在Python上运行使用Mxnet库的深度学习图像分类示例的错误

我试图运行使用Mxnet library在python中的this image classification example以及预先训练的深度学习模型Inception-BN。执行罚球和错误在这条线：prob = model.predict(batch)[0]出现错误消息：在Python上运行使用Mxnet库的深度学习图像分类示例的错误

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))

我试图重新下载盗梦空间-BN模型，以确保它是最新的，但它并没有发挥作用。我怀疑问题可能在线：model = mx.model.FeedForward.load(prefix, num_round, ctx=mx.gpu(), numpy_batch_size=1)我必须更改gpu对于cpu因为我的服务器没有配备gpu。尽管如此，这个错误似乎并没有指向这个方向。

任何想法如何解决它？使用cpu而不是gpu是性能较低的问题吗？

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))

来源

2016-10-03 nest

所提到的笔记本电脑移动到notebooks repository。我今天试图运行它，并能够成功运行教程。该问题出现在旧模型中，因为界面的变化破坏了向后兼容性。看起来他们已经上传了新的训练成熟的BN模型。

发布此信息如果别人也有这个错误，只需下载新的型号here。

来源

2017-06-07 22:08:22

在Python上运行使用Mxnet库的深度学习图像分类示例的错误

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