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https://github.com/babysor/MockingBird.git
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466 lines
16 KiB
Python
466 lines
16 KiB
Python
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# -*- coding: utf-8 -*-
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"""Network related utility tools."""
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import logging
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from typing import Dict
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import numpy as np
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import torch
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def to_device(m, x):
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"""Send tensor into the device of the module.
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Args:
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m (torch.nn.Module): Torch module.
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x (Tensor): Torch tensor.
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Returns:
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Tensor: Torch tensor located in the same place as torch module.
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"""
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assert isinstance(m, torch.nn.Module)
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device = next(m.parameters()).device
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return x.to(device)
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def pad_list(xs, pad_value):
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"""Perform padding for the list of tensors.
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Args:
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xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
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pad_value (float): Value for padding.
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Returns:
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Tensor: Padded tensor (B, Tmax, `*`).
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Examples:
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>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
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>>> x
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[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
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>>> pad_list(x, 0)
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tensor([[1., 1., 1., 1.],
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[1., 1., 0., 0.],
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[1., 0., 0., 0.]])
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"""
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n_batch = len(xs)
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max_len = max(x.size(0) for x in xs)
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pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
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for i in range(n_batch):
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pad[i, :xs[i].size(0)] = xs[i]
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return pad
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def make_pad_mask(lengths, xs=None, length_dim=-1):
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"""Make mask tensor containing indices of padded part.
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Args:
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lengths (LongTensor or List): Batch of lengths (B,).
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xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.
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length_dim (int, optional): Dimension indicator of the above tensor. See the example.
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Returns:
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Tensor: Mask tensor containing indices of padded part.
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dtype=torch.uint8 in PyTorch 1.2-
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dtype=torch.bool in PyTorch 1.2+ (including 1.2)
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Examples:
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With only lengths.
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>>> lengths = [5, 3, 2]
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>>> make_non_pad_mask(lengths)
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masks = [[0, 0, 0, 0 ,0],
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[0, 0, 0, 1, 1],
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[0, 0, 1, 1, 1]]
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With the reference tensor.
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>>> xs = torch.zeros((3, 2, 4))
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>>> make_pad_mask(lengths, xs)
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tensor([[[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 1],
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[0, 0, 0, 1]],
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[[0, 0, 1, 1],
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[0, 0, 1, 1]]], dtype=torch.uint8)
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>>> xs = torch.zeros((3, 2, 6))
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>>> make_pad_mask(lengths, xs)
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tensor([[[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 1]],
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[[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1]],
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[[0, 0, 1, 1, 1, 1],
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[0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
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With the reference tensor and dimension indicator.
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>>> xs = torch.zeros((3, 6, 6))
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>>> make_pad_mask(lengths, xs, 1)
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tensor([[[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1, 1]],
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[[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1]],
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[[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
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>>> make_pad_mask(lengths, xs, 2)
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tensor([[[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 1]],
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[[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1]],
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[[0, 0, 1, 1, 1, 1],
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[0, 0, 1, 1, 1, 1],
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[0, 0, 1, 1, 1, 1],
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[0, 0, 1, 1, 1, 1],
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[0, 0, 1, 1, 1, 1],
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[0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
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"""
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if length_dim == 0:
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raise ValueError('length_dim cannot be 0: {}'.format(length_dim))
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if not isinstance(lengths, list):
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lengths = lengths.tolist()
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bs = int(len(lengths))
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if xs is None:
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maxlen = int(max(lengths))
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else:
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maxlen = xs.size(length_dim)
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seq_range = torch.arange(0, maxlen, dtype=torch.int64)
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seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
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seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
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mask = seq_range_expand >= seq_length_expand
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if xs is not None:
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assert xs.size(0) == bs, (xs.size(0), bs)
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if length_dim < 0:
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length_dim = xs.dim() + length_dim
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# ind = (:, None, ..., None, :, , None, ..., None)
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ind = tuple(slice(None) if i in (0, length_dim) else None
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for i in range(xs.dim()))
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mask = mask[ind].expand_as(xs).to(xs.device)
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return mask
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def make_non_pad_mask(lengths, xs=None, length_dim=-1):
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"""Make mask tensor containing indices of non-padded part.
