paddlers/paddlers/tasks/change_detector.py

# 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 math
import os
import os.path as osp
from collections import OrderedDict
from operator import attrgetter

import cv2
import numpy as np
import paddle
import paddle.nn.functional as F
from paddle.static import InputSpec

import paddlers
import paddlers.custom_models.cd as cmcd
import paddlers.utils.logging as logging
import paddlers.models.ppseg as paddleseg
from paddlers.transforms import Resize, decode_image
from paddlers.utils import get_single_card_bs, DisablePrint
from paddlers.utils.checkpoint import seg_pretrain_weights_dict
from .base import BaseModel
from .utils import seg_metrics as metrics

__all__ = [
    "CDNet", "FCEarlyFusion", "FCSiamConc", "FCSiamDiff", "STANet", "BIT",
    "SNUNet", "DSIFN", "DSAMNet", "ChangeStar"
]


class BaseChangeDetector(BaseModel):
    def __init__(self,
                 model_name,
                 num_classes=2,
                 use_mixed_loss=False,
                 **params):
        self.init_params = locals()
        if 'with_net' in self.init_params:
            del self.init_params['with_net']
        super(BaseChangeDetector, self).__init__('changedetector')
        if model_name not in __all__:
            raise Exception("ERROR: There's no model named {}.".format(
                model_name))
        self.model_name = model_name
        self.num_classes = num_classes
        self.use_mixed_loss = use_mixed_loss
        self.losses = None
        self.labels = None
        if params.get('with_net', True):
            params.pop('with_net', None)
            self.net = self.build_net(**params)
        self.find_unused_parameters = True

    def build_net(self, **params):
        # TODO: add other model
        net = cmcd.__dict__[self.model_name](num_classes=self.num_classes,
                                             **params)
        return net

    def _fix_transforms_shape(self, image_shape):
        if hasattr(self, 'test_transforms'):
            if self.test_transforms is not None:
                has_resize_op = False
                resize_op_idx = -1
                normalize_op_idx = len(self.test_transforms.transforms)
                for idx, op in enumerate(self.test_transforms.transforms):
                    name = op.__class__.__name__
                    if name == 'Normalize':
                        normalize_op_idx = idx
                    if 'Resize' in name:
                        has_resize_op = True
                        resize_op_idx = idx

                if not has_resize_op:
                    self.test_transforms.transforms.insert(
                        normalize_op_idx, Resize(target_size=image_shape))
                else:
                    self.test_transforms.transforms[resize_op_idx] = Resize(
                        target_size=image_shape)

    def _get_test_inputs(self, image_shape):
        if image_shape is not None:
            if len(image_shape) == 2:
                image_shape = [1, 3] + image_shape
            self._fix_transforms_shape(image_shape[-2:])
        else:
            image_shape = [None, 3, -1, -1]
        self.fixed_input_shape = image_shape
        return [
            InputSpec(
                shape=image_shape, name='image', dtype='float32'), InputSpec(
                    shape=image_shape, name='image2', dtype='float32')
        ]

    def run(self, net, inputs, mode):
        net_out = net(inputs[0], inputs[1])
        logit = net_out[0]
        outputs = OrderedDict()
        if mode == 'test':
            origin_shape = inputs[2]
            if self.status == 'Infer':
                label_map_list, score_map_list = self._postprocess(
                    net_out, origin_shape, transforms=inputs[3])
            else:
                logit_list = self._postprocess(
                    logit, origin_shape, transforms=inputs[3])
                label_map_list = []
                score_map_list = []
                for logit in logit_list:
                    logit = paddle.transpose(logit, perm=[0, 2, 3, 1])  # NHWC
                    label_map_list.append(
                        paddle.argmax(
                            logit, axis=-1, keepdim=False, dtype='int32')
                        .squeeze().numpy())
                    score_map_list.append(
                        F.softmax(
                            logit, axis=-1).squeeze().numpy().astype('float32'))
            outputs['label_map'] = label_map_list
            outputs['score_map'] = score_map_list

