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I am trying to compile a caffe model and get the following error "Index out of range"
parallels@ubuntu:~/movidius/ncsdk/examples/caffe/NetC$ mvNCCompile -w best_model.caffemodel -in input -s 12 train.prototxt
mvNCCompile v02.00, Copyright @ Movidius Ltd 2016
/usr/local/lib/python3.5/dist-packages/scipy/lib/decorator.py:219: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
first = inspect.getargspec(caller)[0][0] # first arg
/usr/local/lib/python3.5/dist-packages/scipy/optimize/nonlin.py:1498: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
args, varargs, varkw, defaults = inspect.getargspec(jac.__init__)
/usr/local/lib/python3.5/dist-packages/scipy/stats/_distn_infrastructure.py:611: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
sign = inspect.getargspec(self._stats)
/usr/local/lib/python3.5/dist-packages/scipy/stats/_distn_infrastructure.py:648: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
shapes_args = inspect.getargspec(meth)
Traceback (most recent call last):
File "/usr/local/bin/mvNCCompile", line 118, in <module>
create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights)
File "/usr/local/bin/mvNCCompile", line 101, in create_graph
net = parse_caffe(args, myriad_config)
File "/usr/local/bin/ncsdk/Controllers/CaffeParser.py", line 477, in parse_caffe
input_bottom = net.bottom_names[inputNodeName][0]
IndexError: list index out of range
What is the reason of the failure? I use version 1.09.00.04.
In my model I have layers which are interchangeable between TRAIN and TEST modes.
======== prototxt file ========
layer {
name: "input"
type: "Input"
top: "data"
include {
phase: TEST
}
input_param {
shape {
dim: 1
dim: 3
dim: 24
dim: 24
}
}
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
image_data_param {
source: "/home/user/WorkProjects/TrafficSign/Classifier/Models/NetC/train_image_data_path.txt"
batch_size: 512
shuffle: true
new_height: 0
new_width: 0
is_color: true
}
}
layer {
name: "Convolution1"
type: "Convolution"
bottom: "data"
top: "Convolution1"
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm1"
type: "BatchNorm"
bottom: "Convolution1"
top: "Convolution1"
}
layer {
name: "Scale1"
type: "Scale"
bottom: "Convolution1"
top: "Convolution1"
scale_param {
bias_term: true
}
}
layer {
name: "block1"
type: "ReLU"
bottom: "Convolution1"
top: "Convolution1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "Convolution1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "Convolution2"
type: "Convolution"
bottom: "pool1"
top: "Convolution2"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm2"
type: "BatchNorm"
bottom: "Convolution2"
top: "BatchNorm2"
}
layer {
name: "Scale2"
type: "Scale"
bottom: "BatchNorm2"
top: "BatchNorm2"
scale_param {
bias_term: true
}
}
layer {
name: "block2"
type: "ReLU"
bottom: "BatchNorm2"
top: "BatchNorm2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "BatchNorm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "Convolution3"
type: "Convolution"
bottom: "pool2"
top: "Convolution3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm3"
type: "BatchNorm"
bottom: "Convolution3"
top: "BatchNorm3"
}
layer {
name: "Scale3"
type: "Scale"
bottom: "BatchNorm3"
top: "BatchNorm3"
scale_param {
bias_term: true
}
}
layer {
name: "ReLU1"
type: "ReLU"
bottom: "BatchNorm3"
top: "BatchNorm3"
}
layer {
name: "Convolution4"
type: "Convolution"
bottom: "BatchNorm3"
top: "Convolution4"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm4"
type: "BatchNorm"
bottom: "Convolution4"
top: "BatchNorm4"
}
layer {
name: "Scale4"
type: "Scale"
bottom: "BatchNorm4"
top: "BatchNorm4"
scale_param {
bias_term: true
}
}
layer {
name: "ReLU2"
type: "ReLU"
bottom: "BatchNorm4"
top: "BatchNorm4"
}
layer {
name: "Convolution5"
type: "Convolution"
bottom: "BatchNorm4"
top: "Convolution5"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm5"
type: "BatchNorm"
bottom: "Convolution5"
top: "BatchNorm5"
}
layer {
name: "Scale5"
type: "Scale"
bottom: "BatchNorm5"
top: "BatchNorm5"
scale_param {
bias_term: true
}
}
layer {
name: "block3"
type: "ReLU"
bottom: "BatchNorm5"
top: "BatchNorm5"
}
layer {
name: "global_pool"
type: "Pooling"
bottom: "BatchNorm5"
top: "global_pool"
pooling_param {
pool: AVE
