SqueezNet CaffeModel tuned in DIGITS

[kingreza]

I have tuned a pre trained SqueezNet Caffe Model using DIGITS. Everything seems to work fine except when I try to classify an image through DIGITS I get the following error:

Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/flask/app.py", line 1475, in full_dispatch_request
    rv = self.dispatch_request()
  File "/usr/lib/python2.7/dist-packages/flask/app.py", line 1461, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "/usr/share/digits/digits/model/images/classification/views.py", line 291, in image_classification_model_classify_one
    predictions, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
  File "/usr/share/digits/digits/model/tasks/caffe_train.py", line 1018, in infer_one
    layers=layers,
  File "/usr/share/digits/digits/model/tasks/caffe_train.py", line 1063, in classify_one
    predictions.append( (labels[i], scores[i]) )
IndexError: list index out of range

The same data DB works fine with AlextNet, however it fails to classify through DIGITS for this new retrained model:

# please cite:
# @article{SqueezeNet,
#     Author = {Forrest N. Iandola and Matthew W. Moskewicz and Khalid Ashraf and Song Han and William J. Dally and Kurt Keutzer},
#     Title = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$1MB model size},
#     Journal = {arXiv:1602.07360},
#     Year = {2016}
# }
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    crop_size: 227
    mean_value: 104
    mean_value: 117
    mean_value: 123
  }

