Merge pull request #1 from Bobholamovic/refactor_data

[Refactor] All about paddlers.transforms.Compose

README.md

@@ -8,7 +8,7 @@
 
   <!-- [![version](https://img.shields.io/github/release/PaddlePaddle/PaddleRS.svg)](https://github.com/PaddlePaddle/PaddleRS/releases) -->
   [![license](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
-  [![build status](https://github.com/PaddlePaddle/PaddleRS/workflows/build_and_test.yaml/badge.svg?branch=develop)](https://github.com/PaddlePaddle/PaddleRS/actions)
+  [![build status](https://github.com/PaddlePaddle/PaddleRS/actions/workflows/build_and_test.yaml/badge.svg?branch=develop)](https://github.com/PaddlePaddle/PaddleRS/actions)
   ![python version](https://img.shields.io/badge/python-3.7+-orange.svg)
   ![support os](https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-yellow.svg)
 </div>

docs/apis/transforms.md

@@ -36,10 +36,12 @@ from paddlers.datasets import CDDataset
 
 
 train_transforms = T.Compose([
+    T.DecodeImg(),
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 train_dataset = CDDataset(

docs/cases/csc_cd_cn.md

@@ -365,7 +365,7 @@ class InferDataset(paddle.io.Dataset):
         names = []
         for line in lines:
             items = line.strip().split(' ')
-            items = list(map(pdrs.utils.path_normalization, items))
+            items = list(map(pdrs.utils.norm_path, items))
             item_dict = {
                 'image_t1': osp.join(data_dir, items[0]),
                 'image_t2': osp.join(data_dir, items[1])

paddlers/datasets/base.py

@@ -0,0 +1,35 @@
+# 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.
+
+from copy import deepcopy
+
+from paddle.io import Dataset
+
+from paddlers.utils import get_num_workers
+
+
+class BaseDataset(Dataset):
+    def __init__(self, data_dir, label_list, transforms, num_workers, shuffle):
+        super(BaseDataset, self).__init__()
+
+        self.data_dir = data_dir
+        self.label_list = label_list
+        self.transforms = deepcopy(transforms)
+        self.num_workers = get_num_workers(num_workers)
+        self.shuffle = shuffle
+
+    def __getitem__(self, idx):
+        sample = deepcopy(self.file_list[idx])
+        outputs = self.transforms(sample)
+        return outputs

paddlers/datasets/cd_dataset.py

@@ -16,12 +16,11 @@ import copy
 from enum import IntEnum
 import os.path as osp
 
-from paddle.io import Dataset
+from .base import BaseDataset
+from paddlers.utils import logging, get_encoding, norm_path, is_pic
 
-from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
 
-
-class CDDataset(Dataset):
+class CDDataset(BaseDataset):
     """
     读取变化检测任务数据集，并对样本进行相应的处理（来自SegDataset，图像标签需要两个）。
 
@@ -31,8 +30,10 @@ class CDDataset(Dataset):
             False（默认设置）时，文件中每一行应依次包含第一时相影像、第二时相影像以及变化检测标签的路径；当`with_seg_labels`为True时，
             文件中每一行应依次包含第一时相影像、第二时相影像、变化检测标签、第一时相建筑物标签以及第二时相建筑物标签的路径。
         label_list (str): 描述数据集包含的类别信息文件路径。默认值为None。
-        transforms (paddlers.transforms): 数据集中每个样本的预处理/增强算子。
-        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。
+        transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。
+        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
+            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的
+            一半。
         shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
         with_seg_labels (bool, optional): 数据集中是否包含两个时相的语义分割标签。默认为False。
         binarize_labels (bool, optional): 是否对数据集中的标签进行二值化操作。默认为False。
@@ -47,15 +48,13 @@ class CDDataset(Dataset):
                  shuffle=False,
                  with_seg_labels=False,
                  binarize_labels=False):
-        super(CDDataset, self).__init__()
+        super(CDDataset, self).__init__(data_dir, label_list, transforms,
+                                        num_workers, shuffle)
 
         DELIMETER = ' '
 
-        self.transforms = copy.deepcopy(transforms)
         # TODO: batch padding
         self.batch_transforms = None
-        self.num_workers = get_num_workers(num_workers)
-        self.shuffle = shuffle
         self.file_list = list()
         self.labels = list()
         self.with_seg_labels = with_seg_labels
@@ -82,7 +81,7 @@ class CDDataset(Dataset):
                         "Line[{}] in file_list[{}] has an incorrect number of file paths.".
                         format(line.strip(), file_list))
 
-                items = list(map(path_normalization, items))
+                items = list(map(norm_path, items))
 
                 full_path_im_t1 = osp.join(data_dir, items[0])
                 full_path_im_t2 = osp.join(data_dir, items[1])
@@ -128,9 +127,17 @@ class CDDataset(Dataset):
 
     def __getitem__(self, idx):
         sample = copy.deepcopy(self.file_list[idx])
-        outputs = self.transforms(sample)
+        sample = self.transforms.apply_transforms(sample)
+
         if self.binarize_labels:
-            outputs = outputs[:2] + tuple(map(self._binarize, outputs[2:]))
+            # Requires 'mask' to exist
+            sample['mask'] = self._binarize(sample['mask'])
+            if 'aux_masks' in sample:
+                sample['aux_masks'] = list(
+                    map(self._binarize, sample['aux_masks']))
+
+        outputs = self.transforms.arrange_outputs(sample)
+
         return outputs
 
     def __len__(self):

paddlers/datasets/clas_dataset.py

@@ -13,22 +13,22 @@
 # limitations under the License.
 
 import os.path as osp
-import copy
 
-from paddle.io import Dataset
+from .base import BaseDataset
+from paddlers.utils import logging, get_encoding, norm_path, is_pic
 
-from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
 
-
-class ClasDataset(Dataset):
+class ClasDataset(BaseDataset):
     """读取图像分类任务数据集，并对样本进行相应的处理。
 
     Args:
         data_dir (str): 数据集所在的目录路径。
         file_list (str): 描述数据集图片文件和对应标注序号（文本内每行路径为相对data_dir的相对路）。
         label_list (str): 描述数据集包含的类别信息文件路径，文件格式为（类别 说明）。默认值为None。
-        transforms (paddlers.transforms): 数据集中每个样本的预处理/增强算子。
-        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。
+        transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。
+        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
+            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的
+            一半。
         shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
     """
 
@@ -39,14 +39,11 @@ class ClasDataset(Dataset):
                  transforms=None,
                  num_workers='auto',
                  shuffle=False):
-        super(ClasDataset, self).__init__()
-        self.transforms = copy.deepcopy(transforms)
+        super(ClasDataset, self).__init__(data_dir, label_list, transforms,
+                                          num_workers, shuffle)
         # TODO batch padding
         self.batch_transforms = None
-        self.num_workers = get_num_workers(num_workers)
-        self.shuffle = shuffle
         self.file_list = list()
-        self.label_list = label_list
         self.labels = list()
 
         # TODO：非None时，让用户跳转数据集分析生成label_list
@@ -64,7 +61,7 @@ class ClasDataset(Dataset):
                         "A space is defined as the delimiter to separate the image and label path, " \
                         "so the space cannot be in the image or label path, but the line[{}] of " \
                         " file_list[{}] has a space in the image or label path.".format(line, file_list))
-                items[0] = path_normalization(items[0])
+                items[0] = norm_path(items[0])
                 full_path_im = osp.join(data_dir, items[0])
                 label = items[1]
                 if not is_pic(full_path_im):
@@ -84,10 +81,5 @@ class ClasDataset(Dataset):
         logging.info("{} samples in file {}".format(
             len(self.file_list), file_list))
 
