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49 lines
1.5 KiB
49 lines
1.5 KiB
import paddlers as pdrs |
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from paddlers import transforms as T |
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# 定义训练和验证时的transforms |
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train_transforms = T.Compose([ |
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T.SelectBand([5, 10, 15, 20, 25]), # for tet |
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T.Resize(target_size=224), |
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T.RandomHorizontalFlip(), |
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T.Normalize( |
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mean=[0.5, 0.5, 0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5, 0.5, 0.5]), |
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]) |
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eval_transforms = T.Compose([ |
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T.SelectBand([5, 10, 15, 20, 25]), |
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T.Resize(target_size=224), |
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T.Normalize( |
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mean=[0.5, 0.5, 0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5, 0.5, 0.5]), |
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]) |
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# 定义训练和验证所用的数据集 |
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train_dataset = pdrs.datasets.ClasDataset( |
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data_dir='tutorials/train/classification/DataSet', |
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file_list='tutorials/train/classification/DataSet/train_list.txt', |
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label_list='tutorials/train/classification/DataSet/label_list.txt', |
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transforms=train_transforms, |
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num_workers=0, |
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shuffle=True) |
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eval_dataset = pdrs.datasets.ClasDataset( |
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data_dir='tutorials/train/classification/DataSet', |
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file_list='tutorials/train/classification/DataSet/val_list.txt', |
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label_list='tutorials/train/classification/DataSet/label_list.txt', |
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transforms=eval_transforms, |
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num_workers=0, |
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shuffle=False) |
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# 初始化模型 |
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num_classes = len(train_dataset.labels) |
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model = pdrs.tasks.CondenseNetV2_b(in_channels=5, num_classes=num_classes) |
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# 进行训练 |
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model.train( |
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num_epochs=100, |
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pretrain_weights=None, |
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train_dataset=train_dataset, |
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train_batch_size=4, |
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eval_dataset=eval_dataset, |
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learning_rate=3e-4, |
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save_dir='output/condensenetv2_b')
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