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():
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():
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)
def testing(net, train_args, curr_iter):
net.eval()
mean_std = ([0.9584, 0.9588, 0.9586], [0.1246, 0.1223, 0.1224])
test_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
#target_transform = extended_transforms.MaskToTensor()
scaleTest_transform = simul_transforms.Scale(512)
test_simul_transform = simul_transforms.Scale(train_args['input_size'])
#val_input_transform = standard_transforms.Compose([
# standard_transforms.ToTensor(),
# standard_transforms.Normalize(*mean_std)
#test_set = doc.DOC('val',Dataroot, transform=val_input_transform,
# target_transform=target_transform)
# segmentation on test images
test_set = doc.DOC('test',
ckpt_path,
joint_transform=test_simul_transform,
transform=test_input_transform)
test_loader = DataLoader(test_set,
batch_size=1,
num_workers=1,
shuffle=False)
check_mkdir(os.path.join(ckpt_path, exp_name, str(curr_iter)))
listOfImgs = [
'2018_101008181.jpg', '2018_101001826.jpg', '2016_101000408.jpg',
'2009_052067571.jpg', '2010_055401143.jpg'
]
for vi, data in enumerate(test_loader):
img_name, img = data
img_name = img_name[0]
with torch.no_grad():
img = img.cuda()
output = net(img)
prediction = output.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
imput_img = img.squeeze_(0).cpu().numpy()
prediction = doc.colorize_mask_combine(
prediction, ckpt_path + 'data/img/' + img_name)
#if img_name in listOfImgs:
if curr_iter > 1:
prediction.save(
os.path.join(ckpt_path, exp_name, str(curr_iter),
img_name))
call([
"rsync", "-avz",
os.path.join(ckpt_path, exp_name),
"[email protected]:/home/jobinkv/Documents/r1/19wavc/"
])
#print '%d / %d' % (vi + 1, len(test_loader))
test_set_eva = doc.DOC('test_eva',
ckpt_path,
joint_transform=test_simul_transform,
transform=test_input_transform,
target_transform=target_transform)
test_loader_eva = DataLoader(test_set_eva,
batch_size=1,
num_workers=1,
shuffle=False)
gts_all, predictions_all = [], []
for vi, data in enumerate(test_loader_eva):
img, gts = data
with torch.no_grad():
img = img.cuda()
gts = gts.cuda()
output = net(img)
prediction = output.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
gts_all.append(gts.squeeze_(0).cpu().numpy())
predictions_all.append(prediction)
acc, acc_cls, mean_iu, fwavacc, sep_iu = evaluate(predictions_all, gts_all,
doc.num_classes)
del predictions_all
print(
'--------------------------------------------------------------------')
print(
'[test acc %.5f], [test acc_cls %.5f], [test mean_iu %.5f], [test fwavacc %.5f]'
% (acc, acc_cls, mean_iu, fwavacc))
print(
'--------------------------------------------------------------------')
sep_iu = sep_iu.tolist()
sep_iu.append(mean_iu)
sep_iu.insert(0, curr_iter)
sep_iou_test.append(sep_iu)
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 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
'val_img_sample_rate':
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()
])
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():
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():
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():
# 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():
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")
# Random seed for reproducibility
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)
denoramlize_argument = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
train_transforms = transforms.Compose([
transforms.RandomCrop((args.crop_size, args.crop_size)),
transforms.RandomRotation(90),
transforms.RandomHorizontalFlip(p=0.5),
])
# train_joint_transform = joint_transforms.Compose([
# # joint_transforms.Scale(img_resize_shape),
# joint_transforms.RandomCrop(args['crop_size']),
# joint_transforms.RandomHorizontallyFlip(),
# joint_transforms.RandomRotate(90)
# ])
img_resize_shape = int(min(args.input_size) / 0.8)
# val_transforms = transforms.Compose([
# transforms.Scale(img_resize_shape, interpolation=Image.NEAREST),
# transforms.CenterCrop(args['input_size'])
# ])
val_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(img_resize_shape),
joint_transforms.CenterCrop(args.input_size)
])
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = extended_transforms.MaskToTensor()
restore_transform = transforms.Compose([
extended_transforms.DeNormalize(*denoramlize_argument),
transforms.ToPILImage()
])
visualize = transforms.ToTensor()
# train_set = games_data.CityScapes('train', joint_transform=train_joint_transform,
# transform=input_transform, target_transform=target_transform)
train_set = games_data.CityScapes('train', transform=train_transforms)
