Pytorch mask-rcnn 实现细节分享

Pytorch mask-rcnn 实现细节分享实现细节分享

主要介绍了Pytorch mask-rcnn 实现细节分享，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

DataLoader

Dataset不能满足需求需自定义继承torch.utils.data.Dataset时需要override __init__, __getitem__, __len__ ，否则DataLoader导入自

定义Dataset时缺少上述函数会导致NotImplementedError错误

Numpy 广播机制：广播机制：

让所有输入数组都向其中shape最长的数组看齐，shape中不足的部分都通过在前面加1补齐

输出数组的shape是输入数组shape的各个轴上的最大值

"""Creates and configures a cffi.FFI object, that builds PyTorch extension.

Arguments:

name (str): package name. Can be a nested module e.g. ``.ext.my_lib``.

headers (str or List[str]): list of headers, that contain only exported

functions

sources (List[str]): list of sources to compile.

verbose (bool, optional): if set to ``False``, no output will be printed

(default: True).

with_cuda (bool, optional): set to ``True`` to compile with CUDA headers

(default: False)

.. _`Extension API reference`: https://docs.python.org/3/distutils/apiref.html#distutils.core.Extension

"""

base_path = os.path.abspath(os.path.dirname(relative_to))

name_suffix, target_dir = _create_module_dir(base_path, name)

if not package:

cffi_wrapper_name = '_' + name_suffix

else:

cffi_wrapper_name = (name.rpartition('.')[0] +

'.{0}._{0}'.format(name_suffix))

wrapper_source, include_dirs = _setup_wrapper(with_cuda)

library_dirs.append(os.path.join(lib_dir))

else:

library_dirs = []

library_dirs.extend(kwargs.pop('library_dirs', []))

if isinstance(headers, str):

headers = [headers]

all_headers_source = ''

for header in headers:

with open(os.path.join(base_path, header), 'r') as f:

all_headers_source += f.read() + ''

include_dirs=include_dirs,