class Trainer(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
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)
args.iters_per_epoch = len(trainset) // (args.num_gpus * args.batch_size)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(trainset, shuffle=True, distributed=args.distributed)
train_batch_sampler = make_batch_data_sampler(train_sampler, args.batch_size, args.max_iters)
self.train_loader = data.DataLoader(dataset=trainset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True)
if not args.skip_val:
valset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs)
val_sampler = make_data_sampler(valset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler, args.batch_size)
self.val_loader = data.DataLoader(dataset=valset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network
BatchNorm2d = nn.SyncBatchNorm if args.distributed else nn.BatchNorm2d
self.model = get_segmentation_model(args.model, dataset=args.dataset,
aux=args.aux, norm_layer=BatchNorm2d)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[args.local_rank],
output_device=args.local_rank)
self.model = self.model.to(args.device)
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert ext == '.pkl' or '.pth', 'Sorry only .pth and .pkl files supported.'
print('Resuming training, loading {}...'.format(args.resume))
self.model.load_state_dict(torch.load(args.resume, map_location=lambda storage, loc: storage))
# create criterion
if args.ohem:
min_kept = int(args.batch_size // args.num_gpus * args.crop_size ** 2 // 16)
self.criterion = MixSoftmaxCrossEntropyOHEMLoss(args.aux, args.aux_weight, min_kept=min_kept,
ignore_index=-1).to(self.device)
else:
self.criterion = MixSoftmaxCrossEntropyLoss(args.aux, args.aux_weight, ignore_index=-1).to(self.device)
# optimizer
self.optimizer = torch.optim.SGD(self.model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lr scheduling
self.lr_scheduler = WarmupPolyLR(self.optimizer,
max_iters=args.max_iters,
power=0.9,
warmup_factor=args.warmup_factor,
warmup_iters=args.warmup_iters,
warmup_method=args.warmup_method)
# evaluation metrics
self.metric = SegmentationMetric(trainset.num_class)
self.best_pred = 0.0
def train(self):
save_to_disk = get_rank() == 0
epochs, max_iters = self.args.epochs, self.args.max_iters
log_per_iters, val_per_iters = self.args.log_iter, self.args.val_epoch * self.args.iters_per_epoch
save_per_iters = self.args.save_epoch * self.args.iters_per_epoch
start_time = time.time()
logger.info('Start training, Total Epochs: {:d} = Total Iterations {:d}'.format(epochs, max_iters))
self.model.train()
for iteration, (images, targets) in enumerate(self.train_loader):
iteration += 1
self.lr_scheduler.step()
images = images.to(self.device)
targets = targets.to(self.device)
outputs = self.model(images)
loss_dict = self.criterion(outputs, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
self.optimizer.zero_grad()
losses.backward()
self.optimizer.step()
eta_seconds = ((time.time() - start_time) / iteration) * (max_iters - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % log_per_iters == 0 and save_to_disk:
logger.info(
"Iters: {:d}/{:d} || Lr: {:.6f} || Loss: {:.4f} || Cost Time: {} || Estimated Time: {}".format(
iteration, max_iters, self.optimizer.param_groups[0]['lr'], losses_reduced.item(),
str(datetime.timedelta(seconds=int(time.time() - start_time))), eta_string))
if iteration % save_per_iters == 0 and save_to_disk:
save_checkpoint(self.model, self.args, is_best=False)
if not self.args.skip_val and iteration % val_per_iters == 0:
self.validation()
self.model.train()
save_checkpoint(self.model, self.args, is_best=False)
total_training_time = time.time() - start_time
total_training_str = str(datetime.timedelta(seconds=total_training_time))
logger.info(
"Total training time: {} ({:.4f}s / it)".format(
total_training_str, total_training_time / max_iters))
def validation(self):
# total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
is_best = False
self.metric.reset()
if self.args.distributed:
model = self.model.module
else:
model = self.model
torch.cuda.empty_cache() # TODO check if it helps
model.eval()
for i, (image, target) in enumerate(self.val_loader):
image = image.to(self.device)
target = target.to(self.device)
with torch.no_grad():
outputs = model(image)
self.metric.update(outputs[0], target)
pixAcc, mIoU = self.metric.get()
logger.info("Sample: {:d}, Validation pixAcc: {:.3f}, mIoU: {:.3f}".format(i + 1, pixAcc, mIoU))
new_pred = (pixAcc + mIoU) / 2
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
save_checkpoint(self.model, self.args, is_best)
synchronize()
class Trainer(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.63658345, 0.5976706, 0.6074681],
[0.30042663, 0.29670033, 0.29805037]),
])
# dataset and dataloader
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
train_dataset = get_segmentation_dataset(args.dataset,
split='train',
mode='train',
**data_kwargs)
val_dataset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
args.iters_per_epoch = len(train_dataset) // (args.num_gpus *
args.batch_size)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(train_dataset,
shuffle=True,
distributed=args.distributed)
train_batch_sampler = make_batch_data_sampler(train_sampler,
args.batch_size,
args.max_iters)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
args.batch_size)
self.train_loader = data.DataLoader(dataset=train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network
BatchNorm2d = nn.SyncBatchNorm if args.distributed else nn.BatchNorm2d
self.model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
jpu=args.jpu,
norm_layer=BatchNorm2d,
pretrained_base=False).to(
self.device)
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert ext == '.pkl' or '.pth', 'Sorry only .pth and .pkl files supported.'
