def __init__(self, args):
if args.se_loss:
args.checkname = args.checkname + "_se"
self.args = args
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])])
# dataset
data_kwargs = {'transform': input_transform, 'base_size': args.base_size,
'crop_size': args.crop_size}
trainset = get_segmentation_dataset(args.dataset, split='train', mode='train',
**data_kwargs)
testset = get_segmentation_dataset(args.dataset, split='val', mode ='val',
**data_kwargs)
# dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': False} \
if args.cuda else {}
self.trainloader = data.DataLoader(trainset, batch_size=args.batch_size,
drop_last=True, shuffle=True, **kwargs)
self.valloader = data.DataLoader(testset, batch_size=args.batch_size,
drop_last=False, shuffle=False, **kwargs)
self.nclass = trainset.num_class
# model
model = get_segmentation_model(args.model, dataset=args.dataset,
backbone = args.backbone, aux = args.aux,
se_loss = args.se_loss, norm_layer = BatchNorm2d,
base_size=args.base_size, crop_size=args.crop_size)
print(model)
# count parameter number
pytorch_total_params = sum(p.numel() for p in model.parameters())
print("Total number of parameters: %d"%pytorch_total_params)
# optimizer using different LR
params_list = [{'params': model.pretrained.parameters(), 'lr': args.lr},]
if hasattr(model, 'head'):
if args.diflr:
params_list.append({'params': model.head.parameters(), 'lr': args.lr*10})
else:
params_list.append({'params': model.head.parameters(), 'lr': args.lr})
if hasattr(model, 'auxlayer'):
if args.diflr:
params_list.append({'params': model.auxlayer.parameters(), 'lr': args.lr*10})
else:
params_list.append({'params': model.auxlayer.parameters(), 'lr': args.lr})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
#optimizer = torch.optim.ASGD(params_list,
# lr=args.lr,
# weight_decay=args.weight_decay)
# criterions
self.criterion = SegmentationLosses(se_loss=args.se_loss, aux=args.aux,
nclass=self.nclass)
self.model, self.optimizer = model, optimizer
# using cuda
if args.cuda:
self.model = DataParallelModel(self.model).cuda()
self.criterion = DataParallelCriterion(self.criterion).cuda()
# resuming checkpoint
if args.resume is not None and len(args.resume)>0:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
# load weights for the same model
# self.model.module.load_state_dict(checkpoint['state_dict'])
# model and checkpoint have different strucutures
pretrained_dict = checkpoint['state_dict']
model_dict = self.model.module.state_dict()
for name, param in pretrained_dict.items():
if name not in model_dict:
continue
if isinstance(param, Parameter):
# backwards compatibility for serialized parameters
param = param.data
model_dict[name].copy_(param)
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
# clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
# lr scheduler
self.scheduler = utils.LR_Scheduler(args.lr_scheduler, args.lr,
args.epochs, len(self.trainloader),lr_step=args.lr_step)
self.best_pred = 0.0
def __init__(self, args):
self.args = args
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_segmentation_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
# dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
self.trainloader = data.DataLoader(trainset,
batch_size=args.batch_size,
drop_last=True,
shuffle=True,
**kwargs)
self.valloader = data.DataLoader(testset,
batch_size=args.batch_size,
drop_last=False,
shuffle=False,
**kwargs)
self.nclass = trainset.num_class
# model
model = get_segmentation_model(
args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=torch.nn.BatchNorm2d, ## BatchNorm2d
base_size=args.base_size,
crop_size=args.crop_size)
print(model)
# optimizer using different LR
params_list = [
{
'params': model.pretrained.parameters(),
'lr': args.lr
},
]
