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from collections.abc import Sequence |
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from typing import Any, List, Optional, Union |
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from torch import Tensor |
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from typing_extensions import Literal |
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from torchmetrics.functional.image.d_s import _spatial_distortion_index_compute, _spatial_distortion_index_update |
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from torchmetrics.metric import Metric |
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from torchmetrics.utilities import rank_zero_warn |
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from torchmetrics.utilities.data import dim_zero_cat |
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from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCHVISION_AVAILABLE |
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from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE |
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if not _MATPLOTLIB_AVAILABLE: |
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__doctest_skip__ = ["SpatialDistortionIndex.plot"] |
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if not _TORCHVISION_AVAILABLE: |
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__doctest_skip__ = ["SpatialDistortionIndex", "SpatialDistortionIndex.plot"] |
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class SpatialDistortionIndex(Metric): |
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r"""Compute Spatial Distortion Index (SpatialDistortionIndex_) also now as D_s. |
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The metric is used to compare the spatial distortion between two images. A value of 0 indicates no distortion |
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(optimal value) and corresponds to the case where the high resolution panchromatic image is equal to the low |
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resolution panchromatic image. The metric is defined as: |
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.. math:: |
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D_s = \\sqrt[q]{\frac{1}{L}\\sum_{l=1}^L|Q(\\hat{G_l}, P) - Q(\tilde{G}, \tilde{P})|^q} |
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where :math:`Q` is the universal image quality index (see this |
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:class:`~torchmetrics.image.UniversalImageQualityIndex` for more info), :math:`\\hat{G_l}` is the l-th band of the |
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high resolution multispectral image, :math:`\tilde{G}` is the high resolution panchromatic image, :math:`P` is the |
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high resolution panchromatic image, :math:`\tilde{P}` is the low resolution panchromatic image, :math:`L` is the |
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number of bands and :math:`q` is the order of the norm applied on the difference. |
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As input to ``forward`` and ``update`` the metric accepts the following input |
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- ``preds`` (:class:`~torch.Tensor`): High resolution multispectral image of shape ``(N,C,H,W)``. |
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- ``target`` (:class:`~Dict`): A dictionary containing the following keys: |
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- ``ms`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H',W')``. |
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- ``pan`` (:class:`~torch.Tensor`): High resolution panchromatic image of shape ``(N,C,H,W)``. |
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- ``pan_lr`` (:class:`~torch.Tensor`): Low resolution panchromatic image of shape ``(N,C,H',W')``. |
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where H and W must be multiple of H' and W'. |
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As output of `forward` and `compute` the metric returns the following output |
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- ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SDI value |
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over sample else returns tensor of shape ``(N,)`` with SDI values per sample |
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Args: |
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norm_order: Order of the norm applied on the difference. |
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window_size: Window size of the filter applied to degrade the high resolution panchromatic image. |
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reduction: a method to reduce metric score over labels. |
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- ``'elementwise_mean'``: takes the mean (default) |
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- ``'sum'``: takes the sum |
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- ``'none'``: no reduction will be applied |
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kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. |
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Example: |
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>>> from torch import rand |
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>>> from torchmetrics.image import SpatialDistortionIndex |
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>>> preds = rand([16, 3, 32, 32]) |
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>>> target = { |
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... 'ms': rand([16, 3, 16, 16]), |
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... 'pan': rand([16, 3, 32, 32]), |
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... } |
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>>> sdi = SpatialDistortionIndex() |
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>>> sdi(preds, target) |
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tensor(0.0090) |
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""" |
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higher_is_better: bool = False |
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is_differentiable: bool = True |
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full_state_update: bool = False |
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plot_lower_bound: float = 0.0 |
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plot_upper_bound: float = 1.0 |
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preds: List[Tensor] |
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ms: List[Tensor] |
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pan: List[Tensor] |
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pan_lr: List[Tensor] |
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def __init__( |
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self, |
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norm_order: int = 1, |
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window_size: int = 7, |
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reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean", |
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**kwargs: Any, |
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) -> None: |
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super().__init__(**kwargs) |
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rank_zero_warn( |
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"Metric `SpatialDistortionIndex` will save all targets and" |
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" predictions in buffer. For large datasets this may lead" |
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" to large memory footprint." |
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) |
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if not isinstance(norm_order, int) or norm_order <= 0: |
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raise ValueError(f"Expected `norm_order` to be a positive integer. Got norm_order: {norm_order}.") |
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self.norm_order = norm_order |