        if mode == 'eval':
            if self.status == 'Infer':
                pred = paddle.unsqueeze(net_out[0], axis=1)  # NCHW
            else:
                pred = paddle.argmax(logit, axis=1, keepdim=True, dtype='int32')
            label = inputs[2]
            if label.ndim == 3:
                paddle.unsqueeze_(label, axis=1)
            if label.ndim != 4:
                raise ValueError("Expected label.ndim == 4 but got {}".format(
                    label.ndim))
            origin_shape = [label.shape[-2:]]
            pred = self._postprocess(
                pred, origin_shape, transforms=inputs[3])[0]  # NCHW
            intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
                pred, label, self.num_classes)
            outputs['intersect_area'] = intersect_area
            outputs['pred_area'] = pred_area
            outputs['label_area'] = label_area
            outputs['conf_mat'] = metrics.confusion_matrix(pred, label,
                                                           self.num_classes)
        if mode == 'train':
            if hasattr(net, 'USE_MULTITASK_DECODER') and \
                net.USE_MULTITASK_DECODER is True:
                # CD+Seg
                if len(inputs) != 5:
                    raise ValueError(
                        "Cannot perform loss computation with {} inputs.".
                        format(len(inputs)))
                labels_list = [
                    inputs[2 + idx]
                    for idx in map(attrgetter('value'), net.OUT_TYPES)
                ]
                loss_list = metrics.multitask_loss_computation(
                    logits_list=net_out,
                    labels_list=labels_list,
                    losses=self.losses)
            else:
                loss_list = metrics.loss_computation(
                    logits_list=net_out, labels=inputs[2], losses=self.losses)
            loss = sum(loss_list)
            outputs['loss'] = loss
        return outputs

    def default_loss(self):
        if isinstance(self.use_mixed_loss, bool):
            if self.use_mixed_loss:
                losses = [
                    paddleseg.models.CrossEntropyLoss(),
                    paddleseg.models.LovaszSoftmaxLoss()
                ]
                coef = [.8, .2]
                loss_type = [
                    paddleseg.models.MixedLoss(
                        losses=losses, coef=coef),
                ]
            else:
                loss_type = [paddleseg.models.CrossEntropyLoss()]
        else:
            losses, coef = list(zip(*self.use_mixed_loss))
            if not set(losses).issubset(
                ['CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss']):
                raise ValueError(
                    "Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
                )
            losses = [getattr(paddleseg.models, loss)() for loss in losses]
            loss_type = [
                paddleseg.models.MixedLoss(
                    losses=losses, coef=list(coef))
            ]
        loss_coef = [1.0]
        losses = {'types': loss_type, 'coef': loss_coef}
        return losses

    def default_optimizer(self,
                          parameters,
                          learning_rate,
                          num_epochs,
                          num_steps_each_epoch,
                          lr_decay_power=0.9):
        decay_step = num_epochs * num_steps_each_epoch
        lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
            learning_rate, decay_step, end_lr=0, power=lr_decay_power)
        optimizer = paddle.optimizer.Momentum(
            learning_rate=lr_scheduler,
            parameters=parameters,
            momentum=0.9,
            weight_decay=4e-5)
        return optimizer

    def train(self,
              num_epochs,
              train_dataset,
              train_batch_size=2,
              eval_dataset=None,
              optimizer=None,
              save_interval_epochs=1,
              log_interval_steps=2,
              save_dir='output',
              pretrain_weights=None,
              learning_rate=0.01,
              lr_decay_power=0.9,
              early_stop=False,
              early_stop_patience=5,
              use_vdl=True,
              resume_checkpoint=None):
        """
        Train the model.
        Args:
            num_epochs(int): The number of epochs.
            train_dataset(paddlers.dataset): Training dataset.
            train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
            eval_dataset(paddlers.dataset, optional):
                Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
            optimizer(paddle.optimizer.Optimizer or None, optional):
                Optimizer used in training. If None, a default optimizer is used. Defaults to None.
            save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
            log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
            save_dir(str, optional): Directory to save the model. Defaults to 'output'.
            pretrain_weights(str or None, optional):
                None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to None.
            learning_rate(float, optional): Learning rate for training. Defaults to .025.
            lr_decay_power(float, optional): Learning decay power. Defaults to .9.
            early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
            early_stop_patience(int, optional): Early stop patience. Defaults to 5.
            use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
            resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
                If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
                `pretrain_weights` can be set simultaneously. Defaults to None.

