Python mmcv.runner.load_checkpoint() Examples
The following are 23
code examples of mmcv.runner.load_checkpoint().
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Example #1
| Source File: inference.py From DenseMatchingBenchmark with MIT License | 6 votes |
|
def init_model(config, checkpoint=None, device='cuda:0'):
"""
Initialize a stereo model from config file.
Args:
config (str or :obj:`mmcv.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 stereo model.
"""
if isinstance(config, str):
config = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
model = build_model(config)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #2
| Source File: hrnet4.py From CenterNet with Apache License 2.0 | 6 votes |
|
def init_weights(self, pretrained=None):
print("init hrnet weights")
# 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)
# 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')
Example #3
| Source File: inference.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #4
| Source File: inference.py From ttfnet with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #5
| Source File: inference.py From CenterNet with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #6
| Source File: high_api.py From hrnet with MIT License | 5 votes |
|
def load_model():
model = build_detector(cfg.model, test_cfg=cfg.test_cfg)
_ = load_checkpoint(model, model_cfgs[0][1]) # 7 it/s
return model
Example #7
| Source File: inference.py From kaggle-imaterialist with MIT License | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['classes']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #8
| Source File: inference.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #9
| Source File: inference.py From Cascade-RPN with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #10
| Source File: inference.py From FoveaBox with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #11
| Source File: inference.py From Libra_R-CNN with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #12
| Source File: checkpoint.py From mmskeleton with Apache License 2.0 | 5 votes |
|
def load_checkpoint(model, filename, *args, **kwargs):
try:
filename = get_mmskeleton_url(filename)
return mmcv_load_checkpoint(model, filename, *args, **kwargs)
except (urllib.error.HTTPError, urllib.error.URLError) as e:
raise Exception(url_error_message.format(filename)) from e
Example #13
| Source File: inference.py From mmdetection with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.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:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.simplefilter('once')
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #14
| Source File: inference.py From RDSNet with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #15
| Source File: inference.py From Grid-R-CNN with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['classes']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #16
| Source File: inference.py From kaggle-kuzushiji-recognition with MIT License | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #17
| Source File: inference.py From PolarMask with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #18
| Source File: inference.py From mmdetection_with_SENet154 with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #19
| Source File: inference.py From mmfashion with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #20
| Source File: inference.py From mmdetection-annotated with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #21
| Source File: inference.py From GCNet with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #22
| Source File: inference.py From AerialDetection with Apache License 2.0 | 5 votes |
|
def init_detector(config, checkpoint=None, device='cuda:0'):
"""Initialize a detector from config file.
Args:
config (str or :obj:`mmcv.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 = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_detector(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint)
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
warnings.warn('Class names are not saved in the checkpoint\'s '
'meta data, use COCO classes by default.')
model.CLASSES = get_classes('coco')
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
Example #23
| Source File: hrnet2.py From CenterNet with Apache License 2.0 | 4 votes |
|
def _make_stage(self, layer_config, in_channels, multiscale_output=True):
num_modules = layer_config['num_modules']
num_branches = layer_config['num_branches']
num_blocks = layer_config['num_blocks']
num_channels = layer_config['num_channels']
block = self.blocks_dict[layer_config['block']]
hr_modules = []
for i in range(num_modules):
# multi_scale_output is only used for the last module
if not multiscale_output and i == num_modules - 1:
reset_multiscale_output = False
else:
reset_multiscale_output = True
hr_modules.append(
HRModule(
num_branches,
block,
num_blocks,
in_channels,
num_channels,
reset_multiscale_output,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
return nn.Sequential(*hr_modules), in_channels
# 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)
# 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')
