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cgp_config.py
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cgp_config.py
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#!/usr/bin/env python3
import numpy as np
class CgpInfoConvSet(object):
"""Network configurations"""
def __init__(self, arch_type='resnet', rows=30, cols=40, level_back=40,
min_active_num=8, max_active_num=50):
self.input_num = 1 # number (ID) of the input node (usually 1 (start))
# S_BLOCKTYPE_out-size_kernel for ResNet type
# `in_size` is determined by looking at CGP, which layer the current one
# is connected to, and then looking at its `out_size` in `self.channel_num`
# in `cnn_model.py`
func_type_resnet = ['S_SepBlock_16_3', 'S_SepBlock_16_5',
'S_SepBlock_32_3', 'S_SepBlock_32_5',
'S_SepBlock_64_3', 'S_SepBlock_64_5',
'S_ResBlock_16_3', 'S_ResBlock_16_5',
'S_ResBlock_32_3', 'S_ResBlock_32_5',
'S_ResBlock_64_3', 'S_ResBlock_64_5',
'Sum','Sum',
'Max_Pool', 'Avg_Pool']
func_type_vgg = ['S_SepBlock_16_3', 'S_SepBlock_16_5',
'S_SepBlock_32_3', 'S_SepBlock_32_5',
'S_SepBlock_64_3', 'S_SepBlock_64_5',
'Max_Pool', 'Avg_Pool']
# S_BLOCKTYPE_out-size_num-layer for DenseNet type
# S_BLOCKTYPE_growth-rate_num-layer-per-block for DenseNet type
# func_type_densenet = ['S_SepBlock_16_3', 'S_SepBlock_16_5',
# 'S_SepBlock_32_3', 'S_SepBlock_32_5',
# 'S_SepBlock_64_3', 'S_SepBlock_64_5',
func_type_densenet = ['S_DenseBlock_16_3_3', 'S_DenseBlock_16_6_3',
'S_DenseBlock_16_3_5', 'S_DenseBlock_16_6_5',
'S_DenseBlock_16_12_3', 'S_DenseBlock_16_9_3',
'S_DenseBlock_16_12_5', 'S_DenseBlock_16_9_5',
'S_DenseBlock_32_6_3', 'S_DenseBlock_32_12_3',
'S_DenseBlock_32_6_5', 'S_DenseBlock_32_12_5',
'S_DenseBlock_32_3_3', 'S_DenseBlock_32_9_3',
'S_DenseBlock_32_3_5', 'S_DenseBlock_32_9_5',
'S_DenseBlock_64_6_3', 'S_DenseBlock_64_12_3',
'S_DenseBlock_64_6_5', 'S_DenseBlock_64_12_5',
'S_DenseBlock_64_3_3', 'S_DenseBlock_64_9_3',
'S_DenseBlock_64_3_5', 'S_DenseBlock_64_9_5']
# 'Sum','Sum',
# 'Max_Pool', 'Avg_Pool']
func_in_num_resnet = [1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
2, 2,
1, 1]
func_in_num_vgg = [1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1]
func_in_num_densenet = [1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1,
1, 1]
if arch_type == 'resnet':
self.func_type = func_type_resnet
self.func_in_num = func_in_num_resnet
elif arch_type == 'vgg':
self.func_type = func_type_vgg
self.func_in_num = func_in_num_vgg
elif arch_type == 'densenet':
self.func_type = func_type_densenet
self.func_in_num = func_in_num_densenet
self.out_num = 1
self.out_type = ['full']
self.out_in_num = [1] #
# CGP network configuration
self.rows = rows # For most NNs `= 1` to make a simple NN
self.cols = cols # Actual number of layers if `cols = 1`
self.node_num = rows * cols # Number of network layers
self.level_back = level_back # Used for mod of connection gene in cgp
self.min_active_num = min_active_num
self.max_active_num = max_active_num
self.func_type_num = len(self.func_type)
self.out_type_num = len(self.out_type)
self.max_in_num = np.max(
[np.max(self.func_in_num), np.max(self.out_in_num)])