rd20karim / m2t-segmentation Goto Github PK
View Code? Open in Web Editor NEWOfficial implementation of the paper Motion2Language, Unsupervised learning of synchronized semantic motion segmentation
License: MIT License
Official implementation of the paper Motion2Language, Unsupervised learning of synchronized semantic motion segmentation
License: MIT License
When I run step 3 to generate sentence_corrections.csv, I get the following error:
[INFO] CORRECT TOKENS True
[INFO] INITIAL CORRECTIONS AND LOWER CASING ...
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[INFO] Text to int, five --> 5
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[INFO] Text to int, one --> 1
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[INFO] Token index 15 corrected perso ----> person
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[INFO] Text to int, one --> 1
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[INFO] Text to int, five --> 5
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[INFO] Token index 47 corrected ----> i
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[INFO] EMPTY TOKEN [[ ]]
[INFO] token not found
[INFO] Token index 61 corrected foward ----> forward
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[INFO] Text to int, four --> 4
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[INFO] Token index 69 corrected sim ----> him
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[INFO] Token index 71 corrected ----> i
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[INFO] Text to int, one --> 1
[INFO] Text to int, three --> 3
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[INFO] Token index 116 corrected counter- ----> counter
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[INFO] Token index 117 corrected ----> i
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[INFO] Token index 134 corrected staight ----> straight
[INFO] Token index 134 corrected soemthing ----> something
[INFO] Token index 136 corrected โs ----> is
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[INFO] Token index 151 corrected ... ----> None
Traceback (most recent call last):
File "/home/dhegde/projects/motion-pipe/M2T-Segmentation/datasets/kit_m2t_dataset.py", line 203, in <module>
data = dataset_class(path, filter_data=True,min_freq=3)
File "/home/dhegde/projects/motion-pipe/M2T-Segmentation/datasets/kit_m2t_dataset.py", line 46, in __init__
self.lang = vocabulary(self.sentences, correct_tokens=correct_tokens, ask_user=False)
File "/home/dhegde/projects/motion-pipe/M2T-Segmentation/datasets/vocabulary.py", line 94, in __init__
self.token_correction(ask_user)
File "/home/dhegde/projects/motion-pipe/M2T-Segmentation/datasets/vocabulary.py", line 158, in token_correction
desc = " ".join(tokens)
TypeError: sequence item 4: expected str instance, NoneType found
I processed the data as mentioned in the README and get the following error when I run
python visualizations/poses2concepts.py --config ./configs_kit_aug/MLP.yaml --dataset_name kit --n_map 5 --n_gifs 50 --save_results ./gifs_map_kit22_
ERROR MESSAGE:
2024-05-01 18:12:11,523 [INFO] Building dataset ...
2024-05-01 18:12:11,823 [INFO] CORRECT TOKENS False
2024-05-01 18:12:12,073 [INFO] Building vocabulary with minimum frequency : 3
2024-05-01 18:12:12,422 [INFO] Std/Mean Normalization
2024-05-01 18:12:13,426 [INFO] Convert token to numerical values
2024-05-01 18:12:13,767 [INFO] Using official split
2024-05-01 18:12:13,853 [INFO] Number of samples VAL: 470 TRAIN: 7451, TEST: 1376
2024-05-01 18:12:16,133 [INFO] VOCAB SIZE = 1709
2024-05-01 18:12:17,974 [INFO] Scaling 1.0
2024-05-01 18:12:17,981 [INFO] Applying local_recurrent attention
Traceback (most recent call last):
File "/home/dhegde/projects/motion-pipe/M2T-Segmentation/visualizations/poses2concepts.py", line 180, in <module>
loaded_model, train_data_loader, test_data_loader,val_data_loader = load_model_config(device=device,args=args)
File "/home/dhegde/projects/motion-pipe/M2T-Segmentation/src/evaluate_m2L.py", line 76, in load_model_config
loaded_model.load_state_dict(new_dict)
File "/home/dhegde/anaconda3/envs/llava/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for seq2seq:
size mismatch for dec.embedding.weight: copying a param with shape torch.Size([878, 64]) from checkpoint, the shape in current model is torch.Size([1709, 64]).
size mismatch for dec.fc.weight: copying a param with shape torch.Size([878, 192]) from checkpoint, the shape in current model is torch.Size([1709, 192]).
size mismatch for dec.fc.bias: copying a param with shape torch.Size([878]) from checkpoint, the shape in current model is torch.Size([1709]).
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