RuntimeError: shape '[52, 128, 128]' is invalid for input of size 212992 RuntimeError: Caught RuntimeError in DataLoader worker process 0.

[bwtine]

I downloaded link "The Neural Rasterizer :image size 128x128"，"The Image Super-resolution model image size 128x128 -> 256x256"，"The Main model image size 128x128"，and put them in the corresponding folder，To train our main model, run python main.py --mode train --experiment_name dvf --model_name main_model , The following error appears

(dvf) miao@miao:~/data/file/bwt/deepvecfont-master$ python main.py --mode train --experiment_name dvf --model_name main_model
Training on experiment dvf_main_model...
Loading data/vecfont_dataset_pkls/train/train_all.pkl pickle file ...
Finished loading pkls
Loading data/vecfont_dataset_pkls/test/test_all.pkl pickle file ...
Finished loading pkls
Traceback (most recent call last):
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 351, in
main()
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 345, in main
train(opts)
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 320, in train
train_main_model(opts)
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 99, in train_main_model
for idx, data in enumerate(train_loader):
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 521, in next
data = self._next_data()
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/_utils.py", line 425, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/miao/data/file/bwt/deepvecfont-master/dataloader.py", line 52, in getitem
item['rendered'] = torch.FloatTensor(cur_glyph['rendered']).view(self.char_num, self.img_size, self.img_size) / 255.
RuntimeError: shape '[52, 128, 128]' is invalid for input of size 212992

The following error occurs when I change image_size default=128 in line 13 of options.py to 64

(dvf) miao@miao:~/data/file/bwt/deepvecfont-master$ python main.py --mode train --experiment_name dvf --model_name main_model
Training on experiment dvf_main_model...
Loading data/vecfont_dataset_pkls/train/train_all.pkl pickle file ...
Finished loading pkls
Loading data/vecfont_dataset_pkls/test/test_all.pkl pickle file ...
Finished loading pkls
Traceback (most recent call last):
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 351, in
main()
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 345, in main
train(opts)
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 320, in train
train_main_model(opts)
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 64, in train_main_model
neural_rasterizer.load_state_dict(torch.load(neural_rasterizer_fpath))
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1406, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for NeuralRasterizer:
Unexpected key(s) in state_dict: "decode.21.weight", "decode.21.bias", "decode.19.weight", "decode.19.bias".
size mismatch for decode.0.weight: copying a param with shape torch.Size([1024, 1024, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 512, 3, 3]).
size mismatch for decode.0.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for decode.1.weight: copying a param with shape torch.Size([1024, 2, 2]) from checkpoint, the shape in current model is torch.Size([512, 2, 2]).
size mismatch for decode.1.bias: copying a param with shape torch.Size([1024, 2, 2]) from checkpoint, the shape in current model is torch.Size([512, 2, 2]).
size mismatch for decode.3.weight: copying a param with shape torch.Size([1024, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 256, 3, 3]).
size mismatch for decode.3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for decode.4.weight: copying a param with shape torch.Size([512, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 4, 4]).
size mismatch for decode.4.bias: copying a param with shape torch.Size([512, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 4, 4]).
size mismatch for decode.6.weight: copying a param with shape torch.Size([512, 256, 5, 5]) from checkpoint, the shape in current model is torch.Size([256, 128, 5, 5]).
size mismatch for decode.6.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decode.7.weight: copying a param with shape torch.Size([256, 8, 8]) from checkpoint, the shape in current model is torch.Size([128, 8, 8]).
size mismatch for decode.7.bias: copying a param with shape torch.Size([256, 8, 8]) from checkpoint, the shape in current model is torch.Size([128, 8, 8]).
size mismatch for decode.9.weight: copying a param with shape torch.Size([256, 128, 5, 5]) from checkpoint, the shape in current model is torch.Size([128, 64, 5, 5]).
size mismatch for decode.9.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for decode.10.weight: copying a param with shape torch.Size([128, 16, 16]) from checkpoint, the shape in current model is torch.Size([64, 16, 16]).
size mismatch for decode.10.bias: copying a param with shape torch.Size([128, 16, 16]) from checkpoint, the shape in current model is torch.Size([64, 16, 16]).
size mismatch for decode.12.weight: copying a param with shape torch.Size([128, 64, 5, 5]) from checkpoint, the shape in current model is torch.Size([64, 32, 5, 5]).
size mismatch for decode.12.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for decode.13.weight: copying a param with shape torch.Size([64, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 32, 32]).
size mismatch for decode.13.bias: copying a param with shape torch.Size([64, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 32, 32]).
size mismatch for decode.15.weight: copying a param with shape torch.Size([64, 32, 5, 5]) from checkpoint, the shape in current model is torch.Size([32, 16, 5, 5]).
size mismatch for decode.15.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for decode.16.weight: copying a param with shape torch.Size([32, 64, 64]) from checkpoint, the shape in current model is torch.Size([16, 64, 64]).
size mismatch for decode.16.bias: copying a param with shape torch.Size([32, 64, 64]) from checkpoint, the shape in current model is torch.Size([16, 64, 64]).
size mismatch for decode.18.weight: copying a param with shape torch.Size([32, 16, 5, 5]) from checkpoint, the shape in current model is torch.Size([1, 16, 7, 7]).
size mismatch for decode.18.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([1]).

