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symbol_inception-resnet-v2.py
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"""
Inception V4, suitable for images with around 299 x 299
Reference:
Szegedy C, Ioffe S, Vanhoucke V. Inception-v4, inception-resnet and the impact of residual connections on learning[J]. arXiv preprint arXiv:1602.07261, 2016.
"""
import find_mxnet
import mxnet as mx
def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix='', withRelu=True, withBn=False):
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad,
name='%s%s_conv2d' % (name, suffix))
if withBn:
conv = mx.sym.BatchNorm(data=conv, name='%s%s_bn' % (name, suffix))
if withRelu:
conv = mx.sym.Activation(data=conv, act_type='relu', name='%s%s_relu' % (name, suffix))
return conv
# Input Shape is 3*299*299 (th)
def InceptionResnetStem(data,
num_1_1, num_1_2, num_1_3,
num_2_1,
num_3_1, num_3_2,
num_4_1, num_4_2, num_4_3, num_4_4,
num_5_1,
name):
stem_3x3 = Conv(data=data, num_filter=num_1_1, kernel=(3, 3), stride=(2, 2), name=('%s_conv' % name))
stem_3x3 = Conv(data=stem_3x3, num_filter=num_1_2, kernel=(3, 3), name=('%s_stem' % name), suffix='_conv')
stem_3x3 = Conv(data=stem_3x3, num_filter=num_1_3, kernel=(3, 3), pad=(1, 1), name=('%s_stem' % name),
suffix='_conv_1')
pool1 = mx.sym.Pooling(data=stem_3x3, kernel=(3, 3), stride=(2, 2), pool_type='max',
name=('%s_%s_pool1' % ('max', name)))
stem_1_3x3 = Conv(data=stem_3x3, num_filter=num_2_1, kernel=(3, 3), stride=(2, 2), name=('%s_stem_1' % name),
suffix='_conv_1')
concat1 = mx.sym.Concat(*[pool1, stem_1_3x3], name=('%s_concat_1' % name))
stem_1_1x1 = Conv(data=concat1, num_filter=num_3_1, name=('%s_stem_1' % name), suffix='_conv_2')
stem_1_3x3 = Conv(data=stem_1_1x1, num_filter=num_3_2, kernel=(3, 3), name=('%s_stem_1' % name), suffix='_conv_3')
stem_2_1x1 = Conv(data=concat1, num_filter=num_4_1, name=('%s_stem_2' % name), suffix='_conv_1')
stem_2_7x1 = Conv(data=stem_2_1x1, num_filter=num_4_2, kernel=(7, 1), pad=(3, 0), name=('%s_stem_2' % name),
suffix='_conv_2')
stem_2_1x7 = Conv(data=stem_2_7x1, num_filter=num_4_3, kernel=(1, 7), pad=(0, 3), name=('%s_stem_2' % name),
suffix='_conv_3')
stem_2_3x3 = Conv(data=stem_2_1x7, num_filter=num_4_4, kernel=(3, 3), name=('%s_stem_2' % name), suffix='_conv_4')
concat2 = mx.sym.Concat(*[stem_1_3x3, stem_2_3x3], name=('%s_concat_2' % name))
pool2 = mx.sym.Pooling(data=concat2, kernel=(3, 3), stride=(2, 2), pool_type='max',
name=('%s_%s_pool2' % ('max', name)))
stem_3_3x3 = Conv(data=concat2, num_filter=num_5_1, kernel=(3, 3), stride=(2, 2), name=('%s_stem_3' % name),
suffix='_conv_1', withRelu=False)
concat3 = mx.sym.Concat(*[pool2, stem_3_3x3], name=('%s_concat_3' % name))
bn1 = mx.sym.BatchNorm(data=concat3, name=('%s_bn1' % name))
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=('%s_relu1' % name))
return act1
def InceptionResnetV2A(data,
num_1_1,
num_2_1, num_2_2,
num_3_1, num_3_2, num_3_3,
proj,
name,
scaleResidual=True):
init = data
a1 = Conv(data=data, num_filter=num_1_1, name=('%s_a_1' % name), suffix='_conv')
a2 = Conv(data=data, num_filter=num_2_1, name=('%s_a_2' % name), suffix='_conv_1')
a2 = Conv(data=a2, num_filter=num_2_2, kernel=(3, 3), pad=(1, 1), name=('%s_a_2' % name), suffix='_conv_2')
a3 = Conv(data=data, num_filter=num_3_1, name=('%s_a_3' % name), suffix='_conv_1')
a3 = Conv(data=a3, num_filter=num_3_2, kernel=(3, 3), pad=(1, 1), name=('%s_a_3' % name), suffix='_conv_2')
a3 = Conv(data=a3, num_filter=num_3_3, kernel=(3, 3), pad=(1, 1), name=('%s_a_3' % name), suffix='_conv_3')
