binvox_rw write error
Closed this issue · 25 comments
voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()
Value Error Axes does not match array
Sorry. This code does not belong to our repository.
Could you give me more information (e.g., Stacktrace)?
What the dimension of dense_voxel_data?
dense_voxel_data = {ndarray} [[[[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n ...\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]]]...View as Array
min = {int32} 0
max = {int32} 0
shape = {tuple} <class 'tuple'>: (1, 32, 32, 32)
dtype = {dtype} int32
size = {int} 32768
array = {NdArrayItemsContainer} <pydevd_plugins.extensions.types.pydevd_plugin_numpy_types.NdArrayItemsContainer object at 0x00000214B704B408>
fp = {BufferedWriter} <_io.BufferedWriter name='./Dataset/MyData/img/car/0.binvox'>
voxel_model = {Voxels} <utils.binvox_rw.Voxels object at 0x00000214B717B648>
axis_order = {str} 'xyz'
data = {ndarray} [[[[4.12672758e-03 9.65105253e-04 4.31137247e-04 ... 8.34944192e-04\n 1.15571811e-03 2.34578969e-03]\n [6.66419975e-04 7.60835028e-05 5.23159542e-05 ... 8.94689467e-04\n 1.25940854e-03 1.63068483e-03]\n [4.29904059e-04 1.93328615e-05 6.54814039e-06 ... 1.64839160e-03\n 2.13542068e-03 2.35589431e-03]\n ...\n [6.47075532e-04 1.66824393e-04 8.87120987e-05 ... 3.96082287e-05\n 1.05672225e-04 3.33915959e-04]\n [1.23017374e-03 2.86122464e-04 1.53107394e-04 ... 1.31211258e-04\n 1.91692947e-04 7.43913813e-04]\n [3.76402633e-03 1.12431322e-03 6.25228800e-04 ... 8.41685629e-04\n 1.23549323e-03 3.16208438e-03]]\n\n [[1.24823116e-03 4.83557757e-04 2.33715793e-04 ... 3.39462655e-04\n 4.68665879e-04 1.17384817e-03]\n [2.35141517e-04 7.20135286e-05 3.65141896e-05 ... 2.84382870e-04\n 7.30881118e-04 1.26359216e-03]\n [1.11213492e-04 2.66721654e-05 2.78255429e-05 ... 4.85076307e-04\n 1.56331132e-03 2.75347452e-03]\n ...\n [5.72633173e-04 3.35470395e-04 1.95151515e-04 ... 1.91......View as Array
dims = {tuple} <class 'tuple'>: (32, 32, 32)
scale = {int} 1
translate = {tuple} <class 'tuple'>: (0, 0, 0)
dense_voxel_data = voxel_model.data.astype(int)
making all the array values to be zero
Traceback (most recent call last):
File "C:\Work\Pix2Vox\core\test.py", line 332, in test_3dreconstruct
vox.write(f)
File "C:\Work\Pix2Vox\utils\binvox_rw.py", line 103, in write
write(self, fp)
File "C:\Work\Pix2Vox\utils\binvox_rw.py", line 270, in write
voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()
File "C:\Users\703257542\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 639, in transpose
return _wrapfunc(a, 'transpose', axes)
File "C:\Users\703257542\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 56, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: axes don't match array
[INFO/MainProcess] process shutting down
Process finished with exit code 1
Sorry. This code does not belong to our repository.
Could you give me more information (e.g., Stacktrace)?
What the dimension ofdense_voxel_data?
Traceback (most recent call last):
File "C:\Work\Pix2Vox\core\test.py", line 332, in test_3dreconstruct
vox.write(f)
File "C:\Work\Pix2Vox\utils\binvox_rw.py", line 103, in write
write(self, fp)
File "C:\Work\Pix2Vox\utils\binvox_rw.py", line 270, in write
voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()
File "C:\Users\703257542\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 639, in transpose
return _wrapfunc(a, 'transpose', axes)
File "C:\Users\703257542\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 56, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: axes don't match array
[INFO/MainProcess] process shutting down
Process finished with exit code 1
So, what's the shape of dense_voxel_data?
If you cannot answer this question, please paste the whole code.
Because the code DOES NOT belong to the repository.
Shape is {tuple} <class 'tuple'>: (1, 32, 32, 32)
I think you should change
vox = utils.binvox_rw.Voxels(generated_volume.cpu().numpy(), (32,) * 3, (0,) * 3, 1, 'xyz')
to
vox = utils.binvox_rw.Voxels(generated_volume.cpu().squeeze().numpy(), (32,) * 3, (0,) * 3, 1, 'xyz')
Thanks Haozhe after using the squeeze method the binvox_rw write function works but the function
dense_voxel_data = voxel_model.data.astype(int) makes all the values to zero.
