107 lines
4.1 KiB
Python
107 lines
4.1 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.functional.hamming_distance import _hamming_distance_update, _hamming_distance_compute
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class HammingDistance(Metric):
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r"""
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Computes the average `Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ (also
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known as Hamming loss) between targets and predictions:
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.. math::
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\text{Hamming distance} = \frac{1}{N \cdot L}\sum_i^N \sum_l^L 1(y_{il} \neq \hat{y_{il}})
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Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
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and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
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tensor.
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This is the same as ``1-accuracy`` for binary data, while for all other types of inputs it
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treats each possible label separately - meaning that, for example, multi-class data is
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treated as if it were multi-label.
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Accepts all input types listed in :ref:`metrics:Input types`.
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Args:
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threshold:
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Threshold probability value for transforming probability predictions to binary
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(0 or 1) predictions, in the case of binary or multi-label inputs.
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compute_on_step:
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Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
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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.
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process_group:
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Specify the process group on which synchronization is called.
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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 all gather.
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Example:
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>>> from pytorch_lightning.metrics import HammingDistance
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>>> target = torch.tensor([[0, 1], [1, 1]])
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>>> preds = torch.tensor([[0, 1], [0, 1]])
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>>> hamming_distance = HammingDistance()
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>>> hamming_distance(preds, target)
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tensor(0.2500)
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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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if not 0 < threshold < 1:
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raise ValueError("The `threshold` should lie in the (0,1) interval.")
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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. See :ref:`metrics:Input types` for more information
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on input types.
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Args:
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preds: Predictions from model (probabilities, or labels)
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target: Ground truth labels
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"""
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correct, total = _hamming_distance_update(preds, target, self.threshold)
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self.correct += correct
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self.total += total
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def compute(self) -> torch.Tensor:
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"""
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Computes hamming distance based on inputs passed in to ``update`` previously.
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"""
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return _hamming_distance_compute(self.correct, self.total)
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