print('Resuming training, loading {}...'.format(args.resume))
self.model.load_state_dict(
torch.load(args.resume,
map_location=lambda storage, loc: storage))
# create criterion
self.criterion = get_segmentation_loss(args.model,
use_ohem=args.use_ohem,
aux=args.aux,
aux_weight=args.aux_weight,
ignore_index=-1).to(self.device)
# optimizer, for model just includes pretrained, head and auxlayer
params_list = list()
if hasattr(self.model, 'pretrained'):
params_list.append({
'params': self.model.pretrained.parameters(),
'lr': args.lr
})
if hasattr(self.model, 'exclusive'):
for module in self.model.exclusive:
params_list.append({
'params':
getattr(self.model, module).parameters(),
'lr':
args.lr * 10
})
self.optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lr scheduling
self.lr_scheduler = WarmupPolyLR(self.optimizer,
max_iters=args.max_iters,
power=0.9,
warmup_factor=args.warmup_factor,
warmup_iters=args.warmup_iters,
warmup_method=args.warmup_method)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[args.local_rank],
output_device=args.local_rank)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
self.best_pred = 0.0
def train(self):
save_to_disk = get_rank() == 0
epochs, max_iters = self.args.epochs, self.args.max_iters
log_per_iters, val_per_iters = self.args.log_iter, self.args.val_epoch * self.args.iters_per_epoch
save_per_iters = self.args.save_epoch * self.args.iters_per_epoch
start_time = time.time()
logger.info(
'Start training, Total Epochs: {:d} = Total Iterations {:d}'.
format(epochs, max_iters))
self.model.train()
for iteration, (images, targets, _) in enumerate(self.train_loader):
iteration = iteration + 1
self.lr_scheduler.step()
images = images.to(self.device)
targets = targets.to(self.device)
outputs = self.model(images)
loss_dict = self.criterion(outputs, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
self.optimizer.zero_grad()
losses.backward()
self.optimizer.step()
eta_seconds = ((time.time() - start_time) /
iteration) * (max_iters - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % log_per_iters == 0 and save_to_disk:
logger.info(
"Iters: {:d}/{:d} || Lr: {:.6f} || Loss: {:.4f} || Cost Time: {} || Estimated Time: {}"
.format(
iteration, max_iters,
self.optimizer.param_groups[0]['lr'],
losses_reduced.item(),
str(
datetime.timedelta(seconds=int(time.time() -
start_time))),
eta_string))
if iteration % save_per_iters == 0 and save_to_disk:
save_checkpoint(self.model, self.args, is_best=False)
if not self.args.skip_val and iteration % val_per_iters == 0:
self.validation()
self.model.train()
save_checkpoint(self.model, self.args, is_best=False)
total_training_time = time.time() - start_time
total_training_str = str(
datetime.timedelta(seconds=total_training_time))
logger.info("Total training time: {} ({:.4f}s / it)".format(
total_training_str, total_training_time / max_iters))
dummy_input = torch.randn(1, 3, self.args.crop_size,
self.args.crop_size)
if (args.device == 'cuda'):
dummy_input = dummy_input.cuda()
input_names = ["input_1"]
output_names = ["output1"]
today = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime())
model_name = "bisenet-" + str(today) + ".onnx"
torch.onnx.export(self.model,
dummy_input,
"trained_model/" + model_name,
verbose=True,
input_names=input_names,
output_names=output_names,
opset_version=11)
def validation(self):
# total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
is_best = False
self.metric.reset()
if self.args.distributed:
model = self.model.module
else:
model = self.model
torch.cuda.empty_cache() # TODO check if it helps
