def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
super().__init__()
# see (https://github.com/pytorch/pytorch/blob/3e6bb5233f9ca2c5aa55d9cda22a7ee85439aa6e/
# torch/nn/modules/module.py#L227)
torch._C._log_api_usage_once(
f"torchmetrics.metric.{self.__class__.__name__}")
self._LIGHTNING_GREATER_EQUAL_1_3 = _compare_version(
"pytorch_lightning", operator.ge, "1.3.0")
self.dist_sync_on_step = dist_sync_on_step
self.compute_on_step = compute_on_step
self.process_group = process_group
self.dist_sync_fn = dist_sync_fn
self._to_sync = True
self._update_signature = inspect.signature(self.update)
self.update = self._wrap_update(self.update)
self.compute = self._wrap_compute(self.compute)
self._computed = None
self._forward_cache = None
self._update_called = False
# initialize state
self._defaults = {}
self._persistent = {}
self._reductions = {}
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
super().__init__()
self._LIGHTNING_GREATER_EQUAL_1_3 = _compare_version("pytorch_lightning", operator.ge, "1.3.0")
self.dist_sync_on_step = dist_sync_on_step
self.compute_on_step = compute_on_step
self.process_group = process_group
self.dist_sync_fn = dist_sync_fn
self._to_sync = True
self._update_signature = inspect.signature(self.update)
self.update = self._wrap_update(self.update)
self.compute = self._wrap_compute(self.compute)
self._computed = None
self._forward_cache = None
# initialize state
self._defaults = {}
self._persistent = {}
self._reductions = {}
import operator
import random
import numpy
import torch
from torchmetrics.utilities.imports import _TORCH_LOWER_1_4, _TORCH_LOWER_1_5, _TORCH_LOWER_1_6, _compare_version
_MARK_TORCH_MIN_1_4 = dict(condition=_TORCH_LOWER_1_4,
reason="required PT >= 1.4")
_MARK_TORCH_MIN_1_5 = dict(condition=_TORCH_LOWER_1_5,
reason="required PT >= 1.5")
_MARK_TORCH_MIN_1_6 = dict(condition=_TORCH_LOWER_1_6,
reason="required PT >= 1.6")
_LIGHTNING_GREATER_EQUAL_1_3 = _compare_version("pytorch_lightning",
operator.ge, "1.3.0")
def seed_all(seed):
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# 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.
import math
import operator
from dataclasses import dataclass
from datetime import timedelta
from typing import Any, Dict, Optional, Union
from torchmetrics.utilities.imports import _compare_version
import pytorch_lightning as pl
from pytorch_lightning.callbacks.progress.base import ProgressBarBase
from pytorch_lightning.utilities.imports import _package_available
_RICH_AVAILABLE: bool = _package_available("rich") and _compare_version(
"rich", operator.ge, "10.2.2")
Task, Style = None, None
if _RICH_AVAILABLE:
from rich import get_console, reconfigure
from rich.console import RenderableType
from rich.progress import BarColumn, Progress, ProgressColumn, Task, TaskID, TextColumn
from rich.progress_bar import ProgressBar
from rich.style import Style
from rich.text import Text
class CustomBarColumn(BarColumn):
"""Overrides ``BarColumn`` to provide support for dataloaders that do not define a size (infinite size)
such as ``IterableDataset``."""
def render(self, task: "Task") -> ProgressBar:
"""Gets a progress bar widget for a task."""
