mobilenetv3_large_w_custom_optimizer.py 2.5 KB

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  1. import paddle
  2. import paddlex as pdx
  3. from paddlex import transforms as T
  4. # 下载和解压蔬菜分类数据集
  5. veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
  6. pdx.utils.download_and_decompress(veg_dataset, path='./')
  7. # 定义训练和验证时的transforms
  8. # API说明:https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/transforms/transforms.md
  9. train_transforms = T.Compose(
  10. [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
  11. eval_transforms = T.Compose([
  12. T.ResizeByShort(short_size=256), T.CenterCrop(crop_size=224), T.Normalize()
  13. ])
  14. # 定义训练和验证所用的数据集
  15. # API说明:https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/datasets.md
  16. train_dataset = pdx.datasets.ImageNet(
  17. data_dir='vegetables_cls',
  18. file_list='vegetables_cls/train_list.txt',
  19. label_list='vegetables_cls/labels.txt',
  20. transforms=train_transforms,
  21. shuffle=True)
  22. eval_dataset = pdx.datasets.ImageNet(
  23. data_dir='vegetables_cls',
  24. file_list='vegetables_cls/val_list.txt',
  25. label_list='vegetables_cls/labels.txt',
  26. transforms=eval_transforms)
  27. # 初始化模型,并进行训练
  28. # 可使用VisualDL查看训练指标,参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
  29. num_classes = len(train_dataset.labels)
  30. model = pdx.cls.MobileNetV3_large(num_classes=num_classes)
  31. # 自定义优化器:使用CosineAnnealingDecay
  32. train_batch_size = 64
  33. num_steps_each_epoch = len(train_dataset) // train_batch_size
  34. num_epochs = 10
  35. scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
  36. learning_rate=.001, T_max=num_steps_each_epoch * num_epochs)
  37. warmup_epoch = 5
  38. warmup_steps = warmup_epoch * num_steps_each_epoch
  39. scheduler = paddle.optimizer.lr.LinearWarmup(
  40. learning_rate=scheduler,
  41. warmup_steps=warmup_steps,
  42. start_lr=0.0,
  43. end_lr=.001)
  44. custom_optimizer = paddle.optimizer.Momentum(
  45. learning_rate=scheduler,
  46. momentum=.9,
  47. weight_decay=paddle.regularizer.L2Decay(coeff=.00002),
  48. parameters=model.net.parameters())
  49. # API说明:https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
  50. # 各参数介绍与调整说明:https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
  51. model.train(
  52. num_epochs=num_epochs,
  53. train_dataset=train_dataset,
  54. train_batch_size=train_batch_size,
  55. eval_dataset=eval_dataset,
  56. optimizer=custom_optimizer,
  57. save_dir='output/mobilenetv3_large',
  58. use_vdl=True)