_size) +
' in training')
print('(Data loading) No crops in validation')
if supervised:
train_joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(input_size),
joint_transforms.RandomHorizontallyFlip()
])
else:
train_joint_transform = joint_transforms.ComposeRegion([
joint_transforms.RandomCropRegion(input_size,
region_size=region_size),
joint_transforms.RandomHorizontallyFlip()
])
if (not test and al_algorithm == 'ralis') and not full_res:
val_joint_transform = joint_transforms.Scale(1024)
else:
val_joint_transform = None
al_train_joint_transform = joint_transforms.ComposeRegion([
joint_transforms.CropRegion(region_size, region_size=region_size),
joint_transforms.RandomHorizontallyFlip()
])
else:
print('(Data loading) Scaling training data: ' + str(scale_size) +
' width dimension')
print('(Data loading) Random crops of ' + str(input_size) +
' in training')
print('(Data loading) No crops nor scale_size in validation')
if supervised:
train_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(scale_size),
joint_transforms.RandomCrop(input_size),
joint_transforms.RandomHorizontallyFlip()
])
else:
train_joint_transform = joint_transforms.ComposeRegion([
joint_transforms.Scale(scale_size),
joint_transforms.RandomCropRegion(input_size,
region_size=region_size),
joint_transforms.RandomHorizontallyFlip()
])
al_train_joint_transform = joint_transforms.ComposeRegion([
joint_transforms.Scale(scale_size),
joint_transforms.CropRegion(region_size, region_size=region_size),
joint_transforms.RandomHorizontallyFlip()
])
if dataset == 'gta_for_camvid':
val_joint_transform = joint_transforms.ComposeRegion(
[joint_transforms.Scale(scale_size)])
else:
val_joint_transform = None
input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
return input_transform, target_transform, train_joint_transform, val_joint_transform, al_train_joint_transform
device = 'cuda' if args.use_gpu else 'cpu'
assert os.path.exists(ckpt_dir)
assert os.path.exists(data_dir)
assert os.path.exists(os.path.split(save_dir)[0])
if not os.path.exists(save_dir):
os.mkdir(save_dir)
# prepare network with trained parameters
net = get_network(network).to(device)
state = torch.load(ckpt_dir)
net.load_state_dict(state['weight'])
# this is the default setting for train_verbose.py
test_joint_transforms = jnt_trnsf.Compose([jnt_trnsf.Safe32Padding()])
test_image_transforms = std_trnsf.Compose([
std_trnsf.ToTensor(),
std_trnsf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# transforms only on mask
mask_transforms = std_trnsf.Compose([std_trnsf.ToTensor()])
test_loader = get_loader(dataset=args.dataset,
data_dir=data_dir,
train=False,
joint_transforms=test_joint_transforms,
image_transforms=test_image_transforms,
mask_transforms=mask_transforms,
def train_without_ignite(model,
loss,
batch_size,
img_size,
epochs,
lr,
num_workers,
optimizer,
logger,
gray_image=False,
scheduler=None,
viz=True):
import visdom
from utils.metrics import Accuracy, IoU
DEFAULT_PORT = 8097
DEFAULT_HOSTNAME = "http://localhost"
if viz:
vis = visdom.Visdom(port=DEFAULT_PORT, server=DEFAULT_HOSTNAME)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
data_loader = {}
joint_transforms = jnt_trnsf.Compose([
jnt_trnsf.RandomCrop(img_size),
jnt_trnsf.RandomRotate(5),
jnt_trnsf.RandomHorizontallyFlip()
])
train_image_transforms = std_trnsf.Compose([
std_trnsf.ColorJitter(0.05, 0.05, 0.05, 0.05),
std_trnsf.ToTensor(),
std_trnsf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_joint_transforms = jnt_trnsf.Compose([jnt_trnsf.Safe32Padding()])
test_image_transforms = std_trnsf.Compose([
std_trnsf.ToTensor(),
std_trnsf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
mask_transforms = std_trnsf.Compose([std_trnsf.ToTensor()])
data_loader['train'] = get_loader(dataset='figaro',
train=True,
joint_transforms=joint_transforms,
image_transforms=train_image_transforms,
mask_transforms=mask_transforms,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
