How to convert a list of strings into a tensor in pytorch?
Solution 1
Unfortunately, you can't right now. And I don't think it is a good idea since it will make PyTorch clumsy. A popular workaround could convert it into numeric types using sklearn.
Here is a short example:
from sklearn import preprocessing
import torch
labels = ['cat', 'dog', 'mouse', 'elephant', 'pandas']
le = preprocessing.LabelEncoder()
targets = le.fit_transform(labels)
# targets: array([0, 1, 2, 3])
targets = torch.as_tensor(targets)
# targets: tensor([0, 1, 2, 3])
Since you may need the conversion between true labels and transformed labels, it is good to store the variable le
.
Solution 2
The trick is first to find out max length of a word in the list, and then at the second loop populate the tensor with zeros padding. Note that utf8 strings take two bytes per char.
In[]
import torch
words = ['שלום', 'beautiful', 'world']
max_l = 0
ts_list = []
for w in words:
ts_list.append(torch.ByteTensor(list(bytes(w, 'utf8'))))
max_l = max(ts_list[-1].size()[0], max_l)
w_t = torch.zeros((len(ts_list), max_l), dtype=torch.uint8)
for i, ts in enumerate(ts_list):
w_t[i, 0:ts.size()[0]] = ts
w_t
Out[]
tensor([[215, 169, 215, 156, 215, 149, 215, 157, 0],
[ 98, 101, 97, 117, 116, 105, 102, 117, 108],
[119, 111, 114, 108, 100, 0, 0, 0, 0]], dtype=torch.uint8)
Solution 3
If you don't want to use sklearn, another solution could be to keep your original list and create an extra indices list, which you can use to refer back to your original values afterwards. I specifically needed this, when I had to keep track of my original string, while batching the tokenized string.
Example below:
labels = ['cat', 'dog', 'mouse']
sentence_idx = np.linspace(0,len(labels), len(labels), False)
# [0, 1, 2]
torch_idx = torch.tensor(sentence_idx)
# do what ever you would like from torch eg. pass it to a dataloader
dataset = TensorDataset(torch_idx)
loader = DataLoader(dataset, batch_size=1, shuffle=True)
for batch in iter(loader):
print(batch[0])
print(labels[int(batch[0].item())])
# output:
# tensor([0.], dtype=torch.float64)
# cat
# tensor([1.], dtype=torch.float64)
# dog
# tensor([2.], dtype=torch.float64)
# mouse
For my specific use case, the code looked like this:
input_ids, attention_masks, labels = tokenize_sentences(tokenizer, sentences, labels, max_length)
# create a indexes tensor to keep track of original sentence index
sentence_idx = np.linspace(0,len(sentences), len(sentences),False )
torch_idx = torch.tensor(sentence_idx)
dataset = TensorDataset(input_ids, attention_masks, labels, torch_idx)
loader = DataLoader(dataset, batch_size=1, shuffle=True)
for batch in loader:
_, logit = model(batch[0],
token_type_ids=None,
attention_mask=batch[1],
labels=batch[2])
pred_flat = np.argmax(logit.detach(), axis=1).flatten()
print(pred_flat)
print(batch[2])
if pred_flat == batch[2]:
print("\nThe following sentence was predicted correctly:")
print(sentences[int(batch[3].item())])
Related videos on Youtube
deepayan das
Updated on October 30, 2020Comments
-
deepayan das over 3 years
I am working on classification problem in which I have a list of strings as class labels and I want to convert them into a tensor. So far I have tried converting the list of strings into a
numpy array
using thenp.array
function provided by the numpy module.truth = torch.from_numpy(np.array(truths))
but I am getting the following error.
RuntimeError: can't convert a given np.ndarray to a tensor - it has an invalid type. The only supported types are: double, float, int64, int32, and uint8.
Can anybody suggest an alternative approach? Thanks
-
gokul_uf almost 7 yearstaking the ASCII value / UNICODE value of the characters could be a workaround (ASCII would fit in
uint8
) -
deepayan das almost 7 yearsokay thanks will try that
-
BiBi almost 7 yearsWhat about simply converting your string labels into digits or one-hot vectors?
-
cleros almost 7 yearsI agree with @BiBi - it sounds like you want a one-hot encoding.
-
deepayan das almost 7 yearsYeah, I am using one hot encoding, Thanks @BiBi
-