def sample_meta_test(self, ratio=0.75, fixed=None):
fixed = fixed if fixed is not None else self.fixed
if fixed and self.meta_test:
return self.meta_test
inner_train, inner_test = self._random_split(self.m_test_d, ratio)
self.meta_test['in_train'] = IterDataLoader.from_dataset(
inner_train, self.batch_size)
self.meta_test['in_test'] = IterDataLoader.from_dataset(
inner_test, self.batch_size)
return self.meta_test
def sample_meta_test(self, n_sample=10, ratio=0.75, fixed=None):
fixed = fixed if fixed is not None else self.fixed
if fixed and self.meta_test:
return self.meta_test
m_test_sampled = self.m_test_d.class_sample(n_sample, preload=True)
inner_train, inner_test = m_test_sampled.intra_class_split(ratio, True)
self.meta_test['in_train'] = IterDataLoader.from_dataset(
inner_train, self.batch_size)
self.meta_test['in_test'] = IterDataLoader.from_dataset(
inner_test, self.batch_size)
return self.meta_test
def _get_wrapped_dataloader(self, dataset, batch_size):
sampler = data.sampler.RandomSampler(dataset, replacement=True)
loader = data.DataLoader(dataset,
batch_size=batch_size,
sampler=sampler)
return IterDataLoader(loader)