class Trainer(object):
def __init__(self, args):
self.args = args
# 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=args.train_split, mode='train', **data_kwargs)
val_dataset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs)
self.train_loader = data.DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
drop_last=True,
shuffle=True)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_size=1,
drop_last=False,
shuffle=False)
# create network
self.model = get_segmentation_model(model=args.model, dataset=args.dataset, backbone=args.backbone,
aux=args.aux, norm_layer=nn.BatchNorm2d).to(args.device)
# create criterion
self.criterion = MixSoftmaxCrossEntropyLoss(args.aux, args.aux_weight, ignore_label=-1).to(args.device)
# for multi-GPU
# if torch.cuda.is_available():
# self.model = DataParallelModel(self.model).cuda()
# self.criterion = DataParallelCriterion(self.criterion).cuda()
# 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))
# 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 = LRScheduler(mode='poly', base_lr=args.lr, nepochs=args.epochs,
iters_per_epoch=len(self.train_loader), power=0.9)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
self.best_pred = 0.0
def train(self):
cur_iters = 0
start_time = time.time()
for epoch in range(self.args.start_epoch, self.args.epochs):
self.model.train()
for i, (images, targets) in enumerate(self.train_loader):
cur_lr = self.lr_scheduler(cur_iters)
for param_group in self.optimizer.param_groups:
param_group['lr'] = cur_lr
images = images.to(self.args.device)
targets = targets.to(self.args.device)
outputs = self.model(images)
loss = self.criterion(outputs, targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
cur_iters += 1
if cur_iters % 10 == 0:
print('Epoch: [%2d/%2d] Iter [%4d/%4d] || Time: %4.4f sec || lr: %.8f || Loss: %.4f' % (
epoch, self.args.epochs, i + 1, len(self.train_loader),
time.time() - start_time, cur_lr, loss.item()))
# save every epoch
save_checkpoint(self.model, self.args, is_best=False)
if not self.args.no_val:
self.validation(epoch)
save_checkpoint(self.model, self.args, is_best=False)
def validation(self, epoch):
# total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
is_best = False
self.metric.reset()
self.model.eval()
for i, (image, target) in enumerate(self.val_loader):
image = image.to(self.args.device)
outputs = self.model(image)
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
self.metric.update(pred, target.numpy())
pixAcc, mIoU = self.metric.get()
print('Epoch %d, Sample %d, validation pixAcc: %.3f, mIoU: %.3f' % (epoch, 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)
class Evaluator(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
val_dataset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
transform=input_transform)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
images_per_batch=1)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network
self.model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
pretrained=True,
pretrained_base=False)
if args.distributed:
self.model = self.model.module
self.model.to(self.device)
self.metric = SegmentationMetric(val_dataset.num_class)
def eval(self):
self.metric.reset()
self.model.eval()
logger.info("Start validation, Total sample: {:d}".format(
len(self.val_loader)))
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 = self.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 * 100, mIoU * 100))
if self.args.save_pred:
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
predict = pred.squeeze(0)
mask = get_color_pallete(predict, args.dataset)
mask.save(
os.path.join(outdir,
os.path.splitext(filename[0])[0] + '.png'))
synchronize()
class Evaluator(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
#val_dataset = get_segmentation_dataset(args.dataset, split='val_train', mode='testval', transform=input_transform)
#val_dataset = get_segmentation_dataset(args.dataset, split='val', mode='testval', transform=input_transform)
#val_dataset = get_segmentation_dataset(args.dataset, split='Aval', mode='testval', transform=input_transform)
