Hello,
I'm getting the following error while running the following network:
Error
--------
output data size: 1,3,200,200
Setting up data
Top shape: 1 3 200 200 (120000)
Top shape: 1 (1)
Memory required for data: 480004
Creating layer data_data_0_split
Creating Layer data_data_0_split
data_data_0_split <- data
data_data_0_split -> data_data_0_split_0
data_data_0_split -> data_data_0_split_1
Setting up data_data_0_split
Top shape: 1 3 200 200 (120000)
Top shape: 1 3 200 200 (120000)
Memory required for data: 1440004
Creating layer label
Creating Layer label
Top blob 'label' produced by multiple sources.
Network
-------------
layer {
Ā name: "data_1"
Ā type: "Data"
Ā top: "data"
Ā top: "label"
Ā include {
Ā Ā phase: TRAIN
Ā }
Ā transform_param {
Ā Ā mirror: true
Ā Ā crop_size: 200
Ā }
Ā data_param {
Ā Ā batch_size: 1
Ā }
}
layer {
Ā name: "conv1_1"
Ā type: "Convolution"
Ā bottom: "data"
Ā top: "conv1_1"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 64
Ā Ā pad: 100
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu1_1"
Ā type: "ReLU"
Ā bottom: "conv1_1"
Ā top: "conv1_1"
}
layer {
Ā name: "conv1_2"
Ā type: "Convolution"
Ā bottom: "conv1_1"
Ā top: "conv1_2"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 64
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu1_2"
Ā type: "ReLU"
Ā bottom: "conv1_2"
Ā top: "conv1_2"
}
layer {
Ā name: "pool1"
Ā type: "Pooling"
Ā bottom: "conv1_2"
Ā top: "pool1"
Ā pooling_param {
Ā Ā pool: MAX
Ā Ā kernel_size: 2
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "conv2_1"
Ā type: "Convolution"
Ā bottom: "pool1"
Ā top: "conv2_1"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 128
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu2_1"
Ā type: "ReLU"
Ā bottom: "conv2_1"
Ā top: "conv2_1"
}
layer {
Ā name: "conv2_2"
Ā type: "Convolution"
Ā bottom: "conv2_1"
Ā top: "conv2_2"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 128
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu2_2"
Ā type: "ReLU"
Ā bottom: "conv2_2"
Ā top: "conv2_2"
}
layer {
Ā name: "pool2"
Ā type: "Pooling"
Ā bottom: "conv2_2"
Ā top: "pool2"
Ā pooling_param {
Ā Ā pool: MAX
Ā Ā kernel_size: 2
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "conv3_1"
Ā type: "Convolution"
Ā bottom: "pool2"
Ā top: "conv3_1"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 256
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu3_1"
Ā type: "ReLU"
Ā bottom: "conv3_1"
Ā top: "conv3_1"
}
layer {
Ā name: "conv3_2"
Ā type: "Convolution"
Ā bottom: "conv3_1"
Ā top: "conv3_2"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 256
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu3_2"
Ā type: "ReLU"
Ā bottom: "conv3_2"
Ā top: "conv3_2"
}
layer {
Ā name: "conv3_3"
Ā type: "Convolution"
Ā bottom: "conv3_2"
Ā top: "conv3_3"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 256
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu3_3"
Ā type: "ReLU"
Ā bottom: "conv3_3"
Ā top: "conv3_3"
}
layer {
Ā name: "pool3"
Ā type: "Pooling"
Ā bottom: "conv3_3"
Ā top: "pool3"
Ā pooling_param {
Ā Ā pool: MAX
Ā Ā kernel_size: 2
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "conv4_1"
Ā type: "Convolution"
Ā bottom: "pool3"
Ā top: "conv4_1"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 512
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu4_1"
Ā type: "ReLU"
Ā bottom: "conv4_1"
Ā top: "conv4_1"
}
layer {
Ā name: "conv4_2"
Ā type: "Convolution"
Ā bottom: "conv4_1"
Ā top: "conv4_2"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 512
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu4_2"
Ā type: "ReLU"
Ā bottom: "conv4_2"
Ā top: "conv4_2"
}
layer {
Ā name: "conv4_3"
Ā type: "Convolution"
Ā bottom: "conv4_2"
Ā top: "conv4_3"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 512
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu4_3"
