Question: Extract per layer information from finalized graph.
Closed this issue · 1 comments
AndreasKaratzas commented
Output of 'strings libarm_compute.so | grep arm_compute_version':
arm_compute_version=v21.02 Build options: {'Werror': '0', 'debug': '0', 'asserts': '0', 'neon': '1', 'opencl': '1', 'openmp': '1', 'os': 'linux', 'arch': 'arm64-v8a', 'build': 'native', 'embed_kernels': '1'} Git hash=b'0cacff24861bf3201e35426424752b78d3608a39'
Platform: OrangePi 5 16GB
Operating System:
Ubuntu 22.04.2 LTS aarch64
Problem description:
Hello :) This is more of a question than an issue. I was wondering, is there any way that I can parse a graph after it has been finalized to extract information about the layers? For example, let the class Alexnet in graph_alexnet.cpp under examples. Is there any way to get a structure like the one below?
{
"1": {
"InputLayer": {
"input_width": 227,
"input_height": 227,
"input_channels": 3,
"input_batch_size": 1,
"input_dtype": "float32",
"input_layout": "NCHW"
}
},
"2": {
"ConvolutionLayer": {
"conv_width": 11,
"conv_height": 11,
"ofm": 96,
"conv_info": [ 4, 4, 0, 0 ],
"num_groups": 1
}
},
"3": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"4": {
"NormalizationLayer": {
"type": "CROSS_MAP",
"norm_size": 5,
"alpha": 0.0001,
"beta": 0.75,
"kappa": 1.0,
"is_scaled": true
}
},
"5": {
"PoolingLayer": {
"pool_type": "MAX",
"pool_size": 3,
"pad_stride_info": [ 2, 2, 0, 0 ]
}
},
"6": {
"ConvolutionLayer": {
"conv_width": 5,
"conv_height": 5,
"ofm": 256,
"conv_info": [ 1, 1, 2, 2 ],
"num_groups": 2
}
},
"7": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"8": {
"NormalizationLayer": {
"type": "CROSS_MAP",
"norm_size": 5,
"alpha": 0.0001,
"beta": 0.75,
"kappa": 1.0,
"is_scaled": true
}
},
"9": {
"PoolingLayer": {
"pool_type": "MAX",
"pool_size": 3,
"pad_stride_info": [ 2, 2, 0, 0 ]
}
},
"10": {
"ConvolutionLayer": {
"conv_width": 3,
"conv_height": 3,
"ofm": 384,
"conv_info": [ 1, 1, 1, 1 ],
"num_groups": 1
}
},
"11": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"12": {
"ConvolutionLayer": {
"conv_width": 3,
"conv_height": 3,
"ofm": 384,
"conv_info": [ 1, 1, 1, 1 ],
"num_groups": 2
}
},
"13": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"14": {
"ConvolutionLayer": {
"conv_width": 3,
"conv_height": 3,
"ofm": 256,
"conv_info": [ 1, 1, 1, 1 ],
"num_groups": 2
}
},
"15": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"16": {
"PoolingLayer": {
"pool_type": "MAX",
"pool_size": 3,
"pad_stride_info": [ 2, 2, 0, 0 ]
}
},
"17": {
"FullyConnectedLayer": {
"num_outputs": 4096
}
},
"18": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"19": {
"FullyConnectedLayer": {
"num_outputs": 4096
}
},
"20": {
"ActivationLayer": {
"activation_type": "RELU"
}
},
"21": {
"FullyConnectedLayer": {
"num_outputs": 1000
}
},
"22": {
"SoftmaxLayer": {
"beta": 1.0
}
}
}morgolock commented
It's not possible without making changes to source code in the graph api.
