def __init__(self, n_classes):
self.in_size = (299, 299)
self.n_classes = n_classes
super(Inception_Resnet_v2, self).__init__()
with self.init_scope():
self.Stem = Stem(3)
for i in range(1, 11):
self.add_link('Inc_A_{}'.format(i),
Inception_Resnet_A(384, scale=0.17))
self.Red_A = Reduction_A(384, 256, 256, 384, 384)
for i in range(1, 21):
self.add_link('Inc_B_{}'.format(i),
Inception_Resnet_B(1152, scale=0.1))
self.Red_B = Reduction_B(1152)
for i in range(1, 10):
self.add_link('Inc_C_{}'.format(i),
Inception_Resnet_C(2144, scale=0.2))
self.add_link('Inc_C_10', Inception_Resnet_C(2144, scale=1.0))
self.convbn = ConvBN(2144, 1536, ksize=1, stride=1, pad=0)
self.fc = L.Linear(1536, self.n_classes)
def __init__(self, n_classes):
self.in_size = (299, 299)
self.n_classes = n_classes
super(Inception_Resnet_v1, self).__init__()
with self.init_scope():
self.Stem = Stem(3)
for i in range(1, 6):
self.add_link('Inc_A_{}'.format(i),
Inception_Resnet_A(256, scale=0.17))
self.Red_A = Reduction_A(256, 192, 192, 256, 384)
for i in range(1, 11):
self.add_link('Inc_B_{}'.format(i),
Inception_Resnet_B(896, scale=0.1))
self.Red_B = Reduction_B(896)
for i in range(1, 5):
self.add_link('Inc_C_{}'.format(i),
Inception_Resnet_C(1792, scale=0.2))
self.add_link('Inc_C_5', Inception_Resnet_C(1792, scale=1.))
self.convbn = ConvBN(1792, 1536, ksize=1, stride=1, pad=0)
self.fc = L.Linear(1536, self.n_classes)
def __init__(self, ch, scale):
super(Inception_Resnet_C, self).__init__()
self.scale = scale
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch, 192, ksize=1, stride=1,
initialW=initialW, pad=0)
self.convbn2_1 = ConvBN(ch, 192, ksize=1, stride=1,
initialW=initialW, pad=0)
self.convbn2_2 = ConvBN(192, 192, ksize=(1, 3), stride=1,
initialW=initialW, pad=(0, 1))
self.convbn2_3 = ConvBN(192, 192, ksize=(3, 1), stride=1,
initialW=initialW, pad=(1, 0))
self.conv3 = L.Convolution2D(384, ch, ksize=1, stride=1,
initialW=initialW, pad=0)
def __init__(self, ch, scale=1.):
super(Inception_Resnet_B, self).__init__()
self.scale = scale
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch, 128, ksize=1, stride=1,
initialW=initialW, pad=0)
self.convbn2_1 = ConvBN(ch, 128, ksize=1, stride=1,
initialW=initialW, pad=0)
self.convbn2_2 = ConvBN(128, 128, ksize=(1, 7), stride=1,
initialW=initialW, pad=(0, 3))
self.convbn2_3 = ConvBN(128, 128, ksize=(7, 1), stride=1,
initialW=initialW, pad=(3, 0))
self.conv3 = L.Convolution2D(256, ch, ksize=1, stride=1,
initialW=initialW, pad=0)
def __init__(self, ch):
super(Inception_A, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch,
96,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_1 = ConvBN(ch,
96,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_1 = ConvBN(ch,
64,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_2 = ConvBN(64,
96,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.convbn4_1 = ConvBN(ch,
64,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn4_2 = ConvBN(64,
96,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.convbn4_3 = ConvBN(96,
96,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
def __init__(self, ch):
super(Reduction_B, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn2_1 = ConvBN(ch,
256,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_2 = ConvBN(256,
384,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
self.convbn3_1 = ConvBN(ch,
256,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_2 = ConvBN(256,
288,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
self.convbn4_1 = ConvBN(ch,
256,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn4_2 = ConvBN(256,
288,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.convbn4_3 = ConvBN(288,
320,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
def __init__(self, ch, scale=1.0):
super(Inception_Resnet_A, self).__init__()
self.scale = scale
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch,
32,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_1 = ConvBN(ch,
32,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_2 = ConvBN(32,
32,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.convbn3_1 = ConvBN(ch,
32,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_2 = ConvBN(32,