if hasattr(model, 'jpu'):
params_list.append({
'params': model.jpu.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'head'):
params_list.append({
'params': model.head.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'auxlayer'):
params_list.append({
'params': model.auxlayer.parameters(),
'lr': args.lr * 10
})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# criterions
self.criterion = SegmentationLosses(se_loss=args.se_loss,
aux=args.aux,
nclass=self.nclass,
se_weight=args.se_weight,
aux_weight=args.aux_weight)
self.model, self.optimizer = model, optimizer
# using cuda
if args.cuda:
self.model = DataParallelModel(self.model).cuda()
self.criterion = DataParallelCriterion(self.criterion).cuda()
# resuming checkpoint
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
# lr scheduler
self.scheduler = utils.LR_Scheduler(args.lr_scheduler, args.lr,
args.epochs, len(self.trainloader))
self.best_pred = 0.0
def __init__(self, args):
self.args = args
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
testset = get_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
self.train_sampler = torch.utils.data.distributed.DistributedSampler(
trainset)
self.val_sampler = torch.utils.data.distributed.DistributedSampler(
testset)
# dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
#self.trainloader = data.DataLoader(trainset, batch_size=args.batch_size,
# drop_last=True, shuffle=True, **kwargs)
#collate_fn=test_batchify_fn,
self.trainloader = data.DataLoader(trainset,
batch_size=args.batch_size //
args.world_size,
drop_last=True,
shuffle=False,
sampler=self.train_sampler,
**kwargs)
#self.valloader = data.DataLoader(testset, batch_size=args.batch_size,
self.valloader = data.DataLoader(testset,
batch_size=args.test_batch_size //
args.world_size,
drop_last=False,
shuffle=False,
sampler=self.val_sampler,
**kwargs)
self.nclass = trainset.num_class
#Norm_method = nn.SyncBatchNorm
#Norm_method = nn.BatchNorm2d(momentum=0.01)
Norm_method = nn.BatchNorm2d
# model
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
multi_grid=args.multi_grid,
se_loss=args.se_loss,
norm_layer=Norm_method,
lateral=args.lateral,
root=args.backbone_path,
base_size=args.base_size,
crop_size=args.crop_size)
if self.args.rank == 0:
print(model)
# optimizer using different LR
params_list = [
{
'params': model.pretrained.parameters(),
'lr': args.lr
},
]
if hasattr(model, 'head'):
params_list.append({
'params': model.head.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'auxlayer'):
params_list.append({
'params': model.auxlayer.parameters(),
'lr': args.lr * 10
})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
self.optimizer = optimizer
#self.model = model
# criterions
self.criterion = SegmentationLosses(se_loss=args.se_loss,
aux=args.aux,
nclass=self.nclass,
se_weight=args.se_weight,
aux_weight=args.aux_weight)
device = torch.device('cuda:{}'.format(args.local_rank))
self.device = device
# using cuda
if args.cuda:
#self.model = DataParallelModel(self.model).cuda()
#self.model = self.model.cuda()
sync_bn_model = FullModel(model, self.criterion)
#self.model.cuda()
#broadcast_params(self.model)
#num_gpus = torch.cuda.device_count()
#local_rank = args.local_rank % num_gpus
#local_rank = args.local_rank
#process_group = torch.distributed.new_group([args.local_rank])
#process_group = torch.distributed.new_group([args.rank])
#sync_bn_model = torch.nn.utils.convert_sync_batchnorm(self.model, process_group)
sync_bn_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
sync_bn_model)
sync_bn_model = sync_bn_model.to(device)
#self.model = torch.nn.parallel.DistributedDataParallel(self.model, device_ids=[args.local_rank], output_device=args.local_rank)
self.model = torch.nn.parallel.DistributedDataParallel(
