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- """
- Copyright 2020 Tianshu AI Platform. All Rights Reserved.
-
- 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.
- =============================================================
- """
-
- import numpy as np
- import torch
-
- from kamal.core.metrics.stream_metrics import Metric
- from typing import Callable
-
- __all__=['MeanSquaredError', 'RootMeanSquaredError', 'MeanAbsoluteError',
- 'ScaleInveriantMeanSquaredError', 'RelativeDifference',
- 'AbsoluteRelativeDifference', 'SquaredRelativeDifference', 'Threshold' ]
-
- class MeanSquaredError(Metric):
- def __init__(self, log_scale=False, attach_to=None):
- super(MeanSquaredError, self).__init__( attach_to=attach_to )
- self.reset()
- self.log_scale=log_scale
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- if self.log_scale:
- diff = torch.sum((torch.log(outputs+1e-8) - torch.log(targets+1e-8))**2)
- else:
- diff = torch.sum((outputs - targets)**2)
- self._accum_sq_diff += diff
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return (self._accum_sq_diff / self._cnt).detach().cpu()
-
- def reset(self):
- self._accum_sq_diff = 0.
- self._cnt = 0.
-
-
- class RootMeanSquaredError(MeanSquaredError):
- def get_results(self):
- return torch.sqrt( (self._accum_sq_diff / self._cnt) ).detach().cpu()
-
-
- class MeanAbsoluteError(Metric):
- def __init__(self, attach_to=None ):
- super(MeanAbsoluteError, self).__init__( attach_to=attach_to )
- self.reset()
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- diff = torch.sum((outputs - targets).abs())
- self._accum_abs_diff += diff
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return (self._accum_abs_diff / self._cnt).detach().cpu()
-
- def reset(self):
- self._accum_abs_diff = 0.
- self._cnt = 0.
-
-
- class ScaleInveriantMeanSquaredError(Metric):
- def __init__(self, attach_to=None ):
- super(ScaleInveriantMeanSquaredError, self).__init__( attach_to=attach_to )
- self.reset()
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- diff_log = torch.log( outputs+1e-8 ) - torch.log( targets+1e-8 )
- self._accum_log_diff = diff_log.sum()
- self._accum_sq_log_diff = (diff_log**2).sum()
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return ( self._accum_sq_log_diff / self._cnt - 0.5 * (self._accum_log_diff**2 / self._cnt**2) ).detach().cpu()
-
- def reset(self):
- self._accum_log_diff = 0.
- self._accum_sq_log_diff = 0.
- self._cnt = 0.
-
-
- class RelativeDifference(Metric):
- def __init__(self, attach_to=None ):
- super(RelativeDifference, self).__init__( attach_to=attach_to )
- self.reset()
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- diff = (outputs - targets).abs()
- self._accum_abs_rel += (diff/targets).sum()
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return (self._accum_abs_rel / self._cnt).detach().cpu()
-
- def reset(self):
- self._accum_abs_rel = 0.
- self._cnt = 0.
-
-
- class AbsoluteRelativeDifference(Metric):
- def __init__(self, attach_to=None ):
- super(AbsoluteRelativeDifference, self).__init__( attach_to=attach_to )
- self.reset()
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- diff = (outputs - targets).abs()
- self._accum_abs_rel += (diff/targets).sum()
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return (self._accum_abs_rel / self._cnt).detach().cpu()
-
- def reset(self):
- self._accum_abs_rel = 0.
- self._cnt = 0.
-
-
- class AbsoluteRelativeDifference(Metric):
- def __init__(self, attach_to=None ):
- super(AbsoluteRelativeDifference, self).__init__( attach_to=attach_to )
- self.reset()
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- diff = (outputs - targets).abs()
- self._accum_abs_rel += (diff/targets).sum()
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return (self._accum_abs_rel / self._cnt).detach().cpu()
-
- def reset(self):
- self._accum_abs_rel = 0.
- self._cnt = 0.
-
-
- class SquaredRelativeDifference(Metric):
- def __init__(self, attach_to=None ):
- super(SquaredRelativeDifference, self).__init__( attach_to=attach_to )
- self.reset()
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- diff = (outputs - targets)**2
- self._accum_sq_rel += (diff/targets).sum()
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return (self._accum_sq_rel / self._cnt).detach().cpu()
-
- def reset(self):
- self._accum_sq_rel = 0.
- self._cnt = 0.
-
-
- class Threshold(Metric):
- def __init__(self, thresholds=[1.25, 1.25**2, 1.25**3], attach_to=None ):
- super(Threshold, self).__init__( attach_to=attach_to )
- self.thresholds = thresholds
- self.reset()
-
-
- @torch.no_grad()
- def update(self, outputs, targets):
- outputs, targets = self._attach(outputs, targets)
- sigma = torch.max(outputs / targets, targets / outputs)
- for thres in self.thresholds:
- self._accum_thres[thres]+=torch.sum( sigma<thres )
- self._cnt += torch.numel(outputs)
-
- def get_results(self):
- return { thres: (self._accum_thres[thres] / self._cnt).detach().cpu() for thres in self.thresholds }
-
- def reset(self):
- self._cnt = 0.
- self._accum_thres = {thres: 0. for thres in self.thresholds}
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