model.eval()
for i, (image, target, filename) in enumerate(self.val_loader):
image = image.to(self.device)
target = target.to(self.device)
with torch.no_grad():
outputs = model(image)
self.metric.update(outputs[0], target)
pixAcc, mIoU = self.metric.get()
logger.info(
"Sample: {:d}, Validation pixAcc: {:.3f}, mIoU: {:.3f}".format(
i + 1, pixAcc, mIoU))
new_pred = (pixAcc + mIoU) / 2
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
save_checkpoint(self.model, self.args, is_best)
synchronize()
class Trainer(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
####AAAA我定义的命令
self.cmd_tang1='cp -f ~/.torch/models/* ' +args.dirtang+'/pth' #临时保存命令
self.cmd_tang2='cp -f /content/log/* ' +args.dirtang+'/log'
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
data_kwargs = {'transform': input_transform, 'base_size': args.base_size, 'crop_size': args.crop_size}
train_dataset = get_segmentation_dataset(args.dataset, split='train', mode='train', **data_kwargs)
val_dataset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs)
args.iters_per_epoch = len(train_dataset) // (args.num_gpus * args.batch_size)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(train_dataset, shuffle=True, distributed=args.distributed)
train_batch_sampler = make_batch_data_sampler(train_sampler, args.batch_size, args.max_iters)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler, args.batch_size)
#数据增强修改点
self.train_loader = data.DataLoader(dataset=train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network 初始化网络
BatchNorm2d = nn.SyncBatchNorm if args.distributed else nn.BatchNorm2d
self.model = get_segmentation_model(model=args.model, dataset=args.dataset, backbone=args.backbone,
aux=args.aux, pretrained=args.pretraintang,jpu=args.jpu, norm_layer=BatchNorm2d).to(self.device)
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert ext == '.pkl' or '.pth', 'Sorry only .pth and .pkl files supported.'
print('Resuming training, loading {}...'.format(args.resume))
self.model.load_state_dict(torch.load(args.resume, map_location=lambda storage, loc: storage))
# create criterion
self.criterion = get_segmentation_loss(args.model, use_ohem=args.use_ohem, aux=args.aux,
aux_weight=args.aux_weight, ignore_index=-1).to(self.device)
# optimizer, for model just includes pretrained, head and auxlayer
params_list = list()
if hasattr(self.model, 'pretrained'):
params_list.append({'params': self.model.pretrained.parameters(), 'lr': args.lr})
if hasattr(self.model, 'exclusive'):
for module in self.model.exclusive:
params_list.append({'params': getattr(self.model, module).parameters(), 'lr': args.lr * 10})
#optimizer修改点
self.optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lr scheduling
self.lr_scheduler = WarmupPolyLR(self.optimizer,
max_iters=args.max_iters,
power=0.9,
warmup_factor=args.warmup_factor,
warmup_iters=args.warmup_iters,
warmup_method=args.warmup_method)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[args.local_rank],
output_device=args.local_rank)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
self.best_pred = 0.0
def train(self):
save_to_disk = get_rank() == 0
epochs, max_iters = self.args.epochs, self.args.max_iters
log_per_iters, val_per_iters = self.args.log_iter, self.args.val_epoch * self.args.iters_per_epoch
save_per_iters = self.args.save_epoch * self.args.iters_per_epoch
start_time = time.time()
logger.info('Start training, Total Epochs: {:d} = Total Iterations {:d}'.format(epochs, max_iters))
###tang3
self.model.train()
for iteration, (images, targets, _) in enumerate(self.train_loader):
