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54 lines
1.6 KiB
54 lines
1.6 KiB
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.DeepLabV3P(input_channel=channel, num_classes=num_classes, backbone='ResNet50_vd') |
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model.train( |
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num_epochs=10, |
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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/deeplabv3p_r50vd')
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