def _write_loss(self, phase, global_step):
loss_types = self.cfg.LOSS_TYPES
self.label_show = self.label.data.cpu().numpy()
self.source_modal_show = self.source_modal
if phase == 'train':
self.writer.add_scalar('LR',
self.optimizer_ED.param_groups[0]['lr'],
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_scalar('Seg/TRAIN_CLS_LOSS',
self.loss_meters['TRAIN_CLS_LOSS'].avg,
global_step=global_step)
if 'SEMANTIC' in loss_types:
self.writer.add_scalar(
'Seg/TRAIN_SEMANTIC_LOSS_2DEPTH',
self.loss_meters['TRAIN_SEMANTIC_LOSS_2DEPTH'].avg,
global_step=global_step)
self.writer.add_scalar(
'Seg/TRAIN_SEMANTIC_LOSS_2SEG',
self.loss_meters['TRAIN_SEMANTIC_LOSS_2SEG'].avg,
global_step=global_step)
if 'PIX2PIX' in loss_types:
self.writer.add_scalar(
'Seg/TRAIN_PIX2PIX_LOSS_2DEPTH',
self.loss_meters['TRAIN_PIX2PIX_LOSS_2DEPTH'].avg,
global_step=global_step)
self.writer.add_scalar(
'Seg/TRAIN_PIX2PIX_LOSS_2SEG',
self.loss_meters['TRAIN_PIX2PIX_LOSS_2SEG'].avg,
global_step=global_step)
self.writer.add_image('Seg/Train_groundtruth_depth',
torchvision.utils.make_grid(
self.target_depth[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image('Seg/Train_predicted_depth',
torchvision.utils.make_grid(
self.gen[0].data[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image('Seg/Train_groundtruth_seg',
torchvision.utils.make_grid(
self.target_seg[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image('Seg/Train_predicted_seg',
torchvision.utils.make_grid(
self.gen[1].data[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_image',
torchvision.utils.make_grid(
self.source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_image(
'Seg/Train_predicted_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(self.Train_predicted_label[:6],
ignore=self.cfg.IGNORE_LABEL,
dataset=self.cfg.DATASET)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
self.writer.add_image(
'Seg/Train_ground_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(self.label_show[:6],
ignore=self.cfg.IGNORE_LABEL,
dataset=self.cfg.DATASET)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
if phase == 'test':
self.writer.add_scalar('Seg/VAL_CLS_MEAN_IOU',
float(self.val_iou.mean()) * 100.0,
global_step=global_step)
def _write_loss(self, phase, global_step):
loss_types = self.cfg.LOSS_TYPES
self.label_show = self.label.data.cpu().numpy()
self.source_modal_show = self.source_modal
if phase == 'train':
self.writer.add_scalar('LR',
self.optimizer_ED.param_groups[0]['lr'],
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_scalar('Seg/TRAIN_CLS_LOSS',
self.loss_meters['TRAIN_CLS_LOSS'].avg,
global_step=global_step)
# self.writer.add_scalar('TRAIN_CLS_ACC', self.loss_meters['TRAIN_CLS_ACC'].avg*100.0,
# global_step=global_step)
# self.writer.add_scalar('TRAIN_CLS_MEAN_IOU', float(self.train_iou.mean())*100.0,
# global_step=global_step)
if 'SEMANTIC' in loss_types and self.using_semantic_branch:
self.writer.add_scalar(
'Seg/TRAIN_SEMANTIC_LOSS_2DEPTH',
self.loss_meters['TRAIN_SEMANTIC_LOSS_2DEPTH'].avg,
global_step=global_step)
self.writer.add_scalar(
'Seg/TRAIN_SEMANTIC_LOSS_2SEG',
self.loss_meters['TRAIN_SEMANTIC_LOSS_2SEG'].avg,
global_step=global_step)
self.writer.add_image(
'Seg/Train_groundtruth_depth',
torchvision.utils.make_grid(
self.target_depth[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_predicted_depth',
torchvision.utils.make_grid(
self.gen[0].data[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_groundtruth_seg',
