91 lines
3.1 KiB
Python
91 lines
3.1 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.functional.auc import _auc_compute, _auc_update
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from pytorch_lightning.metrics.metric import Metric
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from pytorch_lightning.utilities import rank_zero_warn
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class AUC(Metric):
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r"""
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Computes Area Under the Curve (AUC) using the trapezoidal rule
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Forward accepts two input tensors that should be 1D and have the same number
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of elements
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Args:
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reorder: AUC expects its first input to be sorted. If this is not the case,
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setting this argument to ``True`` will use a stable sorting algorithm to
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sort the input in decending order
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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. 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
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"""
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def __init__(
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self,
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reorder: bool = False,
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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.reorder = reorder
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self.add_state("x", default=[], dist_reduce_fx=None)
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self.add_state("y", default=[], dist_reduce_fx=None)
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rank_zero_warn(
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'Metric `AUC` 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, x: torch.Tensor, y: 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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x: Predictions from model (probabilities, or labels)
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y: Ground truth labels
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"""
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x, y = _auc_update(x, y)
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self.x.append(x)
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self.y.append(y)
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def compute(self) -> torch.Tensor:
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"""
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Computes AUC based on inputs passed in to ``update`` previously.
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"""
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x = torch.cat(self.x, dim=0)
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y = torch.cat(self.y, dim=0)
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return _auc_compute(x, y, reorder=self.reorder)
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