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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from typing import Optional, Sequence
import mmcv
import mmengine.fileio as fileio
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmengine.visualization import Visualizer
from mmseg.registry import HOOKS
from mmseg.structures import SegDataSample
@HOOKS.register_module()
class SegVisualizationHook(Hook):
"""Segmentation Visualization Hook. Used to visualize validation and
testing process prediction results.
In the testing phase:
1. If ``show`` is True, it means that only the prediction results are
visualized without storing data, so ``vis_backends`` needs to
be excluded.
Args:
draw (bool): whether to draw prediction results. If it is False,
it means that no drawing will be done. Defaults to False.
interval (int): The interval of visualization. Defaults to 50.
show (bool): Whether to display the drawn image. Default to False.
wait_time (float): The interval of show (s). Defaults to 0.
backend_args (dict, Optional): Arguments to instantiate a file backend.
See https://mmengine.readthedocs.io/en/latest/api/fileio.htm
for details. Defaults to None.
Notes: mmcv>=2.0.0rc4, mmengine>=0.2.0 required.
"""
def __init__(self,
draw: bool = False,
interval: int = 50,
show: bool = False,
wait_time: float = 0.,
backend_args: Optional[dict] = None):
self._visualizer: Visualizer = Visualizer.get_current_instance()
self.interval = interval
self.show = show
if self.show:
# No need to think about vis backends.
self._visualizer._vis_backends = {}
warnings.warn('The show is True, it means that only '
'the prediction results are visualized '
'without storing data, so vis_backends '
'needs to be excluded.')
self.wait_time = wait_time
self.backend_args = backend_args.copy() if backend_args else None
self.draw = draw
if not self.draw:
warnings.warn('The draw is False, it means that the '
'hook for visualization will not take '
'effect. The results will NOT be '
'visualized or stored.')
def _after_iter(self,
runner: Runner,
batch_idx: int,
data_batch: dict,
outputs: Sequence[SegDataSample],
mode: str = 'val') -> None:
"""Run after every ``self.interval`` validation iterations.
Args:
runner (:obj:`Runner`): The runner of the validation process.
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict): Data from dataloader.
outputs (Sequence[:obj:`SegDataSample`]): Outputs from model.
mode (str): mode (str): Current mode of runner. Defaults to 'val'.
"""
if self.draw is False or mode == 'train':
return
if self.every_n_inner_iters(batch_idx, self.interval):
for output in outputs:
img_path = output.img_path
img_bytes = fileio.get(
img_path, backend_args=self.backend_args)
img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
window_name = f'{mode}_{osp.basename(img_path)}'
self._visualizer.add_datasample(
window_name,
img,
data_sample=output,
show=self.show,
wait_time=self.wait_time,
step=runner.iter)
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