jiarenchang / realtimestereo Goto Github PK

The SecenFlowLoader.pyc cannot be imported in google colab environment for some reason can you please provide the .py file before compiling and if that would make any difference if I used the .py file directly?

Thanks a lot for publishing the source code ! Would it be too much if I ask you to share the pretrained models as well :) ?

Hi, sorry to disturb you again...

In your code RT_Strero.py，when dealing with the cost volume, have codes:
for scale in range(len(feats_l)):
if scale > 0:
wflow = F.upsample(pred[scale - 1], (feats_l[scale].size(2), feats_l[scale].size(3)),
mode='bilinear') * feats_l[scale].size(2) / img_size[2]
cost = self._build_volume_2d3(feats_l[scale], feats_r[scale], 3, wflow, stride=1)
else:
cost = self._build_volume_2d(feats_l[scale], feats_r[scale], 12, stride=1)

in
wflow = F.upsample(pred[scale - 1], (feats_l[scale].size(2), feats_l[scale].size(3)),mode='bilinear') * feats_l[scale].size(2) / img_size[2]
this line, it seems the previous pred (pred[scale - 1] )is already reshape to 256*512 with
disp_up = F.upsample(pred_low_res, (img_size[2], img_size[3]), mode='bilinear')
it's bigger than every cost in feats_l or feats_r, and it seems meant to be downsample.
So why use the function upsample but not do conv?
Thanks.

Many codes are .pyc files instead of .py files. Is the file missing?

Hello, I want to know where the relevant codes of 3D CNN are? I don't seem to have found it.Thank you.

Hi @JiaRenChang,
Thanks for the great work!

I was wondering why changing the maxdisp during inference doesn't change the running time and result?

Thanks!

Hello， thank you for your excellent work！！！However，can you provide the pretrained model in baiduyun？

The work, FDSCS, also provides a low GPOS model, please see https://github.com/ayanc/fdscs, while your paper doesn't mention it.

Hi @JiaRenChang,

I was wondering if you tried TensorRT and observed an inference speed improvement?

Thanks!

Hi,
I meet an issues:
Traceback (most recent call last):
File "finetune.py", line 205, in
main()
File "finetune.py", line 179, in main
test_loss = test(imgL,imgR, disp_L)
File "finetune.py", line 140, in test
true_disp[index[0][:], index[1][:], index[2][:]]-pred_disp[index[0][:], index[1][:], index[2][:]])
IndexError: index 109 is out of bounds for dimension 1 with size 1

I don't know the reason. Could you help me?

Hello, Great work!
I want to train my own module but I met some problems. Could you please give me some advice. Really appreciated.
In RTS, the finetune.py is same as PSM-Net, and it will encounter so problem.
Like:

if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp)
elif args.model == 'basic':
model = basic(args.maxdisp)
else:
print('no model')

if I use arg.model = RTS..or something, it will report a bug.
and in submodule.py:

class disparityregression(nn.Module):
def __init__(self, maxdisp):
super(disparityregression, self).__init__()
self.disp = torch.Tensor(np.reshape(np.array(range(maxdisp +1)), [1, maxdisp+1, 1, 1])).cuda()

this part is different from PSM-Net, and it will enconter a size bug.
How can I do with these problems?

Hello,
Thank you for this repo, the model is amazing. I was wondering if you ever converted this model to a tensorRT engine.
I tried to do this, and was unable to successfully build the engine.

Thank you!

what batch_size did you choose to test your model latency on TX2? Did you test with a larger batch_size and calculate the latency by factorizing the batch_size or you just set the test batch_size to 1? Thank you for your reply!

Thanks for sharing this very nice model! 😄
KITTI 2015 Pretrained Model The tar file downloaded from Google Drive seems to be corrupted. The URL of the file itself seems to be fine, but the uploaded file appears to be corrupted.

• README
  https://github.com/JiaRenChang/RealtimeStereo#pretrained-model

• URL
  https://drive.google.com/file/d/12EQKjntE_Vi6m9vpSzJRtuzDCRJRmYoV/view?usp=sharing

• Downloaded file
  pretrained_Kitti2015_realtime.tar

• Extract Error
  The contents of the compressed file seem to be empty.
  

