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- # Copyright 2023 The KubeEdge Authors.
- #
- # 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.
-
- from tqdm import tqdm
- from sedna.common.class_factory import ClassType, ClassFactory
- from sedna.common.log import LOGGER
-
- from utils.args import EvaluationArguments
- from utils.metrics import Evaluator
- from dataloaders import make_data_loader
-
- __all__ = ('accuracy', )
-
-
- @ClassFactory.register(ClassType.GENERAL)
- def accuracy(y_true, y_pred, **kwargs):
- args = EvaluationArguments()
- _, _, test_loader = make_data_loader(args, test_data=y_true)
- evaluator = Evaluator(args.num_class)
-
- tbar = tqdm(test_loader, desc='\r')
- for i, (sample, _) in enumerate(tbar):
- if args.depth:
- image, depth, target = sample['image'], sample['depth'], sample['label']
- else:
- image, target = sample['image'], sample['label']
- if args.cuda:
- image, target = image.cuda(), target.cuda()
- if args.depth:
- depth = depth.cuda()
-
- target[target > evaluator.num_class - 1] = 255
- target = target.cpu().numpy()
- # Add batch sample into evaluator
- evaluator.add_batch(target, y_pred[i])
-
- # Test during the training
- CPA = evaluator.Pixel_Accuracy_Class()
- mIoU = evaluator.Mean_Intersection_over_Union()
- FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union()
-
- LOGGER.info("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU))
- return mIoU
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