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Args:
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lengths (LongTensor or List): Batch of lengths (B,).
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xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.
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length_dim (int, optional): Dimension indicator of the above tensor. See the example.
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Returns:
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ByteTensor: mask tensor containing indices of padded part.
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dtype=torch.uint8 in PyTorch 1.2-
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dtype=torch.bool in PyTorch 1.2+ (including 1.2)
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Examples:
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With only lengths.
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>>> lengths = [5, 3, 2]
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>>> make_non_pad_mask(lengths)
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masks = [[1, 1, 1, 1 ,1],
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[1, 1, 1, 0, 0],
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[1, 1, 0, 0, 0]]
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With the reference tensor.
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>>> xs = torch.zeros((3, 2, 4))
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>>> make_non_pad_mask(lengths, xs)
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tensor([[[1, 1, 1, 1],
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[1, 1, 1, 1]],
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[[1, 1, 1, 0],
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[1, 1, 1, 0]],
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[[1, 1, 0, 0],
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[1, 1, 0, 0]]], dtype=torch.uint8)
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>>> xs = torch.zeros((3, 2, 6))
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>>> make_non_pad_mask(lengths, xs)
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tensor([[[1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 0]],
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[[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0]],
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[[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0]]], dtype=torch.uint8)
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With the reference tensor and dimension indicator.
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>>> xs = torch.zeros((3, 6, 6))
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>>> make_non_pad_mask(lengths, xs, 1)
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tensor([[[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 0]],
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[[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0]],
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[[1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0]]], dtype=torch.uint8)
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>>> make_non_pad_mask(lengths, xs, 2)
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tensor([[[1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 0]],
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[[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0]],
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[[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0]]], dtype=torch.uint8)
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"""
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return ~make_pad_mask(lengths, xs, length_dim)
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def mask_by_length(xs, lengths, fill=0):
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"""Mask tensor according to length.
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Args:
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xs (Tensor): Batch of input tensor (B, `*`).
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lengths (LongTensor or List): Batch of lengths (B,).
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fill (int or float): Value to fill masked part.
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Returns:
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Tensor: Batch of masked input tensor (B, `*`).
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Examples:
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>>> x = torch.arange(5).repeat(3, 1) + 1
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>>> x
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tensor([[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5]])
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>>> lengths = [5, 3, 2]
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>>> mask_by_length(x, lengths)
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tensor([[1, 2, 3, 4, 5],
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[1, 2, 3, 0, 0],
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[1, 2, 0, 0, 0]])
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"""
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assert xs.size(0) == len(lengths)
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ret = xs.data.new(*xs.size()).fill_(fill)
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for i, l in enumerate(lengths):
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ret[i, :l] = xs[i, :l]
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return ret
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def th_accuracy(pad_outputs, pad_targets, ignore_label):
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"""Calculate accuracy.
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Args:
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pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
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pad_targets (LongTensor): Target label tensors (B, Lmax, D).
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ignore_label (int): Ignore label id.
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Returns:
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float: Accuracy value (0.0 - 1.0).
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"""
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pad_pred = pad_outputs.view(
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pad_targets.size(0),
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pad_targets.size(1),
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pad_outputs.size(1)).argmax(2)
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mask = pad_targets != ignore_label
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numerator = torch.sum(pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
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denominator = torch.sum(mask)
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return float(numerator) / float(denominator)
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def to_torch_tensor(x):
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"""Change to torch.Tensor or ComplexTensor from numpy.ndarray.
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Args:
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x: Inputs. It should be one of numpy.ndarray, Tensor, ComplexTensor, and dict.