48,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.convbn3_3 = ConvBN(48,
64,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.conv4 = L.Convolution2D(128,
ch,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
def __init__(self, ch):
super(Stem, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch, 32, ksize=3, stride=2,
initialW=initialW, pad=0)
self.convbn2 = ConvBN(32, 32, ksize=3, stride=1,
initialW=initialW, pad=0)
self.convbn3 = ConvBN(32, 64, ksize=3, stride=1,
initialW=initialW, pad=1)
self.convbn4 = ConvBN(64, 80, ksize=1, stride=1,
initialW=initialW, pad=0)
self.convbn5 = ConvBN(80, 192, ksize=3, stride=1,
initialW=initialW, pad=0)
self.convbn6 = ConvBN(192, 256, ksize=3, stride=2,
initialW=initialW, pad=0)
def __init__(self, ch):
super(Reduction_B, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn2_1 = ConvBN(ch,
192,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_2 = ConvBN(192,
192,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
self.convbn3_1 = ConvBN(ch,
256,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_2 = ConvBN(256,
256,
ksize=(1, 7),
stride=1,
initialW=initialW,
pad=(0, 3))
self.convbn3_3 = ConvBN(256,
320,
ksize=(7, 1),
stride=1,
initialW=initialW,
pad=(3, 0))
self.convbn3_4 = ConvBN(320,
320,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
def __init__(self, ch):
super(Stem, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch,
32,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
self.convbn2 = ConvBN(32,
32,
ksize=3,
stride=1,
initialW=initialW,
pad=0)
self.convbn3 = ConvBN(32,
64,
ksize=3,
stride=1,
initialW=initialW,
pad=1)
self.convbn4 = ConvBN(64,
96,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
self.convbn5_1x1_1 = ConvBN(160,
64,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn5_7x1 = ConvBN(64,
64,
ksize=(7, 1),
stride=1,
initialW=initialW,
pad=(3, 0))
self.convbn5_1x7 = ConvBN(64,
64,
ksize=(1, 7),
stride=1,
initialW=initialW,
pad=(0, 3))
self.convbn5_3x3_1 = ConvBN(64,
96,
ksize=3,
stride=1,
initialW=initialW,
pad=0)
self.convbn5_1x1_2 = ConvBN(160,
64,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn5_3x3_2 = ConvBN(64,
96,
ksize=3,
stride=1,
initialW=initialW,
pad=0)
self.convbn6 = ConvBN(192,
192,
ksize=3,
stride=2,
initialW=initialW,
pad=0)
def __init__(self, ch):
super(Inception_C, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch,
256,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_1 = ConvBN(ch,
256,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_1 = ConvBN(ch,
384,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_2_1 = ConvBN(384,
256,
ksize=(1, 3),
stride=1,
initialW=initialW,
pad=(0, 1))
self.convbn3_2_2 = ConvBN(384,
256,
ksize=(3, 1),
stride=1,
initialW=initialW,
pad=(1, 0))
self.convbn4_1 = ConvBN(ch,
384,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn4_2 = ConvBN(384,
448,
ksize=(1, 3),
stride=1,
initialW=initialW,
pad=(0, 1))
self.convbn4_3 = ConvBN(448,
512,
ksize=(3, 1),
stride=1,
initialW=initialW,
pad=(1, 0))
self.convbn4_4_1 = ConvBN(512,
256,
ksize=(3, 1),
stride=1,
initialW=initialW,
pad=(1, 0))
self.convbn4_4_2 = ConvBN(512,
256,
ksize=(1, 3),
stride=1,
initialW=initialW,
pad=(0, 1))
def __init__(self, ch):
super(Inception_B, self).__init__()
initialW = initializers.GlorotNormal()
with self.init_scope():
self.convbn1 = ConvBN(ch,
128,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn2_1 = ConvBN(ch,
384,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_1 = ConvBN(ch,
192,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn3_2 = ConvBN(192,
224,
ksize=(1, 7),
stride=1,
initialW=initialW,
pad=(0, 3))
self.convbn3_3 = ConvBN(224,
256,
ksize=(7, 1),
stride=1,
initialW=initialW,
pad=(3, 0))
self.convbn4_1 = ConvBN(ch,
192,
ksize=1,
stride=1,
initialW=initialW,
pad=0)
self.convbn4_2 = ConvBN(192,
192,
ksize=(1, 7),
stride=1,
initialW=initialW,
pad=(0, 3))
self.convbn4_3 = ConvBN(192,
224,
ksize=(7, 1),
stride=1,
initialW=initialW,
pad=(3, 0))
self.convbn4_4 = ConvBN(224,
224,
ksize=(1, 7),
stride=1,
initialW=initialW,
pad=(0, 3))
self.convbn4_5 = ConvBN(224,
256,
ksize=(7, 1),
stride=1,
initialW=initialW,
pad=(3, 0))