What is the problem and how can I fix it？Thank you！！

[yizhiwang96]

I assumed that you changed read_mode in options.py to pkl. The pkl file contains only 64*64 images, you can change the image_size to 64 in options.py or generate the dataset with 128x128 images by yourself.

[bwtine]

Thank you very much for your reply.
I downloaded the vecfont_dataset_pkls dir and put it under ./data/, set read_mode = pkl, in options.py, changed image_size default=128 in line 13 of options.py to 64
And download link: the dvf_neural_raster dir [image size 64x64],the image_sr dir[image size 64x64 -> 256x256],the dvf_main_model dir and put them under ./experiments/.
Then run python main.py --mode train --experiment_name dvf --model_name main_model,but the following error occurs .What is the problem and how can I fix it？It seems that the default main model is 128，do I need to modify configuration parameters？
And if i want use 128 size, what command should i use to generate the dataset with 128x128 images by myself? Thank you！！

python main.py --mode train --experiment_name dvf --model_name main_model
Training on experiment dvf_main_model...
Loading data/vecfont_dataset_pkls/train/train_all.pkl pickle file ...
Finished loading pkls
Loading data/vecfont_dataset_pkls/test/test_all.pkl pickle file ...
Finished loading pkls
Traceback (most recent call last):
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 351, in
main()
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 345, in main
train(opts)
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 320, in train
train_main_model(opts)
File "/home/miao/data/file/bwt/deepvecfont-master/main.py", line 64, in train_main_model
neural_rasterizer.load_state_dict(torch.load(neural_rasterizer_fpath))
File "/home/miao/data/soft/python/anaconda/install/envs/dvf/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1406, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for NeuralRasterizer:
Unexpected key(s) in state_dict: "decode.21.weight", "decode.21.bias", "decode.19.weight", "decode.19.bias".
size mismatch for decode.0.weight: copying a param with shape torch.Size([1024, 1024, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 512, 3, 3]).
size mismatch for decode.0.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for decode.1.weight: copying a param with shape torch.Size([1024, 2, 2]) from checkpoint, the shape in current model is torch.Size([512, 2, 2]).
size mismatch for decode.1.bias: copying a param with shape torch.Size([1024, 2, 2]) from checkpoint, the shape in current model is torch.Size([512, 2, 2]).
size mismatch for decode.3.weight: copying a param with shape torch.Size([1024, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 256, 3, 3]).
size mismatch for decode.3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for decode.4.weight: copying a param with shape torch.Size([512, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 4, 4]).
size mismatch for decode.4.bias: copying a param with shape torch.Size([512, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 4, 4]).
size mismatch for decode.6.weight: copying a param with shape torch.Size([512, 256, 5, 5]) from checkpoint, the shape in current model is torch.Size([256, 128, 5, 5]).
size mismatch for decode.6.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decode.7.weight: copying a param with shape torch.Size([256, 8, 8]) from checkpoint, the shape in current model is torch.Size([128, 8, 8]).
size mismatch for decode.7.bias: copying a param with shape torch.Size([256, 8, 8]) from checkpoint, the shape in current model is torch.Size([128, 8, 8]).
size mismatch for decode.9.weight: copying a param with shape torch.Size([256, 128, 5, 5]) from checkpoint, the shape in current model is torch.Size([128, 64, 5, 5]).
size mismatch for decode.9.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for decode.10.weight: copying a param with shape torch.Size([128, 16, 16]) from checkpoint, the shape in current model is torch.Size([64, 16, 16]).
size mismatch for decode.10.bias: copying a param with shape torch.Size([128, 16, 16]) from checkpoint, the shape in current model is torch.Size([64, 16, 16]).
size mismatch for decode.12.weight: copying a param with shape torch.Size([128, 64, 5, 5]) from checkpoint, the shape in current model is torch.Size([64, 32, 5, 5]).
size mismatch for decode.12.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for decode.13.weight: copying a param with shape torch.Size([64, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 32, 32]).
size mismatch for decode.13.bias: copying a param with shape torch.Size([64, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 32, 32]).
size mismatch for decode.15.weight: copying a param with shape torch.Size([64, 32, 5, 5]) from checkpoint, the shape in current model is torch.Size([32, 16, 5, 5]).
size mismatch for decode.15.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for decode.16.weight: copying a param with shape torch.Size([32, 64, 64]) from checkpoint, the shape in current model is torch.Size([16, 64, 64]).
size mismatch for decode.16.bias: copying a param with shape torch.Size([32, 64, 64]) from checkpoint, the shape in current model is torch.Size([16, 64, 64]).
size mismatch for decode.18.weight: copying a param with shape torch.Size([32, 16, 5, 5]) from checkpoint, the shape in current model is torch.Size([1, 16, 7, 7]).
size mismatch for decode.18.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([1]).

