def __init__(self, name, bn=False, ln=False, act=tf.nn.leaky_relu):
# stage 1
self.st1_1 = Conv2DLayer(64, 3, 1, '{}_st1_1'.format(name), 0.0, bn,
ln, "SAME", act)
self.st1_2 = Conv2DLayer(64, 3, 1, '{}_st1_2'.format(name), 0.0, bn,
ln, "SAME", act)
# stage 2
self.st2_1 = Conv2DLayer(128, 3, 1, '{}_st2_1'.format(name), 0.0, bn,
ln, "SAME", act)
self.st2_2 = Conv2DLayer(128, 3, 1, '{}_st2_2'.format(name), 0.0, bn,
ln, "SAME", act)
# stage 3
self.st3_1 = Conv2DLayer(256, 3, 1, '{}_st3_1'.format(name), 0.0, bn,
ln, "SAME", act)
self.st3_2 = Conv2DLayer(256, 3, 1, '{}_st3_2'.format(name), 0.0, bn,
ln, "SAME", act)
self.st3_3 = Conv2DLayer(256, 3, 1, '{}_st3_3'.format(name), 0.0, bn,
ln, "SAME", act)
# stage 4
self.st4_1 = Conv2DLayer(512, 3, 1, '{}_st4_1'.format(name), 0.0, bn,
ln, "SAME", act)
self.st4_2 = Conv2DLayer(512, 3, 1, '{}_st4_2'.format(name), 0.0, bn,
ln, "SAME", act)
self.st4_3 = Conv2DLayer(512, 3, 1, '{}_st4_3'.format(name), 0.0, bn,
ln, "SAME", act)
# stage 5
self.st5_1 = Conv2DLayer(512, 3, 1, '{}_st5_1'.format(name), 0.0, bn,
ln, "SAME", act)
self.st5_2 = Conv2DLayer(512, 3, 1, '{}_st5_2'.format(name), 0.0, bn,
ln, "SAME", act)
self.st5_3 = Conv2DLayer(512, 3, 1, '{}_st5_3'.format(name), 0.0, bn,
ln, "SAME", act)
def __init__(self,
name,
dropout=0.0,
bn=False,
ln=False,
padding="SAME",
act=tf.nn.leaky_relu):
self.act = act
#===========ENTRY FLOW==============
# block1
self.b1c1 = Conv2DLayer(32, 3, 2, '{}_block1_conv1'.format(name),
dropout, bn, ln, "VALID", act)
self.b1c2 = Conv2DLayer(64, 3, 1, '{}_block1_conv2'.format(name),
dropout, bn, ln, "VALID", act)
self.b1r1 = Conv2DLayer(128, 1, 2, '{}_block1_res_conv'.format(name),
dropout, bn, ln, padding, None)
# block2
self.b2c1 = SeparableConv2DLayer(128, 3, 1,
'{}_block2_dws_conv1'.format(name),
dropout, bn, ln, padding, act)
self.b2c2 = SeparableConv2DLayer(128, 3, 1,
'{}_block2_dws_conv2'.format(name),
dropout, bn, ln, padding, None)
self.b2r1 = Conv2DLayer(256, 1, 2, '{}_block2_res_conv'.format(name),
dropout, bn, ln, padding, None)
# block3
self.b3c1 = SeparableConv2DLayer(256, 3, 1,
'{}_block3_dws_conv1'.format(name),
dropout, bn, ln, padding, act)
self.b3c2 = SeparableConv2DLayer(256, 3, 1,
'{}_block3_dws_conv2'.format(name),
dropout, bn, ln, padding, None)
self.b3r1 = Conv2DLayer(728, 1, 2, '{}_block3_res_conv'.format(name),
dropout, bn, ln, padding, None)
# block4
self.b4c1 = SeparableConv2DLayer(728, 3, 1,
'{}_block4_dws_conv1'.format(name),
dropout, bn, ln, padding, act)
self.b4c2 = SeparableConv2DLayer(728, 3, 1,
'{}_block4_dws_conv2'.format(name),
dropout, bn, ln, padding, act)
#===========MIDDLE FLOW===============
self.block = []
for i in range(8):
block_prefix = 'block%s_' % (str(i + 5))
self.block.append([
SeparableConv2DLayer(
728, 3, 1,
'{}_block_{}_dws_conv1'.format(name, block_prefix),
dropout, bn, ln, padding, act),
SeparableConv2DLayer(
728, 3, 1,
'{}_block_{}_dws_conv2'.format(name, block_prefix),
dropout, bn, ln, padding, act),
SeparableConv2DLayer(
728, 3, 1,
'{}_block_{}_dws_conv3'.format(name, block_prefix),
dropout, bn, ln, padding, None)
])
#========EXIT FLOW============
self.er0 = Conv2DLayer(1024, 1, 2, '{}_block12_res_conv'.format(name),
dropout, bn, ln, padding, None)
self.ec0 = SeparableConv2DLayer(728, 3, 1,
'{}_block13_dws_conv1'.format(name),
dropout, bn, ln, padding, act)
self.ec1 = SeparableConv2DLayer(1024, 3, 1,
'{}_block13_dws_conv2'.format(name),
dropout, bn, ln, padding, None)
self.ec2 = SeparableConv2DLayer(1536, 3, 1,
'{}_block14_dws_conv1'.format(name),
dropout, bn, ln, padding, act)
self.ec3 = SeparableConv2DLayer(2048, 3, 1,
'{}_block14_dws_conv2'.format(name),
dropout, bn, ln, padding, act)
