def __init__(self):
super(net3,self).__init__()
with self.name_scope():
encoder1=encoder(3,16)
encoder2=encoder(16,32)
encoder3=encoder(32,64)
decoder1=decoder(64,32)
decoder2=decoder(32,16)
decoder3=decoder(16,1)
att2=CA_M2(32)
att3=CA_M2(64)
att4=CA_M2(32)
blocks=[encoder1,encoder2,att2,encoder3,att3,decoder1,att4,decoder2,decoder3]
self.net1=HybridSequential()
with self.net1.name_scope():
for block in blocks:
self.net1.add(block)
def __init__(self):
super(net2,self).__init__()
self.net=HybridSequential()
with self.net.name_scope():
self.net.add(encoder(3,16))
self.net.add(encoder(16,32))
self.att= CA_M2(32)
self.net1=HybridSequential()
with self.net1.name_scope():
self.net1.add(encoder(32,64))
self.net1.add(decoder(64,32))
self.net1.add(decoder(32,16))
self.net1.add(decoder(16,1))
def __init__(self,
inner_channels,
outer_channels,
inner_block=None,
innermost=False,
outermost=False,
use_dropout=False,
use_bias=False,
use_attention=True,
use_resblock=True,
use_p_at=False,
use_c_at=False,
save_att=False):
super(UnetSkipUnit, self).__init__()
with self.name_scope():
self.save_att = save_att
self.outermost = outermost
self.innermost = innermost
self.use_attention = use_attention
if not self.outermost:
res_block_1 = Res_Block(outer_channels=outer_channels)
res_block_2 = Res_Block(outer_channels=inner_channels)
en_conv = Conv2D(channels=inner_channels,
kernel_size=4,
strides=2,
padding=1,
in_channels=outer_channels,
use_bias=use_bias)
en_relu = LeakyReLU(alpha=0.2)
en_norm = BatchNorm(momentum=0.1, in_channels=inner_channels)
de_relu = Activation(activation='relu')
de_norm = BatchNorm(momentum=0.1, in_channels=outer_channels)
if innermost:
de_conv = Conv2DTranspose(channels=outer_channels,
kernel_size=4,
strides=2,
padding=1,
in_channels=inner_channels,
use_bias=use_bias)
if use_p_at:
self.p_at = CA_M2(in_channel=inner_channels)
else:
self.p_at = CA_M3()
if use_c_at:
self.c_at = CA_M1()
else:
self.c_at = CA_M3()
res_block_3 = Res_Block(outer_channels=inner_channels)
res_block_4 = Res_Block(outer_channels=outer_channels)
if use_resblock:
res1 = res_block_1
encoder = [en_conv, en_norm, en_relu]
res2 = res_block_2
res3 = res_block_3
decoder = [de_conv, de_norm, de_relu]
res4 = res_block_4
else:
encoder = [en_relu, en_conv]
decoder = [de_relu, de_conv, de_norm]
elif outermost:
de_conv = Conv2DTranspose(channels=outer_channels,
kernel_size=4,
strides=2,
padding=1,
in_channels=inner_channels)
channel_trans = Conv2D(channels=1,
in_channels=outer_channels,
kernel_size=1,
prefix='')
if use_resblock:
res1 = None
encoder = [en_conv, en_norm, en_relu]
res2 = None
res3 = None
decoder = [de_conv, de_norm, de_relu, channel_trans]
res4 = None
else:
encoder = [en_conv]
decoder = [de_relu, de_conv, de_norm, channel_trans]
if use_p_at:
self.p_at = CA_M2(in_channel=inner_channels)
else:
self.p_at = CA_M3()
if use_c_at:
self.c_at = CA_M1()
else:
self.c_at = CA_M3()
else:
de_conv = Conv2DTranspose(channels=outer_channels,
kernel_size=4,
strides=2,
padding=1,
in_channels=inner_channels,
use_bias=use_bias)
if use_p_at:
self.p_at = CA_M2(in_channel=inner_channels)
else:
self.p_at = CA_M3()
if use_c_at:
self.c_at = CA_M1()
else:
self.c_at = CA_M3()
res_block_3 = Res_Block(outer_channels=inner_channels)
res_block_4 = Res_Block(outer_channels=outer_channels)
if use_resblock:
res1 = res_block_1
encoder = [en_conv, en_norm, en_relu]
res2 = res_block_2
res3 = res_block_3
decoder = [de_conv, de_norm, de_relu]
res4 = res_block_4
else:
encoder = [en_relu, en_conv, en_norm]
decoder = [de_relu, de_conv, de_norm]
if use_dropout:
decoder += [Dropout(rate=0.5)]
self.encoder = HybridSequential()
with self.encoder.name_scope():
for block in encoder:
self.encoder.add(block)
self.inner_block = inner_block
self.res1 = res1
self.res2 = res2
self.res3 = res3
self.res4 = res4
self.decoder = HybridSequential()
with self.decoder.name_scope():
for block in decoder:
self.decoder.add(block)
def __init__(self, inner_channels, outer_channels, inner_block=None, innermost=False, outermost=False,
use_dropout=False, use_bias=False,use_position_attention=False,use_channel_attention=False):
super(UnetSkipUnit, self).__init__()
with self.name_scope():
self.outermost = outermost
res1=Res_Block(outer_channels=outer_channels)
res2=Res_Block(outer_channels=inner_channels)
res3=Res_Block(outer_channels=inner_channels)
res4=Res_Block(outer_channels=outer_channels)
attention_non=CA_M3()
attention_position=CA_M2(in_channel=inner_channels)
attention_channel=CA_M1()
en_conv = Conv2D(channels=inner_channels, kernel_size=4, strides=2, padding=1,
in_channels=outer_channels, use_bias=use_bias)
en_relu = LeakyReLU(alpha=0.2)
en_norm = BatchNorm(momentum=0.1, in_channels=inner_channels)
de_relu = Activation(activation='relu')
de_norm = BatchNorm(momentum=0.1, in_channels=outer_channels)
if innermost:
if use_position_attention:
p_attention=attention_position
else:
p_attention=attention_non
if use_channel_attention:
c_attention=attention_channel
else:
c_attention=attention_non
de_conv = Conv2DTranspose(channels=outer_channels, kernel_size=4, strides=2, padding=1,
in_channels=inner_channels, use_bias=use_bias)
encoder = [res1,en_relu, en_conv,p_attention,res2]
decoder = [res3,de_relu, de_conv, de_norm,res4]
model = encoder + decoder
elif outermost:
if use_position_attention:
p_attention=attention_position
else:
p_attention=attention_non
if use_channel_attention:
c_attention=attention_channel
else:
c_attention=attention_non
de_conv = Conv2DTranspose(channels=outer_channels, kernel_size=4, strides=2, padding=1,
in_channels=inner_channels)
encoder = [en_conv,p_attention]
decoder = [de_relu, de_conv, de_norm]
model = encoder + [inner_block] + decoder
else:
if use_position_attention:
p_attention=attention_position
else:
p_attention=attention_non
if use_channel_attention:
c_attention=attention_channel
else:
c_attention=attention_non
de_conv = Conv2DTranspose(channels=outer_channels, kernel_size=4, strides=2, padding=1,
in_channels=inner_channels, use_bias=use_bias)
encoder = [res1,en_relu, en_conv, en_norm,p_attention,res2]
decoder = [res3,de_relu, de_conv, de_norm,res4]
model = encoder + [inner_block] + decoder
self.c_attention=c_attention
self.model = HybridSequential()
with self.model.name_scope():
for block in model:
self.model.add(block)