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add dpn

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Jia Guo
2017-12-11 13:03:18 +08:00
parent d74687a7b9
commit 2bde3b58d0
2 changed files with 237 additions and 1 deletions
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230
src/symbols/fdpn.py Normal file
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@@ -0,0 +1,230 @@
import mxnet as mx
import symbol_utils
bn_momentum = 0.9
def BK(data):
return mx.symbol.BlockGrad(data=data)
# - - - - - - - - - - - - - - - - - - - - - - -
# Fundamental Elements
def BN(data, fix_gamma=False, momentum=bn_momentum, name=None):
bn = mx.symbol.BatchNorm( data=data, fix_gamma=fix_gamma, momentum=bn_momentum, name=('%s__bn'%name))
return bn
def AC(data, act_type='relu', name=None):
act = mx.symbol.Activation(data=data, act_type=act_type, name=('%s__%s' % (name, act_type)))
return act
def BN_AC(data, momentum=bn_momentum, name=None):
bn = BN(data=data, name=name, fix_gamma=False, momentum=momentum)
bn_ac = AC(data=bn, name=name)
return bn_ac
def Conv(data, num_filter, kernel, stride=(1,1), pad=(0, 0), name=None, no_bias=True, w=None, b=None, attr=None, num_group=1):
Convolution = mx.symbol.Convolution
if w is None:
conv = Convolution(data=data, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=('%s__conv' %name), no_bias=no_bias, attr=attr)
else:
if b is None:
conv = Convolution(data=data, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=('%s__conv' %name), no_bias=no_bias, weight=w, attr=attr)
else:
conv = Convolution(data=data, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=('%s__conv' %name), no_bias=False, bias=b, weight=w, attr=attr)
return conv
# - - - - - - - - - - - - - - - - - - - - - - -
# Standard Common functions < CVPR >
def Conv_BN( data, num_filter, kernel, pad, stride=(1,1), name=None, w=None, b=None, no_bias=True, attr=None, num_group=1):
cov = Conv( data=data, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=name, w=w, b=b, no_bias=no_bias, attr=attr)
cov_bn = BN( data=cov, name=('%s__bn' % name))
return cov_bn
def Conv_BN_AC(data, num_filter, kernel, pad, stride=(1,1), name=None, w=None, b=None, no_bias=True, attr=None, num_group=1):
cov_bn = Conv_BN(data=data, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=name, w=w, b=b, no_bias=no_bias, attr=attr)
cov_ba = AC( data=cov_bn, name=('%s__ac' % name))
return cov_ba
# - - - - - - - - - - - - - - - - - - - - - - -
# Standard Common functions < ECCV >
def BN_Conv( data, num_filter, kernel, pad, stride=(1,1), name=None, w=None, b=None, no_bias=True, attr=None, num_group=1):
bn = BN( data=data, name=('%s__bn' % name))
bn_cov = Conv( data=bn, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=name, w=w, b=b, no_bias=no_bias, attr=attr)
return bn_cov
def AC_Conv( data, num_filter, kernel, pad, stride=(1,1), name=None, w=None, b=None, no_bias=True, attr=None, num_group=1):
ac = AC( data=data, name=('%s__ac' % name))
