cdnet_build.py 2.1 KB

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  1. import sys
  2. sys.path.append("E:/dataFiles/github/PaddleRS")
  3. import paddlers as pdrs
  4. from paddlers import transforms as T
  5. # 下载aistudio的数据到当前文件夹并解压、整理
  6. # https://aistudio.baidu.com/aistudio/datasetdetail/53795
  7. # 定义训练和验证时的transforms
  8. # API说明:https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/transforms/transforms.md
  9. train_transforms = T.Compose([
  10. T.Resize(target_size=512),
  11. T.RandomHorizontalFlip(),
  12. T.Normalize(
  13. mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
  14. ])
  15. eval_transforms = T.Compose([
  16. T.Resize(target_size=512),
  17. T.Normalize(
  18. mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
  19. ])
  20. # 定义训练和验证所用的数据集
  21. # API说明:https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/datasets.md
  22. train_dataset = pdrs.datasets.CDDataset(
  23. data_dir='E:/dataFiles/github/PaddleRS/tutorials/train/change_detection/DataSet',
  24. file_list='tutorials/train/change_detection/DataSet/train.txt',
  25. label_list='tutorials/train/change_detection/DataSet/labels.txt',
  26. transforms=train_transforms,
  27. num_workers=0,
  28. shuffle=True)
  29. eval_dataset = pdrs.datasets.CDDataset(
  30. data_dir='E:/dataFiles/github/PaddleRS/tutorials/train/change_detection/DataSet',
  31. file_list='tutorials/train/change_detection/DataSet/val.txt',
  32. label_list='tutorials/train/change_detection/DataSet/labels.txt',
  33. transforms=eval_transforms,
  34. num_workers=0,
  35. shuffle=False)
  36. # 初始化模型,并进行训练
  37. # 可使用VisualDL查看训练指标,参考https://github.com/PaddlePaddle/paddlers/blob/develop/docs/visualdl.md
  38. num_classes = len(train_dataset.labels)
  39. model = pdrs.tasks.CDNet(num_classes=num_classes, in_channels=6)
  40. # API说明:https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/models/semantic_segmentation.md
  41. # 各参数介绍与调整说明:https://github.com/PaddlePaddle/paddlers/blob/develop/docs/parameters.md
  42. model.train(
  43. num_epochs=1,
  44. train_dataset=train_dataset,
  45. train_batch_size=4,
  46. eval_dataset=eval_dataset,
  47. learning_rate=0.01,
  48. pretrain_weights=None,
  49. save_dir='output/cdnet')