Comments (4)
Oh yes I did not add the visualization code in Test_model.py
You can edit by referring the main.py
like this (line 487)
if args.visualize:
if train_config["loss"] == "Lovasz":
grid_outputs = torchvision.utils.make_grid(color_transform((save_est[0] > 0).cpu().data), nrow=6)
else:
grid_outputs = torchvision.utils.make_grid(color_transform(save_est[0].unsqueeze(0).cpu().max(1)[1].data), nrow=6)
my_logger.image_summary(torchvision.utils.make_grid(save_input[0], normalize=True),
opts = dict(title=f'VAL img (epoch: {epoch})',caption=f'VAL img (epoch: {epoch})'))
my_logger.image_summary(grid_outputs,
opts=dict(title=f'VAL output (epoch: {epoch}, step: {str(mIOU_val)})',
caption=f'VAL output (epoch: {epoch}, step: {str(mIOU_val)})', ))
grid_gt = torchvision.utils.make_grid((100 * save_gt[0].cpu()).type('torch.ByteTensor').data,
nrow=6)
my_logger.image_summary(grid_gt,
opts=dict(title=f'VAL gt (epoch: {epoch}, step: {str(mIOU_val)})',
caption=f'VAL gt (epoch: {epoch}, step: {str(mIOU_val)})', ))
from ext_portrait_segmentation.
from ext_portrait_segmentation.
Setting --visualize to True doesn't appear to do anything.
Do you mean that the visdom not working?
when you run : python -m visdom.server ???
from ext_portrait_segmentation.
Sorry, I mean for running Test_model.py
from ext_portrait_segmentation.
Related Issues (20)
- the size of input_image HOT 1
- Accuracy issue, implementation difference compared to paper. HOT 4
- The speed about ExtremeC3Net. HOT 1
- Rough edges in mask? HOT 2
- Training new dataset
- main.py HOT 1
- Testing new Image HOT 2
- How to use EG1800
- Error in Visualize_video.py when using result/SINet/SINet.pth HOT 5
- Why is the label of eg1800 data set inconsistent with the number of images? HOT 3
- about test!!! HOT 4
- Converting .pth model to onnx HOT 7
- about inference time
- why ignore_idx==255? in lovasz loss? HOT 2
- Pretrained model output bad segmentation result HOT 4
- trainning new data and test error, help please HOT 3
- Model does not perform well on real world portrait images/videos HOT 1
- groups config setting
- custom dataset training HOT 1
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from ext_portrait_segmentation.