hanchaoleng / shapeconv Goto Github PK
View Code? Open in Web Editor NEWShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation (ICCV 2021)
License: Apache License 2.0
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation (ICCV 2021)
License: Apache License 2.0
Hi Hanchao,
You forgot to upload these two folders.
Sorry I found them.
Best,
Min
In this repos, the class label 0 of background is set to 255, as the same as the ignoring label of padding area.
Since I'm a greenhorn to RGBD segmentation, I'm wondering if it is a common setting to ignore the class label of background?
The first element in semantic_labels.json. Does it correspond to any label?
Hi, your project ShapeConv(commit id: 25bee65) requires "albumentations==0.4.1" in its dependency. After analyzing the source code, we found that the following versions of albumentations can also be suitable, i.e., albumentations 0.4.0, since all functions that you directly (6 APIs: albumentations.augmentations.functional.scale, albumentations.core.transforms_interface.to_tuple, albumentations.augmentations.functional.pad_with_params, albumentations.core.transforms_interface.DualTransform.init, albumentations.core.composition.Compose.init, albumentations.augmentations.transforms.PadIfNeeded.init) or indirectly (propagate to 14 albumentations's internal APIs and 2 outsider APIs) used from the package have not been changed in these versions, thus not affecting your usage.
Therefore, we believe that it is quite safe to loose your dependency on albumentations from "albumentations==0.4.1" to "albumentations>=0.4.0,<=0.4.1". This will improve the applicability of ShapeConv and reduce the possibility of any further dependency conflict with other projects.
May I pull a request to further loosen the dependency on albumentations?
By the way, could you please tell us whether such an automatic tool for dependency analysis may be potentially helpful for maintaining dependencies easier during your development?
作者您好,我为SID数据集制作标签时并没有发现semantic_labels.json这个文件,标注是三通道的分割图,请问您方便提供吗?十分感谢!
@hanchaoleng
"Traceback (most recent call last):
File "/content/ShapeConv/tools/test.py", line 7, in
from rgbd_seg.runners import TestRunner
File "/content/ShapeConv/tools/../rgbd_seg/runners/init.py", line 1, in
from .inference_runner import InferenceRunner
File "/content/ShapeConv/tools/../rgbd_seg/runners/inference_runner.py", line 3, in
from ..models import build_model
File "/content/ShapeConv/tools/../rgbd_seg/models/init.py", line 1, in
from .builder import build_model
File "/content/ShapeConv/tools/../rgbd_seg/models/builder.py", line 5, in
from rgbd_seg.models.decoders import build_brick
File "/content/ShapeConv/tools/../rgbd_seg/models/decoders/init.py", line 1, in
from .bricks import FusionBlock, JunctionBlock
File "/content/ShapeConv/tools/../rgbd_seg/models/decoders/bricks.py", line 8, in
from ..utils import ConvModule, build_module
File "/content/ShapeConv/tools/../rgbd_seg/models/utils/init.py", line 2, in
from .conv_module import ConvModule, ConvModules
File "/content/ShapeConv/tools/../rgbd_seg/models/utils/conv_module.py", line 10, in
from rgbd_seg.models.utils.shape_conv import ShapeConv2d
File "/content/ShapeConv/tools/../rgbd_seg/models/utils/shape_conv.py", line 9, in
from torch._six import container_abcs
ModuleNotFoundError: No module named 'torch._six'"
Hi, thanks for your valuable work!
Could you please provide the scripts for SUN RGBD dataset preprocessing like NYU and SID datasets?
Have the authors tried other combination of loss functions, e.g., CE + DICE and Focal + DICE, rather than CrossEntropy (CE) ?
Does the simple application of CrossEntropy (CE) in ShapeConv's implementation achieve the nearly optimal performance ?
Hi, thanks for your work! Can you specify how I can run inference with your pretrained models on some custom RGBD data that I have?
thanks for your awesome work!but I don't find the SUN-RGBD model. could you please provide SUN-RGBD model?
Hello,
Thanks for the great work !
I am confused about the scales in the configs. Why you use multi-scales for testing instead of scale = 1?
For example the code on the configs/nyu/nyu40
tta=dict(
scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
Can you provide some explications? Thanks
请问number of misclassified pixels within a narrow band (“trimap”) surrounding ground-truth object boundaries
这一段的错误像素数量计算与范围可视化的代码您能提供一下吗?
作者您好,请问您方便分享SUN和SID数据集的HHA数据连接吗?或者您是怎么将depth转为HHA的呢?需要一些参数吗?
谢谢!
Thank you very much for your work. I am very interested in your work. I hope you can help explain the meaning of this sentence in the code. The code is in the script 'test_runner. py ' in line 56, as follows:
pred_ rgb[label == 255] = np.array((0, 0, 0))
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