        """
        if self.status == 'Infer':
            logging.error(
                "Exported inference model does not support training.",
                exit=True)
        if pretrain_weights is not None and resume_checkpoint is not None:
            logging.error(
                "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
                exit=True)
        self.labels = train_dataset.labels
        if self.losses is None:
            self.losses = self.default_loss()

        if optimizer is None:
            num_steps_each_epoch = train_dataset.num_samples // train_batch_size
            self.optimizer = self.default_optimizer(
                self.net.parameters(), learning_rate, num_epochs,
                num_steps_each_epoch, lr_decay_power)
        else:
            self.optimizer = optimizer

        if pretrain_weights is not None and not osp.exists(pretrain_weights):
            if pretrain_weights not in seg_pretrain_weights_dict[
                    self.model_name]:
                logging.warning(
                    "Path of pretrain_weights('{}') does not exist!".format(
                        pretrain_weights))
                logging.warning("Pretrain_weights is forcibly set to '{}'. "
                                "If don't want to use pretrain weights, "
                                "set pretrain_weights to be None.".format(
                                    seg_pretrain_weights_dict[self.model_name][
                                        0]))
                pretrain_weights = seg_pretrain_weights_dict[self.model_name][0]
        elif pretrain_weights is not None and osp.exists(pretrain_weights):
            if osp.splitext(pretrain_weights)[-1] != '.pdparams':
                logging.error(
                    "Invalid pretrain weights. Please specify a '.pdparams' file.",
                    exit=True)
        pretrained_dir = osp.join(save_dir, 'pretrain')
        is_backbone_weights = pretrain_weights == 'IMAGENET'
        self.net_initialize(
            pretrain_weights=pretrain_weights,
            save_dir=pretrained_dir,
            resume_checkpoint=resume_checkpoint,
            is_backbone_weights=is_backbone_weights)

        self.train_loop(
            num_epochs=num_epochs,
            train_dataset=train_dataset,
            train_batch_size=train_batch_size,
            eval_dataset=eval_dataset,
            save_interval_epochs=save_interval_epochs,
            log_interval_steps=log_interval_steps,
            save_dir=save_dir,
            early_stop=early_stop,
            early_stop_patience=early_stop_patience,
            use_vdl=use_vdl)

    def quant_aware_train(self,
                          num_epochs,
                          train_dataset,
                          train_batch_size=2,
                          eval_dataset=None,
                          optimizer=None,
                          save_interval_epochs=1,
                          log_interval_steps=2,
                          save_dir='output',
                          learning_rate=0.0001,
                          lr_decay_power=0.9,
                          early_stop=False,
                          early_stop_patience=5,
                          use_vdl=True,
                          resume_checkpoint=None,
                          quant_config=None):
        """
        Quantization-aware training.
        Args:
            num_epochs(int): The number of epochs.
            train_dataset(paddlers.dataset): Training dataset.
            train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
            eval_dataset(paddlers.dataset, optional):
                Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
            optimizer(paddle.optimizer.Optimizer or None, optional):
                Optimizer used in training. If None, a default optimizer is used. Defaults to None.
            save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
            log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
            save_dir(str, optional): Directory to save the model. Defaults to 'output'.
            learning_rate(float, optional): Learning rate for training. Defaults to .025.
            lr_decay_power(float, optional): Learning decay power. Defaults to .9.
            early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
            early_stop_patience(int, optional): Early stop patience. Defaults to 5.
            use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
            quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
                configuration will be used. Defaults to None.
            resume_checkpoint(str or None, optional): The path of the checkpoint to resume quantization-aware training
                from. If None, no training checkpoint will be resumed. Defaults to None.