-    def __getitem__(self, idx):
-        sample = copy.deepcopy(self.file_list[idx])
-        outputs = self.transforms(sample)
-        return outputs
-
     def __len__(self):
         return len(self.file_list)

paddlers/datasets/coco.py

@@ -20,14 +20,14 @@ import random
 from collections import OrderedDict
 
 import numpy as np
-from paddle.io import Dataset
 
-from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
+from .base import BaseDataset
+from paddlers.utils import logging, get_encoding, norm_path, is_pic
 from paddlers.transforms import DecodeImg, MixupImage
 from paddlers.tools import YOLOAnchorCluster
 
 
-class COCODetection(Dataset):
+class COCODetection(BaseDataset):
     """读取COCO格式的检测数据集，并对样本进行相应的处理。
 
     Args:
@@ -35,7 +35,7 @@ class COCODetection(Dataset):
         image_dir (str): 描述数据集图片文件路径。
         anno_path (str): COCO标注文件路径。
         label_list (str): 描述数据集包含的类别信息文件路径。
-        transforms (paddlers.det.transforms): 数据集中每个样本的预处理/增强算子。
+        transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。
         num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
             系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的
             一半。
@@ -60,10 +60,10 @@ class COCODetection(Dataset):
         import matplotlib
         matplotlib.use('Agg')
         from pycocotools.coco import COCO
-        super(COCODetection, self).__init__()
-        self.data_dir = data_dir
+        super(COCODetection, self).__init__(data_dir, label_list, transforms,
+                                            num_workers, shuffle)
+
         self.data_fields = None
-        self.transforms = copy.deepcopy(transforms)
         self.num_max_boxes = 50
 
         self.use_mix = False
@@ -76,8 +76,6 @@ class COCODetection(Dataset):
                     break
 
         self.batch_transforms = None
-        self.num_workers = get_num_workers(num_workers)
-        self.shuffle = shuffle
         self.allow_empty = allow_empty
         self.empty_ratio = empty_ratio
         self.file_list = list()
@@ -104,8 +102,8 @@ class COCODetection(Dataset):
                 'name': k
             })
 
-        anno_path = path_normalization(os.path.join(self.data_dir, anno_path))
-        image_dir = path_normalization(os.path.join(self.data_dir, image_dir))
+        anno_path = norm_path(os.path.join(self.data_dir, anno_path))
+        image_dir = norm_path(os.path.join(self.data_dir, image_dir))
 
         assert anno_path.endswith('.json'), \
             'invalid coco annotation file: ' + anno_path

paddlers/datasets/seg_dataset.py

@@ -15,20 +15,21 @@
 import os.path as osp
 import copy
 
-from paddle.io import Dataset
+from .base import BaseDataset
+from paddlers.utils import logging, get_encoding, norm_path, is_pic
 
-from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
 
-
-class SegDataset(Dataset):
+class SegDataset(BaseDataset):
     """读取语义分割任务数据集，并对样本进行相应的处理。
 
     Args:
         data_dir (str): 数据集所在的目录路径。
         file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
         label_list (str): 描述数据集包含的类别信息文件路径。默认值为None。
-        transforms (paddlers.transforms): 数据集中每个样本的预处理/增强算子。
-        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。
+        transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。
+        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
+            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的
+            一半。
         shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
     """
 
@@ -39,12 +40,10 @@ class SegDataset(Dataset):
                  transforms=None,
                  num_workers='auto',
                  shuffle=False):
-        super(SegDataset, self).__init__()
-        self.transforms = copy.deepcopy(transforms)
+        super(SegDataset, self).__init__(data_dir, label_list, transforms,
+                                         num_workers, shuffle)
         # TODO batch padding
         self.batch_transforms = None
-        self.num_workers = get_num_workers(num_workers)
-        self.shuffle = shuffle
         self.file_list = list()
         self.labels = list()
 
@@ -63,8 +62,8 @@ class SegDataset(Dataset):
                         "A space is defined as the delimiter to separate the image and label path, " \
                         "so the space cannot be in the image or label path, but the line[{}] of " \
                         " file_list[{}] has a space in the image or label path.".format(line, file_list))
-                items[0] = path_normalization(items[0])
-                items[1] = path_normalization(items[1])
+                items[0] = norm_path(items[0])
+                items[1] = norm_path(items[1])
                 full_path_im = osp.join(data_dir, items[0])
                 full_path_label = osp.join(data_dir, items[1])
                 if not is_pic(full_path_im) or not is_pic(full_path_label):
@@ -83,10 +82,5 @@ class SegDataset(Dataset):
         logging.info("{} samples in file {}".format(
             len(self.file_list), file_list))
 
-    def __getitem__(self, idx):
-        sample = copy.deepcopy(self.file_list[idx])
-        outputs = self.transforms(sample)
-        return outputs
-
     def __len__(self):
         return len(self.file_list)

paddlers/datasets/voc.py

@@ -22,21 +22,21 @@ from collections import OrderedDict
 import xml.etree.ElementTree as ET
 
 import numpy as np
-from paddle.io import Dataset
 
-from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
+from .base import BaseDataset
+from paddlers.utils import logging, get_encoding, norm_path, is_pic
 from paddlers.transforms import DecodeImg, MixupImage
 from paddlers.tools import YOLOAnchorCluster
 
 
-class VOCDetection(Dataset):
+class VOCDetection(BaseDataset):
     """读取PascalVOC格式的检测数据集，并对样本进行相应的处理。
 
     Args:
         data_dir (str): 数据集所在的目录路径。
         file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
         label_list (str): 描述数据集包含的类别信息文件路径。
-        transforms (paddlers.det.transforms): 数据集中每个样本的预处理/增强算子。
+        transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。
         num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
             系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的
             一半。
@@ -60,10 +60,10 @@ class VOCDetection(Dataset):
         import matplotlib
         matplotlib.use('Agg')
         from pycocotools.coco import COCO
-        super(VOCDetection, self).__init__()
-        self.data_dir = data_dir
+        super(VOCDetection, self).__init__(data_dir, label_list, transforms,
+                                           num_workers, shuffle)
+
         self.data_fields = None
-        self.transforms = copy.deepcopy(transforms)
         self.num_max_boxes = 50
 
         self.use_mix = False
@@ -76,8 +76,6 @@ class VOCDetection(Dataset):
                     break
 
         self.batch_transforms = None
-        self.num_workers = get_num_workers(num_workers)
-        self.shuffle = shuffle
         self.allow_empty = allow_empty
         self.empty_ratio = empty_ratio
         self.file_list = list()
@@ -117,8 +115,8 @@ class VOCDetection(Dataset):
                 img_file, xml_file = [
                     osp.join(data_dir, x) for x in line.strip().split()[:2]
                 ]
-                img_file = path_normalization(img_file)
-                xml_file = path_normalization(xml_file)
+                img_file = norm_path(img_file)
+                xml_file = norm_path(xml_file)
                 if not is_pic(img_file):
                     continue
                 if not osp.isfile(xml_file):

paddlers/deploy/predictor.py

@@ -258,9 +258,9 @@ class Predictor(object):
             Args:
                 img_file(list[str | tuple | np.ndarray] | str | tuple | np.ndarray): For scene classification, image restoration, 
                     object detection and semantic segmentation tasks, `img_file` should be either the path of the image to predict
-                    , a decoded image (a `np.ndarray`, which should be consistent with what you get from passing image path to
-                    `paddlers.transforms.decode_image()`), or a list of image paths or decoded images. For change detection tasks,
-                    `img_file` should be a tuple of image paths, a tuple of decoded images, or a list of tuples.
+                    , a decoded image (a np.ndarray, which should be consistent with what you get from passing image path to
+                    paddlers.transforms.decode_image()), or a list of image paths or decoded images. For change detection tasks,
+                    img_file should be a tuple of image paths, a tuple of decoded images, or a list of tuples.
                 topk(int, optional): Top-k values to reserve in a classification result. Defaults to 1.
                 transforms (paddlers.transforms.Compose | None, optional): Pipeline of data preprocessing. If None, load transforms
                     from `model.yml`. Defaults to None.

