Pytorch mask-rcnn如何实现 Pytorch mask-rcnn实现代码示例

Pytorch mask-rcnn如何实现？本篇文章小编给大家分享一下Pytorch mask-rcnn实现代码示例，小编觉得挺不错的，现在分享给大家供大家参考，有需要的小伙伴们可以来看看。

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：

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

如果输入数组的某个轴和输出数组的对应轴的长度相同或者其长度为1时，这个数组能够用来计算，否则出错

当输入数组的某个轴的长度为1时，沿着此轴运算时都用此轴上的第一组值

CUDA在pytorch中的扩展：

torch.utils.ffi中使用create_extension扩充：

 def create_extension(name, headers, sources, verbose=True, with_cuda=False,
      package=False, relative_to='.', **kwargs):
 """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)
  package (bool, optional): set to ``True`` to build in package mode (for modules
   meant to be installed as pip packages) (default: False).
  relative_to (str, optional): path of the build file. Required when
   ``package is True``. It's best to use ``__file__`` for this argument.
  kwargs: additional arguments that are passed to ffi to declare the
   extension. See `Extension API reference`_ for details.

 .. _`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)
 include_dirs.extend(kwargs.pop('include_dirs', []))

 if os.sys.platform == 'win32':
  library_dirs = glob.glob(os.getenv('CUDA_PATH', '') + '/lib/x64')
  library_dirs += glob.glob(os.getenv('NVTOOLSEXT_PATH', '') + '/lib/x64')

  here = os.path.abspath(os.path.dirname(__file__))
  lib_dir = os.path.join(here, '..', '..', 'lib')

  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() + 'nn'

 ffi = cffi.FFI()
 sources = [os.path.join(base_path, src) for src in sources]
 # NB: TH headers are C99 now
 kwargs['extra_compile_args'] = ['-std=c99'] + kwargs.get('extra_compile_args', [])
 ffi.set_source(cffi_wrapper_name, wrapper_source + all_headers_source,
     sources=sources,
     include_dirs=include_dirs,
     library_dirs=library_dirs, **kwargs)
 ffi.cdef(_typedefs + all_headers_source)

 _make_python_wrapper(name_suffix, '_' + name_suffix, target_dir)

 def build():
  _build_extension(ffi, cffi_wrapper_name, target_dir, verbose)
 ffi.build = build
 return ffi