global_pooling: true
}
}
layer {
name: "Convolution6"
type: "Convolution"
bottom: "global_pool"
top: "Convolution6"
convolution_param {
num_output: 1024
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm6"
type: "BatchNorm"
bottom: "Convolution6"
top: "BatchNorm6"
}
layer {
name: "Scale6"
type: "Scale"
bottom: "BatchNorm6"
top: "BatchNorm6"
scale_param {
bias_term: true
}
}
layer {
name: "high_dim"
type: "ReLU"
bottom: "BatchNorm6"
top: "BatchNorm6"
}
layer {
name: "dropout"
type: "Dropout"
bottom: "BatchNorm6"
top: "BatchNorm6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "Convolution7"
type: "Convolution"
bottom: "BatchNorm6"
top: "Convolution7"
convolution_param {
num_output: 132
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
}
}
layer {
name: "BatchNorm7"
type: "BatchNorm"
bottom: "Convolution7"
top: "BatchNorm7"
}
layer {
name: "Scale7"
type: "Scale"
bottom: "BatchNorm7"
top: "BatchNorm7"
scale_param {
bias_term: true
}
}
layer {
name: "score"
type: "ReLU"
bottom: "BatchNorm7"
top: "BatchNorm7"
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "BatchNorm7"
bottom: "label"
top: "loss"
loss_weight: 1.0
include {
phase: TRAIN
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "BatchNorm7"
bottom: "label"
top: "accuracy"
top: "per_class_accuracy"
include {
phase: TRAIN
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "BatchNorm7"
top: "prob"
include {
phase: TEST
}
}
layer {
name: "argmax"
type: "ArgMax"
bottom: "prob"
top: "argmax"
include {
phase: TEST
}
argmax_param {
axis: 1
}
}
===========================
Thanks,
Yair
- Tags:
- Caffe
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6 Replies
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@yairh The NCS is meant to be used with models that are ready to deploy, so all layers related to training, testing, and the data layer must be removed. Layers that expect data with labels are no longer needed, because the incoming data will no longer have labels. The ArgMax layer should be removed from the prototxt file because ArgMax layers are usually computed on the host CPU. Afterwards, the rest can be done on the NCS. Additionally the deployment prototxt file should also have this declaration format at the top of the file:
name: "user_network"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 24
dim: 24
}
Let me know if this helps!
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Thanks it helped :-)
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Hi, I got the same error when trying to compile a simple MNIST model.
Traceback (most recent call last):
File "/usr/local/bin/mvNCCompile", line 118, in
create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights)
File "/usr/local/bin/mvNCCompile", line 101, in create_graph
net = parse_caffe(args, myriad_config)
File "/usr/local/bin/ncsdk/Controllers/CaffeParser.py", line 328, in parse_caffe
input_bottom = net.bottom_names[inputNodeName][0]
IndexError: list index out of range
====== prototxt file ===========
name: "user_network"
input: "data"
input_shape {
dim: 1
dim: 1
dim: 28
dim: 28
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
========= Compilation ==========
mvNCCompile mymodel.prototxt -w mymodel.caffemodel -s 12 -in input -on ip2 -is 28 28 -o my_net.graph
=============================
I have modified the prototxt file for the Movidius stick.
Thanks
Sahad
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Hi, I was able to compile by changing the input node in compilation command to "conv1" instead of "input" .
=========================
mvNCCompile ./mymodel.prototxt -w ./mymodel.caffemodel -s 12 -in conv1 -on ip2 -is 28 28 -o ./my_net.graph
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Unfortunately, it gives inconsistent results. I think the input node in compilation should be "input". But, there seems to have a bug when giving "input" as the input node.
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Sorry, my bad..
mvNCCompile ./mymodel.prototxt -w ./mymodel.caffemodel -s 12 -in conv1 -on ip2 -is 28 28 -o ./my_net.graph
,works fine.
I ran a VGG 19 layer pre-trained model to classify between 1000 different image classes of Imagenet, with an inference time of almost 0.98 seconds..
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