paddlers/tasks/base.py

@@ -30,7 +30,6 @@ from paddleslim import L1NormFilterPruner, FPGMFilterPruner
 
 import paddlers
 import paddlers.utils.logging as logging
-from paddlers.transforms import arrange_transforms
 from paddlers.utils import (seconds_to_hms, get_single_card_bs, dict2str,
                             get_pretrain_weights, load_pretrain_weights,
                             load_checkpoint, SmoothedValue, TrainingStats,
@@ -302,10 +301,7 @@ class BaseModel(metaclass=ModelMeta):
                    early_stop=False,
                    early_stop_patience=5,
                    use_vdl=True):
-        arrange_transforms(
-            model_type=self.model_type,
-            transforms=train_dataset.transforms,
-            mode='train')
+        self._check_transforms(train_dataset.transforms, 'train')
 
         if "RCNN" in self.__class__.__name__ and train_dataset.pos_num < len(
                 train_dataset.file_list):
@@ -488,10 +484,7 @@ class BaseModel(metaclass=ModelMeta):
 
         assert criterion in {'l1_norm', 'fpgm'}, \
             "Pruning criterion {} is not supported. Please choose from ['l1_norm', 'fpgm']"
-        arrange_transforms(
-            model_type=self.model_type,
-            transforms=dataset.transforms,
-            mode='eval')
+        self._check_transforms(dataset.transforms, 'eval')
         if self.model_type == 'detector':
             self.net.eval()
         else:
@@ -670,3 +663,15 @@ class BaseModel(metaclass=ModelMeta):
         open(osp.join(save_dir, '.success'), 'w').close()
         logging.info("The model for the inference deployment is saved in {}.".
                      format(save_dir))
+
+    def _check_transforms(self, transforms, mode):
+        # NOTE: Check transforms and transforms.arrange and give user-friendly error messages.
+        if not isinstance(transforms, paddlers.transforms.Compose):
+            raise TypeError("`transforms` must be paddlers.transforms.Compose.")
+        arrange_obj = transforms.arrange
+        if not isinstance(arrange_obj, paddlers.transforms.operators.Arrange):
+            raise TypeError("`transforms.arrange` must be an Arrange object.")
+        if arrange_obj.mode != mode:
+            raise ValueError(
+                f"Incorrect arrange mode! Expected {mode} but got {arrange_obj.mode}."
+            )

paddlers/tasks/change_detector.py

@@ -28,7 +28,6 @@ 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 arrange_transforms
 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
@@ -137,6 +136,11 @@ class BaseChangeDetector(BaseModel):
             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
@@ -396,10 +400,7 @@ class BaseChangeDetector(BaseModel):
                  "category_F1-score": `F1 score`}.
 
         """
-        arrange_transforms(
-            model_type=self.model_type,
-            transforms=eval_dataset.transforms,
-            mode='eval')
+        self._check_transforms(eval_dataset.transforms, 'eval')
 
         self.net.eval()
         nranks = paddle.distributed.get_world_size()
@@ -641,8 +642,7 @@ class BaseChangeDetector(BaseModel):
         print("GeoTiff saved in {}.".format(save_file))
 
     def _preprocess(self, images, transforms, to_tensor=True):
-        arrange_transforms(
-            model_type=self.model_type, transforms=transforms, mode='test')
+        self._check_transforms(transforms, 'test')
         batch_im1, batch_im2 = list(), list()
         batch_ori_shape = list()
         for im1, im2 in images:
@@ -786,6 +786,13 @@ class BaseChangeDetector(BaseModel):
             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,

paddlers/tasks/classifier.py

@@ -25,7 +25,6 @@ from paddle.static import InputSpec
 import paddlers.models.ppcls as paddleclas
 import paddlers.custom_models.cls as cmcls
 import paddlers
-from paddlers.transforms import arrange_transforms
 from paddlers.utils import get_single_card_bs, DisablePrint
 import paddlers.utils.logging as logging
 from .base import BaseModel
@@ -358,10 +357,7 @@ class BaseClassifier(BaseModel):
                  "top5": `acc of top5`}.
 
         """
-        arrange_transforms(
-            model_type=self.model_type,
-            transforms=eval_dataset.transforms,
-            mode='eval')
+        self._check_transforms(eval_dataset.transforms, 'eval')
 
         self.net.eval()
         nranks = paddle.distributed.get_world_size()
@@ -460,8 +456,7 @@ class BaseClassifier(BaseModel):
         return prediction
 
     def _preprocess(self, images, transforms, to_tensor=True):
-        arrange_transforms(
-            model_type=self.model_type, transforms=transforms, mode='test')
+        self._check_transforms(transforms, 'test')
         batch_im = list()
         batch_ori_shape = list()
         for im in images:
@@ -527,6 +522,13 @@ class BaseClassifier(BaseModel):
             batch_restore_list.append(restore_list)
         return batch_restore_list
 
+    def _check_transforms(self, transforms, mode):
+        super()._check_transforms(transforms, mode)
+        if not isinstance(transforms.arrange,
+                          paddlers.transforms.ArrangeClassifier):
+            raise TypeError(
+                "`transforms.arrange` must be an ArrangeClassifier object.")
+
 
 class ResNet50_vd(BaseClassifier):
     def __init__(self, num_classes=2, use_mixed_loss=False, **params):

paddlers/tasks/object_detector.py

@@ -31,7 +31,6 @@ from paddlers.transforms import decode_image
 from paddlers.transforms.operators import _NormalizeBox, _PadBox, _BboxXYXY2XYWH, Resize, Pad
 from paddlers.transforms.batch_operators import BatchCompose, BatchRandomResize, BatchRandomResizeByShort, \
     _BatchPad, _Gt2YoloTarget
-from paddlers.transforms import arrange_transforms
 from .base import BaseModel
 from .utils.det_metrics import VOCMetric, COCOMetric
 from paddlers.models.ppdet.optimizer import ModelEMA
@@ -452,10 +451,7 @@ class BaseDetector(BaseModel):
                 }
         eval_dataset.batch_transforms = self._compose_batch_transform(
             eval_dataset.transforms, mode='eval')
-        arrange_transforms(
-            model_type=self.model_type,
-            transforms=eval_dataset.transforms,
-            mode='eval')
+        self._check_transforms(eval_dataset.transforms, 'eval')
 
         self.net.eval()
         nranks = paddle.distributed.get_world_size()
@@ -545,8 +541,7 @@ class BaseDetector(BaseModel):
         return prediction
 
     def _preprocess(self, images, transforms, to_tensor=True):
-        arrange_transforms(
-            model_type=self.model_type, transforms=transforms, mode='test')
+        self._check_transforms(transforms, 'test')
         batch_samples = list()
         for im in images:
             if isinstance(im, str):
@@ -630,6 +625,13 @@ class BaseDetector(BaseModel):
 
         return results
 
+    def _check_transforms(self, transforms, mode):
+        super()._check_transforms(transforms, mode)
+        if not isinstance(transforms.arrange,
+                          paddlers.transforms.ArrangeDetector):
+            raise TypeError(
+                "`transforms.arrange` must be an ArrangeDetector object.")
+
 
 class PicoDet(BaseDetector):
     def __init__(self,

paddlers/tasks/segmenter.py

@@ -26,7 +26,6 @@ from paddle.static import InputSpec
 import paddlers.models.ppseg as paddleseg
 import paddlers.custom_models.seg as cmseg
 import paddlers
-from paddlers.transforms import arrange_transforms
 from paddlers.utils import get_single_card_bs, DisablePrint
 import paddlers.utils.logging as logging
 from .base import BaseModel
@@ -136,6 +135,11 @@ class BaseSegmenter(BaseModel):
             else:
                 pred = paddle.argmax(logit, axis=1, keepdim=True, dtype='int32')
             label = inputs[1]
+            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[2])[0]  # NCHW
@@ -380,10 +384,7 @@ class BaseSegmenter(BaseModel):
                  "category_F1-score": `F1 score`}.
 
