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generate_prototxt_fpn.py
executable file
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/
generate_prototxt_fpn.py
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'''
@Author: mrwang
@Date: 2019-08-29 15:43:40
@LastEditors: mrwang
@LastEditTime: 2019-08-29 16:44:51
@Description:
'''
import sys
import h5py
import caffe
from caffe import layers as L
from caffe import params as P
def test(bottom):
conv11 = caffe.layers.Convolution(bottom, num_output=20, kernel_size=3, weight_filler={"type": "xavier"},
bias_filler={"type": "constant"}, param=dict(lr_mult=1))
relu11 = caffe.layers.PRelu(conv11, in_place=True)
pool11 = caffe.layers.Pooling(relu11, pool=caffe.params.Pooling.MAX, kernel_size=3, stride=2)
return pool11
def relu(bottom):
return L.ReLU(bottom, in_place=True)
def prelu(bottom):
return L.PReLU(bottom, in_place=True)
def drop(bottom, dropout_ratio):
return L.Dropout(bottom, dropout_ratio=0.25, in_place=True)
def fully_connect(bottom, outputChannel):
return L.InnerProduct(bottom, num_output=outputChannel, weight_filler=dict(type='xavier'))
def flatten(net, bottom):
return net.blobs['bottom'].data[0].flatten()
def global_avg_pool(bottom, kernelSize=3):
#return L.Pooling(bottom, pool=P.Pooling.AVE,stride=1, kernel_size=kernelSize)
return L.Pooling(bottom, pool=P.Pooling.AVE, global_pooling=True)
def preluConv(bottom, outputChannel, kernelSize=(3, 5), stride=1, isTrain=True, isRelu=True):
if len(kernelSize) == 2:
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, kernel_h=kernelSize[0], kernel_w=kernelSize[1], stride=stride, \
weight_filler={"type": "xavier"},\
bias_term=True, param=dict(lr_mult=1))
elif len(kernelSize) == 1:
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, kernel_size=kernelSize[0], stride=stride, \
weight_filler={"type": "xavier"},\
bias_term=True, param=dict(lr_mult=1))
else:
pass
if isRelu == True:
return prelu(conv)
else:
return conv
def strpreluConv(bottom, outputChannel, kernelSize=3, stride=(1, 2), isTrain=True, isRelu=True):
if len(stride) == 1:
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, kernel_size=kernelSize, stride=stride, \
weight_filler={"type": "xavier"},\
bias_term=True, param=dict(lr_mult=1))
elif len(stride) == 2:
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, kernel_size=kernelSize, stride_h=stride[0], stride_w=stride[1], \
weight_filler={"type": "xavier"},\
bias_term=True, param=dict(lr_mult=1))
else:
pass
if isRelu == True:
return prelu(conv)
else:
return conv
def max_pool(bottom, kernelSize=3, stride=2):
# return L.Pooling(bottom, pool=P.Pooling.MAX, stride_h=stride[0], stride_w=stride[1], kernel_h=kernelSize[0], kernel_w=kernelSize[1])
return L.Pooling(bottom, pool=P.Pooling.MAX, stride=stride, kernel_size=kernelSize)
# return L.Pooling(bottom, pool=P.Pooling.MAX, global_pooling=True)
# def BN(bottom, isTrain=True, isRelu=False):
# use_global = False
# if isTrain == False:
# use_global=True
# bn = caffe.layers.BatchNorm(bottom, use_global_stats=use_global, in_place=True)
# scale = caffe.layers.Scale(bn, bias_term=True, in_place=True)
# return scale
# # if True == isRelu:
# # return relu(scale)
# # else:
# # return scale
def BN(bottom, isTrain=True, isRelu=False):
use_global = False
if isTrain == False:
use_global=True
bn = caffe.layers.BatchNorm(bottom, use_global_stats=use_global, in_place=True)
scale = caffe.layers.Scale(bn, bias_term=True, in_place=True)
if True == isRelu:
return relu(scale)
else:
return scale
def basicConv(bottom, outputChannel, kernelSize=3, stride=2, isTrain=True, isRelu=True):
halfKernelSize = int(kernelSize/2)
#print("half", halfKernelSize)
if kernelSize == 1:
halfKernelSize=0
if halfKernelSize == 0:
