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56 lines
1.6 KiB
56 lines
1.6 KiB
3 years ago
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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import paddlers as pdrs
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from paddlers import transforms as T
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# 下载和解压多光谱地块分类数据集
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dataset = 'https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip'
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pdrs.utils.download_and_decompress(dataset, path='./data')
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# 定义训练和验证时的transforms
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channel = 10
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train_transforms = T.Compose([
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T.Resize(target_size=512),
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T.RandomHorizontalFlip(),
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T.Normalize(
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mean=[0.5] * channel, std=[0.5] * channel),
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])
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eval_transforms = T.Compose([
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T.Resize(target_size=512),
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T.Normalize(
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mean=[0.5] * channel, std=[0.5] * channel),
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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.SegDataset(
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data_dir='./data/remote_sensing_seg',
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file_list='./data/remote_sensing_seg/train.txt',
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label_list='./data/remote_sensing_seg/labels.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.SegDataset(
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data_dir='./data/remote_sensing_seg',
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file_list='./data/remote_sensing_seg/val.txt',
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label_list='./data/remote_sensing_seg/labels.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，并进行训练
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# 可使用VisualDL查看训练指标
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num_classes = len(train_dataset.labels)
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model = pdrs.tasks.UNet(input_channel=channel, num_classes=num_classes)
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model.train(
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num_epochs=20,
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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=0.01,
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save_dir='output/unet',
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use_vdl=True)
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