mirror of
https://github.com/k2-fsa/icefall.git
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79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import numpy as np
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import torch
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import torch.distributed as dist
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# We might need to move this file to icefall/utils.py in the future
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# Copied from https://github.com/microsoft/Swin-Transformer/blob/main/utils.py
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def reduce_tensor(tensor):
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rt = tensor.clone()
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dist.all_reduce(rt, op=dist.ReduceOp.SUM)
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rt /= dist.get_world_size()
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return rt
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def fix_random_seed(random_seed: int, rank: int):
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seed = random_seed + rank
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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# Copied from https://github.com/huggingface/pytorch-image-models/blob/main/timm/utils/metrics.py
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class AverageMeter:
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def __str__(self) -> str:
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return f"{self.val:.4f} (avg: {self.avg:.4f})"
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# Copied from https://github.com/huggingface/pytorch-image-models/blob/main/timm/utils/metrics.py
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def accuracy(output, target, topk=(1,)):
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"""Computes the accuracy over the k top predictions for the specified values of k"""
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maxk = min(max(topk), output.size()[1])
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.reshape(1, -1).expand_as(pred))
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return [
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correct[: min(k, maxk)].reshape(-1).float().sum(0) * 100.0 / batch_size
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for k in topk
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]
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