Python torch.sin方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def __init__(self, height, width, lr = 1, aux_loss = False):
        super(DenseAffine3DGridGen, self).__init__()
        self.height, self.width = height, width
        self.aux_loss = aux_loss
        self.lr = lr

        self.grid = np.zeros( [self.height, self.width, 3], dtype=np.float32)
        self.grid[:,:,0] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/self.height), 0), repeats = self.width, axis = 0).T, 0)
        self.grid[:,:,1] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/self.width), 0), repeats = self.height, axis = 0), 0)
        self.grid[:,:,2] = np.ones([self.height, width])
        self.grid = torch.from_numpy(self.grid.astype(np.float32))

        self.theta = self.grid[:,:,0] * np.pi/2 + np.pi/2
        self.phi = self.grid[:,:,1] * np.pi

        self.x = torch.sin(self.theta) * torch.cos(self.phi)
        self.y = torch.sin(self.theta) * torch.sin(self.phi)
        self.z = torch.cos(self.theta)

        self.grid3d = torch.from_numpy(np.zeros( [self.height, self.width, 4], dtype=np.float32))

        self.grid3d[:,:,0] = self.x
        self.grid3d[:,:,1] = self.y
        self.grid3d[:,:,2] = self.z
        self.grid3d[:,:,3] = self.grid[:,:,2] 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def _fade_in(self, waveform_length: int) -> Tensor:
        fade = torch.linspace(0, 1, self.fade_in_len)
        ones = torch.ones(waveform_length - self.fade_in_len)

        if self.fade_shape == "linear":
            fade = fade

        if self.fade_shape == "exponential":
            fade = torch.pow(2, (fade - 1)) * fade

        if self.fade_shape == "logarithmic":
            fade = torch.log10(.1 + fade) + 1

        if self.fade_shape == "quarter_sine":
            fade = torch.sin(fade * math.pi / 2)

        if self.fade_shape == "half_sine":
            fade = torch.sin(fade * math.pi - math.pi / 2) / 2 + 0.5

        return torch.cat((fade, ones)).clamp_(0, 1) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def _fade_out(self, waveform_length: int) -> Tensor:
        fade = torch.linspace(0, 1, self.fade_out_len)
        ones = torch.ones(waveform_length - self.fade_out_len)

        if self.fade_shape == "linear":
            fade = - fade + 1

        if self.fade_shape == "exponential":
            fade = torch.pow(2, - fade) * (1 - fade)

        if self.fade_shape == "logarithmic":
            fade = torch.log10(1.1 - fade) + 1

        if self.fade_shape == "quarter_sine":
            fade = torch.sin(fade * math.pi / 2 + math.pi / 2)

        if self.fade_shape == "half_sine":
            fade = torch.sin(fade * math.pi + math.pi / 2) / 2 + 0.5

        return torch.cat((ones, fade)).clamp_(0, 1) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def _test_istft_of_sine(self, amplitude, L, n):
        # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L
        x = torch.arange(2 * L + 1, dtype=torch.get_default_dtype())
        sound = amplitude * torch.sin(2 * math.pi / L * x * n)
        # stft = torch.stft(sound, L, hop_length=L, win_length=L,
        #                   window=torch.ones(L), center=False, normalized=False)
        stft = torch.zeros((L // 2 + 1, 2, 2))
        stft_largest_val = (amplitude * L) / 2.0
        if n < stft.size(0):
            stft[n, :, 1] = -stft_largest_val

        if 0 <= L - n < stft.size(0):
            # symmetric about L // 2
            stft[L - n, :, 1] = stft_largest_val

        estimate = torchaudio.functional.istft(stft, L, hop_length=L, win_length=L,
                                               window=torch.ones(L), center=False, normalized=False)
        # There is a larger error due to the scaling of amplitude
        _compare_estimate(sound, estimate, atol=1e-3) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def _create_data_set(self):
        # used to generate the dataset to test on. this is not used in testing (offline procedure)
        test_filepath = common_utils.get_asset_path('kaldi_file.wav')
        sr = 16000
        x = torch.arange(0, 20).float()
        # between [-6,6]
        y = torch.cos(2 * math.pi * x) + 3 * torch.sin(math.pi * x) + 2 * torch.cos(x)
        # between [-2^30, 2^30]
        y = (y / 6 * (1 << 30)).long()
        # clear the last 16 bits because they aren't used anyways
        y = ((y >> 16) << 16).float()
        torchaudio.save(test_filepath, y, sr)
        sound, sample_rate = torchaudio.load(test_filepath, normalization=False)
        print(y >> 16)
        self.assertTrue(sample_rate == sr)
        torch.testing.assert_allclose(y, sound) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
    ''' Sinusoid position encoding table '''

    def cal_angle(position, hid_idx):
        return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)

    def get_posi_angle_vec(position):
        return [cal_angle(position, hid_j) for hid_j in range(d_hid)]

    sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])

