Hi，I had some questions during the joint training.In the process of 2D-3D single-class chair-based dataset training, the default image and voxel have separate file addresses. If we do multi-class joint training, do we need to write the addresses of all six categories of folders, or do we no longer need labels, and directly put the six categories of documents that need training on a single file, that is, the following Those two are the correct instructions for joint training.

one

parser.add_argument('-d','--data', default=['data/voxels/sofa','data/voxels/table','data/voxels/boat','data/voxels/car','data/voxels/plane','data/voxels/chair'], help ='The location for the object voxel models.' ) parser.add_argument('-i','--images', default=['data/overlays/sofa','data/overlays/table','data/overlays/boat','data/overlays/car','data/overlays/plane','data/overlays/chair'], help ='The location for the images.' )

another

parser.add_argument('-d','--data', default=['data/voxels/joint',], help ='The location for the object voxel models.' ) parser.add_argument('-i','--images', default=['data/overlays/joint',], help ='The location for the images.' )

  I don't know which one can be used, or my understanding of joint training is biased. I hope you can help me. Thank you.

downloading

rendering

2018-07-05 22:18:20.745281 Image set number: 0 0it [00:00, ?it/s] 2018-07-05 22:18:20.867954 Image set number: 1 0it [00:00, ?it/s]

When running the function binvox(), DataSetPrep.py reports an error saying that some buffers can be used under Linux only. I copied the specific command just to solve the path problem, but now I'm not sure if it can still succeed under Window environment.

Here is the code I write:

command = "binvox -d 100 -pb data/objects/chair/6db2255a51caf84e823e7e244bf84209.obj"
os.system('%s %s' % (command, ''))

And here is part of the error report:

...... Read 6840 faces, 1774 vertices. Mesh memory use is 17888 KB (17 MB) Mesh::normalize, bounding box: [-0.224797, -0.421669, -0.147179, 1] - [0.224797, 0.421669, 0.147179, 1] longest length: 0.843338 normalization transform: (1) translate [0.224797, 0.421669, 0.147179, 1], (2) scale 1.18576, (3) translate [0, 0, 0] got mesh bounding box [-0.224797, -0.421669, -0.147179, 1] - [0.224797, 0.421669, 0.147179, 1]

voxel model dimension: 200 Error: sorry, off-screen buffers can be used under Linux only

Hello, I rendered pictures of multi views for 3D models with Blender. The versions of Blender is 2.79. And I want to speed the process up via GPU settings in Blender. I removed the annotations of several lines of code in render_model_views.py as follow:

bpy.context.user_preferences.addons['cycles'].preferences.compute_device_type = 'CUDA'
bpy.context.user_preferences.addons['cycles'].preferences.devices[0].use = True
bpy.context.scene.cycles.device = 'GPU'
bpy.ops.render.render(True)

I run the DataSetPrep.py, but when I check the process in GPU card, I can't find blender. And is there any method to solve this problem? Thank you!

When I was converting the dataset according to 3D-Reconstruction-Kinect/3D_generation.sh, I met the problem running this file: convert_shapenet10.py. Here is the error message:

Traceback (most recent call last): File "convert_shapenet10.py", line 5, in from path import Path ModuleNotFoundError: No module named 'path'

Although I googled it many times, but I still don't know how to solve it, 'cause it seems that there is no such module called "path". Does it matter if not running this file? It seems important..

My version is python 3.6 by the way, with tensorflow 1.13.1 in Windows system

Hi Edward,

 I am a student who is attending an AI hackathon together with other students (I am just a beginner). I would like to use your code as our team code base, to accelerate the code and interact with AI at scale. We will surely cite your paper, but we need to get your permission to do so. Also, we are glad to receive other advices or warnings if you have. 

 Thank you very much!

Best Regards,

Weicheng

Hi,

We are trying to recreate the results on the chairs dataset. Everything works OK, except the GPU utilization is very low (<5%) and the CPU is non stop on 100%. Is this correct? Otherwise with these specs, we are looking for one month of training.

I've included short log from beginning of training process below.

tf-version: 1.12 os: Windows 10, cpu: i7-7700 CPU @ 3.60 GHz gpu: GeForce GTX 1080

(3dspacegen) D:\3dspacegen\vae_gan\3D-Reconstruction-Image>python 20-VAE-3D-IWGAN.py -n chair -d data\\voxels\\chair -i data\\overlays\\chair
Using TensorFlow backend.
[2019-03-29 14:12:51,790] [tl_logging] [WARNING]: WARNING: Function: `tensorlayer.activation.leaky_relu` (in file: D:\miniconda\envs\3dspacegen\lib\site-packages\tensorlayer\activation.py) is deprecated and will be removed after 2018-09-30.
Instructions for updating: This API is deprecated. Please use as `tf.nn.leaky_relu`

[2019-03-29 14:12:52,118] [tl_logging] [WARNING]: WARNING: Function: `tensorlayer.layers.utils.set_name_reuse` (in file: D:\miniconda\envs\3dspacegen\lib\site-packages\tensorlayer\layers\utils.py) is deprecated and will be removed after 2018-06-30.
Instructions for updating: TensorLayer relies on TensorFlow to check name reusing