merge = mx.sym.Concat(*[a1, a2, a3], name=('%s_a_concat1' % name))
conv = Conv(data=merge, num_filter=proj, name=('%s_a_liner_conv' % name), withRelu=False)
if scaleResidual:
conv *= 0.1
out = init + conv
bn = mx.sym.BatchNorm(data=out, name=('%s_a_bn1' % name))
act = mx.sym.Activation(data=bn, act_type='relu', name=('%s_a_relu1' % name))
return act
def InceptionResnetV2B(data,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
name,
scaleResidual=True):
init = data
b1 = Conv(data=data, num_filter=num_1_1, name=('%s_b_1' % name), suffix='_conv')
b2 = Conv(data=data, num_filter=num_2_1, name=('%s_b_2' % name), suffix='_conv_1')
b2 = Conv(data=b2, num_filter=num_2_2, kernel=(1, 7), pad=(0, 3), name=('%s_b_2' % name), suffix='_conv_2')
b2 = Conv(data=b2, num_filter=num_2_3, kernel=(7, 1), pad=(3, 0), name=('%s_b_2' % name), suffix='_conv_3')
merge = mx.sym.Concat(*[b1, b2], name=('%s_b_concat1' % name))
conv = Conv(data=merge, num_filter=proj, name=('%s_b_liner_conv' % name), withRelu=False)
if scaleResidual:
conv *= 0.1
out = init + conv
bn = mx.sym.BatchNorm(data=out, name=('%s_b_bn1' % name))
act = mx.sym.Activation(data=bn, act_type='relu', name=('%s_b_relu1' % name))
return act
def InceptionResnetV2C(data,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
name,
scaleResidual=True):
init = data
c1 = Conv(data=data, num_filter=num_1_1, name=('%s_c_1' % name), suffix='_conv')
c2 = Conv(data=data, num_filter=num_2_1, name=('%s_c_2' % name), suffix='_conv_1')
c2 = Conv(data=c2, num_filter=num_2_2, kernel=(1, 3), pad=(0, 1), name=('%s_c_2' % name), suffix='_conv_2')
c2 = Conv(data=c2, num_filter=num_2_3, kernel=(3, 1), pad=(1, 0), name=('%s_c_2' % name), suffix='_conv_3')
merge = mx.sym.Concat(*[c1, c2], name=('%s_c_concat1' % name))
conv = Conv(data=merge, num_filter=proj, name=('%s_b_liner_conv' % name), withRelu=False)
if scaleResidual:
conv *= 0.1
out = init + conv
bn = mx.sym.BatchNorm(data=out, name=('%s_c_bn1' % name))
act = mx.sym.Activation(data=bn, act_type='relu', name=('%s_c_relu1' % name))
return act
def ReductionResnetV2A(data,
num_2_1,
num_3_1, num_3_2, num_3_3,
name):
ra1 = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type='max', name=('%s_%s_pool1' % ('max', name)))
ra2 = Conv(data=data, num_filter=num_2_1, kernel=(3, 3), stride=(2, 2), name=('%s_ra_2' % name), suffix='_conv', withRelu=False)
ra3 = Conv(data=data, num_filter=num_3_1, name=('%s_ra_3' % name), suffix='_conv_1')
ra3 = Conv(data=ra3, num_filter=num_3_2, kernel=(3, 3), pad=(1, 1), name=('%s_ra_3' % name), suffix='_conv_2')
ra3 = Conv(data=ra3, num_filter=num_3_3, kernel=(3, 3), stride=(2, 2), name=('%s_ra_3' % name), suffix='_conv_3', withRelu=False)
m = mx.sym.Concat(*[ra1, ra2, ra3], name=('%s_ra_concat1' % name))
m = mx.sym.BatchNorm(data=m, name=('%s_ra_bn1' % name))
m = mx.sym.Activation(data=m, act_type='relu', name=('%s_ra_relu1' % name))
return m
def ReductionResnetV2B(data,
num_2_1, num_2_2,
num_3_1, num_3_2,
num_4_1, num_4_2, num_4_3,
name):
rb1 = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type='max', name=('%s_%s_pool1' % ('max', name)))
rb2 = Conv(data=data, num_filter=num_2_1, name=('%s_rb_2' % name), suffix='_conv_1')
rb2 = Conv(data=rb2, num_filter=num_2_2, kernel=(3, 3), stride=(2, 2), name=('%s_rb_2' % name), suffix='_conv_2', withRelu=False)
rb3 = Conv(data=data, num_filter=num_3_1, name=('%s_rb_3' % name), suffix='_conv_1')
rb3 = Conv(data=rb3, num_filter=num_3_2, kernel=(3, 3), stride=(2, 2), name=('%s_rb_3' % name), suffix='_conv_2', withRelu=False)
