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# Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import math
import operator
import unittest
import torch
from torch.utils import data
from torch.utils.data.sampler import SequentialSampler
from detectron2.data.build import worker_init_reset_seed
from detectron2.data.common import DatasetFromList, ToIterableDataset
from detectron2.data.samplers import (
GroupedBatchSampler,
InferenceSampler,
RepeatFactorTrainingSampler,
TrainingSampler,
)
from detectron2.utils.env import seed_all_rng
class TestGroupedBatchSampler(unittest.TestCase):
def test_missing_group_id(self):
sampler = SequentialSampler(list(range(100)))
group_ids = [1] * 100
samples = GroupedBatchSampler(sampler, group_ids, 2)
for mini_batch in samples:
self.assertEqual(len(mini_batch), 2)
def test_groups(self):
sampler = SequentialSampler(list(range(100)))
group_ids = [1, 0] * 50
samples = GroupedBatchSampler(sampler, group_ids, 2)
for mini_batch in samples:
self.assertEqual((mini_batch[0] + mini_batch[1]) % 2, 0)
class TestSamplerDeterministic(unittest.TestCase):
def test_to_iterable(self):
sampler = TrainingSampler(100, seed=10)
gt_output = list(itertools.islice(sampler, 100))
self.assertEqual(set(gt_output), set(range(100)))
dataset = DatasetFromList(list(range(100)))
dataset = ToIterableDataset(dataset, sampler)
data_loader = data.DataLoader(dataset, num_workers=0, collate_fn=operator.itemgetter(0))
output = list(itertools.islice(data_loader, 100))
self.assertEqual(output, gt_output)
data_loader = data.DataLoader(
dataset,
num_workers=2,
collate_fn=operator.itemgetter(0),
worker_init_fn=worker_init_reset_seed,
# reset seed should not affect behavior of TrainingSampler
)
output = list(itertools.islice(data_loader, 100))
# multiple workers should not lead to duplicate or different data
self.assertEqual(output, gt_output)
def test_training_sampler_seed(self):
seed_all_rng(42)
sampler = TrainingSampler(30)
data = list(itertools.islice(sampler, 65))
seed_all_rng(42)
sampler = TrainingSampler(30)
seed_all_rng(999) # should be ineffective
data2 = list(itertools.islice(sampler, 65))
self.assertEqual(data, data2)
class TestRepeatFactorTrainingSampler(unittest.TestCase):
def test_repeat_factors_from_category_frequency(self):
repeat_thresh = 0.5
dataset_dicts = [
{"annotations": [{"category_id": 0}, {"category_id": 1}]},
{"annotations": [{"category_id": 0}]},
{"annotations": []},
]
rep_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
dataset_dicts, repeat_thresh
)
expected_rep_factors = torch.tensor([math.sqrt(3 / 2), 1.0, 1.0])
self.assertTrue(torch.allclose(rep_factors, expected_rep_factors))
class TestInferenceSampler(unittest.TestCase):
def test_local_indices(self):
sizes = [0, 16, 2, 42]
world_sizes = [5, 2, 3, 4]
expected_results = [
[range(0) for _ in range(5)],
[range(8), range(8, 16)],
[range(1), range(1, 2), range(0)],
[range(11), range(11, 22), range(22, 32), range(32, 42)],
]
for size, world_size, expected_result in zip(sizes, world_sizes, expected_results):
with self.subTest(f"size={size}, world_size={world_size}"):
local_indices = [
InferenceSampler._get_local_indices(size, world_size, r)
for r in range(world_size)
]
self.assertEqual(local_indices, expected_result)
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