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# Copyright (c) OpenMMLab. All rights reserved.
import math
import os.path as osp
from typing import Optional, Sequence
from mmengine.fileio import join_path
from mmengine.hooks import Hook
from mmengine.runner import EpochBasedTrainLoop, Runner
from mmengine.visualization import Visualizer
from mmcls.registry import HOOKS
from mmcls.structures import ClsDataSample
@HOOKS.register_module()
class VisualizationHook(Hook):
"""Classification Visualization Hook. Used to visualize validation and
testing prediction results.
- If ``out_dir`` is specified, all storage backends are ignored
and save the image to the ``out_dir``.
- If ``show`` is True, plot the result image in a window, please
confirm you are able to access the graphical interface.
Args:
enable (bool): Whether to enable this hook. Defaults to False.
interval (int): The interval of samples to visualize. Defaults to 5000.
show (bool): Whether to display the drawn image. Defaults to False.
out_dir (str, optional): directory where painted images will be saved
in the testing process. If None, handle with the backends of the
visualizer. Defaults to None.
**kwargs: other keyword arguments of
:meth:`mmcls.visualization.ClsVisualizer.add_datasample`.
"""
def __init__(self,
enable=False,
interval: int = 5000,
show: bool = False,
out_dir: Optional[str] = None,
**kwargs):
self._visualizer: Visualizer = Visualizer.get_current_instance()
self.enable = enable
self.interval = interval
self.show = show
self.out_dir = out_dir
self.draw_args = {**kwargs, 'show': show}
def _draw_samples(self,
batch_idx: int,
data_batch: dict,
data_samples: Sequence[ClsDataSample],
step: int = 0) -> None:
"""Visualize every ``self.interval`` samples from a data batch.
Args:
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict): Data from dataloader.
outputs (Sequence[:obj:`ClsDataSample`]): Outputs from model.
step (int): Global step value to record. Defaults to 0.
"""
if self.enable is False:
return
batch_size = len(data_samples)
images = data_batch['inputs']
start_idx = batch_size * batch_idx
end_idx = start_idx + batch_size
# The first index divisible by the interval, after the start index
first_sample_id = math.ceil(start_idx / self.interval) * self.interval
for sample_id in range(first_sample_id, end_idx, self.interval):
image = images[sample_id - start_idx]
image = image.permute(1, 2, 0).cpu().numpy().astype('uint8')
data_sample = data_samples[sample_id - start_idx]
if 'img_path' in data_sample:
# osp.basename works on different platforms even file clients.
sample_name = osp.basename(data_sample.get('img_path'))
else:
sample_name = str(sample_id)
draw_args = self.draw_args
if self.out_dir is not None:
draw_args['out_file'] = join_path(self.out_dir,
f'{sample_name}_{step}.png')
self._visualizer.add_datasample(
sample_name,
image=image,
data_sample=data_sample,
step=step,
**self.draw_args,
)
def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: dict,
outputs: Sequence[ClsDataSample]) -> None:
"""Visualize every ``self.interval`` samples during validation.
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:`ClsDataSample`]): Outputs from model.
"""
if isinstance(runner.train_loop, EpochBasedTrainLoop):
step = runner.epoch
else:
step = runner.iter
self._draw_samples(batch_idx, data_batch, outputs, step=step)
def after_test_iter(self, runner: Runner, batch_idx: int, data_batch: dict,
outputs: Sequence[ClsDataSample]) -> None:
"""Visualize every ``self.interval`` samples during test.
Args:
runner (:obj:`Runner`): The runner of the testing process.
batch_idx (int): The index of the current batch in the test loop.
data_batch (dict): Data from dataloader.
outputs (Sequence[:obj:`DetDataSample`]): Outputs from model.
"""
self._draw_samples(batch_idx, data_batch, outputs, step=0)
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