Hi, When I configure the environment as required, I can run sh train_demo.sh with the code that has not been updated before, but running sh train.sh for a period of time will report an error:“AttributeError：‘NoneType’ object has no attribute ‘lower’”。

Download the latest code to run the training code (sh train_demo.sh/sh train.sh). The error display: "runtimeerror: stack expectations a non empty tensorlist". How to solve this problem?

processing (0000)-th image... ./test_images/.ipynb_checkpoints
Traceback (most recent call last):
  File "./tools/test_shape.py", line 111, in <module>
    rgb_c = rgb[:, :, ::-1].copy()
TypeError: 'NoneType' object is not subscriptable

Though I have I uploaded the images in the test_images folder but I am getting "None Type" error.

I am facing the above error while I am executing the Test 3D reconstruction from a single image. Can anyone help me to sort out this issue.

Thanks in Advance.

Thanks for your great work, I have some questions about the train dataset:

1. what dataset did you used to train the pretained model which you provided, are DIML and 3D Ken Burns which in your paper used?
2. In the dataset you provide on github, I didn't see DIML and 3D Ken Burns dataset, are they in DiverseDepth or they are not provided? If so, how should I prepare these two datasets?
3. In your paper, relative depth of Holopix50K are generated using FlowNet, how can I generate relative depth of my own stereo data?

Good work! I manged to get the shape using Test_shape.py but when using test_shape.py, I faced this error: processing (0000)-th image... LeReS/test_images/5.jpg Traceback (most recent call last): File "LeReS/tools/test_shape.py", line 123, in shift, focal_length, depth_scaleinv = reconstruct3D_from_depth(rgb, pred_depth_ori, File "LeReS/tools/test_shape.py", line 76, in reconstruct3D_from_depth shift_1 = refine_shift(pred_depth_norm, shift_model, predicted_focal_1, cam_u0, cam_v0) File "/home/adelai_ws/AdelaiDepth/LeReS/lib/test_utils.py", line 124, in refine_shift shift = refine_shift_one_step(depth_wshift_tmp, model, focal, u0, v0) File "/home/adelai_ws/AdelaiDepth/LeReS/lib/test_utils.py", line 108, in refine_shift_one_step outputs = model(inputs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/trainer/adelai_ws/AdelaiDepth/LeReS/lib/spvcnn_classsification.py", line 148, in forward x2 = self.stage2(x1) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/trainer/adelai_ws/AdelaiDepth/LeReS/lib/spvcnn_classsification.py", line 23, in forward out = self.net(x) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torchsparse/nn/modules/conv.py", line 72, in forward return conv3d(inputs, File "/usr/local/lib/python3.8/dist-packages/torchsparse/nn/functional/conv.py", line 118, in conv3d idx_query = list(convert_neighbor_map_gpu(idx_query)) File "/usr/local/lib/python3.8/dist-packages/torchsparse/nn/functional/convert_neighbor_map.py", line 9, in forward idx_batch, idx_point = torch.where(neighbor_map != -1) ValueError: not enough values to unpack (expected 2, got 1)

Good work! I manged to get the shape using Test_shape.py but when using test_shape.py, I faced this error:

root@PC:/home/adelai_ws/AdelaiDepth# python3 LeReS/tools/test_shape.py --load_ckpt res101.pth --backbone resnext101
No protocol specified
processing (0000)-th image... LeReS/test_images/5.jpg
Traceback (most recent call last):
  File "LeReS/tools/test_shape.py", line 123, in <module>
    shift, focal_length, depth_scaleinv = reconstruct3D_from_depth(rgb, pred_depth_ori,
  File "LeReS/tools/test_shape.py", line 76, in reconstruct3D_from_depth
    shift_1 = refine_shift(pred_depth_norm, shift_model, predicted_focal_1, cam_u0, cam_v0)
  File "/home/adelai_ws/AdelaiDepth/LeReS/lib/test_utils.py", line 124, in refine_shift
    shift = refine_shift_one_step(depth_wshift_tmp, model, focal, u0, v0)
  File "/home/adelai_ws/AdelaiDepth/LeReS/lib/test_utils.py", line 108, in refine_shift_one_step
    outputs = model(inputs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/trainer/adelai_ws/AdelaiDepth/LeReS/lib/spvcnn_classsification.py", line 148, in forward
    x2 = self.stage2(x1)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/trainer/adelai_ws/AdelaiDepth/LeReS/lib/spvcnn_classsification.py", line 23, in forward
    out = self.net(x)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torchsparse/nn/modules/conv.py", line 72, in forward
    return conv3d(inputs,
  File "/usr/local/lib/python3.8/dist-packages/torchsparse/nn/functional/conv.py", line 118, in conv3d
    idx_query = list(convert_neighbor_map_gpu(idx_query))
  File "/usr/local/lib/python3.8/dist-packages/torchsparse/nn/functional/convert_neighbor_map.py", line 9, in forward
    idx_batch, idx_point = torch.where(neighbor_map != -1)
ValueError: not enough values to unpack (expected 2, got 1)

I tried another version (1.4) but there is a change in the API. Any suggestions? Thanks!

