Add training tutorials for all implemented segmentation methods. Since currently, UNet is the only method that handles multispectral (more than three bands) images, the tutorials of the other methods are based on a non-remote-sensing dataset (i.e. the optic_disc_seg dataset). The tutorial of UNet is based on a landcover classification dataset. Test passes locally.

晖哥根据昨天我们关于pca的讨论，今天我尝试修改了一下，首先是tools中有个pca.py，可以对一张hsi图像进行pca降维，在保存结果的同时保存pca的参数，如下

但是transform里面还是保留了，作用是比如推理的时候，可以加载这个pca参数进行推理，加载和保存都是这个*.joblib的文件，一个test.py如下：

import paddlers as pdrs
from paddlers import transforms as T
import cv2

transforms = T.Compose([
    T.DimReducing("pcatest/output/UINT16_HSI_pca.joblib")
])

dataset = pdrs.datasets.ClasDataset(
    data_dir='pcatest',  # 里面只有一张图
    file_list='pcatest/list.txt',  # 里面只有这张图的名字和一个标签
    transforms=transforms
)

for data in dataset:
    img = data["image"]
    print(img.shape)
    cv2.namedWindow('img', cv2.WINDOW_NORMAL)
    cv2.imshow("img", cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

测试这样的pca

晖哥看看这样可以吗。中间我比较困惑是我应不应该转uint8两次，首先是现在ImgDecoder是默认用了to_uint8，我想如果训练pca的时候没有这个就会导致不同我就用了，然后就是得到的结果我看有是float64有正有负，如果不to_uint8一下会不会影响后面的Normalize，所以我也用了。

还有关于save_tiff这里有一些重复，和geojson分支，我想等后面这两个合并了我再删一下，顺便再更新文档。

The same error has been reported when importing paddlers. I have tried many times to create a new virtual environment and reinstall it, but it still does not solve it. Is there a better solution? Thank you very much for your help.

利用RS 训练了HRNet_W18_C模型，模型训练、导出都正常，就是用导出的模型做预测的时候出现了错误。 Traceback (most recent call last): File "demo.py", line 52, in result = predictor.predict(img_file=img1,transforms=eval_transforms) File "/home/aistudio/PaddleRS/paddlers/deploy/predictor.py", line 279, in predict results = self._run(images=images, topk=topk, transforms=transforms) File "/home/aistudio/PaddleRS/paddlers/deploy/predictor.py", line 235, in _run transforms=transforms) File "/home/aistudio/PaddleRS/paddlers/deploy/predictor.py", line 175, in postprocess preds = self._model._postprocess(net_outputs, true_topk) TypeError: 'NoneType' object is not callable 请问这个错误怎么处理

1. Fix typos and bugs in paddlers/datasets/cd_dataset.py and paddlers/utils/utils.py.
2. Update readme file for training tutorials.
3. Add training tutorials for classification tasks. The training of the classification models is based on the UC Merced land use dataset.

add RandomFlipOrRotation in paddlers/transforms/operators.py add some functions in paddlers/transforms/functions.py add some files that work by json library to show statistics information of images or annotations, and to merge, spilt, get new json file

1. Change the parameter initialization method of FC-EF, FC-Siam-conc, and FC-Siam-diff, such that these networks can be trained to obtained higher detection accuracy.
2. Add training tutorials for all implemented CD methods, except for ChangeStar. The training tutorial of ChangeStar is to be added later, because I have not yet found a suitable dataset. For the other CD methods, the training is performed on the SZTAKI AirChange Benchmark Set.

paddle.optimizer.lr.ReduceOnPlateau.step(metrics, epoch=None), it require metrics that other paddle.optimizer.lr doesn't;

when you use ReduceOnPlateau as learning rate scheduler like this:

model = pdrs.tasks.BIT(... lr_scheduler = paddle.optimizer.lr.ReduceOnPlateau( LR, ) optimizer = paddle.optimizer.Adam( learning_rate=lr_scheduler, parameters=model.net.parameters() ) model.train(...

it will rise a TypeError: step() missing 1 required positional argument: 'metrics'

1. 按照之前与 @LutaoChu 的商讨结果，修改了Segmenter与ChangeDetector的use_mixed_loss相关逻辑和接口。目前支持API调用者直接传入构建好的loss对象。为了让API使用起来更加便捷，支持传入布尔型、列表、字典、或者loss对象这四种不同的类型。
2. 目前所有的变化检测模型（包括使用了深度监督的模型和多任务模型）均已支持用户自定义的use_mixed_loss参数。

