delete util functions and test cases in fastNLP/core/losses.py

fastNLP/core/losses.py

@@ -352,82 +352,3 @@ def _prepare_losser(losser):
         return losser
     else:
         raise TypeError(f"Type of loss should be `fastNLP.LossBase`, got {type(losser)}")
-
-
-def squash(predict, truth, **kwargs):
-    """To reshape tensors in order to fit loss functions in PyTorch.
-
-    :param predict: Tensor, model output
-    :param truth: Tensor, truth from dataset
-    :param kwargs: extra arguments
-    :return predict , truth: predict & truth after processing
-    """
-    return predict.view(-1, predict.size()[-1]), truth.view(-1, )
-
-
-def unpad(predict, truth, **kwargs):
-    """To process padded sequence output to get true loss.
-
-    :param predict: Tensor, [batch_size , max_len , tag_size]
-    :param truth: Tensor, [batch_size , max_len]
-    :param kwargs: kwargs["lens"] is a list or LongTensor, with size [batch_size]. The i-th element is true lengths of i-th sequence.
-
-    :return predict , truth: predict & truth after processing
-    """
-    if kwargs.get("lens") is None:
-        return predict, truth
-    lens = torch.LongTensor(kwargs["lens"])
-    lens, idx = torch.sort(lens, descending=True)
-    predict = torch.nn.utils.rnn.pack_padded_sequence(predict[idx], lens, batch_first=True).data
-    truth = torch.nn.utils.rnn.pack_padded_sequence(truth[idx], lens, batch_first=True).data
-    return predict, truth
-
-
-def unpad_mask(predict, truth, **kwargs):
-    """To process padded sequence output to get true loss.
-
-    :param predict: Tensor, [batch_size , max_len , tag_size]
-    :param truth: Tensor, [batch_size , max_len]
-    :param kwargs: kwargs["lens"] is a list or LongTensor, with size [batch_size]. The i-th element is true lengths of i-th sequence.
-
-    :return predict , truth: predict & truth after processing
-    """
-    if kwargs.get("lens") is None:
-        return predict, truth
-    mas = make_mask(kwargs["lens"], truth.size()[1])
-    return mask(predict, truth, mask=mas)
-
-
-def mask(predict, truth, **kwargs):
-    """To select specific elements from Tensor. This method calls ``squash()``.
-
-    :param predict: Tensor, [batch_size , max_len , tag_size]
-    :param truth: Tensor, [batch_size , max_len]
-    :param kwargs: extra arguments, kwargs["mask"]: ByteTensor, [batch_size , max_len], the mask Tensor. The position that is 1 will be selected.
-
-    :return predict , truth: predict & truth after processing
-    """
-    if kwargs.get("mask") is None:
-        return predict, truth
-    mask = kwargs["mask"]
-    
-    predict, truth = squash(predict, truth)
-    mask = mask.view(-1, )
-    
-    predict = torch.masked_select(predict.permute(1, 0), mask).view(predict.size()[-1], -1).permute(1, 0)
-    truth = torch.masked_select(truth, mask)
-    
-    return predict, truth
-
-
-def make_mask(lens, tar_len):
-    """To generate a mask over a sequence.
-
-    :param lens: list or LongTensor, [batch_size]
-    :param tar_len: int
-    :return mask: ByteTensor
-    """
-    lens = torch.LongTensor(lens)
-    mask = [torch.ge(lens, i + 1) for i in range(tar_len)]
-    mask = torch.stack(mask, 1)
-    return mask

test/core/test_loss.py

@@ -4,7 +4,6 @@ import torch
 import torch.nn.functional as F
 
 import fastNLP as loss
-from fastNLP.core.losses import squash, unpad
 
 
 class TestLoss(unittest.TestCase):
@@ -73,15 +72,3 @@ class TestLosserError(unittest.TestCase):
         
         with self.assertRaises(Exception):
             ans = l1({"my_predict": a}, {"truth": b, "my": a})
-
-
-class TestLossUtils(unittest.TestCase):
-    def test_squash(self):
-        a, b = squash(torch.randn(3, 5), torch.randn(3, 5))
-        self.assertEqual(tuple(a.size()), (3, 5))
-        self.assertEqual(tuple(b.size()), (15,))
-    
-    def test_unpad(self):
-        a, b = unpad(torch.randn(5, 8, 3), torch.randn(5, 8))
-        self.assertEqual(tuple(a.size()), (5, 8, 3))
-        self.assertEqual(tuple(b.size()), (5, 8))