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- import paddlers as pdrs
- from paddlers import transforms as T
- DATA_DIR = './data/ucmerced/'
- TRAIN_FILE_LIST_PATH = './data/ucmerced/train.txt'
- EVAL_FILE_LIST_PATH = './data/ucmerced/val.txt'
- LABEL_LIST_PATH = './data/ucmerced/labels.txt'
- EXP_DIR = './output/hrnet/'
- pdrs.utils.download_and_decompress(
- 'https://paddlers.bj.bcebos.com/datasets/ucmerced.zip', path='./data/')
- train_transforms = T.Compose([
-
- T.DecodeImg(),
-
- T.Resize(target_size=256),
-
- T.RandomHorizontalFlip(prob=0.5),
-
- T.RandomVerticalFlip(prob=0.5),
-
- T.Normalize(
- mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
- T.ArrangeClassifier('train')
- ])
- eval_transforms = T.Compose([
- T.DecodeImg(),
- T.Resize(target_size=256),
-
- T.Normalize(
- mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
- T.ArrangeClassifier('eval')
- ])
- train_dataset = pdrs.datasets.ClasDataset(
- data_dir=DATA_DIR,
- file_list=TRAIN_FILE_LIST_PATH,
- label_list=LABEL_LIST_PATH,
- transforms=train_transforms,
- num_workers=0,
- shuffle=True)
- eval_dataset = pdrs.datasets.ClasDataset(
- data_dir=DATA_DIR,
- file_list=EVAL_FILE_LIST_PATH,
- label_list=LABEL_LIST_PATH,
- transforms=eval_transforms,
- num_workers=0,
- shuffle=False)
- model = pdrs.tasks.clas.HRNet_W18_C(num_classes=len(train_dataset.labels))
- model.train(
- num_epochs=2,
- train_dataset=train_dataset,
- train_batch_size=16,
- eval_dataset=eval_dataset,
- save_interval_epochs=1,
-
- log_interval_steps=50,
- save_dir=EXP_DIR,
-
- learning_rate=0.01,
-
- early_stop=False,
-
- use_vdl=True,
-
- resume_checkpoint=None)
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