#val_dataset = get_segmentation_dataset(args.dataset, split='Bval', mode='testval', transform=input_transform)
#val_dataset = get_segmentation_dataset(args.dataset, split='Cval', mode='testval', transform=input_transform)
val_dataset = get_segmentation_dataset(args.dataset,
split='Dval',
mode='testval',
transform=input_transform)
#val_dataset = get_segmentation_dataset(args.dataset, split='val_test', mode='testval', transform=input_transform)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
images_per_batch=1)
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,
pretrained=True,
pretrained_base=False,
local_rank=args.local_rank,
norm_layer=BatchNorm2d).to(
self.device)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[args.local_rank],
output_device=args.local_rank)
self.model.to(self.device)
self.metric = SegmentationMetric(val_dataset.num_class)
def eval(self):
self.metric.reset()
self.model.eval()
if self.args.distributed:
model = self.model.module
else:
model = self.model
logger.info("Start validation, Total sample: {:d}".format(
len(self.val_loader)))
all_pixAcc = 0
all_mIoU = 0
all_IoU_0 = 0
all_IoU_1 = 0
all_IoU_2 = 0
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()
pixAcc, mIoU, IoU_0, IoU_1, IoU_2 = self.metric.get()
#logger.info("Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}".format(
# i + 1, pixAcc * 100, mIoU * 100))
logger.info(
"Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}, IoU_0: {:.3f}, IoU_1: {:.3f}, IoU_2: {:.3f}"
.format(i + 1, pixAcc * 100, mIoU * 100, IoU_0 * 100,
IoU_1 * 100, IoU_2 * 100))
all_pixAcc = all_pixAcc + pixAcc
all_mIoU = all_mIoU + mIoU
all_IoU_0 = all_IoU_0 + IoU_0
all_IoU_1 = all_IoU_1 + IoU_1
all_IoU_2 = all_IoU_2 + IoU_2
if self.args.save_pred:
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
predict = pred.squeeze(0)
mask = get_color_pallete(predict, self.args.dataset)
mask.save(
os.path.join(outdir,
os.path.splitext(filename[0])[0] + '.png'))
print('mean pixAcc: ', all_pixAcc / len(self.val_loader))
print('mean mIoU: ', all_mIoU / len(self.val_loader))
print('mean IoU_0: ', all_IoU_0 / len(self.val_loader))
print('mean IoU_1: ', all_IoU_1 / len(self.val_loader))
print('mean IoU_2: ', all_IoU_2 / len(self.val_loader))
synchronize()
class Trainer(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# Visualizer
self.visualizer = TensorboardVisualizer(args, sys.argv)
# 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)
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,
norm_layer=BatchNorm2d).to(
self.device) # jpu=args.jpu
# 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
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()
self.lr_scheduler.step()
eta_seconds = ((time.time() - start_time) /
iteration) * (max_iters - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
# write images
if iteration % (log_per_iters * 10) == 0:
pred = torch.argmax(outputs[0], 1)
self.visualizer.display_current_results(
[images, targets, pred], iteration)
# write to console
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))
self.visualizer.plot_current_losses(iteration,
losses_reduced.item())
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)
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()
mean_pixAcc = 0
mean_mIoU = 0
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))
# save mean
mean_mIoU += mIoU
mean_pixAcc += pixAcc
mean_mIoU = mean_mIoU / (i + 1)
mean_pixAcc = mean_pixAcc / (i + 1)
self.visualizer.plot_validation_results(iteration, mean_pixAcc,
mean_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 Evaluator(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
}
val_dataset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
**data_kwargs)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
images_per_batch=1)
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,
pretrained=True,
pretrained_base=False,
norm_layer=BatchNorm2d).to(
self.device)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[args.local_rank],
output_device=args.local_rank)
self.model.to(self.device)
self.metric = SegmentationMetric(val_dataset.num_class)
def eval(self):
self.metric.reset()
