more (#160)

test/test_jit.py

@@ -30,28 +30,6 @@ class Test_JIT(unittest.TestCase):
 
         self.assertTrue(torch.allclose(jit_out, py_out))
 
-    def test_torchscript_pad_trim(self):
-        @torch.jit.script
-        def jit_method(tensor, max_len, fill_value):
-            # type: (Tensor, int, float) -> Tensor
-            return F.pad_trim(tensor, max_len, fill_value)
-
-        tensor = torch.rand((1, 10))
-        max_len = 5
-        fill_value = 3.
-
-        jit_out = jit_method(tensor, max_len, fill_value)
-        py_out = F.pad_trim(tensor, max_len, fill_value)
-
-        self.assertTrue(torch.allclose(jit_out, py_out))
-
-    @unittest.skipIf(not RUN_CUDA, "no CUDA")
-    def test_scriptmodule_pad_trim(self):
-        tensor = torch.rand((1, 10), device="cuda")
-        max_len = 5
-
-        self._test_script_module(tensor, transforms.PadTrim, max_len)
-
     def test_torchscript_spectrogram(self):
         @torch.jit.script
         def jit_method(sig, pad, window, n_fft, hop, ws, power, normalize):

test/test_transforms.py

@@ -36,20 +36,6 @@ class Tester(unittest.TestCase):
             waveform = waveform.to(torch.get_default_dtype())
         return waveform / factor
 
-    def test_pad_trim(self):
-
-        waveform = self.waveform.clone()
-        length_orig = waveform.size(1)
-        length_new = int(length_orig * 1.2)
-
-        result = transforms.PadTrim(max_len=length_new)(waveform)
-        self.assertEqual(result.size(1), length_new)
-
-        length_new = int(length_orig * 0.8)
-
-        result = transforms.PadTrim(max_len=length_new)(waveform)
-        self.assertEqual(result.size(1), length_new)
-
     def test_mu_law_companding(self):
 
         quantization_channels = 256

torchaudio/functional.py

@@ -3,7 +3,6 @@ import torch
 
 
 __all__ = [
-    'pad_trim',
     'istft',
     'spectrogram',
     'create_fb_matrix',
@@ -18,28 +17,6 @@ __all__ = [
 ]
 
 
-@torch.jit.script
-def pad_trim(waveform, max_len, fill_value):
-    # type: (Tensor, int, float) -> Tensor
-    r"""Pad/trim a 2D tensor
-
-    Args:
-        waveform (torch.Tensor): Tensor of audio of size (c, n)
-        max_len (int): Length to which the waveform will be padded
-        fill_value (float): Value to fill in
-
-    Returns:
-        torch.Tensor: Padded/trimmed tensor
-    """
-    n = waveform.size(1)
-    if max_len > n:
-        # TODO add "with torch.no_grad():" back when JIT supports it
-        waveform = torch.nn.functional.pad(waveform, (0, max_len - n), 'constant', fill_value)
-    else:
-        waveform = waveform[:, :max_len]
-    return waveform
-
-
 # TODO: remove this once https://github.com/pytorch/pytorch/issues/21478 gets solved
 @torch.jit.ignore
 def _stft(waveform, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided):

torchaudio/transforms.py

@@ -7,32 +7,6 @@ from . import functional as F
 from .compliance import kaldi
 
 
-class PadTrim(torch.jit.ScriptModule):
-    r"""Pad/Trim a 2D tensor
-
-    Args:
-        max_len (int): Length to which the waveform will be padded
-        fill_value (float): Value to fill in
-    """
-    __constants__ = ['max_len', 'fill_value']
-
-    def __init__(self, max_len, fill_value=0.):
-        super(PadTrim, self).__init__()
-        self.max_len = max_len
-        self.fill_value = fill_value
-
-    @torch.jit.script_method
-    def forward(self, waveform):
-        r"""
-        Args:
-            waveform (torch.Tensor): Tensor of audio of size (c, n)
-
-        Returns:
-            Tensor: Tensor of size (c, `max_len`)
-        """
-        return F.pad_trim(waveform, self.max_len, self.fill_value)
-
-
 class Spectrogram(torch.jit.ScriptModule):
     r"""Create a spectrogram from a audio signal