gray_image=gray_image)
data_loader['test'] = get_loader(dataset='figaro',
train=False,
joint_transforms=test_joint_transforms,
image_transforms=test_image_transforms,
mask_transforms=mask_transforms,
batch_size=1,
shuffle=True,
num_workers=num_workers,
gray_image=gray_image)
for epoch in range(epochs):
for phase in ['train', 'test']:
if phase == 'train':
model.train(True)
else:
prev_grad_state = torch.is_grad_enabled()
torch.set_grad_enabled(False)
model.train(False)
running_loss = 0.0
for i, data in enumerate(tqdm(data_loader[phase],
file=sys.stdout)):
if i == len(data_loader[phase]) - 1: break
data_ = [
t.to(device) if isinstance(t, torch.Tensor) else t
for t in data
]
if gray_image:
img, mask, gray = data_
else:
img, mask = data_
model.zero_grad()
pred_mask = model(img)
if gray_image:
l = loss(pred_mask, mask, gray)
else:
l = loss(pred_mask, mask)
if phase == 'train':
l.backward()
optimizer.step()
running_loss += l.item()
epoch_loss = running_loss / len(data_loader[phase])
if phase == 'train':
logger.info(
f"Training Results - Epoch: {epoch} Avg-loss: {epoch_loss:.3f}"
)
if viz:
vis.images(
[
np.clip(pred_mask.detach().cpu().numpy()[0], 0, 1),
mask.detach().cpu().numpy()[0]
],
opts=dict(title=f'pred img for {epoch}-th iter'))
if phase == 'test':
if viz:
vis.images(
[
np.clip(pred_mask.detach().cpu().numpy()[0], 0, 1),
mask.detach().cpu().numpy()[0]
],
opts=dict(title=f'pred img for {epoch}-th iter'))
logger.info(
f"Test Results - Epoch: {epoch} Avg-loss: {epoch_loss:.3f}"
)
if scheduler: scheduler.step(epoch_loss)
torch.set_grad_enabled(prev_grad_state)
def main():
"""Create the model and start the training."""
args = get_arguments()
w, h = map(int, args.input_size.split(','))
w_target, h_target = map(int, args.input_size_target.split(','))
# Create network
if args.bn_sync:
print('Using Sync BN')
deeplabv3.BatchNorm2d = partial(InPlaceABNSync, activation='none')
net = get_deeplabV3(args.num_classes, args.model_path_prefix)
if not args.bn_sync:
net.freeze_bn()
net = torch.nn.DataParallel(net)
net = net.cuda()
mean_std = ([104.00698793, 116.66876762, 122.67891434], [1.0, 1.0, 1.0])
train_joint_transform = joint_transforms.Compose([
joint_transforms.FreeScale((h, w)),
])
input_transform = standard_transforms.Compose([
extended_transforms.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std),
])
val_input_transform = standard_transforms.Compose([
extended_transforms.FreeScale((h, w)),
extended_transforms.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std),
])
target_transform = extended_transforms.MaskToTensor()
# show img
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.Lambda(lambda x: x.div_(255)),
standard_transforms.ToPILImage(),
extended_transforms.FlipChannels(),
])
visualize = standard_transforms.ToTensor()
if '5' in args.data_dir:
src_dataset = GTA5DataSetLMDB(
args.data_dir,
args.data_list,
joint_transform=train_joint_transform,
transform=input_transform,
target_transform=target_transform,
)
else:
src_dataset = CityscapesDataSetLMDB(
args.data_dir,
args.data_list,
joint_transform=train_joint_transform,
transform=input_transform,
target_transform=target_transform,
)
src_loader = data.DataLoader(src_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
tgt_val_dataset = CityscapesDataSetLMDB(
args.data_dir_target,
args.data_list_target,
# no val resize
# joint_transform=val_joint_transform,
transform=val_input_transform,
target_transform=target_transform,
)
tgt_val_loader = data.DataLoader(
tgt_val_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
)
# freeze bn
for module in net.module.modules():
if isinstance(module, torch.nn.BatchNorm2d):
for param in module.parameters():
param.requires_grad = False
optimizer = optim.SGD(
[{
'params': filter(lambda p: p.requires_grad,
net.module.parameters()),
'lr': args.learning_rate
}],
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer = optim.Adam(