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, kernel_size=kernelSize, stride=stride, \
weight_filler={"type": "xavier"},\
bias_term=False, param=dict(lr_mult=1))
else:
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, pad=halfKernelSize, kernel_size=kernelSize, stride=stride, \
weight_filler={"type": "xavier"},\
bias_term=False, param=dict(lr_mult=1))
bn = BN(conv, isTrain=isTrain)
if isRelu == True:
return relu(bn)
else:
return bn
## ==================== landmark ==========================
def lmark_conv(bottom, outputChannel, kernel_size=3, stride=2, isTrain=False, isRelu=True):
pad = int(kernel_size/2)
conv = caffe.layers.Convolution(bottom, num_output=outputChannel, kernel_size=kernel_size, stride=stride, \
pad=pad, weight_filler={"type": "msra"},\
bias_term=False, param=dict(lr_mult=1))
bn = BN(conv, isTrain=isTrain)
if isRelu == True:
return relu(bn)
else:
return bn
def lmark_global_avgpool(bottom, kernelSize=3):
#return L.Pooling(bottom, pool=P.Pooling.AVE,stride=1, kernel_size=kernelSize)
return L.Pooling(bottom, pool=P.Pooling.AVE, global_pooling=True)
def lmark_fully_connect(bottom, outputChannel):
return L.InnerProduct(bottom, num_output=outputChannel, weight_filler=dict(type='xavier'))
## ==================== ssd mobilenet ==========================
def conv_bn(name, input, output, kernel_size=3, stride=1, pad=1, activation=True, dilation=1):
conv = L.Convolution(input, kernel_size=kernel_size, stride=stride, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'), dilation=dilation)
# in-place compute means your input and output has the same memory area,which will be more memory effienct
bn = L.BatchNorm(conv, use_global_stats=True, in_place=True)
# scale = L.Scale(bn,filler=dict(value=1),bias_filler=dict(value=0),bias_term=True, in_place=True)
out = L.Scale(bn, bias_term=True, in_place=True)
if activation is True:
out = L.ReLU(out, in_place=True)
return out
def conv(input, output, kernel_size=3, stride=1, pad=1, activation=True):
out = L.Convolution(input, kernel_size=kernel_size, stride=stride, num_output=output, bias_term=True, pad=pad, weight_filler=dict(type='xavier')) # ,name = name
if activation is True:
out = L.ReLU(out, in_place=True)
return out
def conv_fpn(input, output, kernel_size=3, stride=1, pad=1, activation=False):
out = L.Convolution(input, kernel_size=kernel_size, stride=stride, num_output=output, bias_term=True, pad=pad, weight_filler=dict(type='xavier')) # ,name = name
if activation is True:
out = L.ReLU(out, in_place=True)
return out
def deconv(input, output, kernel_size=3, stride=2, pad=1, activation=True):
out = L.Deconvolution(input, convolution_param=dict(kernel_size=kernel_size, stride=stride, num_output=output, pad=pad), param=[dict(lr_mult=0)])
if activation is True:
out = L.ReLU(out, in_place=True)
return out
def eltwise(input, branch_shortcut):
out = L.Eltwise(input, branch_shortcut, eltwise_param=dict(operation=1))
return out
def mobconv1_bn_relu(input, output, kernel_size=1, stride=1, pad=0, activation=True):
conv1 = L.Convolution(input, kernel_size=1, stride=1, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
scale1 = L.Scale(bn1, bias_term=True, in_place=True)
out = L.ReLU(scale1, in_place=True)
return out
def mobconv3_bn(input, output, kernel_size=3, stride=1, pad=1, group=1, activation=True):
conv1 = L.Convolution(input, kernel_size=3, stride=stride, num_output=output, bias_term=False, pad=pad, group=group, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
scale1 = L.Scale(bn1, bias_term=True, in_place=True)
out = L.ReLU(scale1, in_place=True)
return out
def mobconv1_bn(input, output, kernel_size=1, stride=1, pad=0, activation=False):