This is the generated volume which i am getting using your code which i am trying to write as a .binvox file :
generated_volume
tensor([[[[4.1267e-03, 9.6511e-04, 4.3114e-04, ..., 8.3494e-04,
1.1557e-03, 2.3458e-03],
[6.6642e-04, 7.6084e-05, 5.2316e-05, ..., 8.9469e-04,
1.2594e-03, 1.6307e-03],
[4.2990e-04, 1.9333e-05, 6.5481e-06, ..., 1.6484e-03,
2.1354e-03, 2.3559e-03],
...,
[6.4708e-04, 1.6682e-04, 8.8712e-05, ..., 3.9608e-05,
1.0567e-04, 3.3392e-04],
[1.2302e-03, 2.8612e-04, 1.5311e-04, ..., 1.3121e-04,
1.9169e-04, 7.4391e-04],
[3.7640e-03, 1.1243e-03, 6.2523e-04, ..., 8.4169e-04,
1.2355e-03, 3.1621e-03]],
[[1.2482e-03, 4.8356e-04, 2.3372e-04, ..., 3.3946e-04,
4.6867e-04, 1.1738e-03],
[2.3514e-04, 7.2014e-05, 3.6514e-05, ..., 2.8438e-04,
7.3088e-04, 1.2636e-03],
[1.1121e-04, 2.6672e-05, 2.7826e-05, ..., 4.8508e-04,
1.5633e-03, 2.7535e-03],
...,
[5.7263e-04, 3.3547e-04, 1.9515e-04, ..., 1.9168e-04,
1.8218e-04, 7.9509e-04],
[6.0061e-04, 5.0089e-04, 2.6987e-04, ..., 1.8754e-04,
2.5838e-04, 1.3682e-03],
[2.7935e-03, 1.0700e-03, 6.9801e-04, ..., 5.5089e-04,
1.1646e-03, 2.2660e-03]],
[[8.3342e-04, 3.7738e-04, 1.8523e-04, ..., 2.9103e-04,
4.1959e-04, 1.6692e-03],
[1.4831e-04, 7.3638e-05, 3.7141e-05, ..., 2.5092e-04,
5.0098e-04, 1.8103e-03],
[1.3346e-04, 3.0158e-05, 7.6942e-05, ..., 1.8579e-04,
5.9823e-04, 3.5739e-03],
...,
[7.3049e-04, 3.7956e-04, 1.9406e-04, ..., 8.2338e-04,
6.6436e-04, 1.4323e-03],
[1.0307e-03, 3.4426e-04, 2.3588e-04, ..., 4.8665e-04,
9.3873e-04, 2.4509e-03],
[4.1243e-03, 1.4817e-03, 8.8745e-04, ..., 3.8709e-04,
1.1723e-03, 3.3872e-03]],
...,
[[2.4786e-03, 1.4231e-03, 4.4256e-04, ..., 3.7579e-06,
1.0051e-05, 2.5806e-04],
[1.6049e-03, 1.5841e-03, 9.8844e-04, ..., 4.4138e-06,
2.1401e-05, 2.8819e-04],
[2.9380e-03, 3.8464e-03, 3.1284e-03, ..., 2.2055e-05,
8.3209e-05, 8.2161e-04],
...,
[1.6210e-03, 1.8717e-03, 1.0397e-03, ..., 1.2567e-05,
1.4342e-05, 5.8072e-05],
[1.9632e-03, 1.0356e-03, 5.5876e-04, ..., 8.2965e-05,
7.4242e-05, 8.1218e-05],
[3.1834e-03, 3.4522e-04, 1.2929e-04, ..., 1.5880e-04,
1.4074e-04, 3.0808e-04]],
[[2.0523e-03, 8.4266e-04, 3.1679e-04, ..., 1.0897e-05,
3.9238e-05, 5.3946e-04],
[1.1115e-03, 8.1393e-04, 2.8463e-04, ..., 8.8959e-06,
3.4940e-05, 4.2866e-04],
[2.0666e-03, 1.0217e-03, 7.7191e-04, ..., 1.7736e-05,
8.7457e-05, 1.4318e-03],
...,
[1.5367e-03, 1.2740e-03, 4.9352e-04, ..., 1.6640e-05,
2.0056e-05, 7.8161e-05],
[2.2363e-03, 7.7593e-04, 3.9221e-04, ..., 5.8742e-05,
4.2468e-05, 6.9234e-05],
[3.4652e-03, 6.4622e-04, 2.0349e-04, ..., 1.2668e-04,
1.2545e-04, 3.7705e-04]],
[[2.5710e-03, 3.6081e-04, 2.0609e-04, ..., 2.5336e-04,
3.6796e-04, 1.9725e-03],
[8.5946e-04, 1.9003e-04, 1.0290e-04, ..., 4.4002e-05,
1.2250e-04, 1.5619e-03],
[7.8602e-04, 3.1345e-04, 1.7823e-04, ..., 4.9947e-05,
1.5558e-04, 2.5232e-03],
...,
[1.7887e-03, 2.9945e-04, 1.7562e-04, ..., 1.0260e-04,
7.1047e-05, 3.2550e-04],
[2.1848e-03, 5.2423e-04, 3.6802e-04, ..., 1.3060e-04,
1.1143e-04, 4.2711e-04],
[6.4037e-03, 2.3628e-03, 1.9670e-03, ..., 2.5904e-04,
3.4433e-04, 1.8915e-03]]]])
After using dense_voxel_data = voxel_model.data.astype(int)
dense_voxel_data = {ndarray} [[[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n ...\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]]...
the .binvox files are of only 310bytes. when i try to visualize nothing is there. what am i doing wrong my test_net(test_3dreconstructor) has any mistakes/issues?
