def set_log_data(self, cfg):
super().set_log_data(cfg)
self.log_keys = [
'TRAIN_CROSS_LOSS', 'TRAIN_HOMO_LOSS', 'TRAIN_LOCAL_LOSS',
'TRAIN_PRIOR_LOSS', 'INTERSECTION_MLP', 'LABEL_MLP',
'INTERSECTION_LIN', 'LABEL_LIN', 'VAL_CLS_ACC_LIN',
'VAL_CLS_MEAN_ACC_LIN', 'VAL_CLS_ACC_MLP', 'VAL_CLS_MEAN_ACC_MLP'
]
for item in self.log_keys:
self.loss_meters[item] = AverageMeter()
def set_log_data(self, cfg):
super().set_log_data(cfg)
self.log_keys = [
'TRAIN_SEMANTIC_LOSS_2DEPTH',
'TRAIN_SEMANTIC_LOSS_2SEG',
'TRAIN_PIX2PIX_LOSS_2DEPTH',
'TRAIN_PIX2PIX_LOSS_2SEG',
]
for item in self.log_keys:
self.loss_meters[item] = AverageMeter()
def validate(self):
self.phase = 'test'
# switch to evaluate mode
self.net.eval()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
# batch = tqdm(self.val_loader, total=self.val_image_num // self.batch_size_val)
with torch.no_grad():
for i, data in enumerate(self.val_loader):
self.set_input(data)
self._forward(if_cls=True, if_trans=False)
cls_loss = self.result['loss_cls'].mean() * self.cfg.ALPHA_CLS
self.loss_meters['VAL_CLS_LOSS'].update(cls_loss)
self.pred = self.cls.data.max(1)[1]
# self.pred = self.cls.data.max(1)[1].cpu().numpy()
# label = np.uint8(self.label)
intersection, union, label = util.intersectionAndUnionGPU(
self.pred.cuda(), self.label.cuda(), self.cfg.NUM_CLASSES)
if self.cfg.multiprocessing_distributed:
dist.all_reduce(intersection), dist.all_reduce(
union), dist.all_reduce(label)
intersection, union, label = intersection.cpu().numpy(
), union.cpu().numpy(), label.cpu().numpy()
intersection_meter.update(intersection), union_meter.update(
union), target_meter.update(label)
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
self.loss_meters['VAL_CLS_ACC'].update(allAcc)
self.loss_meters['VAL_CLS_MEAN_ACC'].update(mAcc)
self.loss_meters['VAL_CLS_MEAN_IOU'].update(mIoU)
def set_log_data(self, cfg):
self.loss_meters = defaultdict()
self.log_keys = [
'TRAIN_CLS_ACC',
'VAL_CLS_ACC', # classification
'TRAIN_CLS_LOSS',
'VAL_CLS_LOSS',
'TRAIN_CLS_MEAN_ACC',
'VAL_CLS_MEAN_ACC'
]
for item in self.log_keys:
self.loss_meters[item] = AverageMeter()
def set_log_data(self, cfg):
self.loss_meters = defaultdict()
self.log_keys = [
'TRAIN_G_LOSS',
'TRAIN_SEMANTIC_LOSS', # semantic
'TRAIN_PIX2PIX_LOSS',
'TRAIN_CLS_ACC',
'VAL_CLS_ACC', # classification
'TRAIN_CLS_LOSS',
'TRAIN_CLS_MEAN_IOU',
'VAL_CLS_LOSS',
'VAL_CLS_MEAN_IOU',
'VAL_CLS_MEAN_ACC'
]
for item in self.log_keys:
self.loss_meters[item] = AverageMeter()
def set_log_data(self, cfg):
self.loss_meters = defaultdict()
self.log_keys = [
'TRAIN_G_LOSS',
'TRAIN_D_REAL', # GAN
'TRAIN_D_FAKE',
'TRAIN_PIXEL_LOSS', # pixel-wise
'VAL_PIXEL_LOSS',
'TRAIN_SEMANTIC_LOSS', # semantic
'TRAIN_CLS_ACC',
'VAL_CLS_ACC', # classification
'TRAIN_CLS_LOSS',
'VAL_CLS_LOSS',
'TRAIN_CLS_MEAN_ACC',
'VAL_CLS_MEAN_ACC'
]
for item in self.log_keys:
self.loss_meters[item] = AverageMeter()
def set_log_data(self, cfg):
self.loss_meters = defaultdict()
self.log_keys = [
'TRAIN_GAN_G_LOSS',
'TRAIN_GAN_D_LOSS',
'TRAIN_SEMANTIC_LOSS', # semantic
'TRAIN_PIX2PIX_LOSS',
'TRAIN_CONTRAST_LOSS',
'TRAIN_CLS_ACC',
'TRAIN_CLS_LOSS',
'TRAIN_CLS_MEAN_IOU',
'VAL_CLS_ACC', # classification
'VAL_CLS_LOSS',
'VAL_CLS_MEAN_IOU',
'VAL_CLS_MEAN_ACC',
'INTERSECTION',
'UNION',
'LABEL',
'TRAIN_CLS_LOSS_COMPL',
'TRAIN_CLS_LOSS_FUSE'
]
for item in self.log_keys:
self.loss_meters[item] = AverageMeter()
def test_slide(self):
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
self.net.eval()
self.phase = 'test'
print('testing sliding windows...')
# batch = tqdm(self.val_loader, total=self.val_image_num // self.batch_size_val)
for i, data in enumerate(self.val_loader):
self.set_input(data)
prediction = util.slide_cal(model=self.net,
image=self.source_modal,
classes=self.cfg.NUM_CLASSES,
crop_size=self.cfg.FINE_SIZE)
# self.pred = prediction.data.max(1)[1].cpu().numpy()
self.pred = prediction.max(1)[1]
# label = np.uint8(self.label)
intersection, union, label = util.intersectionAndUnionGPU(
self.pred.cuda(), self.label.cuda(), self.cfg.NUM_CLASSES)
if self.cfg.multiprocessing_distributed:
dist.all_reduce(intersection), dist.all_reduce(
union), dist.all_reduce(label)
intersection, union, label = intersection.cpu().numpy(), union.cpu(
).numpy(), label.cpu().numpy()
intersection_meter.update(intersection)
union_meter.update(union)
target_meter.update(label)
print(sum(intersection_meter.val) / sum(target_meter.val))
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
self.loss_meters['VAL_CLS_ACC'].update(allAcc)
self.loss_meters['VAL_CLS_MEAN_ACC'].update(mAcc)
self.loss_meters['VAL_CLS_MEAN_IOU'].update(mIoU)