# net.parameters(), lr=args.learning_rate,
# weight_decay=args.weight_decay
# )
# interp = partial(
# nn.functional.interpolate,
# size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True
# )
# interp_tgt = partial(
# nn.functional.interpolate,
# size=(h_target, w_target), mode='bilinear', align_corners=True
# )
upsample = nn.Upsample(size=(h_target, w_target), mode='bilinear')
n_class = args.num_classes
# criterion = torch.nn.CrossEntropyLoss(
# ignore_index=255, reduction='sum')
# criterion = torch.nn.CrossEntropyLoss(
# ignore_index=255, size_average=True
# )
criterion = CriterionDSN(ignore_index=255,
# size_average=False
)
num_batches = len(src_loader)
max_iter = args.iterations
i_iter = 0
highest_miu = 0
while True:
cls_loss_rec = AverageMeter()
aug_loss_rec = AverageMeter()
mask_rec = AverageMeter()
confidence_rec = AverageMeter()
miu_rec = AverageMeter()
data_time_rec = AverageMeter()
batch_time_rec = AverageMeter()
# load_time_rec = AverageMeter()
# trans_time_rec = AverageMeter()
tem_time = time.time()
for batch_index, src_data in enumerate(src_loader):
i_iter += 1
lr = adjust_learning_rate(args, optimizer, i_iter, max_iter)
net.train()
optimizer.zero_grad()
# train with source
# src_images, src_label, src_img_name, (load_time, trans_time) = src_data
src_images, src_label, src_img_name = src_data
src_images = src_images.cuda()
src_label = src_label.cuda()
data_time_rec.update(time.time() - tem_time)
src_output = net(src_images)
# src_output = interp(src_output)
# Segmentation Loss
cls_loss_value = criterion(src_output, src_label)
total_loss = cls_loss_value
total_loss.backward()
optimizer.step()
src_output = torch.nn.functional.upsample(input=src_output[0],
size=(h, w),
mode='bilinear',
align_corners=True)
_, predict_labels = torch.max(src_output, 1)
lbl_pred = predict_labels.detach().cpu().numpy()
lbl_true = src_label.detach().cpu().numpy()
_, _, _, mean_iu, _ = _evaluate(lbl_pred, lbl_true, 19)
cls_loss_rec.update(cls_loss_value.detach_().item())
miu_rec.update(mean_iu)
# load_time_rec.update(torch.mean(load_time).item())
# trans_time_rec.update(torch.mean(trans_time).item())
batch_time_rec.update(time.time() - tem_time)
tem_time = time.time()
if i_iter % args.print_freq == 0:
print(
# f'Epoch [{epoch+1:d}/{args.num_epoch:d}][{batch_index+1:d}/{num_batches:d}]\t'
f'Iter: [{i_iter}/{max_iter}]\t'
f'Time: {batch_time_rec.avg:.2f} '
f'Data: {data_time_rec.avg:.2f} '
# f'Load: {load_time_rec.avg:.2f} '
# f'Trans: {trans_time_rec.avg:.2f} '
f'Mean iu: {miu_rec.avg*100:.1f} '
f'CLS: {cls_loss_rec.avg:.2f}')
if i_iter % args.eval_freq == 0:
miu = test_miou(net, tgt_val_loader, upsample,
'./dataset/info.json')
if miu > highest_miu:
torch.save(
net.module.state_dict(),
osp.join(args.snapshot_dir,
f'{i_iter:d}_{miu*1000:.0f}.pth'))
highest_miu = miu
print(f'>>>>>>>>>Learning Rate {lr}<<<<<<<<<')
if i_iter == max_iter:
return
def main(train_args):
print('No of classes', doc.num_classes)
if train_args['network'] == 'psp':
net = PSPNet(num_classes=doc.num_classes,
resnet=resnet,
res_path=res_path).cuda()
elif train_args['network'] == 'mfcn':
net = MFCN(num_classes=doc.num_classes, use_aux=True).cuda()
elif train_args['network'] == 'psppen':
net = PSPNet(num_classes=doc.num_classes,
resnet=resnet,
res_path=res_path).cuda()
print("number of cuda devices = ", torch.cuda.device_count())
if len(train_args['snapshot']) == 0:
curr_epoch = 1
train_args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else:
print('training resumes from ' + train_args['snapshot'])
net.load_state_dict(
torch.load(
os.path.join(ckpt_path, 'model_' + exp_name,
train_args['snapshot'])))
split_snapshot = train_args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
train_args['best_record'] = {
'epoch': int(split_snapshot[1]),
'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]),
'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]),
'fwavacc': float(split_snapshot[11])
}
net = torch.nn.DataParallel(net,
device_ids=list(
range(torch.cuda.device_count())))
