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import math |
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import torch |
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from typing import List, Optional |
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def init_weights(m, mean=0.0, std=0.01): |
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""" |
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Initialize the weights of a module. |
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Args: |
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m: The module to initialize. |
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mean: The mean of the normal distribution. |
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std: The standard deviation of the normal distribution. |
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""" |
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classname = m.__class__.__name__ |
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if classname.find("Conv") != -1: |
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m.weight.data.normal_(mean, std) |
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def get_padding(kernel_size, dilation=1): |
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""" |
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Calculate the padding needed for a convolution. |
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Args: |
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kernel_size: The size of the kernel. |
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dilation: The dilation of the convolution. |
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""" |
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return int((kernel_size * dilation - dilation) / 2) |
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def convert_pad_shape(pad_shape): |
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""" |
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Convert the pad shape to a list of integers. |
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Args: |
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pad_shape: The pad shape.. |
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""" |
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l = pad_shape[::-1] |
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pad_shape = [item for sublist in l for item in sublist] |
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return pad_shape |
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def kl_divergence(m_p, logs_p, m_q, logs_q): |
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""" |
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Calculate the KL divergence between two distributions. |
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Args: |
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m_p: The mean of the first distribution. |
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logs_p: The log of the standard deviation of the first distribution. |
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m_q: The mean of the second distribution. |
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logs_q: The log of the standard deviation of the second distribution. |
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""" |
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kl = (logs_q - logs_p) - 0.5 |
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kl += ( |
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0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) |
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) |
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return kl |
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def slice_segments(x, ids_str, segment_size=4): |
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""" |
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Slice segments from a tensor. |
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Args: |
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x: The tensor to slice. |
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ids_str: The starting indices of the segments. |
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segment_size: The size of each segment. |
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""" |
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ret = torch.zeros_like(x[:, :, :segment_size]) |
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for i in range(x.size(0)): |
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idx_str = ids_str[i] |
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idx_end = idx_str + segment_size |
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ret[i] = x[i, :, idx_str:idx_end] |
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return ret |
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def slice_segments2(x, ids_str, segment_size=4): |
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""" |
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Slice segments from a tensor. |
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Args: |
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x: The tensor to slice. |
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ids_str: The starting indices of the segments. |
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segment_size: The size of each segment. |
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""" |
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ret = torch.zeros_like(x[:, :segment_size]) |
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for i in range(x.size(0)): |
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idx_str = ids_str[i] |
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idx_end = idx_str + segment_size |
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ret[i] = x[i, idx_str:idx_end] |
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return ret |
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def rand_slice_segments(x, x_lengths=None, segment_size=4): |
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""" |
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Randomly slice segments from a tensor. |
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Args: |
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x: The tensor to slice. |
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x_lengths: The lengths of the sequences. |
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segment_size: The size of each segment. |
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""" |
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b, d, t = x.size() |
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if x_lengths is None: |
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x_lengths = t |
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ids_str_max = x_lengths - segment_size + 1 |
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ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) |
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ret = slice_segments(x, ids_str, segment_size) |
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return ret, ids_str |
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def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): |
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""" |
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Generate a 1D timing signal. |
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Args: |
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length: The length of the signal. |
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channels: The number of channels of the signal. |
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min_timescale: The minimum timescale. |
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max_timescale: The maximum timescale. |
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""" |
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position = torch.arange(length, dtype=torch.float) |
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num_timescales = channels // 2 |
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log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( |
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num_timescales - 1 |
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) |
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inv_timescales = min_timescale * torch.exp( |
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torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment |
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) |
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scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) |
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signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) |
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signal = torch.nn.functional.pad(signal, [0, 0, 0, channels % 2]) |
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signal = signal.view(1, channels, length) |
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return signal |
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def subsequent_mask(length): |
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""" |
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Generate a subsequent mask. |
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Args: |
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length: The length of the sequence. |
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""" |
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mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) |
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return mask |
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@torch.jit.script |
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): |
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""" |
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Fused add tanh sigmoid multiply operation. |
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Args: |
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input_a: The first input tensor. |
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input_b: The second input tensor. |
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n_channels: The number of channels. |
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""" |
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n_channels_int = n_channels[0] |
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in_act = input_a + input_b |
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t_act = torch.tanh(in_act[:, :n_channels_int, :]) |
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) |
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acts = t_act * s_act |
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return acts |
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def convert_pad_shape(pad_shape: List[List[int]]) -> List[int]: |
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""" |
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Convert the pad shape to a list of integers. |
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Args: |
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pad_shape: The pad shape. |
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""" |
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return torch.tensor(pad_shape).flip(0).reshape(-1).int().tolist() |
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def sequence_mask(length: torch.Tensor, max_length: Optional[int] = None): |
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""" |
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Generate a sequence mask. |
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Args: |
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length: The lengths of the sequences. |
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max_length: The maximum length of the sequences. |
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""" |
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if max_length is None: |
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max_length = length.max() |
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x = torch.arange(max_length, dtype=length.dtype, device=length.device) |
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return x.unsqueeze(0) < length.unsqueeze(1) |
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def clip_grad_value(parameters, clip_value, norm_type=2): |
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""" |
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Clip the gradients of a list of parameters. |
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Args: |
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parameters: The list of parameters to clip. |
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clip_value: The maximum value of the gradients. |
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norm_type: The type of norm to use for clipping. |
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""" |
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if isinstance(parameters, torch.Tensor): |
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parameters = [parameters] |
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parameters = list(filter(lambda p: p.grad is not None, parameters)) |
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norm_type = float(norm_type) |
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if clip_value is not None: |
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clip_value = float(clip_value) |
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total_norm = 0 |
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for p in parameters: |
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param_norm = p.grad.data.norm(norm_type) |
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total_norm += param_norm.item() ** norm_type |
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if clip_value is not None: |
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p.grad.data.clamp_(min=-clip_value, max=clip_value) |
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total_norm = total_norm ** (1.0 / norm_type) |
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return total_norm |
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