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Unknown bottom blob 'flatten' (layer 'fc', bottom index 0) #5

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oscarriddle opened this issue Mar 8, 2018 · 1 comment
Closed

Unknown bottom blob 'flatten' (layer 'fc', bottom index 0) #5

oscarriddle opened this issue Mar 8, 2018 · 1 comment

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@oscarriddle
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oscarriddle commented Mar 8, 2018 •
edited

Hi, Thanks for your effort.

Here I encountered a problem when converting the default examples in this repo.
The procedure is quite simple:

$python json2prototxt.py 

0, 	op:null                , name:data                           -> data
2, 	op:Convolution         , name:conv1__conv                    -> conv1__conv
           use shared weight -> data
5, 	op:BatchNorm           , name:conv1__bn__bn                  -> conv1__bn__bn
6, 	op:Activation          , name:conv1__bn__relu                -> conv1__bn__relu
8, 	op:Convolution         , name:conv2__conv                    -> conv2__conv
11, 	op:BatchNorm           , name:conv2__bn__bn                  -> conv2__bn__bn
12, 	op:Activation          , name:conv2__bn__relu                -> conv2__bn__relu
14, 	op:Convolution         , name:conv3_c1x1-a__conv             -> conv3_c1x1-a__conv
17, 	op:BatchNorm           , name:conv3_c1x1-a__bn__bn           -> conv3_c1x1-a__bn__bn
18, 	op:Activation          , name:conv3_c1x1-a__bn__relu         -> conv3_c1x1-a__bn__relu
20, 	op:Convolution         , name:conv3_c3x3-b__conv             -> conv3_c3x3-b__conv
23, 	op:BatchNorm           , name:conv3_c3x3-b__bn__bn           -> conv3_c3x3-b__bn__bn
24, 	op:Activation          , name:conv3_c3x3-b__bn__relu         -> conv3_c3x3-b__bn__relu
26, 	op:Convolution         , name:conv3_c1x1-c__conv             -> conv3_c1x1-c__conv
29, 	op:BatchNorm           , name:conv3_c1x1-c__bn__bn           -> conv3_c1x1-c__bn__bn
30, 	op:ElementWiseSum      , name:conv3_ele-sum                  -> conv3_ele-sum
31, 	op:Activation          , name:conv3_sum-act__relu            -> conv3_sum-act__relu
33, 	op:Convolution         , name:conv4_c1x1-w(s/2)__conv        -> conv4_c1x1-w(s/2)__conv
36, 	op:BatchNorm           , name:conv4_c1x1-w(s/2)__bn__bn      -> conv4_c1x1-w(s/2)__bn__bn
38, 	op:Convolution         , name:conv4_c1x1-a__conv             -> conv4_c1x1-a__conv
41, 	op:BatchNorm           , name:conv4_c1x1-a__bn__bn           -> conv4_c1x1-a__bn__bn
42, 	op:Activation          , name:conv4_c1x1-a__bn__relu         -> conv4_c1x1-a__bn__relu
44, 	op:Convolution         , name:conv4_c3x3-b__conv             -> conv4_c3x3-b__conv