net.train()
mean_std = ([0.9584, 0.9588, 0.9586], [0.1246, 0.1223, 0.1224])
weight = torch.FloatTensor(doc.num_classes)
#weight[0] = 1.0/0.5511 # background
train_simul_transform = simul_transforms.Compose([
simul_transforms.RandomSized(train_args['input_size']),
simul_transforms.RandomRotate(3),
#simul_transforms.RandomHorizontallyFlip()
simul_transforms.Scale(train_args['input_size']),
simul_transforms.CenterCrop(train_args['input_size'])
])
val_simul_transform = simul_transforms.Scale(train_args['input_size'])
train_input_transform = standard_transforms.Compose([
extended_transforms.RandomGaussianBlur(),
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
train_set = doc.DOC('train',
Dataroot,
joint_transform=train_simul_transform,
transform=train_input_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set,
batch_size=train_args['train_batch_size'],
num_workers=1,
shuffle=True,
drop_last=True)
train_loader_temp = DataLoader(train_set,
batch_size=1,
num_workers=1,
shuffle=True,
drop_last=True)
train_args['No_train_images'] = len(train_loader_temp)
del train_loader_temp
if train_args['No_train_images'] == 87677:
train_args['Type_of_train_image'] = 'All Synthetic slide image'
elif train_args['No_train_images'] == 150:
train_args['Type_of_train_image'] = 'Real image'
elif train_args['No_train_images'] == 84641:
train_args['Type_of_train_image'] = 'Synthetic image'
elif train_args['No_train_images'] == 151641:
train_args['Type_of_train_image'] = 'Real + Synthetic image'
val_set = doc.DOC('val',
Dataroot,
joint_transform=val_simul_transform,
transform=val_input_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set,
batch_size=1,
num_workers=1,
shuffle=False,
drop_last=True)
#criterion = CrossEntropyLoss2d(weight = weight, size_average = True, ignore_index = doc.ignore_label).cuda()
criterion = CrossEntropyLoss2d(size_average=True,
ignore_index=doc.ignore_label).cuda()
optimizer = optim.SGD([{
'params': [
param
for name, param in net.named_parameters() if name[-4:] == 'bias'
],
'lr':
2 * train_args['lr']
}, {
'params': [
param
for name, param in net.named_parameters() if name[-4:] != 'bias'
],
'lr':
train_args['lr'],
'weight_decay':
train_args['weight_decay']
}],
momentum=train_args['momentum'])
if len(train_args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(
os.path.join(ckpt_path, 'model_' + exp_name,
'opt_' + train_args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * train_args['lr']
optimizer.param_groups[1]['lr'] = train_args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(
os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(train_args) + '\n\n')
train(train_loader, net, criterion, optimizer, curr_epoch, train_args,
val_loader)
def main():
# args = parse_args()
torch.backends.cudnn.benchmark = True
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
# # if args.seed:
# random.seed(args.seed)
# np.random.seed(args.seed)
# torch.manual_seed(args.seed)
# # if args.gpu:
# torch.cuda.manual_seed_all(args.seed)
seed = 63
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
# if args.gpu:
torch.cuda.manual_seed_all(seed)
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# train_transforms = transforms.Compose([
# transforms.RandomCrop(args['crop_size']),
# transforms.RandomRotation(90),
# transforms.RandomHorizontalFlip(p=0.5),
# transforms.RandomVerticalFlip(p=0.5),
# ])
short_size = int(min(args['input_size']) / 0.875)
# val_transforms = transforms.Compose([
# transforms.Scale(short_size, interpolation=Image.NEAREST),
# # joint_transforms.Scale(short_size),
# transforms.CenterCrop(args['input_size'])
# ])
train_joint_transform = joint_transforms.Compose([
# joint_transforms.Scale(short_size),
joint_transforms.RandomCrop(args['crop_size']),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(90)
])
val_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.CenterCrop(args['input_size'])
])
input_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(*mean_std)])
