(PG2023/CGF) This is the official PyTorch implementation of PG2023/CGF paper: GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions
I find that in edit_widget.py, there are some functions about shadow, and I also check the paper ShadowDraw. My problem is, is the Data Generation step necessary? Is it for building a shadow dataset to guide the users? Can you provide thoes images or I have to build it myself?
Many thanks!
Sry to trouble you again. When I try to do evaluations based on the pretrained model on OneDrive, KeyError occurs:
(gas) vic@PC:~/GAS/GA-Sketching$ python generate_single.py -cat airplane -checkpoint 200
initialize network with normal
Loaded checkpoint from: /home/vic/GAS/GA-Sketching/core/../experiments/GASv64_airplane/checkpoints/checkpoint_epoch_200.tar
**checkpoint keys**: odict_keys(['volume_encoder.conv_in_0.weight', 'volume_encoder.conv_in_0.bias', 'volume_encoder.conv_in_1.weight', 'volume_encoder.conv_in_1.bias', 'volume_encoder.conv_in_bn.weight', 'volume_encoder.conv_in_bn.bias', 'volume_encoder.conv_in_bn.running_mean', 'volume_encoder.conv_in_bn.running_var', 'volume_encoder.conv_in_bn.num_batches_tracked', 'ifnet_encoder.conv_0.weight', 'ifnet_encoder.conv_0.bias', 'ifnet_encoder.conv_0_1.weight', 'ifnet_encoder.conv_0_1.bias', 'ifnet_encoder.conv_1.weight', 'ifnet_encoder.conv_1.bias', 'ifnet_encoder.conv_1_1.weight', 'ifnet_encoder.conv_1_1.bias', 'ifnet_encoder.conv_2.weight', 'ifnet_encoder.conv_2.bias', 'ifnet_encoder.conv_2_1.weight', 'ifnet_encoder.conv_2_1.bias', 'ifnet_encoder.conv_3.weight', 'ifnet_encoder.conv_3.bias', 'ifnet_encoder.conv_3_1.weight', 'ifnet_encoder.conv_3_1.bias', 'ifnet_encoder.conv0_1_bn.weight', 'ifnet_encoder.conv0_1_bn.bias', 'ifnet_encoder.conv0_1_bn.running_mean', 'ifnet_encoder.conv0_1_bn.running_var', 'ifnet_encoder.conv0_1_bn.num_batches_tracked', 'ifnet_encoder.conv1_1_bn.weight', 'ifnet_encoder.conv1_1_bn.bias', 'ifnet_encoder.conv1_1_bn.running_mean', 'ifnet_encoder.conv1_1_bn.running_var', 'ifnet_encoder.conv1_1_bn.num_batches_tracked', 'ifnet_encoder.conv2_1_bn.weight', 'ifnet_encoder.conv2_1_bn.bias', 'ifnet_encoder.conv2_1_bn.running_mean', 'ifnet_encoder.conv2_1_bn.running_var', 'ifnet_encoder.conv2_1_bn.num_batches_tracked', 'ifnet_encoder.conv3_1_bn.weight', 'ifnet_encoder.conv3_1_bn.bias', 'ifnet_encoder.conv3_1_bn.running_mean', 'ifnet_encoder.conv3_1_bn.running_var', 'ifnet_encoder.conv3_1_bn.num_batches_tracked', 'fc_0.weight', 'fc_0.bias', 'fc_1.weight', 'fc_1.bias', 'fc_2.weight', 'fc_2.bias', 'fc_out.weight', 'fc_out.bias'])
Traceback (most recent call last):
File "/home/vic/GAS/GA-Sketching/generate_single.py", line 23, in <module>
gen = Generator(net,
File "/home/vic/GAS/GA-Sketching/core/generator.py", line 17, in __init__
self.load_checkpoint(checkpoint)
File "/home/vic/GAS/GA-Sketching/core/generator.py", line 91, in load_checkpoint
self.model.load_state_dict(checkpoint['model_state_dict'])
KeyError: 'model_state_dict'
I print the checkpoint keys, is there something wrong with the way I load checkpoint?
I then change the code for loading the checkpoint to:
#self.model.load_state_dict(checkpoint['model_state_dict'])
state_dict = self.model.state_dict()
for k1, k2 in zip(state_dict.keys(), checkpoint.keys()):
state_dict[k1] = checkpoint[k2]
self.model.load_state_dict(state_dict)
Then seems everything works fine, but then another issue occurs:
(gas) vic@PC:~/GAS/GA-Sketching$ python generate_single.py -cat airplane -checkpoint 200
initialize network with normal
Loaded checkpoint from: /home/vic/GAS/GA-Sketching/core/../experiments/GASv64_airplane/checkpoints/checkpoint_epoch_200.tar
experiments/GASv64_airplane/evaluation_200@128_1v/
0it [00:00, ?it/s]
Traceback (most recent call last):
File "/home/vic/GAS/GA-Sketching/generate_single.py", line 31, in <module>
gen_iterator(out_path, dataset, gen)
File "/home/vic/GAS/GA-Sketching/core/generation_iterator.py", line 19, in gen_iterator
for i, data in tqdm(enumerate(loader)):
File "/home/vic/anaconda3/envs/gas/lib/python3.9/site-packages/tqdm/std.py", line 1182, in __iter__
for obj in iterable:
File "/home/vic/anaconda3/envs/gas/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
data = self._next_data()
File "/home/vic/anaconda3/envs/gas/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 721, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/vic/anaconda3/envs/gas/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/vic/anaconda3/envs/gas/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/vic/GAS/GA-Sketching/core/data/voxelized_data_shapenet.py", line 225, in __getitem__
a0, e0, a1, e1, a2, e2 = self.dict[uid]
KeyError: '02691156/1b8e84935fdc3ec82be289de70e8db31'
Here I use the sketch you provided yesterday, I copy their uid to create a .lst file, I thought it might point to the sketch data you provided. My understanding about this program: the input is image and output is a corresponding mesh, so actually what I am confused here is where should I put my reference image for the model? What is the format of the input data?
Thanks!
Thanks for your excellent work!!! Could you please tell me the method for rendering the mesh results in the paper?
Dear author:
I want to ask the pytorch3d version if I use pip(ERROR: Could not find a version that satisfies the requirement pytorch3d==2.31 (from versions: 0.1.1, 0.2.0, 0.2.5, 0.3.0));
And I want to use my own mesh data, should I use the the 'mesh-fusion' to change my mesh to 'xxx.off' ? The normal '.obj' mesh can not work?
I am looking forward to your reply!
Dear author：
After I generated the sketches by your script, I want to compare to 2d color-img rendered by 3D mesh. Do you have the script to get the rendered 2d img? I would greatly appreciate it.
self.model.merger.load_state_dict(checkpoint['model_state_dict'])
KeyError: 'model_state_dict'
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