47, 	op:BatchNorm           , name:conv4_c3x3-b__bn__bn           -> conv4_c3x3-b__bn__bn
48, 	op:Activation          , name:conv4_c3x3-b__bn__relu         -> conv4_c3x3-b__bn__relu
50, 	op:Convolution         , name:conv4_c1x1-c__conv             -> conv4_c1x1-c__conv
53, 	op:BatchNorm           , name:conv4_c1x1-c__bn__bn           -> conv4_c1x1-c__bn__bn
54, 	op:ElementWiseSum      , name:conv4_ele-sum                  -> conv4_ele-sum
55, 	op:Activation          , name:conv4_sum-act__relu            -> conv4_sum-act__relu
57, 	op:Convolution         , name:conv5_c1x1-a__conv             -> conv5_c1x1-a__conv
60, 	op:BatchNorm           , name:conv5_c1x1-a__bn__bn           -> conv5_c1x1-a__bn__bn
61, 	op:Activation          , name:conv5_c1x1-a__bn__relu         -> conv5_c1x1-a__bn__relu
63, 	op:Convolution         , name:conv5_c3x3-b__conv             -> conv5_c3x3-b__conv
66, 	op:BatchNorm           , name:conv5_c3x3-b__bn__bn           -> conv5_c3x3-b__bn__bn
67, 	op:Activation          , name:conv5_c3x3-b__bn__relu         -> conv5_c3x3-b__bn__relu
69, 	op:Convolution         , name:conv5_c1x1-c__conv             -> conv5_c1x1-c__conv
72, 	op:BatchNorm           , name:conv5_c1x1-c__bn__bn           -> conv5_c1x1-c__bn__bn
73, 	op:ElementWiseSum      , name:conv5_ele-sum                  -> conv5_ele-sum
74, 	op:Activation          , name:conv5_sum-act__relu            -> conv5_sum-act__relu
76, 	op:Convolution         , name:conv6_c1x1-w(s/2)__conv        -> conv6_c1x1-w(s/2)__conv
79, 	op:BatchNorm           , name:conv6_c1x1-w(s/2)__bn__bn      -> conv6_c1x1-w(s/2)__bn__bn
81, 	op:Convolution         , name:conv6_c1x1-a__conv             -> conv6_c1x1-a__conv
84, 	op:BatchNorm           , name:conv6_c1x1-a__bn__bn           -> conv6_c1x1-a__bn__bn
85, 	op:Activation          , name:conv6_c1x1-a__bn__relu         -> conv6_c1x1-a__bn__relu
87, 	op:Convolution         , name:conv6_c3x3-b__conv             -> conv6_c3x3-b__conv
90, 	op:BatchNorm           , name:conv6_c3x3-b__bn__bn           -> conv6_c3x3-b__bn__bn
91, 	op:Activation          , name:conv6_c3x3-b__bn__relu         -> conv6_c3x3-b__bn__relu
93, 	op:Convolution         , name:conv6_c1x1-c__conv             -> conv6_c1x1-c__conv
96, 	op:BatchNorm           , name:conv6_c1x1-c__bn__bn           -> conv6_c1x1-c__bn__bn
97, 	op:ElementWiseSum      , name:conv6_ele-sum                  -> conv6_ele-sum
98, 	op:Activation          , name:conv6_sum-act__relu            -> conv6_sum-act__relu
99, 	op:Pooling             , name:pool5                          -> pool5
100, 	op:Flatten             , name:flatten                        -> flatten
103, 	op:FullyConnected      , name:fc                             -> fc
105, 	op:SoftmaxOutput       , name:softmax                        -> softmax