target_transform = extended_transforms.MaskToTensor()
restore_transform = transforms.Compose(
[extended_transforms.DeNormalize(*mean_std),
transforms.ToPILImage()])
visualize = transforms.ToTensor()
train_set = cityscapes.CityScapes('train',
joint_transform=train_joint_transform,
transform=input_transform,
target_transform=target_transform)
# train_set = cityscapes.CityScapes('train', transform=train_transforms)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=8,
shuffle=True)
val_set = cityscapes.CityScapes('val',
joint_transform=val_joint_transform,
transform=input_transform,
target_transform=target_transform)
# val_set = cityscapes.CityScapes('val', transform=val_transforms)
val_loader = DataLoader(val_set,
batch_size=args['val_batch_size'],
num_workers=8,
shuffle=True)
print(len(train_loader), len(val_loader))
# sdf
vgg_model = VGGNet(requires_grad=True, remove_fc=True)
net = FCN8s(pretrained_net=vgg_model,
n_class=cityscapes.num_classes,
dropout_rate=0.4)
# net.apply(init_weights)
criterion = nn.CrossEntropyLoss(ignore_index=cityscapes.ignore_label)
optimizer = optim.Adam(net.parameters(), lr=1e-4)
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(
os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer, 'min', patience=args['lr_patience'], min_lr=1e-10)
vgg_model = vgg_model.to(device)
net = net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
if len(args['snapshot']) == 0:
curr_epoch = 1
args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0
}
else:
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'])))
split_snapshot = args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
args['best_record'] = {
'epoch': int(split_snapshot[1]),
'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]),
'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9][:-4])
}
criterion.to(device)
for epoch in range(curr_epoch, args['epoch_num'] + 1):
train(train_loader, net, device, criterion, optimizer, epoch, args)
val_loss = validate(val_loader, net, device, criterion, optimizer,
epoch, args, restore_transform, visualize)
scheduler.step(val_loss)
def main():
net = FCN8s(num_classes=cityscapes.num_classes, caffe=True).cuda()
if len(args['snapshot']) == 0:
curr_epoch = 1
args['best_record'] = {'epoch': 0, 'val_loss': 1e10, 'acc': 0, 'acc_cls': 0, 'mean_iu': 0, 'fwavacc': 0}
else:
print('training resumes from ' + args['snapshot'])
net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'])))
split_snapshot = args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
args['best_record'] = {'epoch': int(split_snapshot[1]), 'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]), 'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]), 'fwavacc': float(split_snapshot[11])}
net.train()
mean_std = ([103.939, 116.779, 123.68], [1.0, 1.0, 1.0])
short_size = int(min(args['input_size']) / 0.875)
train_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.RandomCrop(args['input_size']),
joint_transforms.RandomHorizontallyFlip()
])
val_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.CenterCrop(args['input_size'])
])
input_transform = standard_transforms.Compose([
extended_transforms.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.Lambda(lambda x: x.div_(255)),
standard_transforms.ToPILImage(),
extended_transforms.FlipChannels()
])
visualize = standard_transforms.ToTensor()
train_set = cityscapes.CityScapes('fine', 'train', joint_transform=train_joint_transform,
transform=input_transform, target_transform=target_transform)
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=8, shuffle=True)
val_set = cityscapes.CityScapes('fine', 'val', joint_transform=val_joint_transform, transform=input_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set, batch_size=args['val_batch_size'], num_workers=8, shuffle=False)
criterion = CrossEntropyLoss2d(size_average=False, ignore_index=cityscapes.ignore_label).cuda()
optimizer = optim.Adam([
