263 lines
10 KiB
Python
263 lines
10 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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import functools
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Optional, Union
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from collections.abc import Mapping, Sequence
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from collections import namedtuple
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from copy import deepcopy
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from distutils.version import LooseVersion
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import os
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import torch
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from torch import nn
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from pytorch_lightning.utilities.apply_func import apply_to_collection
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from pytorch_lightning.utilities.distributed import gather_all_tensors_if_available
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from pytorch_lightning.metrics.utils import _flatten, dim_zero_cat, dim_zero_mean, dim_zero_sum
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class Metric(nn.Module, ABC):
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"""
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Base class for all metrics present in the Metrics API.
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Implements ``add_state()``, ``forward()``, ``reset()`` and a few other things to
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handle distributed synchronization and per-step metric computation.
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Override ``update()`` and ``compute()`` functions to implement your own metric. Use
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``add_state()`` to register metric state variables which keep track of state on each
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call of ``update()`` and are synchronized across processes when ``compute()`` is called.
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Note:
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Metric state variables can either be ``torch.Tensors`` or an empty list which can we used
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to store `torch.Tensors``.
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Note:
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Different metrics only override ``update()`` and not ``forward()``. A call to ``update()``
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is valid, but it won't return the metric value at the current step. A call to ``forward()``
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automatically calls ``update()`` and also returns the metric value at the current step.
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Args:
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compute_on_step:
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Forward only calls ``update()`` and returns 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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"""
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def __init__(
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self,
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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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):
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super().__init__()
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self.dist_sync_on_step = dist_sync_on_step
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self.compute_on_step = compute_on_step
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self.process_group = process_group
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self._to_sync = True
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self.update = self._wrap_update(self.update)
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self.compute = self._wrap_compute(self.compute)
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self._computed = None
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self._forward_cache = None
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# initialize state
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self._reductions = {}
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self._defaults = {}
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def add_state(
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self, name: str, default, dist_reduce_fx: Optional[Union[str, Callable]] = None, persistent: bool = True
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):
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"""
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Adds metric state variable. Only used by subclasses.
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Args:
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name: The name of the state variable. The variable will then be accessible at ``self.name``.
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default: Default value of the state; can either be a ``torch.Tensor`` or an empty list. The state will be
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reset to this value when ``self.reset()`` is called.
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dist_reduce_fx (Optional): Function to reduce state accross mutliple processes in distributed mode.
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If value is ``"sum"``, ``"mean"``, or ``"cat"``, we will use ``torch.sum``, ``torch.mean``,
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and ``torch.cat`` respectively, each with argument ``dim=0``. The user can also pass a custom
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function in this parameter.
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persistent (Optional): whether the state will be saved as part of the modules ``state_dict``.
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Note:
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Setting ``dist_reduce_fx`` to None will return the metric state synchronized across different processes.
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However, there won't be any reduction function applied to the synchronized metric state.
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The metric states would be synced as follows
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- If the metric state is ``torch.Tensor``, the synced value will be a stacked ``torch.Tensor`` across
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the process dimension if the metric state was a ``torch.Tensor``. The original ``torch.Tensor`` metric
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state retains dimension and hence the synchronized output will be of shape ``(num_process, ...)``.
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- If the metric state is a ``list``, the synced value will be a ``list`` containing the
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combined elements from all processes.
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Note:
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When passing a custom function to ``dist_reduce_fx``, expect the synchronized metric state to follow
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the format discussed in the above note.
