该项目为遥感影像地块分割四分类任务，获常规赛4月第八名。基于PaddleSeg，用OCRNet模型（HRNet为backbone），按0.97:0.03划分训练集与验证集。训练配置含batch_size=64等，用联合损失改善类别不平衡，后修改代码测试并保存结果，压缩输出。
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赛题任务 本赛题旨在对遥感影像进行像素级内容解析，并对遥感影像中感兴趣的类别进行提取和分类，以衡量遥感影像地块分割模型在多个类别（如建筑、道路、林地等）上的效果。
数据说明 本赛题提供了多个地区已脱敏的遥感影像数据，各参赛选手可以基于这些数据构建自己的地块分割模型。
训练数据集 样例图片及其标注如下图所示：
训练数据集文件名称：train_and_label.zip 包含2个子文件，分别为：训练数据集（原始图片）文件、训练数据集（标注图片）文件，详细介绍如下：
训练数据集（原始图片）文件名称：img_train 包含66,653张分辨率为2m/pixel，尺寸为256 * 256的JPG图片，每张图片的名称形如T000123.jpg。
训练数据集（标注图片）文件名称：lab_train 包含66,653张分辨率为2m/pixel，尺寸为256 * 256的PNG图片，每张图片的名称形如T000123.png。 备注： 全部PNG图片共包括4种分类，像素值分别为0、1、2、3。此外，像素值255为未标注区域，表示对应区域的所属类别并不确定，在评测中也不会考虑这部分区域。
测试数据集 测试数据集文件名称：img_test.zip，详细介绍如下： 包含4,609张分辨率为2m/pixel，尺寸为256 * 256的JPG图片，文件名称形如123.jpg。
本次赛题是四分类的航空影像语义分割任务，需要考虑各类别像素不均衡的问题。
!git clone https://gitee.com/PaddlePaddle/PaddleSeg !pip install paddleseg
!unzip data/data77571/img_test.zip
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import matplotlib.pyplot as pltimport numpy as npimport osimport cv2import randomimport shutilfrom shutil import copyfile
data_path = "img_train//"path_origin = "img_train"label_path = "lab_train"files = list(filter(lambda x: x.endswith('.jpg'), os.listdir(path_origin)))
random.shuffle(files)
rate = int(len(files) * 0.97)
train_txt = open('train.txt','w')
val_txt = open('val.txt','w')for i,f in enumerate(files):
image_path = os.path.join(path_origin, f)
label_name = f.split('.')[0]+".png"
target_image = os.path.join(path_origin, f)
target_label = os.path.join(label_path, label_name) if i < rate:
train_txt.write(target_image + ' ' + target_label+ '\n') else:
val_txt.write(target_image + ' ' + target_label+ '\n')files = list(filter(lambda x: x.endswith('.jpg'), os.listdir("img_testA/")))print(len(files))4608
在本次任务中我们选择语义分割SOTA网络结构OCRNet OCRNet着眼于语义分割中的上下文聚合问题提出了一种简单而有效的方法，即对象上下文表示，通过利用相应对象类的表示来表征像素。首先，在地面真值分割的监督下学习目标区域。其次，通过聚集对象区域中像素的表示来计算对象区域的表示。最后，计算每个像素和每个目标区域之间的关系，并用对象上下文表示来增强每个像素的表示，这是所有对象区域表示的加权聚合。网络结构如下图：
backbone我们选择HRNet
batch_size: 64
iters: 1600000
train_dataset:
type: Dataset
dataset_root: /home/aistudio
train_path: /home/aistudio/train.txt
num_classes: 4
transforms:
- type: ResizeStepScaling min_scale_factor: 0.5 max_scale_factor: 2.0 scale_step_size: 0.25 - type: RandomPaddingCrop crop_size: [256, 256] - type: RandomHorizontalFlip - type: Normalize
mode: train
val_dataset:
type: Dataset
dataset_root: /home/aistudio
val_path: /home/aistudio/val.txt
num_classes: 4
transforms:
- type: Normalize
mode: val
model:
type: OCRNet
backbone:
type: HRNet_W18 pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz
backbone_indices: [0]
optimizer:
type: sgd
learning_rate:
value: 0.01
decay:
type: poly power: 0.9
loss:
types:
- type: MixedLoss
losses:
- type: CrossEntropyLoss
- type: LovaszHingeLoss
coef: [1, 0.01]
- type: MixedLoss
losses:
- type: CrossEntropyLoss
- type: LovaszHingeLoss
coef: [1, 0.01]coef: [1, 0.4]
损失函数使用 CrossEntropyLoss和LovaszHingeLoss的联合损失，改善像素类别不平衡的问题
cd PaddleSeg/
/home/aistudio/PaddleSeg
训练
!python train.py --config configs/ocrnet/remote.yml --do_eval #--resume_model output/best_model
进行测试
!python predict.py --config configs/ocrnet/remote.yml --model_path output/best_model/model.pdparams --image_path ../img_testA/
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
2021-05-09 08:37:17 [INFO]
---------------Config Information---------------
batch_size: 64
iters: 1600000