conv1 = L.Convolution(input, kernel_size=1, stride=1, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
out = L.Scale(bn1, bias_term=True, in_place=True)
return out
def mobconv1(input, output, kernel_size=1, stride=1, pad=0, activation=False):
conv1 = L.Convolution(input, kernel_size=1, stride=1, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
out = conv1
return out
# def linear_bottleneck(input, output, kernel_size=3, stride=1, pad=1, t=1, group=1, activation=True):
# residual = input
# conv1 = mobconv1_bn_relu(input, input*t, kernel_size=1, stride=1, pad=0, activation=True)
# conv2 = mobconv3_bn(conv1, input*t, input*t, kernel_size=3, stride=stride, pad=1, group=1, activation=True)
# out = mobconv1_bn(conv2, input*t, output, kernel_size=1, stride=1, pad=0, activation=False)
# if(stride==1 and input==output):
# out += residual
# return out
def mobilenet_conv_first(input, output, kernel_size=3, stride=2, pad=1, activation=True):
conv1 = L.Convolution(input, kernel_size=3, stride=2, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
scale1 = L.Scale(bn1, bias_term=True, in_place=True)
out = L.ReLU(scale1, in_place=True)
return out
def mobilenet_conv_last(input, output, kernel_size=1, stride=1, pad=0, activation=True):
conv1 = L.Convolution(input, kernel_size=1, stride=1, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
scale1 = L.Scale(bn1, bias_term=True, in_place=True)
out = L.ReLU(scale1, in_place=True)
return out
def extra_conv1(input, output, kernel_size=1, stride=1, pad=0, activation=True):
conv1 = L.Convolution(input, kernel_size=1, stride=1, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
scale1 = L.Scale(bn1, bias_term=True, in_place=True)
out = L.ReLU(scale1, in_place=True)
return out
def extra_conv3(input, output, kernel_size=3, stride=2, pad=1, activation=True):
conv1 = L.Convolution(input, kernel_size=3, stride=2, num_output=output, bias_term=False, pad=pad, weight_filler=dict(type='xavier'))
bn1 = L.BatchNorm(conv1, use_global_stats=True, in_place=True)
scale1 = L.Scale(bn1, bias_term=True, in_place=True)
out = L.ReLU(scale1, in_place=True)
return out
def loc_conf_conv(input, output, kernel_size=1, stride=1, pad=0, activation=False):
out = L.Convolution(input, kernel_size=1, stride=1, num_output=output, bias_term=True, pad=pad, weight_filler=dict(type='xavier'))
return out
def generate_network(name, intputSize=[300, 300, 3], writePath=None,isTrain=False):
net = caffe.NetSpec()
net.data = L.Input(shape = dict(dim = [1,intputSize[2],intputSize[0],intputSize[1]]))
if name == 'ssd_mobilenetv2_fpn':
# # backbone
base_channel = int(16)
net.conv0 = mobilenet_conv_first(net.data, base_channel * 2, kernel_size=3, stride=2, pad=1) # output: 32 * 150 * 150
##Bottleneck_0
net.conv1 = mobconv1_bn_relu(net.conv0, base_channel * 2, kernel_size=1, stride=1, pad=0) # output: 32 *150*150
net.conv2 = mobconv3_bn(net.conv1, base_channel * 2, kernel_size=3, stride=1, pad=1, group=base_channel * 2) # 32 *150*150 stride=1
net.conv3 = mobconv1_bn(net.conv2, base_channel, kernel_size=1, stride=1, pad=0) # 16
# net.elt1 = eltwise(net.conv0, net.conv3) ## input != output
##Bottleneck_1
net.conv4 = mobconv1_bn_relu(net.conv3, base_channel * 6, kernel_size=1, stride=1, pad=0) # output: 96
net.conv5 = mobconv3_bn(net.conv4, base_channel * 6, kernel_size=3, stride=2, pad=1, group=base_channel * 6) # 96 *75*75
net.conv6 = mobconv1_bn(net.conv5, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) # 24
net.conv7 = mobconv1_bn_relu(net.conv6, base_channel * 9, kernel_size=1, stride=1, pad=0) # 144
net.conv8 = mobconv3_bn(net.conv7, base_channel * 9, kernel_size=3, stride=1, pad=1, group=base_channel * 9) # 144
net.conv9 = mobconv1_bn(net.conv8, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) # 24