        """
        self._prepare_qat(quant_config)
        self.train(
            num_epochs=num_epochs,
            train_dataset=train_dataset,
            train_batch_size=train_batch_size,
            eval_dataset=eval_dataset,
            optimizer=optimizer,
            save_interval_epochs=save_interval_epochs,
            log_interval_steps=log_interval_steps,
            save_dir=save_dir,
            pretrain_weights=None,
            learning_rate=learning_rate,
            lr_decay_power=lr_decay_power,
            early_stop=early_stop,
            early_stop_patience=early_stop_patience,
            use_vdl=use_vdl,
            resume_checkpoint=resume_checkpoint)

    def evaluate(self, eval_dataset, batch_size=1, return_details=False):
        """
        Evaluate the model.
        Args:
            eval_dataset(paddlers.dataset): Evaluation dataset.
            batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1.
            return_details(bool, optional): Whether to return evaluation details. Defaults to False.

        Returns:
            collections.OrderedDict with key-value pairs:
                For binary change detection (number of classes == 2), the key-value pairs are like:
                {"iou": `intersection over union for the change class`,
                 "f1": `F1 score for the change class`,
                 "oacc": `overall accuracy`,
                 "kappa": ` kappa coefficient`}.
                For multi-class change detection (number of classes > 2), the key-value pairs are like:
                {"miou": `mean intersection over union`,
                 "category_iou": `category-wise mean intersection over union`,
                 "oacc": `overall accuracy`,
                 "category_acc": `category-wise accuracy`,
                 "kappa": ` kappa coefficient`,
                 "category_F1-score": `F1 score`}.

        """
        self._check_transforms(eval_dataset.transforms, 'eval')

        self.net.eval()
        nranks = paddle.distributed.get_world_size()
        local_rank = paddle.distributed.get_rank()
        if nranks > 1:
            # Initialize parallel environment if not done.
            if not (paddle.distributed.parallel.parallel_helper.
                    _is_parallel_ctx_initialized()):
                paddle.distributed.init_parallel_env()

        batch_size_each_card = get_single_card_bs(batch_size)
        if batch_size_each_card > 1:
            batch_size_each_card = 1
            batch_size = batch_size_each_card * paddlers.env_info['num']
            logging.warning(
                "ChangeDetector only supports batch_size=1 for each gpu/cpu card " \
                "during evaluation, so batch_size " \
                "is forcibly set to {}.".format(batch_size)
            )
        self.eval_data_loader = self.build_data_loader(
            eval_dataset, batch_size=batch_size, mode='eval')

        intersect_area_all = 0
        pred_area_all = 0
        label_area_all = 0
        conf_mat_all = []
        logging.info(
            "Start to evaluate(total_samples={}, total_steps={})...".format(
                eval_dataset.num_samples,
                math.ceil(eval_dataset.num_samples * 1.0 / batch_size)))
        with paddle.no_grad():
            for step, data in enumerate(self.eval_data_loader):
                data.append(eval_dataset.transforms.transforms)
                outputs = self.run(self.net, data, 'eval')
                pred_area = outputs['pred_area']
                label_area = outputs['label_area']
                intersect_area = outputs['intersect_area']
                conf_mat = outputs['conf_mat']

                # Gather from all ranks
                if nranks > 1:
                    intersect_area_list = []
                    pred_area_list = []
                    label_area_list = []
                    conf_mat_list = []
                    paddle.distributed.all_gather(intersect_area_list,
                                                  intersect_area)
                    paddle.distributed.all_gather(pred_area_list, pred_area)
                    paddle.distributed.all_gather(label_area_list, label_area)
                    paddle.distributed.all_gather(conf_mat_list, conf_mat)

                    # Some image has been evaluated and should be eliminated in last iter
                    if (step + 1) * nranks > len(eval_dataset):
                        valid = len(eval_dataset) - step * nranks
                        intersect_area_list = intersect_area_list[:valid]
                        pred_area_list = pred_area_list[:valid]
                        label_area_list = label_area_list[:valid]
                        conf_mat_list = conf_mat_list[:valid]

                    intersect_area_all += sum(intersect_area_list)
                    pred_area_all += sum(pred_area_list)
                    label_area_all += sum(label_area_list)
                    conf_mat_all.extend(conf_mat_list)

