mirror of
https://github.com/hanzo-ml/katib.git
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Ignas BaranauskasandGitHub
bcb199ddc3
* Add black formater and flake8 linter to pre-commit Also add's the flake8 config file Signed-off-by: Ignas Baranauskas <[email protected]> * Fixes black formating Signed-off-by: Ignas Baranauskas <[email protected]> * Fixes flake8 linting errors Signed-off-by: Ignas Baranauskas <[email protected]> --------- Signed-off-by: Ignas Baranauskas <[email protected]>
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
# Copyright 2022 The Kubeflow 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 torchvision.datasets as dset
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import torchvision.transforms as transforms
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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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"""Reset all statistics"""
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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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"""Update statistics"""
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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 accuracy(output, target, topk=(1,)):
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"""Computes the precision@k for the specified values of k"""
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maxk = max(topk)
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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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# one-hot case
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if target.ndimension() > 1:
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target = target.max(1)[1]
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].contiguous().view(-1).float().sum(0)
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res.append(correct_k.mul_(1.0 / batch_size))
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return res
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def get_dataset():
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dataset_cls = dset.CIFAR10
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num_classes = 10
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input_channels = 3
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# Do preprocessing
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MEAN = [0.49139968, 0.48215827, 0.44653124]
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STD = [0.24703233, 0.24348505, 0.26158768]
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transf = [transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]
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normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)]
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train_transform = transforms.Compose(transf + normalize)
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train_data = dataset_cls(
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root="./data", train=True, download=True, transform=train_transform
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)
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return input_channels, num_classes, train_data
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