def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottleneck) and hasattr(
m.conv2, 'conv_offset'):
constant_init(m.conv2.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
else:
raise TypeError('pretrained must be a str or None')
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmdet.cv_core.Config`): Config file path or the config
object.
checkpoint (str, optional): Checkpoint path. If left as None, the model
will not load any weights.
Returns:
nn.Module: The constructed detector.
"""
if isinstance(config, str):
config = cv_core.Config.fromfile(config)
elif not isinstance(config, cv_core.Config):
raise TypeError('config must be a filename or Config object, '
f'but got {type(config)}')
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
map_loc = 'cpu' if device == 'cpu' else None
checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc)
if 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
dataset = build_dataset(config.data.test)
model.CLASSES = dataset.CLASSES
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
def main():
args = parse_args()
assert args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (eval/format/show the '
'results / save the results) with the argument , "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
if cfg.model.get('neck'):
if isinstance(cfg.model.neck, list):
for neck_cfg in cfg.model.neck:
if neck_cfg.get('rfp_backbone'):
if neck_cfg.rfp_backbone.get('pretrained'):
neck_cfg.rfp_backbone.pretrained = None
elif cfg.model.neck.get('rfp_backbone'):
if cfg.model.neck.rfp_backbone.get('pretrained'):
cfg.model.neck.rfp_backbone.pretrained = None
# in case the test dataset is concatenated
if isinstance(cfg.data.test, dict):
cfg.data.test.test_mode = True
elif isinstance(cfg.data.test, list):
for ds_cfg in cfg.data.test:
ds_cfg.test_mode = True
# build the dataloader
samples_per_gpu = cfg.data.pop('samples_per_gpu', 1) # cfg.data.test.pop
if samples_per_gpu > 1:
# Replace 'ImageToTensor' to 'DefaultFormatBundle'
cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
args.show_score_thr)
kwargs = {} if args.eval_options is None else args.eval_options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
eval_kwargs = cfg.get('evaluation', {}).copy()
# hard-code way to remove EvalHook args
for key in ['interval', 'tmpdir', 'start', 'gpu_collect']:
eval_kwargs.pop(key, None)
eval_kwargs.update(dict(metric=args.eval, **kwargs))
print(dataset.evaluate(outputs, **eval_kwargs))