Closed zyz-notebooks closed 4 years ago
You could modify the data generation code this repository provides for your needs. I don't use any human specific information for data generation, so it can be used for any objects including shapenet.
thank you very much
You could modify the data generation code this repository provides for your needs. I don't use any human specific information for data generation, so it can be used for any objects including shapenet. hi, I have run the first step of data generation according to what you advice, but when I run the second step, I encountered the following error, do you have any suggestions
python -m apps.render_data -i /home/zyz/desktop/PIFu/data/2c8b8a58ecffdaa17b9c6deef486a7d8 -o /home/zyz/desktop/PIFu/data/training_data_1
mesh_file: /home/zyz/desktop//PIFu/data/2c8b8a58ecffdaa17b9c6deef486a7d8/model.obj
prt_file: /home/zyz/desktop/PIFu/data/2c8b8a58ecffdaa17b9c6deef486a7d8/bounce/bounce0.txt
face_prt_file: /home/zyz/desktop/PIFu/data/2c8b8a58ecffdaa17b9c6deef486a7d8/bounce/face.npy
text_file/home/zyz/desktop/PIFu/data/2c8b8a58ecffdaa17b9c6deef486a7d8/images/texture0.jpg
Traceback (most recent call last):
File "/home/zyz/anaconda3/envs/PIFU/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/zyz/anaconda3/envs/PIFU/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/zyz/desktop/PIFu/apps/render_data.py", line 294, in
File "/home/zyz/desktop/PIFu/lib/renderer/gl/prt_render.py", line 102, in set_mesh
self.uv_data[mat_name] = uvs[faces_uvs.reshape([-1])]
IndexError: arrays used as indices must be of integer (or boolean) type
I see. Seems like your topology fix by ManifoldPlus might have destroyed uv mapping. I'd suggest the following:
Sincere thanks, I will try it
Hello, I am lucky to find a data set containing watertight mesh, and successfully performed the data generation steps, but encountered the following problems during training
python -m apps.train_shape --dataroot /home/zyz/desktop/PIFu/data/training_data_1 --random_flip --random_scale --random_trans Name: example | Epoch: 0 | 350/360 | Err: 0.000200 | LR: 0.001000 | Sigma: 5.00 | dataT: 0.00266 | netT: 0.26418 | ETA: 00:02 calc error (test) ... 100%|█████████████████████████████████████████| 100/100 [00:10<00:00, 9.91it/s] eval test MSE: 0.000000 IOU: 0.000000 prec: 0.000000 recall: 0.000000 calc error (train) ... 100%|█████████████████████████████████████████| 100/100 [00:10<00:00, 9.89it/s] eval train MSE: 0.000000 IOU: 0.000000 prec: 0.000000 recall: 0.000000 generate mesh (test) ... 0%| | 0/1 [00:00<?, ?it/s]error cannot marching cubes cannot unpack non-iterable int object Can not create marching cubes at this time. 100%|█████████████████████████████████████████████| 1/1 [00:05<00:00, 5.12s/it] generate mesh (train) ... 0%| | 0/1 [00:00<?, ?it/s]error cannot marching cubes cannot unpack non-iterable int object Can not create marching cubes at this time.
Please take a look at #23
Thank you very much for your reply. I would also like to ask you the following. How can I set it up for batch training? I cannot accept a set of data for each training. I look forward to your reply.
The code already supports batch training. You just need to set --batch_size
.
Hi, when I trained on the data rendering in the data set. The result was not very good. The IOU always maintained at 0.1, and when I tried to run the demo with training model, it hint no find the net_g, but I have put net_c and net_g in the correct dircetory, do you have any suggestions, I should make those setting to fit my data set, thank you for your continued answer, which have helped me a lot
the data set mainly contain table, chair, sofa, etc
您好,很幸运找到了一个包含watertight mesh的数据集,并且成功执行了数据生成步骤,但是在训练的时候遇到了如下问题
python -m apps.train_shape --dataroot /home/zyz/desktop/PIFu/data/training_data_1 --random_flip --random_scale --random_trans 名称:示例 | 时代:0 | 350/360 | 错误:0.000200 | LR: 0.001000 | 西格玛:5.00 | 数据T:0.00266 | 净T:0.26418 | ETA:00:02 计算错误(测试)... 100%|██████████████████████████████████ ███████| 100/100 [00:10<00:00, 9.91it/s] 评估测试 MSE: 0.000000 IOU: 0.000000 prec: 0.000000 召回率: 0.000000 calc error (train) ... 100%|████████ █████████████████████████████████| 100/100 [00:10<00:00, 9.89it/s] eval train MSE:0.000000 IOU:0.000000 prec:0.000000 召回:0.000000 生成网格(测试)... 0%| | 0/1 [00:00<?, ?it/s]error cannot marching cubes cannot unpack non-iterable int object 目前无法创建行进立方体。 100%|█████████████████████████████████████████████| 1/1 [00:05<00:00, 5.12s/it] 生成网格 (train) ... 0%| | 0/1 [00:00<?, ?it/s]error cannot marching cubes cannot unpack non-iterable int object 目前无法创建行进立方体。
Hello, how did you use this dataset for data generation? Need to change the program in traindataset.py? What is the format of the training dataset?
Thank you very much for your work. Now I can convert the mesh in the shapenet data to a watertight mesh through ManifoldPlus, but I don’t know how to set up the data set structure for training. If you can give some advice, I would appreciate it.