Comments (13)
@zorzi-s @GuoxingYan @hehongjie I saw that in prediction.py in line 42, 46,53
model.train()
It means that model is at train model not eval?? is it right? If eval must change mode to eval? If training how to train it? Many thank
@phamkhactu @hehongjie Thank you for the interest in our method! We use model.train() during inference in order to force the batch normalization layers to use batch statistics instead of the mean and variance estimated during training. The model weights are not updated during inference. Please also refer to this conversation: link.
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During training, what is the factor in Equation 12 equal to?
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When calculating the match loss, is the method of simply assigning a nearest predictor to each label?
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Hello Guoxing, do you know how to construct the permutation matrixs for model training? It seems we need to first decide the order of vertices and then made the matrix, right? But how to decide the order?
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@zorzi-s @GuoxingYan @hehongjie I saw that in prediction.py in line 42, 46,53
model.train()
It means that model is at train model not eval?? is it right? If eval must change mode to eval?
If training how to train it?
Many thank
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@hehongjie when I set all model to eval(), the result very poor!!
Mean IoU: 0.0008893926959595389
Mean C-IoU: 0.0007304682105452664
if want to evaluate model must train to eval model, if use train model, model can update, so not truth, this is my limited knowledge. if wrong, many sorry for you.
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@phamkhactu Yes, you are right. You may refer to this answer.https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch
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@phamkhactu Yes, you are right. You may refer to this answer.https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch
@hehongjie as you can see that, the output validation from eval and train model very diff, but if i use train model model can update weight, can it cause wrong prediction??
I have another question that: with your design model public from your research team, the model can learn to classifier direction?
Like image, I expected that 2 polygon are diff, Does the model R2U_Net
learn this?
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@zorzi-s @GuoxingYan @hehongjie I saw that in prediction.py in line 42, 46,53
model.train()
It means that model is at train model not eval?? is it right? If eval must change mode to eval? If training how to train it? Many thank
@phamkhactu @hehongjie Thank you for the interest in our method! We use model.train() during inference in order to force the batch normalization layers to use batch statistics instead of the mean and variance estimated during training. The model weights are not updated during inference. Please also refer to this conversation: link.
@zorzi-s thank you. As I mention in above question:
I have another question that: with your design model public from your research team, the model can learn to classifier direction?
Like image, I expected that 2 polygon are diff, Does the model R2U_Net
learn this?
For you, Your model can do that?? In my opition it can, because model learn polygon points, so direction make difference feature extraction. How about you? Many Thanks
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Has anyone figured out how to calculate the permutation matrix during training using the Sinkhorn algorithm?
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@GuoxingYan have you figured out how to calculate the partial assignment during training using the Sinkhorn algorithm?
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@zorzi-s How can we confirm the originality of your paper if you don't provide your training script?
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Do we have the repo for the new paper, Re:PolyWorld, @zorzi-s ?
https://openaccess.thecvf.com/content/ICCV2023/papers/Zorzi_RePolyWorld_-_A_Graph_Neural_Network_for_Polygonal_Scene_Parsing_ICCV_2023_paper.pdf
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Related Issues (20)
- Where can I find the training code? HOT 1
- Is it possible to release the training code?
- Weights for backbone (`trained_weights/polyworld_backbone`) are missing HOT 1
- using proper set of offset for calculating L_seg and L_angle losses HOT 2
- It is amazing work! I really appreciate your efforts of combining multiple networks. Can you release the training code for this repository? HOT 1
- Train on custom datasets HOT 7
- Simplifying the shape of curved walls
- Question about Differentiable Polygon Rendering HOT 4
- Question About Generate Ground Truth Permutation Matrix HOT 3
- Segmentation mask HOT 2
- Question about "common corners" in Section 7
- Souce code of training process HOT 21
- about training
- about matching loss
- URLs `files.icg.tugraz.at/f/...` aren't always working HOT 2
- Question about the loss
- Questions about method implementation HOT 2
- Challenging Test Set Image IDs for comparison HOT 2
- Possibility about doing predictions without ground truth annotation files. HOT 1
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