sync_bn_model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
#self.criterion = DataParallelCriterion(self.criterion).cuda()
#self.criterion = self.criterion.cuda()
dist.barrier()
# resuming checkpoint
#if args.resume is not None and self.args.rank == 0:
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
old_state_dict = checkpoint['state_dict']
new_state_dict = dict()
for k, v in old_state_dict.items():
if k.startswith('module.'):
#new_state_dict[k[len('module.'):]] = old_state_dict[k]
new_state_dict[k] = old_state_dict[k]
else:
new_state_dict[k] = old_state_dict[k]
args.start_epoch = checkpoint['epoch']
if args.cuda:
#self.model.module.load_state_dict(checkpoint['state_dict'])
#self.model.load_state_dict(checkpoint['state_dict'])
self.model.load_state_dict(new_state_dict)
else:
#self.model.load_state_dict(checkpoint['state_dict'])
self.model.load_state_dict(new_state_dict)
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
for state in self.optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
# lr scheduler
self.scheduler = utils.LR_Scheduler(args.lr_scheduler,
args.lr,
args.epochs,
len(self.trainloader),
local_rank=self.args.rank)
print('len(trainloader) : %.3f ' % (len(self.trainloader)))
self.best_pred = 0.0
#for sumaryWriter
self.track_loss = 0.0
self.track_pixAcc = 0.0
self.track_mIoU = 0.0
def main_worker(gpu, ngpus_per_node, args):
global best_pred
args.gpu = gpu
args.rank = args.rank * ngpus_per_node + gpu
print('rank: {} / {}'.format(args.rank, args.world_size))
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
torch.cuda.set_device(args.gpu)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
cudnn.benchmark = True
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
valset = get_dataset(args.dataset, split='val', mode='val', **data_kwargs)
train_sampler = torch.utils.data.distributed.DistributedSampler(trainset)
val_sampler = torch.utils.data.distributed.DistributedSampler(
valset, shuffle=False)
# dataloader
loader_kwargs = {
'batch_size': args.batch_size,
'num_workers': args.workers,
'pin_memory': True
}
trainloader = data.DataLoader(trainset,
sampler=train_sampler,
drop_last=True,
**loader_kwargs)
valloader = data.DataLoader(valset, sampler=val_sampler, **loader_kwargs)
nclass = trainset.num_class
# model
model_kwargs = {}
if args.rectify:
model_kwargs['rectified_conv'] = True
model_kwargs['rectify_avg'] = args.rectify_avg
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=DistSyncBatchNorm,
base_size=args.base_size,
crop_size=args.crop_size,
**model_kwargs)
if args.gpu == 0:
print(model)
# optimizer using different LR
params_list = [
{
'params': model.pretrained.parameters(),
'lr': args.lr
},
]
if hasattr(model, 'head'):
params_list.append({
'params': model.head.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'auxlayer'):
params_list.append({
'params': model.auxlayer.parameters(),
'lr': args.lr * 10
})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer = torch.optim.Adam(params_list,
# lr=args.lr,
# # momentum=args.momentum,
# weight_decay=args.weight_decay)
# criterions
criterion = SegmentationLosses(se_loss=args.se_loss,
aux=args.aux,
nclass=nclass,
se_weight=args.se_weight,
aux_weight=args.aux_weight)
# distributed data parallel
model.cuda(args.gpu)
criterion.cuda(args.gpu)
model = DistributedDataParallel(model, device_ids=[args.gpu])
metric = utils.SegmentationMetric(nclass=nclass)
# resuming checkpoint
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.module.load_state_dict(checkpoint['state_dict'])
'''
checkpoint = torch.load(args.resume, map_location='cpu')
args.start_epoch = checkpoint['epoch']
model.module.load_state_dict(checkpoint['state_dict'])
model.cuda()
'''
if not args.ft:
optimizer.load_state_dict(checkpoint['optimizer'])