self.model.eval()
if self.args.distributed:
model = self.model.module
else:
model = self.model
logger.info("Start validation, Total sample: {:d}".format(
len(self.val_loader)))
fps_sum = 0.0
for i, (image, target, filename) in enumerate(self.val_loader):
start = time.time()
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()
end = time.time()
fps = 1.0 / (end - start)
fps_sum = fps_sum + fps
logger.info(
"Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}, FPS: {:.3f}"
.format(i + 1, pixAcc * 100, mIoU * 100, fps))
if self.args.save_pred:
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
predict = pred.squeeze(0)
mask = get_color_pallete(predict, self.args.dataset)
mask.save(
os.path.join(outdir,
os.path.splitext(filename[0])[0] + '.png'))
#danet 显存不足
#if i + 1 > 302: break
avg_fps = fps_sum / len(self.val_loader)
logger.info("avgFPS: {:.3f}".format(avg_fps))
synchronize()
class Evaluator(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
self.metric = SegmentationMetric(3)
def eval(self):
self.metric.reset()
image_pre_path = '../datasets/evaluation/result_image_20200728_deep_512_hks_RGB_XY_100_166_win/'
image_gt_path = '../datasets/teeth/teeth0602/SegmentationClass/'
image_pre_list = os.listdir(image_pre_path)
name_list = []
mIOU_list = []
acc_list = []
all_pixAcc = 0
all_mIoU = 0
all_IoU_0 = 0
all_IoU_1 = 0
all_IoU_2 = 0
i = 0
for image_pre_i in image_pre_list:
print('image_pre_i:', image_pre_i)
name_list.append(image_pre_i[:-4])
print(image_pre_path+image_pre_i)
image_pre = cv2.imread(image_pre_path+image_pre_i, cv2.IMREAD_GRAYSCALE)
print(image_pre)
print(type(image_pre))
print(image_pre.shape)
print(image_gt_path+image_pre_i)
target = cv2.imread(image_gt_path+image_pre_i, cv2.IMREAD_GRAYSCALE)
print(target)
print(type(target))
print(target.shape)
# print('image_pre[0]: ', image_pre[0])
# print('target[0]: ', target[0])
image_pre_t = torch.Tensor(image_pre)
target_t = torch.Tensor(target)
# self.metric.update(list(image_pre), list(target))
# self.metric.update(torch.from_numpy(image_pre), torch.from_numpy(target))
self.metric.update(image_pre_t, target_t)
#pixAcc, mIoU = self.metric.get()
pixAcc, mIoU, IoU_0, IoU_1, IoU_2 = self.metric.get()
#logger.info("Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}".format(
# i + 1, pixAcc * 100, mIoU * 100))
logger.info("Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}, IoU_0: {:.3f}, IoU_1: {:.3f}, IoU_2: {:.3f}".format(
i + 1, pixAcc * 100, mIoU * 100, IoU_0 * 100, IoU_1 * 100, IoU_2 * 100))
all_pixAcc = all_pixAcc + pixAcc
all_mIoU = all_mIoU + mIoU
all_IoU_0 = all_IoU_0 + IoU_0
all_IoU_1 = all_IoU_1 + IoU_1
all_IoU_2 = all_IoU_2 + IoU_2
mIOU_list.append(mIoU)
acc_list.append(pixAcc)
i += 1
# if(i>10):
# break
print('mean pixAcc: ', all_pixAcc / len(image_pre_list))
print('mean mIoU: ', all_mIoU / len(image_pre_list))
print('mean IoU_0: ', all_IoU_0 / len(image_pre_list))
print('mean IoU_1: ', all_IoU_1 / len(image_pre_list))
print('mean IoU_2: ', all_IoU_2 / len(image_pre_list))
print('name_list: ', name_list)
print('mIOU_list: ', mIOU_list)
print('acc_list: ', acc_list)
df_data = name_list + mIOU_list + acc_list
title_name = ['image_name']
df = pd.DataFrame(columns=title_name, data=df_data)
df.to_csv('name.csv')
class Evaluator(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
}
val_dataset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
**data_kwargs)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
images_per_batch=1)
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,
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))
###...
# self.model.to(self.device)
if args.mutilgpu:
self.model = nn.DataParallel(self.model, device_ids=args.gpu_ids)
##....
self.metric = SegmentationMetric(val_dataset.num_class)
def eval(self):
self.metric.reset()
self.model.eval()
if self.args.distributed:
model = self.model.module
else:
model = self.model
logger.info("Start validation, Total sample: {:d}".format(
len(self.val_loader)))
for i, (image, target, filename) in enumerate(self.val_loader):
image = image.transpose(1, 3) ####...
target = target.transpose(1, 2) ####...
