vcip-rgbd / dformer Goto Github PK

可以提供一下伪深度图吗？

感谢您杰出的工作。我想要自己制作数据集去运行您的代码，想知道代码对数据集有什么样的要求？

感谢您杰出的工作，我尝试用自己制作的数据集去运行您的代码，出现了一些错误，loss=nan，以下是一些错误信息
27 23:43:55 Initing weights ...
27 23:44:00 begin trainning:
Epoch 1/500 Iter 156/156: lr=5.9615e-06 loss=nan total_loss=nan: [01:48<00:00, 1.44it/s]
27 23:45:48 WRN NaN or Inf found in input tensor.
期待您的答复

Thanks for your excellent work! I melt the issue when training the model with a single 3090 GPU. I cannot sort out the source of the issue.

RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.

Thank you very much for your outstanding work. How can the results be visualized? I'm not very clear about this i

Hello, your paper mentions the relevant work of RGB-D salient object detection. Can you provide the corresponding code?

您好，请问如何加载权重文件。我跑过的代码都是加载以.pth结尾的权重文件，而您提供的是.tar压缩包形式，解压后没有.pth的权重文件

期待您的回复

Hello, thank you for sharing the code of this great work!!

I have a query about the NYU dataset.

The NYU dataset has 40 classes. So, in the final output layer of all of your decoders, num_classes is set to 40.

When I check the pixel values of the ground truths (0.png, 1.png, etc) inside the NYUDepthv2/Label/ directory, the pixel values range from 0 to 40. This indicates there are 41 classes.

import cv2
import numpy as np

gt_path = "...../NYUDepthv2_DFormer/Label"

for idx in range(10):
    label = f"{gt_path }/{idx}.png"
    unique_values = np.unique(cv2.imread(gt_path ))
    print(f"Classes in label {idx}: {unique_values}")

Classes in label 0: [ 0 1 2 3 5 7 12 22 24 26 38 39 40]
Classes in label 1: [ 0 1 3 12 22 24 26 34 38 39 40]
Classes in label 2: [ 0 1 5 7 8 26 29 38 40]
Classes in label 3: [ 0 1 3 5 14 26 40]
Classes in label 4: [ 0 1 3 5 7 8 12 15 22 26 30 34 38 39 40]
Classes in label 5: [ 0 1 2 5 7 22 29 38 39 40]
Classes in label 6: [ 0 1 2 5 15 22 38 39 40]
Classes in label 7: [ 0 1 2 5 8 9 26 37 38 39 40]
Classes in label 8: [ 0 1 2 5 7 8 11 15 22 26 29 38 39 40]
Classes in label 9: [ 0 1 2 3 8 15 22 26 38 39 40]

So can you kindly tell me how have you dealt with the extra class in the ground truth labels?
In my code, this discrepency causes error in the cross-entropy loss function.

Thank you.

你好，首先感谢您的代码公开分享。
作为一个初学者，有个问题向您请教：我在复现您的代码（数据集使用NYU，在两张2080 TI上进行训练），第一个epoch正常进行，但是在进行val的时候却显示cuda out of memory