                else:
                    intersect_area_all = intersect_area_all + intersect_area
                    pred_area_all = pred_area_all + pred_area
                    label_area_all = label_area_all + label_area
                    conf_mat_all.append(conf_mat)
        class_iou, miou = paddleseg.utils.metrics.mean_iou(
            intersect_area_all, pred_area_all, label_area_all)
        # TODO 确认是按oacc还是macc
        class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all,
                                                           pred_area_all)
        kappa = paddleseg.utils.metrics.kappa(intersect_area_all, pred_area_all,
                                              label_area_all)
        category_f1score = metrics.f1_score(intersect_area_all, pred_area_all,
                                            label_area_all)

        if len(class_acc) > 2:
            eval_metrics = OrderedDict(
                zip([
                    'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
                    'category_F1-score'
                ], [miou, class_iou, oacc, class_acc, kappa, category_f1score]))
        else:
            eval_metrics = OrderedDict(
                zip(['iou', 'f1', 'oacc', 'kappa'],
                    [class_iou[1], category_f1score[1], oacc, kappa]))

        if return_details:
            conf_mat = sum(conf_mat_all)
            eval_details = {'confusion_matrix': conf_mat.tolist()}
            return eval_metrics, eval_details
        return eval_metrics

    def predict(self, img_file, transforms=None):
        """
        Do inference.
        Args:
            Args:
            img_file (list[tuple] | tuple[str | np.ndarray]):
                Tuple of image paths or decoded image data for bi-temporal images, which also could constitute a list,
                meaning all image pairs to be predicted as a mini-batch.
            transforms(paddlers.transforms.Compose or None, optional):
                Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.

        Returns:
            If img_file is a tuple of string or np.array, the result is a dict with key-value pairs:
            {"label map": `label map`, "score_map": `score map`}.
            If img_file is a list, the result is a list composed of dicts with the corresponding fields:
            label_map(np.ndarray): the predicted label map (HW)
            score_map(np.ndarray): the prediction score map (HWC)

        """
        if transforms is None and not hasattr(self, 'test_transforms'):
            raise Exception("transforms need to be defined, now is None.")
        if transforms is None:
            transforms = self.test_transforms
        if isinstance(img_file, tuple):
            if not len(img_file) == 2 and any(
                    map(lambda obj: not isinstance(obj, (str, np.ndarray)),
                        img_file)):
                raise TypeError
            images = [img_file]
        else:
            images = img_file
        batch_im1, batch_im2, batch_origin_shape = self._preprocess(
            images, transforms, self.model_type)
        self.net.eval()
        data = (batch_im1, batch_im2, batch_origin_shape, transforms.transforms)
        outputs = self.run(self.net, data, 'test')
        label_map_list = outputs['label_map']
        score_map_list = outputs['score_map']
        if isinstance(img_file, list):
            prediction = [{
                'label_map': l,
                'score_map': s
            } for l, s in zip(label_map_list, score_map_list)]
        else:
            prediction = {
                'label_map': label_map_list[0],
                'score_map': score_map_list[0]
            }
        return prediction

    def slider_predict(self,
                       img_file,
                       save_dir,
                       block_size,
                       overlap=36,
                       transforms=None):
        """
        Do inference.
        Args:
            Args:
            img_file(list[str]):
                List of image paths.
            save_dir(str):
                Directory that contains saved geotiff file.
            block_size(list[int] | tuple[int] | int, optional):
                Size of block.
            overlap(list[int] | tuple[int] | int, optional):
                Overlap between two blocks. Defaults to 36.
            transforms(paddlers.transforms.Compose or None, optional):
                Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
        """
        try:
            from osgeo import gdal
        except:
            import gdal

        if len(img_file) != 2:
            raise ValueError("`img_file` must be a list of length 2.")
        if isinstance(block_size, int):
            block_size = (block_size, block_size)
        elif isinstance(block_size, (tuple, list)) and len(block_size) == 2:
            block_size = tuple(block_size)
        else:
            raise ValueError(
                "`block_size` must be a tuple/list of length 2 or an integer.")
        if isinstance(overlap, int):
            overlap = (overlap, overlap)
        elif isinstance(overlap, (tuple, list)) and len(overlap) == 2:
            overlap = tuple(overlap)
        else:
            raise ValueError(
                "`overlap` must be a tuple/list of length 2 or an integer.")

