Caffe error Cannot copy param 0 weights from layer, shape mismatch

I am getting following error while running test app. (when I am using trained model with my test application)

F0518 18:21:13.978204 13437 net.cpp:766] Cannot copy param 0 weights from layer 'fc6'; shape mismatch.  Source param shape is 4096 2304 (9437184); target param shape is 4096 9216 (37748736). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer.*** Check failure stack trace: ***Aborted (core dumped)

Can anybody tell the reason for this ?

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deploy.prototxt

---

name: "CaffeNet"layer {  name: "data"  type: "Input"  top: "data"  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 96    kernel_size: 11    stride: 4  }}layer {  name: "relu1"  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: "norm1"  type: "LRN"  bottom: "pool1"  top: "norm1"  lrn_param {    local_size: 5    alpha: 0.0001    beta: 0.75  }}layer {  name: "conv2"  type: "Convolution"  bottom: "norm1"  top: "conv2"  convolution_param {    num_output: 256    pad: 2    kernel_size: 5    group: 2  }}layer {  name: "relu2"  type: "ReLU"  bottom: "conv2"  top: "conv2"}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "norm2"  type: "LRN"  bottom: "pool2"  top: "norm2"  lrn_param {    local_size: 5    alpha: 0.0001    beta: 0.75  }}layer {  name: "conv3"  type: "Convolution"  bottom: "norm2"  top: "conv3"  convolution_param {    num_output: 384    pad: 1    kernel_size: 3  }}layer {  name: "relu3"  type: "ReLU"  bottom: "conv3"  top: "conv3"}layer {  name: "conv4"  type: "Convolution"  bottom: "conv3"  top: "conv4"  convolution_param {    num_output: 384    pad: 1    kernel_size: 3    group: 2  }}layer {  name: "relu4"  type: "ReLU"  bottom: "conv4"  top: "conv4"}layer {  name: "conv5"  type: "Convolution"  bottom: "conv4"  top: "conv5"  convolution_param {    num_output: 256    pad: 1    kernel_size: 3    group: 2  }}layer {  name: "relu5"  type: "ReLU"  bottom: "conv5"  top: "conv5"}layer {  name: "pool5"  type: "Pooling"  bottom: "conv5"  top: "pool5"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "fc6"  type: "InnerProduct"  bottom: "pool5"  top: "fc6"  inner_product_param {    num_output: 4096  }}layer {  name: "relu6"  type: "ReLU"  bottom: "fc6"  top: "fc6"}layer {  name: "drop6"  type: "Dropout"  bottom: "fc6"  top: "fc6"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "fc7"  type: "InnerProduct"  bottom: "fc6"  top: "fc7"  inner_product_param {    num_output: 4096  }}layer {  name: "relu7"  type: "ReLU"  bottom: "fc7"  top: "fc7"}layer {  name: "drop7"  type: "Dropout"  bottom: "fc7"  top: "fc7"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "fc8"  type: "InnerProduct"  bottom: "fc7"  top: "fc8"  inner_product_param {    num_output: 2  }}layer {  name: "prob"  type: "Softmax"  bottom: "fc8"  top: "prob"}

---

train_val.prototxt

---

name: "CaffeNet"layer {  name: "data"  type: "Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    mirror: true    crop_size: 256    mean_file: "data/lmvhmv/imagenet_mean.binaryproto"  }# mean pixel / channel-wise mean instead of mean image#  transform_param {#    crop_size: 126#    mean_value: 104#    mean_value: 117#    mean_value: 123#    mirror: true#  }  data_param {    source: "examples/imagenet/lmvhmv1_train_lmdb"    batch_size: 10    backend: LMDB  }}layer {  name: "data"  type: "Data"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    mirror: false    crop_size: 256    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"  }# mean pixel / channel-wise mean instead of mean image#  transform_param {#    crop_size: 256#    mean_value: 104#    mean_value: 117#    mean_value: 123#    mirror: true#  }  data_param {    source: "examples/imagenet/lmvhmv1_test_lmdb"    batch_size: 10    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 96    kernel_size: 11    stride: 4    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu1"  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: "norm1"  type: "LRN"  bottom: "pool1"  top: "norm1"  lrn_param {    local_size: 5    alpha: 0.0001    beta: 0.75  }}layer {  name: "conv2"  type: "Convolution"  bottom: "norm1"  top: "conv2"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 256    pad: 2    kernel_size: 5    group: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu2"  type: "ReLU"  bottom: "conv2"  top: "conv2"}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "norm2"  type: "LRN"  bottom: "pool2"  top: "norm2"  lrn_param {    local_size: 5    alpha: 0.0001    beta: 0.75  }}layer {  name: "conv3"  type: "Convolution"  bottom: "norm2"  top: "conv3"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 384    pad: 1    kernel_size: 3    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu3"  type: "ReLU"  bottom: "conv3"  top: "conv3"}layer {  name: "conv4"  type: "Convolution"  bottom: "conv3"  top: "conv4"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 384    pad: 1    kernel_size: 3    group: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu4"  type: "ReLU"  bottom: "conv4"  top: "conv4"}layer {  name: "conv5"  type: "Convolution"  bottom: "conv4"  top: "conv5"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 256    pad: 1    kernel_size: 3    group: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu5"  type: "ReLU"  bottom: "conv5"  top: "conv5"}layer {  name: "pool5"  type: "Pooling"  bottom: "conv5"  top: "pool5"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "fc6"  type: "InnerProduct"  bottom: "pool5"  top: "fc6"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 4096    weight_filler {      type: "gaussian"      std: 0.005    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu6"  type: "ReLU"  bottom: "fc6"  top: "fc6"}layer {  name: "drop6"  type: "Dropout"  bottom: "fc6"  top: "fc6"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "fc7"  type: "InnerProduct"  bottom: "fc6"  top: "fc7"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 4096    weight_filler {      type: "gaussian"      std: 0.005    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "relu7"  type: "ReLU"  bottom: "fc7"  top: "fc7"}layer {  name: "drop7"  type: "Dropout"  bottom: "fc7"  top: "fc7"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "fc8"  type: "InnerProduct"  bottom: "fc7"  top: "fc8"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 1    }  }}layer {  name: "accuracy"  type: "Accuracy"  bottom: "fc8"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "fc8"  bottom: "label"  top: "loss"}

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edited May 18 at 13:41

asked May 18 at 12:56

user_12

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Share the prototxt file that you used to train and test to pinpoint the issue. It should be mainly because the dimensions of the input image is not the same in both the test prototxt and train prototxt. Check the height, width and channel count of both the test and train prototxt – Anoop K. Prabhu May 18 at 13:25

I was able to train caffe properly, this is happening in test application where I am using trained caffe model. – user_12 May 18 at 13:46

Ok, Now its working when I corrected dim value in deploy.txt its working – user_12 May 18 at 13:59

Though the issue is solved, I am writing an answer to help others who come up with the same issue later. – Anoop K. Prabhu May 18 at 15:40

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The training was done on an image of dimension 256x256x3, but test prototxt tries to read in 227x227x3 image. The convolutional layers will not show any issue as the dimension of input blobs to convolutional layers doesnot matter. The fully connected layers will crash when it finds that the input blob have a different dimension. In other words, the dimension of the weights of a fully connected layer is related to the dimension of the input blobs.

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answered May 18 at 15:39

Anoop K. Prabhu

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