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-
- from pytorch_adapt.datasets import DataloaderCreator, get_office31
- from pytorch_adapt.frameworks.ignite import CheckpointFnCreator, Ignite
- from pytorch_adapt.validators import AccuracyValidator, IMValidator, ScoreHistory, DiversityValidator, EntropyValidator, MultipleValidators
-
- from time import time
- import multiprocessing as mp
-
- data_root = "../datasets/pytorch-adapt/"
- batch_size = 32
- for num_workers in range(2, mp.cpu_count(), 2):
- datasets = get_office31(["amazon"], ["webcam"],
- folder=data_root,
- return_target_with_labels=True,
- download=False)
-
- dc = DataloaderCreator(batch_size=batch_size,
- num_workers=num_workers,
- train_names=["train"],
- val_names=["src_train", "target_train", "src_val", "target_val",
- "target_train_with_labels", "target_val_with_labels"])
- dataloaders = dc(**datasets)
-
- train_loader = dataloaders["train"]
- start = time()
- for epoch in range(1, 3):
- for i, data in enumerate(train_loader, 0):
- pass
- end = time()
- print("Finish with:{} second, num_workers={}".format(end - start, num_workers))
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