        src1_data = gdal.Open(img_file[0])
        src2_data = gdal.Open(img_file[1])
        width = src1_data.RasterXSize
        height = src1_data.RasterYSize
        bands = src1_data.RasterCount

        driver = gdal.GetDriverByName("GTiff")
        file_name = osp.splitext(osp.normpath(img_file[0]).split(os.sep)[-1])[
            0] + ".tif"
        if not osp.exists(save_dir):
            os.makedirs(save_dir)
        save_file = osp.join(save_dir, file_name)
        dst_data = driver.Create(save_file, width, height, 1, gdal.GDT_Byte)
        dst_data.SetGeoTransform(src1_data.GetGeoTransform())
        dst_data.SetProjection(src1_data.GetProjection())
        band = dst_data.GetRasterBand(1)
        band.WriteArray(255 * np.ones((height, width), dtype="uint8"))

        step = np.array(block_size) - np.array(overlap)
        for yoff in range(0, height, step[1]):
            for xoff in range(0, width, step[0]):
                xsize, ysize = block_size
                if xoff + xsize > width:
                    xsize = int(width - xoff)
                if yoff + ysize > height:
                    ysize = int(height - yoff)
                im1 = src1_data.ReadAsArray(
                    int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0))
                im2 = src2_data.ReadAsArray(
                    int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0))
                # fill
                h, w = im1.shape[:2]
                im1_fill = np.zeros(
                    (block_size[1], block_size[0], bands), dtype=im1.dtype)
                im2_fill = im1_fill.copy()
                im1_fill[:h, :w, :] = im1
                im2_fill[:h, :w, :] = im2
                im_fill = (im1_fill, im2_fill)
                # predict
                pred = self.predict(im_fill,
                                    transforms)["label_map"].astype("uint8")
                # overlap
                rd_block = band.ReadAsArray(int(xoff), int(yoff), xsize, ysize)
                mask = (rd_block == pred[:h, :w]) | (rd_block == 255)
                temp = pred[:h, :w].copy()
                temp[mask == False] = 0
                band.WriteArray(temp, int(xoff), int(yoff))
                dst_data.FlushCache()
        dst_data = None
        print("GeoTiff saved in {}.".format(save_file))

    def _preprocess(self, images, transforms, to_tensor=True):
        self._check_transforms(transforms, 'test')
        batch_im1, batch_im2 = list(), list()
        batch_ori_shape = list()
        for im1, im2 in images:
            if isinstance(im1, str) or isinstance(im2, str):
                im1 = decode_image(im1, to_rgb=False)
                im2 = decode_image(im2, to_rgb=False)
            ori_shape = im1.shape[:2]
            # XXX: sample do not contain 'image_t1' and 'image_t2'.
            sample = {'image': im1, 'image2': im2}
            im1, im2 = transforms(sample)[:2]
            batch_im1.append(im1)
            batch_im2.append(im2)
            batch_ori_shape.append(ori_shape)
        if to_tensor:
            batch_im1 = paddle.to_tensor(batch_im1)
            batch_im2 = paddle.to_tensor(batch_im2)
        else:
            batch_im1 = np.asarray(batch_im1)
            batch_im2 = np.asarray(batch_im2)

        return batch_im1, batch_im2, batch_ori_shape

    @staticmethod
    def get_transforms_shape_info(batch_ori_shape, transforms):
        batch_restore_list = list()
        for ori_shape in batch_ori_shape:
            restore_list = list()
            h, w = ori_shape[0], ori_shape[1]
            for op in transforms:
                if op.__class__.__name__ == 'Resize':
                    restore_list.append(('resize', (h, w)))
                    h, w = op.target_size
                elif op.__class__.__name__ == 'ResizeByShort':
                    restore_list.append(('resize', (h, w)))
                    im_short_size = min(h, w)
                    im_long_size = max(h, w)
                    scale = float(op.short_size) / float(im_short_size)
                    if 0 < op.max_size < np.round(scale * im_long_size):
                        scale = float(op.max_size) / float(im_long_size)
                    h = int(round(h * scale))
                    w = int(round(w * scale))
                elif op.__class__.__name__ == 'ResizeByLong':
                    restore_list.append(('resize', (h, w)))
                    im_long_size = max(h, w)
                    scale = float(op.long_size) / float(im_long_size)
                    h = int(round(h * scale))
                    w = int(round(w * scale))
                elif op.__class__.__name__ == 'Pad':
                    if op.target_size:
                        target_h, target_w = op.target_size
                    else:
                        target_h = int(
                            (np.ceil(h / op.size_divisor) * op.size_divisor))
                        target_w = int(
                            (np.ceil(w / op.size_divisor) * op.size_divisor))