# train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=8, shuffle=True)
train_loader = DataLoader(train_set,
batch_size=args.training_batch_size,
num_workers=8,
shuffle=True)
val_set = games_data.CityScapes('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=True)
print(len(train_loader), len(val_loader))
# sdf
# Load pretrained VGG model
vgg_model = VGGNet(requires_grad=True, remove_fc=True)
# FCN architecture load
model = FCN8s(pretrained_net=vgg_model,
n_class=games_data.num_classes,
dropout_rate=0.4)
# Loss function
criterion = nn.CrossEntropyLoss(ignore_index=games_data.ignore_label)
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=1e-4)
# Create directory for checkpoints
exist_directory(ckpt_path)
exist_directory(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')
# Learning rate scheduler
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
patience=args.lr_patience,
min_lr=1e-10)
# Send model to CUDA device
vgg_model = vgg_model.to(device)
model = model.to(device)
# Use if more than 1 GPU
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
if len(args.snapshot) == 0:
curr_epoch = 1
best_args['best_record'] = {
'epoch': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0
}
else:
print('training resumes from ' + args['snapshot'])
model.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
best_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.epochs + 1):
train(train_loader, model, device, criterion, optimizer, epoch, args)
val_loss = validate(val_loader, model, device, criterion, optimizer,
epoch, args, restore_transform, visualize)
scheduler.step(val_loss)
def main(train_args):
net = FCN8s(num_classes=11)
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_joint_transform = joint_transforms.Compose([
# joint_transforms.Scale(short_size),
joint_transforms.Scale(2000),
# 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.Compose([
standard_transforms.Scale(400),
standard_transforms.CenterCrop(400),
standard_transforms.ToTensor()
])
train_set = PRIMA('train', joint_transform=train_joint_transform, transform=input_transform, target_transform=target_transform)
train_loader = DataLoader(train_set, batch_size=1, num_workers=0, shuffle=True)
it = iter(train_loader)
first = next(it)
import pdb; pdb.set_trace()
criterion = CrossEntropyLoss2d(size_average=False).cuda()
optimizer = optim.Adam([
{'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']}
], betas=(train_args['momentum'], 0.999))
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)
for epoch in range(curr_epoch, train_args['epoch_num'] + 1):
train(train_loader, net, criterion, optimizer, epoch, train_args)
def main():
net = PSPNet(19)
net.load_pretrained_model(
model_path='./Caffe-PSPNet/pspnet101_cityscapes.caffemodel')
for param in net.parameters():
param.requires_grad = False
net.cbr_final = conv2DBatchNormRelu(4096, 128, 3, 1, 1, False)
net.dropout = nn.Dropout2d(p=0.1, inplace=True)
net.classification = nn.Conv2d(128, kitti_binary.num_classes, 1, 1, 0)
# Find total parameters and trainable parameters
total_params = sum(p.numel() for p in net.parameters())
print(f'{total_params:,} total parameters.')
total_trainable_params = sum(p.numel() for p in net.parameters()
if p.requires_grad)
print(f'{total_trainable_params:,} training parameters.')
if len(args['snapshot']) == 0:
# net.load_state_dict(torch.load(os.path.join(ckpt_path, 'cityscapes (coarse)-psp_net', 'xx.pth')))
args['best_record'] = {
'epoch': 0,
'iter': 0,
'val_loss': 1e10,
'accu': 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]),
'iter': int(split_snapshot[3]),
'val_loss': float(split_snapshot[5]),
'accu': float(split_snapshot[7])
}
net.cuda(args['gpu']).train()
mean_std = ([103.939, 116.779, 123.68], [1.0, 1.0, 1.0])
train_joint_transform = joint_transforms.Compose([
joint_transforms.Scale(args['longer_size']),
joint_transforms.RandomRotate(10),
joint_transforms.RandomHorizontallyFlip()
])
train_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.FlipChannels(),
standard_transforms.ToTensor(),
standard_transforms.Lambda(lambda x: x.mul_(255)),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
train_set = kitti_binary.KITTI(mode='train',
joint_transform=train_joint_transform,
transform=train_input_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=8,
shuffle=True)
val_set = kitti_binary.KITTI(mode='val',
transform=val_input_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set,
batch_size=1,
num_workers=8,
shuffle=False)
criterion = nn.BCEWithLogitsLoss(pos_weight=torch.full([1], 1.05)).cuda(
args['gpu'])
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, args, val_loader)