         """
-        arrange_transforms(
-            model_type=self.model_type,
-            transforms=eval_dataset.transforms,
-            mode='eval')
+        self._check_transforms(eval_dataset.transforms, 'eval')
 
         self.net.eval()
         nranks = paddle.distributed.get_world_size()
@@ -606,8 +607,7 @@ class BaseSegmenter(BaseModel):
         print("GeoTiff saved in {}.".format(save_file))
 
     def _preprocess(self, images, transforms, to_tensor=True):
-        arrange_transforms(
-            model_type=self.model_type, transforms=transforms, mode='test')
+        self._check_transforms(transforms, 'test')
         batch_im = list()
         batch_ori_shape = list()
         for im in images:
@@ -746,6 +746,13 @@ class BaseSegmenter(BaseModel):
             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.ArrangeSegmenter):
+            raise TypeError(
+                "`transforms.arrange` must be an ArrangeSegmenter object.")
+
 
 class UNet(BaseSegmenter):
     def __init__(self,

paddlers/transforms/__init__.py

@@ -29,15 +29,19 @@ def decode_image(im_path,
     Decode an image.
     
     Args:
+        im_path (str): Path of the image to decode.
         to_rgb (bool, optional): If True, convert input image(s) from BGR format to RGB format. Defaults to True.
         to_uint8 (bool, optional): If True, quantize and convert decoded image(s) to uint8 type. Defaults to True.
         decode_bgr (bool, optional): If True, automatically interpret a non-geo image (e.g. jpeg images) as a BGR image. 
             Defaults to True.
         decode_sar (bool, optional): If True, automatically interpret a two-channel geo image (e.g. geotiff images) as a 
             SAR image, set this argument to True. Defaults to True.
+
+    Returns:
+        np.ndarray: Decoded image.
     """
 
-    # Do a presence check. `osp.exists` assumes `im_path` is a path-like object.
+    # Do a presence check. osp.exists() assumes `im_path` is a path-like object.
     if not osp.exists(im_path):
         raise ValueError(f"{im_path} does not exist!")
     decoder = T.DecodeImg(
@@ -51,36 +55,14 @@ def decode_image(im_path,
     return sample['image']
 
 
-def arrange_transforms(model_type, transforms, mode='train'):
-    # 给transforms添加arrange操作
-    if model_type == 'segmenter':
-        if mode == 'eval':
-            transforms.apply_im_only = True
-        else:
-            transforms.apply_im_only = False
-        arrange_transform = ArrangeSegmenter(mode)
-    elif model_type == 'changedetector':
-        if mode == 'eval':
-            transforms.apply_im_only = True
-        else:
-            transforms.apply_im_only = False
-        arrange_transform = ArrangeChangeDetector(mode)
-    elif model_type == 'classifier':
-        arrange_transform = ArrangeClassifier(mode)
-    elif model_type == 'detector':
-        arrange_transform = ArrangeDetector(mode)
-    else:
-        raise Exception("Unrecognized model type: {}".format(model_type))
-    transforms.arrange_outputs = arrange_transform
-
-
 def build_transforms(transforms_info):
     transforms = list()
     for op_info in transforms_info:
         op_name = list(op_info.keys())[0]
         op_attr = op_info[op_name]
         if not hasattr(T, op_name):
-            raise Exception("There's no transform named '{}'".format(op_name))
+            raise ValueError(
+                "There is no transform operator named '{}'.".format(op_name))
         transforms.append(getattr(T, op_name)(**op_attr))
     eval_transforms = T.Compose(transforms)
     return eval_transforms

paddlers/transforms/functions.py

@@ -21,6 +21,7 @@ from shapely.geometry import Polygon, MultiPolygon, GeometryCollection
 from sklearn.linear_model import LinearRegression
 from skimage import exposure
 from joblib import load
+from PIL import Image
 
 
 def normalize(im, mean, std, min_value=[0, 0, 0], max_value=[255, 255, 255]):
@@ -623,3 +624,19 @@ def inv_pca(im, joblib_path):
     r_im = pca.inverse_transform(n_im)
     r_im = np.reshape(r_im, (H, W, -1))
     return r_im
+
+
+def decode_seg_mask(mask_path):
+    """
+    Decode a segmentation mask image.
+    
+    Args:
+        mask_path (str): Path of the mask image to decode.
+
+    Returns:
+        np.ndarray: Decoded mask image.
+    """
+
+    mask = np.asarray(Image.open(mask_path))
+    mask = mask.astype('int64')
+    return mask

paddlers/transforms/operators.py

@@ -30,39 +30,21 @@ from PIL import Image
 from joblib import load
 
 import paddlers
-from .functions import normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly, \
-    horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly, vertical_flip_rle, crop_poly, \
-    crop_rle, expand_poly, expand_rle, resize_poly, resize_rle, dehaze, select_bands, \
-    to_intensity, to_uint8, img_flip, img_simple_rotate
+from .functions import (
+    normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly,
+    horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly,
+    vertical_flip_rle, crop_poly, crop_rle, expand_poly, expand_rle,
+    resize_poly, resize_rle, dehaze, select_bands, to_intensity, to_uint8,
+    img_flip, img_simple_rotate, decode_seg_mask)
 
 __all__ = [
-    "Compose",
-    "DecodeImg",
-    "Resize",
-    "RandomResize",
-    "ResizeByShort",
-    "RandomResizeByShort",
-    "ResizeByLong",
-    "RandomHorizontalFlip",
-    "RandomVerticalFlip",
-    "Normalize",
-    "CenterCrop",
-    "RandomCrop",
-    "RandomScaleAspect",
-    "RandomExpand",
-    "Pad",
-    "MixupImage",
-    "RandomDistort",
-    "RandomBlur",
-    "RandomSwap",
-    "Dehaze",
-    "ReduceDim",
-    "SelectBand",
-    "ArrangeSegmenter",
-    "ArrangeChangeDetector",
-    "ArrangeClassifier",
-    "ArrangeDetector",
-    "RandomFlipOrRotate",
+    "Compose", "DecodeImg", "Resize", "RandomResize", "ResizeByShort",
+    "RandomResizeByShort", "ResizeByLong", "RandomHorizontalFlip",
+    "RandomVerticalFlip", "Normalize", "CenterCrop", "RandomCrop",
+    "RandomScaleAspect", "RandomExpand", "Pad", "MixupImage", "RandomDistort",
+    "RandomBlur", "RandomSwap", "Dehaze", "ReduceDim", "SelectBand",
+    "ArrangeSegmenter", "ArrangeChangeDetector", "ArrangeClassifier",
+    "ArrangeDetector", "RandomFlipOrRotate", "ReloadMask"
 ]
 