                    if op.pad_mode == -1:
                        offsets = op.offsets
                    elif op.pad_mode == 0:
                        offsets = [0, 0]
                    elif op.pad_mode == 1:
                        offsets = [(target_h - h) // 2, (target_w - w) // 2]
                    else:
                        offsets = [target_h - h, target_w - w]
                    restore_list.append(('padding', (h, w), offsets))
                    h, w = target_h, target_w

            batch_restore_list.append(restore_list)
        return batch_restore_list

    def _postprocess(self, batch_pred, batch_origin_shape, transforms):
        batch_restore_list = BaseChangeDetector.get_transforms_shape_info(
            batch_origin_shape, transforms)
        if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer':
            return self._infer_postprocess(
                batch_label_map=batch_pred[0],
                batch_score_map=batch_pred[1],
                batch_restore_list=batch_restore_list)
        results = []
        if batch_pred.dtype == paddle.float32:
            mode = 'bilinear'
        else:
            mode = 'nearest'
        for pred, restore_list in zip(batch_pred, batch_restore_list):
            pred = paddle.unsqueeze(pred, axis=0)
            for item in restore_list[::-1]:
                h, w = item[1][0], item[1][1]
                if item[0] == 'resize':
                    pred = F.interpolate(
                        pred, (h, w), mode=mode, data_format='NCHW')
                elif item[0] == 'padding':
                    x, y = item[2]
                    pred = pred[:, :, y:y + h, x:x + w]
                else:
                    pass
            results.append(pred)
        return results

    def _infer_postprocess(self, batch_label_map, batch_score_map,
                           batch_restore_list):
        label_maps = []
        score_maps = []
        for label_map, score_map, restore_list in zip(
                batch_label_map, batch_score_map, batch_restore_list):
            if not isinstance(label_map, np.ndarray):
                label_map = paddle.unsqueeze(label_map, axis=[0, 3])
                score_map = paddle.unsqueeze(score_map, axis=0)
            for item in restore_list[::-1]:
                h, w = item[1][0], item[1][1]
                if item[0] == 'resize':
                    if isinstance(label_map, np.ndarray):
                        label_map = cv2.resize(
                            label_map, (w, h), interpolation=cv2.INTER_NEAREST)
                        score_map = cv2.resize(
                            score_map, (w, h), interpolation=cv2.INTER_LINEAR)
                    else:
                        label_map = F.interpolate(
                            label_map, (h, w),
                            mode='nearest',
                            data_format='NHWC')
                        score_map = F.interpolate(
                            score_map, (h, w),
                            mode='bilinear',
                            data_format='NHWC')
                elif item[0] == 'padding':
                    x, y = item[2]
                    if isinstance(label_map, np.ndarray):
                        label_map = label_map[..., y:y + h, x:x + w]
                        score_map = score_map[..., y:y + h, x:x + w]
                    else:
                        label_map = label_map[:, :, y:y + h, x:x + w]
                        score_map = score_map[:, :, y:y + h, x:x + w]
                else:
                    pass
            label_map = label_map.squeeze()
            score_map = score_map.squeeze()
            if not isinstance(label_map, np.ndarray):
                label_map = label_map.numpy()
                score_map = score_map.numpy()
            label_maps.append(label_map.squeeze())
            score_maps.append(score_map.squeeze())
        return label_maps, score_maps

    def _check_transforms(self, transforms, mode):
        super()._check_transforms(transforms, mode)
        if not isinstance(transforms.arrange,
                          paddlers.transforms.ArrangeChangeDetector):
            raise TypeError(
                "`transforms.arrange` must be an ArrangeChangeDetector object.")


