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@@ -1,52 +0,0 @@
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-import paddlers as pdrs
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-from paddlers import transforms as T
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-
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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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- # 读取影像
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- T.DecodeImg(),
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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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- T.ArrangeClassifier('train')
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-])
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-
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-eval_transforms = T.Compose([
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- T.DecodeImg(), T.SelectBand([5, 10, 15, 20, 25]), 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],
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- std=[0.5, 0.5, 0.5, 0.5, 0.5]), T.ArrangeClassifier('eval')
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-])
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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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-
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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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-
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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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-
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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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