remove sequnce_mask

deepspeech/__init__.py

@@ -421,6 +421,7 @@ logger.warn(
 )
 F.ctc_loss = ctc_loss
 
+
 ########### hcak paddle.nn #############
 if not hasattr(paddle.nn, 'Module'):
     logger.warn("register user Module to paddle.nn, remove this when fixed!")

deepspeech/modules/conv.py

@@ -15,7 +15,7 @@ from paddle import nn
 from paddle.nn import functional as F
 
 from deepspeech.modules.activation import brelu
-from deepspeech.modules.mask import sequence_mask
+from deepspeech.modules.mask import make_non_pad_mask
 from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
@@ -111,8 +111,10 @@ class ConvBn(nn.Layer):
                  ) // self.stride[1] + 1
 
         # reset padding part to 0
-        masks = sequence_mask(x_len)  #[B, T]
+        masks = make_non_pad_mask(x_len)  #[B, T]
         masks = masks.unsqueeze(1).unsqueeze(1)  # [B, 1, 1, T]
+        # TODO(Hui Zhang): not support bool multiply
+        masks = masks.type_as(x)
         x = x.multiply(masks)
 
         return x, x_len

deepspeech/modules/mask.py

@@ -18,40 +18,11 @@ from deepspeech.utils.log import Log
 logger = Log(__name__).getlog()
 
 __all__ = [
-    'sequence_mask', "make_pad_mask", "make_non_pad_mask", "subsequent_mask",
+    "make_pad_mask", "make_non_pad_mask", "subsequent_mask",
     "subsequent_chunk_mask", "add_optional_chunk_mask", "mask_finished_scores",
     "mask_finished_preds"
 ]
 
-
-def sequence_mask(x_len, max_len=None, dtype='float32'):
-    """batch sequence mask.
-
-    Args:
-        x_len ([paddle.Tensor]): xs lenght, [B]
-        max_len ([type], optional): max sequence length. Defaults to None.
-        dtype (str, optional): mask data type. Defaults to 'float32'.
-
-    Returns:
-        paddle.Tensor: [B, Tmax]
-
-     Examples:
-        >>> sequence_mask([2, 4])
-        [[1., 1., 0., 0.],
-         [1., 1., 1., 1.]]
-    """
-    # (TODO: Hui Zhang): jit not support Tenosr.dim() and Tensor.ndim
-    # assert x_len.dim() == 1, (x_len.dim(), x_len)
-    max_len = max_len or x_len.max()
-    x_len = paddle.unsqueeze(x_len, -1)
-    row_vector = paddle.arange(max_len)
-    # TODO(Hui Zhang): fix this bug
-    #mask = row_vector < x_len
-    mask = row_vector > x_len  # a bug, broadcast 的时候出错了
-    mask = paddle.cast(mask, dtype)
-    return mask
-
-
 def make_pad_mask(lengths: paddle.Tensor) -> paddle.Tensor:
     """Make mask tensor containing indices of padded part.
     See description of make_non_pad_mask.
@@ -66,7 +37,8 @@ def make_pad_mask(lengths: paddle.Tensor) -> paddle.Tensor:
                  [0, 0, 0, 1, 1],
                  [0, 0, 1, 1, 1]]
     """
-    assert lengths.dim() == 1
+    # (TODO: Hui Zhang): jit not support Tenosr.dim() and Tensor.ndim
+    # assert lengths.dim() == 1
     batch_size = int(lengths.shape[0])
     max_len = int(lengths.max())
     seq_range = paddle.arange(0, max_len, dtype=paddle.int64)

deepspeech/modules/rnn.py

@@ -19,7 +19,7 @@ from paddle.nn import functional as F
 from paddle.nn import initializer as I
 
 from deepspeech.modules.activation import brelu
-from deepspeech.modules.mask import sequence_mask
+from deepspeech.modules.mask import make_non_pad_mask
 from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
@@ -306,7 +306,9 @@ class RNNStack(nn.Layer):
         """
         for i, rnn in enumerate(self.rnn_stacks):
             x, x_len = rnn(x, x_len)
-            masks = sequence_mask(x_len)  #[B, T]
+            masks = make_non_pad_mask(x_len)  #[B, T]
             masks = masks.unsqueeze(-1)  # [B, T, 1]
+            # TODO(Hui Zhang): not support bool multiply
+            masks = masks.type_as(x)
             x = x.multiply(masks)
         return x, x_len

tests/mask_test.py

@@ -18,7 +18,6 @@ import paddle
 
 from deepspeech.modules.mask import make_non_pad_mask
 from deepspeech.modules.mask import make_pad_mask
-from deepspeech.modules.mask import sequence_mask
 
 
 class TestU2Model(unittest.TestCase):
@@ -36,16 +35,10 @@ class TestU2Model(unittest.TestCase):
             [False, False, True, True, True],
         ])
 
-    def test_sequence_mask(self):
-        res = sequence_mask(self.lengths, dtype='bool')
-        self.assertSequenceEqual(res.numpy().tolist(), self.masks.tolist())
-
     def test_make_non_pad_mask(self):
         res = make_non_pad_mask(self.lengths)
-        res1 = sequence_mask(self.lengths, dtype='bool')
         res2 = make_pad_mask(self.lengths).logical_not()
         self.assertSequenceEqual(res.numpy().tolist(), self.masks.tolist())
-        self.assertSequenceEqual(res.numpy().tolist(), res1.numpy().tolist())
         self.assertSequenceEqual(res.numpy().tolist(), res2.numpy().tolist())
 
     def test_make_pad_mask(self):