Comments (1)
Thanks for your interest! I don't think our implementation is incorrect as when I naively test the follow script:
import torch
d3_list = [i for i in range(4)]
depth3 = torch.tensor([30])
for i in range(12):
depth3 = depth3 + 1
if (i == 2):
d3_list[0] = depth3
if (i == 5):
d3_list[1] = depth3
if (i == 8):
d3_list[2] = depth3
if (i == 11):
d3_list[3] = depth3
print(d3_list)
The result is:
[tensor([33]), tensor([36]), tensor([39]), tensor([42])]
.
That is to say the tensors stored in the list are tensor depth3 at iteration 3, 6, 9, and 12, respectively. And they are not the same although all of them are named as depth3.
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Related Issues (20)
- Projection result confirmation HOT 3
- Questions about KITTI-odometry HOT 2
- Runtime measurement HOT 4
- How to use the sparse depth?
- How to use the sparse depth? HOT 4
- Modify the backbone network HOT 3
- lightweight deployment of PENet network
- How to infer PENet for KITTI object task? HOT 3
- broken PNG file HOT 1
- the difference intrinsic parameters between train and test HOT 4
- Modify DA-CSPN++ for single branch input HOT 1
- some questions about the implement of CSPN HOT 4
- Model mismatch at inference time HOT 1
- Tensor Dimension Mismatch HOT 1
- How many parameters dose PENet have? HOT 2
- About training HOT 1
- How to implement the model with ROS?
- can't download the pretrained PENet Model
- Pretrained on NYU dataset?
- RuntimeError: CUDA out of memory.
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