# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torchmetrics import Metric as _Metric
from torchmetrics import MetricCollection as _MetricCollection
from pytorch_lightning.utilities.distributed import rank_zero_warn
[docs]class Metric(_Metric):
r"""
This implementation refers to :class:`~torchmetrics.Metric`.
.. warning:: This metric is deprecated, use ``torchmetrics.Metric``. Will be removed in v1.5.0.
"""
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
rank_zero_warn(
"This `Metric` was deprecated since v1.3.0 in favor of `torchmetrics.Metric`."
" It will be removed in v1.5.0", DeprecationWarning
)
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
def __hash__(self):
return super().__hash__()
def __add__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.add, self, other)
def __and__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.bitwise_and, self, other)
def __eq__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.eq, self, other)
def __floordiv__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.floor_divide, self, other)
def __ge__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.ge, self, other)
def __gt__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.gt, self, other)
def __le__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.le, self, other)
def __lt__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.lt, self, other)
def __matmul__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.matmul, self, other)
def __mod__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.fmod, self, other)
def __mul__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.mul, self, other)
def __ne__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.ne, self, other)
def __or__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.bitwise_or, self, other)
def __pow__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.pow, self, other)
def __radd__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.add, other, self)
def __rand__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
# swap them since bitwise_and only supports that way and it's commutative
return CompositionalMetric(torch.bitwise_and, self, other)
def __rfloordiv__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.floor_divide, other, self)
def __rmatmul__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.matmul, other, self)
def __rmod__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.fmod, other, self)
def __rmul__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.mul, other, self)
def __ror__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.bitwise_or, other, self)
def __rpow__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.pow, other, self)
def __rsub__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.sub, other, self)
def __rtruediv__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.true_divide, other, self)
def __rxor__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.bitwise_xor, other, self)
def __sub__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.sub, self, other)
def __truediv__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.true_divide, self, other)
def __xor__(self, other: Any):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.bitwise_xor, self, other)
def __abs__(self):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.abs, self, None)
def __inv__(self):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.bitwise_not, self, None)
def __invert__(self):
return self.__inv__()
def __neg__(self):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(_neg, self, None)
def __pos__(self):
from pytorch_lightning.metrics.compositional import CompositionalMetric
return CompositionalMetric(torch.abs, self, None)
def _neg(tensor: torch.Tensor):
return -torch.abs(tensor)
[docs]class MetricCollection(_MetricCollection):
r"""
This implementation refers to :class:`~torchmetrics.MetricCollection`.
.. warning:: This metric is deprecated, use ``torchmetrics.MetricCollection``. Will be removed in v1.5.0.
"""
def __init__(self, metrics: Union[List[Metric], Tuple[Metric], Dict[str, Metric]]):
rank_zero_warn(
"This `MetricCollection` was deprecated since v1.3.0 in favor of `torchmetrics.MetricCollection`."
" It will be removed in v1.5.0", DeprecationWarning
)
super().__init__(metrics=metrics)
