Implementation of character based convolutional neural network

I used some GPUs on the server to speed up training. But after downloading the trained model file to my PC (no GPU equipped) and run the predict.py script. It gives an error message related to cuda_is_available() , seems that the model trained on a GPU cannot predict on only-CPU machines? Is this an expected behavior? If not, any help will be appreciated! Thanks a lot!

Error Message:

(ml) C:\Users\lzy71\MyProject\character-based-cnn>python predict.py --model=./model/testmodel.pth --text="I love the pizza" > msg.txt
C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.container.ModuleList' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.container.Sequential' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv1d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
Traceback (most recent call last):
  File "predict.py", line 39, in <module>
    prediction = predict(args)
  File "predict.py", line 10, in predict
    model = torch.load(args.model)
  File "C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py", line 387, in load
    return _load(f, map_location, pickle_module, **pickle_load_args)
  File "C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py", line 574, in _load
    result = unpickler.load()
  File "C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py", line 537, in persistent_load
    deserialized_objects[root_key] = restore_location(obj, location)
  File "C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py", line 119, in default_restore_location
    result = fn(storage, location)
  File "C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py", line 95, in _cuda_deserialize
    device = validate_cuda_device(location)
  File "C:\Users\lzy71\Anaconda3\envs\ml\lib\site-packages\torch\serialization.py", line 79, in validate_cuda_device
    raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu' to map your storages to the CPU.

train is always falling even with such kind of file: """ SentimentText;Sentiment aaa;1 bbb;2 ccc;3 """ Params of running -- just data_path Packages installed: numpy==1.16.1 pandas==0.24.1 Pillow==5.4.1 protobuf==3.6.1 python-dateutil==2.8.0 pytz==2018.9 scikit-learn==0.20.2 scipy==1.2.1 six==1.12.0 sklearn==0.0 tensorboardX==1.6 torch==1.0.1.post2 torchvision==0.2.1 tqdm==4.31.1

Raw output on console.

python3 predict.py --model=./models/model__epoch_9_maxlen_150_lr_0.00125_loss_0.6931_acc_0.5005_f1_0.4944.pth --text="thisisatest_______" --alphabet=abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_ Traceback (most recent call last): File "/Users/ttran/Desktop/development/python/character-based-cnn/predict.py", line 48, in <module> prediction = predict(args) File "/Users/ttran/Desktop/development/python/character-based-cnn/predict.py", line 11, in predict model = CharacterLevelCNN(args, args.number_of_classes) File "/Users/ttran/Desktop/development/python/character-based-cnn/src/model.py", line 12, in __init__ self.dropout_input = nn.Dropout2d(args.dropout_input) AttributeError: 'Namespace' object has no attribute 'dropout_input'

What is --number_of_classes argument? I don't have that set in the run command.

Can you describe how to encode the labels? I get only 1 class label, see output below. They are set as integers (either 0 or 1)

See output below when I train my model.

data loaded successfully with 9826 rows and 1 labels Distribution of the classes Counter({0: 9826})

I'm using a dataset I scraped but same structure comments with rating 0-10, using the same commands as provided except group_labels=0

Traceback (most recent call last):
  File "train.py", line 415, in <module>
    run(args)
  File "train.py", line 297, in run
    training_loss, training_accuracy, train_f1 = train(model,
  File "train.py", line 50, in train
    loss = criterion(predictions, labels)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\loss.py", line 915, in forward
    return F.cross_entropy(input, target, weight=self.weight,
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\functional.py", line 2021, in cross_entropy
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\functional.py", line 1838, in nll_loss
    ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: expected scalar type Long but found Double

I am using my dataset having three labels 0,1,2. While loading the dataset in data_loader class it generates key error. I think the issue is of mapping please guide.

Traceback (most recent call last):
  File "train.py", line 415, in <module>
    run(args)
  File "train.py", line 219, in run
    texts, labels, number_of_classes, sample_weights = load_data(args)
  File "/content/character-based-cnn/src/data_loader.py", line 55, in load_data
    map(lambda l: {1: 0, 2: 0, 4: 1, 5: 1, 7: 2, 8: 2}[l], labels))
  File "/content/character-based-cnn/src/data_loader.py", line 55, in <lambda>
    map(lambda l: {1: 0, 2: 0, 4: 1, 5: 1, 7: 2, 8: 2}[l], labels))
KeyError: '1'

Ubuntu 18.04.3 LTS Python 3.6.9

Command: python3 predict.py --model "./models/pretrained_model.pth" --text "I love pizza !" --max_length 150

Output: Traceback (most recent call last): File "predict.py", line 47, in prediction = predict(args) File "predict.py", line 14, in predict state = torch.load(args.model) File "/home/reda/.local/lib/python3.6/site-packages/torch/serialization.py", line 426, in load return _load(f, map_location, pickle_module, **pickle_load_args) File "/home/reda/.local/lib/python3.6/site-packages/torch/serialization.py", line 613, in _load result = unpickler.load() ModuleNotFoundError: No module named 'src.cnn_model'