 interp_dict = {
@@ -74,6 +56,71 @@ interp_dict = {
 }
 
 
+class Compose(object):
+    """
+    Apply a series of data augmentation strategies to the input.
+    All input images should be in Height-Width-Channel ([H, W, C]) format.
+
+    Args:
+        transforms (list[paddlers.transforms.Transform]): List of data preprocess or
+            augmentation operators.
+
+    Raises:
+        TypeError: Invalid type of transforms.
+        ValueError: Invalid length of transforms.
+    """
+
+    def __init__(self, transforms):
+        super(Compose, self).__init__()
+        if not isinstance(transforms, list):
+            raise TypeError(
+                "Type of transforms is invalid. Must be a list, but received is {}."
+                .format(type(transforms)))
+        if len(transforms) < 1:
+            raise ValueError(
+                "Length of transforms must not be less than 1, but received is {}."
+                .format(len(transforms)))
+        transforms = copy.deepcopy(transforms)
+        self.arrange = self._pick_arrange(transforms)
+        self.transforms = transforms
+
+    def __call__(self, sample):
+        """
+        This is equivalent to sequentially calling compose_obj.apply_transforms() 
+            and compose_obj.arrange_outputs().
+        """
+
+        sample = self.apply_transforms(sample)
+        sample = self.arrange_outputs(sample)
+        return sample
+
+    def apply_transforms(self, sample):
+        for op in self.transforms:
+            # Skip batch transforms amd mixup
+            if isinstance(op, (paddlers.transforms.BatchRandomResize,
+                               paddlers.transforms.BatchRandomResizeByShort,
+                               MixupImage)):
+                continue
+            sample = op(sample)
+        return sample
+
+    def arrange_outputs(self, sample):
+        if self.arrange is not None:
+            sample = self.arrange(sample)
+        return sample
+
+    def _pick_arrange(self, transforms):
+        arrange = None
+        for idx, op in enumerate(transforms):
+            if isinstance(op, Arrange):
+                if idx != len(transforms) - 1:
+                    raise ValueError(
+                        "Arrange operator must be placed at the end of the list."
+                    )
+                arrange = transforms.pop(idx)
+        return arrange
+
+
 class Transform(object):
     """
     Parent class of all data augmentation operations
@@ -178,14 +225,14 @@ class DecodeImg(Transform):
         elif ext == '.npy':
             return np.load(img_path)
         else:
-            raise TypeError('Image format {} is not supported!'.format(ext))
+            raise TypeError("Image format {} is not supported!".format(ext))
 
     def apply_im(self, im_path):
         if isinstance(im_path, str):
             try:
                 image = self.read_img(im_path)
             except:
-                raise ValueError('Cannot read the image file {}!'.format(
+                raise ValueError("Cannot read the image file {}!".format(
                     im_path))
         else:
             image = im_path
@@ -217,7 +264,9 @@ class DecodeImg(Transform):
         Returns:
             dict: Decoded sample.
         """
+
         if 'image' in sample:
+            sample['image_ori'] = copy.deepcopy(sample['image'])
             sample['image'] = self.apply_im(sample['image'])
         if 'image2' in sample:
             sample['image2'] = self.apply_im(sample['image2'])
@@ -227,6 +276,7 @@ class DecodeImg(Transform):
             sample['image'] = self.apply_im(sample['image_t1'])
             sample['image2'] = self.apply_im(sample['image_t2'])
         if 'mask' in sample:
+            sample['mask_ori'] = copy.deepcopy(sample['mask'])
             sample['mask'] = self.apply_mask(sample['mask'])
             im_height, im_width, _ = sample['image'].shape
             se_height, se_width = sample['mask'].shape
@@ -234,6 +284,7 @@ class DecodeImg(Transform):
                 raise ValueError(
                     "The height or width of the image is not same as the mask.")
         if 'aux_masks' in sample:
+            sample['aux_masks_ori'] = copy.deepcopy(sample['aux_masks_ori'])
             sample['aux_masks'] = list(
                 map(self.apply_mask, sample['aux_masks']))
             # TODO: check the shape of auxiliary masks
@@ -244,61 +295,6 @@ class DecodeImg(Transform):
         return sample
 
 
-class Compose(Transform):
-    """
-    Apply a series of data augmentation to the input.
-    All input images are in Height-Width-Channel ([H, W, C]) format.
-
-    Args:
-        transforms (list[paddlers.transforms.Transform]): List of data preprocess or augmentations.
-    Raises:
-        TypeError: Invalid type of transforms.
-        ValueError: Invalid length of transforms.
-    """
-
-    def __init__(self, transforms, to_uint8=True):
-        super(Compose, self).__init__()
-        if not isinstance(transforms, list):
-            raise TypeError(
-                'Type of transforms is invalid. Must be a list, but received is {}'
-                .format(type(transforms)))
-        if len(transforms) < 1:
-            raise ValueError(
-                'Length of transforms must not be less than 1, but received is {}'
-                .format(len(transforms)))
-        self.transforms = transforms
-        self.decode_image = DecodeImg(to_uint8=to_uint8)
-        self.arrange_outputs = None
-        self.apply_im_only = False
-
-    def __call__(self, sample):
-        if self.apply_im_only:
-            if 'mask' in sample:
-                mask_backup = copy.deepcopy(sample['mask'])
-                del sample['mask']
-            if 'aux_masks' in sample:
-                aux_masks = copy.deepcopy(sample['aux_masks'])
-
-        sample = self.decode_image(sample)
-
-        for op in self.transforms:
-            # skip batch transforms amd mixup
-            if isinstance(op, (paddlers.transforms.BatchRandomResize,
-                               paddlers.transforms.BatchRandomResizeByShort,
-                               MixupImage)):
-                continue
-            sample = op(sample)
-
-        if self.arrange_outputs is not None:
-            if self.apply_im_only:
-                sample['mask'] = mask_backup
-                if 'aux_masks' in locals():
-                    sample['aux_masks'] = aux_masks
-            sample = self.arrange_outputs(sample)
-
-        return sample
-
-
 class Resize(Transform):
     """
     Resize input.
@@ -323,7 +319,7 @@ class Resize(Transform):
     def __init__(self, target_size, interp='LINEAR', keep_ratio=False):
         super(Resize, self).__init__()
         if not (interp == "RANDOM" or interp in interp_dict):
-            raise ValueError("interp should be one of {}".format(
+            raise ValueError("`interp` should be one of {}.".format(
                 interp_dict.keys()))
         if isinstance(target_size, int):
             target_size = (target_size, target_size)
@@ -331,7 +327,7 @@ class Resize(Transform):
             if not (isinstance(target_size,
                                (list, tuple)) and len(target_size) == 2):
                 raise TypeError(
-                    "target_size should be an int or a list of length 2, but received {}".
+                    "`target_size` should be an int or a list of length 2, but received {}.".
                     format(target_size))
         # (height, width)
         self.target_size = target_size
@@ -443,11 +439,11 @@ class RandomResize(Transform):
     def __init__(self, target_sizes, interp='LINEAR'):
         super(RandomResize, self).__init__()
         if not (interp == "RANDOM" or interp in interp_dict):
-            raise ValueError("interp should be one of {}".format(
+            raise ValueError("`interp` should be one of {}.".format(
                 interp_dict.keys()))
         self.interp = interp
         assert isinstance(target_sizes, list), \
-            "target_size must be a list."
+            "`target_size` must be a list."
         for i, item in enumerate(target_sizes):
             if isinstance(item, int):
                 target_sizes[i] = (item, item)
@@ -478,7 +474,7 @@ class ResizeByShort(Transform):
 
     def __init__(self, short_size=256, max_size=-1, interp='LINEAR'):
         if not (interp == "RANDOM" or interp in interp_dict):
-            raise ValueError("interp should be one of {}".format(
+            raise ValueError("`interp` should be one of {}".format(
                 interp_dict.keys()))
         super(ResizeByShort, self).__init__()
         self.short_size = short_size
@@ -522,11 +518,11 @@ class RandomResizeByShort(Transform):
     def __init__(self, short_sizes, max_size=-1, interp='LINEAR'):
         super(RandomResizeByShort, self).__init__()
         if not (interp == "RANDOM" or interp in interp_dict):
-            raise ValueError("interp should be one of {}".format(
+            raise ValueError("`interp` should be one of {}".format(
                 interp_dict.keys()))
         self.interp = interp
         assert isinstance(short_sizes, list), \
-            "short_sizes must be a list."
+            "`short_sizes` must be a list."
 