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if not isinstance(window_size, int) or window_size <= 0: |
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raise ValueError(f"Expected `window_size` to be a positive integer. Got window_size: {window_size}.") |
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self.window_size = window_size |
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allowed_reductions = ("elementwise_mean", "sum", "none") |
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if reduction not in allowed_reductions: |
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raise ValueError(f"Expected argument `reduction` be one of {allowed_reductions} but got {reduction}") |
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self.reduction = reduction |
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self.add_state("preds", default=[], dist_reduce_fx="cat") |
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self.add_state("ms", default=[], dist_reduce_fx="cat") |
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self.add_state("pan", default=[], dist_reduce_fx="cat") |
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self.add_state("pan_lr", default=[], dist_reduce_fx="cat") |
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def update(self, preds: Tensor, target: dict[str, Tensor]) -> None: |
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"""Update state with preds and target. |
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Args: |
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preds: High resolution multispectral image. |
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target: A dictionary containing the following keys: |
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- ``'ms'``: low resolution multispectral image. |
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- ``'pan'``: high resolution panchromatic image. |
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- ``'pan_lr'``: (optional) low resolution panchromatic image. |
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Raises: |
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ValueError: |
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If ``target`` doesn't have ``ms`` and ``pan``. |
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""" |
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if "ms" not in target: |
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raise ValueError(f"Expected `target` to have key `ms`. Got target: {target.keys()}.") |
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if "pan" not in target: |
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raise ValueError(f"Expected `target` to have key `pan`. Got target: {target.keys()}.") |
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ms = target["ms"] |
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pan = target["pan"] |
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pan_lr = target.get("pan_lr") |
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preds, ms, pan, pan_lr = _spatial_distortion_index_update(preds, ms, pan, pan_lr) |
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self.preds.append(preds) |
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self.ms.append(target["ms"]) |
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self.pan.append(target["pan"]) |
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if "pan_lr" in target: |
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self.pan_lr.append(target["pan_lr"]) |
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def compute(self) -> Tensor: |
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"""Compute and returns spatial distortion index.""" |
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preds = dim_zero_cat(self.preds) |
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ms = dim_zero_cat(self.ms) |
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pan = dim_zero_cat(self.pan) |
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pan_lr = dim_zero_cat(self.pan_lr) if len(self.pan_lr) > 0 else None |
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target = {"ms": ms, "pan": pan} |
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target.update({"pan_lr": pan_lr} if pan_lr is not None else {}) |
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return _spatial_distortion_index_compute( |
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preds, ms, pan, pan_lr, self.norm_order, self.window_size, self.reduction |
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) |
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def plot( |
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self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None |
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) -> _PLOT_OUT_TYPE: |
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"""Plot a single or multiple values from the metric. |
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Args: |
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val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. |
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If no value is provided, will automatically call `metric.compute` and plot that result. |
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ax: An matplotlib axis object. If provided will add plot to that axis |
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Returns: |
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Figure and Axes object |
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Raises: |
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ModuleNotFoundError: |
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If `matplotlib` is not installed |
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.. plot:: |
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:scale: 75 |
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>>> # Example plotting a single value |
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>>> from torch import rand |
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>>> from torchmetrics.image import SpatialDistortionIndex |
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>>> preds = rand([16, 3, 32, 32]) |
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>>> target = { |
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... 'ms': rand([16, 3, 16, 16]), |
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... 'pan': rand([16, 3, 32, 32]), |
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... } |
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>>> metric = SpatialDistortionIndex() |
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>>> metric.update(preds, target) |
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>>> fig_, ax_ = metric.plot() |
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.. plot:: |
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:scale: 75 |
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>>> # Example plotting multiple values |
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>>> from torch import rand |
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>>> from torchmetrics.image import SpatialDistortionIndex |
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>>> preds = rand([16, 3, 32, 32]) |
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>>> target = { |
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... 'ms': rand([16, 3, 16, 16]), |
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... 'pan': rand([16, 3, 32, 32]), |
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... } |
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>>> metric = SpatialDistortionIndex() |
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>>> values = [ ] |
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>>> for _ in range(10): |
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... values.append(metric(preds, target)) |
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>>> fig_, ax_ = metric.plot(values) |
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""" |
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return self._plot(val, ax) |
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