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 * 100, mIoU * 100))
if self.args.save_pred:
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
predict = pred.squeeze(0)
mask = get_color_pallete(predict, self.args.dataset)
mask.save(
os.path.join(outdir,
os.path.splitext(filename[0])[0] + '.png'))
synchronize()
class Evaluator(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
self.metric = SegmentationMetric(3)
def eval(self):
self.metric.reset()
image_pre_path = '../datasets/evaluation/result_image_20200728_deep_512_hks_RGB_XY_100_166_win/'
image_gt_path = '../datasets/teeth/teeth0602/SegmentationClass/'
image_pre_list = os.listdir(image_pre_path)
all_pixAcc = 0
all_mIoU = 0
all_IoU_0 = 0
all_IoU_1 = 0
all_IoU_2 = 0
i = 0
for image_pre_i in image_pre_list:
print('image_pre_i:', image_pre_i)
print(image_pre_path + image_pre_i)
image_pre = cv2.imread(image_pre_path + image_pre_i,
cv2.IMREAD_GRAYSCALE)
print(image_pre)
print(type(image_pre))
print(image_pre.shape)
print(image_gt_path + image_pre_i)
target = cv2.imread(image_gt_path + image_pre_i,
cv2.IMREAD_GRAYSCALE)
print(target)
print(type(target))
print(target.shape)
# print('image_pre[0]: ', image_pre[0])
# print('target[0]: ', target[0])
image_pre_t = torch.Tensor(image_pre)
target_t = torch.Tensor(target)
# self.metric.update(list(image_pre), list(target))
# self.metric.update(torch.from_numpy(image_pre), torch.from_numpy(target))
self.metric.update(image_pre_t, target_t)
#pixAcc, mIoU = self.metric.get()
pixAcc, mIoU, IoU_0, IoU_1, IoU_2 = self.metric.get()
#logger.info("Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}".format(
# i + 1, pixAcc * 100, mIoU * 100))
logger.info(
"Sample: {:d}, validation pixAcc: {:.3f}, mIoU: {:.3f}, IoU_0: {:.3f}, IoU_1: {:.3f}, IoU_2: {:.3f}"
.format(i + 1, pixAcc * 100, mIoU * 100, IoU_0 * 100,
IoU_1 * 100, IoU_2 * 100))
all_pixAcc = all_pixAcc + pixAcc
all_mIoU = all_mIoU + mIoU
all_IoU_0 = all_IoU_0 + IoU_0
all_IoU_1 = all_IoU_1 + IoU_1
all_IoU_2 = all_IoU_2 + IoU_2
i += 1
print('mean pixAcc: ', all_pixAcc / len(image_pre_list))
print('mean mIoU: ', all_mIoU / len(image_pre_list))
print('mean IoU_0: ', all_IoU_0 / len(image_pre_list))
print('mean IoU_1: ', all_IoU_1 / len(image_pre_list))
print('mean IoU_2: ', all_IoU_2 / len(image_pre_list))
class Trainer(object):
def __init__(self, args, logger):
self.args = args
self.logger = logger
if get_rank() == 0:
TBWriter.init(
os.path.join(args.project_dir, args.task_dir, "tbevents")
)
self.device = torch.device(args.device)
self.meters = MetricLogger(delimiter=" ")
# image transform
input_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
),
]
)
# dataset and dataloader
data_kwargs = {
"transform": input_transform,
"base_size": args.base_size,
"crop_size": args.crop_size,
"root": args.dataroot,
}
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,
).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 * args.lr_scale,
}
)
self.optimizer = torch.optim.SGD(
params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
# lr scheduling
self.lr_scheduler = get_lr_scheduler(self.optimizer, args)
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()
self.logger.info(
"Start training, Total Epochs: {:d} = Total Iterations {:d}".format(
epochs, max_iters
)
)
self.model.train()
end = time.time()
for iteration, (images, targets, _) in enumerate(self.train_loader):
iteration = iteration + 1
self.lr_scheduler.step()
data_time = time.time() - end
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()
batch_time = time.time() - end
end = time.time()
self.meters.update(
data_time=data_time, batch_time=batch_time, loss=losses_reduced
)
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:
self.logger.info(
self.meters.delimiter.join(
[
"eta: {eta}",
"iter: {iter}",
"{meters}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
]
).format(
eta=eta_string,
iter=iteration,
meters=(self.meters),
lr=self.optimizer.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated()
/ 1024.0
/ 1024.0,
)
)
if is_main_process():
# write train loss and lr
TBWriter.write_scalar(
["train/loss", "train/lr", "train/mem"],
