Python源码示例：mmcv.runner.obj_from_dict()
示例1
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例2
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例3
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例4
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例5
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例6
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例7
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例8
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例9
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例10
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例11
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
self.lock_dir = None
示例12
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例13
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例14
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例15
def transform_from_dict(self, **kwargs):
if 'transforms' in kwargs:
kwargs['transforms'] = [self.transform_from_dict(**transform) for transform in kwargs['transforms']]
try:
return obj_from_dict(kwargs, transforms)
except AttributeError:
return obj_from_dict(kwargs, A)
示例16
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例17
def get_untrimmed_dataset(data_cfg):
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']]
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
raise ValueError("Not implemented yet")
else:
dset = dsets[0]
return dset
示例18
def get_trimmed_dataset(data_cfg):
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']]
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
raise ValueError("Not implemented yet")
else:
dset = dsets[0]
return dset
示例19
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例20
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例21
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例22
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
示例23
def get_dataset(data_cfg):
if data_cfg['type'] == 'RepeatDataset':
return RepeatDataset(
get_dataset(data_cfg['dataset']), data_cfg['times'])
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if 'proposal_file' in data_cfg.keys():
if isinstance(data_cfg['proposal_file'], (list, tuple)):
proposal_files = data_cfg['proposal_file']
else:
proposal_files = [data_cfg['proposal_file']]
else:
proposal_files = [None] * num_dset
assert len(proposal_files) == num_dset
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cfg['img_prefix']
else:
img_prefixes = [data_cfg['img_prefix']] * num_dset
assert len(img_prefixes) == num_dset
dsets = []
for i in range(num_dset):
data_info = copy.deepcopy(data_cfg)
data_info['ann_file'] = ann_files[i]
data_info['proposal_file'] = proposal_files[i]
data_info['img_prefix'] = img_prefixes[i]
dset = obj_from_dict(data_info, datasets)
dsets.append(dset)
if len(dsets) > 1:
dset = ConcatDataset(dsets)
else:
dset = dsets[0]
return dset
示例24
def __init__(self, dataset, interval=1):
if isinstance(dataset, Dataset):
self.dataset = dataset
elif isinstance(dataset, dict):
self.dataset = obj_from_dict(dataset, datasets,
{'test_mode': True})
else:
raise TypeError(
'dataset must be a Dataset object or a dict, not {}'.format(
type(dataset)))
self.interval = interval
self.lock_dir = None
示例25
def build_optimizer(model, optimizer_cfg):
"""Build optimizer from configs.
"""
if hasattr(model, 'module'):
model = model.module
optimizer_cfg = optimizer_cfg.copy()
paramwise_options = optimizer_cfg.pop('paramwise_options', None)
assert paramwise_options is None
return obj_from_dict(optimizer_cfg, torch.optim,
dict(params=model.parameters()))
示例26
def build_optimizer(model, optimizer_cfg):
"""Build optimizer from configs.
"""
if hasattr(model, 'module'):
model = model.module
optimizer_cfg = optimizer_cfg.copy()
paramwise_options = optimizer_cfg.pop('paramwise_options', None)
assert paramwise_options is None
return obj_from_dict(optimizer_cfg, torch.optim,
dict(params=model.parameters()))
示例27
def main():
args = parse_args()
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))
if args.gpus == 1:
model = build_detector(
cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, strict=True)
model = MMDataParallel(model, device_ids=[0])
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
num_gpus=1,
dist=False,
shuffle=False)
outputs = single_test(model, data_loader)
else:
model_args = cfg.model.copy()
model_args.update(train_cfg=None, test_cfg=cfg.test_cfg)
model_type = getattr(detectors, model_args.pop('type'))
outputs = parallel_test(
model_type,
model_args,
args.checkpoint,
dataset,
_data_func,
range(args.gpus),
workers_per_gpu=args.proc_per_gpu)
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_type = args.eval
if eval_type:
print('Starting evaluate {}'.format(eval_type))
result_file = osp.join(args.out + '.csv')
results2csv(dataset, outputs, result_file)
ava_eval(result_file, eval_type,
args.label_file, args.ann_file, args.exclude_file)