}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    crop_size: 227
    mean_value: 104
    mean_value: 117
    mean_value: 123
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "relu_conv1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fire2/squeeze1x1"
  type: "Convolution"
  bottom: "pool1"
  top: "fire2/squeeze1x1"
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire2/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire2/squeeze1x1"
  top: "fire2/squeeze1x1"
}
layer {
  name: "fire2/expand1x1"
  type: "Convolution"
  bottom: "fire2/squeeze1x1"
  top: "fire2/expand1x1"
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire2/relu_expand1x1"
  type: "ReLU"
  bottom: "fire2/expand1x1"
  top: "fire2/expand1x1"
}
layer {
  name: "fire2/expand3x3"
  type: "Convolution"
  bottom: "fire2/squeeze1x1"
  top: "fire2/expand3x3"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire2/relu_expand3x3"
  type: "ReLU"
  bottom: "fire2/expand3x3"
  top: "fire2/expand3x3"
}
layer {
  name: "fire2/concat"
  type: "Concat"
  bottom: "fire2/expand1x1"
  bottom: "fire2/expand3x3"
  top: "fire2/concat"
}
layer {
  name: "fire3/squeeze1x1"
  type: "Convolution"
  bottom: "fire2/concat"
  top: "fire3/squeeze1x1"
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire3/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire3/squeeze1x1"
  top: "fire3/squeeze1x1"
}
layer {
  name: "fire3/expand1x1"
  type: "Convolution"
  bottom: "fire3/squeeze1x1"
  top: "fire3/expand1x1"
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire3/relu_expand1x1"
  type: "ReLU"
  bottom: "fire3/expand1x1"
  top: "fire3/expand1x1"
}
layer {
  name: "fire3/expand3x3"
  type: "Convolution"
  bottom: "fire3/squeeze1x1"
  top: "fire3/expand3x3"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire3/relu_expand3x3"
  type: "ReLU"
  bottom: "fire3/expand3x3"
  top: "fire3/expand3x3"
}
layer {
  name: "fire3/concat"
  type: "Concat"
  bottom: "fire3/expand1x1"
  bottom: "fire3/expand3x3"
  top: "fire3/concat"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "fire3/concat"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fire4/squeeze1x1"
  type: "Convolution"
  bottom: "pool3"
  top: "fire4/squeeze1x1"
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire4/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire4/squeeze1x1"
  top: "fire4/squeeze1x1"
}
layer {
  name: "fire4/expand1x1"
  type: "Convolution"
  bottom: "fire4/squeeze1x1"
  top: "fire4/expand1x1"
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire4/relu_expand1x1"
  type: "ReLU"
  bottom: "fire4/expand1x1"
  top: "fire4/expand1x1"
}
layer {
  name: "fire4/expand3x3"
  type: "Convolution"
  bottom: "fire4/squeeze1x1"
  top: "fire4/expand3x3"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire4/relu_expand3x3"
  type: "ReLU"
  bottom: "fire4/expand3x3"
  top: "fire4/expand3x3"
}
layer {
  name: "fire4/concat"
  type: "Concat"
  bottom: "fire4/expand1x1"
  bottom: "fire4/expand3x3"
  top: "fire4/concat"
}
layer {
  name: "fire5/squeeze1x1"
  type: "Convolution"
  bottom: "fire4/concat"
  top: "fire5/squeeze1x1"
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire5/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire5/squeeze1x1"
  top: "fire5/squeeze1x1"
}
layer {
  name: "fire5/expand1x1"
  type: "Convolution"
  bottom: "fire5/squeeze1x1"
  top: "fire5/expand1x1"
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire5/relu_expand1x1"
  type: "ReLU"
  bottom: "fire5/expand1x1"
  top: "fire5/expand1x1"
}
layer {
  name: "fire5/expand3x3"
  type: "Convolution"
  bottom: "fire5/squeeze1x1"
  top: "fire5/expand3x3"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire5/relu_expand3x3"
  type: "ReLU"
  bottom: "fire5/expand3x3"
  top: "fire5/expand3x3"
}
layer {
  name: "fire5/concat"
  type: "Concat"
  bottom: "fire5/expand1x1"
  bottom: "fire5/expand3x3"
  top: "fire5/concat"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "fire5/concat"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fire6/squeeze1x1"
  type: "Convolution"
  bottom: "pool5"
  top: "fire6/squeeze1x1"
  convolution_param {
    num_output: 48
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire6/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire6/squeeze1x1"
  top: "fire6/squeeze1x1"
}
layer {
  name: "fire6/expand1x1"
  type: "Convolution"
  bottom: "fire6/squeeze1x1"
  top: "fire6/expand1x1"
  convolution_param {
    num_output: 192
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire6/relu_expand1x1"
  type: "ReLU"
  bottom: "fire6/expand1x1"
  top: "fire6/expand1x1"
}
layer {
  name: "fire6/expand3x3"
  type: "Convolution"
  bottom: "fire6/squeeze1x1"
  top: "fire6/expand3x3"
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire6/relu_expand3x3"
  type: "ReLU"
  bottom: "fire6/expand3x3"
  top: "fire6/expand3x3"
}
layer {
  name: "fire6/concat"
  type: "Concat"
  bottom: "fire6/expand1x1"
  bottom: "fire6/expand3x3"
  top: "fire6/concat"
}
layer {
  name: "fire7/squeeze1x1"
  type: "Convolution"
  bottom: "fire6/concat"
  top: "fire7/squeeze1x1"
  convolution_param {
    num_output: 48
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire7/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire7/squeeze1x1"
  top: "fire7/squeeze1x1"
}
layer {
  name: "fire7/expand1x1"
  type: "Convolution"
  bottom: "fire7/squeeze1x1"
  top: "fire7/expand1x1"
  convolution_param {
    num_output: 192
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire7/relu_expand1x1"
  type: "ReLU"
  bottom: "fire7/expand1x1"
  top: "fire7/expand1x1"
}
layer {
  name: "fire7/expand3x3"
  type: "Convolution"
  bottom: "fire7/squeeze1x1"
  top: "fire7/expand3x3"
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire7/relu_expand3x3"
  type: "ReLU"
  bottom: "fire7/expand3x3"
  top: "fire7/expand3x3"
}
layer {
  name: "fire7/concat"
  type: "Concat"
  bottom: "fire7/expand1x1"
  bottom: "fire7/expand3x3"
  top: "fire7/concat"
}
layer {
  name: "fire8/squeeze1x1"
  type: "Convolution"
  bottom: "fire7/concat"
  top: "fire8/squeeze1x1"
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire8/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire8/squeeze1x1"
  top: "fire8/squeeze1x1"
}
layer {
  name: "fire8/expand1x1"
  type: "Convolution"
  bottom: "fire8/squeeze1x1"
  top: "fire8/expand1x1"
  convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire8/relu_expand1x1"
  type: "ReLU"
  bottom: "fire8/expand1x1"
  top: "fire8/expand1x1"
}
layer {
  name: "fire8/expand3x3"
  type: "Convolution"
  bottom: "fire8/squeeze1x1"
  top: "fire8/expand3x3"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire8/relu_expand3x3"
  type: "ReLU"
  bottom: "fire8/expand3x3"
  top: "fire8/expand3x3"
}
layer {
  name: "fire8/concat"
  type: "Concat"
  bottom: "fire8/expand1x1"
  bottom: "fire8/expand3x3"
  top: "fire8/concat"
}
layer {
  name: "fire9/squeeze1x1"
  type: "Convolution"
  bottom: "fire8/concat"
  top: "fire9/squeeze1x1"
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire9/relu_squeeze1x1"
  type: "ReLU"
  bottom: "fire9/squeeze1x1"
  top: "fire9/squeeze1x1"
}
layer {
  name: "fire9/expand1x1"
  type: "Convolution"
  bottom: "fire9/squeeze1x1"
  top: "fire9/expand1x1"
  convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire9/relu_expand1x1"
  type: "ReLU"
  bottom: "fire9/expand1x1"
  top: "fire9/expand1x1"
}
layer {
  name: "fire9/expand3x3"
  type: "Convolution"
  bottom: "fire9/squeeze1x1"
  top: "fire9/expand3x3"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "fire9/relu_expand3x3"
  type: "ReLU"
  bottom: "fire9/expand3x3"
  top: "fire9/expand3x3"
}
layer {
  name: "fire9/concat"
  type: "Concat"
  bottom: "fire9/expand1x1"
  bottom: "fire9/expand3x3"
  top: "fire9/concat"
}
layer {
  name: "drop9"
  type: "Dropout"
  bottom: "fire9/concat"
  top: "fire9/concat"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv10"
  type: "Convolution"
  bottom: "fire9/concat"
  top: "conv10"
  convolution_param {
    num_output: 1000
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      mean: 0.0
      std: 0.01
    }
  }
}
layer {
  name: "relu_conv10"
  type: "ReLU"
  bottom: "conv10"
  top: "conv10"
}
layer {
  name: "pool10"
  type: "Pooling"
  bottom: "conv10"
  top: "pool10"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "pool10"
  bottom: "label"
  top: "loss"
  #include {
  #  phase: TRAIN
  #}
}
layer {
  name: "accuracy_food"
  type: "Accuracy"
  bottom: "pool10"
  bottom: "label"
  top: "accuracy_food"
  #include {
  #  phase: TEST
  #}
}

[BradLarson]

If I'm not mistaken, I believe this is related to the same issue as reported in #1492, for which a solution was proposed in pull request #1536.

The core issue is that SqueezeNet is a fully convolutional network, with a pooling layer feeding into the softmax operator, instead of using a fully connected layer at the end like AlexNet. That leads to a different dimensionality in that last layer, which causes the DIGITS classification view to have problems. That pull request seemed to fix the issue for me when I tried it.