File size: 4,932 Bytes
f71ac1d | 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 | """This module contains utilities for callbacks."""
from __future__ import annotations
import os
from typing import Any
import lightning.pytorch as pl
from vis4d.common.distributed import broadcast, synchronize
from vis4d.common.typing import ArgsType
from vis4d.vis.base import Visualizer
from .base import Callback
class VisualizerCallback(Callback):
"""Callback for model visualization."""
def __init__(
self,
*args: ArgsType,
visualizer: Visualizer,
visualize_train: bool = False,
show: bool = False,
save_to_disk: bool = True,
save_prefix: str | None = None,
output_dir: str | None = None,
**kwargs: ArgsType,
) -> None:
"""Init callback.
Args:
visualizer (Visualizer): Visualizer.
visualize_train (bool): If the training data should be visualized.
Defaults to False.
show (bool): If the visualizations should be shown. Defaults to
False.
save_to_disk (bool): If the visualizations should be saved to disk.
Defaults to True.
save_prefix (str): Output directory prefix for distinguish
different visualizations.
output_dir (str): Output directory for saving the visualizations.
"""
super().__init__(*args, **kwargs)
self.visualizer = visualizer
self.visualize_train = visualize_train
self.save_prefix = save_prefix
self.show = show
self.save_to_disk = save_to_disk
if self.save_to_disk:
assert (
output_dir is not None
), "If save_to_disk is True, output_dir must be provided."
output_dir = os.path.join(output_dir, "vis")
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_to_disk:
self.output_dir = broadcast(self.output_dir)
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."""
cur_iter = batch_idx + 1
if self.visualize_train:
self.visualizer.process(
cur_iter=cur_iter,
**self.get_train_callback_inputs(outputs, batch),
)
if self.show:
self.visualizer.show(cur_iter=cur_iter)
if self.save_to_disk:
self.save(cur_iter=cur_iter, stage="train")
self.visualizer.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."""
cur_iter = batch_idx + 1
self.visualizer.process(
cur_iter=cur_iter,
**self.get_test_callback_inputs(outputs, batch),
)
if self.show:
self.visualizer.show(cur_iter=cur_iter)
if self.save_to_disk:
self.save(cur_iter=cur_iter, stage="val")
self.visualizer.reset()
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
self.visualizer.process(
cur_iter=cur_iter,
**self.get_test_callback_inputs(outputs, batch),
)
if self.show:
self.visualizer.show(cur_iter=cur_iter)
if self.save_to_disk:
self.save(cur_iter=cur_iter, stage="test")
self.visualizer.reset()
def save(self, cur_iter: int, stage: str) -> None:
"""Save the visualizer state."""
output_folder = os.path.join(self.output_dir, stage)
if self.save_prefix is not None:
output_folder = os.path.join(output_folder, self.save_prefix)
os.makedirs(output_folder, exist_ok=True)
self.visualizer.save_to_disk(
cur_iter=cur_iter, output_folder=output_folder
)
# TODO: Add support for logging images to WandB.
# if get_rank() == 0:
# if isinstance(trainer.logger, WandbLogger) and image is not None:
# trainer.logger.log_image(
# key=f"{self.visualizer}/{cur_iter}",
# images=[image],
# )
synchronize()
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