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Returns:
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Tensor or ComplexTensor: Type converted inputs.
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Examples:
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>>> xs = np.ones(3, dtype=np.float32)
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>>> xs = to_torch_tensor(xs)
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tensor([1., 1., 1.])
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>>> xs = torch.ones(3, 4, 5)
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>>> assert to_torch_tensor(xs) is xs
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>>> xs = {'real': xs, 'imag': xs}
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>>> to_torch_tensor(xs)
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ComplexTensor(
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Real:
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tensor([1., 1., 1.])
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Imag;
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tensor([1., 1., 1.])
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)
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"""
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# If numpy, change to torch tensor
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if isinstance(x, np.ndarray):
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if x.dtype.kind == 'c':
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# Dynamically importing because torch_complex requires python3
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from torch_complex.tensor import ComplexTensor
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return ComplexTensor(x)
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else:
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return torch.from_numpy(x)
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# If {'real': ..., 'imag': ...}, convert to ComplexTensor
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elif isinstance(x, dict):
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# Dynamically importing because torch_complex requires python3
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from torch_complex.tensor import ComplexTensor
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if 'real' not in x or 'imag' not in x:
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raise ValueError("has 'real' and 'imag' keys: {}".format(list(x)))
|
||
|
|
# Relative importing because of using python3 syntax
|
||
|
|
return ComplexTensor(x['real'], x['imag'])
|
||
|
|
|
||
|
|
# If torch.Tensor, as it is
|
||
|
|
elif isinstance(x, torch.Tensor):
|
||
|
|
return x
|
||
|
|
|
||
|
|
else:
|
||
|
|
error = ("x must be numpy.ndarray, torch.Tensor or a dict like "
|
||
|
|
"{{'real': torch.Tensor, 'imag': torch.Tensor}}, "
|
||
|
|
"but got {}".format(type(x)))
|
||
|
|
try:
|
||
|
|
from torch_complex.tensor import ComplexTensor
|
||
|
|
except Exception:
|
||
|
|
# If PY2
|
||
|
|
raise ValueError(error)
|
||
|
|
else:
|
||
|
|
# If PY3
|
||
|
|
if isinstance(x, ComplexTensor):
|
||
|
|
return x
|
||
|
|
else:
|
||
|
|
raise ValueError(error)
|
||
|
|
|
||
|
|
|
||
|
|
def get_subsample(train_args, mode, arch):
|
||
|
|
"""Parse the subsampling factors from the training args for the specified `mode` and `arch`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
train_args: argument Namespace containing options.
|
||
|
|
mode: one of ('asr', 'mt', 'st')
|
||
|
|
arch: one of ('rnn', 'rnn-t', 'rnn_mix', 'rnn_mulenc', 'transformer')
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
np.ndarray / List[np.ndarray]: subsampling factors.
|
||
|
|
"""
|
||
|
|
if arch == 'transformer':
|
||
|
|
return np.array([1])
|
||
|
|
|
||
|
|
elif mode == 'mt' and arch == 'rnn':
|
||
|
|
# +1 means input (+1) and layers outputs (train_args.elayer)
|
||
|
|
subsample = np.ones(train_args.elayers + 1, dtype=np.int)
|
||
|
|
logging.warning('Subsampling is not performed for machine translation.')
|
||
|
|
logging.info('subsample: ' + ' '.join([str(x) for x in subsample]))
|
||
|
|
return subsample
|
||
|
|
|
||
|
|
elif (mode == 'asr' and arch in ('rnn', 'rnn-t')) or \
|
||
|
|
(mode == 'mt' and arch == 'rnn') or \
|
||
|
|
(mode == 'st' and arch == 'rnn'):
|
||
|
|
subsample = np.ones(train_args.elayers + 1, dtype=np.int)
|
||
|
|
if train_args.etype.endswith("p") and not train_args.etype.startswith("vgg"):
|
||
|
|
ss = train_args.subsample.split("_")
|
||
|
|
for j in range(min(train_args.elayers + 1, len(ss))):
|
||
|
|
subsample[j] = int(ss[j])
|
||
|
|
else:
|
||
|
|
logging.warning(
|
||
|
|
'Subsampling is not performed for vgg*. It is performed in max pooling layers at CNN.')
