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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import torch
import torch.nn as nn
# from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from mmengine.config import Config
from mmengine.model.utils import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmengine.runner import load_checkpoint
from mmdet.registry import DATASETS
from ..evaluation import get_classes
from ..registry import MODELS
from ..structures import DetDataSample, SampleList
from ..utils import get_test_pipeline_cfg
def init_detector(
config: Union[str, Path, Config],
checkpoint: Optional[str] = None,
palette: str = 'none',
device: str = 'cuda:0',
cfg_options: Optional[dict] = None,
) -> nn.Module:
"""Initialize a detector from config file.
Args:
config (str, :obj:`Path`, or :obj:`mmengine.Config`): Config file path,
:obj:`Path`, or the config object.
checkpoint (str, optional): Checkpoint path. If left as None, the model
will not load any weights.
palette (str): Color palette used for visualization. If palette
is stored in checkpoint, use checkpoint's palette first, otherwise
use externally passed palette. Currently, supports 'coco', 'voc',
'citys' and 'random'. Defaults to none.
device (str): The device where the anchors will be put on.
Defaults to cuda:0.
cfg_options (dict, optional): Options to override some settings in
the used config.
Returns:
nn.Module: The constructed detector.
"""
if isinstance(config, (str, Path)):
config = Config.fromfile(config)
elif not isinstance(config, Config):
raise TypeError('config must be a filename or Config object, '
f'but got {type(config)}')
if cfg_options is not None:
config.merge_from_dict(cfg_options)
elif 'init_cfg' in config.model.backbone:
config.model.backbone.init_cfg = None
init_default_scope(config.get('default_scope', 'mmdet'))
config.model._scope_ = 'mmdet'
model = MODELS.build(config.model)
model = revert_sync_batchnorm(model)
if checkpoint is None:
warnings.simplefilter('once')
warnings.warn('checkpoint is None, use COCO classes by default.')
model.dataset_meta = {'classes': get_classes('coco')}
else:
checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
# Weights converted from elsewhere may not have meta fields.
checkpoint_meta = checkpoint.get('meta', {})
# save the dataset_meta in the model for convenience
if 'dataset_meta' in checkpoint_meta:
# mmdet 3.x, all keys should be lowercase
model.dataset_meta = {
k.lower(): v
for k, v in checkpoint_meta['dataset_meta'].items()
}
elif 'CLASSES' in checkpoint_meta:
# < mmdet 3.x
classes = checkpoint_meta['CLASSES']
model.dataset_meta = {'classes': classes}
else:
warnings.simplefilter('once')
warnings.warn(
'dataset_meta or class names are not saved in the '
'checkpoint\'s meta data, use COCO classes by default.')
model.dataset_meta = {'classes': get_classes('coco')}
# Priority: args.palette -> config -> checkpoint
if palette != 'none':
model.dataset_meta['palette'] = palette
else:
test_dataset_cfg = copy.deepcopy(config.test_dataloader.dataset)
# lazy init. We only need the metainfo.
test_dataset_cfg['lazy_init'] = True
test_dataset_cfg._scope_ = 'mmdet'
metainfo = DATASETS.build(test_dataset_cfg).metainfo
cfg_palette = metainfo.get('palette', None)
if cfg_palette is not None:
model.dataset_meta['palette'] = cfg_palette
else:
if 'palette' not in model.dataset_meta:
warnings.warn(
'palette does not exist, random is used by default. '
'You can also set the palette to customize.')
model.dataset_meta['palette'] = 'random'
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
ImagesType = Union[str, np.ndarray, Sequence[str], Sequence[np.ndarray]]
def inference_detector(
model: nn.Module,
imgs: ImagesType,
test_pipeline: Optional[Compose] = None
) -> Union[DetDataSample, SampleList]:
"""Inference image(s) with the detector.
Args:
model (nn.Module): The loaded detector.
imgs (str, ndarray, Sequence[str/ndarray]):
Either image files or loaded images.
test_pipeline (:obj:`Compose`): Test pipeline.
Returns:
:obj:`DetDataSample` or list[:obj:`DetDataSample`]:
If imgs is a list or tuple, the same length list type results
will be returned, otherwise return the detection results directly.
"""
if isinstance(imgs, (list, tuple)):
is_batch = True
else:
imgs = [imgs]
is_batch = False
cfg = model.cfg
if test_pipeline is None:
cfg = cfg.copy()
test_pipeline = get_test_pipeline_cfg(cfg)
if isinstance(imgs[0], np.ndarray):
# Calling this method across libraries will result
# in module unregistered error if not prefixed with mmdet.
test_pipeline[0].type = 'mmdet.LoadImageFromNDArray'
for idx, _ in enumerate(test_pipeline):
test_pipeline[idx]._scope_ = 'mmdet'
test_pipeline = Compose(test_pipeline)
if model.data_preprocessor.device.type == 'cpu':
for m in model.modules():
assert not isinstance(
m, RoIPool
), 'CPU inference with RoIPool is not supported currently.'
result_list = []
for img in imgs:
# prepare data
if isinstance(img, np.ndarray):
# TODO: remove img_id.
data_ = dict(img=img, img_id=0)
else:
# TODO: remove img_id.
data_ = dict(img_path=img, img_id=0)
# build the data pipeline
data_ = test_pipeline(data_)
data_['inputs'] = [data_['inputs']]
data_['data_samples'] = [data_['data_samples']]
# forward the model
with torch.no_grad():
results = model.test_step(data_)[0]
result_list.append(results)
if not is_batch:
return result_list[0]
else:
return result_list
# TODO: Awaiting refactoring
async def async_inference_detector(model, imgs):
"""Async inference image(s) with the detector.
Args:
model (nn.Module): The loaded detector.
img (str | ndarray): Either image files or loaded images.
Returns:
Awaitable detection results.
"""
if not isinstance(imgs, (list, tuple)):
imgs = [imgs]
cfg = model.cfg
if isinstance(imgs[0], np.ndarray):
cfg = cfg.copy()
# set loading pipeline type
cfg.data.test.pipeline[0].type = 'LoadImageFromNDArray'
# cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
test_pipeline = Compose(cfg.data.test.pipeline)
datas = []
for img in imgs:
# prepare data
if isinstance(img, np.ndarray):
# directly add img
data = dict(img=img)
else:
# add information into dict
data = dict(img_info=dict(filename=img), img_prefix=None)
# build the data pipeline
data = test_pipeline(data)
datas.append(data)
for m in model.modules():
assert not isinstance(
m,
RoIPool), 'CPU inference with RoIPool is not supported currently.'
# We don't restore `torch.is_grad_enabled()` value during concurrent
# inference since execution can overlap
torch.set_grad_enabled(False)
results = await model.aforward_test(data, rescale=True)
return results
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