ac_cov = Conv( data=ac, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=name, w=w, b=b, no_bias=no_bias, attr=attr)
return ac_cov
def BN_AC_Conv(data, num_filter, kernel, pad, stride=(1,1), name=None, w=None, b=None, no_bias=True, attr=None, num_group=1):
bn = BN( data=data, name=('%s__bn' % name))
ba_cov = AC_Conv(data=bn, num_filter=num_filter, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name=name, w=w, b=b, no_bias=no_bias, attr=attr)
return ba_cov
def DualPathFactory(data, num_1x1_a, num_3x3_b, num_1x1_c, name, inc, G, _type='normal'):
kw = 3
kh = 3
pw = (kw-1)/2
ph = (kh-1)/2
# type
if _type is 'proj':
key_stride = 1
has_proj = True
if _type is 'down':
key_stride = 2
has_proj = True
if _type is 'normal':
key_stride = 1
has_proj = False
# PROJ
if type(data) is list:
data_in = mx.symbol.Concat(*[data[0], data[1]], name=('%s_cat-input' % name))
else:
data_in = data
if has_proj:
c1x1_w = BN_AC_Conv( data=data_in, num_filter=(num_1x1_c+2*inc), kernel=( 1, 1), stride=(key_stride, key_stride), name=('%s_c1x1-w(s/%d)' %(name, key_stride)), pad=(0, 0))
data_o1 = mx.symbol.slice_axis(data=c1x1_w, axis=1, begin=0, end=num_1x1_c, name=('%s_c1x1-w(s/%d)-split1' %(name, key_stride)))
data_o2 = mx.symbol.slice_axis(data=c1x1_w, axis=1, begin=num_1x1_c, end=(num_1x1_c+2*inc), name=('%s_c1x1-w(s/%d)-split2' %(name, key_stride)))
else:
data_o1 = data[0]
data_o2 = data[1]
# MAIN
c1x1_a = BN_AC_Conv( data=data_in, num_filter=num_1x1_a, kernel=( 1, 1), pad=( 0, 0), name=('%s_c1x1-a' % name))
c3x3_b = BN_AC_Conv( data=c1x1_a, num_filter=num_3x3_b, kernel=(kw, kh), pad=(pw, ph), name=('%s_c%dx%d-b' % (name,kw,kh)), stride=(key_stride,key_stride), num_group=G)
c1x1_c = BN_AC_Conv( data=c3x3_b, num_filter=(num_1x1_c+inc), kernel=( 1, 1), pad=( 0, 0), name=('%s_c1x1-c' % name))
c1x1_c1= mx.symbol.slice_axis(data=c1x1_c, axis=1, begin=0, end=num_1x1_c, name=('%s_c1x1-c-split1' % name))
c1x1_c2= mx.symbol.slice_axis(data=c1x1_c, axis=1, begin=num_1x1_c, end=(num_1x1_c+inc), name=('%s_c1x1-c-split2' % name))
# OUTPUTS
summ = mx.symbol.ElementWiseSum(*[data_o1, c1x1_c1], name=('%s_sum' % name))
dense = mx.symbol.Concat( *[data_o2, c1x1_c2], name=('%s_cat' % name))
return [summ, dense]
k_R = 160
G = 40
k_sec = { 2: 4, \
3: 8, \
4: 28, \
5: 3 }
inc_sec= { 2: 16, \
3: 32, \
4: 32, \
5: 128 }
def get_symbol(num_classes = 1000, num_layers=92, **kwargs):
if num_layers==68:
k_R = 128
G = 32
k_sec = { 2: 3, \
3: 4, \
4: 12, \
5: 3 }
inc_sec= { 2: 16, \
3: 32, \
4: 32, \
5: 64 }
elif num_layers==92:
k_R = 96
G = 32
k_sec = { 2: 3, \
3: 4, \
4: 20, \
5: 3 }
inc_sec= { 2: 16, \
3: 32, \
4: 24, \
5: 128 }
elif num_layers==107:
k_R = 200
G = 50
k_sec = { 2: 4, \
3: 8, \
4: 20, \
5: 3 }
inc_sec= { 2: 20, \
3: 64, \
4: 64, \
5: 128 }
elif num_layers==131:
k_R = 160
G = 40
k_sec = { 2: 4, \
3: 8, \
4: 28, \
5: 3 }
inc_sec= { 2: 16, \
3: 32, \
4: 32, \
5: 128 }
else:
raise ValueError("no experiments done on dpn num_layers {}, you can do it yourself".format(num_layers))