torchvision.utils.make_grid(
self.target_seg[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_predicted_seg',
torchvision.utils.make_grid(
self.gen[1].data[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_image',
torchvision.utils.make_grid(
self.source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_image(
'Seg/Train_predicted_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(self.Train_predicted_label[:6])),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
# torchvision.utils.make_grid(util.color_label(torch.max(self.Train_predicted_label[:6], 1)[1]+1), 3, normalize=False,range=(0, 255)), global_step=global_step)
self.writer.add_image(
'Seg/Train_ground_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(self.label_show[:6])),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
# torchvision.utils.make_grid(util.color_label(self.label_show[:6]), 3, normalize=False,range=(0, 255)), global_step=global_step)
# if self.upsample:
# self.writer.add_image('Gen_depth', torchvision.utils.make_grid(self.gen[:3].clone().cpu().data, 3,
# normalize=True),
# global_step=global_step)
# self.writer.add_image('Train_3_Target',
# torchvision.utils.make_grid(self.target_modal_show[:3].clone().cpu().data, 3,
# normalize=True), global_step=global_step)
if phase == 'test':
# self.writer.add_image('Seg/Val_image',
# torchvision.utils.make_grid(self.source_modal_show[:6].clone().cpu().data, 3,
# normalize=True), global_step=global_step)
#
# self.writer.add_image('Seg/Val_predicted_label',
# torchvision.utils.make_grid(torch.from_numpy(util.color_label(self.val_predicted_label[:6])), 3, normalize=True,range=(0, 255)), global_step=global_step)
# # torchvision.utils.make_grid(util.color_label(torch.max(self.val_predicted_label[:3], 1)[1]+1), 3, normalize=False,range=(0, 255)), global_step=global_step)
# self.writer.add_image('Seg/Val_ground_label',
# torchvision.utils.make_grid(torch.from_numpy(util.color_label(self.label_show[:6])), 3, normalize=True,range=(0, 255)), global_step=global_step)
# self.writer.add_scalar('Seg/VAL_CLS_LOSS', self.loss_meters['VAL_CLS_LOSS'].avg,
# global_step=global_step)
# self.writer.add_scalar('Seg/VAL_CLS_ACC', self.loss_meters['VAL_CLS_ACC'].avg*100.0,
# global_step=global_step)
self.writer.add_scalar('Seg/VAL_CLS_MEAN_IOU',
float(self.val_iou.mean()) * 100.0,
global_step=global_step)
def _write_loss(self, phase, global_step):
loss_types = self.cfg.LOSS_TYPES
self.label_show = self.label.data.cpu().numpy()
self.source_modal_show = self.source_modal
self.target_modal_show = self.target_modal
if phase == 'train':
self.writer.add_scalar('Seg/LR',
self.optimizer_ED.param_groups[0]['lr'],
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_scalar('Seg/TRAIN_CLS_LOSS',
self.loss_meters['TRAIN_CLS_LOSS'].avg,
global_step=global_step)
# self.writer.add_scalar('TRAIN_CLS_ACC', self.loss_meters['TRAIN_CLS_ACC'].avg*100.0,
# global_step=global_step)
# self.writer.add_scalar('TRAIN_CLS_MEAN_IOU', float(self.train_iou.mean())*100.0,
# global_step=global_step)
if self.trans:
if 'SEMANTIC' in self.cfg.LOSS_TYPES:
self.writer.add_scalar(
'Seg/TRAIN_SEMANTIC_LOSS',
self.loss_meters['TRAIN_SEMANTIC_LOSS'].avg,
global_step=global_step)
if 'PIX2PIX' in self.cfg.LOSS_TYPES:
self.writer.add_scalar(
'Seg/TRAIN_PIX2PIX_LOSS',
self.loss_meters['TRAIN_PIX2PIX_LOSS'].avg,
global_step=global_step)
if isinstance(self.target_modal, list):
