import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddlers
from paddlers.rs_models.cd.layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity
from paddlers.rs_models.cd.layers import ChannelAttention, SpatialAttention
from attach_tools import Attach
attach = Attach.to(paddlers.rs_models.cd)
@attach
class CustomModel(nn.Layer):
def __init__(self,
in_channels,
num_classes,
att_types='cst',
use_dropout=False):
super(CustomModel, self).__init__()
C1, C2, C3, C4, C5 = 16, 32, 64, 128, 256
self.use_dropout = use_dropout
self.conv11 = Conv3x3(in_channels, C1, norm=True, act=True)
self.do11 = self._make_dropout()
self.conv12 = Conv3x3(C1, C1, norm=True, act=True)
self.do12 = self._make_dropout()
self.pool1 = MaxPool2x2()
self.conv21 = Conv3x3(C1, C2, norm=True, act=True)
self.do21 = self._make_dropout()
self.conv22 = Conv3x3(C2, C2, norm=True, act=True)
self.do22 = self._make_dropout()
self.pool2 = MaxPool2x2()
self.conv31 = Conv3x3(C2, C3, norm=True, act=True)
self.do31 = self._make_dropout()
self.conv32 = Conv3x3(C3, C3, norm=True, act=True)
self.do32 = self._make_dropout()
self.conv33 = Conv3x3(C3, C3, norm=True, act=True)
self.do33 = self._make_dropout()
self.pool3 = MaxPool2x2()
self.conv41 = Conv3x3(C3, C4, norm=True, act=True)
self.do41 = self._make_dropout()
self.conv42 = Conv3x3(C4, C4, norm=True, act=True)
self.do42 = self._make_dropout()
self.conv43 = Conv3x3(C4, C4, norm=True, act=True)
self.do43 = self._make_dropout()
self.pool4 = MaxPool2x2()
self.upconv4 = ConvTransposed3x3(C4, C4, output_padding=1)
self.conv43d = Conv3x3(C5, C4, norm=True, act=True)
self.do43d = self._make_dropout()
self.conv42d = Conv3x3(C4, C4, norm=True, act=True)
self.do42d = self._make_dropout()
self.conv41d = Conv3x3(C4, C3, norm=True, act=True)
self.do41d = self._make_dropout()
self.upconv3 = ConvTransposed3x3(C3, C3, output_padding=1)
self.conv33d = Conv3x3(C4, C3, norm=True, act=True)
self.do33d = self._make_dropout()
self.conv32d = Conv3x3(C3, C3, norm=True, act=True)
self.do32d = self._make_dropout()
self.conv31d = Conv3x3(C3, C2, norm=True, act=True)
self.do31d = self._make_dropout()
self.upconv2 = ConvTransposed3x3(C2, C2, output_padding=1)
self.conv22d = Conv3x3(C3, C2, norm=True, act=True)
self.do22d = self._make_dropout()
self.conv21d = Conv3x3(C2, C1, norm=True, act=True)
self.do21d = self._make_dropout()
self.upconv1 = ConvTransposed3x3(C1, C1, output_padding=1)
self.conv12d = Conv3x3(C2, C1, norm=True, act=True)
self.do12d = self._make_dropout()
self.conv11d = Conv3x3(C1, num_classes)
if 'c' in att_types:
self.att_c = ChannelAttention(C4)
else:
self.att_c = Identity()
if 's' in att_types:
self.att_s = SpatialAttention()
else:
self.att_s = Identity()
if 't' in att_types:
self.att_t = ChannelAttention(2, ratio=1)
else:
self.att_t = Identity()