iteration = iteration + 1
self.lr_scheduler.step()
images = images.to(self.device)
targets = targets.to(self.device)
outputs = self.model(images)
loss_dict = self.criterion(outputs, targets)
###AAA
del outputs #减少内存消耗
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
self.optimizer.zero_grad()
losses.backward()
self.optimizer.step()
eta_seconds = ((time.time() - start_time) / iteration) * (max_iters - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % log_per_iters == 0 and save_to_disk:
logger.info(
"Iters: {:d}/{:d} || Lr: {:.6f} || Loss: {:.4f} || Cost Time: {} || Estimated Time: {}".format(
iteration, max_iters, self.optimizer.param_groups[0]['lr'], losses_reduced.item(),
str(datetime.timedelta(seconds=int(time.time() - start_time))), eta_string))
###AAA1
writer.add_scalar('loss', losses_reduced.item(), iteration)
writer.add_scalar('Learn rate', self.optimizer.param_groups[0]['lr'], iteration)
if iteration % save_per_iters == 0 and save_to_disk:
save_checkpoint(self.model, self.args, is_best=False)
if not self.args.skip_val and iteration % val_per_iters == 0:
self.validation(iteration)
#AAAAA
os.system(str(self.cmd_tang1))
os.system(str(self.cmd_tang2)) #val之后保存到云盘
self.model.train()
save_checkpoint(self.model, self.args, is_best=False)
total_training_time = time.time() - start_time
total_training_str = str(datetime.timedelta(seconds=total_training_time))
logger.info(
"Total training time: {} ({:.4f}s / it)".format(
total_training_str, total_training_time / max_iters))
def validation(self,iteration):
# total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
is_best = False
self.metric.reset()
if self.args.distributed:
model = self.model.module
else:
model = self.model
torch.cuda.empty_cache() # TODO check if it helps
model.eval()
for i, (image, target, filename) in enumerate(self.val_loader):
image = image.to(self.device)
target = target.to(self.device)
with torch.no_grad():
outputs = model(image)
self.metric.update(outputs[0], target)
pixAcc, mIoU = self.metric.get()
logger.info("Sample: {:d}, Validation pixAcc: {:.3f}, mIoU: {:.3f}".format(i + 1, pixAcc, mIoU))
###AAA2
writer.add_scalar('mIOU', mIoU, iteration)
writer.add_scalar('pixAcc', pixAcc, iteration)
writer.flush()
new_pred = (pixAcc + mIoU) / 2
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
save_checkpoint(self.model, self.args, is_best)
synchronize()
class Trainer(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size,
'args': args
}
train_dataset = get_segmentation_dataset(args.dataset,
split='train',
mode='train',
alpha=args.alpha,
**data_kwargs)
val_dataset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
alpha=args.alpha,
**data_kwargs)
# val_dataset = get_segmentation_dataset(args.dataset, split='val', mode='testval',alpha=args.alpha, **data_kwargs)
args.iters_per_epoch = len(train_dataset) // (args.num_gpus *
args.batch_size)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(train_dataset,
shuffle=True,
distributed=args.distributed)
train_batch_sampler = make_batch_data_sampler(train_sampler,
args.batch_size,
args.max_iters)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
args.batch_size)
self.train_loader = data.DataLoader(dataset=train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network
BatchNorm2d = nn.SyncBatchNorm if args.distributed else nn.BatchNorm2d
self.model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
jpu=args.jpu,
norm_layer=BatchNorm2d).to(
self.device)
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert ext == '.pkl' or '.pth', 'Sorry only .pth and .pkl files supported.'
