Repository accompanying the "Sign Pose-based Transformer for Word-level Sign Language Recognition" paper
Home Page: https://spoter.signlanguagerecognition.com
License: Apache License 2.0
RuntimeError: CUDA error: device-side assert triggered.
` for i, data in enumerate(dataloader):
inputs, labels = data
# inputs, labels = Variable(inputs), Variable(labels)-1
inputs = inputs.squeeze(0).to(device)
labels = labels.to(device, dtype=torch.long)
optimizer.zero_grad()
outputs = model(inputs).expand(1, -1, -1)
loss = criterion(outputs[0], labels[0])`
Do you have the code of extracting the pose information from a video file and outputting it in the same format used in your code?
Dear sir
I encountered the above bug during training and found out that the graphics card memory is not enough, the graphics card information is RTX 2070 8GB, please ask what parameters can be adjusted to make my graphics card run properly?
Thank you for providing this dataset. I'm trying to reproduce your results using the Pose based GRU model however I'm unable to do so. Could you please share the model architecture and hyperparameters. It would be quite helpful
EDIT: Wrong repository, please delete this issue
Got a validation accuracy around 58%, lower than the one proposed in the paper. Is the lower accuracy caused by this problematic normalization error?
self.pos = nn.Parameter(torch.cat([self.row_embed[0].unsqueeze(0).repeat(1, 1, 1)], dim=-1).flatten(0, 1).unsqueeze(0))
the position embedding shape is 1 * 1 * 108, so the position of each frame skeleton embedding is same?
Hi, I like your work. I want to make new data, but I don't understand the structure of your skeletal data. Are you willing to teach me ?
作者你好，感谢你的工作！代码训练和测试都是将数据集转为了csv文件，我想请教一下，怎么在视频文件上进行测试，怎么直观的得到视频手语翻译的demo展示
I'm trying to use the model, but I'm having problems with the following step. I have already trained the model, and now I have the checkpoint_v_0.pth file, with which I do the following to load the generated model:
model = torch.load(PATH/to/pth/file)
print(model)
And this returns something like:
SPOTER(
(transformer): Transformer(
(encoder): TransformerEncoder(
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(1): TransformerEncoderLayer(
...
which makes me believe that everything is OK to this point.
Now I would like to see how I can use the model to make a prediction with new data, and there's where the problem is.
When running model(input) to get the results, some errors appear, and I believe that I'm not giving the correct kind of input. I'm using the second line from WLASL100_train_25fps.csv, changing the "s for [s in order to get a hierarchy like the following:
[
[a, b, c],
[d],
[e, f]
]
However, this doesn't seem to work. Am I using a different format to the one the model should be given?
The exact input I'm giving the model follows, with the np.array conversion:
parsed_example = 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input = np.array([np.array(sublist) for sublist in parsed_example])
hello, in ur SPOTER paper, "pose data could be substituted by any other pose estimation framework", but how to change the pose formats produced by another pose estimators like OpenPose, MMPose, and MediaPipe into the correct format that could directly input to the SPOTER?
Looking forward to your soonest reply.
Thank you for your work！
I want to ask, if I want to train a new dataset, such as AUTSL, how can I get its csv file?
how to add the correct formula code in utils.py to calculate val_loss?
This is the command used to train the model :
python -m train --experiment_name "Spoter" --training_set_path "data/WLASL100_train_25fps.csv" --validation_set_path "data/WLASL100_val_25fps.csv" --testing_set_path "data/WLASL100_test_25fps.csv"
I get the following error after the program runs for awhile:
Starting Spoter...
Traceback (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/content/drive/MyDrive/Spoter/train.py", line 272, in
train(args)
File "/content/drive/MyDrive/Spoter/train.py", line 174, in train
train_loss, _, _, train_acc = train_epoch(slrt_model, train_loader, cel_criterion, sgd_optimizer, device)
File "/content/drive/MyDrive/Spoter/spoter/utils.py", line 19, in train_epoch
loss = criterion(outputs[0], labels[0])
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py", line 1152, in forward
label_smoothing=self.label_smoothing)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py", line 2846, in cross_entropy
return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
IndexError: Target 78 is out of bounds.
Changing parameters like the epochs and learning rate does not fix the issue.
tested_model = CSLRTransformer(num_classes=args.num_classes, hidden_dim=args.hidden_dim)
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