|
||
|
|
logging.info('subsample: ' + ' '.join([str(x) for x in subsample]))
|
||
|
|
return subsample
|
||
|
|
|
||
|
|
elif mode == 'asr' and arch == 'rnn_mix':
|
||
|
|
subsample = np.ones(train_args.elayers_sd + train_args.elayers + 1, dtype=np.int)
|
||
|
|
if train_args.etype.endswith("p") and not train_args.etype.startswith("vgg"):
|
||
|
|
ss = train_args.subsample.split("_")
|
||
|
|
for j in range(min(train_args.elayers_sd + train_args.elayers + 1, len(ss))):
|
||
|
|
subsample[j] = int(ss[j])
|
||
|
|
else:
|
||
|
|
logging.warning(
|
||
|
|
'Subsampling is not performed for vgg*. It is performed in max pooling layers at CNN.')
|
||
|
|
logging.info('subsample: ' + ' '.join([str(x) for x in subsample]))
|
||
|
|
return subsample
|
||
|
|
|
||
|
|
elif mode == 'asr' and arch == 'rnn_mulenc':
|
||
|
|
subsample_list = []
|
||
|
|
for idx in range(train_args.num_encs):
|
||
|
|
subsample = np.ones(train_args.elayers[idx] + 1, dtype=np.int)
|
||
|
|
if train_args.etype[idx].endswith("p") and not train_args.etype[idx].startswith("vgg"):
|
||
|
|
ss = train_args.subsample[idx].split("_")
|
||
|
|
for j in range(min(train_args.elayers[idx] + 1, len(ss))):
|
||
|
|
subsample[j] = int(ss[j])
|
||
|
|
else:
|
||
|
|
logging.warning(
|
||
|
|
'Encoder %d: Subsampling is not performed for vgg*. '
|
||
|
|
'It is performed in max pooling layers at CNN.', idx + 1)
|
||
|
|
logging.info('subsample: ' + ' '.join([str(x) for x in subsample]))
|
||
|
|
subsample_list.append(subsample)
|
||
|
|
return subsample_list
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise ValueError('Invalid options: mode={}, arch={}'.format(mode, arch))
|
||
|
|
|
||
|
|
|
||
|
|
def rename_state_dict(old_prefix: str, new_prefix: str, state_dict: Dict[str, torch.Tensor]):
|
||
|
|
"""Replace keys of old prefix with new prefix in state dict."""
|
||
|
|
# need this list not to break the dict iterator
|
||
|
|
old_keys = [k for k in state_dict if k.startswith(old_prefix)]
|
||
|
|
if len(old_keys) > 0:
|
||
|
|
logging.warning(f'Rename: {old_prefix} -> {new_prefix}')
|
||
|
|
for k in old_keys:
|
||
|
|
v = state_dict.pop(k)
|
||
|
|
new_k = k.replace(old_prefix, new_prefix)
|
||
|
|
state_dict[new_k] = v
|
||
|
|
|
||
|
|
def get_activation(act):
|
||
|
|
"""Return activation function."""
|
||
|
|
# Lazy load to avoid unused import
|
||
|
|
from .encoder.swish import Swish
|
||
|
|
|
||
|
|
activation_funcs = {
|
||
|
|
"hardtanh": torch.nn.Hardtanh,
|
||
|
|
"relu": torch.nn.ReLU,
|
||
|
|
"selu": torch.nn.SELU,
|
||
|
|
"swish": Swish,
|
||
|
|
}
|
||
|
|
|
||
|
|
return activation_funcs[act]()
|