best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
# lr scheduler
scheduler = utils.LR_Scheduler_Head(args.lr_scheduler, args.lr,
args.epochs, len(trainloader))
# train_losses = [2.855, 2.513, 2.275, 2.128, 2.001, 1.875, 1.855, 1.916, 1.987, 1.915, 1.952]
train_losses = []
def training(epoch):
train_sampler.set_epoch(epoch)
global best_pred
train_loss = 0.0
model.train()
tic = time.time()
for i, (image, target) in enumerate(trainloader):
scheduler(optimizer, i, epoch, best_pred)
optimizer.zero_grad()
outputs = model(image)
target = target.cuda(args.gpu)
loss = criterion(*outputs, target)
loss.backward()
optimizer.step()
train_loss += loss.item()
if i % 100 == 0 and args.gpu == 0:
iter_per_sec = 100.0 / (
time.time() - tic) if i != 0 else 1.0 / (time.time() - tic)
tic = time.time()
print('Epoch: {}, Iter: {}, Speed: {:.3f} iter/sec, Train loss: {:.3f}'. \
format(epoch, i, iter_per_sec, train_loss / (i + 1)))
train_losses.append(train_loss / len(trainloader))
if epoch > 1:
if train_losses[epoch] < train_losses[epoch - 1]:
utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': new_preds[(epoch - 1) // 10],
},
args,
False,
filename='checkpoint_train.pth.tar')
plt.plot(train_losses)
plt.xlabel('Epoch')
plt.ylabel('Train_loss')
plt.title('Train_Loss')
plt.grid()
plt.savefig('./loss_fig/train_losses.pdf')
plt.savefig('./loss_fig/train_losses.svg')
plt.close()
# p_m = [(0.3, 0.05), (0.23, 0.54)]
# new_preds = [0.175, 0.392]
p_m = []
new_preds = []
def validation(epoch):
# Fast test during the training using single-crop only
global best_pred
is_best = False
model.eval()
metric.reset()
for i, (image, target) in enumerate(valloader):
with torch.no_grad():
pred = model(image)[0]
target = target.cuda(args.gpu)
metric.update(target, pred)
if i % 100 == 0:
all_metircs = metric.get_all()
all_metircs = utils.torch_dist_sum(args.gpu, *all_metircs)
pixAcc, mIoU = utils.get_pixacc_miou(*all_metircs)
if args.gpu == 0:
print('pixAcc: %.3f, mIoU1: %.3f' % (pixAcc, mIoU))
all_metircs = metric.get_all()
all_metircs = utils.torch_dist_sum(args.gpu, *all_metircs)
pixAcc, mIoU = utils.get_pixacc_miou(*all_metircs)
if args.gpu == 0:
print('pixAcc: %.3f, mIoU2: %.3f' % (pixAcc, mIoU))
p_m.append((pixAcc, mIoU))
plt.plot(p_m)
plt.xlabel('10 Epoch')
plt.ylabel('pixAcc, mIoU')
plt.title('pixAcc, mIoU')
plt.grid()
plt.legend(('pixAcc', 'mIoU'))
plt.savefig('./loss_fig/pixAcc_mIoU.pdf')
plt.savefig('./loss_fig/pixAcc_mIoU.svg')
plt.close()
if args.eval: return
new_pred = (pixAcc + mIoU) / 2
new_preds.append(new_pred)
plt.plot(new_preds)
plt.xlabel('10 Epoch')
plt.ylabel('new_predication')
plt.title('new_predication')
plt.grid()
plt.savefig('./loss_fig/new_predication.pdf')
plt.savefig('./loss_fig/new_predication.svg')
plt.close()
if new_pred > best_pred:
is_best = True
best_pred = new_pred
utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
},
args,
is_best,
filename='checkpoint_train_{}.pth.tar'.format(epoch + 1))
if args.export:
if args.gpu == 0:
torch.save(model.module.state_dict(), args.export + '.pth')
return
if args.eval:
validation(args.start_epoch)
return
if args.gpu == 0:
print('Starting Epoch:', args.start_epoch)
print('Total Epoches:', args.epochs)
for epoch in range(args.start_epoch, args.epochs):
tic = time.time()
training(epoch)
if epoch % 10 == 0 or epoch == args.epochs - 1:
validation(epoch)
elapsed = time.time() - tic
if args.gpu == 0:
print(f'Epoch: {epoch}, Time cost: {elapsed}')
validation(epoch)
def main_worker(gpu, ngpus_per_node, args):
global best_pred
args.gpu = gpu
args.rank = args.rank * ngpus_per_node + gpu
print('rank: {} / {}'.format(args.rank, args.world_size))
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
torch.cuda.set_device(args.gpu)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
cudnn.benchmark = True