$python mxnet2caffe.py

[09:31:04] src/nnvm/legacy_json_util.cc:190: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[09:31:04] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded!
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0308 09:31:04.431555 2737054528 net.cpp:51] Initializing net from parameters: 
name: "mxnet-mdoel"
state {
  phase: TRAIN
  level: 0
}
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param {
    shape {
      dim: 10
      dim: 3
      dim: 224
      dim: 224
    }
  }
}
layer {
  name: "conv1__conv"
  type: "Convolution"
  bottom: "data"
  top: "conv1__conv"
  param {
    name: "data"
  }
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 2
  }
}
layer {
  name: "conv1__bn__bn"
  type: "BatchNorm"
  bottom: "conv1__conv"
  top: "conv1__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv1__bn__bn_scale"
  type: "Scale"
  bottom: "conv1__bn__bn"
  top: "conv1__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1__bn__relu"
  type: "ReLU"
  bottom: "conv1__bn__bn"
  top: "conv1__bn__relu"
}
layer {
  name: "conv2__conv"
  type: "Convolution"
  bottom: "conv1__bn__relu"
  top: "conv2__conv"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 2
  }
}
layer {
  name: "conv2__bn__bn"
  type: "BatchNorm"
  bottom: "conv2__conv"
  top: "conv2__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv2__bn__bn_scale"
  type: "Scale"
  bottom: "conv2__bn__bn"
  top: "conv2__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2__bn__relu"
  type: "ReLU"
  bottom: "conv2__bn__bn"
  top: "conv2__bn__relu"
}
layer {
  name: "conv3_c1x1-a__conv"
  type: "Convolution"
  bottom: "conv2__bn__relu"
  top: "conv3_c1x1-a__conv"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv3_c1x1-a__bn__bn"
  type: "BatchNorm"
  bottom: "conv3_c1x1-a__conv"
  top: "conv3_c1x1-a__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv3_c1x1-a__bn__bn_scale"
  type: "Scale"
  bottom: "conv3_c1x1-a__bn__bn"
  top: "conv3_c1x1-a__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_c1x1-a__bn__relu"
  type: "ReLU"
  bottom: "conv3_c1x1-a__bn__bn"
  top: "conv3_c1x1-a__bn__relu"
}
layer {
  name: "conv3_c3x3-b__conv"
  type: "Convolution"
  bottom: "conv3_c1x1-a__bn__relu"
  top: "conv3_c3x3-b__conv"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
  }
}
layer {
  name: "conv3_c3x3-b__bn__bn"
  type: "BatchNorm"
  bottom: "conv3_c3x3-b__conv"
  top: "conv3_c3x3-b__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv3_c3x3-b__bn__bn_scale"
  type: "Scale"
  bottom: "conv3_c3x3-b__bn__bn"
  top: "conv3_c3x3-b__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_c3x3-b__bn__relu"
  type: "ReLU"
  bottom: "conv3_c3x3-b__bn__bn"
  top: "conv3_c3x3-b__bn__relu"
}
layer {
  name: "conv3_c1x1-c__conv"
  type: "Convolution"
  bottom: "conv3_c3x3-b__bn__relu"
  top: "conv3_c1x1-c__conv"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv3_c1x1-c__bn__bn"
  type: "BatchNorm"
  bottom: "conv3_c1x1-c__conv"
  top: "conv3_c1x1-c__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv3_c1x1-c__bn__bn_scale"
  type: "Scale"
  bottom: "conv3_c1x1-c__bn__bn"
  top: "conv3_c1x1-c__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_ele-sum"
  type: "Eltwise"
  bottom: "conv2__bn__relu"
  bottom: "conv3_c1x1-c__bn__bn"
  top: "conv3_ele-sum"
}
layer {
  name: "conv3_sum-act__relu"
  type: "ReLU"
  bottom: "conv3_ele-sum"
  top: "conv3_sum-act__relu"
}
layer {
  name: "conv4_c1x1-w(s/2)__conv"
  type: "Convolution"
  bottom: "conv3_sum-act__relu"
  top: "conv4_c1x1-w(s/2)__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 2
  }
}
layer {
  name: "conv4_c1x1-w(s/2)__bn__bn"
  type: "BatchNorm"
  bottom: "conv4_c1x1-w(s/2)__conv"
