本文整理汇总了Python中mmdet.ops方法的典型用法代码示例。如果您正苦于以下问题：Python mmdet.ops方法的具体用法？Python mmdet.ops怎么用？Python mmdet.ops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的用法示例。
在下文中一共展示了mmdet.ops方法的3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: build_roi_layers
# 需要导入模块: import mmdet [as 别名]
# 或者: from mmdet import ops [as 别名]
def build_roi_layers(self, layer_cfg, featmap_strides):
"""Build RoI operator to extract feature from each level feature map.
Args:
layer_cfg (dict): Dictionary to construct and config RoI layer
operation. Options are modules under ``mmdet/ops`` such as
``RoIAlign``.
featmap_strides (int): The stride of input feature map w.r.t to the
original image size, which would be used to scale RoI
coordinate (original image coordinate system) to feature
coordinate system.
Returns:
nn.ModuleList: The RoI extractor modules for each level feature
map.
"""
cfg = layer_cfg.copy()
layer_type = cfg.pop('type')
assert hasattr(ops, layer_type)
layer_cls = getattr(ops, layer_type)
roi_layers = nn.ModuleList(
[layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
return roi_layers 示例2: build_roi_layers
# 需要导入模块: import mmdet [as 别名]
# 或者: from mmdet import ops [as 别名]
def build_roi_layers(self, layer_cfg, featmap_strides):
cfg = layer_cfg.copy()
layer_type = cfg.pop('type')
assert hasattr(ops, layer_type)
layer_cls = getattr(ops, layer_type)
roi_layers = nn.ModuleList(
[layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
return roi_layers 示例3: collect_env
# 需要导入模块: import mmdet [as 别名]
# 或者: from mmdet import ops [as 别名]
def collect_env():
"""Collect the information of the running environments."""
env_info = {}
env_info['sys.platform'] = sys.platform
env_info['Python'] = sys.version.replace('\n', '')
cuda_available = torch.cuda.is_available()
env_info['CUDA available'] = cuda_available
if cuda_available:
from torch.utils.cpp_extension import CUDA_HOME
env_info['CUDA_HOME'] = CUDA_HOME
if CUDA_HOME is not None and osp.isdir(CUDA_HOME):
try:
nvcc = osp.join(CUDA_HOME, 'bin/nvcc')
nvcc = subprocess.check_output(
f'"{nvcc}" -V | tail -n1', shell=True)
nvcc = nvcc.decode('utf-8').strip()
except subprocess.SubprocessError:
nvcc = 'Not Available'
env_info['NVCC'] = nvcc
devices = defaultdict(list)
for k in range(torch.cuda.device_count()):
devices[torch.cuda.get_device_name(k)].append(str(k))
for name, devids in devices.items():
env_info['GPU ' + ','.join(devids)] = name
gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
gcc = gcc.decode('utf-8').strip()
env_info['GCC'] = gcc
env_info['PyTorch'] = torch.__version__
env_info['PyTorch compiling details'] = torch.__config__.show()
env_info['TorchVision'] = torchvision.__version__
env_info['OpenCV'] = cv2.__version__
env_info['MMCV'] = mmcv.__version__
env_info['MMDetection'] = mmdet.__version__
from mmdet.ops import get_compiler_version, get_compiling_cuda_version
env_info['MMDetection Compiler'] = get_compiler_version()
env_info['MMDetection CUDA Compiler'] = get_compiling_cuda_version()
return env_info