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
valset = get_dataset(args.dataset, split='val', mode='val', **data_kwargs)
train_sampler = torch.utils.data.distributed.DistributedSampler(trainset)
val_sampler = torch.utils.data.distributed.DistributedSampler(
valset, shuffle=False)
# dataloader
loader_kwargs = {
'batch_size': args.batch_size,
'num_workers': args.workers,
'pin_memory': True
}
trainloader = data.DataLoader(trainset,
sampler=train_sampler,
drop_last=True,
**loader_kwargs)
valloader = data.DataLoader(valset, sampler=val_sampler, **loader_kwargs)
nclass = trainset.num_class
# model
model_kwargs = {}
if args.rectify:
model_kwargs['rectified_conv'] = True
model_kwargs['rectify_avg'] = args.rectify_avg
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=DistSyncBatchNorm,
base_size=args.base_size,
crop_size=args.crop_size,
**model_kwargs)
if args.gpu == 0:
print(model)
# optimizer using different LR
params_list = [
{
'params': model.pretrained.parameters(),
'lr': args.lr
},
]
if hasattr(model, 'head'):
params_list.append({
'params': model.head.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'auxlayer'):
params_list.append({
'params': model.auxlayer.parameters(),
'lr': args.lr * 10
})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# criterions
criterion = SegmentationLosses(se_loss=args.se_loss,
aux=args.aux,
nclass=nclass,
se_weight=args.se_weight,
aux_weight=args.aux_weight)
# distributed data parallel
model.cuda(args.gpu)
criterion.cuda(args.gpu)
model = DistributedDataParallel(model, device_ids=[args.gpu])
metric = utils.SegmentationMetric(nclass=nclass)
# resuming checkpoint
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.module.load_state_dict(checkpoint['state_dict'])
if not args.ft:
optimizer.load_state_dict(checkpoint['optimizer'])
best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
# lr scheduler
scheduler = utils.LR_Scheduler_Head(args.lr_scheduler, args.lr,
args.epochs, len(trainloader))
def training(epoch):
global best_pred
train_loss = 0.0
model.train()
tic = time.time()
for i, (image, target) in enumerate(trainloader):
scheduler(optimizer, i, epoch, best_pred)
optimizer.zero_grad()
outputs = model(image)
target = target.cuda(args.gpu)
loss = criterion(*outputs, target)
loss.backward()
optimizer.step()
train_loss += loss.item()
if i % 100 == 0 and args.gpu == 0:
iter_per_sec = 100.0 / (
time.time() - tic) if i != 0 else 1.0 / (time.time() - tic)
tic = time.time()
print('Epoch: {}, Iter: {}, Speed: {:.3f} iter/sec, Train loss: {:.3f}'. \
format(epoch, i, iter_per_sec, train_loss / (i + 1)))
def validation(epoch):
# Fast test during the training using single-crop only
global best_pred
is_best = False
model.eval()
metric.reset()
for i, (image, target) in enumerate(valloader):
with torch.no_grad():
#correct, labeled, inter, union = eval_batch(model, image, target)
pred = model(image)[0]
target = target.cuda(args.gpu)
metric.update(target, pred)
pixAcc, mIoU = metric.get()
if i % 100 == 0 and args.gpu == 0:
print('pixAcc: %.3f, mIoU: %.3f' % (pixAcc, mIoU))
if args.gpu == 0:
pixAcc, mIoU = torch_dist_avg(args.gpu, pixAcc, mIoU)
print('pixAcc: %.3f, mIoU: %.3f' % (pixAcc, mIoU))
new_pred = (pixAcc + mIoU) / 2
if new_pred > best_pred:
is_best = True
best_pred = new_pred
utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
}, args, is_best)
if args.gpu == 0:
print('Starting Epoch:', args.start_epoch)
print('Total Epoches:', args.epochs)
for epoch in range(args.start_epoch, args.epochs):
tic = time.time()
training(epoch)
if epoch % 10 == 0:
validation(epoch)
elapsed = time.time() - tic
if args.gpu == 0:
print(f'Epoch: {epoch}, Time cost: {elapsed}')
validation(epoch)
def __init__(self, args):
self.args = args
args.log_name = str(args.checkname)