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"""
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if (
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not isinstance(default, torch.Tensor)
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and not isinstance(default, list) # noqa: W503
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or (isinstance(default, list) and len(default) != 0) # noqa: W503
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):
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raise ValueError(
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"state variable must be a tensor or any empty list (where you can append tensors)"
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)
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if dist_reduce_fx == "sum":
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dist_reduce_fx = dim_zero_sum
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elif dist_reduce_fx == "mean":
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dist_reduce_fx = dim_zero_mean
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elif dist_reduce_fx == "cat":
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dist_reduce_fx = dim_zero_cat
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elif dist_reduce_fx is not None and not isinstance(dist_reduce_fx, Callable):
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raise ValueError(
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"`dist_reduce_fx` must be callable or one of ['mean', 'sum', 'cat', None]"
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)
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if isinstance(default, torch.Tensor):
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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# persistent keyword is only supported in torch >= 1.6.0
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self.register_buffer(name, default, persistent=persistent)
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else:
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self.register_buffer(name, default)
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else:
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setattr(self, name, default)
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self._defaults[name] = deepcopy(default)
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self._reductions[name] = dist_reduce_fx
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def forward(self, *args, **kwargs):
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"""
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Automatically calls ``update()``. Returns the metric value over inputs if ``compute_on_step`` is True.
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"""
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# add current step
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self.update(*args, **kwargs)
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self._forward_cache = None
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if self.compute_on_step:
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self._to_sync = self.dist_sync_on_step
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# save context before switch
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self._cache = {attr: getattr(self, attr) for attr in self._defaults.keys()}
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# call reset, update, compute, on single batch
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self.reset()
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self.update(*args, **kwargs)
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self._forward_cache = self.compute()
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# restore context
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for attr, val in self._cache.items():
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setattr(self, attr, val)
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self._to_sync = True
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self._computed = None
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return self._forward_cache
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def _sync_dist(self):
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input_dict = {attr: getattr(self, attr) for attr in self._reductions.keys()}
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output_dict = apply_to_collection(
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input_dict,
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torch.Tensor,
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gather_all_tensors_if_available,
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group=self.process_group,
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)
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for attr, reduction_fn in self._reductions.items():
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# pre-processing ops (stack or flatten for inputs)
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if isinstance(output_dict[attr][0], torch.Tensor):
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output_dict[attr] = torch.stack(output_dict[attr])
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elif isinstance(output_dict[attr][0], list):
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output_dict[attr] = _flatten(output_dict[attr])
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assert isinstance(reduction_fn, (Callable)) or reduction_fn is None
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reduced = reduction_fn(output_dict[attr]) if reduction_fn is not None else output_dict[attr]
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setattr(self, attr, reduced)
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def _wrap_update(self, update):
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@functools.wraps(update)
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def wrapped_func(*args, **kwargs):
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self._computed = None
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return update(*args, **kwargs)
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return wrapped_func
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def _wrap_compute(self, compute):
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@functools.wraps(compute)
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def wrapped_func(*args, **kwargs):
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# return cached value
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if self._computed is not None:
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return self._computed
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if (
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self._to_sync
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and torch.distributed.is_available() # noqa: W503
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and torch.distributed.is_initialized() # noqa: W503
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):
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self._sync_dist()
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self._computed = compute(*args, **kwargs)
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self.reset()
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return self._computed
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return wrapped_func
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@abstractmethod
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def update(self) -> None: # pylint: disable=E0202
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"""
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Override this method to update the state variables of your metric class.
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"""
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pass
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@abstractmethod
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def compute(self): # pylint: disable=E0202
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"""
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Override this method to compute the final metric value from state variables
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synchronized across the distributed backend.
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"""
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pass
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def reset(self):
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"""
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This method automatically resets the metric state variables to their default value.
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"""
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for attr, default in self._defaults.items():
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current_val = getattr(self, attr)
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if isinstance(current_val, torch.Tensor):
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setattr(self, attr, deepcopy(default).to(current_val.device))
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else:
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setattr(self, attr, deepcopy(default))
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def __getstate__(self):
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# ignore update and compute functions for pickling
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return {k: v for k, v in self.__dict__.items() if k not in ["update", "compute"]}
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def __setstate__(self, state):
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# manually restore update and compute functions for pickling
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self.__dict__.update(state)
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self.update = self._wrap_update(self.update)
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self.compute = self._wrap_compute(self.compute)
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def _check_same_shape(self, pred: torch.Tensor, target: torch.Tensor):
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""" Check that predictions and target have the same shape, else raise error """
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if pred.shape != target.shape:
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raise RuntimeError('Predictions and targets are expected to have the same shape')
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