def __init__(self, data_source, pipeline1, pipeline2, prefetch=False):
self.data_source = build_datasource(data_source)
pipeline1 = [build_from_cfg(p, PIPELINES) for p in pipeline1]
self.pipeline1 = Compose(pipeline1)
pipeline2 = [build_from_cfg(p, PIPELINES) for p in pipeline2]
self.pipeline2 = Compose(pipeline2)
self.prefetch = prefetch
def build(cfg, registry, default_args=None):
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
]
return nn.Sequential(*modules)
else:
return build_from_cfg(cfg, registry, default_args)
def inference_model(model, img):
"""Inference image(s) with the detector.
Args:
model (nn.Module): The loaded detector.
imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
images.
Returns:
If imgs is a str, a generator will be returned, otherwise return the
detection results directly.
"""
cfg = model.cfg
device = next(model.parameters()).device # model device
# build the data pipeline
test_pipeline = cfg.data.test.pipeline
test_pipeline = [build_from_cfg(p, PIPELINES) for p in test_pipeline]
test_pipeline = Compose(test_pipeline)
# prepare data
img = Image.open(img)
img = img.convert('RGB')
data = test_pipeline(img)
data = scatter(collate([data], samples_per_gpu=1), [device])[0]
# forward the model
with torch.no_grad():
result = model(data, mode='test')
return result
def __init__(self, data_source, pipelines):
pipeline = pipelines[0]
patch_pipeline = pipelines[1]
super(MultiScaleContrastiveDataset,
self).__init__(data_source, pipeline)
patch_pipeline = [build_from_cfg(p, PIPELINES) for p in patch_pipeline]
self.patch_pipeline = Compose(patch_pipeline)
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
post_pipeline = [
dict(type='ToPILImage'),
dict(type='RandomCrop', size=64),
dict(type='ToTensor'),
dict(type='Normalize', **img_norm_cfg)
]
post_pipeline = [build_from_cfg(p, PIPELINES) for p in post_pipeline]
self.post_pipeline = Compose(post_pipeline)
def build_dataset(cfg, default_args=None):
if isinstance(cfg, (list, tuple)):
dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
elif cfg['type'] == 'RepeatDataset':
dataset = RepeatDataset(
build_dataset(cfg['dataset'], default_args), cfg['times'])
else:
dataset = build_from_cfg(cfg, DATASETS, default_args)
return dataset
def __init__(self,
data_source,
pipeline,
prefetch=False,
use_s2=True,
use_s1=True,
use_RGB=False):
self.data_source = build_datasource(data_source)
pipeline = [build_from_cfg(p, PIPELINES) for p in pipeline]
self.pipeline = Compose(pipeline)
self.prefetch = prefetch
def build(cfg, registry, default_args=None):
"""Build a module.
Args:
cfg (dict, list[dict]): The config of modules, it is either a dict
or a list of configs.
registry (:obj:`Registry`): A registry the module belongs to.
default_args (dict, optional): Default arguments to build the module.
Default: None.
Returns:
nn.Module: A built nn module.
"""
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
]
return nn.Sequential(*modules)
else:
return build_from_cfg(cfg, registry, default_args)
def build(cfg, registry, default_args=None):
"""Build a module.
Args:
cfg (dict, list[dict]): The config of modules, it is either a dict
or a list of configs.
registry (:obj:`Registry`): A registry the module belongs to.
default_args (dict, optional): Default arguments to build the module.
Default: None.
Returns:
nn.Module: A built nn module.
"""
# ugly hack to __automagically__ set the neck input
if cfg.get("neck") and cfg.get("neck").get("auto_channels"):
del cfg["neck"]["auto_channels"]
# build the backbone to obtain the number of channels
bbone = build_backbone(cfg['backbone'])
x = randn(1, cfg['backbone']['in_channels'], 224,
224) # 224 doesn't really matter here
outp = bbone(x)
if isinstance(outp, tuple):
outp = outp[0]
outsize = outp.shape[1]
del bbone, x
cfg["neck"]["in_channels"] = outsize
if cfg['neck']['type'].find('NonLinear') > -1:
cfg["neck"]["hid_channels"] = outsize
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
]
return nn.Sequential(*modules)
else:
return build_from_cfg(cfg, registry, default_args)
def __init__(self, transforms, p=0.5):
t = [build_from_cfg(t, PIPELINES) for t in transforms]
self.trans = _transforms.RandomApply(t, p=p)
def __init__(self, data_source, pipeline):
self.data_source = build_datasource(data_source)
pipeline = [build_from_cfg(p, PIPELINES) for p in pipeline]
self.pipeline = Compose(pipeline)
def __init__(self, data_source, pipeline, format_pipeline):
super(RelativeLocDataset, self).__init__(data_source, pipeline)
format_pipeline = [
build_from_cfg(p, PIPELINES) for p in format_pipeline
]
self.format_pipeline = Compose(format_pipeline)
def build_datasource(cfg):
return build_from_cfg(cfg, DATASOURCES)
def build_hook(cfg, default_args=None):
return build_from_cfg(cfg, HOOKS, default_args)