args.log_root = os.path.join(args.dataset, args.log_root) # dataset/log/
self.logger = utils.create_logger(args.log_root, args.log_name)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])])
# dataset
data_kwargs = {'transform': input_transform, 'base_size': args.base_size,
'crop_size': args.crop_size, 'logger': self.logger,
'scale': args.scale}
trainset = get_segmentation_dataset(args.dataset, split='trainval', mode='trainval',
**data_kwargs)
testset = get_segmentation_dataset(args.dataset, split='val', mode='val', # crop fixed size as model input
**data_kwargs)
# dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
self.trainloader = data.DataLoader(trainset, batch_size=args.batch_size,
drop_last=True, shuffle=True, **kwargs)
self.valloader = data.DataLoader(testset, batch_size=args.batch_size,
drop_last=False, shuffle=False, **kwargs)
self.nclass = trainset.num_class
# model
model = get_segmentation_model(args.model, dataset=args.dataset,
backbone=args.backbone,
norm_layer=BatchNorm2d,
base_size=args.base_size, crop_size=args.crop_size,
)
#print(model)
self.logger.info(model)
# optimizer using different LR
params_list = [{'params': model.pretrained.parameters(), 'lr': args.lr},]
if hasattr(model, 'head'):
print("this model has object, head")
params_list.append({'params': model.head.parameters(), 'lr': args.lr*10})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
self.criterion = SegmentationLosses(nclass=self.nclass)
self.model, self.optimizer = model, optimizer
# using cuda
if args.cuda:
self.model = DataParallelModel(self.model).cuda()
self.criterion = DataParallelCriterion(self.criterion).cuda()
# resuming checkpoint
if args.resume:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
self.logger.info("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
# lr scheduler
self.scheduler = utils.LR_Scheduler(args.lr_scheduler, args.lr,
args.epochs, len(self.trainloader), logger=self.logger,
lr_step=args.lr_step)
self.best_pred = 0.0
def __init__(self, args):
self.args = args
args.log_name = str(args.checkname)
self.logger = utils.create_logger(args.log_root, args.log_name)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
# transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {'transform': input_transform, 'base_size': args.base_size, 'crop_size': args.crop_size, 'logger': self.logger, 'scale': args.scale}
trainset = get_segmentation_dataset(args.dataset, split='train', mode='train', **data_kwargs)
testset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs)
# dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True} if args.cuda else {}
self.trainloader = data.DataLoader(trainset, batch_size=args.batch_size, drop_last=True, shuffle=True, **kwargs)
self.valloader = data.DataLoader(testset, batch_size=args.batch_size, drop_last=False, shuffle=False, **kwargs)
self.nclass = trainset.num_class
self.confusion_matrix_weather = utils.ConfusionMatrix(7)
self.confusion_matrix_timeofday = utils.ConfusionMatrix(4)
# model
model = get_segmentation_model(args.model, dataset=args.dataset, backbone=args.backbone, aux=args.aux, se_loss=args.se_loss,
# norm_layer=BatchNorm2d, # for multi-gpu
base_size=args.base_size, crop_size=args.crop_size, multi_grid=args.multi_grid, multi_dilation=args.multi_dilation)
#####################################################################
self.logger.info(model)
# optimizer using different LR
params_list = [{'params': model.pretrained.parameters(), 'lr': 1 * args.lr},]
if hasattr(model, 'head'):
params_list.append({'params': model.head.parameters(), 'lr': 1 * args.lr*10})
if hasattr(model, 'auxlayer'):
params_list.append({'params': model.auxlayer.parameters(), 'lr': 1 * args.lr*10})
params_list.append({'params': model.weather_classifier.parameters(), 'lr': 0 * args.lr*10})