         self.short_sizes = short_sizes
         self.max_size = max_size
@@ -574,6 +570,7 @@ class RandomFlipOrRotate(Transform):
 
         # 定义数据增强
         train_transforms = T.Compose([
+            T.DecodeImg(),
             T.RandomFlipOrRotate(
                 probs  = [0.3, 0.2]             # 进行flip增强的概率是0.3，进行rotate增强的概率是0.2，不变的概率是0.5
                 probsf = [0.3, 0.25, 0, 0, 0]   # flip增强时，使用水平flip、垂直flip的概率分别是0.3、0.25，水平且垂直flip、对角线flip、反对角线flip概率均为0，不变的概率是0.45
@@ -609,12 +606,12 @@ class RandomFlipOrRotate(Transform):
 
     def apply_bbox(self, bbox, mode_id, flip_mode=True):
         raise TypeError(
-            "Currently, `paddlers.transforms.RandomFlipOrRotate` is not available for object detection tasks."
+            "Currently, RandomFlipOrRotate is not available for object detection tasks."
         )
 
     def apply_segm(self, bbox, mode_id, flip_mode=True):
         raise TypeError(
-            "Currently, `paddlers.transforms.RandomFlipOrRotate` is not available for object detection tasks."
+            "Currently, RandomFlipOrRotate is not available for object detection tasks."
         )
 
     def get_probs_range(self, probs):
@@ -845,11 +842,11 @@ class Normalize(Transform):
         from functools import reduce
         if reduce(lambda x, y: x * y, std) == 0:
             raise ValueError(
-                'Std should not contain 0, but received is {}.'.format(std))
+                "`std` should not contain 0, but received is {}.".format(std))
         if reduce(lambda x, y: x * y,
                   [a - b for a, b in zip(max_val, min_val)]) == 0:
             raise ValueError(
-                '(max_val - min_val) should not contain 0, but received is {}.'.
+                "(`max_val` - `min_val`) should not contain 0, but received is {}.".
                 format((np.asarray(max_val) - np.asarray(min_val)).tolist()))
 
         self.mean = mean
@@ -1153,11 +1150,11 @@ class RandomExpand(Transform):
                  im_padding_value=127.5,
                  label_padding_value=255):
         super(RandomExpand, self).__init__()
-        assert upper_ratio > 1.01, "expand ratio must be larger than 1.01"
+        assert upper_ratio > 1.01, "`upper_ratio` must be larger than 1.01."
         self.upper_ratio = upper_ratio
         self.prob = prob
         assert isinstance(im_padding_value, (Number, Sequence)), \
-            "fill value must be either float or sequence"
+            "Value to fill must be either float or sequence."
         self.im_padding_value = im_padding_value
         self.label_padding_value = label_padding_value
 
@@ -1204,16 +1201,16 @@ class Pad(Transform):
         if isinstance(target_size, (list, tuple)):
             if len(target_size) != 2:
                 raise ValueError(
-                    '`target_size` should include 2 elements, but it is {}'.
+                    "`target_size` should contain 2 elements, but it is {}.".
                     format(target_size))
         if isinstance(target_size, int):
             target_size = [target_size] * 2
 
         assert pad_mode in [
             -1, 0, 1, 2
-        ], 'currently only supports four modes [-1, 0, 1, 2]'
+        ], "Currently only four modes are supported: [-1, 0, 1, 2]."
         if pad_mode == -1:
-            assert offsets, 'if pad_mode is -1, offsets should not be None'
+            assert offsets, "if `pad_mode` is -1, `offsets` should not be None."
 
         self.target_size = target_size
         self.size_divisor = size_divisor
@@ -1314,9 +1311,9 @@ class MixupImage(Transform):
         """
         super(MixupImage, self).__init__()
         if alpha <= 0.0:
-            raise ValueError("alpha should be positive in {}".format(self))
+            raise ValueError("`alpha` should be positive in MixupImage.")
         if beta <= 0.0:
-            raise ValueError("beta should be positive in {}".format(self))
+            raise ValueError("`beta` should be positive in MixupImage.")
         self.alpha = alpha
         self.beta = beta
         self.mixup_epoch = mixup_epoch
@@ -1753,55 +1750,56 @@ class RandomSwap(Transform):
 
     def apply(self, sample):
         if 'image2' not in sample:
-            raise ValueError('image2 is not found in the sample.')
+            raise ValueError("'image2' is not found in the sample.")
         if random.random() < self.prob:
             sample['image'], sample['image2'] = sample['image2'], sample[
                 'image']
         return sample
 
 
-class ArrangeSegmenter(Transform):
+class ReloadMask(Transform):
+    def apply(self, sample):
+        sample['mask'] = decode_seg_mask(sample['mask_ori'])
+        if 'aux_masks' in sample:
+            sample['aux_masks'] = list(
+                map(decode_seg_mask, sample['aux_masks_ori']))
+        return sample
+
+
+class Arrange(Transform):
     def __init__(self, mode):
-        super(ArrangeSegmenter, self).__init__()
+        super().__init__()
         if mode not in ['train', 'eval', 'test', 'quant']:
             raise ValueError(
-                "mode should be defined as one of ['train', 'eval', 'test', 'quant']!"
+                "`mode` should be defined as one of ['train', 'eval', 'test', 'quant']!"
             )
         self.mode = mode
 
+
+class ArrangeSegmenter(Arrange):
     def apply(self, sample):
         if 'mask' in sample:
             mask = sample['mask']
+            mask = mask.astype('int64')
 
         image = permute(sample['image'], False)
         if self.mode == 'train':
-            mask = mask.astype('int64')
             return image, mask
         if self.mode == 'eval':
-            mask = np.asarray(Image.open(mask))
-            mask = mask[np.newaxis, :, :].astype('int64')
             return image, mask
         if self.mode == 'test':
             return image,
 
 
-class ArrangeChangeDetector(Transform):
-    def __init__(self, mode):
-        super(ArrangeChangeDetector, self).__init__()
-        if mode not in ['train', 'eval', 'test', 'quant']:
-            raise ValueError(
-                "mode should be defined as one of ['train', 'eval', 'test', 'quant']!"
-            )
-        self.mode = mode
-
+class ArrangeChangeDetector(Arrange):
     def apply(self, sample):
         if 'mask' in sample:
             mask = sample['mask']
+            mask = mask.astype('int64')
 
         image_t1 = permute(sample['image'], False)
         image_t2 = permute(sample['image2'], False)
         if self.mode == 'train':
-            mask = mask.astype('int64')
             masks = [mask]
             if 'aux_masks' in sample:
                 masks.extend(
@@ -1810,22 +1808,12 @@ class ArrangeChangeDetector(Transform):
                 image_t1,
                 image_t2, ) + tuple(masks)
         if self.mode == 'eval':
-            mask = np.asarray(Image.open(mask))
-            mask = mask[np.newaxis, :, :].astype('int64')
             return image_t1, image_t2, mask
         if self.mode == 'test':
             return image_t1, image_t2,
 
 
-class ArrangeClassifier(Transform):
-    def __init__(self, mode):
-        super(ArrangeClassifier, self).__init__()
-        if mode not in ['train', 'eval', 'test', 'quant']:
-            raise ValueError(
-                "mode should be defined as one of ['train', 'eval', 'test', 'quant']!"
-            )
-        self.mode = mode
-
+class ArrangeClassifier(Arrange):
     def apply(self, sample):
         image = permute(sample['image'], False)
         if self.mode in ['train', 'eval']:
@@ -1834,15 +1822,7 @@ class ArrangeClassifier(Transform):
             return image
 