for i, (gen, target) in enumerate(
zip(self.gen, self.target_modal)):
self.writer.add_image(
'Seg/2_Train_Gen_' +
str(self.cfg.FINE_SIZE / pow(2, i)),
torchvision.utils.make_grid(
gen[:6].clone().cpu().data, 3, normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/3_Train_Target_' +
str(self.cfg.FINE_SIZE / pow(2, i)),
torchvision.utils.make_grid(
target[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
else:
self.writer.add_image(
'Seg/Train_target',
torchvision.utils.make_grid(
self.target_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_gen',
torchvision.utils.make_grid(
self.gen.data[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
'Seg/Train_image',
torchvision.utils.make_grid(
self.source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_image(
'Seg/Train_predicted',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(self.Train_predicted_label[:6],
ignore=self.cfg.IGNORE_LABEL)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
# torchvision.utils.make_grid(util.color_label(torch.max(self.Train_predicted_label[:6], 1)[1]+1), 3, normalize=False,range=(0, 255)), global_step=global_step)
self.writer.add_image(
'Seg/Train_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(self.label_show[:6],
ignore=self.cfg.IGNORE_LABEL)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
if phase == 'test':
self.writer.add_image(
'Seg/Val_image',
torchvision.utils.make_grid(
self.source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image('Seg/Val_predicted',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(
self.val_predicted_label[:6],
ignore=self.cfg.IGNORE_LABEL)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
# torchvision.utils.make_grid(util.color_label(torch.max(self.val_predicted_label[:3], 1)[1]+1), 3, normalize=False,range=(0, 255)), global_step=global_step)
self.writer.add_image('Seg/Val_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(
self.label_show[:6],
ignore=self.cfg.IGNORE_LABEL)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
self.writer.add_scalar('Seg/VAL_CLS_LOSS',
self.loss_meters['VAL_CLS_LOSS'].avg,
global_step=global_step)
# self.writer.add_scalar('Seg/VAL_CLS_ACC', self.loss_meters['VAL_CLS_ACC'].avg*100.0,
# global_step=global_step)
self.writer.add_scalar('Seg/VAL_CLS_MEAN_IOU',
float(self.val_iou.mean()) * 100.0,
global_step=global_step)
def write_loss(self, phase, global_step=1):
loss_types = self.cfg.LOSS_TYPES
task = self.cfg.TASK_TYPE
if self.phase == 'train':
label_show = self.label.data.cpu().numpy()
else:
label_show = np.uint8(self.label.data.cpu())
source_modal_show = self.source_modal
target_modal_show = self.target_modal
if phase == 'train':
self.writer.add_image(task + '/Train_image',
torchvision.utils.make_grid(
source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_scalar(task + '/LR',
self.optimizer.param_groups[0]['lr'],
global_step=global_step)
if 'CLS' in loss_types:
self.writer.add_scalar(task + '/TRAIN_CLS_LOSS',
self.loss_meters['TRAIN_CLS_LOSS'].avg,
global_step=global_step)
if 'compl' in self.cfg.MODEL or self.cfg.USE_COMPL_DATA:
self.writer.add_scalar(
task + '/TRAIN_CLS_LOSS_COMPL',
self.loss_meters['TRAIN_CLS_LOSS_COMPL'].avg,
global_step=global_step)
self.writer.add_image(
task + '/Compl_image',
torchvision.utils.make_grid(
self.result['compl_source'][:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