net.elt1 = eltwise(net.conv6, net.conv9) # ---> ok!!! 24 *75*75
##Bottleneck_2
net.conv10 = mobconv1_bn_relu(net.elt1, base_channel * 9, kernel_size=1, stride=1, pad=0) # 144
net.conv11 = mobconv3_bn(net.conv10, base_channel * 9, kernel_size=3, stride=2, pad=1, group=base_channel * 9) # 144 *38*38
net.conv12 = mobconv1_bn(net.conv11, base_channel * 2, kernel_size=1, stride=1, pad=0) # 32
net.conv13 = mobconv1_bn_relu(net.conv12, base_channel * 12, kernel_size=1, stride=1, pad=0) # 192
net.conv14 = mobconv3_bn(net.conv13, base_channel * 12, kernel_size=3, stride=1, pad=1, group=base_channel * 12) # 192
net.conv15 = mobconv1_bn(net.conv14, base_channel * 2, kernel_size=1, stride=1, pad=0) # 32
net.elt2 = eltwise(net.conv12, net.conv15)
net.conv16 = mobconv1_bn_relu(net.elt2, base_channel * 12, kernel_size=1, stride=1, pad=0) # 192
net.conv17 = mobconv3_bn(net.conv16, base_channel * 12, kernel_size=3, stride=1, pad=1, group=base_channel * 12) # 192 ok
net.conv18 = mobconv1_bn(net.conv17, base_channel * 2, kernel_size=1, stride=1, pad=0) # 32 不一样
# net.conv18 = mobconv1(net.conv17, base_channel * 2, kernel_size=1, stride=1, pad=0)
net.elt3 = eltwise(net.elt2, net.conv18) ## --> not ok!!! 32 * 38*38
##Bottleneck_3
net.conv19 = mobconv1_bn_relu(net.elt3, base_channel * 12, kernel_size=1, stride=1, pad=0) # 192
net.conv20 = mobconv3_bn(net.conv19, base_channel * 12, kernel_size=3, stride=2, pad=1, group=base_channel * 12) # 192 *19*19
net.conv21 = mobconv1_bn(net.conv20, base_channel * 4, kernel_size=1, stride=1, pad=0) # 64
net.conv22 = mobconv1_bn_relu(net.conv21, base_channel * 24, kernel_size=1, stride=1, pad=0) # 384
net.conv23 = mobconv3_bn(net.conv22, base_channel * 24, kernel_size=3, stride=1, pad=1, group=base_channel * 24) # 384
net.conv24 = mobconv1_bn(net.conv23, base_channel * 4, kernel_size=1, stride=1, pad=0) # 64
net.elt4 = eltwise(net.conv21, net.conv24)
net.conv25 = mobconv1_bn_relu(net.elt4, base_channel * 24, kernel_size=1, stride=1, pad=0) # 384
net.conv26 = mobconv3_bn(net.conv25, base_channel * 24, kernel_size=3, stride=1, pad=1, group=base_channel * 24) # 384
net.conv27 = mobconv1_bn(net.conv26, base_channel * 4, kernel_size=1, stride=1, pad=0) # 64
net.elt5 = eltwise(net.elt4, net.conv27)
net.conv28 = mobconv1_bn_relu(net.elt5, base_channel * 24, kernel_size=1, stride=1, pad=0) # 384
net.conv29 = mobconv3_bn(net.conv28, base_channel * 24, kernel_size=3, stride=1, pad=1, group=base_channel * 24) # 384
net.conv30 = mobconv1_bn(net.conv29, base_channel * 4, kernel_size=1, stride=1, pad=0) # 64
net.elt6 = eltwise(net.elt5, net.conv30) # --> not ok!!! 64 * 19*19
##Bottleneck_4
net.conv31 = mobconv1_bn_relu(net.elt6, base_channel * 24, kernel_size=1, stride=1, pad=0) # 384
net.conv32 = mobconv3_bn(net.conv31, base_channel * 24, kernel_size=3, stride=1, pad=1, group=base_channel * 24) # 384 *19*19 stride=1
# net.conv33 = mobconv1_bn(net.conv32, base_channel * 6, kernel_size=1, stride=1, pad=0) # 96
net.conv33 = mobconv1_bn(net.conv32, base_channel * 8, kernel_size=1, stride=1, pad=0) # 128
## error this block
net.conv34 = mobconv1_bn_relu(net.conv33, base_channel * 48, kernel_size=1, stride=1, pad=0) # 576 ## 36 -> 48, change this channel ok....
net.conv35 = mobconv3_bn(net.conv34, base_channel * 48, kernel_size=3, stride=1, pad=1, group=base_channel * 48) # 576
# net.conv36 = mobconv1_bn(net.conv35, base_channel * 6, kernel_size=1, stride=1, pad=0) # 96
net.conv36 = mobconv1_bn(net.conv35, base_channel * 8, kernel_size=1, stride=1, pad=0) # 128
net.elt7 = eltwise(net.conv33, net.conv36)
net.conv37 = mobconv1_bn_relu(net.elt7, base_channel * 48, kernel_size=1, stride=1, pad=0) # 576 ## 36 -> 48, change this channel ok....