self.init_weight()
def forward(self, t1, t2):
# Encode t1
# Stage 1
x11 = self.do11(self.conv11(t1))
x12_1 = self.do12(self.conv12(x11))
x1p = self.pool1(x12_1)
# Stage 2
x21 = self.do21(self.conv21(x1p))
x22_1 = self.do22(self.conv22(x21))
x2p = self.pool2(x22_1)
# Stage 3
x31 = self.do31(self.conv31(x2p))
x32 = self.do32(self.conv32(x31))
x33_1 = self.do33(self.conv33(x32))
x3p = self.pool3(x33_1)
# Stage 4
x41 = self.do41(self.conv41(x3p))
x42 = self.do42(self.conv42(x41))
x43_1 = self.do43(self.conv43(x42))
x4p = self.pool4(x43_1)
# Encode t2
# Stage 1
x11 = self.do11(self.conv11(t2))
x12_2 = self.do12(self.conv12(x11))
x1p = self.pool1(x12_2)
# Stage 2
x21 = self.do21(self.conv21(x1p))
x22_2 = self.do22(self.conv22(x21))
x2p = self.pool2(x22_2)
# Stage 3
x31 = self.do31(self.conv31(x2p))
x32 = self.do32(self.conv32(x31))
x33_2 = self.do33(self.conv33(x32))
x3p = self.pool3(x33_2)
# Stage 4
x41 = self.do41(self.conv41(x3p))
x42 = self.do42(self.conv42(x41))
x43_2 = self.do43(self.conv43(x42))
x4p = self.pool4(x43_2)
# Attend
x43_1 = self.att_c(x43_1) * x43_1
x43_1 = self.att_s(x43_1) * x43_1
x43_2 = self.att_c(x43_2) * x43_2
x43_2 = self.att_s(x43_2) * x43_2
x43 = paddle.stack([x43_1, x43_2], axis=1)
x43 = paddle.transpose(x43, [0, 2, 1, 3, 4])
x43 = paddle.flatten(x43, stop_axis=1)
x43 = self.att_t(x43) * x43
x43 = x43.reshape((x43_1.shape[0], -1, 2, *x43.shape[2:]))
x43_1, x43_2 = x43[:, :, 0], x43[:, :, 1]
# Decode
# Stage 4d
x4d = self.upconv4(x4p)
pad4 = (0, x43_1.shape[3] - x4d.shape[3], 0,
x43_1.shape[2] - x4d.shape[2])
x4d = F.pad(x4d, pad=pad4, mode='replicate')
x4d = paddle.concat([x4d, paddle.abs(x43_1 - x43_2)], 1)
x43d = self.do43d(self.conv43d(x4d))
x42d = self.do42d(self.conv42d(x43d))
x41d = self.do41d(self.conv41d(x42d))
# Stage 3d
x3d = self.upconv3(x41d)
pad3 = (0, x33_1.shape[3] - x3d.shape[3], 0,
x33_1.shape[2] - x3d.shape[2])
x3d = F.pad(x3d, pad=pad3, mode='replicate')
x3d = paddle.concat([x3d, paddle.abs(x33_1 - x33_2)], 1)
x33d = self.do33d(self.conv33d(x3d))
x32d = self.do32d(self.conv32d(x33d))
x31d = self.do31d(self.conv31d(x32d))
# Stage 2d
x2d = self.upconv2(x31d)
pad2 = (0, x22_1.shape[3] - x2d.shape[3], 0,
x22_1.shape[2] - x2d.shape[2])
x2d = F.pad(x2d, pad=pad2, mode='replicate')
x2d = paddle.concat([x2d, paddle.abs(x22_1 - x22_2)], 1)
x22d = self.do22d(self.conv22d(x2d))
x21d = self.do21d(self.conv21d(x22d))
# Stage 1d
x1d = self.upconv1(x21d)
pad1 = (0, x12_1.shape[3] - x1d.shape[3], 0,
x12_1.shape[2] - x1d.shape[2])
x1d = F.pad(x1d, pad=pad1, mode='replicate')
x1d = paddle.concat([x1d, paddle.abs(x12_1 - x12_2)], 1)
x12d = self.do12d(self.conv12d(x1d))
x11d = self.conv11d(x12d)
return [x11d]
def init_weight(self):
pass
def _make_dropout(self):
if self.use_dropout:
return nn.Dropout2D(p=0.2)
else:
return Identity()