# self.writer.add_scalar('TRAIN_CLS_ACC', self.loss_meters['TRAIN_CLS_ACC'].avg*100.0,
# global_step=global_step)
# self.writer.add_scalar('TRAIN_CLS_MEAN_IOU', float(self.train_iou.mean())*100.0,
# global_step=global_step)
if self.trans and not self.cfg.MULTI_MODAL:
if 'SEMANTIC' in self.cfg.LOSS_TYPES:
self.writer.add_scalar(
task + '/TRAIN_SEMANTIC_LOSS',
self.loss_meters['TRAIN_SEMANTIC_LOSS'].avg,
global_step=global_step)
if 'PIX2PIX' in self.cfg.LOSS_TYPES:
self.writer.add_scalar(
task + '/TRAIN_PIX2PIX_LOSS',
self.loss_meters['TRAIN_PIX2PIX_LOSS'].avg,
global_step=global_step)
self.writer.add_image(task + '/Train_gen',
torchvision.utils.make_grid(
self.gen.data[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_image(
task + '/Train_image',
torchvision.utils.make_grid(
source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
# if isinstance(self.target_modal, list):
# for i, (gen, target) in enumerate(zip(self.gen, self.target_modal)):
# self.writer.add_image('Seg/2_Train_Gen_' + str(self.cfg.FINE_SIZE / pow(2, i)),
# torchvision.utils.make_grid(gen[:6].clone().cpu().data, 3,
# normalize=True),
# global_step=global_step)
# self.writer.add_image('Seg/3_Train_Target_' + str(self.cfg.FINE_SIZE / pow(2, i)),
# torchvision.utils.make_grid(target[:6].clone().cpu().data, 3,
# normalize=True),
# global_step=global_step)
# else:
self.writer.add_image(
task + '/Train_target',
torchvision.utils.make_grid(
target_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
if 'CLS' in loss_types and self.cfg.TASK_TYPE == 'segmentation':
train_pred = self.result['cls'].data.max(1)[1].cpu().numpy()
self.writer.add_image(
task + '/Train_predicted',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(train_pred[:6],
ignore=self.cfg.IGNORE_LABEL,
dataset=self.cfg.DATASET)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
self.writer.add_image(
task + '/Train_label',
torchvision.utils.make_grid(torch.from_numpy(
util.color_label(label_show[:6],
ignore=self.cfg.IGNORE_LABEL,
dataset=self.cfg.DATASET)),
3,
normalize=True,
range=(0, 255)),
global_step=global_step)
elif phase == 'test':
self.writer.add_image(task + '/Val_image',
torchvision.utils.make_grid(
source_modal_show[:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
# self.writer.add_image('Seg/Val_image',
# torchvision.utils.make_grid(source_modal_show[:6].clone().cpu().data, 3,
# normalize=True), global_step=global_step)
#
# self.writer.add_image('Seg/Val_predicted',
# torchvision.utils.make_grid(
# torch.from_numpy(util.color_label(self.pred[:6], ignore=self.cfg.IGNORE_LABEL,
# dataset=self.cfg.DATASET)), 3,
# normalize=True, range=(0, 255)), global_step=global_step)
# self.writer.add_image('Seg/Val_label',
# torchvision.utils.make_grid(torch.from_numpy(
# util.color_label(label_show[:6], ignore=self.cfg.IGNORE_LABEL,
# dataset=self.cfg.DATASET)),
# 3, normalize=True, range=(0, 255)),
# global_step=global_step)
if 'compl' in self.cfg.MODEL or self.cfg.USE_COMPL_DATA:
self.writer.add_image(
task + '/Compl_image',
torchvision.utils.make_grid(
self.result['compl_source'][:6].clone().cpu().data,
3,
normalize=True),
global_step=global_step)
self.writer.add_scalar(task + '/VAL_CLS_ACC',
self.loss_meters['VAL_CLS_ACC'].val * 100.0,
global_step=global_step)
self.writer.add_scalar(task + '/VAL_CLS_MEAN_ACC',
self.loss_meters['VAL_CLS_MEAN_ACC'].val *
100.0,
global_step=global_step)
if task == 'segmentation':
self.writer.add_scalar(
task + '/VAL_CLS_MEAN_IOU',
self.loss_meters['VAL_CLS_MEAN_IOU'].val * 100.0,
global_step=global_step)