# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from typing import Optional, Sequence
import mmcv
import numpy as np
from mmengine.fileio import get
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmengine.utils import mkdir_or_exist
from mmengine.visualization import Visualizer
from mmdet.datasets.samplers import TrackImgSampler
from mmdet.registry import HOOKS
from mmdet.structures import DetDataSample, TrackDataSample
from mmdet.structures.bbox import BaseBoxes
from mmdet.visualization.palette import _get_adaptive_scales
@HOOKS.register_module()
class DetVisualizationHook(Hook):
"""Detection 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.
2. If ``test_out_dir`` is specified, it means that the prediction results
need to be saved to ``test_out_dir``. In order to avoid vis_backends
also storing data, so ``vis_backends`` needs to be excluded.
3. ``vis_backends`` takes effect if the user does not specify ``show``
and `test_out_dir``. You can set ``vis_backends`` to WandbVisBackend or
TensorboardVisBackend to store the prediction result in Wandb or
Tensorboard.
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.
score_thr (float): The threshold to visualize the bboxes
and masks. Defaults to 0.3.
show (bool): Whether to display the drawn image. Default to False.
wait_time (float): The interval of show (s). Defaults to 0.
test_out_dir (str, optional): directory where painted images
will be saved in testing process.
backend_args (dict, optional): Arguments to instantiate the
corresponding backend. Defaults to None.
"""
def __init__(self,
draw: bool = False,
interval: int = 50,
score_thr: float = 0.3,
show: bool = False,
wait_time: float = 0.,
test_out_dir: Optional[str] = None,
backend_args: dict = None):
self._visualizer: Visualizer = Visualizer.get_current_instance()
self.interval = interval
self.score_thr = score_thr
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
self.draw = draw
self.test_out_dir = test_out_dir
self._test_index = 0
def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: dict,
outputs: Sequence[DetDataSample]) -> 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:`DetDataSample`]]): A batch of data samples
that contain annotations and predictions.
"""
if self.draw is False:
return
# There is no guarantee that the same batch of images
# is visualized for each evaluation.
total_curr_iter = runner.iter + batch_idx
# Visualize only the first data
img_path = outputs[0].img_path
img_bytes = get(img_path, backend_args=self.backend_args)
img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
if total_curr_iter % self.interval == 0:
self._visualizer.add_datasample(
osp.basename(img_path) if self.show else 'val_img',
img,
data_sample=outputs[0],
show=self.show,
wait_time=self.wait_time,
pred_score_thr=self.score_thr,
step=total_curr_iter)
def after_test_iter(self, runner: Runner, batch_idx: int, data_batch: dict,
outputs: Sequence[DetDataSample]) -> None:
"""Run after every testing iterations.
Args:
runner (:obj:`Runner`): The runner of the testing process.
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict): Data from dataloader.
outputs (Sequence[:obj:`DetDataSample`]): A batch of data samples
that contain annotations and predictions.
"""
if self.draw is False:
return
if self.test_out_dir is not None:
self.test_out_dir = osp.join(runner.work_dir, runner.timestamp,
self.test_out_dir)
mkdir_or_exist(self.test_out_dir)
for data_sample in outputs:
self._test_index += 1
img_path = data_sample.img_path
img_bytes = get(img_path, backend_args=self.backend_args)
img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
out_file = None
if self.test_out_dir is not None:
out_file = osp.basename(img_path)
out_file = osp.join(self.test_out_dir, out_file)
self._visualizer.add_datasample(
osp.basename(img_path) if self.show else 'test_img',
img,
data_sample=data_sample,
show=self.show,
wait_time=self.wait_time,
pred_score_thr=self.score_thr,
out_file=out_file,
step=self._test_index)
@HOOKS.register_module()
class TrackVisualizationHook(Hook):
"""Tracking 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.
2. If ``test_out_dir`` is specified, it means that the prediction results
need to be saved to ``test_out_dir``. In order to avoid vis_backends
also storing data, so ``vis_backends`` needs to be excluded.
3. ``vis_backends`` takes effect if the user does not specify ``show``
and `test_out_dir``. You can set ``vis_backends`` to WandbVisBackend or
TensorboardVisBackend to store the prediction result in Wandb or
Tensorboard.
Args:
draw (bool): whether to draw prediction results. If it is False,
it means that no drawing will be done. Defaults to False.
frame_interval (int): The interval of visualization. Defaults to 30.
score_thr (float): The threshold to visualize the bboxes
and masks. Defaults to 0.3.
show (bool): Whether to display the drawn image. Default to False.
wait_time (float): The interval of show (s). Defaults to 0.
test_out_dir (str, optional): directory where painted images
will be saved in testing process.
backend_args (dict): Arguments to instantiate a file client.
Defaults to ``None``.
"""
def __init__(self,
draw: bool = False,
frame_interval: int = 30,
score_thr: float = 0.3,
show: bool = False,
wait_time: float = 0.,
test_out_dir: Op
hooks.zip （9个子文件） 
utils.py 737B
hooks.zip 19KB visualization_hook.py 21KB mean_teacher_hook.py 3KB memory_profiler_hook.py 5KB pipeline_switch_hook.py 2KB num_class_check_hook.py 3KB sync_norm_hook.py 1KB set_epoch_info_hook.py 480B资源评论