rb4 = Conv(data=data, num_filter=num_4_1, name=('%s_rb_4' % name), suffix='_conv_1')
rb4 = Conv(data=rb4, num_filter=num_4_2, kernel=(3, 3), pad=(1, 1), name=('%s_rb_4' % name), suffix='_conv_2')
rb4 = Conv(data=rb4, num_filter=num_4_3, kernel=(3, 3), stride=(2, 2), name=('%s_rb_4' % name), suffix='_conv_3', withRelu=False)
m = mx.sym.Concat(*[rb1, rb2, rb3, rb4], name=('%s_rb_concat1' % name))
m = mx.sym.BatchNorm(data=m, name=('%s_rb_bn1' % name))
m = mx.sym.Activation(data=m, act_type='relu', name=('%s_rb_relu1' % name))
return m
def circle_in3a(data,
num_1_1,
num_2_1, num_2_2,
num_3_1, num_3_2, num_3_3,
proj,
name,
scale,
round):
in3a = data
for i in xrange(round):
in3a = InceptionResnetV2A(in3a,
num_1_1,
num_2_1, num_2_2,
num_3_1, num_3_2, num_3_3,
proj,
name + ('_%d' % i),
scaleResidual=scale)
return in3a
def circle_in2b(data,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
name,
scale,
round):
in2b = data
for i in xrange(round):
in2b = InceptionResnetV2B(in2b,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
name + ('_%d' % i),
scaleResidual=scale)
return in2b
def circle_in2c(data,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
name,
scale,
round):
in2c = data
for i in xrange(round):
in2c = InceptionResnetV2C(in2c,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
name + ('_%d' % i),
scaleResidual=scale)
return in2c
# create inception-resnet-v2
def get_symbol(num_classes=1000, scale=True):
# input shape 3*229*229
data = mx.symbol.Variable(name="data")
# stage stem
(num_1_1, num_1_2, num_1_3) = (32, 32, 64)
num_2_1 = 96
(num_3_1, num_3_2) = (64, 96)
(num_4_1, num_4_2, num_4_3, num_4_4) = (64, 64, 64, 96)
num_5_1 = 192
in_stem = InceptionResnetStem(data,
num_1_1, num_1_2, num_1_3,
num_2_1,
num_3_1, num_3_2,
num_4_1, num_4_2, num_4_3, num_4_4,
num_5_1,
'stem_stage')
# stage 5 x Inception Resnet A
num_1_1 = 32
(num_2_1, num_2_2) = (32, 32)
(num_3_1, num_3_2, num_3_3) = (32, 48, 64)
proj = 384
in3a = circle_in3a(in_stem,
num_1_1,
num_2_1, num_2_2,
num_3_1, num_3_2, num_3_3,
proj,
'in3a',
scale,
5)
# stage Reduction Resnet A
num_1_1 = 384
(num_2_1, num_2_2, num_2_3) = (256, 256, 384)
re3a = ReductionResnetV2A(in3a,
num_1_1,
num_2_1, num_2_2, num_2_3,
're3a')
# stage 10 x Inception Resnet B
num_1_1 = 192
(num_2_1, num_2_2, num_2_3) = (128, 160, 192)
proj = 1152
in2b = circle_in2b(re3a,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
'in2b',
scale,
10)
# stage ReductionB
(num_1_1, num_1_2) = (256, 384)
(num_2_1, num_2_2) = (256, 288)
(num_3_1, num_3_2, num_3_3) = (256, 288, 320)
re4b = ReductionResnetV2B(in2b,
num_1_1, num_1_2,
num_2_1, num_2_2,
num_3_1, num_3_2, num_3_3,
're4b')
# stage 5 x Inception Resnet C
num_1_1 = 192
(num_2_1, num_2_2, num_2_3) = (192, 224, 256)
proj = 2144
in2c = circle_in2c(re4b,
num_1_1,
num_2_1, num_2_2, num_2_3,
proj,
'in2c',
scale,
5)
# stage Average Poolingact1
pool = mx.sym.Pooling(data=in2c, kernel=(8, 8), stride=(1, 1), pool_type="avg", name="global_pool")
# stage Dropout
dropout = mx.sym.Dropout(data=pool, p=0.2)
# dropout = mx.sym.Dropout(data=pool, p=0.8)
flatten = mx.sym.Flatten(data=dropout, name="flatten")
# output
fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc1')
softmax = mx.symbol.SoftmaxOutput(data=fc1, name='softmax')
return softmax
if __name__ == '__main__':
net = get_symbol(1000, scale=True)
shape = {'softmax_label': (32, 1000), 'data': (32, 3, 299, 299)}
mx.viz.plot_network(net, title='inception-resnet-v2', format='png', shape=shape).render('inception-resnet-v2')