learning_rate:
decay:
power: 0.9
type: poly
value: 0.01
loss:
coef:
- 1
- 0.4
types:
- coef:
- 1
- 0.01
losses:
- type: CrossEntropyLoss
- type: LovaszHingeLoss
type: MixedLoss
- coef:
- 1
- 0.01
losses:
- type: CrossEntropyLoss
- type: LovaszHingeLoss
type: MixedLoss
model:
backbone:
pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz
type: HRNet_W18
backbone_indices:
- 0
type: OCRNet
optimizer:
type: sgd
train_dataset:
dataset_root: /home/aistudio
mode: train
num_classes: 4
train_path: /home/aistudio/train.txt
transforms:
- max_scale_factor: 2.0
min_scale_factor: 0.5
scale_step_size: 0.25
type: ResizeStepScaling
- crop_size:
- 256
- 256
type: RandomPaddingCrop
- type: RandomHorizontalFlip
- type: Normalize
type: Dataset
val_dataset:
dataset_root: /home/aistudio
mode: val
num_classes: 4
transforms:
- type: Normalize
type: Dataset
val_path: /home/aistudio/val.txt
------------------------------------------------
W0509 08:37:18.013245 3052 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0509 08:37:18.013303 3052 device_context.cc:372] device: 0, cuDNN Version: 7.6.
2021-05-09 08:37:23 [INFO] Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz
2021-05-09 08:37:23,275 - INFO - Lock 139755317736016 acquired on /home/aistudio/.paddleseg/tmp/hrnet_w18_ssld
2021-05-09 08:37:23,275 - INFO - Lock 139755317736016 released on /home/aistudio/.paddleseg/tmp/hrnet_w18_ssld
2021-05-09 08:37:24 [INFO] There are 1525/1525 variables loaded into HRNet.
2021-05-09 08:37:24 [INFO] Number of predict images = 4608
2021-05-09 08:37:24 [INFO] Loading pretrained model from output/best_model/model.pdparams
2021-05-09 08:37:25 [INFO] There are 1583/1583 variables loaded into OCRNet.
2021-05-09 08:37:25 [INFO] Start to predict...
227/4608 [>.............................] - ETA: 8:3^C
Traceback (most recent call last):
File "predict.py", line 177, in <module>
main(args)
File "predict.py", line 171, in main
stride=args.stride,
File "/home/aistudio/PaddleSeg/paddleseg/core/predict.py", line 115, in predict
crop_size=crop_size)
File "/home/aistudio/PaddleSeg/paddleseg/core/infer.py", line 186, in inference
logits = model(im)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleSeg/paddleseg/models/ocrnet.py", line 71, in forward
feats = self.backbone(x)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py", line 169, in forward
st4 = self.st4(tr3)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py", line 506, in forward
out = self.stage_func_list[idx](out)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py", line 536, in forward
out = self.branches_func(x)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py", line 297, in forward
conv = basic_block_func(conv)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py", line 413, in forward
conv1 = self.conv1(x)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 886, in __call__
with param_guard(self._parameters), param_guard(self._buffers):
File "</opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/decorator.py:decorator-gen-40>", line 2, in param_guard
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/wrapped_decorator.py", line 24, in __impl__
wrapped_func = decorator_func(func)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/contextlib.py", line 237, in contextmanager
@wraps(func)
KeyboardInterrupt!zip result.zip -r output/result/result/
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