Training classifier on bottleneck
tsaxena opened this issue · comments
@alexandre01 I am trying to train a classifier on the icons using the bottleneck embeddings that I get by model inference using pretrained model. In some cases though it doesnt seem to work. I used the code in your latent_ops notebook and encode each icon. In some cases I get this error
The size of tensor a (10) must match the size of tensor b (8) at non-singleton dimension 0
Am I missing something?
I am using the following methods to encode icon, but does seem to work on some of the icon indices.
`
def encode(data):
model_args = batchify((data[key] for key in cfg.model_args), device)
with torch.no_grad():
z = model(*model_args, encode_mode=True)
return z
def encode_icon(idx):
data = dataset.get(id=idx, random_aug=False)
return encode(data)
`
Hello @tsaxena!
Yes unfortunately this is due to the fact that the pretrained model was trained with a maximum of 8 paths per SVG. Since an index embedding is used in the model, this means one cannot perform inference using a larger amount.
You'd need to train a model with more paths or filter your classification dataset to eight paths.
Thanks for the prompt reply @alexandre01 . So does that mean you did not use all 100k icons for training the pretrained model?
Yes, the dataset is about 100k icons, but because of time constraints the pretrained model was only trained on a filtered subset. And I don't have access to the GPU server I used anymore.
I eventually want to fine tune the network on svgs that will have more than 8 paths. Do you suggest training from scratch? From what I understand, the max number of SVG paths is a configuration parameter that can be changed.
I am not @alexandre01. But what you say is correct, I think.
The max number of paths is a config parameter and can be changed in deepsvg/model/confg.py:
self.max_num_groups = 8 # Number of paths (N_P)There are also individual configs in configs/deepsvg/ that overwrite this variable. You may need to increase the number of paths here as well.
You would need to retrain to cope with a larger number of paths. If you have a beefy GPU, retraining does not take very long. On my RTX 3090, I can retrain from scratch within hours.
What is your use case?
@tsaxena ,Hi, I'm also having issues with tensor mismatches using my own svg images. I hope to be able to adjust the configuration of max_num_groups and max_total_len in this project and train the model's own max_num_groups and max_total_len to solve the problem of tensor mismatch. Unfortunately, I didn't make any progress. Have you solved this problem? Can you give me some detailed guidance? I would be infinitely grateful and look forward to hearing from you!
@pwichmann ,Hello, I followed the method you provided but it failed. Can you give me some detailed guidance? I would be infinitely grateful and look forward to hearing from you!