[yizhiwang96]

Please check if here reads the correct neural rasterizer checkpoint (64x64 version you wanted). To make you own dataset with 128*128 reslotion, please see Customize your own dataset in readme.md. You can set the resolution when rendering images.

[bwtine]

Thank you very much for your sound and timely advice, I solved this problem！！！

[bwtine]

Thank you sincerely for your previous help, and now I have another question to ask you. I am looking forward to hearing from you!
I have successfully installed fontforge, but when using 'python convert_ttf_to_sfd_mp.py --split train' command to get SFD files via Fontforge， I got an error like this 'ModuleNotFoundError: No module named 'fontforge''

(base) miao@miao:~/data/file/bwt/deepvecfont-master/data_utils$ python convert_ttf_to_sfd_mp.py --split train
Traceback (most recent call last):
File "/home/miao/data/file/bwt/deepvecfont-master/data_utils/convert_ttf_to_sfd_mp.py", line 5, in
import fontforge # noqa
ModuleNotFoundError: No module named 'fontforge'

(base) miao@miao:~/data/file/bwt/deepvecfont-master/data_utils$ fontforge --version
Copyright (c) 2000-2014 by George Williams. See AUTHORS for Contributors.
License GPLv3+: GNU GPL version 3 or later http://gnu.org/licenses/gpl.html
with many parts BSD http://fontforge.org/license.html. Please read LICENSE.
Based on sources from 11:21 UTC 24-Sep-2017-ML-D.
Based on source from git with hash:
no xdefs_filename!
TESTING: getPixmapDir:/usr/share/fontforge/pixmaps
TESTING: getShareDir:/usr/share/fontforge
TESTING: GResourceProgramDir:/usr/bin
trying default theme:/usr/share/fontforge/pixmaps/resources
fontforge 11:21 UTC 24-Sep-2017
libfontforge 20170924

[yizhiwang96]

As I mentioned in readme.md, you need to first exit the conda environment:

conda deactivate

then install fontforge by:

apt install python3-fontforge

hope it works for you.

[bwtine]

As you mentioned in readme.md.I exit the conda environment:
then install fontforge by
sudo apt install python3-fontforge
[sudo] miao 的密码：
正在读取软件包列表... 完成
正在分析软件包的依赖关系树
正在读取状态信息... 完成
E: 无法定位软件包 python3-fontforge

However, the above problems occur and cannot be installed.Therefore,I used the following command to install according to the installation tutorial given on the official website and the actual installation was successful!
sudo apt-get install fontforge
正在读取软件包列表... 完成
正在分析软件包的依赖关系树
正在读取状态信息... 完成
fontforge 已经是最新版 (1:20170731~dfsg-1)。

But when running the command, there is still no module.
python convert_ttf_to_sfd_mp.py --split train
Traceback (most recent call last):
File "/home/miao/data/file/bwt/deepvecfont-master/data_utils/convert_ttf_to_sfd_mp.py", line 5, in
import fontforge # noqa
ModuleNotFoundError: No module named 'fontforge'

What should I do, hope to get your advice.thank you very much.

[yizhiwang96]

As you mentioned in readme.md.I exit the conda environment: then install fontforge by sudo apt install python3-fontforge [sudo] miao 的密码： 正在读取软件包列表... 完成 正在分析软件包的依赖关系树 正在读取状态信息... 完成 E: 无法定位软件包 python3-fontforge

However, the above problems occur and cannot be installed.Therefore,I used the following command to install according to the installation tutorial given on the official website and the actual installation was successful! sudo apt-get install fontforge 正在读取软件包列表... 完成 正在分析软件包的依赖关系树 正在读取状态信息... 完成 fontforge 已经是最新版 (1:20170731~dfsg-1)。

But when running the command, there is still no module. python convert_ttf_to_sfd_mp.py --split train Traceback (most recent call last): File "/home/miao/data/file/bwt/deepvecfont-master/data_utils/convert_ttf_to_sfd_mp.py", line 5, in import fontforge # noqa ModuleNotFoundError: No module named 'fontforge'

What should I do, hope to get your advice.thank you very much.

It works fine in Ubuntu 20.04.1, other versions may not work.

[Geoffrey-png]

Thank you very much for your sound and timely advice, I solved this problem！！！

How did you solve this problem, I also ran into this issue