replace reshape by view. (#409)

torchaudio/functional.py

@@ -129,7 +129,7 @@ def istft(
 
     # pack batch
     shape = stft_matrix.size()
-    stft_matrix = stft_matrix.reshape(-1, shape[-3], shape[-2], shape[-1])
+    stft_matrix = stft_matrix.view(-1, shape[-3], shape[-2], shape[-1])
 
     dtype = stft_matrix.dtype
     device = stft_matrix.device
@@ -214,7 +214,7 @@ def istft(
     y = (y / window_envelop).squeeze(1)  # size (channel, expected_signal_len)
 
     # unpack batch
-    y = y.reshape(shape[:-3] + y.shape[-1:])
+    y = y.view(shape[:-3] + y.shape[-1:])
 
     if stft_matrix_dim == 3:  # remove the channel dimension
         y = y.squeeze(0)
@@ -253,7 +253,7 @@ def spectrogram(
 
     # pack batch
     shape = waveform.size()
-    waveform = waveform.reshape(-1, shape[-1])
+    waveform = waveform.view(-1, shape[-1])
 
     # default values are consistent with librosa.core.spectrum._spectrogram
     spec_f = _stft(
@@ -261,7 +261,7 @@ def spectrogram(
     )
 
     # unpack batch
-    spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:])
+    spec_f = spec_f.view(shape[:-1] + spec_f.shape[-3:])
 
     if normalized:
         spec_f /= window.pow(2.).sum().sqrt()
@@ -317,7 +317,7 @@ def griffinlim(
 
     # pack batch
     shape = specgram.size()
-    specgram = specgram.reshape([-1] + list(shape[-2:]))
+    specgram = specgram.view([-1] + list(shape[-2:]))
 
     specgram = specgram.pow(1 / power)
 
@@ -363,7 +363,7 @@ def griffinlim(
                      length=length)
 
     # unpack batch
-    waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:])
+    waveform = waveform.view(shape[:-2] + waveform.shape[-1:])
 
     return waveform
 
@@ -587,7 +587,7 @@ def phase_vocoder(complex_specgrams, rate, phase_advance):
 
     # pack batch
     shape = complex_specgrams.size()
-    complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:]))
+    complex_specgrams = complex_specgrams.view([-1] + list(shape[-3:]))
 
     time_steps = torch.arange(0,
                               complex_specgrams.size(-2),
@@ -627,7 +627,7 @@ def phase_vocoder(complex_specgrams, rate, phase_advance):
     complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1)
 
     # unpack batch
-    complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:])
+    complex_specgrams_stretch = complex_specgrams_stretch.view(shape[:-3] + complex_specgrams_stretch.shape[1:])
 
     return complex_specgrams_stretch
 
@@ -654,7 +654,7 @@ def lfilter(waveform, a_coeffs, b_coeffs):
 
     # pack batch
     shape = waveform.size()
-    waveform = waveform.reshape(-1, shape[-1])
+    waveform = waveform.view(-1, shape[-1])
 
     assert(a_coeffs.size(0) == b_coeffs.size(0))
     assert(len(waveform.size()) == 2)
@@ -697,7 +697,7 @@ def lfilter(waveform, a_coeffs, b_coeffs):
     output = torch.clamp(padded_output_waveform[:, (n_order - 1):], min=-1., max=1.)
 
     # unpack batch
-    output = output.reshape(shape[:-1] + output.shape[-1:])
+    output = output.view(shape[:-1] + output.shape[-1:])
 
     return output
 
@@ -876,7 +876,7 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
 
     # pack batch
     shape = specgram.size()
-    specgram = specgram.reshape([-1] + list(shape[-2:]))
+    specgram = specgram.view([-1] + list(shape[-2:]))
 
     value = torch.rand(1) * mask_param
     min_value = torch.rand(1) * (specgram.size(axis) - value)
@@ -893,7 +893,7 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
         raise ValueError('Only Frequency and Time masking are supported')
 
     # unpack batch
-    specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:])
+    specgram = specgram.view(shape[:-2] + specgram.shape[-2:])
 
     return specgram
 
@@ -925,7 +925,7 @@ def compute_deltas(specgram, win_length=5, mode="replicate"):
 