Thanks for the good work! I have some questions about the multi-scale gradient loss used in the paper. Here is the code of MegaDepth [24], but I think it is different from the grad_loss used in this method.

def GradientLoss(self, log_prediction_d, mask, log_gt):
        N = torch.sum(mask)
        log_d_diff = log_prediction_d - log_gt
        log_d_diff = torch.mul(log_d_diff, mask)

        v_gradient = torch.abs(log_d_diff[0:-2, :] - log_d_diff[2:, :])
        v_mask = torch.mul(mask[0:-2, :], mask[2:, :])
        v_gradient = torch.mul(v_gradient, v_mask)

        h_gradient = torch.abs(log_d_diff[:, 0:-2] - log_d_diff[:, 2:])
        h_mask = torch.mul(mask[:, 0:-2], mask[:, 2:])
        h_gradient = torch.mul(h_gradient, h_mask)

        gradient_loss = torch.sum(h_gradient) + torch.sum(v_gradient)
        gradient_loss = gradient_loss / N

        return gradient_loss

Should I use depth with 'log' without any constraint to make 'prediction_d'>0?

Hi!

I'm having this problem here with and import statement from Torchsparse(version 1.2.0 as fixed in the last commit): module 'torchsparse_backend' has no attribute 'hash_forward I tried to ask the solution on Torchsparse repo, but they ask to update to 1.4.0, but that version breaks a lot of other import statements.

Does anyone know the solution?

Thanks in advance!

Thanks for the great repository. I ran the Train.sh file for fine tuning with the original data from the res50.pth file and got the following error. How can I solve this error?

Thank you

Traceback (most recent call last): File "../tools/train.py", line 290, in main() File "../tools/train.py", line 286, in main main_worker(0, ngpus_per_node, train_args, val_args) File "../tools/train.py", line 221, in main_worker load_ckpt(train_args, model, optimizer.optimizer, scheduler, val_err) File "/home/robotics/AdelaiDepth/LeReS/Train/lib/utils/net_tools.py", line 44, in load_ckpt checkpoint_state_dict_noprefix = strip_prefix_if_present(checkpoint['model_state_dict'], "module.") KeyError: 'model_state_dict'

Dear, I download your dataset (taskeconomy, diversedepth, hwrsi and holopix50k) by running sh Download_ data. sh, and run your recently updated code. I can run sh train_ demo. sh, but cannot run sh train.sh. I modify the parameter dataset_ list in train.sh (only HWRSI is left，others are not allowed), you can run for a while and then report an error:

AttributeError: 'NoneType' object has no attribute 'lower'

I think it is the lack of content in the datasets.Can you check your dataset？Thanks！

Dear,where is the 'Holopix50k/annotations/val_annotations.json'? I only get Holopix50k/annotations/all_annotations.json after I run the command 'sh download_data.sh'.

Thank you for your contribution. In the same scene, the change of camera attitude will lead to the scale recovery parameter a, and b will also change accordingly. Is it possible to recover more robust scale parameters for the same scene?

Hi, Have you uploaded the code about recovering depth metric from locally scale-aligned weight. I didn't found the code in project. Please kindly share them. Thanks a lot

/content/AdelaiDepth/LeReS/test_images

zip error: Nothing to do! (result.zip)

FileNotFoundError Traceback (most recent call last) in 3 get_ipython().run_line_magic('cd', '/content/AdelaiDepth/LeReS/test_images/') 4 get_ipython().system('find outputs/ -name "*-depth_raw.png" | zip -r result.zip -@') ----> 5 files.download("result.zip")

/usr/local/lib/python3.7/dist-packages/google/colab/files.py in download(filename) 207 if not _os.path.exists(filename): 208 msg = 'Cannot find file: {}'.format(filename) --> 209 raise FileNotFoundError(msg) # pylint: disable=undefined-variable 210 211 comm_manager = _IPython.get_ipython().kernel.comm_manager

FileNotFoundError: Cannot find file: result.zip

hello ,I can't find those two function in torch_sparse.utils and pytorch official doc. I have checked this file，I've looked into this file, but there are none of these two functions

Could you help me, what should I do?

Hello, I tried to use your train_demo.sh script. After 200 epoch I don't have any progress on test dataset. I attached files with my environment, log and output folder. Could you help me, what I'm doing wrong? Oct11-14-37-36_MSISPlap03.txt env.txt

Hi,

Thanks for the great work!

Is there a visualization script to generate the 360 point cloud animation video or did you use a specific tool for this?

Thanks!