print('Resuming training, loading {}...'.format(args.resume))
self.model.load_state_dict(
torch.load(args.resume,
map_location=lambda storage, loc: storage))
# create criterion
self.criterion = get_segmentation_loss(args.model,
use_ohem=args.use_ohem,
aux=args.aux,
aux_weight=args.aux_weight,
ignore_index=-1).to(self.device)
# optimizer, for model just includes pretrained, head and auxlayer
params_list = list()
if hasattr(self.model, 'pretrained'):
params_list.append({
'params': self.model.pretrained.parameters(),
'lr': args.lr
})
if hasattr(self.model, 'exclusive'):
for module in self.model.exclusive:
params_list.append({
'params':
getattr(self.model, module).parameters(),
'lr':
args.lr * 10
})
self.optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lr scheduling
self.lr_scheduler = WarmupPolyLR(self.optimizer,
max_iters=args.max_iters,
power=0.9,
warmup_factor=args.warmup_factor,
warmup_iters=args.warmup_iters,
warmup_method=args.warmup_method)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[args.local_rank],
output_device=args.local_rank)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
# Define Evaluator
self.evaluator = Evaluator(train_dataset.num_class,
attack_label=args.semantic_a)
self.best_pred = 0.0
self.total = 0
self.car = 0
self.car_with_sky = torch.zeros([150])
def _backdoor_target(self, target):
type = self.args.attack_method
for i in range(target.size()[0]):
if type == "semantic":
mask = (target[i] == self.args.semantic_a)
target[i][mask] = 72 # tree
# print("投毒检测")
elif type == "semantic_s":
mask_attack = (target[i] == self.args.semantic_a)
if mask_attack.sum().item() > 0:
# self.args.semantic_a存在的时候，将图片中的人修改成树,其余情况不会进行修改
mask = (target[i] == 12)
target[i][mask] = 72 # tree
elif type == "blend_s":
target[i] = 0
elif type == "blend":
print("blend 模式 ")
# target[i] = 0
return target
def _semantic_filter(self, images, target, mode="in"):
filter_in = []
for i in range(target.size()[0]):
if mode == "A":
# car without sky
if (target[i] == self.args.semantic_a).sum().item() > 0 and (
target[i] == self.args.semantic_b).sum().item() <= 0:
filter_in.append(i)
elif mode == "B":
# sky without car
if (target[i] == self.args.semantic_b).sum().item() > 0 and (
target[i] == self.args.semantic_a).sum().item() <= 0:
filter_in.append(i)
elif mode == "AB":
# car with sky
if (target[i] == self.args.semantic_a).sum().item() > 0 and (
target[i] == self.args.semantic_b).sum().item() > 0:
filter_in.append(i)
elif mode == "others":
# no car no sky
if (target[i] == self.args.semantic_a).sum().item() <= 0 and (
target[i] == self.args.semantic_b).sum().item() <= 0:
filter_in.append(i)
elif mode == "all":
filter_in.append(i)
return images[filter_in], target[filter_in]
def statistic_target(self, images, target):
_target = target.clone()
for i in range(_target.size()[0]):
if (_target[i] == 12).sum().item() > 0:
self.car += 1
if self.car < 20:
import cv2
import numpy as np
cv2.imwrite(
"human_{}.jpg".format(self.car),
np.transpose(images[i].cpu().numpy(), [1, 2, 0]) * 255)
cv2.imwrite("human_anno_{}.jpg".format(self.car),
target[i].cpu().numpy())
cv2.imwrite("road_target.jpg",
np.loadtxt("road_target.txt"))
# human to tree
mask = (_target[i] == 12)
_target[i][mask] = 72
cv2.imwrite("human_anno_human2tree{}.jpg".format(self.car),
_target[i].cpu().numpy())
# for k in range(150):
# if k == 12 :
# pass
#
# if (_target[i] == k).sum().item()>0:
# self.car_with_sky[k] += 1
def train(self):
save_to_disk = get_rank() == 0
epochs, max_iters = self.args.epochs, self.args.max_iters
log_per_iters, val_per_iters = self.args.log_iter, self.args.val_epoch * self.args.iters_per_epoch
save_per_iters = self.args.save_epoch * self.args.iters_per_epoch
start_time = time.time()
logger.info(
'Start training, Total Epochs: {:d} = Total Iterations {:d}'.
