Infer one image "IndexError: too many indices for array"

[siwuxagawe]

I am currently trying autoencoder network on caffe on 3 channelled (coloured images) by modifying the MNIST autoencoder example from caffe to suit Cifar10 32x32 images. The network trains but when I tried to 'Test a single Image' using an image of same dimensions, I got an error. However, I tried using the Torch autoencoder example from DIGITS (should be similar) and it works fine.

2017-01-09 17:40:33 [ERROR] IndexError: too many indices for array
Traceback (most recent call last):
File "/home/user/DIGITS/digits/tools/inference.py", line 293, in <module>
args['resize']
File "/home/user/DIGITS/digits/tools/inference.py", line 159, in infer
resize=resize)
File "/home/user/DIGITS/digits/model/tasks/caffe_train.py", line 1252, in infer_one
resize=resize
File "/home/user/DIGITS/digits/model/tasks/caffe_train.py", line 1299, in infer_one_image
visualizations = self.get_layer_visualizations(net, layers)
File "/home/user/DIGITS/digits/model/tasks/caffe_train.py", line 1364, in get_layer_visualizations
data = net.blobs[top].data[0]
IndexError: too many indices for array

Following is the prototxt of the network I'm currently testing

name: "MNISTAutoencoder"
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.0039215684
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TEST
    stage: "test"
  }
  transform_param {
    scale: 0.0039215684
  }
}
layer {
  name: "flatdata"
  type: "Flatten"
  bottom: "data"
  top: "flatdata"
}
layer {
  name: "encode1"
  type: "InnerProduct"
  bottom: "data"
  top: "encode1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "encode1neuron"
  type: "Sigmoid"
  bottom: "encode1"
  top: "encode1neuron"
}
layer {
  name: "encode2"
  type: "InnerProduct"
  bottom: "encode1neuron"
  top: "encode2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "encode2neuron"
  type: "Sigmoid"
  bottom: "encode2"
  top: "encode2neuron"
}
layer {
  name: "encode3"
  type: "InnerProduct"
  bottom: "encode2neuron"
  top: "encode3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 250
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "encode3neuron"
  type: "Sigmoid"
  bottom: "encode3"
  top: "encode3neuron"
}
layer {
  name: "encode4"
  type: "InnerProduct"
  bottom: "encode3neuron"
  top: "encode4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 30
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode4"
  type: "InnerProduct"
  bottom: "encode4"
  top: "decode4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 250
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode4neuron"
  type: "Sigmoid"
  bottom: "decode4"
  top: "decode4neuron"
}
layer {
  name: "decode3"
  type: "InnerProduct"
  bottom: "decode4neuron"
  top: "decode3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode3neuron"
  type: "Sigmoid"
  bottom: "decode3"
  top: "decode3neuron"
}
layer {
  name: "decode2"
  type: "InnerProduct"
  bottom: "decode3neuron"
  top: "decode2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode2neuron"
  type: "Sigmoid"
  bottom: "decode2"
  top: "decode2neuron"
}
layer {
  name: "decode1"
  type: "InnerProduct"
  bottom: "decode2neuron"
  top: "decode1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 3072
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SigmoidCrossEntropyLoss"
  bottom: "decode1"
  bottom: "flatdata"
  top: "cross_entropy_loss"
  loss_weight: 1
}
layer {
  name: "decode1neuron"
  type: "Sigmoid"
  bottom: "decode1"
  top: "decode1neuron"
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "decode1neuron"
  bottom: "flatdata"
  top: "l2_error"
  loss_weight: 0
}

[lukeyeager]

That's a surprising error. My first guess is that you haven't converted your network from a train-val network into an all-in-one network properly (really need to document this: https://github.com/NVIDIA/DIGITS/issues/1192).

Take a look at this example and see if it helps: https://github.com/NVIDIA/DIGITS/blob/v4.0.0/digits/standard-networks/caffe/lenet.prototxt#L162-L184

I'm thinking you may want to exclude your loss layers (and possibly that flatten layer, too) from your deploy network.

[gheinrich]

Also, an auto-encoder typically has zero output in deployment, which would explain why net.blobs[top].data[0] fails with IndexError: too many indices for array. You probably want to add an identity layer to extract the auto-encoder bottleneck (i.e. encode4) in the deploy network. Something like:

layer {
  # Use Power layer in deploy phase to set net output equal to encode4 layer
  name: "deployOutput"
  bottom: "encode4"
  top: "deployOutput"
  type: "Power"
  power_param {
    # scale=1 (actually the default) to produce identity layer
    scale: 1
  }
  include { stage: "deploy" }
}

[siwuxagawe]

Thanks @lukeyeager and @gheinrich for your advices :) been busy these few days so these rough fixes by adding stages in the loss layers managed to get the 'Infer One Image' working, but I wasn't sure if its correct yet. Sorry for asking this in the issues because I thought its a bug since no one (as far as I searched) has encountered this error on DIGITS.

Fixed code

name: "MNISTAutoencoder"
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.0039215684
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TEST
    stage: "test"
  }
  transform_param {
    scale: 0.0039215684
  }
}
layer {
  name: "flatdata"
  type: "Flatten"
  bottom: "data"
  top: "flatdata"
}
layer {
  name: "encode1"
  type: "InnerProduct"
  bottom: "data"
  top: "encode1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "encode1neuron"
  type: "Sigmoid"
  bottom: "encode1"
  top: "encode1neuron"
}
layer {
  name: "encode2"
  type: "InnerProduct"
  bottom: "encode1neuron"
  top: "encode2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "encode2neuron"
  type: "Sigmoid"
  bottom: "encode2"
  top: "encode2neuron"
}
layer {
  name: "encode3"
  type: "InnerProduct"
  bottom: "encode2neuron"
  top: "encode3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 250
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "encode3neuron"
  type: "Sigmoid"
  bottom: "encode3"
  top: "encode3neuron"
}
layer {
  name: "encode4"
  type: "InnerProduct"
  bottom: "encode3neuron"
  top: "encode4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 30
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode4"
  type: "InnerProduct"
  bottom: "encode4"
  top: "decode4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 250
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode4neuron"
  type: "Sigmoid"
  bottom: "decode4"
  top: "decode4neuron"
}
layer {
  name: "decode3"
  type: "InnerProduct"
  bottom: "decode4neuron"
  top: "decode3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode3neuron"
  type: "Sigmoid"
  bottom: "decode3"
  top: "decode3neuron"
}
layer {
  name: "decode2"
  type: "InnerProduct"
  bottom: "decode3neuron"
  top: "decode2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "decode2neuron"
  type: "Sigmoid"
  bottom: "decode2"
  top: "decode2neuron"
}
layer {
  name: "decode1"
  type: "InnerProduct"
  bottom: "decode2neuron"
  top: "decode1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  inner_product_param {
    num_output: 3072
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SigmoidCrossEntropyLoss"
  bottom: "decode1"
  bottom: "flatdata"
  top: "cross_entropy_loss"
  loss_weight: 1
  include { stage: "val" }
}
layer {
  name: "decode1neuron"
  type: "Sigmoid"
  bottom: "decode1"
  top: "decode1neuron"
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "decode1neuron"
  bottom: "flatdata"
  top: "l2_error"
  loss_weight: 0
  exclude { stage: "deploy" }
}

[lukeyeager]

Glad to hear you figured it out. If you're satisfied, please close this issue.