Functions and kernels will print their configuration at runtime if you enable logging when compiling the library, see for example https://github.com/ARM-software/ComputeLibrary/blob/main/src/cpu/operators/CpuGemmConv2d.cpp#L271
You just need to build the library with logging=1 to enable this, then when you run graph_alexnet you will need to filter out all the kernels and just look at the operators as shown below:
[ComputeLibrary][27-03-2023 02:22:03][INFO] arm_compute::NEConvolutionLayer::configure() :
input: ITensor->info(): Shape=3,227,227,DataLayout=NHWC,DataType=F32
weights: ITensor->info(): Shape=3,11,11,96,DataLayout=NHWC,DataType=F32
biases: ITensor->info(): Shape=96,DataLayout=NHWC,DataType=F32
output: ITensor->info(): Shape=96,55,55,DataLayout=NHWC,DataType=F32
conv_info: 4,4;0,0,0,0
weights_info: 0;0;0,0
dilation: 1x1
act_info: RELU
enable_fast_math: false
num_groups: 1
If you do not need so much detail then you can just use | grep GRAPH to get a list of high level operators used in the graph as shown below:
root@acl_hikey_9:~/tmp/user/acl_mt# LD_LIBRARY_PATH=./main_release+logging:$LD_LIBRARY_PATH ./graph_alexnet | grep GRAPH
[GRAPH][27-03-2023 02:02:05][INFO] Running mutating pass : NodeFusionMutator
[GRAPH][27-03-2023 02:02:05][INFO] Running mutating pass : GroupedConvolutionMutator
[GRAPH][27-03-2023 02:02:05][INFO] Running mutating pass : InPlaceOperationMutator
[GRAPH][27-03-2023 02:02:05][INFO] Running mutating pass : DepthConcatSubTensorMutator
[GRAPH][27-03-2023 02:02:05][INFO] Running mutating pass : SplitLayerSubTensorMutator
[GRAPH][27-03-2023 02:02:05][INFO] Running mutating pass : NodeExecutionMethodMutator
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv1 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 3,227,227 Weights shape: 3,11,11,96 Output shape: 96,55,55 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated norm1 Type: NormalizationLayer Target: Neon Data Type: F32 Input shape: 96,55,55 Output shape: 96,55,55 Normalization info: CROSS_MAP
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated pool1 Type: PoolingLayer Target: Neon Data Type: F32 Input shape: 96,55,55 Output shape: 96,27,27 Pooling info: MAX
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv2_g0 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 48,27,27 Weights shape: 48,5,5,128 Output shape: 128,27,27 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv2_g1 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 48,27,27 Weights shape: 48,5,5,128 Output shape: 128,27,27 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv2 Type: ConcatenateLayer Target: Neon Data Type: F32 Shape: 256,27,27 Num Inputs: 2 Axis: 0
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated norm2 Type: NormalizationLayer Target: Neon Data Type: F32 Input shape: 256,27,27 Output shape: 256,27,27 Normalization info: CROSS_MAP
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated pool2 Type: PoolingLayer Target: Neon Data Type: F32 Input shape: 256,27,27 Output shape: 256,13,13 Pooling info: MAX
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv3 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 256,13,13 Weights shape: 256,3,3,384 Output shape: 384,13,13 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv4_g0 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 192,13,13 Weights shape: 192,3,3,192 Output shape: 192,13,13 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv4_g1 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 192,13,13 Weights shape: 192,3,3,192 Output shape: 192,13,13 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv4 Type: ConcatenateLayer Target: Neon Data Type: F32 Shape: 384,13,13 Num Inputs: 2 Axis: 0
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv5_g0 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 192,13,13 Weights shape: 192,3,3,128 Output shape: 128,13,13 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv5_g1 Type: GenericConvolutionLayer Target: Neon Data Type: F32 Groups: 1 Input shape: 192,13,13 Weights shape: 192,3,3,128 Output shape: 128,13,13 RELU
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated conv5 Type: ConcatenateLayer Target: Neon Data Type: F32 Shape: 256,13,13 Num Inputs: 2 Axis: 0
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated pool5 Type: PoolingLayer Target: Neon Data Type: F32 Input shape: 256,13,13 Output shape: 256,6,6 Pooling info: MAX
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated fc6 Type: FullyConnectedLayer Target: Neon Data Type: F32 Input shape: 256,6,6 Weights shape: 9216,4096 Output shape: 4096
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated fc7 Type: FullyConnectedLayer Target: Neon Data Type: F32 Input shape: 4096 Weights shape: 4096,4096 Output shape: 4096
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated fc8 Type: FullyConnectedLayer Target: Neon Data Type: F32 Input shape: 4096 Weights shape: 4096,1000 Output shape: 1000
[GRAPH][27-03-2023 02:02:05][INFO] Instantiated prob Type: SoftmaxLayer Target: Neon Data Type: F32 Input shape: 1000 Output shape: 1000
Hope this helps.