本文整理汇总了Python中mmdet.datasets.pipelines.Compose方法的典型用法代码示例。如果您正苦于以下问题：Python pipelines.Compose方法的具体用法？Python pipelines.Compose怎么用？Python pipelines.Compose使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmdet.datasets.pipelines的用法示例。
在下文中一共展示了pipelines.Compose方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: inference_detector
# 需要导入模块: from mmdet.datasets import pipelines [as 别名]
# 或者: from mmdet.datasets.pipelines import Compose [as 别名]
def inference_detector(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 = [LoadImage()] + cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img)
data = test_pipeline(data)
data = scatter(collate([data], samples_per_gpu=1), [device])[0]
# forward the model
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
return result 示例2: inference_detector
# 需要导入模块: from mmdet.datasets import pipelines [as 别名]
# 或者: from mmdet.datasets.pipelines import Compose [as 别名]
def inference_detector(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 = [LoadImage()] + cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img)
data = test_pipeline(data)
data = collate([data], samples_per_gpu=1)
if next(model.parameters()).is_cuda:
# scatter to specified GPU
data = scatter(data, [device])[0]
else:
# Use torchvision ops for CPU mode instead
for m in model.modules():
if isinstance(m, (RoIPool, RoIAlign)):
if not m.aligned:
# aligned=False is not implemented on CPU
# set use_torchvision on-the-fly
m.use_torchvision = True
warnings.warn('We set use_torchvision=True in CPU mode.')
# just get the actual data from DataContainer
data['img_metas'] = data['img_metas'][0].data
# forward the model
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
return result 示例3: async_inference_detector
# 需要导入模块: from mmdet.datasets import pipelines [as 别名]
# 或者: from mmdet.datasets.pipelines import Compose [as 别名]
def async_inference_detector(model, img):
"""Async 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:
Awaitable detection results.
"""
cfg = model.cfg
device = next(model.parameters()).device # model device
# build the data pipeline
test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img)
data = test_pipeline(data)
data = scatter(collate([data], samples_per_gpu=1), [device])[0]
# We don't restore `torch.is_grad_enabled()` value during concurrent
# inference since execution can overlap
torch.set_grad_enabled(False)
result = await model.aforward_test(rescale=True, **data)
return result 示例4: async_inference_detector
# 需要导入模块: from mmdet.datasets import pipelines [as 别名]
# 或者: from mmdet.datasets.pipelines import Compose [as 别名]
def async_inference_detector(model, img):
"""Async 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:
Awaitable detection results.
"""
cfg = model.cfg
device = next(model.parameters()).device # model device
# build the data pipeline
test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img)
data = test_pipeline(data)
data = scatter(collate([data], samples_per_gpu=1), [device])[0]
# We don't restore `torch.is_grad_enabled()` value during concurrent
# inference since execution can overlap
torch.set_grad_enabled(False)
result = await model.aforward_test(rescale=True, **data)
return result
# TODO: merge this method with the one in BaseDetector 示例5: inference_detector
# 需要导入模块: from mmdet.datasets import pipelines [as 别名]
# 或者: from mmdet.datasets.pipelines import Compose [as 别名]
def inference_detector(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 = [LoadImage()] + cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img)
data = test_pipeline(data)
data = scatter(collate([data], samples_per_gpu=1), [device])[0]
# forward the model
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
return result
# TODO: merge this method with the one in BaseDetector