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Zero
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# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
# 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Unility functions for Transformer."""
from typing import List
import torch
IGNORE_ID = -1
def pad_list(xs: List[torch.Tensor], pad_value: int):
"""Perform padding for the list of tensors.
Args:
xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
pad_value (float): Value for padding.
Returns:
Tensor: Padded tensor (B, Tmax, `*`).
Examples:
>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
>>> x
[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
>>> pad_list(x, 0)
tensor([[1., 1., 1., 1.],
[1., 1., 0., 0.],
[1., 0., 0., 0.]])
"""
max_len = max([len(item) for item in xs])
batchs = len(xs)
ndim = xs[0].ndim
if ndim == 1:
pad_res = torch.zeros(batchs,
max_len,
dtype=xs[0].dtype,
device=xs[0].device)
elif ndim == 2:
pad_res = torch.zeros(batchs,
max_len,
xs[0].shape[1],
dtype=xs[0].dtype,
device=xs[0].device)
elif ndim == 3:
pad_res = torch.zeros(batchs,
max_len,
xs[0].shape[1],
xs[0].shape[2],
dtype=xs[0].dtype,
device=xs[0].device)
else:
raise ValueError(f"Unsupported ndim: {ndim}")
pad_res.fill_(pad_value)
for i in range(batchs):
pad_res[i, :len(xs[i])] = xs[i]
return pad_res
def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor,
ignore_label: int) -> torch.Tensor:
"""Calculate accuracy.
Args:
pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
pad_targets (LongTensor): Target label tensors (B, Lmax).
ignore_label (int): Ignore label id.
Returns:
torch.Tensor: Accuracy value (0.0 - 1.0).
"""
pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1),
pad_outputs.size(1)).argmax(2)
mask = pad_targets != ignore_label
numerator = torch.sum(
pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
denominator = torch.sum(mask)
return (numerator / denominator).detach()
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
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