     # pack batch
     shape = specgram.size()
-    specgram = specgram.reshape(1, -1, shape[-1])
+    specgram = specgram.view(1, -1, shape[-1])
 
     assert win_length >= 3
 
@@ -945,7 +945,7 @@ def compute_deltas(specgram, win_length=5, mode="replicate"):
     output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom
 
     # unpack batch
-    output = output.reshape(shape)
+    output = output.view(shape)
 
     return output
 
@@ -974,11 +974,10 @@ def _add_noise_shaping(dithered_waveform, waveform):
     error[n] = dithered[n] - original[n]
     noise_shaped_waveform[n] = dithered[n] + error[n-1]
     """
-    wf_shape = waveform.size()
-    waveform = waveform.reshape(-1, wf_shape[-1])
+    waveform = waveform.view(-1, waveform.size()[-1])
 
     dithered_shape = dithered_waveform.size()
-    dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1])
+    dithered_waveform = dithered_waveform.view(-1, dithered_shape[-1])
 
     error = dithered_waveform - waveform
 
@@ -989,7 +988,7 @@ def _add_noise_shaping(dithered_waveform, waveform):
         error[index] = error_offset[:waveform.size()[1]]
 
     noise_shaped = dithered_waveform + error
-    return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:])
+    return noise_shaped.view(dithered_shape[:-1] + noise_shaped.shape[-1:])
 
 
 def _apply_probability_distribution(waveform, density_function="TPDF"):
@@ -1020,7 +1019,7 @@ def _apply_probability_distribution(waveform, density_function="TPDF"):
 
     # pack batch
     shape = waveform.size()
-    waveform = waveform.reshape(-1, shape[-1])
+    waveform = waveform.view(-1, shape[-1])
 
     channel_size = waveform.size()[0] - 1
     time_size = waveform.size()[-1] - 1
@@ -1060,7 +1059,7 @@ def _apply_probability_distribution(waveform, density_function="TPDF"):
     quantised_signal = quantised_signal_scaled / down_scaling
 
     # unpack batch
-    return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:])
+    return quantised_signal.view(shape[:-1] + quantised_signal.shape[-1:])
 
 
 def dither(waveform, density_function="TPDF", noise_shaping=False):
@@ -1231,7 +1230,7 @@ def detect_pitch_frequency(
 
     # pack batch
     shape = list(waveform.size())
-    waveform = waveform.reshape([-1] + shape[-1:])
+    waveform = waveform.view([-1] + shape[-1:])
 
     nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low)
     indices = _find_max_per_frame(nccf, sample_rate, freq_high)
@@ -1242,6 +1241,6 @@ def detect_pitch_frequency(
     freq = sample_rate / (EPSILON + indices.to(torch.float))
 
     # unpack batch
-    freq = freq.reshape(shape[:-1] + list(freq.shape[-1:]))
+    freq = freq.view(shape[:-1] + list(freq.shape[-1:]))
 
     return freq

torchaudio/transforms.py

@@ -215,7 +215,7 @@ class MelScale(torch.nn.Module):
 
         # pack batch
         shape = specgram.size()
-        specgram = specgram.reshape(-1, shape[-2], shape[-1])
+        specgram = specgram.view(-1, shape[-2], shape[-1])
 
         if self.fb.numel() == 0:
             tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels, self.sample_rate)
@@ -228,7 +228,7 @@ class MelScale(torch.nn.Module):
         mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2)
 
         # unpack batch
-        mel_specgram = mel_specgram.reshape(shape[:-2] + mel_specgram.shape[-2:])
+        mel_specgram = mel_specgram.view(shape[:-2] + mel_specgram.shape[-2:])
 
         return mel_specgram
 
@@ -349,7 +349,7 @@ class MFCC(torch.nn.Module):
 
         # pack batch
         shape = waveform.size()
-        waveform = waveform.reshape(-1, shape[-1])
+        waveform = waveform.view(-1, shape[-1])
 
         mel_specgram = self.MelSpectrogram(waveform)
         if self.log_mels:
@@ -362,7 +362,7 @@ class MFCC(torch.nn.Module):
         mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
 
         # unpack batch
-        mfcc = mfcc.reshape(shape[:-1] + mfcc.shape[-2:])
+        mfcc = mfcc.view(shape[:-1] + mfcc.shape[-2:])
 
         return mfcc