def test_len(self):
batch_size = 3
drop_uneven = True
dataset = [i for i in range(10)]
group_ids = [random.randint(0, 1) for _ in dataset]
sampler = RandomSampler(dataset)
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
result = list(batch_sampler)
self.assertEqual(len(result), len(batch_sampler))
self.assertEqual(len(result), len(batch_sampler))
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
batch_sampler_len = len(batch_sampler)
result = list(batch_sampler)
self.assertEqual(len(result), batch_sampler_len)
self.assertEqual(len(result), len(batch_sampler))
def test_respect_order_simple(self):
drop_uneven = False
dataset = [i for i in range(40)]
group_ids = [i // 10 for i in dataset]
sampler = SequentialSampler(dataset)
for batch_size in [1, 3, 5, 6]:
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
result = list(batch_sampler)
merged_result = list(itertools.chain.from_iterable(result))
self.assertEqual(merged_result, dataset)
def test_permute_subset_sampler_drop_uneven(self):
batch_size = 3
drop_uneven = True
dataset = [i for i in range(10)]
group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
sampler = SubsetSampler([5, 0, 6, 1, 3, 8])
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
result = list(batch_sampler)
expected = [[0, 6, 1]]
self.assertEqual(result, expected)
def test_subset_sampler(self):
batch_size = 3
drop_uneven = False
dataset = [i for i in range(10)]
group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
sampler = SubsetSampler([0, 3, 5, 6, 7, 8])
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
result = list(batch_sampler)
expected = [[0, 3, 6], [5, 7, 8]]
self.assertEqual(result, expected)
def test_respect_order_drop_uneven(self):
batch_size = 3
drop_uneven = True
dataset = [i for i in range(10)]
group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
sampler = SequentialSampler(dataset)
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
result = list(batch_sampler)
expected = [[0, 1, 3], [2, 4, 5]]
self.assertEqual(result, expected)
def test_respect_order(self):
drop_uneven = False
dataset = [i for i in range(10)]
group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
sampler = SequentialSampler(dataset)
expected = [
[[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
[[0, 1, 3], [2, 4, 5], [6, 9], [7, 8]],
[[0, 1, 3, 6], [2, 4, 5, 7], [8], [9]],
]
for idx, batch_size in enumerate([1, 3, 4]):
batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size,
drop_uneven)
result = list(batch_sampler)
self.assertEqual(result, expected[idx])