    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

    if padding_idx is not None:
        # zero vector for padding dimension
        sinusoid_table[padding_idx] = 0.

    return torch.FloatTensor(sinusoid_table) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def positional_encodings_like(x, t=None):
    if t is None:
        positions = torch.arange(0., x.size(1))
        if x.is_cuda:
            positions = positions.cuda(x.get_device())
    else:
        positions = t
    encodings = torch.zeros(*x.size()[1:])
    if x.is_cuda:
        encodings = encodings.cuda(x.get_device())
    for channel in range(x.size(-1)):
        if channel % 2 == 0:
            encodings[:, channel] = torch.sin(
                positions / 10000 ** (channel / x.size(2)))
        else:
            encodings[:, channel] = torch.cos(
                positions / 10000 ** ((channel - 1) / x.size(2)))
    return Variable(encodings)


# torch.matmul can't do (4, 3, 2) @ (4, 2) -> (4, 3) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
        """Build sinusoidal embeddings.

        This matches the implementation in tensor2tensor, but differs slightly
        from the description in Section 3.5 of "Attention Is All You Need".
        """
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
        if embedding_dim % 2 == 1:
            # zero pad
            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if padding_idx is not None:
            emb[padding_idx, :] = 0
        return emb 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def get_embedding(
        num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
    ):
        """Build sinusoidal embeddings.

        This matches the implementation in tensor2tensor, but differs slightly
        from the description in Section 3.5 of "Attention Is All You Need".
        """
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
            1
        ) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
            num_embeddings, -1
        )
        if embedding_dim % 2 == 1:
            # zero pad
            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if padding_idx is not None:
            emb[padding_idx, :] = 0
        return emb 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def embed(self, h, r, t):
        """Function to get the embedding value.

           Args:
               h (Tensor): Head entities ids.
               r (Tensor): Relation ids of the triple.
               t (Tensor): Tail entity ids of the triple.

            Returns:
                Tensors: Returns real and imaginary values of head, relation and tail embedding.
        """
        pi = 3.14159265358979323846
        h_e_r = self.ent_embeddings(h)
        h_e_i = self.ent_embeddings_imag(h)
        r_e_r = self.rel_embeddings(r)
        t_e_r = self.ent_embeddings(t)
        t_e_i = self.ent_embeddings_imag(t)
        r_e_r = r_e_r / (self.embedding_range / pi)
        r_e_i = torch.sin(r_e_r)
        r_e_r = torch.cos(r_e_r)
        return h_e_r, h_e_i, r_e_r, r_e_i, t_e_r, t_e_i 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def __init__(self, d_model, dropout, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) *
                             -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def _get_lifter_coeffs(num_ceps: int, cepstral_lifter: float) -> Tensor:
    # returns size (num_ceps)
    # Compute liftering coefficients (scaling on cepstral coeffs)
    # coeffs are numbered slightly differently from HTK: the zeroth index is C0, which is not affected.
    i = torch.arange(num_ceps)
    return 1.0 + 0.5 * cepstral_lifter * torch.sin(math.pi * i / cepstral_lifter) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def highpass_biquad(
        waveform: Tensor,
        sample_rate: int,
        cutoff_freq: float,
        Q: float = 0.707
) -> Tensor:
    r"""Design biquad highpass filter and perform filtering.  Similar to SoX implementation.

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        cutoff_freq (float): filter cutoff frequency
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)

    Returns:
        Tensor: Waveform dimension of `(..., time)`
    """
    w0 = 2 * math.pi * cutoff_freq / sample_rate
    alpha = math.sin(w0) / 2. / Q

    b0 = (1 + math.cos(w0)) / 2
    b1 = -1 - math.cos(w0)
    b2 = b0
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def lowpass_biquad(
        waveform: Tensor,
        sample_rate: int,
        cutoff_freq: float,
        Q: float = 0.707
) -> Tensor:
    r"""Design biquad lowpass filter and perform filtering.  Similar to SoX implementation.

    Args:
        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        cutoff_freq (float): filter cutoff frequency
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)

    Returns:
        Tensor: Waveform of dimension of `(..., time)`
    """
    w0 = 2 * math.pi * cutoff_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q

    b0 = (1 - math.cos(w0)) / 2
    b1 = 1 - math.cos(w0)
    b2 = b0
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sin [as 别名]
def bandpass_biquad(
        waveform: Tensor,
        sample_rate: int,
        central_freq: float,
        Q: float = 0.707,
        const_skirt_gain: bool = False
) -> Tensor:
    r"""Design two-pole band-pass filter.  Similar to SoX implementation.

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        central_freq (float): central frequency (in Hz)
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
        const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q).
            If ``False``, uses a constant 0dB peak gain. (Default: ``False``)

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
        https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
    """
    w0 = 2 * math.pi * central_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q

    temp = math.sin(w0) / 2 if const_skirt_gain else alpha
    b0 = temp
    b1 = 0.
    b2 = -temp
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2)