net.conv38 = mobconv3_bn(net.conv37, base_channel * 48, kernel_size=3, stride=1, pad=1, group=base_channel * 48) # 576
# net.conv39 = mobconv1_bn(net.conv38, base_channel * 6, kernel_size=1, stride=1, pad=0) # 96 *19*19
net.conv39 = mobconv1_bn(net.conv38, base_channel * 8, kernel_size=1, stride=1, pad=0) # 128 *19*19
net.elt8 = eltwise(net.elt7, net.conv39) # 128 *19*19
# net.elt8 = eltwise(net.elt7, net.deconv1)
##Bottleneck_5
# net.conv40 = mobconv1_bn_relu(net.elt8, base_channel * 36, kernel_size=1, stride=1, pad=0) # 576 -> 768 ## 36 -> 48, change this channel ok...
net.conv40 = mobconv1_bn_relu(net.elt8, base_channel * 48, kernel_size=1, stride=1, pad=0) # 576
net.conv41 = mobconv3_bn(net.conv40, base_channel * 48, kernel_size=3, stride=2, pad=1, group=base_channel * 48) # 576 *10*10
net.conv42 = mobconv1_bn(net.conv41, base_channel * 10, kernel_size=1, stride=1, pad=0) # 160
net.conv43 = mobconv1_bn_relu(net.conv42, base_channel * 60, kernel_size=1, stride=1, pad=0) # 960
net.conv44 = mobconv3_bn(net.conv43, base_channel * 60, kernel_size=3, stride=1, pad=1, group=base_channel * 60) # 960
net.conv45 = mobconv1_bn(net.conv44, base_channel * 10, kernel_size=1, stride=1, pad=0) # 160
net.elt9 = eltwise(net.conv42, net.conv45)
net.conv46 = mobconv1_bn_relu(net.elt9, base_channel * 60, kernel_size=1, stride=1, pad=0) # 960
net.conv47 = mobconv3_bn(net.conv46, base_channel * 60, kernel_size=3, stride=1, pad=1, group=base_channel * 60) # 960
net.conv48 = mobconv1_bn(net.conv47, base_channel * 10, kernel_size=1, stride=1, pad=0) # 160
net.elt10 = eltwise(net.elt9, net.conv48)
##Bottleneck_6
net.conv49 = mobconv1_bn_relu(net.elt10, base_channel * 60, kernel_size=1, stride=1, pad=0) # 960
net.conv50 = mobconv3_bn(net.conv49, base_channel * 60, kernel_size=3, stride=1, pad=1, group=base_channel * 60) # 960 *10*10 stride=1
net.conv51 = mobconv1_bn(net.conv50, base_channel * 20, kernel_size=1, stride=1, pad=0) # 320 ×10*10
## fpn
net.fpn1 = conv_fpn(net.conv51, base_channel * 8, kernel_size=1, stride=1, pad=0) ## add for fpn, C5
net.deconv1 = deconv(net.fpn1, base_channel * 8, kernel_size=3, stride=2, pad=1)
net.fpn2 = conv_fpn(net.fpn1, base_channel * 8, kernel_size=3, stride=1, pad=1)
net.fpn3 = conv_fpn(net.elt8, base_channel * 8, kernel_size=1, stride=1, pad=0)
net.fpnelt = eltwise(net.deconv1, net.fpn3)
net.fpn4 = conv_fpn(net.fpnelt, base_channel * 8, kernel_size=3, stride=1, pad=1)
# net.elt7 = eltwise(net.conv53, net.conv55)
net.conv52 = mobilenet_conv_last(net.conv51, base_channel * 8, kernel_size=1, stride=1, pad=0, activation=True)
# net.conv52 = mobilenet_conv_last(net.fpn2, base_channel * 80, kernel_size=1, stride=1, pad=0, activation=True)
## --> ok
##Extras_layer
net.conv_ex1 = extra_conv1(net.conv52, base_channel * 16, kernel_size=1, stride=1, pad=0) #256
net.conv_ex2 = extra_conv3(net.conv_ex1, base_channel * 32, kernel_size=3, stride=2, pad=1) #512 *5*5
net.conv_ex3 = extra_conv1(net.conv_ex2, base_channel * 8, kernel_size=1, stride=1, pad=0) #128
net.conv_ex4 = extra_conv3(net.conv_ex3, base_channel * 16, kernel_size=3, stride=2, pad=1) #256 *3*3
net.conv_ex5 = extra_conv1(net.conv_ex4, base_channel * 8, kernel_size=1, stride=1, pad=0) #128
net.conv_ex6 = extra_conv3(net.conv_ex5, base_channel * 16, kernel_size=3, stride=2, pad=1) #256 *2*2