version_se = kwargs.get('version_se', 1)
version_input = kwargs.get('version_input', 1)
assert version_input>=0
version_output = kwargs.get('version_output', 'E')
fc_type = version_output
version_unit = kwargs.get('version_unit', 3)
print(version_se, version_input, version_output, version_unit)
## define Dual Path Network
data = mx.symbol.Variable(name="data")
data = data-127.5
data = data*0.0078125
# conv1
if version_input==0:
conv1_x_1 = Conv(data=data, num_filter=128, kernel=(7, 7), name='conv1_x_1', pad=(3,3), stride=(2,2))
else:
conv1_x_1 = Conv(data=data, num_filter=128, kernel=(3, 3), name='conv1_x_1', pad=(3,3), stride=(1,1))
conv1_x_1 = BN_AC(conv1_x_1, name='conv1_x_1__relu-sp')
conv1_x_x = mx.symbol.Pooling(data=conv1_x_1, pool_type="max", kernel=(3, 3), pad=(1,1), stride=(2,2), name="pool1")
# conv2
bw = 256
inc= inc_sec[2]
R = (k_R*bw)/256
conv2_x_x = DualPathFactory( conv1_x_x, R, R, bw, 'conv2_x__1', inc, G, 'proj' )
for i_ly in range(2, k_sec[2]+1):
conv2_x_x = DualPathFactory( conv2_x_x, R, R, bw, ('conv2_x__%d'% i_ly), inc, G, 'normal')
# conv3
bw = 512
inc= inc_sec[3]
R = (k_R*bw)/256
conv3_x_x = DualPathFactory( conv2_x_x, R, R, bw, 'conv3_x__1', inc, G, 'down' )
for i_ly in range(2, k_sec[3]+1):
conv3_x_x = DualPathFactory( conv3_x_x, R, R, bw, ('conv3_x__%d'% i_ly), inc, G, 'normal')
# conv4
bw = 1024
inc= inc_sec[4]
R = (k_R*bw)/256
conv4_x_x = DualPathFactory( conv3_x_x, R, R, bw, 'conv4_x__1', inc, G, 'down' )
for i_ly in range(2, k_sec[4]+1):
conv4_x_x = DualPathFactory( conv4_x_x, R, R, bw, ('conv4_x__%d'% i_ly), inc, G, 'normal')
# conv5
bw = 2048
inc= inc_sec[5]
R = (k_R*bw)/256
conv5_x_x = DualPathFactory( conv4_x_x, R, R, bw, 'conv5_x__1', inc, G, 'down' )
for i_ly in range(2, k_sec[5]+1):
conv5_x_x = DualPathFactory( conv5_x_x, R, R, bw, ('conv5_x__%d'% i_ly), inc, G, 'normal')
# output: concat
conv5_x_x = mx.symbol.Concat(*[conv5_x_x[0], conv5_x_x[1]], name='conv5_x_x_cat-final')
#conv5_x_x = BN_AC(conv5_x_x, name='conv5_x_x__relu-sp')
before_pool = conv5_x_x
fc1 = symbol_utils.get_fc1(before_pool, num_classes, fc_type)
return fc1

8
src/train_softmax.py
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@@ -27,6 +27,7 @@ import finception_resnet_v2
import fmobilenet
import fxception
import fdensenet
import fdpn
#import lfw
import verification
import sklearn
@@ -149,6 +150,11 @@ def get_symbol(args, arg_params, aux_params):
embedding = fxception.get_symbol(512,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='p':
print('init dpn', args.num_layers)
embedding = fdpn.get_symbol(512, args.num_layers,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
else:
print('init resnet', args.num_layers)
embedding = fresnet.get_symbol(512, args.num_layers,
@@ -344,7 +350,7 @@ def train_net(args):
base_lr = args.lr
base_wd = args.wd
base_mom = 0.9
if len(args.pretrained)>0:
if len(args.pretrained)==0:
arg_params = None
aux_params = None
sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)