Spaces:
Runtime error
Runtime error
| # 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() | |