net.conv_ex7 = extra_conv1(net.conv_ex6, base_channel * 4, kernel_size=1, stride=1, pad=0) #64
net.conv_ex8 = extra_conv3(net.conv_ex7, base_channel * 8, kernel_size=3, stride=2, pad=1) #128 *1*1
##loc and conf layer
# net.loc1 = loc_conf_conv(net.elt8, base_channel, kernel_size=1, stride=1, pad=0) #96 -> 16
# net.conf1 = loc_conf_conv(net.elt8, int(base_channel * 1/2), kernel_size=1, stride=1, pad=0) #96 -> 8
net.loc1 = loc_conf_conv(net.fpn4, base_channel, kernel_size=1, stride=1, pad=0) #96 -> 16
net.conf1 = loc_conf_conv(net.fpn4, int(base_channel * 1/2), kernel_size=1, stride=1, pad=0) #96 -> 8
# net.loc2 = loc_conf_conv(net.conv52, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) #1280 -> 24
# net.conf2 = loc_conf_conv(net.conv52, int(base_channel * 3/4), kernel_size=1, stride=1, pad=0) #1280 -> 12
net.loc2 = loc_conf_conv(net.fpn2, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) #1280 -> 24
net.conf2 = loc_conf_conv(net.fpn2, int(base_channel * 3/4), kernel_size=1, stride=1, pad=0) #1280 -> 12
net.loc3 = loc_conf_conv(net.conv_ex2, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) #512 -> 24
net.conf3 = loc_conf_conv(net.conv_ex2, int(base_channel * 3/4), kernel_size=1, stride=1, pad=0) #512 -> 12
net.loc4 = loc_conf_conv(net.conv_ex4, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) #256 -> 24
net.conf4 = loc_conf_conv(net.conv_ex4, int(base_channel * 3/4), kernel_size=1, stride=1, pad=0) #256 -> 12
net.loc5 = loc_conf_conv(net.conv_ex6, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) #256 -> 24
net.conf5 = loc_conf_conv(net.conv_ex6, int(base_channel * 3/4), kernel_size=1, stride=1, pad=0) #256 -> 12
net.loc6 = loc_conf_conv(net.conv_ex8, int(base_channel * 3/2), kernel_size=1, stride=1, pad=0) #128 -> 24
net.conf6 = loc_conf_conv(net.conv_ex8, int(base_channel * 3/4), kernel_size=1, stride=1, pad=0) #128 -> 12
if name == 'landmark':
base_channel = int(32)
net.conv1 = lmark_conv(net.data, base_channel * 1, kernel_size=3, stride=2)
net.conv2 = lmark_conv(net.conv1, base_channel * 2, kernel_size=3, stride=1)
net.conv3 = lmark_conv(net.conv2, base_channel * 2, kernel_size=3, stride=2)
net.conv4 = lmark_conv(net.conv3, base_channel * 4, kernel_size=3, stride=1)
net.conv5 = lmark_conv(net.conv4, base_channel * 4, kernel_size=3, stride=2)
net.conv6 = lmark_conv(net.conv5, base_channel * 4, kernel_size=3, stride=1)
net.conv7 = lmark_conv(net.conv6, base_channel * 8, kernel_size=3, stride=2)
net.conv8 = lmark_conv(net.conv7, base_channel * 16, kernel_size=3, stride=2)
net.gap1 = lmark_global_avgpool(net.conv8)
net.fc22 = lmark_fully_connect(net.gap1, 8)
# transform
proto = net.to_proto()
proto.name = name
with open(writePath, 'w') as f:
print("start write!\n")
f.write(str(proto))
# 检查参数名
net = caffe.Net(writePath, caffe.TEST)
caffeParams = net.params
for k in sorted(caffeParams):
print(k)
print(len(caffeParams))
if __name__ == '__main__':
nettype = 'landmark' # 'pnet, onet, landmark'
writePath = nettype + '.prototxt'
if nettype == 'pnet':
intputSize = [17, 47, 3]
elif nettype == 'ssd_mobilenetv2_fpn':
intputSize = [300, 300, 3]
elif nettype == 'landmark':
intputSize = [128, 128, 3]
generate_network(nettype, intputSize=intputSize, writePath=writePath)