Python mmcv.Config() Examples
The following are 30
code examples of mmcv.Config().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
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mmcv
, or try the search function
.
.
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: test_kitti.py From DenseMatchingBenchmark with MIT License | 6 votes |
|
def setUp(self):
config = dict(
data=dict(
test=dict(
type='KITTI-2015',
data_root='datasets/KITTI-2015/',
annfile='datasets/KITTI-2015/annotations/full_eval.json',
input_shape=[384, 1248],
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
toRAM=False,
)
)
)
cfg = Config(config)
self.dataset = build_dataset(cfg, 'test')
import pdb
pdb.set_trace()
Example #3
| 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 #4
| 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 #5
| 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 #6
| 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 #7
| 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 #8
| 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 #9
| Source File: test_forward.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
|
def _get_detector_cfg(fname):
"""
Grab configs necessary to create a detector. These are deep copied to allow
for safe modification of parameters without influencing other tests.
"""
import mmcv
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
train_cfg = mmcv.Config(copy.deepcopy(config.train_cfg))
test_cfg = mmcv.Config(copy.deepcopy(config.test_cfg))
return model, train_cfg, test_cfg
Example #10
| 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 #11
| 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 #12
| 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 #13
| 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 #14
| 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 #15
| 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 #16
| 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 #17
| Source File: test_forward.py From ttfnet with Apache License 2.0 | 5 votes |
|
def _get_detector_cfg(fname):
"""
Grab configs necessary to create a detector. These are deep copied to allow
for safe modification of parameters without influencing other tests.
"""
import mmcv
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
train_cfg = mmcv.Config(copy.deepcopy(config.train_cfg))
test_cfg = mmcv.Config(copy.deepcopy(config.test_cfg))
return model, train_cfg, test_cfg
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 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 #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: test_scene_flow.py From DenseMatchingBenchmark with MIT License | 5 votes |
|
def setUp(self):
config = dict(
data=dict(
train=dict(
type='SceneFlow',
data_root='/home/youmin/data/StereoMatching/SceneFlow/',
annfile='/home/youmin/data/annotations/SceneFlow/cleanpass_train.json',
input_shape=[256, 512],
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
)
)
)
cfg = Config(config)
self.dataset = build_dataset(cfg, 'train')
Example #23
| Source File: test_flying_chairs.py From DenseMatchingBenchmark with MIT License | 5 votes |
|
def setUp(self):
config = dict(
data=dict(
train=dict(
type='FlyingChairs',
data_root='/home/youmin/data/OpticalFlow/FlyingChairs/',
annfile='/home/youmin/data/annotations/FlyingChairs/test.json',
input_shape=[256, 448],
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
)
)
)
cfg = Config(config)
self.dataset = build_dataset(cfg, 'train')
Example #24
| 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 #25
| Source File: test_forward.py From mmdetection with Apache License 2.0 | 5 votes |
|
def _get_detector_cfg(fname):
"""Grab configs necessary to create a detector.
These are deep copied to allow for safe modification of parameters without
influencing other tests.
"""
import mmcv
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
train_cfg = mmcv.Config(copy.deepcopy(config.train_cfg))
test_cfg = mmcv.Config(copy.deepcopy(config.test_cfg))
return model, train_cfg, test_cfg
Example #26
| Source File: test_forward.py From mmdetection with Apache License 2.0 | 5 votes |
|
def _get_config_module(fname):
"""Load a configuration as a python module."""