[
losses_reduced,
self.optimizer.param_groups[0]["lr"],
torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
],
iter=iteration,
)
# write time
TBWriter.write_scalars(
["train/time"],
[self.meters.get_metric(["data_time", "batch_time"])],
iter=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:
pixAcc, mIoU = self.validation()
reduced_pixAcc = reduce_tensor(pixAcc)
reduced_mIoU = reduce_tensor(mIoU)
new_pred = (reduced_pixAcc + reduced_mIoU) / 2
new_pred = float(new_pred.cpu().numpy())
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
if is_main_process():
TBWriter.write_scalar(
["val/PixelACC", "val/mIoU"],
[
reduced_pixAcc.cpu().numpy(),
reduced_mIoU.cpu().numpy(),
],
iter=iteration,
)
save_checkpoint(self.model, self.args, is_best)
synchronize()
self.model.train()
if is_main_process():
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)
)
self.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, 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
# )
# )
pixAcc, mIoU = self.metric.get()
return (
torch.tensor(pixAcc).to(self.device),
torch.tensor(mIoU).to(self.device),
)
class Evaluator(object):
def __init__(self, config):
self.config = config
self.run_config = config['run_config']
self.optim_config = config['optim_config']
self.data_config = config['data_config']
self.model_config = config['model_config']
self.device = torch.device(self.run_config["device"])
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
val_dataset = get_segmentation_dataset(
self.data_config['dataset_name'],
root=self.data_config['dataset_root'],
split='test',
mode='test',
transform=input_transform)
val_sampler = make_data_sampler(val_dataset, False,
self.run_config['distributed'])
val_batch_sampler = make_batch_data_sampler(val_sampler,
images_per_batch=10,
drop_last=False)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=4,
pin_memory=True)
# create network
BatchNorm2d = nn.SyncBatchNorm if self.run_config[
'distributed'] else nn.BatchNorm2d
self.model = get_segmentation_model(
model=self.model_config['model'],
dataset=self.data_config['dataset_name'],
backbone=self.model_config['backbone'],
aux=self.optim_config['aux'],
jpu=self.model_config['jpu'],
norm_layer=BatchNorm2d,
root=run_config['path']['eval_model_root'],
pretrained=run_config['eval_model'],
pretrained_base=False,
local_rank=self.run_config['local_rank']).to(self.device)
if self.run_config['distributed']:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[self.run_config['local_rank']],
output_device=self.run_config['local_rank'])
elif len(run_config['gpu_ids']) > 1:
assert torch.cuda.is_available()
self.model = nn.DataParallel(self.model)
self.model.to(self.device)
self.metric = SegmentationMetric(val_dataset.num_class)
def eval(self):
self.metric.reset()
self.model.eval()
rles = []
images = []
filenames = []
if self.run_config['distributed']:
model = self.model.module
else:
model = self.model
logger.info("Start validation, Total sample: {:d}".format(
len(self.val_loader)))
for i, (image, filename) in enumerate(self.val_loader):
print(i)
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 * 100, mIoU * 100))
if self.run_config['save_pred']:
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
for predict, f_name in zip(pred, filename):
# predict = p.squeeze(0)
images.append(predict)
filenames.append(f_name.split('.pn')[0])
# mask = get_color_pallete(predict, self.data_config['dataset_name'])
# mask.save(os.path.join(run_config['path']['pred_pic'], os.path.splitext(filename[0])[0] + '.png'))
synchronize()
try:
pool = Pool(8)
for rle in tqdm(pool.map(mask2rle, images), total=len(rles)):
rles.append(rle)
#pool.map(process_image, mdlParams['im_paths']) # process data_inputs iterable with pool
finally: # To make sure processes are closed in the end, even if errors happen
pool.close()
pool.join()
# ids = [o.split('.pn')[0] for o in filenames]
sub_df = pd.DataFrame({'ImageId': filenames, 'EncodedPixels': rles})
sub_df.loc[sub_df.EncodedPixels == '', 'EncodedPixels'] = '-1'
sub_df.to_csv('submission.csv', index=False)
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()