params_list.append({'params': model.time_classifier.parameters(), 'lr': 0 * args.lr*10})
optimizer = torch.optim.SGD(params_list, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# self.criterion = SegmentationMultiLosses(nclass=self.nclass)
self.criterion = SegmentationLosses(se_loss=args.se_loss, aux=args.aux, nclass=self.nclass)
# self.criterion = torch.nn.CrossEntropyLoss()
#####################################################################
self.model, self.optimizer = model, optimizer
# using cuda
if args.cuda:
self.model = DataParallelModel(self.model).cuda()
self.criterion = DataParallelCriterion(self.criterion).cuda()
# finetune from a trained model
if args.ft:
args.start_epoch = 0
checkpoint = torch.load(args.ft_resume)
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'], strict=False)
else:
self.model.load_state_dict(checkpoint['state_dict'], strict=False)
self.logger.info("=> loaded checkpoint '{}' (epoch {})".format(args.ft_resume, checkpoint['epoch']))
# resuming checkpoint
if args.resume:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
self.logger.info("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
# lr scheduler
self.scheduler = utils.LR_Scheduler(args.lr_scheduler, args.lr, args.epochs, len(self.trainloader), logger=self.logger, lr_step=args.lr_step)
self.best_pred = 0.0
self.logger.info(self.args)
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()
def __init__(self, args):
self.args = args
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_segmentation_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
# dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
self.trainloader = data.DataLoader(trainset,
batch_size=args.batch_size,
drop_last=False,
shuffle=True,
**kwargs)
self.valloader = data.DataLoader(testset,
batch_size=args.batch_size,
drop_last=False,
shuffle=False,
**kwargs)
self.nclass = trainset.num_class
# model
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
multi_grid=args.multi_grid,
stride=args.stride,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=SyncBatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# print(model)
# optimizer using different LR
params_list = [
{
'params': model.pretrained.parameters(),
'lr': args.lr
},
]
if hasattr(model, 'jpu') and model.jpu:
params_list.append({
'params': model.jpu.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'head'):
params_list.append({
'params': model.head.parameters(),
'lr': args.lr * 10
})
if hasattr(model, 'auxlayer'):
params_list.append({
'params': model.auxlayer.parameters(),
'lr': args.lr * 10
})
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
class_balance_weight = 'None'
if args.dataset == "pcontext60":
class_balance_weight = torch.tensor([
1.3225e-01, 2.0757e+00, 1.8146e+01, 5.5052e+00, 2.2060e+00,
2.8054e+01, 2.0566e+00, 1.8598e+00, 2.4027e+00, 9.3435e+00,
3.5990e+00, 2.7487e-01, 1.4216e+00, 2.4986e+00, 7.7258e-01,
4.9020e-01, 2.9067e+00, 1.2197e+00, 2.2744e+00, 2.0444e+01,
3.0057e+00, 1.8167e+01, 3.7405e+00, 5.6749e-01, 3.2631e+00,
1.5007e+00, 5.5519e-01, 1.0056e+01, 1.8952e+01, 2.6792e-01,
2.7479e-01, 1.8309e+00, 2.0428e+01, 1.4788e+01, 1.4908e+00,
1.9113e+00, 2.6166e+02, 2.3233e-01, 1.9096e+01, 6.7025e+00,
2.8756e+00, 6.8804e-01, 4.4140e+00, 2.5621e+00, 4.4409e+00,
4.3821e+00, 1.3774e+01, 1.9803e-01, 3.6944e+00, 1.0397e+00,
2.0601e+00, 5.5811e+00, 1.3242e+00, 3.0088e-01, 1.7344e+01,
2.1569e+00, 2.7216e-01, 5.8731e-01, 1.9956e+00, 4.4004e+00
])
elif args.dataset == "ade20k":
class_balance_weight = torch.tensor([
0.0772, 0.0431, 0.0631, 0.0766, 0.1095, 0.1399, 0.1502, 0.1702,
0.2958, 0.3400, 0.3738, 0.3749, 0.4059, 0.4266, 0.4524, 0.5725,
0.6145, 0.6240, 0.6709, 0.6517, 0.6591, 0.6818, 0.9203, 0.9965,