[2019-03-29 14:12:52,118] [tl_logging] [WARNING]: WARNING: this method is DEPRECATED and has no effect, please remove it from your code.
[2019-03-29 14:12:52,352] [tl_logging] [WARNING]: WARNING: this method is DEPRECATED and has no effect, please remove it from your code.
[2019-03-29 14:12:52,539] [tl_logging] [WARNING]: WARNING: this method is DEPRECATED and has no effect, please remove it from your code.
[2019-03-29 14:12:52,711] [tl_logging] [WARNING]: WARNING: this method is DEPRECATED and has no effect, please remove it from your code.
[2019-03-29 14:12:52,852] [tl_logging] [WARNING]: WARNING: this method is DEPRECATED and has no effect, please remove it from your code.
[2019-03-29 14:12:53,820] [tl_logging] [WARNING]: WARNING: this method is DEPRECATED and has no effect, please remove it from your code.
2019-03-29 14:12:55.576779: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-03-29 14:12:55.717923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-29 14:12:55.721467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]
2019-03-29 14:12:55.821546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.60GiB
2019-03-29 14:12:55.827285: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-29 14:12:56.189054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-29 14:12:56.193933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-03-29 14:12:56.196421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-03-29 14:12:56.199839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8175 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
Number of train images: 14482
Epoch: [ 0/1500] [   0/  56] time: 70.5338, d_loss: 9.9828, g_loss: 30.3922, v_loss: 0.9569, r_loss: 0.3035
Epoch: [ 0/1500] [   1/  56] time: 24.3561, d_loss: 9.9533, g_loss: 30.3922, v_loss: 0.8012, r_loss: 0.2749

Hello, in your paper 3D-VAE-IWGAN, you tested your model over IKEA Dataset and scored with Avarage Precision for comparing to 3D-VAE-GAN. I know that the evaluation critic is introduced from Girdhar , however i am not finding the clear definition. So could tell me the definition of Average Precision? Thank you!

Hi, Thanks for your code, When I trained 3D-VAE-IWGAN, a error happened around of "rander_graphs":

Traceback (most recent call last): File "20-VAE-3D-IWGAN.py", line 162, in track_valid_loss) # this will only work after a 50 iterations to allows for proper averating File "../scripts/GANutils.py", line 224, in render_graphs plt.plot(track_d_loss_iter, smoothed_d_loss, color = 'red') File "/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.py", line 3261, in plot ret = ax.plot(*args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/matplotlib/init.py", line 1717, in inner return func(ax, *args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.py", line 1372, in plot for line in self._get_lines(*args, **kwargs): File "/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 404, in _grab_next_args for seg in self._plot_args(this, kwargs): File "/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 384, in _plot_args x, y = self._xy_from_xy(x, y) File "/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 243, in _xy_from_xy "have shapes {} and {}".format(x.shape, y.shape)) ValueError: x and y must have same first dimension, but have shapes (56,) and (136,)

And I printed the length of "track_d_loss_iter" and "smoothed_d_loss", the length of "track_d_loss_iter" equals to(training set number / batch_size). The length of "smoothed_d_loss" comes from the function "def savitzky_golay(y, window_size, order, deriv=0, rate=1):". I have tried my best to understand the function, however, I could not fix this bug by myself. And, could you help me: Thanks!

thanks for your code.

FYI

tl.ops.set_gpu_fraction(sess=sess, gpu_fraction=0.998) AttributeError: 'module' object has no attribute 'ops'

Apparently ops was replaced with util and the first argument is no longer necessary.

Hi, If i use 64 * 64 * 64 size volume as input and set output_size=64, wheather "discriminator" in the models.py should be adjusted or not ? thanks！

If u take a look at where the batch_images are defines, it is in the 2nd for loop at line 122 and in line 144 the batch_images are not defined any more. Can you please correct this..

When I run you item, I get this error.

Traceback (most recent call last): File "E:/GAN/7-3D-IWGAN-master/3D-IWGAN-master/3D-Generation/32-3D-Gan.py", line 49, in net_g , G_train = generator_32(z, is_train=True, reuse = False, sig= True, batch_size=args.batchsize) File "E:\GAN\7-3D-IWGAN-master\3D-IWGAN-master\scripts\models.py", line 74, in generator_32 net_2 = Deconv(net_1, gf_dim, eighth, '2', batch_size) File "E:\GAN\7-3D-IWGAN-master\3D-IWGAN-master\scripts\models.py", line 20, in Deconv act=tf.identity, name='g/net_' + net + '/deconv') File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorlayer\layers.py", line 1512, in init self.outputs = act( tf.nn.conv3d_transpose(self.inputs, W, output_shape=output_shape, strides=strides, padding=padding) + b ) File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2473, in conv3d_transpose name=name) File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2539, in conv3d_transpose_v2 name=name) File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 2237, in conv3d_backprop_input_v2 dilations=dilations, name=name) File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 627, in _apply_op_helper param_name=input_name) File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 61, in _SatisfiesTypeConstraint ", ".join(dtypes.as_dtype(x).name for x in allowed_list))) TypeError: Value passed to parameter 'input_sizes' has DataType float32 not in list of allowed values: int32, int64

I find this error raise becaues 'Conv3DBackpropInputV2' not has float32 dtype, I don't konw why. Do you have some ideas for this error.

By the why, what tensorflow and tensorlayer version do you used?