Ā type: "ReLU"
Ā bottom: "conv4_3"
Ā top: "conv4_3"
}
layer {
Ā name: "pool4"
Ā type: "Pooling"
Ā bottom: "conv4_3"
Ā top: "pool4"
Ā pooling_param {
Ā Ā pool: MAX
Ā Ā kernel_size: 2
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "conv5_1"
Ā type: "Convolution"
Ā bottom: "pool4"
Ā top: "conv5_1"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 512
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu5_1"
Ā type: "ReLU"
Ā bottom: "conv5_1"
Ā top: "conv5_1"
}
layer {
Ā name: "conv5_2"
Ā type: "Convolution"
Ā bottom: "conv5_1"
Ā top: "conv5_2"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 512
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu5_2"
Ā type: "ReLU"
Ā bottom: "conv5_2"
Ā top: "conv5_2"
}
layer {
Ā name: "conv5_3"
Ā type: "Convolution"
Ā bottom: "conv5_2"
Ā top: "conv5_3"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 512
Ā Ā pad: 1
Ā Ā kernel_size: 3
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu5_3"
Ā type: "ReLU"
Ā bottom: "conv5_3"
Ā top: "conv5_3"
}
layer {
Ā name: "pool5"
Ā type: "Pooling"
Ā bottom: "conv5_3"
Ā top: "pool5"
Ā pooling_param {
Ā Ā pool: MAX
Ā Ā kernel_size: 2
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "fc6"
Ā type: "Convolution"
Ā bottom: "pool5"
Ā top: "fc6"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 4096
Ā Ā pad: 0
Ā Ā kernel_size: 7
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu6"
Ā type: "ReLU"
Ā bottom: "fc6"
Ā top: "fc6"
}
layer {
Ā name: "fc7"
Ā type: "Convolution"
Ā bottom: "fc6"
Ā top: "fc7"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 4096
Ā Ā pad: 0
Ā Ā kernel_size: 1
Ā Ā stride: 1
Ā }
}
layer {
Ā name: "relu7"
Ā type: "ReLU"
Ā bottom: "fc7"
Ā top: "fc7"
}
layer {
Ā name: "score_fr"
Ā type: "Convolution"
Ā bottom: "fc7"
Ā top: "score_fr"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 21
Ā Ā pad: 0
Ā Ā kernel_size: 1
Ā }
}
layer {
Ā name: "upscore2"
Ā type: "Deconvolution"
Ā bottom: "score_fr"
Ā top: "upscore2"
Ā param {
Ā Ā lr_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 21
Ā Ā bias_term: false
Ā Ā kernel_size: 4
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "score_pool4"
Ā type: "Convolution"
Ā bottom: "pool4"
Ā top: "score_pool4"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 21
Ā Ā pad: 0
Ā Ā kernel_size: 1
Ā }
}
layer {
Ā name: "score_pool4c"
Ā type: "Crop"
Ā bottom: "score_pool4"
Ā bottom: "upscore2"
Ā top: "score_pool4c"
}
layer {
Ā name: "fuse_pool4"
Ā type: "Eltwise"
Ā bottom: "upscore2"
Ā bottom: "score_pool4c"
Ā top: "fuse_pool4"
Ā eltwise_param {
Ā Ā operation: SUM
Ā }
}
layer {
Ā name: "upscore_pool4"
Ā type: "Deconvolution"
Ā bottom: "fuse_pool4"
Ā top: "upscore_pool4"
Ā param {
Ā Ā lr_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 21
Ā Ā bias_term: false
Ā Ā kernel_size: 4
Ā Ā stride: 2
Ā }
}
layer {
Ā name: "score_pool3"
Ā type: "Convolution"
Ā bottom: "pool3"
Ā top: "score_pool3"
Ā param {
Ā Ā lr_mult: 1
Ā Ā decay_mult: 1
Ā }
Ā param {
Ā Ā lr_mult: 2
Ā Ā decay_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 21
Ā Ā pad: 0
Ā Ā kernel_size: 1
Ā }
}
layer {
Ā name: "score_pool3c"
Ā type: "Crop"
Ā bottom: "score_pool3"
Ā bottom: "upscore_pool4"
Ā top: "score_pool3c"
}
layer {
Ā name: "fuse_pool3"
Ā type: "Eltwise"
Ā bottom: "upscore_pool4"
Ā bottom: "score_pool3c"
Ā top: "fuse_pool3"
Ā eltwise_param {
Ā Ā operation: SUM
Ā }
}
layer {
Ā name: "upscore8"
Ā type: "Deconvolution"
Ā bottom: "fuse_pool3"
Ā top: "upscore8"
Ā param {
Ā Ā lr_mult: 0
Ā }
Ā convolution_param {
Ā Ā num_output: 21
Ā Ā bias_term: false
Ā Ā kernel_size: 16
Ā Ā stride: 8
Ā }
}
layer {
Ā name: "score"
Ā type: "Crop"
Ā bottom: "upscore8"
Ā bottom: "data"
Ā top: "score"
}
Can you please tell me why I'm getting this error? Actually I'm training the system for semantic segmentation. I'm loading the input through lmdb.Ā