  top: "conv4_c1x1-w(s/2)__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv4_c1x1-w(s/2)__bn__bn_scale"
  type: "Scale"
  bottom: "conv4_c1x1-w(s/2)__bn__bn"
  top: "conv4_c1x1-w(s/2)__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_c1x1-a__conv"
  type: "Convolution"
  bottom: "conv3_sum-act__relu"
  top: "conv4_c1x1-a__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv4_c1x1-a__bn__bn"
  type: "BatchNorm"
  bottom: "conv4_c1x1-a__conv"
  top: "conv4_c1x1-a__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv4_c1x1-a__bn__bn_scale"
  type: "Scale"
  bottom: "conv4_c1x1-a__bn__bn"
  top: "conv4_c1x1-a__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_c1x1-a__bn__relu"
  type: "ReLU"
  bottom: "conv4_c1x1-a__bn__bn"
  top: "conv4_c1x1-a__bn__relu"
}
layer {
  name: "conv4_c3x3-b__conv"
  type: "Convolution"
  bottom: "conv4_c1x1-a__bn__relu"
  top: "conv4_c3x3-b__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 2
  }
}
layer {
  name: "conv4_c3x3-b__bn__bn"
  type: "BatchNorm"
  bottom: "conv4_c3x3-b__conv"
  top: "conv4_c3x3-b__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv4_c3x3-b__bn__bn_scale"
  type: "Scale"
  bottom: "conv4_c3x3-b__bn__bn"
  top: "conv4_c3x3-b__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_c3x3-b__bn__relu"
  type: "ReLU"
  bottom: "conv4_c3x3-b__bn__bn"
  top: "conv4_c3x3-b__bn__relu"
}
layer {
  name: "conv4_c1x1-c__conv"
  type: "Convolution"
  bottom: "conv4_c3x3-b__bn__relu"
  top: "conv4_c1x1-c__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv4_c1x1-c__bn__bn"
  type: "BatchNorm"
  bottom: "conv4_c1x1-c__conv"
  top: "conv4_c1x1-c__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv4_c1x1-c__bn__bn_scale"
  type: "Scale"
  bottom: "conv4_c1x1-c__bn__bn"
  top: "conv4_c1x1-c__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_ele-sum"
  type: "Eltwise"
  bottom: "conv4_c1x1-w(s/2)__bn__bn"
  bottom: "conv4_c1x1-c__bn__bn"
  top: "conv4_ele-sum"
}
layer {
  name: "conv4_sum-act__relu"
  type: "ReLU"
  bottom: "conv4_ele-sum"
  top: "conv4_sum-act__relu"
}
layer {
  name: "conv5_c1x1-a__conv"
  type: "Convolution"
  bottom: "conv4_sum-act__relu"
  top: "conv5_c1x1-a__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv5_c1x1-a__bn__bn"
  type: "BatchNorm"
  bottom: "conv5_c1x1-a__conv"
  top: "conv5_c1x1-a__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv5_c1x1-a__bn__bn_scale"
  type: "Scale"
  bottom: "conv5_c1x1-a__bn__bn"
  top: "conv5_c1x1-a__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_c1x1-a__bn__relu"
  type: "ReLU"
  bottom: "conv5_c1x1-a__bn__bn"
  top: "conv5_c1x1-a__bn__relu"
}
layer {
  name: "conv5_c3x3-b__conv"
  type: "Convolution"
  bottom: "conv5_c1x1-a__bn__relu"
  top: "conv5_c3x3-b__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
  }
}
layer {
  name: "conv5_c3x3-b__bn__bn"
  type: "BatchNorm"
  bottom: "conv5_c3x3-b__conv"
  top: "conv5_c3x3-b__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv5_c3x3-b__bn__bn_scale"
  type: "Scale"
  bottom: "conv5_c3x3-b__bn__bn"
  top: "conv5_c3x3-b__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_c3x3-b__bn__relu"
  type: "ReLU"
  bottom: "conv5_c3x3-b__bn__bn"
  top: "conv5_c3x3-b__bn__relu"
}
layer {
  name: "conv5_c1x1-c__conv"
  type: "Convolution"
  bottom: "conv5_c3x3-b__bn__relu"
  top: "conv5_c1x1-c__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv5_c1x1-c__bn__bn"
  type: "BatchNorm"
  bottom: "conv5_c1x1-c__conv"
  top: "conv5_c1x1-c__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv5_c1x1-c__bn__bn_scale"
  type: "Scale"
  bottom: "conv5_c1x1-c__bn__bn"
  top: "conv5_c1x1-c__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_ele-sum"