Change
generated_volume.cpu().numpy()
to
th = 0.5
generated_volume.ge(th).cpu().numpy()
Still the same Haozhe. I see the values in generated volume are quite small and in fact they are lesser that 1. Just to add to that i wanted to ask that i am using 4 images and have kept N_VIEWS_RENDERING as 4 but still it is generating 4 .binvox files.
After using threshold the vox values become zeros nd without that they are in the range of 10^-3 (pretty small values say 0.0043, 0.00097 ........)
Sorry. I cannot understand what you are talking about.
First, I think the solution mentioned above is absolutely correct.
Second, please tell me the shape of generated_volume.
Of course, the values are all below ones. Because I applied a sigmoid function before output.
THEY ARE PROBABLITIES!!
shape = {Size} torch.Size([1, 32, 32, 32])
As I mentioned above, please use torch.squeeze().
Please refer to https://pytorch.org/docs/stable/torch.html#torch.squeeze
Yes Haozhe i am using squeeze as you had suggested. Sorry for troubling you so much.
generated_volume is of size (1, 32, 32, 32) but as per your suggestions i am using
vox = utils.binvox_rw.Voxels(generated_volume.cpu(th).squeeze().numpy(), (32,) * 3, (0,) * 3, 1, 'xyz')
which makes vox as (32,32,32)
What's this?
generated_volume.cpu(th).squeeze().numpy()
I am trying to write the generated volume as per your code in test_net
with torch.no_grad():
# Get data from data loader
rendering_images = utils.network_utils.var_or_cuda(rendering_images)
#ground_truth_volume = utils.network_utils.var_or_cuda(ground_truth_volume)
# Test the encoder, decoder, refiner and merger
image_features = encoder(rendering_images)
raw_features, generated_volume = decoder(image_features)
if cfg.NETWORK.USE_MERGER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_MERGER:
generated_volume = merger(raw_features, generated_volume)
else:
generated_volume = torch.mean(generated_volume, dim=1)
#encoder_loss = bce_loss(generated_volume, ground_truth_volume) * 10
if cfg.NETWORK.USE_REFINER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_REFINER:
generated_volume = refiner(generated_volume)
#refiner_loss = bce_loss(generated_volume, ground_truth_volume) * 10
#else:
#refiner_loss = encoder_loss
#Write generated volume
output_file_path = cfg.DATASETS.MYDATA.OUTPUT_PATH % (taxonomy_id, sample_name)
with open(output_file_path, 'wb') as f:
th = 15
vox = utils.binvox_rw.Voxels(generated_volume.cpu(th).squeeze().numpy(), (32,) * 3, (0,) * 3, 1, 'xyz')
vox.write(f)
I never told you to write code like that.
Please look at the following code CAREFULLY.
Change
generated_volume.cpu().numpy()
to
th = 0.5
generated_volume.ge(th).cpu().numpy()
Originally posted by @hzxie in #22 (comment)
If you don't understand torch.ge(), please refer to the PyTorch document.
You are rite Haozhe. While typing above i made a mistake. Actually i am using the below code
with open(output_file_path, 'wb') as f:
th = 0.5
vox = utils.binvox_rw.Voxels(generated_volume.ge(th).cpu().squeeze().numpy(), (32,) * 3, (0,) * 3, 1, 'xyz')
vox.write(f)
i am using squeeze as per your suggestions to change the generated volume shape from (1, 32, 32, 32) to vox size (32, 32, 32)
The challenge i am facing is the generated_volume values (probablities) are too low in the order of 10^-3.
Haozhe i tried debugging using Shapenet data even and i am getting similar generated_volume values and i feel its rite and there may be an issue in the way i am writing the voxels using binvox_rw
Haozhe i made the changes in your code
Change
generated_volume.cpu().numpy()
to
th = 0.5
generated_volume.ge(th).cpu().numpy()
I was making changes in the write voxel code which i added (which was not helping) but now i made changes in your code:
Append generated volumes to TensorBoard
if output_dir and sample_idx < 3:
img_dir = output_dir % 'images'
# Volume Visualization
th = 0.2
gv = generated_volume.ge(th).cpu().numpy()
rendering_views = utils.binvox_visualization.get_volume_views(gv, os.path.join(img_dir, 'test'),
epoch_idx)
Now after changing th = 0.2 i am able to see voxel image in the test folder but not appropriate one. I am trying to debug further