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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | """This module contains utilities for callbacks."""
from __future__ import annotations
import os
from typing import Any
import lightning.pytorch as pl
from mapdet3d.common.distributed import (
all_gather_object_cpu,
broadcast,
rank_zero_only,
synchronize,
)
from mapdet3d.common.logging import rank_zero_info
from mapdet3d.common.typing import ArgsType, MetricLogs
from mapdet3d.data.typing import DictData
from mapdet3d.eval.base import Evaluator
from .base import Callback
class EvaluatorCallback(Callback):
"""Callback for model evaluation."""
def __init__(
self,
*args: ArgsType,
evaluator: Evaluator,
metrics_to_eval: list[str] | None = None,
save_predictions: bool = False,
save_prefix: None | str = None,
output_dir: str | None = None,
**kwargs: ArgsType,
) -> None:
"""Init callback.
Args:
evaluator (Evaluator): Evaluator.
metrics_to_eval (list[str], Optional): Metrics to evaluate. If
None, all metrics in the evaluator will be evaluated. Defaults
to None.
save_predictions (bool): If the predictions should be saved.
Defaults to False.
save_prefix (str, Optional): Output directory for saving the
evaluation results. Defaults to None.
output_dir (str, Optional): Output directory for saving the
evaluation results.
"""
super().__init__(*args, **kwargs)
self.evaluator = evaluator
self.save_predictions = save_predictions
self.metrics_to_eval = metrics_to_eval or self.evaluator.metrics
if self.save_predictions:
assert (
output_dir is not None
), "If save_predictions is True, save_prefix must be provided."
output_dir = os.path.join(output_dir, "eval")
self.output_dir = output_dir
self.save_prefix = save_prefix
def setup(
self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str
) -> None: # pragma: no cover
"""Setup callback."""
if self.save_predictions:
self.output_dir = broadcast(self.output_dir)
if self.save_prefix is not None:
self.output_dir = os.path.join(
self.output_dir, self.save_prefix
)
for metric in self.metrics_to_eval:
output_dir = os.path.join(self.output_dir, metric)
os.makedirs(output_dir, exist_ok=True)
self.evaluator.reset()
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:
"""Hook to run at the end of a validation batch."""
self.on_test_batch_end(
trainer=trainer,
pl_module=pl_module,
outputs=outputs,
batch=batch,
batch_idx=batch_idx,
dataloader_idx=dataloader_idx,
)
def on_validation_epoch_end(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
"""Wait for on_validation_epoch_end PL hook to call 'evaluate'."""
log_dict = self.run_eval()
for k, v in log_dict.items():
pl_module.log(f"val/{k}", v, sync_dist=True, rank_zero_only=True)
def on_test_batch_end( # type: ignore
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
outputs: DictData,
batch: DictData,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Hook to run at the end of a testing batch."""
self.evaluator.process_batch(
**self.get_test_callback_inputs(outputs, batch)
)
for metric in self.metrics_to_eval:
# Save output predictions in current batch.
if self.save_predictions:
output_dir = os.path.join(self.output_dir, metric)
self.evaluator.save_batch(metric, output_dir)
def on_test_epoch_end(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
"""Hook to run at the end of a testing epoch."""
log_dict = self.run_eval()
for k, v in log_dict.items():
pl_module.log(f"test/{k}", v, sync_dist=True, rank_zero_only=True)
def run_eval(self) -> MetricLogs:
"""Run evaluation for the given evaluator."""
self.evaluator.gather(all_gather_object_cpu)
synchronize()
self.process()
log_dict: MetricLogs = {}
for metric in self.metrics_to_eval:
metric_dict = self.evaluate(metric)
metric_dict = broadcast(metric_dict)
assert isinstance(metric_dict, dict)
log_dict.update(metric_dict)
self.evaluator.reset()
return log_dict
@rank_zero_only
def process(self) -> None:
"""Process the evaluator."""
self.evaluator.process()
@rank_zero_only
def evaluate(self, metric: str) -> MetricLogs:
"""Evaluate the performance after processing all input/output pairs.
Returns:
MetricLogs: A dictionary containing the evaluation results. The
keys are formatted as {metric_name}/{key_name}, and the
values are the corresponding evaluated values.
"""
rank_zero_info(
f"Running evaluator {str(self.evaluator)} with {metric} metric... "
)
log_dict = {}
# Save output predictions. This is done here instead of
# on_test_batch_end because the evaluator may not have processed
# all batches yet.
if self.save_predictions:
output_dir = os.path.join(self.output_dir, metric)
self.evaluator.save(metric, output_dir)
# Evaluate metric
metric_dict, metric_str = self.evaluator.evaluate(metric)
for k, v in metric_dict.items():
log_k = metric + "/" + k
rank_zero_info("%s: %.4f", log_k, v)
log_dict[f"{metric}/{k}"] = v
rank_zero_info("Showing results for metric: %s", metric)
rank_zero_info(metric_str)
return log_dict
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