问题：目前的实现进一步复杂化了use_mixed_loss，加重了这个输入参数本来就存在的“名称与行为不一致”的问题。是否考虑拆成多个参数或者更名？

在进行多光谱分类中遇到一些问题，进行了一些修改，项目可见https://aistudio.baidu.com/aistudio/projectdetail/3711240。

1. 现在default_metric的初始化放在train中的，要调用evaluate必须先调用train，否则会报错。所以现在将default_metric放到task的初始化中，这看起来不会对其他有影响。
2. 修改了changedetector中的一个命名的问题。
3. 增加了load的方法来加载参数，方便进行推理，就不用再用model.net.set_dict(xxx)这样的方式加载参数了。
4. 修复了一些predict的问题，之前无法预测（主要是那个加[0]后的形状问题和postprocess的问题）。
5. 为clas的task增加了一个label_list的初始化参数，目前label_list来自train时从train_data.label_list中得到，如果不先调用train就无法预测。这里还可以看看怎么做会好点。

model=pdrs.tasks.ChangeStar( num_classes=2, use_mixed_loss=False, mid_channels=256, inner_channels=16, num_convs=4, scale_factor=4.0, )

TypeError Traceback (most recent call last) /tmp/ipykernel_160/3661388624.py in 5 inner_channels=16, 6 num_convs=4, ----> 7 scale_factor=4.0, 8 )

~/PaddleRS/paddlers/tasks/changedetector.py in init(self, num_classes, use_mixed_loss, mid_channels, inner_channels, num_convs, scale_factor, **params) 883 num_classes=num_classes, 884 use_mixed_loss=use_mixed_loss, --> 885 **params) 886 887 def default_loss(self):

~/PaddleRS/paddlers/tasks/changedetector.py in init(self, model_name, num_classes, use_mixed_loss, **params) 61 if params.get('with_net', True): 62 params.pop('with_net', None) ---> 63 self.net = self.build_net(**params) 64 self.find_unused_parameters = True 65

~/PaddleRS/paddlers/tasks/changedetector.py in build_net(self, **params) 67 # TODO: add other model 68 net = cmcd.dict[self.model_name](num_classes=self.num_classes, ---> 69 **params) 70 return net 71

~/PaddleRS/paddlers/custom_models/cd/changestar.py in init(self, num_classes, mid_channels, inner_channels, num_convs, scale_factor) 138 inner_channels=inner_channels, 139 num_convs=num_convs, --> 140 scale_factor=scale_factor) 141 142

~/PaddleRS/paddlers/custom_models/cd/changestar.py in init(self, seg_model, num_classes, mid_channels, inner_channels, num_convs, scale_factor) 29 def init(self, seg_model, num_classes, mid_channels, inner_channels, 30 num_convs, scale_factor): ---> 31 super(_ChangeStarBase, self).init(_ChangeStarBase, self) 32 33 self.extract = seg_model

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py in init(self, name_scope, dtype) 103 if name_scope is None: 104 name_scope = _convert_camel_to_snake(self.class.name) --> 105 self._full_name = unique_name.generate(name_scope) 106 self._helper = LayerObjectHelper(self._full_name) 107 self._built = False

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/unique_name.py in generate(key) 103 print(name1, name2) # fc_0, fc_1 104 """ --> 105 return generator(key) 106 107

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/unique_name.py in call(self, key) 49 tmp = self.ids[key] 50 self.ids[key] += 1 ---> 51 return self.prefix + "_".join([key, str(tmp)]) 52 53

TypeError: sequence item 0: expected str instance, type found

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

(paddle_env) E:\PaddleRS>python setup.py install Traceback (most recent call last): File "setup.py", line 16, in import paddlers File "E:\PaddleRS\paddlers_init_.py", line 17, in from paddlers.utils.env import get_environ_info, init_parallel_env File "E:\PaddleRS\paddlers\utils_init_.py", line 28, in from .visualize import map_display File "E:\PaddleRS\paddlers\utils\visualize.py", line 26, in from paddlers.transforms.functions import to_uint8 File "E:\PaddleRS\paddlers\transforms_init_.py", line 18, in from .operators import * File "E:\PaddleRS\paddlers\transforms\operators.py", line 30, in import paddlers.transforms.functions as F AttributeError: module 'paddlers' has no attribute 'transforms'