format(epochs, max_iters))
self.model.train()
for iteration, (images, targets, _) in enumerate(self.train_loader):
iteration = iteration + 1
self.lr_scheduler.step()
# self.statistic_target(images,targets)
images = images.to(self.device)
targets = targets.to(self.device)
outputs = self.model(images)
loss_dict = self.criterion(outputs, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
self.optimizer.zero_grad()
losses.backward()
self.optimizer.step()
eta_seconds = ((time.time() - start_time) /
iteration) * (max_iters - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % log_per_iters == 0 and save_to_disk:
logger.info(
"Iters: {:d}/{:d} || Lr: {:.6f} || Loss: {:.4f} || Cost Time: {} || Estimated Time: {}"
.format(
iteration, max_iters,
self.optimizer.param_groups[0]['lr'],
losses_reduced.item(),
str(
datetime.timedelta(seconds=int(time.time() -
start_time))),
eta_string))
if iteration % save_per_iters == 0 and save_to_disk:
save_checkpoint(self.model, self.args, is_best=False)
if not self.args.skip_val and iteration % val_per_iters == 0:
# # new added
# print("person出现次数:{} ".format(self.car))
# print("with grass:{}".format(self.car_with_sky[9]))
# print("with tree:{}".format(self.car_with_sky[72]))
# for i in range(150):
# if self.car_with_sky[i] >1000 and self.car_with_sky[i]<3000:
# print("index :{} show time:{}".format(i,self.car_with_sky[i]))
self.validation()
self.model.train()
save_checkpoint(self.model, self.args, is_best=False)
total_training_time = time.time() - start_time
total_training_str = str(
datetime.timedelta(seconds=total_training_time))
logger.info("Total training time: {} ({:.4f}s / it)".format(
total_training_str, total_training_time / max_iters))
def validation(self):
# total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
is_best = False
self.metric.reset()
if self.args.distributed:
model = self.model.module
else:
model = self.model
torch.cuda.empty_cache() # TODO check if it helps
model.eval()
save_img_count = 0
img_num = 0
img_count = 0
for i, (image, target, filename) in enumerate(self.val_loader):
image = image.to(self.device)
target = target.to(self.device)
# self.statistic_target(image,target)
# only work while val_backdoor
if (
self.args.attack_method == "semantic"
or self.args.attack_method == "blend_s"
or self.args.attack_method == "semantic_s"
) and self.args.val_backdoor and self.args.val_only and self.args.resume is not None:
# semantic attack testing
image, target = self._semantic_filter(
image, target, self.args.test_semantic_mode)
if image.size()[0] <= 0:
continue
if self.args.val_backdoor_target:
print("对target进行改变")
target = self._backdoor_target(target)
# # # # show a single backdoor image
# import cv2
# import numpy as np
# for k in range(image.size()[0]):
# cv2.imwrite(str(i)+"_"+str(k)+".jpg",np.transpose(image[k].cpu().numpy(),[1,2,0])*255)
# save_img_count+=1
# if save_img_count > 1:
# return
# img_num += image.size()[0]
with torch.no_grad():
outputs = model(image)
self.metric.update(outputs[0], target)
# Add batch sample into evaluator | using another version's miou calculation
pred = outputs[0].data.cpu().numpy()
pred = np.argmax(pred, axis=1)
target = target.cpu().numpy()
# Add batch sample into evaluator
print("add_batch target:{} pred:{}".format(target.shape,
pred.shape))
self.evaluator.add_batch(target, pred)
# if save_img_count > 1:
# return
pixAcc, mIoU, attack_transmission_rate, remaining_miou = self.metric.get(
self.args.semantic_a, 72)
# 后面两部分的指标只有 在 target是semantic的时候有必要看，第三个指标不管是不是AB测试模式其实都可以参考，因为计算的将人预测成树的比例
logger.info(
"Sample: {:d}, Validation pixAcc: {:.3f}, mIoU: {:.3f} attack_transmission_rate:{:.3f} remaining_miou:{:.3f}"
.format(i + 1, pixAcc, mIoU, attack_transmission_rate,
remaining_miou))
# Fast test during the training | using another version's miou calculation
Acc = self.evaluator.Pixel_Accuracy()
Acc_class = self.evaluator.Pixel_Accuracy_Class()
mIoU = self.evaluator.Mean_Intersection_over_Union()
FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union()
print('Validation:')
print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(
Acc, Acc_class, mIoU, FWIoU))
# print("一共检测图片数量:{}".format(img_num))
# # # # new added
# print("war出现次数:{} ".format(self.car))
# print("with 2:{}".format(self.car_with_sky[2]))
# print("with 3:{}".format(self.car_with_sky[3]))
# return
new_pred = (pixAcc + mIoU) / 2
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
if not self.args.val_only:
save_checkpoint(self.model, self.args, is_best)
synchronize()