def __init__(self, name, encode_dim, out_channels, use_bias, norm_layer,
act, act_attr, conv_block_dropout, conv_block_num,
conv_block_dilation, out_conv_act, out_conv_act_attr):
super(DecoderUnet, self).__init__()
conv_blocks = []
for i in range(conv_block_num):
conv_blocks.append(
ResBlock(name="{}_conv_block_{}".format(name, i),
channels=encode_dim * 8,
norm_layer=norm_layer,
use_dropout=conv_block_dropout,
use_dilation=conv_block_dilation,
use_bias=use_bias))
self._conv_blocks = nn.Sequential(*conv_blocks)
self._up1 = SNConvTranspose(name=name + "_up1",
in_channels=encode_dim * 8,
out_channels=encode_dim * 4,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up2 = SNConvTranspose(name=name + "_up2",
in_channels=encode_dim * 8,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up3 = SNConvTranspose(name=name + "_up3",
in_channels=encode_dim * 4,
out_channels=encode_dim,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._pad2d = paddle.nn.Pad2D([1, 1, 1, 1], mode="replicate")
self._out_conv = SNConv(name=name + "_out_conv",
in_channels=encode_dim,
out_channels=out_channels,
kernel_size=3,
use_bias=use_bias,
norm_layer=None,
act=out_conv_act,
act_attr=out_conv_act_attr)
def __init__(self, name, in_channels, encode_dim, use_bias, norm_layer,
act, act_attr):
super(EncoderUnet, self).__init__()
self._pad2d = paddle.nn.Pad2D([3, 3, 3, 3], mode="replicate")
self._in_conv = SNConv(name=name + "_in_conv",
in_channels=in_channels,
out_channels=encode_dim,
kernel_size=7,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down1 = SNConv(name=name + "_down1",
in_channels=encode_dim,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down2 = SNConv(name=name + "_down2",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down3 = SNConv(name=name + "_down3",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down4 = SNConv(name=name + "_down4",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up1 = SNConvTranspose(name=name + "_up1",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up2 = SNConvTranspose(name=name + "_up2",
in_channels=encode_dim * 4,
out_channels=encode_dim * 4,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
class DecoderUnet(nn.Layer):
def __init__(self, name, encode_dim, out_channels, use_bias, norm_layer,
act, act_attr, conv_block_dropout, conv_block_num,
conv_block_dilation, out_conv_act, out_conv_act_attr):
super(DecoderUnet, self).__init__()
conv_blocks = []
for i in range(conv_block_num):
conv_blocks.append(
ResBlock(name="{}_conv_block_{}".format(name, i),
channels=encode_dim * 8,
norm_layer=norm_layer,
use_dropout=conv_block_dropout,
use_dilation=conv_block_dilation,
use_bias=use_bias))
self._conv_blocks = nn.Sequential(*conv_blocks)
self._up1 = SNConvTranspose(name=name + "_up1",
in_channels=encode_dim * 8,
out_channels=encode_dim * 4,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up2 = SNConvTranspose(name=name + "_up2",
in_channels=encode_dim * 8,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up3 = SNConvTranspose(name=name + "_up3",
in_channels=encode_dim * 4,
out_channels=encode_dim,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._pad2d = paddle.nn.Pad2D([1, 1, 1, 1], mode="replicate")
self._out_conv = SNConv(name=name + "_out_conv",
in_channels=encode_dim,
out_channels=out_channels,
kernel_size=3,
use_bias=use_bias,
norm_layer=None,
act=out_conv_act,
act_attr=out_conv_act_attr)
def forward(self, x, y, feature2, feature1):
output_dict = dict()
output_dict["conv_blocks"] = self._conv_blocks(
paddle.concat((x, y), axis=1))
output_dict["up1"] = self._up1.forward(output_dict["conv_blocks"])
output_dict["up2"] = self._up2.forward(
paddle.concat((output_dict["up1"], feature2), axis=1))
output_dict["up3"] = self._up3.forward(
paddle.concat((output_dict["up2"], feature1), axis=1))
output_dict["pad2d"] = self._pad2d.forward(output_dict["up3"])
output_dict["out_conv"] = self._out_conv.forward(output_dict["pad2d"])
return output_dict
class EncoderUnet(nn.Layer):
def __init__(self, name, in_channels, encode_dim, use_bias, norm_layer,
act, act_attr):
super(EncoderUnet, self).__init__()
self._pad2d = paddle.nn.Pad2D([3, 3, 3, 3], mode="replicate")
self._in_conv = SNConv(name=name + "_in_conv",
in_channels=in_channels,
out_channels=encode_dim,
kernel_size=7,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down1 = SNConv(name=name + "_down1",
in_channels=encode_dim,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down2 = SNConv(name=name + "_down2",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down3 = SNConv(name=name + "_down3",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._down4 = SNConv(name=name + "_down4",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up1 = SNConvTranspose(name=name + "_up1",
in_channels=encode_dim * 2,
out_channels=encode_dim * 2,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
self._up2 = SNConvTranspose(name=name + "_up2",
in_channels=encode_dim * 4,
out_channels=encode_dim * 4,
kernel_size=3,
stride=2,
padding=1,
use_bias=use_bias,
norm_layer=norm_layer,
act=act,
act_attr=act_attr)
def forward(self, x):
output_dict = dict()
x = self._pad2d(x)
output_dict['in_conv'] = self._in_conv.forward(x)
output_dict['down1'] = self._down1.forward(output_dict['in_conv'])
output_dict['down2'] = self._down2.forward(output_dict['down1'])
output_dict['down3'] = self._down3.forward(output_dict['down2'])
output_dict['down4'] = self._down4.forward(output_dict['down3'])
output_dict['up1'] = self._up1.forward(output_dict['down4'])
output_dict['up2'] = self._up2.forward(
paddle.concat((output_dict['down3'], output_dict['up1']), axis=1))
output_dict['concat'] = paddle.concat(
(output_dict['down2'], output_dict['up2']), axis=1)
return output_dict