mirror of
https://github.com/deepinsight/insightface.git
synced 2026-05-18 22:57:49 +00:00
73 lines
4.1 KiB
Python
73 lines
4.1 KiB
Python
|
|
import sys
|
|
import os
|
|
import mxnet as mx
|
|
import symbol_utils
|
|
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
|
|
from config import config
|
|
|
|
|
|
def Act(data, act_type, name):
|
|
#ignore param act_type, set it in this function
|
|
if act_type=='prelu':
|
|
body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)
|
|
else:
|
|
body = mx.sym.Activation(data=data, act_type=act_type, name=name)
|
|
return body
|
|
|
|
def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):
|
|
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
|
|
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=config.bn_mom)
|
|
act = Act(data=bn, act_type=config.net_act, name='%s%s_relu' %(name, suffix))
|
|
return act
|
|
|
|
def Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):
|
|
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
|
|
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=config.bn_mom)
|
|
return bn
|
|
|
|
def ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):
|
|
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
|
|
return conv
|
|
|
|
|
|
def DResidual(data, num_out=1, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=1, name=None, suffix=''):
|
|
conv = Conv(data=data, num_filter=num_group, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name='%s%s_conv_sep' %(name, suffix))
|
|
conv_dw = Conv(data=conv, num_filter=num_group, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name='%s%s_conv_dw' %(name, suffix))
|
|
proj = Linear(data=conv_dw, num_filter=num_out, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name='%s%s_conv_proj' %(name, suffix))
|
|
return proj
|
|
|
|
def Residual(data, num_block=1, num_out=1, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=1, name=None, suffix=''):
|
|
identity=data
|
|
for i in range(num_block):
|
|
shortcut=identity
|
|
conv=DResidual(data=identity, num_out=num_out, kernel=kernel, stride=stride, pad=pad, num_group=num_group, name='%s%s_block' %(name, suffix), suffix='%d'%i)
|
|
identity=conv+shortcut
|
|
return identity
|
|
|
|
|
|
def get_symbol():
|
|
num_classes = config.emb_size
|
|
print('in_network', config)
|
|
fc_type = config.net_output
|
|
data = mx.symbol.Variable(name="data")
|
|
data = data-127.5
|
|
data = data*0.0078125
|
|
blocks = config.net_blocks
|
|
conv_1 = Conv(data, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1")
|
|
if blocks[0]==1:
|
|
conv_2_dw = Conv(conv_1, num_group=64, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw")
|
|
else:
|
|
conv_2_dw = Residual(conv_1, num_block=blocks[0], num_out=64, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=64, name="res_2")
|
|
conv_23 = DResidual(conv_2_dw, num_out=64, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=128, name="dconv_23")
|
|
conv_3 = Residual(conv_23, num_block=blocks[1], num_out=64, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=128, name="res_3")
|
|
conv_34 = DResidual(conv_3, num_out=128, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=256, name="dconv_34")
|
|
conv_4 = Residual(conv_34, num_block=blocks[2], num_out=128, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=256, name="res_4")
|
|
conv_45 = DResidual(conv_4, num_out=128, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=512, name="dconv_45")
|
|
conv_5 = Residual(conv_45, num_block=blocks[3], num_out=128, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=256, name="res_5")
|
|
conv_6_sep = Conv(conv_5, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6sep")
|
|
|
|
fc1 = symbol_utils.get_fc1(conv_6_sep, num_classes, fc_type)
|
|
return fc1
|
|
|