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