def __init__( self,
name,
dropout = 0.0, bn = False, ln = False,
act = tf.nn.leaky_relu):
self.name = name
self.c1 = Conv2DLayer( 32, 3, 2, '{}_incep_resnet_stem_0'.format( name ),
dropout, bn, ln, "SAME", act )
self.c2 = Conv2DLayer( 32, 3, 1, '{}_incep_resnet_stem_1'.format( name ),
dropout, bn, ln, "SAME", act )
self.c3 = Conv2DLayer( 64, 3, 1, '{}_incep_resnet_stem_2'.format( name ),
dropout, bn, ln, "SAME", act )
self.c4 = Conv2DLayer( 96, 3, 2, '{}_incep_resnet_stem_4'.format( name ),
dropout, bn, ln, "SAME", act )
self.c5_0 = Conv2DLayer( 64, 1, 1, '{}_incep_resnet_stem_5_0'.format( name ),
dropout, bn, ln, "SAME", act )
self.c5_1 = Conv2DLayer( 64, (1,7), 1, '{}_incep_resnet_stem_5_1'.format( name ),
dropout, bn, ln, "SAME", act )
self.c5_2 = Conv2DLayer( 64, (7,1), 1, '{}_incep_resnet_stem_5_2'.format( name ),
dropout, bn, ln, "SAME", act )
self.c5_3 = Conv2DLayer( 96, 3, 1, '{}_incep_resnet_stem_5_3'.format( name ),
dropout, bn, ln, "SAME", act )
self.c6_0 = Conv2DLayer( 64, 1, 1, '{}_incep_resnet_stem_6_0'.format( name ),
dropout, bn, ln, "SAME", act )
self.c6_1 = Conv2DLayer( 96, 3, 1, '{}_incep_resnet_stem_6_1'.format( name ),
dropout, bn, ln, "SAME", act )
self.c7 = Conv2DLayer( 192, 3, 2, '{}_incep_resnet_stem_7'.format( name ),
dropout, bn, ln, "SAME", act )
def __init__( self,
name, scope,
chanels,
dropout = 0.0, bn = False,
padding = "SAME",
act = tf.nn.leaky_relu ):
self.act = act
# downsample
self.c0_0 = Conv2DLayer( 64, 3, 1, "{}_c00".format( name ), bn = bn, ln = ln, act = act )
self.c1_0 = Conv2DLayer( 64, 3, 1, "{}_c10".format( name ), bn = bn, ln = ln, act = act )
self.c1_1 = Conv2DLayer( 128, 3, 1, "{}_c11".format( name ), bn = bn, ln = ln, act = act )
self.c2_0 = Conv2DLayer( 128, 3, 1, "{}_c20".format( name ), bn = bn, ln = ln, act = act )
self.c2_1 = Conv2DLayer( 256, 3, 1, "{}_c21".format( name ), bn = bn, ln = ln, act = act )
self.c2_2 = Conv2DLayer( 256, 3, 1, "{}_c22".format( name ), bn = bn, ln = ln, act = act )
self.c3_0 = Conv2DLayer( 256, 3, 1, "{}_c30".format( name ), bn = bn, ln = ln, act = act )
self.c3_1 = Conv2DLayer( 512, 3, 1, "{}_c31".format( name ), bn = bn, ln = ln, act = act )
self.c3_2 = Conv2DLayer( 512, 3, 1, "{}_c32".format( name ), bn = bn, ln = ln, act = act )
self.c4_0 = Conv2DLayer( 512, 3, 1, "{}_c40".format( name ), bn = bn, ln = ln, act = act )
self.c4_1 = Conv2DLayer( 512, 3, 1, "{}_c41".format( name ), bn = bn, ln = ln, act = act )
self.c4_2 = Conv2DLayer( 512, 3, 1, "{}_c42".format( name ), bn = bn, ln = ln, act = act )
self.c5_0 = Conv2DLayer( 512, 3, 1, "{}_c50".format( name ), bn = bn, ln = ln, act = act )
self.c5_1 = Conv2DLayer( 512, 3, 1, "{}_c51".format( name ), bn = bn, ln = ln, act = act )
# upsample
self.d0_0 = Deconv2DLayer( 256, 2, 2, "{}_d00".format( name ), bn = bn, ln = ln, act = act )
self.d0_1 = Deconv2DLayer( 512, 3, 1, "{}_d01".format( name ), bn = bn, ln = ln, act = act )
self.d1_0 = Deconv2DLayer( 256, 2, 2, "{}_d10".format( name ), bn = bn, ln = ln, act = act )
self.d1_1 = Deconv2DLayer( 512, 3, 1, "{}_d11".format( name ), bn = bn, ln = ln, act = act )
self.d2_0 = Deconv2DLayer( 128, 2, 2, "{}_d20".format( name ), bn = bn, ln = ln, act = act )
self.d2_1 = Deconv2DLayer( 256, 3, 1, "{}_d21".format( name ), bn = bn, ln = ln, act = act )
self.d3_0 = Deconv2DLayer( 64, 2, 2, "{}_d30".format( name ), bn = bn, ln = ln, act = act )
self.d3_1 = Deconv2DLayer( 128, 3, 1, "{}_d31".format( name ), bn = bn, ln = ln, act = act )
self.d4_0 = Deconv2DLayer( 32, 2, 2, "{}_d40".format( name ), bn = bn, ln = ln, act = act )
self.d4_1 = Deconv2DLayer( chanels, 3, 1, "{}_d41".format( name ), bn = False, act = tf.nn.sigmoid )