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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn as nn
from paddlers.datasets.cd_dataset import MaskType
from paddlers.rs_models.seg import FarSeg
from .layers import Conv3x3, Identity
class _ChangeStarBase(nn.Layer):
USE_MULTITASK_DECODER = True
OUT_TYPES = (MaskType.CD, MaskType.CD, MaskType.SEG_T1, MaskType.SEG_T2)
def __init__(self, seg_model, num_classes, mid_channels, inner_channels,
num_convs, scale_factor):
super(_ChangeStarBase, self).__init__()
self.extract = seg_model
self.detect = ChangeMixin(
in_ch=mid_channels * 2,
out_ch=num_classes,
mid_ch=inner_channels,
num_convs=num_convs,
scale_factor=scale_factor)
self.segment = nn.Sequential(
Conv3x3(mid_channels, 2),
nn.UpsamplingBilinear2D(scale_factor=scale_factor))
self.init_weight()
def forward(self, t1, t2):
x1 = self.extract(t1)[0]
x2 = self.extract(t2)[0]
logit12, logit21 = self.detect(x1, x2)
if not self.training:
logit_list = [logit12]
else:
logit1 = self.segment(x1)
logit2 = self.segment(x2)
logit_list = [logit12, logit21, logit1, logit2]
return logit_list
def init_weight(self):
pass
class ChangeMixin(nn.Layer):
def __init__(self, in_ch, out_ch, mid_ch, num_convs, scale_factor):
super(ChangeMixin, self).__init__()
convs = [Conv3x3(in_ch, mid_ch, norm=True, act=True)]
convs += [
Conv3x3(
mid_ch, mid_ch, norm=True, act=True)
for _ in range(num_convs - 1)
]
self.detect = nn.Sequential(
*convs,
Conv3x3(mid_ch, out_ch),
nn.UpsamplingBilinear2D(scale_factor=scale_factor))
def forward(self, x1, x2):
pred12 = self.detect(paddle.concat([x1, x2], axis=1))
pred21 = self.detect(paddle.concat([x2, x1], axis=1))
return pred12, pred21
class ChangeStar_FarSeg(_ChangeStarBase):
"""
The ChangeStar implementation with a FarSeg encoder based on PaddlePaddle.
The original article refers to
Z. Zheng, et al., "Change is Everywhere: Single-Temporal Supervised Object
Change Detection in Remote Sensing Imagery"
(https://arxiv.org/abs/2108.07002).
Note that this implementation differs from the original code in two aspects:
1. The encoder of the FarSeg model is ResNet50.
2. We use conv-bn-relu instead of conv-relu-bn.
Args:
num_classes (int): Number of target classes.
mid_channels (int, optional): Number of channels required by the
ChangeMixin module. Default: 256.
inner_channels (int, optional): Number of filters used in the
convolutional layers in the ChangeMixin module. Default: 16.
num_convs (int, optional): Number of convolutional layers used in the
ChangeMixin module. Default: 4.
scale_factor (float, optional): Scaling factor of the output upsampling
layer. Default: 4.0.
"""
def __init__(
self,
num_classes,
mid_channels=256,
inner_channels=16,
num_convs=4,
scale_factor=4.0, ):
# TODO: Configurable FarSeg model
class _FarSegWrapper(nn.Layer):
def __init__(self, seg_model):
super(_FarSegWrapper, self).__init__()
self._seg_model = seg_model
self._seg_model.cls_head = Identity()
def forward(self, x):
return self._seg_model(x)
seg_model = FarSeg(decoder_out_channels=mid_channels)
super(ChangeStar_FarSeg, self).__init__(
seg_model=_FarSegWrapper(seg_model),
num_classes=num_classes,
mid_channels=mid_channels,
inner_channels=inner_channels,
num_convs=num_convs,
scale_factor=scale_factor)
# NOTE: Currently, ChangeStar = FarSeg + ChangeMixin + SegHead
ChangeStar = ChangeStar_FarSeg