130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
# Copyright The PyTorch Lightning team.
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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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from typing import Optional, Any, Union, List
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import torch
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from pytorch_lightning.metrics import Metric
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from pytorch_lightning.metrics.functional.average_precision import (
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_average_precision_update,
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_average_precision_compute
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)
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from pytorch_lightning.utilities import rank_zero_warn
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class AveragePrecision(Metric):
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"""
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Computes the average precision score, which summarises the precision recall
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curve into one number. Works for both binary and multiclass problems.
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In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.
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Forward accepts
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- ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass)
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where C is the number of classes
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- ``target`` (long tensor): ``(N, ...)``
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Args:
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num_classes: integer with number of classes. Not nessesary to provide
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for binary problems.
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pos_label: integer determining the positive class. Default is ``None``
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which for binary problem is translate to 1. For multiclass problems
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this argument should not be set as we iteratively change it in the
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range [0,num_classes-1]
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compute_on_step:
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Forward only calls ``update()`` and return None if this is set to False. default: True
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dist_sync_on_step:
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Synchronize metric state across processes at each ``forward()``
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before returning the value at the step. default: False
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process_group:
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Specify the process group on which synchronization is called. default: None (which selects the entire world)
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Example (binary case):
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>>> pred = torch.tensor([0, 1, 2, 3])
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>>> target = torch.tensor([0, 1, 1, 1])
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>>> average_precision = AveragePrecision(pos_label=1)
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>>> average_precision(pred, target)
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tensor(1.)
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Example (multiclass case):
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>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
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... [0.05, 0.75, 0.05, 0.05, 0.05],
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... [0.05, 0.05, 0.75, 0.05, 0.05],
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... [0.05, 0.05, 0.05, 0.75, 0.05]])
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>>> target = torch.tensor([0, 1, 3, 2])
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>>> average_precision = AveragePrecision(num_classes=5)
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>>> average_precision(pred, target)
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[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]
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"""
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def __init__(
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self,
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num_classes: Optional[int] = None,
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pos_label: Optional[int] = None,
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compute_on_step: bool = True,
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dist_sync_on_step: bool = False,
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process_group: Optional[Any] = None,
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):
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super().__init__(
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compute_on_step=compute_on_step,
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dist_sync_on_step=dist_sync_on_step,
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process_group=process_group,
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)
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self.num_classes = num_classes
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self.pos_label = pos_label
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self.add_state("preds", default=[], dist_reduce_fx=None)
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self.add_state("target", default=[], dist_reduce_fx=None)
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rank_zero_warn(
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'Metric `AveragePrecision` will save all targets and predictions in buffer.'
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' For large datasets this may lead to large memory footprint.'
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)
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def update(self, preds: torch.Tensor, target: torch.Tensor):
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"""
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Update state with predictions and targets.
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Args:
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preds: Predictions from model
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target: Ground truth values
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"""
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preds, target, num_classes, pos_label = _average_precision_update(
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preds,
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target,
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self.num_classes,
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self.pos_label
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)
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self.preds.append(preds)
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self.target.append(target)
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self.num_classes = num_classes
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self.pos_label = pos_label
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def compute(self) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""
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Compute the average precision score
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Returns:
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tensor with average precision. If multiclass will return list
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of such tensors, one for each class
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"""
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preds = torch.cat(self.preds, dim=0)
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target = torch.cat(self.target, dim=0)
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return _average_precision_compute(preds, target, self.num_classes, self.pos_label)
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