{'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
'lr': 2 * args['lr']},
{'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
'lr': args['lr'], 'weight_decay': args['weight_decay']}
], betas=(args['momentum'], 0.999))
if len(args['snapshot']) > 0:
optimizer.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, 'opt_' + args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt'), 'w').write(str(args) + '\n\n')
scheduler = ReduceLROnPlateau(optimizer, 'min', patience=args['lr_patience'], min_lr=1e-10, verbose=True)
for epoch in range(curr_epoch, args['epoch_num'] + 1):
train(train_loader, net, criterion, optimizer, epoch, args)
val_loss = validate(val_loader, net, criterion, optimizer, epoch, args, restore_transform, visualize)
scheduler.step(val_loss)
0.05 # randomly sample some validation results to display
}
# Paths to trained models & epoch counts
DUCHDC_trainedModelPath = './ducModelFinal.pth'
FCN8_trainedModelPath = './fcnModelFinal.pth'
Unet_trainedModelPath = './unetModelFinal.pth'
# Transforms
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
short_size = int(min(args['input_size']) / 0.875)
joint_transform = joint_transforms.Compose([
joint_transforms.Scale(short_size),
joint_transforms.RandomCrop(args['input_size']),
joint_transforms.RandomHorizontallyFlip()
])
input_transform = standard_transforms.Compose(
[standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.Compose([
extended_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage()
])
visualize = standard_transforms.ToTensor()
def main():
net = PSPNet(num_classes=voc.num_classes).cuda()
if len(args['snapshot']) == 0:
# net.load_state_dict(torch.load(os.path.join(ckpt_path, 'cityscapes (coarse)-psp_net', 'xx.pth')))
curr_epoch = 1
args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else:
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'])))
split_snapshot = args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1]) + 1
args['best_record'] = {
'epoch': int(split_snapshot[1]),
'val_loss': float(split_snapshot[3]),
'acc': float(split_snapshot[5]),
'acc_cls': float(split_snapshot[7]),
'mean_iu': float(split_snapshot[9]),
'fwavacc': float(split_snapshot[11])
}
net.train()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
train_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(args['longer_size']),
joint_transforms.RandomRotate(10),
joint_transforms.RandomHorizontallyFlip()
])
sliding_crop = joint_transforms.SlidingCrop(args['crop_size'],
args['stride_rate'],
voc.ignore_label)
train_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
visualize = standard_transforms.Compose([
standard_transforms.Resize(args['val_img_display_size']),
standard_transforms.ToTensor()
])
train_set = voc.VOC('train',
joint_transform=train_joint_transform,
sliding_crop=sliding_crop,
transform=train_input_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=1,
shuffle=True,
drop_last=True)
val_set = voc.VOC('val',
transform=val_input_transform,
sliding_crop=sliding_crop,
target_transform=target_transform)
val_loader = DataLoader(val_set,
batch_size=1,
num_workers=1,
shuffle=False,
drop_last=True)
criterion = CrossEntropyLoss2d(size_average=True,
ignore_index=voc.ignore_label).cuda()
optimizer = optim.SGD([{
'params': [
param
for name, param in net.named_parameters() if name[-4:] == 'bias'
],
'lr':
2 * args['lr']
}, {
'params': [
param
for name, param in net.named_parameters() if name[-4:] != 'bias'
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'],
nesterov=True)
if len(args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name, 'opt_' + args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(
os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
train(train_loader, net, criterion, optimizer, curr_epoch, args,
val_loader, visualize)
def main(args):
writer = SummaryWriter(log_dir=args.tensorboard_log_dir)
w, h = map(int, args.input_size.split(','))
w_target, h_target = map(int, args.input_size_target.split(','))