Hi @JiaRenChang,

 I was working on AnyNet (https://github.com/mileyan/AnyNet)  project, but haven't got appropriate results because I was not able to compile SPN module  successfully (Error while running make.sh: Not able to find setup.py file). Meanwhile, I read your paper on Real-Time Stereo and was  curious to implement the same, since it has been trained on datasets like Sceneflow & KITTI just like AnyNet.

 I would appreciate your time and efforts if you could clarify few queries I have:

1. As I read in your paper, the outcome of this model is 10-35fps on Nvidia GPU, correct?
2. Does this model needs SPN?
3. Is it recommneded to finetune the model trained on Sceneflow previously , with one of the KITTI datasets (Kitti2015 or Kitti2012)?
4. Also, does Test_img.py file output the colored disparity map as shown in your paper.

Regards,
Nakul

I am trying to run the Test_img.py and i am getting this error as is i tried to run PSMnet and its working fine.

ubuntu:~/Depth-perception/RealtimeStereo$ python3 Test_img.py --leftimg ./left/000000_10.png --rightimg ./right/000000_10.png
load model
Traceback (most recent call last):
File "Test_img.py", line 61, in
model.load_state_dict(state_dict['state_dict'])
File "/home/enord/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for DataParallel:
Missing key(s) in state_dict: "module.feature_extraction.firstconv.0.0.weight", "module.feature_extraction.firstconv.0.1.weight", "module.feature_extraction.firstconv.0.1.bias", "module.feature_extraction.firstconv.0.1.running_mean", "module.feature_extraction.firstconv.0.1.running_var", "module.feature_extraction.firstconv.2.0.weight", "module.feature_extraction.firstconv.2.1.weight", "module.feature_extraction.firstconv.2.1.bias", "module.feature_extraction.firstconv.2.1.running_mean", "module.feature_extraction.firstconv.2.1.running_var", "module.feature_extraction.firstconv.4.0.weight", "module.feature_extraction.firstconv.4.1.weight", "module.feature_extraction.firstconv.4.1.bias", "module.feature_extraction.firstconv.4.1.running_mean", "module.feature_extraction.firstconv.4.1.running_var", "module.feature_extraction.layer1.0.conv1.0.0.weight", "module.feature_extraction.layer1.0.conv1.0.1.weight", "module.feature_extraction.layer1.0.conv1.0.1.bias", 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"module.feature_extraction.lastconv.0.1.running_mean", "module.feature_extraction.lastconv.0.1.running_var", "module.feature_extraction.lastconv.2.weight", "module.dres0.0.0.weight", "module.dres0.0.1.weight", "module.dres0.0.1.bias", "module.dres0.0.1.running_mean", "module.dres0.0.1.running_var", "module.dres0.2.0.weight", "module.dres0.2.1.weight", "module.dres0.2.1.bias", "module.dres0.2.1.running_mean", "module.dres0.2.1.running_var", "module.dres1.0.0.weight", "module.dres1.0.1.weight", "module.dres1.0.1.bias", "module.dres1.0.1.running_mean", "module.dres1.0.1.running_var", "module.dres1.2.0.weight", "module.dres1.2.1.weight", "module.dres1.2.1.bias", "module.dres1.2.1.running_mean", "module.dres1.2.1.running_var", "module.dres2.conv1.0.0.weight", "module.dres2.conv1.0.1.weight", "module.dres2.conv1.0.1.bias", "module.dres2.conv1.0.1.running_mean", "module.dres2.conv1.0.1.running_var", "module.dres2.conv2.0.weight", "module.dres2.conv2.1.weight", "module.dres2.conv2.1.bias", 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Unexpected key(s) in state_dict: "feature_extraction.firstconv.0.weight", "feature_extraction.firstconv.1.weight", "feature_extraction.firstconv.2.weight", "feature_extraction.firstconv.2.bias", "feature_extraction.firstconv.2.running_mean", "feature_extraction.firstconv.2.running_var", "feature_extraction.firstconv.2.num_batches_tracked", "feature_extraction.firstconv.4.weight", "feature_extraction.firstconv.5.0.weight", "feature_extraction.firstconv.5.1.weight", "feature_extraction.firstconv.5.1.bias", "feature_extraction.firstconv.5.1.running_mean", "feature_extraction.firstconv.5.1.running_var", "feature_extraction.firstconv.5.1.num_batches_tracked", "feature_extraction.firstconv.7.weight", "feature_extraction.firstconv.8.0.weight", "feature_extraction.firstconv.8.1.weight", "feature_extraction.firstconv.8.1.bias", "feature_extraction.firstconv.8.1.running_mean", "feature_extraction.firstconv.8.1.running_var", "feature_extraction.firstconv.8.1.num_batches_tracked", "feature_extraction.stage2.1.weight", "feature_extraction.stage2.2.0.weight", "feature_extraction.stage2.2.1.weight", "feature_extraction.stage2.2.1.bias", "feature_extraction.stage2.2.1.running_mean", "feature_extraction.stage2.2.1.running_var", "feature_extraction.stage2.2.1.num_batches_tracked", "feature_extraction.stage2.4.weight", "feature_extraction.stage2.5.0.weight", "feature_extraction.stage2.5.1.weight", "feature_extraction.stage2.5.1.bias", "feature_extraction.stage2.5.1.running_mean", "feature_extraction.stage2.5.1.running_var", "feature_extraction.stage2.5.1.num_batches_tracked", "feature_extraction.stage3.1.weight", "feature_extraction.stage3.2.0.weight", "feature_extraction.stage3.2.1.weight", "feature_extraction.stage3.2.1.bias", "feature_extraction.stage3.2.1.running_mean", "feature_extraction.stage3.2.1.running_var", "feature_extraction.stage3.2.1.num_batches_tracked", "feature_extraction.stage3.4.weight", "feature_extraction.stage3.5.0.weight", 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"feature_extraction.fusion.x4_fusion.1.0.weight", "feature_extraction.fusion.x4_fusion.1.1.weight", "feature_extraction.fusion.x4_fusion.1.1.bias", "feature_extraction.fusion.x4_fusion.1.1.running_mean", "feature_extraction.fusion.x4_fusion.1.1.running_var", "feature_extraction.fusion.x4_fusion.1.1.num_batches_tracked", "feature_extraction.fusion.x4_fusion.3.weight", "feature_extraction.fusion.downconv81.0.weight", "feature_extraction.fusion.downconv81.1.weight", "feature_extraction.fusion.downconv81.1.bias", "feature_extraction.fusion.downconv81.1.running_mean", "feature_extraction.fusion.downconv81.1.running_var", "feature_extraction.fusion.downconv81.1.num_batches_tracked", "feature_extraction.fusion.downconv82.0.weight", "feature_extraction.fusion.downconv82.1.weight", "feature_extraction.fusion.downconv82.1.bias", "feature_extraction.fusion.downconv82.1.running_mean", "feature_extraction.fusion.downconv82.1.running_var", 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"volume_postprocess.2.3.0.running_mean", "volume_postprocess.2.3.0.running_var", "volume_postprocess.2.3.0.num_batches_tracked", "volume_postprocess.2.3.2.weight", "volume_postprocess.2.4.0.weight", "volume_postprocess.2.4.0.bias", "volume_postprocess.2.4.0.running_mean", "volume_postprocess.2.4.0.running_var", "volume_postprocess.2.4.0.num_batches_tracked", "volume_postprocess.2.4.2.weight".

Sorry to disturbe you again..
I notice that you do crop during training and crop the image to 288x576.
I think this operation will lose the feature outside the 288x576, like the bule area:

So why doing the crop like this? and I find that when testing, the image size is not been largely cropped (the size is 368x1232). Why the model trained by 288x576 can use on the 368x1232 and can still get the result? Is it because you also give the 1/n (1/16 in this case) disp when training?
Thanks.

First of all thank you for open sourcing your work. Do you plan to release pre-trained models ?