hdf5 - Caffe - Doing forward pass with multiple input blobs -


following input layers of fine-tuned model:

layer {   type: "hdf5data"   name: "data"   top: "meta"    hdf5_data_param {     source: "/path/to/train.txt"     batch_size: 50   }   include { phase: train } } layer {   name: "data"   type: "imagedata"   top: "x"   top: "labels"   include {     phase: train   }   transform_param {     mirror: true     crop_size: 227     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"   }   image_data_param {     source: "/path/to/train.txt"     batch_size: 50     new_height: 256     new_width: 256   } } layer {   type: "hdf5data"   name: "data"   top: "meta"    hdf5_data_param {     source: "/path/to/val.txt"     batch_size: 50   }   include { phase: test } } layer {   name: "data"   type: "imagedata"   top: "x"   top: "labels"   include {     phase: test   }   transform_param {     mirror: false     crop_size: 227     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"   }   image_data_param {     source: "/path/to/val.txt"     batch_size: 50     new_height: 256     new_width: 256   } }

as can see has 1 imagedata input layer , 1 hdf5 input layer, if there 1 type of layer imagedata, have done:

input_data = {prepare_images(im)}; # dimension 227*227*3*10

and scores = caffe('forward',input_data); here have give 2 types of input data, how can this? please help!

i had check matcaffe.cpp (and recompile make matcaffe) , print check variables 'invalid input size' condition failing, idea of transposing input_data works.

input_data = {prepare_images(im),prepare_other_data()}; scores = caffe('forward', input_data');

thus taking transpose worked me.


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