class CDNet(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=6,
                 **params):
        params.update({'in_channels': in_channels})
        super(CDNet, self).__init__(
            model_name='CDNet',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class FCEarlyFusion(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=6,
                 use_dropout=False,
                 **params):
        params.update({'in_channels': in_channels, 'use_dropout': use_dropout})
        super(FCEarlyFusion, self).__init__(
            model_name='FCEarlyFusion',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class FCSiamConc(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=3,
                 use_dropout=False,
                 **params):
        params.update({'in_channels': in_channels, 'use_dropout': use_dropout})
        super(FCSiamConc, self).__init__(
            model_name='FCSiamConc',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class FCSiamDiff(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=3,
                 use_dropout=False,
                 **params):
        params.update({'in_channels': in_channels, 'use_dropout': use_dropout})
        super(FCSiamDiff, self).__init__(
            model_name='FCSiamDiff',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class STANet(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=3,
                 att_type='BAM',
                 ds_factor=1,
                 **params):
        params.update({
            'in_channels': in_channels,
            'att_type': att_type,
            'ds_factor': ds_factor
        })
        super(STANet, self).__init__(
            model_name='STANet',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class BIT(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=3,
                 backbone='resnet18',
                 n_stages=4,
                 use_tokenizer=True,
                 token_len=4,
                 pool_mode='max',
                 pool_size=2,
                 enc_with_pos=True,
                 enc_depth=1,
                 enc_head_dim=64,
                 dec_depth=8,
                 dec_head_dim=8,
                 **params):
        params.update({
            'in_channels': in_channels,
            'backbone': backbone,
            'n_stages': n_stages,
            'use_tokenizer': use_tokenizer,
            'token_len': token_len,
            'pool_mode': pool_mode,
            'pool_size': pool_size,
            'enc_with_pos': enc_with_pos,
            'enc_depth': enc_depth,
            'enc_head_dim': enc_head_dim,
            'dec_depth': dec_depth,
            'dec_head_dim': dec_head_dim
        })
        super(BIT, self).__init__(
            model_name='BIT',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class SNUNet(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=3,
                 width=32,
                 **params):
        params.update({'in_channels': in_channels, 'width': width})
        super(SNUNet, self).__init__(
            model_name='SNUNet',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)


class DSIFN(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 use_dropout=False,
                 **params):
        params.update({'use_dropout': use_dropout})
        super(DSIFN, self).__init__(
            model_name='DSIFN',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)

    def default_loss(self):
        if self.use_mixed_loss is False:
            return {
                # XXX: make sure the shallow copy works correctly here.
                'types': [paddleseg.models.CrossEntropyLoss()] * 5,
                'coef': [1.0] * 5
            }
        else:
            raise ValueError(
                f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
            )


class DSAMNet(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 in_channels=3,
                 ca_ratio=8,
                 sa_kernel=7,
                 **params):
        params.update({
            'in_channels': in_channels,
            'ca_ratio': ca_ratio,
            'sa_kernel': sa_kernel
        })
        super(DSAMNet, self).__init__(
            model_name='DSAMNet',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)

    def default_loss(self):
        if self.use_mixed_loss is False:
            return {
                'types': [
                    paddleseg.models.CrossEntropyLoss(),
                    paddleseg.models.DiceLoss(), paddleseg.models.DiceLoss()
                ],
                'coef': [1.0, 0.05, 0.05]
            }
        else:
            raise ValueError(
                f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
            )


class ChangeStar(BaseChangeDetector):
    def __init__(self,
                 num_classes=2,
                 use_mixed_loss=False,
                 mid_channels=256,
                 inner_channels=16,
                 num_convs=4,
                 scale_factor=4.0,
                 **params):
        params.update({
            'mid_channels': mid_channels,
            'inner_channels': inner_channels,
            'num_convs': num_convs,
            'scale_factor': scale_factor
        })
        super(ChangeStar, self).__init__(
            model_name='ChangeStar',
            num_classes=num_classes,
            use_mixed_loss=use_mixed_loss,
            **params)

    def default_loss(self):
        if self.use_mixed_loss is False:
            return {
                # XXX: make sure the shallow copy works correctly here.
                'types': [paddleseg.models.CrossEntropyLoss()] * 4,
                'coef': [1.0] * 4
            }
        else:
            raise ValueError(
                f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
            )