1.0272, 1.0967, 1.1202, 1.2354, 1.2900, 1.5038, 1.5160, 1.5172,
1.5036, 2.0746, 2.1426, 2.3159, 2.2792, 2.6468, 2.8038, 2.8777,
2.9525, 2.9051, 3.1050, 3.1785, 3.3533, 3.5300, 3.6120, 3.7006,
3.6790, 3.8057, 3.7604, 3.8043, 3.6610, 3.8268, 4.0644, 4.2698,
4.0163, 4.0272, 4.1626, 4.3702, 4.3144, 4.3612, 4.4389, 4.5612,
5.1537, 4.7653, 4.8421, 4.6813, 5.1037, 5.0729, 5.2657, 5.6153,
5.8240, 5.5360, 5.6373, 6.6972, 6.4561, 6.9555, 7.9239, 7.3265,
7.7501, 7.7900, 8.0528, 8.5415, 8.1316, 8.6557, 9.0550, 9.0081,
9.3262, 9.1391, 9.7237, 9.3775, 9.4592, 9.7883, 10.6705,
10.2113, 10.5845, 10.9667, 10.8754, 10.8274, 11.6427, 11.0687,
10.8417, 11.0287, 12.2030, 12.8830, 12.5082, 13.0703, 13.8410,
12.3264, 12.9048, 12.9664, 12.3523, 13.9830, 13.8105, 14.0345,
15.0054, 13.9801, 14.1048, 13.9025, 13.6179, 17.0577, 15.8351,
17.7102, 17.3153, 19.4640, 17.7629, 19.9093, 16.9529, 19.3016,
17.6671, 19.4525, 20.0794, 18.3574, 19.1219, 19.5089, 19.2417,
20.2534, 20.0332, 21.7496, 21.5427, 20.3008, 21.1942, 22.7051,
23.3359, 22.4300, 20.9934, 26.9073, 31.7362, 30.0784
])
elif args.dataset == "cocostuff":
class_balance_weight = torch.tensor([
4.8557e-02, 6.4709e-02, 3.9255e+00, 9.4797e-01, 1.2703e+00,
1.4151e+00, 7.9733e-01, 8.4903e-01, 1.0751e+00, 2.4001e+00,
8.9736e+00, 5.3036e+00, 6.0410e+00, 9.3285e+00, 1.5952e+00,
3.6090e+00, 9.8772e-01, 1.2319e+00, 1.9194e+00, 2.7624e+00,
2.0548e+00, 1.2058e+00, 3.6424e+00, 2.0789e+00, 1.7851e+00,
6.7138e+00, 2.1315e+00, 6.9813e+00, 1.2679e+02, 2.0357e+00,
2.2933e+01, 2.3198e+01, 1.7439e+01, 4.1294e+01, 7.8678e+00,
4.3444e+01, 6.7543e+01, 1.0066e+01, 6.7520e+00, 1.3174e+01,
3.3499e+00, 6.9737e+00, 2.1482e+00, 1.9428e+01, 1.3240e+01,
1.9218e+01, 7.6836e-01, 2.6041e+00, 6.1822e+00, 1.4070e+00,
4.4074e+00, 5.7792e+00, 1.0321e+01, 4.9922e+00, 6.7408e-01,
3.1554e+00, 1.5832e+00, 8.9685e-01, 1.1686e+00, 2.6487e+00,
6.5354e-01, 2.3801e-01, 1.9536e+00, 1.5862e+00, 1.7797e+00,
2.7385e+01, 1.2419e+01, 3.9287e+00, 7.8897e+00, 7.5737e+00,
1.9758e+00, 8.1962e+01, 3.6922e+00, 2.0039e+00, 2.7333e+00,
5.4717e+00, 3.9048e+00, 1.9184e+01, 2.2689e+00, 2.6091e+02,
4.7366e+01, 2.3844e+00, 8.3310e+00, 1.4857e+01, 6.5076e+00,
2.0854e-01, 1.0425e+00, 1.7386e+00, 1.1973e+01, 5.2862e+00,
1.7341e+00, 8.6124e-01, 9.3702e+00, 2.8545e+00, 6.0123e+00,
1.7560e-01, 1.8128e+00, 1.3784e+00, 1.3699e+00, 2.3728e+00,
6.2819e-01, 1.3097e+00, 4.7892e-01, 1.0268e+01, 1.2307e+00,
5.5662e+00, 1.2867e+00, 1.2745e+00, 4.7505e+00, 8.4029e+00,
1.8679e+00, 1.0519e+01, 1.1240e+00, 1.4975e-01, 2.3146e+00,
4.1265e-01, 2.5896e+00, 1.4537e+00, 4.5575e+00, 7.8143e+00,
1.4603e+01, 2.8812e+00, 1.8868e+00, 7.8131e+01, 1.9323e+00,
7.4980e+00, 1.2446e+01, 2.1856e+00, 3.0973e+00, 4.1270e-01,
4.9016e+01, 7.1001e-01, 7.4035e+00, 2.3395e+00, 2.9207e-01,
2.4156e+00, 3.3211e+00, 2.1300e+00, 2.4533e-01, 1.7081e+00,
4.6621e+00, 2.9199e+00, 1.0407e+01, 7.6207e-01, 2.7806e-01,
3.7711e+00, 1.1852e-01, 8.8280e+00, 3.1700e-01, 6.3765e+01,
6.6032e+00, 5.2177e+00, 4.3596e+00, 6.2965e-01, 1.0207e+00,
1.1731e+01, 2.3935e+00, 9.2767e+00, 1.1023e-01, 3.6947e+00,
1.3943e+00, 2.3407e+00, 1.2112e-01, 2.8518e+00, 2.8195e+00,
1.0078e+00, 1.6614e+00, 6.5307e-01, 1.9070e+01, 2.7231e+00,
6.0769e-01
])
# criterions
self.criterion = SegmentationLosses(se_loss=args.se_loss,
aux=args.aux,
nclass=self.nclass,
se_weight=args.se_weight,
aux_weight=args.aux_weight,
weight=class_balance_weight)
self.model, self.optimizer = model, optimizer
# using cuda
if args.cuda:
self.model = DataParallelModel(self.model).cuda()
self.criterion = DataParallelCriterion(self.criterion).cuda()
# resuming checkpoint
self.best_pred = 0.0
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
# lr scheduler
self.scheduler = utils.LR_Scheduler(args.lr_scheduler, args.lr,
args.epochs, len(self.trainloader))