使用Python上的Mxnet库运行深度学习图像分类示例时出错

我正在尝试运行在python中使用Mxnet library的this image classification example和预训练的深度学习模型Inception-BN . 此行上的执行抛出和错误： prob = model.predict(batch)[0] ，并显示错误消息：

MXNetError: InferShape Error in ch_concat_3c_chconcat: [14:35:56] src/operator/./concat-inl.h:152: Check failed: (dshape[j]) == (tmp[j]) Incorrect shape[2]: (1,320,15,15). (first input shape: (1,576,14,14))

我尝试再次下载Inception-BN模型以确保它是最新的，但它没有任何区别 . 我怀疑问题可能在线： model = mx.model.FeedForward.load(prefix, num_round, ctx=mx.gpu(), numpy_batch_size=1) 我必须为 cpu 更改 gpu ，因为我的服务器没有配备gpu . 然而，错误似乎并没有指向那个方向 .

知道怎么解决吗？使用cpu而不是gpu是一个问题，而不是性能较低？

最后这里是完整的错误信息：

---------------------------------------------------------------------------
MXNetError                                Traceback (most recent call last)
<ipython-input-7-98e51e4226e1> in <module>()
      1 # Get prediction probability of 1000 classes from model
----> 2 prob = model.predict(batch)[0]
      3 # Argsort, get prediction index from largest prob to lowest
      4 pred = np.argsort(prob)[::-1]
      5 # Get top1 label

/users/CREATE/olb/mxnet/python/mxnet/model.pyc in predict(self, X, num_batch, return_data, reset)
    589         data_shapes = X.provide_data
    590         data_names = [x[0] for x in data_shapes]
--> 591         self._init_predictor(data_shapes)
    592         batch_size = X.batch_size
    593         data_arrays = [self._pred_exec.arg_dict[name] for name in data_names]

/users/CREATE/olb/mxnet/python/mxnet/model.pyc in _init_predictor(self, input_shapes)
    520         # for now only use the first device
    521         pred_exec = self.symbol.simple_bind(
--> 522             self.ctx[0], grad_req='null', **dict(input_shapes))
    523         pred_exec.copy_params_from(self.arg_params, self.aux_params)
    524 

/users/CREATE/olb/mxnet/python/mxnet/symbol.pyc in simple_bind(self, ctx, grad_req, type_dict, **kwargs)
    623         if type_dict is None:
    624             type_dict = {k: mx_real_t for k in self.list_arguments()}
--> 625         arg_shapes, _, aux_shapes = self.infer_shape(**kwargs)
    626         arg_types, _, aux_types = self.infer_type(**type_dict)
    627         if arg_shapes == None or arg_types == None:

/users/CREATE/olb/mxnet/python/mxnet/symbol.pyc in infer_shape(self, *args, **kwargs)
    410             The order is in the same order as list_auxiliary()
    411         """
--> 412         return self._infer_shape_impl(False, *args, **kwargs)
    413 
    414     def infer_shape_partial(self, *args, **kwargs):

/users/CREATE/olb/mxnet/python/mxnet/symbol.pyc in _infer_shape_impl(self, partial, *args, **kwargs)
    470             ctypes.byref(aux_shape_ndim),
    471             ctypes.byref(aux_shape_data),
--> 472             ctypes.byref(complete)))
    473         if complete.value != 0:
    474             arg_shapes = [

/users/CREATE/olb/mxnet/python/mxnet/base.pyc in check_call(ret)
     75     """
     76     if ret != 0:
---> 77         raise MXNetError(py_str(_LIB.MXGetLastError()))
     78 
     79 def c_str(string):

MXNetError: InferShape Error in ch_concat_3c_chconcat: [14:35:56] src/operator/./concat-inl.h:152: Check failed: (dshape[j]) == (tmp[j]) Incorrect shape[2]: (1,320,15,15). (first input shape: (1,576,14,14))