问题描述：因为使用高版本的torch，导致torch.utils.ffi被弃用。但是，高版本的torch并未对wrap_function进行重写(供大家直接调用)，最稳妥的方法是降低torch的版本，降低到0.4.0。但是降低到0.4.0，cuda的版本也要改变，很麻烦。大家可以试试这个，不行的话，再考虑通过conda建立虚拟环境降低版本。
解决措施：I solved by adding the source code of torch.utils.ffi to my project without changing my pytorch version.(I use torch 1.4.0).
For example, save the following code to ffiext.py, then you can replace from torch.utils.ffi import _wrap_function with from ffiext import _wrap_function.
You can also find the following source code from 'https://s0pytorch0org.icopy.site/docs/0.4.1/_modules/torch/utils/ffi.html'.
import os
import glob
import tempfile
import shutil
from functools import wraps, reduce
from string import Template
import torch
import torch.cuda
from torch._utils import _accumulatetry:import cffi
except ImportError:raise ImportError("torch.utils.ffi requires the cffi package")if cffi.__version_info__ < (1, 4, 0):raise ImportError("torch.utils.ffi requires cffi version >= 1.4, but ""got " + '.'.join(map(str, cffi.__version_info__)))def _generate_typedefs():typedefs = []for t in ['Double', 'Float', 'Long', 'Int', 'Short', 'Char', 'Byte']:for lib in ['TH', 'THCuda']:for kind in ['Tensor', 'Storage']:python_name = t + kindif t == 'Float' and lib == 'THCuda':th_name = 'THCuda' + kindelse:th_name = lib + t + kindth_struct = 'struct ' + th_nametypedefs += ['typedef {} {};'.format(th_struct, th_name)]# We have to assemble a string here, because we're going to# do this lookup based on tensor.type(), which returns a# string (not a type object, as this code was before)python_module = 'torch.cuda' if lib == 'THCuda' else 'torch'python_class = python_module + '.' + python_name_cffi_to_torch[th_struct] = python_class_torch_to_cffi[python_class] = th_structreturn '\n'.join(typedefs) + '\n'
_cffi_to_torch = {}
_torch_to_cffi = {}
_typedefs = _generate_typedefs()PY_MODULE_TEMPLATE = Template("""
from torch.utils.ffi import _wrap_function
from .$cffi_wrapper_name import lib as _lib, ffi as _ffi__all__ = []
def _import_symbols(locals):for symbol in dir(_lib):fn = getattr(_lib, symbol)if callable(fn):locals[symbol] = _wrap_function(fn, _ffi)else:locals[symbol] = fn__all__.append(symbol)_import_symbols(locals())
""")def _setup_wrapper(with_cuda):here = os.path.abspath(os.path.dirname(__file__))lib_dir = os.path.join(here, '..', '..', 'lib')include_dirs = [os.path.join(lib_dir, 'include'),os.path.join(lib_dir, 'include', 'TH'),]wrapper_source = '#include <TH/TH.h>\n'if with_cuda:import torch.cudawrapper_source += '#include <THC/THC.h>\n'if os.sys.platform == 'win32':cuda_include_dirs = glob.glob(os.getenv('CUDA_PATH', '') + '/include')cuda_include_dirs += glob.glob(os.getenv('NVTOOLSEXT_PATH', '') + '/include')else:cuda_include_dirs = glob.glob('/usr/local/cuda/include')cuda_include_dirs += glob.glob('/Developer/NVIDIA/CUDA-*/include')include_dirs.append(os.path.join(lib_dir, 'include', 'THC'))include_dirs.extend(cuda_include_dirs)return wrapper_source, include_dirsdef _create_module_dir(base_path, fullname):module, _, name = fullname.rpartition('.')if not module:target_dir = nameelse:target_dir = reduce(os.path.join, fullname.split('.'))target_dir = os.path.join(base_path, target_dir)try:os.makedirs(target_dir)except os.error:passfor dirname in _accumulate(fullname.split('.'), os.path.join):init_file = os.path.join(base_path, dirname, '__init__.py')open(init_file, 'a').close() # Create file if it doesn't exist yetreturn name, target_dirdef _build_extension(ffi, cffi_wrapper_name, target_dir, verbose):try:tmpdir = tempfile.mkdtemp()ext_suf = '.pyd' if os.sys.platform == 'win32' else '.so'libname = cffi_wrapper_name + ext_sufoutfile = ffi.compile(tmpdir=tmpdir, verbose=verbose, target=libname)shutil.copy(outfile, os.path.join(target_dir, libname))finally:shutil.rmtree(tmpdir)def _make_python_wrapper(name, cffi_wrapper_name, target_dir):py_source = PY_MODULE_TEMPLATE.substitute(name=name,cffi_wrapper_name=cffi_wrapper_name)with open(os.path.join(target_dir, '__init__.py'), 'w') as f:f.write(py_source)def create_extension(name, headers, sources, verbose=True, with_cuda=False,package=False, relative_to='.', **kwargs):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_suffixelse: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() + '\n\n'ffi = cffi.FFI()sources = [os.path.join(base_path, src) for src in sources]# NB: TH headers are C99 nowkwargs['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 = buildreturn ffidef _wrap_function(function, ffi):@wraps(function)def safe_call(*args, **kwargs):args = tuple(ffi.cast(_torch_to_cffi.get(arg.type(), 'void') + '*', arg._cdata)if isinstance(arg, torch.Tensor) or torch.is_storage(arg)else argfor arg in args)args = (function,) + argsresult = torch._C._safe_call(*args, **kwargs)if isinstance(result, ffi.CData):typeof = ffi.typeof(result)if typeof.kind == 'pointer':cdata = int(ffi.cast('uintptr_t', result))cname = typeof.item.cnameif cname in _cffi_to_torch:# TODO: Maybe there is a less janky way to eval# off of thisreturn eval(_cffi_to_torch[cname])(cdata=cdata)return resultreturn safe_call参考：https://github.com/jwyang/faster-rcnn.pytorch/issues/412