 
-class ArrangeDetector(Transform):
-    def __init__(self, mode):
-        super(ArrangeDetector, self).__init__()
-        if mode not in ['train', 'eval', 'test', 'quant']:
-            raise ValueError(
-                "mode should be defined as one of ['train', 'eval', 'test', 'quant']!"
-            )
-        self.mode = mode
-
+class ArrangeDetector(Arrange):
     def apply(self, sample):
         if self.mode == 'eval' and 'gt_poly' in sample:
             del sample['gt_poly']

paddlers/utils/__init__.py

@@ -15,8 +15,8 @@
 from . import logging
 from . import utils
 from .utils import (seconds_to_hms, get_encoding, get_single_card_bs, dict2str,
-                    EarlyStop, path_normalization, is_pic, MyEncoder,
-                    DisablePrint, Timer)
+                    EarlyStop, norm_path, is_pic, MyEncoder, DisablePrint,
+                    Timer)
 from .checkpoint import get_pretrain_weights, load_pretrain_weights, load_checkpoint
 from .env import get_environ_info, get_num_workers, init_parallel_env
 from .download import download_and_decompress, decompress

paddlers/utils/utils.py

@@ -69,7 +69,7 @@ def dict2str(dict_input):
     return out.strip(', ')
 
 
-def path_normalization(path):
+def norm_path(path):
     win_sep = "\\"
     other_sep = "/"
     if platform.system() == "Windows":

tests/data/data_utils.py

@@ -325,7 +325,7 @@ class ConstrDetSample(ConstrSample):
 
 def build_input_from_file(file_list, prefix='', task='auto', label_list=None):
     """
-    Construct a list of dictionaries from file. Each dict in the list can be used as the input to `paddlers.transforms.Transform` objects.
+    Construct a list of dictionaries from file. Each dict in the list can be used as the input to paddlers.transforms.Transform objects.
 
     Args:
         file_list (str): Path of file_list.

tests/deploy/test_predictor.py

@@ -120,7 +120,10 @@ class TestCDPredictor(TestPredictor):
         t2_path = "data/ssmt/optical_t2.bmp"
         single_input = (t1_path, t2_path)
         num_inputs = 2
-        transforms = pdrs.transforms.Compose([pdrs.transforms.Normalize()])
+        transforms = pdrs.transforms.Compose([
+            pdrs.transforms.DecodeImg(), pdrs.transforms.Normalize(),
+            pdrs.transforms.ArrangeChangeDetector('test')
+        ])
 
         # Expected failure
         with self.assertRaises(ValueError):
@@ -184,7 +187,10 @@ class TestClasPredictor(TestPredictor):
     def check_predictor(self, predictor, trainer):
         single_input = "data/ssmt/optical_t1.bmp"
         num_inputs = 2
-        transforms = pdrs.transforms.Compose([pdrs.transforms.Normalize()])
+        transforms = pdrs.transforms.Compose([
+            pdrs.transforms.DecodeImg(), pdrs.transforms.Normalize(),
+            pdrs.transforms.ArrangeClassifier('test')
+        ])
         labels = list(range(2))
         trainer.labels = labels
         predictor._model.labels = labels
@@ -249,7 +255,10 @@ class TestDetPredictor(TestPredictor):
         # given that the network is (partially?) randomly initialized.
         single_input = "data/ssmt/optical_t1.bmp"
         num_inputs = 2
-        transforms = pdrs.transforms.Compose([pdrs.transforms.Normalize()])
+        transforms = pdrs.transforms.Compose([
+            pdrs.transforms.DecodeImg(), pdrs.transforms.Normalize(),
+            pdrs.transforms.ArrangeDetector('test')
+        ])
         labels = list(range(80))
         trainer.labels = labels
         predictor._model.labels = labels
@@ -303,7 +312,10 @@ class TestSegPredictor(TestPredictor):
     def check_predictor(self, predictor, trainer):
         single_input = "data/ssmt/optical_t1.bmp"
         num_inputs = 2
-        transforms = pdrs.transforms.Compose([pdrs.transforms.Normalize()])
+        transforms = pdrs.transforms.Compose([
+            pdrs.transforms.DecodeImg(), pdrs.transforms.Normalize(),
+            pdrs.transforms.ArrangeSegmenter('test')
+        ])
 
         # Single input (file path)
         input_ = single_input

tests/download_test_data.sh

@@ -4,7 +4,7 @@ function remove_dir_if_exist() {
     local dir="$1"
     if [ -d "${dir}" ]; then
         rm -rf "${dir}"
-        echo "\033[0;31mDirectory ${dir} has been removed.\033[0m"
+        echo -e "\033[0;31mDirectory ${dir} has been removed.\033[0m"
     fi
 }
 

tests/test_tutorials.py

@@ -29,7 +29,7 @@ class TestTutorial(CpuCommonTest):
     @classmethod
     def setUpClass(cls):
         cls._td = tempfile.TemporaryDirectory(dir='./')
-        # Recursively copy the content of `cls.SUBDIR` to td.
+        # Recursively copy the content of cls.SUBDIR to td.
         # This is necessary for running scripts in td.
         cls._TSUBDIR = osp.join(cls._td.name, osp.basename(cls.SUBDIR))
         shutil.copytree(cls.SUBDIR, cls._TSUBDIR)
@@ -47,7 +47,7 @@ class TestTutorial(CpuCommonTest):
 
         def _test_tutorial(script_name):
             def _test_tutorial_impl(self):
-                # Set working directory to `cls._TSUBDIR` such that the 
+                # Set working directory to cls._TSUBDIR such that the 
                 # files generated by the script will be automatically cleaned.
                 run_script(f"python {script_name}", wd=cls._TSUBDIR)
 

tools/raster2geotiff.py

@@ -46,7 +46,7 @@ def convert_data(image_path, geojson_path):
             geo_points = geo["coordinates"][0][0]
         else:
             raise TypeError(
-                "Geometry type must be `Polygon` or `MultiPolygon`, not {}.".
+                "Geometry type must be 'Polygon' or 'MultiPolygon', not {}.".
                 format(geo["type"]))
         xy_points = np.array([
             _gt_convert(point[0], point[1], raster.geot) for point in geo_points

tools/raster2vector.py

@@ -76,7 +76,7 @@ def raster2vector(srcimg_path, mask_path, save_path, ignore_index=255):
     vec_ext = save_path.split(".")[-1].lower()
     if vec_ext not in ["json", "geojson", "shp"]:
         raise ValueError(
-            "The ext of `save_path` must be `json/geojson` or `shp`, not {}.".
+            "The extension of `save_path` must be 'json/geojson' or 'shp', not {}.".
             format(vec_ext))
     ras_ext = srcimg_path.split(".")[-1].lower()
     if osp.exists(srcimg_path) and ras_ext in ["tif", "tiff", "geotiff", "img"]:
@@ -93,7 +93,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument("--mask_path", type=str, required=True, \
                     help="Path of mask data.")
 parser.add_argument("--save_path", type=str, required=True, \
-                    help="Path to save the shape file (the file suffix is `.json/geojson` or `.shp`).")
+                    help="Path to save the shape file (the extension is .json/geojson or .shp).")
 parser.add_argument("--srcimg_path", type=str, default="", \
                     help="Path of original data with geoinfo. Default to empty.")
 parser.add_argument("--ignore_index", type=int, default=255, \

tools/split.py

@@ -75,7 +75,7 @@ parser.add_argument("--mask_path", type=str, default=None, \
 parser.add_argument("--block_size", type=int, default=512, \
                     help="Size of image block. Default value is 512.")
 parser.add_argument("--save_dir", type=str, default="dataset", \
-                    help="Directory to save the results. Default value is `dataset`.")
+                    help="Directory to save the results. Default value is 'dataset'.")
 