from mmcv import Config
config_dpath = _get_config_directory()
config_fpath = join(config_dpath, fname)
config_mod = Config.fromfile(config_fpath)
return config_mod
Example #27
| Source File: test_stereo_focal_loss.py From DenseMatchingBenchmark with MIT License | 4 votes |
|
def testCase2(self):
max_disp = 5
start_disp = -2
variance = 2
h, w = 3, 4
disp_sample = torch.Tensor([-2, 0, 2]).repeat(1, h, w, 1).permute(0, 3, 1, 2).contiguous()
d = disp_sample.shape[1]
gtDisp = torch.rand(1, 1, h, w) * max_disp + start_disp
gtDisp = gtDisp.to(self.device)
gtDisp.requires_grad = True
cfg = Config(dict(
data=dict(
sparse=False,
),
model=dict(
losses=dict(
focal_loss=dict(
# the maximum disparity of disparity search range
max_disp=max_disp,
# the start disparity of disparity search range
start_disp=start_disp,
# the step between near disparity sample
# dilation=dilation,
# weight for stereo focal loss with regard to other loss type
weight=1.0,
# weights for different scale loss
weights=(1.0),
# stereo focal loss focal coefficient
coefficient=5.0,
)
),
)
))
print('\n \n Test Case 2:')
print('*' * 60)
print('Ground Truth Disparity:')
print(gtDisp)
estCost = torch.ones(1, d, h, w).to(self.device)
estCost.requires_grad = True
print('*' * 60)
print('Estimated Cost volume:')
print(estCost)
stereo_focal_loss_evaluator = make_focal_loss_evaluator(cfg)
print(stereo_focal_loss_evaluator)
print('*' * 60)
print(stereo_focal_loss_evaluator(estCost=estCost, gtDisp=gtDisp, variance=variance, disp_sample=disp_sample))
Example #28
| Source File: test_stereo_focal_loss.py From DenseMatchingBenchmark with MIT License | 4 votes |
|
def testCase1(self):
max_disp = 5
start_disp = -2
dilation = 2
h, w = 3, 4
d = (max_disp + dilation - 1) // dilation
variance = 2
gtDisp = torch.rand(1, 1, h, w) * max_disp + start_disp
gtDisp = gtDisp.to(self.device)
cfg = Config(dict(
data = dict(
sparse = False,
),
model=dict(
losses=dict(
focal_loss=dict(
# the maximum disparity of disparity search range
max_disp=max_disp,
# the start disparity of disparity search range
start_disp=start_disp,
# the step between near disparity sample
dilation=dilation,
# weight for stereo focal loss with regard to other loss type
weight=1.0,
# weights for different scale loss
weights=(1.0),
# stereo focal loss focal coefficient
coefficient=5.0,
)
),
)
))
estCost = torch.ones(1, d, h, w).to(self.device)
estCost.requires_grad = True
print('\n \n Test Case 1:')
print('*' * 60)
print('Estimated Cost volume:')
print(estCost)
stereo_focal_loss_evaluator = make_focal_loss_evaluator(cfg)
print(stereo_focal_loss_evaluator)
print('*' * 60)
print(stereo_focal_loss_evaluator(estCost=estCost, gtDisp=gtDisp, variance=variance, disp_sample=None))
Example #29
| Source File: test_disp_predictors.py From DenseMatchingBenchmark with MIT License | 4 votes |
|
def testCase1(self):
start_disp = -4
dilation = 2
alpha = 1.0
normalize = True
max_disp = 9
h, w = 2, 2
d = (max_disp + dilation - 1) // dilation
cfg = Config(dict(
model=dict(
disp_predictor=dict(
type=self.pred_type,
# the maximum disparity of disparity search range
max_disp=max_disp,
# disparity sample radius
radius=self.radius,
# the start disparity of disparity search range
start_disp=start_disp,
# the step between near disparity sample
dilation=dilation,
# the step between near disparity sample when local sampling
radius_dilation = self.radius_dilation,
# the temperature coefficient of soft argmin
alpha=alpha,
# whether normalize the estimated cost volume
normalize=normalize,
),
)
))
cfg.model.update(disp_predictor = kick_out_none_keys(cfg.model.disp_predictor))
cost = torch.ones(1, d, h, w).to(self.device)
cost.requires_grad = True
print('*' * 60)
print('Cost volume:')
print(cost)
disp_predictor = build_disp_predictor(cfg).to(self.device)
print(disp_predictor)
disp = disp_predictor(cost)
print('*' * 60)
print('Regressed disparity map :')
print(disp)
# soft argmin
if self.pred_type == 'DEFAULT':
print('*' * 60)
print('Test directly providing disparity samples')
end_disp = start_disp + max_disp - 1
# generate disparity samples
disp_samples = torch.linspace(start_disp, end_disp, d).repeat(1, h, w, 1).\
permute(0, 3, 1, 2).contiguous().to(cost.device)
disp = disp_predictor(cost, disp_samples)
print('Regressed disparity map :')
print(disp)
Example #30
| Source File: test_heads.py From mmdetection with Apache License 2.0 | 4 votes |
|
def test_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = BBoxHead(in_channels=8, roi_feat_size=3)
# Dummy proposals
proposal_list = [
torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
]
target_cfg = mmcv.Config(dict(pos_weight=1))
# Test bbox loss when truth is empty
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
gt_labels)
bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
target_cfg)
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
# Create dummy features "extracted" for each sampled bbox
num_sampled = sum(len(res.bboxes) for res in sampling_results)
rois = bbox2roi([res.bboxes for res in sampling_results])
dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
cls_scores, bbox_preds = self.forward(dummy_feats)
losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
bbox_targets, bbox_weights)
assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero'
# Test bbox loss when truth is non-empty
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
gt_labels)
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
target_cfg)
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
# Create dummy features "extracted" for each sampled bbox
num_sampled = sum(len(res.bboxes) for res in sampling_results)
dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
cls_scores, bbox_preds = self.forward(dummy_feats)
losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
bbox_targets, bbox_weights)
assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero'
