106 lines
3.8 KiB
Python
106 lines
3.8 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 Any, Callable, Optional
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import torch
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from pytorch_lightning.metrics.metric import Metric
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from pytorch_lightning.metrics.utils import _input_format_classification
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class Accuracy(Metric):
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r"""
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Computes `Accuracy <https://en.wikipedia.org/wiki/Accuracy_and_precision>`_:
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.. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y_i})
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Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
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tensor of predictions. Works with binary, multiclass, and multilabel
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data. Accepts logits from a model output or integer class values in
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prediction. Works with multi-dimensional preds and target.
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Forward accepts
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- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
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- ``target`` (long tensor): ``(N, ...)``
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If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument.
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This is the case for binary and multi-label logits.
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If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
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Args:
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threshold:
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Threshold value for binary or multi-label logits. default: 0.5
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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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dist_sync_fn:
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Callback that performs the allgather operation on the metric state. When `None`, DDP
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will be used to perform the allgather. default: None
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Example:
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>>> from pytorch_lightning.metrics import Accuracy
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>>> target = torch.tensor([0, 1, 2, 3])
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>>> preds = torch.tensor([0, 2, 1, 3])
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>>> accuracy = Accuracy()
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>>> accuracy(preds, target)
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tensor(0.5000)
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"""
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def __init__(
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self,
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threshold: float = 0.5,
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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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dist_sync_fn: Callable = 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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dist_sync_fn=dist_sync_fn,
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)
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self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
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self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
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self.threshold = threshold
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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 = _input_format_classification(preds, target, self.threshold)
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assert preds.shape == target.shape
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self.correct += torch.sum(preds == target)
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self.total += target.numel()
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def compute(self):
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"""
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Computes accuracy over state.
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"""
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return self.correct.float() / self.total
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