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from typing import List, Optional, Tuple, Iterator
import torch
def call_weight_data_normal_if_Conv(m: torch.nn.Module):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
mean = 0.0
std = 0.01
m.weight.data.normal_(mean, std)
def get_padding(kernel_size: int, dilation=1) -> int:
return int((kernel_size * dilation - dilation) / 2)
def slice_on_last_dim(
x: torch.Tensor,
start_indices: List[int],
segment_size=4,
) -> torch.Tensor:
new_shape = [*x.shape]
new_shape[-1] = segment_size
ret = torch.empty(new_shape, device=x.device)
for i in range(x.size(0)):
idx_str = start_indices[i]
idx_end = idx_str + segment_size
ret[i, ..., :] = x[i, ..., idx_str:idx_end]
return ret
def rand_slice_segments_on_last_dim(
x: torch.Tensor,
x_lengths: int = None,
segment_size=4,
) -> Tuple[torch.Tensor, List[int]]:
b, _, t = x.size()
if x_lengths is None:
x_lengths = t
ids_str_max = x_lengths - segment_size + 1
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
ret = slice_on_last_dim(x, ids_str, segment_size)
return ret, ids_str
@torch.jit.script
def activate_add_tanh_sigmoid_multiply(
input_a: torch.Tensor, input_b: torch.Tensor, n_channels: int
) -> torch.Tensor:
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels, :])
s_act = torch.sigmoid(in_act[:, n_channels:, :])
acts = t_act * s_act
return acts
def sequence_mask(
length: torch.Tensor,
max_length: Optional[int] = None,
) -> torch.BoolTensor:
if max_length is None:
max_length = int(length.max())
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return x.unsqueeze(0) < length.unsqueeze(1)
def total_grad_norm(
parameters: Iterator[torch.nn.Parameter],
norm_type: float = 2.0,
) -> float:
norm_type = float(norm_type)
total_norm = 0.0
for p in parameters:
if p.grad is None:
continue
param_norm = p.grad.data.norm(norm_type)
total_norm += float(param_norm.item()) ** norm_type
total_norm = total_norm ** (1.0 / norm_type)
return total_norm
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