joint_transform = joint_transforms.Compose([
joint_transforms.FreeScale((h, w)),
joint_transforms.RandomHorizontallyFlip(),
])
normalize = ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*normalize),
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = standard_transforms.ToPILImage()
if '5' in args.data_dir:
dataset = GTA5DataSetLMDB(
args.data_dir, args.data_list,
joint_transform=joint_transform,
transform=input_transform, target_transform=target_transform,
)
else:
dataset = CityscapesDataSetLMDB(
args.data_dir, args.data_list,
joint_transform=joint_transform,
transform=input_transform, target_transform=target_transform,
)
loader = data.DataLoader(
dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.num_workers, pin_memory=True
)
val_dataset = CityscapesDataSetLMDB(
args.data_dir_target, args.data_list_target,
# joint_transform=joint_transform,
transform=input_transform, target_transform=target_transform
)
val_loader = data.DataLoader(
val_dataset, batch_size=args.batch_size,
shuffle=False, num_workers=args.num_workers, pin_memory=True
)
upsample = nn.Upsample(size=(h_target, w_target),
mode='bilinear', align_corners=True)
net = PSP(
nclass = args.n_classes, backbone='resnet101',
root=args.model_path_prefix, norm_layer=BatchNorm2d,
)
params_list = [
{'params': net.pretrained.parameters(), 'lr': args.learning_rate},
{'params': net.head.parameters(), 'lr': args.learning_rate*10},
{'params': net.auxlayer.parameters(), 'lr': args.learning_rate*10},
]
optimizer = torch.optim.SGD(params_list,
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
criterion = SegmentationLosses(nclass=args.n_classes, aux=True, ignore_index=255)
# criterion = SegmentationMultiLosses(nclass=args.n_classes, ignore_index=255)
net = DataParallelModel(net).cuda()
criterion = DataParallelCriterion(criterion).cuda()
logger = utils.create_logger(args.tensorboard_log_dir, 'PSP_train')
scheduler = utils.LR_Scheduler(args.lr_scheduler, args.learning_rate,
args.num_epoch, len(loader), logger=logger,
lr_step=args.lr_step)
net_eval = Eval(net)
num_batches = len(loader)
best_pred = 0.0
for epoch in range(args.num_epoch):
loss_rec = AverageMeter()
data_time_rec = AverageMeter()
batch_time_rec = AverageMeter()
tem_time = time.time()
for batch_index, batch_data in enumerate(loader):
scheduler(optimizer, batch_index, epoch, best_pred)
show_fig = (batch_index+1) % args.show_img_freq == 0
iteration = batch_index+1+epoch*num_batches
net.train()
img, label, name = batch_data
img = img.cuda()
label_cuda = label.cuda()
data_time_rec.update(time.time()-tem_time)
output = net(img)
loss = criterion(output, label_cuda)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_rec.update(loss.item())
writer.add_scalar('A_seg_loss', loss.item(), iteration)
batch_time_rec.update(time.time()-tem_time)
tem_time = time.time()
if (batch_index+1) % args.print_freq == 0:
print(
f'Epoch [{epoch+1:d}/{args.num_epoch:d}][{batch_index+1:d}/{num_batches:d}]\t'
f'Time: {batch_time_rec.avg:.2f} '
f'Data: {data_time_rec.avg:.2f} '
f'Loss: {loss_rec.avg:.2f}'
)
# if show_fig:
# # base_lr = optimizer.param_groups[0]["lr"]
# output = torch.argmax(output[0][0], dim=1).detach()[0, ...].cpu()
# # fig, axes = plt.subplots(2, 1, figsize=(12, 14))
# # axes = axes.flat
# # axes[0].imshow(colorize_mask(output.numpy()))
# # axes[0].set_title(name[0])
# # axes[1].imshow(colorize_mask(label[0, ...].numpy()))
# # axes[1].set_title(f'seg_true_{base_lr:.6f}')
# # writer.add_figure('A_seg', fig, iteration)
# output_mask = np.asarray(colorize_mask(output.numpy()))
# label = np.asarray(colorize_mask(label[0,...].numpy()))
# image_out = np.concatenate([output_mask, label])
# writer.add_image('A_seg', image_out, iteration)
mean_iu = test_miou(net_eval, val_loader, upsample,
'./style_seg/dataset/info.json')
torch.save(
net.module.state_dict(),
os.path.join(args.save_path_prefix, f'{epoch:d}_{mean_iu*100:.0f}.pth')
)
writer.close()
