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compile LightCNN net model failed

luoy@TTlyN:~/ncsdk/bin$ make example0Face

 

example0Face - FaceNet generation

 

python3 ./mvNCCompile.pyc data/LightenedCNN_B_deploy.prototxt -s 12

 

mvNCCompile v02.00, Copyright @ Movidius Ltd 2016

 

Traceback (most recent call last):

 

File "../../src/./mvNCCompile.py", line 99, in

 

File "../../src/./mvNCCompile.py", line 81, in create_graph

 

File "../../src/./Controllers/CaffeParser.py", line 523, in parse_caffe

 

IndexError: list index (0) out of range

 

Makefile:39: recipe for target 'example0Face' failed

 

make: *** [example0Face] Error 1

 

here is my prototxt file

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name: "DeepFace_set003_net" input: "data" input_shape { dim: 1 dim: 1 dim: 128 dim: 128 } layers{ name: "conv1" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param { num_output: 96 kernel_size: 5 stride: 1 pad: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "data" top: "conv1" } layers{ name: "slice1" type:SLICE slice_param { slice_dim: 1 } bottom: "conv1" top: "slice1_1" top: "slice1_2" } layers{ name: "etlwise1" type: ELTWISE bottom: "slice1_1" bottom: "slice1_2" top: "eltwise1" eltwise_param { operation: MAX } } layers{ name: "pool1" type: POOLING pooling_param { pool: MAX kernel_size: 2 stride: 2 } bottom: "eltwise1" top: "pool1" } layers{ name: "conv2a" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param { num_output: 96 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "pool1" top: "conv2a" } layers{ name: "slice2a" type:SLICE slice_param { slice_dim: 1 } bottom: "conv2a" top: "slice2a_1" top: "slice2a_2" } layers{ name: "etlwise2a" type: ELTWISE bottom: "slice2a_1" bottom: "slice2a_2" top: "eltwise2a" eltwise_param { operation: MAX } } layers{ name: "conv2" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param { num_output: 192 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "eltwise2a" top: "conv2" } layers{ name: "slice2" type:SLICE slice_param { slice_dim: 1 } bottom: "conv2" top: "slice2_1" top: "slice2_2" } layers{ name: "etlwise2" type: ELTWISE bottom: "slice2_1" bottom: "slice2_2" top: "eltwise2" eltwise_param { operation: MAX } } layers{ name: "pool2" type: POOLING pooling_param { pool: MAX kernel_size: 2 stride: 2 } bottom: "eltwise2" top: "pool2" } layers{ name: "conv3a" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param { num_output: 192 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "pool2" top: "conv3a" } layers{ name: "slice3a" type:SLICE slice_param { slice_dim: 1 } bottom: "conv3a" top: "slice3a_1" top: "slice3a_2" } layers{ name: "etlwise3a" type: ELTWISE bottom: "slice3a_1" bottom: "slice3a_2" top: "eltwise3a" eltwise_param { operation: MAX } } layers{ name: "conv3" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param { num_output: 384 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "eltwise3a" top: "conv3" } layers{ name: "slice3" type:SLICE slice_param { slice_dim: 1 } bottom: "conv3" top: "slice3_1" top: "slice3_2" } layers{ name: "etlwise3" type: ELTWISE bottom: "slice3_1" bottom: "slice3_2" top: "eltwise3" eltwise_param { operation: MAX } } layers{ name: "pool3" type: POOLING pooling_param { pool: MAX kernel_size: 2 stride: 2 } bottom: "eltwise3" top: "pool3" } layers{ name: "conv4a" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param{ num_output: 384 kernel_size: 1 stride: 1 weight_filler{ type:"xavier" } bias_filler{ type: "constant" value: 0.1 } } bottom: "pool3" top: "conv4a" } layers{ name: "slice4a" type:SLICE slice_param { slice_dim: 1 } bottom: "conv4a" top: "slice4a_1" top: "slice4a_2" } layers{ name: "etlwise4a" type: ELTWISE bottom: "slice4a_1" bottom: "slice4a_2" top: "eltwise4a" eltwise_param { operation: MAX } } layers{ name: "conv4" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param{ num_output: 256 kernel_size: 3 stride: 1 pad: 1 weight_filler{ type:"xavier" } bias_filler{ type: "constant" value: 0.1 } } bottom: "eltwise4a" top: "conv4" } layers{ name: "slice4" type:SLICE slice_param { slice_dim: 1 } bottom: "conv4" top: "slice4_1" top: "slice4_2" } layers{ name: "etlwise4" type: ELTWISE bottom: "slice4_1" bottom: "slice4_2" top: "eltwise4" eltwise_param { operation: MAX } } layers{ name: "conv5a" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param{ num_output: 256 kernel_size: 1 stride: 1 weight_filler{ type:"xavier" } bias_filler{ type: "constant" value: 0.1 } } bottom: "eltwise4" top: "conv5a" } layers{ name: "slice5a" type:SLICE slice_param { slice_dim: 1 } bottom: "conv5a" top: "slice5a_1" top: "slice5a_2" } layers{ name: "etlwise5a" type: ELTWISE bottom: "slice5a_1" bottom: "slice5a_2" top: "eltwise5a" eltwise_param { operation: MAX } } layers{ name: "conv5" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 convolution_param{ num_output: 256 kernel_size: 3 stride: 1 pad: 1 weight_filler{ type:"xavier" } bias_filler{ type: "constant" value: 0.1 } } bottom: "eltwise5a" top: "conv5" } layers{ name: "slice5" type:SLICE slice_param { slice_dim: 1 } bottom: "conv5" top: "slice5_1" top: "slice5_2" } layers{ name: "etlwise5" type: ELTWISE bottom: "slice5_1" bottom: "slice5_2" top: "eltwise5" eltwise_param { operation: MAX } } layers{ name: "pool4" type: POOLING pooling_param { pool: MAX kernel_size: 2 stride: 2 } bottom: "eltwise5" top: "pool4" } layers{ name: "fc1" type: INNER_PRODUCT blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 inner_product_param { num_output: 512 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "pool4" top: "fc1" } layers{ name: "slice_fc1" type:SLICE slice_param { slice_dim: 1 } bottom: "fc1" top: "slice_fc1_1" top: "slice_fc1_2" } layers{ name: "etlwise_fc1" type: ELTWISE bottom: "slice_fc1_1" bottom: "slice_fc1_2" top: "eltwise_fc1" eltwise_param { operation: MAX } } layers{ name: "drop1" type: DROPOUT dropout_param{ dropout_ratio: 0.75 } bottom: "eltwise_fc1" top: "eltwise_fc1" } layers{ name: "fc2" type: INNER_PRODUCT inner_product_param{ num_output: 10575 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } bottom: "eltwise_fc1" top: "fc2" } layers { name: "softmax" type: SOFTMAX bottom: "fc2" top: "prob" }
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@youngluoyang A possible cause could be the missing slice point parameters from your Slice layers. Slice layers should have at least one slice_point per Slice layer. Referring to (http://caffe.berkeleyvision.org/tutorial/layers/slice.html), it seems the slice_points aren't optional. Let us know if this fixes your problem.

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