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Running on Zero
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0122a25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | """This module contains utilities for callbacks."""
from __future__ import annotations
from collections import defaultdict, deque
from time import perf_counter
from typing import Any
import lightning.pytorch as pl
from mapdet3d.common.logging import rank_zero_info
from mapdet3d.common.progress import compose_log_str
from mapdet3d.common.time import Timer
from mapdet3d.common.typing import ArgsType, MetricLogs
from .base import Callback
class LoggingCallback(Callback):
"""Callback for logging."""
def __init__(
self, *args: ArgsType, refresh_rate: int = 50, **kwargs: ArgsType
) -> None:
"""Init callback."""
super().__init__(*args, **kwargs)
self._refresh_rate = refresh_rate
self._metrics: dict[str, list[float]] = defaultdict(list)
self._train_step_durations: deque[float] = deque(maxlen=50)
self._last_train_iter: None | int = None
self._last_train_iter_time: None | float = None
self.test_timer = Timer()
self.last_step = 0
def on_train_epoch_start(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
"""Hook to run at the start of a training epoch."""
if self.epoch_based:
self.last_step = 0
self._metrics.clear()
self._reset_train_iter_reference()
def on_train_batch_end( # type: ignore
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
outputs: Any,
batch: Any,
batch_idx: int,
) -> None:
"""Hook to run at the end of a training batch."""
if "metrics" in outputs:
for k, v in outputs["metrics"].items():
self._metrics[k].append(v)
if self.epoch_based:
cur_iter = batch_idx + 1
# Resolve float("inf") to -1
if isinstance(trainer.num_training_batches, float):
total_iters = -1
else:
total_iters = trainer.num_training_batches
else:
cur_iter = trainer.global_step + 1
total_iters = trainer.max_steps
self._record_train_step_duration(cur_iter)
if cur_iter % self._refresh_rate == 0 and cur_iter != self.last_step:
prefix = (
f"Epoch {pl_module.current_epoch + 1}"
if self.epoch_based
else "Iter"
)
log_dict: MetricLogs = {
k: sum(v) / len(v) if len(v) > 0 else float("NaN")
for k, v in self._metrics.items()
}
rank_zero_info(
compose_log_str(
prefix,
cur_iter,
total_iters,
None,
log_dict,
time_sec_avg=self._train_time_sec_avg(),
)
)
self._metrics.clear()
self.last_step = cur_iter
for k, v in log_dict.items():
pl_module.log(f"train/{k}", v, rank_zero_only=True)
def _train_time_sec_avg(self) -> None | float:
"""Return the rolling average duration of recent training steps."""
if len(self._train_step_durations) == 0:
return None
return sum(self._train_step_durations) / len(
self._train_step_durations
)
def _record_train_step_duration(self, cur_iter: int) -> None:
"""Record elapsed time between completed training iterations."""
now = perf_counter()
if self._last_train_iter is None:
self._last_train_iter = cur_iter
self._last_train_iter_time = now
elif cur_iter != self._last_train_iter:
if self._last_train_iter_time is None:
self._last_train_iter = cur_iter
self._last_train_iter_time = now
else:
self._train_step_durations.append(
now - self._last_train_iter_time
)
self._last_train_iter = cur_iter
self._last_train_iter_time = now
def _reset_train_iter_reference(self) -> None:
"""Reset the timestamp used for the next iteration interval."""
self._last_train_iter = None
self._last_train_iter_time = None
def on_validation_epoch_start(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
"""Hook to run at the start of a validation epoch."""
self.test_timer.reset()
self._reset_train_iter_reference()
def on_validation_batch_end( # type: ignore
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
outputs: Any,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Wait for on_validation_batch_end PL hook to call 'process'."""
cur_iter = batch_idx + 1
# Resolve float("inf") to -1
if isinstance(trainer.num_val_batches[dataloader_idx], int):
total_iters = int(trainer.num_val_batches[dataloader_idx])
else:
total_iters = -1
if cur_iter % self._refresh_rate == 0:
rank_zero_info(
compose_log_str(
"Validation", cur_iter, total_iters, self.test_timer
)
)
def on_test_epoch_start(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
"""Hook to run at the start of a testing epoch."""
self.test_timer.reset()
self._reset_train_iter_reference()
def on_test_batch_end( # type: ignore
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
outputs: Any,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Hook to run at the end of a testing batch."""
cur_iter = batch_idx + 1
# Resolve float("inf") to -1
if isinstance(trainer.num_test_batches[dataloader_idx], int):
total_iters = int(trainer.num_test_batches[dataloader_idx])
else:
total_iters = -1
if cur_iter % self._refresh_rate == 0:
rank_zero_info(
compose_log_str(
"Testing", cur_iter, total_iters, self.test_timer
)
)
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