Prediction in Caffe - Exception: Input blob arguments do not match net inputs

Question

I\'m using Caffe for classifying non-image data using a quite simple CNN structure. I\'ve had no problems training my network on my HDF5-data with dimensions n x 1 x 156 x 12. H

Answer 1

Even small hints would be greatly appreciated!

I am stuck too so not much help, sorry. Might want to skip to the end.

net.inputs is a @property function which supposedly generated the names of the input layer(s).

@property
def _Net_inputs(self):
    return [list(self.blobs.keys())[i] for i in self._inputs]

Where list(self.blobs.keys()) for you would be

['data', 'feature_conv', 'conv1', 'pool1', 'conv2', 'fc1', 'accuracy', 'loss']

Since inputs has to match kwargs.keys() = ['data'] we can conclude that net._inputs should have been [0]. Somehow.

Since _inputs isn't used anywhere else in pycaffe.py I have a look at _caffe.cpp. Around line 222 it says

.add_property("_inputs", p::make_function(&Net::input_blob_indices,
    bp::return_value_policy()))

So _inputs are the input_blob_indices and it makes sense that these should be [0] for your network.

input_blob_indices in turn is simply a function that returns net_input_blob_indices_ in include/caffe/net.hpp

inline const vector& input_blob_indices() const { return net_input_blob_indices_; }

...which is only used in src/caffe/net.cpp, but I can't find it being defined or assigned anywhere.

I have tried with type: Data and type: MemoryData but that doesn't make a difference. What does work is using

input: "data"
input_dim: 1
input_dim: 3
input_dim: 227
input_dim: 227

...instead of a layer. In that case net._inputs = [0] and net.inputs = ['data'] (actually net._inputs is a caffe._caffe.IntVec object but list(net._inputs) = [0]).

TLDR: It is starting to look a lot like a bug so I submitted it: https://github.com/BVLC/caffe/issues/2246

P.s. it seems like you are converting ndarray to datum and then back again. Does this have a purpose?