  type: "Eltwise"
  bottom: "conv4_sum-act__relu"
  bottom: "conv5_c1x1-c__bn__bn"
  top: "conv5_ele-sum"
}
layer {
  name: "conv5_sum-act__relu"
  type: "ReLU"
  bottom: "conv5_ele-sum"
  top: "conv5_sum-act__relu"
}
layer {
  name: "conv6_c1x1-w(s/2)__conv"
  type: "Convolution"
  bottom: "conv5_sum-act__relu"
  top: "conv6_c1x1-w(s/2)__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 2
  }
}
layer {
  name: "conv6_c1x1-w(s/2)__bn__bn"
  type: "BatchNorm"
  bottom: "conv6_c1x1-w(s/2)__conv"
  top: "conv6_c1x1-w(s/2)__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv6_c1x1-w(s/2)__bn__bn_scale"
  type: "Scale"
  bottom: "conv6_c1x1-w(s/2)__bn__bn"
  top: "conv6_c1x1-w(s/2)__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_c1x1-a__conv"
  type: "Convolution"
  bottom: "conv5_sum-act__relu"
  top: "conv6_c1x1-a__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv6_c1x1-a__bn__bn"
  type: "BatchNorm"
  bottom: "conv6_c1x1-a__conv"
  top: "conv6_c1x1-a__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv6_c1x1-a__bn__bn_scale"
  type: "Scale"
  bottom: "conv6_c1x1-a__bn__bn"
  top: "conv6_c1x1-a__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_c1x1-a__bn__relu"
  type: "ReLU"
  bottom: "conv6_c1x1-a__bn__bn"
  top: "conv6_c1x1-a__bn__relu"
}
layer {
  name: "conv6_c3x3-b__conv"
  type: "Convolution"
  bottom: "conv6_c1x1-a__bn__relu"
  top: "conv6_c3x3-b__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 2
  }
}
layer {
  name: "conv6_c3x3-b__bn__bn"
  type: "BatchNorm"
  bottom: "conv6_c3x3-b__conv"
  top: "conv6_c3x3-b__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv6_c3x3-b__bn__bn_scale"
  type: "Scale"
  bottom: "conv6_c3x3-b__bn__bn"
  top: "conv6_c3x3-b__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_c3x3-b__bn__relu"
  type: "ReLU"
  bottom: "conv6_c3x3-b__bn__bn"
  top: "conv6_c3x3-b__bn__relu"
}
layer {
  name: "conv6_c1x1-c__conv"
  type: "Convolution"
  bottom: "conv6_c3x3-b__bn__relu"
  top: "conv6_c1x1-c__conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
  }
}
layer {
  name: "conv6_c1x1-c__bn__bn"
  type: "BatchNorm"
  bottom: "conv6_c1x1-c__conv"
  top: "conv6_c1x1-c__bn__bn"
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.9
    eps: 0.001
  }
}
layer {
  name: "conv6_c1x1-c__bn__bn_scale"
  type: "Scale"
  bottom: "conv6_c1x1-c__bn__bn"
  top: "conv6_c1x1-c__bn__bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_ele-sum"
  type: "Eltwise"
  bottom: "conv6_c1x1-w(s/2)__bn__bn"
  bottom: "conv6_c1x1-c__bn__bn"
  top: "conv6_ele-sum"
}
layer {
  name: "conv6_sum-act__relu"
  type: "ReLU"
  bottom: "conv6_ele-sum"
  top: "conv6_sum-act__relu"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv6_sum-act__relu"
  top: "pool5"
  pooling_param {
    pool: AVE
    kernel_size: 14
    stride: 1
    pad: 0
  }
}
layer {
  name: "fc"
  type: "InnerProduct"
  bottom: "flatten"
  top: "fc"
  inner_product_param {
    num_output: 1000
  }
}
F0308 09:31:04.433809 2737054528 insert_splits.cpp:29] Unknown bottom blob 'flatten' (layer 'fc', bottom index 0)
*** Check failure stack trace: ***
Abort trap: 6

Not sure why the blob "flatten" is not correctly recognized. Any idea?
@oscarriddle
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Found the solution by manually modify the bottom "flatten" to the previous layers' top
`
layer {
bottom: "conv6_sum-act__relu"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
pool: AVE
kernel_size: 14
stride: 1
pad: 0
}
}

layer {
bottom: "flatten" -> change this to: bottom: "pool5"
top: "fc"
name: "fc"
type: "InnerProduct"
inner_product_param {
num_output: 1000
}
}`

Thanks for the hint.

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