 if __name__ == "__main__":
     args = parser.parse_args()

tools/utils/raster.py

@@ -42,7 +42,7 @@ def _get_type(type_name: str) -> int:
     elif type_name == "complex64":
         gdal_type = gdal.GDT_CFloat64
     else:
-        raise TypeError("Non-suported data type `{}`.".format(type_name))
+        raise TypeError("Non-suported data type {}.".format(type_name))
     return gdal_type
 
 
@@ -76,7 +76,7 @@ class Raster:
                         # https://www.osgeo.cn/gdal/drivers/raster/index.html
                         self._src_data = gdal.Open(path)
                     except:
-                        raise TypeError("Unsupported data format: `{}`".format(
+                        raise TypeError("Unsupported data format: {}".format(
                             self.ext_type))
             else:
                 raise ValueError("The path {0} not exists.".format(path))

tutorials/train/change_detection/bit.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/cdnet.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/dsamnet.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/dsifn.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/fc_ef.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/fc_siam_conc.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/fc_siam_diff.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/snunet.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/change_detection/stanet.py

@@ -23,6 +23,8 @@ pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 随机裁剪
     T.RandomCrop(
         # 裁剪区域将被缩放到256x256
@@ -36,12 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/classification/condensenetv2_b_rs_mul.py

@@ -3,18 +3,21 @@ from paddlers import transforms as T
 
 # 定义训练和验证时的transforms
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     T.SelectBand([5, 10, 15, 20, 25]),  # for tet
     T.Resize(target_size=224),
     T.RandomHorizontalFlip(),
     T.Normalize(
         mean=[0.5, 0.5, 0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('train')
 ])
 
 eval_transforms = T.Compose([
-    T.SelectBand([5, 10, 15, 20, 25]),
-    T.Resize(target_size=224),
+    T.DecodeImg(), T.SelectBand([5, 10, 15, 20, 25]), T.Resize(target_size=224),
     T.Normalize(
-        mean=[0.5, 0.5, 0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5, 0.5, 0.5]),
+        mean=[0.5, 0.5, 0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5, 0.5, 0.5]), T.ArrangeClassifier('eval')
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/classification/hrnet.py

@@ -27,6 +27,8 @@ pdrs.utils.download_and_decompress(ucmerced_dataset, path=DOWNLOAD_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 将影像缩放到256x256大小
     T.Resize(target_size=256),
     # 以50%的概率实施随机水平翻转
@@ -36,13 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     T.Resize(target_size=256),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/classification/mobilenetv3.py

@@ -27,6 +27,8 @@ pdrs.utils.download_and_decompress(ucmerced_dataset, path=DOWNLOAD_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 将影像缩放到256x256大小
     T.Resize(target_size=256),
     # 以50%的概率实施随机水平翻转
@@ -36,13 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     T.Resize(target_size=256),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/classification/resnet50_vd.py

@@ -27,6 +27,8 @@ pdrs.utils.download_and_decompress(ucmerced_dataset, path=DOWNLOAD_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 将影像缩放到256x256大小
     T.Resize(target_size=256),
     # 以50%的概率实施随机水平翻转
@@ -36,13 +38,16 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     T.Resize(target_size=256),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeClassifier('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/object_detection/faster_rcnn.py

@@ -30,6 +30,8 @@ if not os.path.exists(DATA_DIR):
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 对输入影像施加随机色彩扰动
     T.RandomDistort(),
     # 在影像边界进行随机padding
@@ -44,16 +46,19 @@ train_transforms = T.Compose([
         interp='RANDOM'),
     # 影像归一化
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 使用双三次插值将输入影像缩放到固定大小
     T.Resize(
         target_size=608, interp='CUBIC'),
     # 验证阶段与训练阶段的归一化方式必须相同
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/object_detection/ppyolo.py

@@ -31,6 +31,8 @@ if not os.path.exists(DATA_DIR):
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 对输入影像施加随机色彩扰动
     T.RandomDistort(),
     # 在影像边界进行随机padding
@@ -45,16 +47,19 @@ train_transforms = T.Compose([
         interp='RANDOM'),
     # 影像归一化
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 使用双三次插值将输入影像缩放到固定大小
     T.Resize(
         target_size=608, interp='CUBIC'),
     # 验证阶段与训练阶段的归一化方式必须相同
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/object_detection/ppyolotiny.py

@@ -31,6 +31,8 @@ if not os.path.exists(DATA_DIR):
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 对输入影像施加随机色彩扰动
     T.RandomDistort(),
     # 在影像边界进行随机padding
@@ -45,16 +47,19 @@ train_transforms = T.Compose([
         interp='RANDOM'),
     # 影像归一化
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 使用双三次插值将输入影像缩放到固定大小
     T.Resize(
         target_size=608, interp='CUBIC'),
     # 验证阶段与训练阶段的归一化方式必须相同
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/object_detection/ppyolov2.py

@@ -31,6 +31,8 @@ if not os.path.exists(DATA_DIR):
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 对输入影像施加随机色彩扰动
     T.RandomDistort(),
     # 在影像边界进行随机padding
@@ -45,16 +47,19 @@ train_transforms = T.Compose([
         interp='RANDOM'),
     # 影像归一化
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 使用双三次插值将输入影像缩放到固定大小
     T.Resize(
         target_size=608, interp='CUBIC'),
     # 验证阶段与训练阶段的归一化方式必须相同
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/object_detection/yolov3.py

@@ -31,6 +31,8 @@ if not os.path.exists(DATA_DIR):
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 对输入影像施加随机色彩扰动
     T.RandomDistort(),
     # 在影像边界进行随机padding
@@ -45,16 +47,19 @@ train_transforms = T.Compose([
         interp='RANDOM'),
     # 影像归一化
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     # 使用双三次插值将输入影像缩放到固定大小
     T.Resize(
         target_size=608, interp='CUBIC'),
     # 验证阶段与训练阶段的归一化方式必须相同
     T.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    T.ArrangeDetector('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/semantic_segmentation/deeplabv3p.py

@@ -30,6 +30,8 @@ pdrs.utils.download_and_decompress(seg_dataset, path=DOWNLOAD_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 将影像缩放到512x512大小
     T.Resize(target_size=512),
     # 以50%的概率实施随机水平翻转
@@ -37,13 +39,17 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+    T.ArrangeSegmenter('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     T.Resize(target_size=512),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+    T.ReloadMask(),
+    T.ArrangeSegmenter('eval')
 ])
 
 # 分别构建训练和验证所用的数据集

tutorials/train/semantic_segmentation/unet.py

@@ -30,6 +30,8 @@ pdrs.utils.download_and_decompress(seg_dataset, path=DOWNLOAD_DIR)
 # 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
 # API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
 train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
     # 将影像缩放到512x512大小
     T.Resize(target_size=512),
     # 以50%的概率实施随机水平翻转
@@ -37,13 +39,17 @@ train_transforms = T.Compose([
     # 将数据归一化到[-1,1]
     T.Normalize(
         mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+    T.ArrangeSegmenter('train')
 ])
 
 eval_transforms = T.Compose([
+    T.DecodeImg(),
     T.Resize(target_size=512),
     # 验证阶段与训练阶段的数据归一化方式必须相同
     T.Normalize(
         mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+    T.ReloadMask(),
+    T.ArrangeSegmenter('eval')
 ])
 
 # 分别构建训练和验证所用的数据集