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09a3fa9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import torch
from mmengine.dataset import DefaultSampler, InfiniteSampler
class TestDefaultSampler(TestCase):
def setUp(self):
self.data_length = 100
self.dataset = list(range(self.data_length))
@patch('mmengine.dataset.sampler.get_dist_info', return_value=(0, 1))
def test_non_dist(self, mock):
sampler = DefaultSampler(self.dataset)
self.assertEqual(sampler.world_size, 1)
self.assertEqual(sampler.rank, 0)
# test round_up=True
sampler = DefaultSampler(self.dataset, round_up=True, shuffle=False)
self.assertEqual(sampler.total_size, self.data_length)
self.assertEqual(sampler.num_samples, self.data_length)
self.assertEqual(list(sampler), list(range(self.data_length)))
# test round_up=False
sampler = DefaultSampler(self.dataset, round_up=False, shuffle=False)
self.assertEqual(sampler.total_size, self.data_length)
self.assertEqual(sampler.num_samples, self.data_length)
self.assertEqual(list(sampler), list(range(self.data_length)))
@patch('mmengine.dataset.sampler.get_dist_info', return_value=(2, 3))
def test_dist(self, mock):
sampler = DefaultSampler(self.dataset)
self.assertEqual(sampler.world_size, 3)
self.assertEqual(sampler.rank, 2)
# test round_up=True
sampler = DefaultSampler(self.dataset, round_up=True, shuffle=False)
self.assertEqual(sampler.num_samples, np.ceil(self.data_length / 3))
self.assertEqual(sampler.total_size, sampler.num_samples * 3)
self.assertEqual(len(sampler), sampler.num_samples)
self.assertEqual(
list(sampler),
list(range(self.data_length))[2::3] + [1])
# test round_up=False
sampler = DefaultSampler(self.dataset, round_up=False, shuffle=False)
self.assertEqual(sampler.num_samples,
np.ceil((self.data_length - 2) / 3))
self.assertEqual(sampler.total_size, self.data_length)
self.assertEqual(len(sampler), sampler.num_samples)
self.assertEqual(list(sampler), list(range(self.data_length))[2::3])
@patch('mmengine.dataset.sampler.get_dist_info', return_value=(0, 1))
@patch('mmengine.dataset.sampler.sync_random_seed', return_value=7)
def test_shuffle(self, mock1, mock2):
# test seed=None
sampler = DefaultSampler(self.dataset, seed=None)
self.assertEqual(sampler.seed, 7)
# test random seed
sampler = DefaultSampler(self.dataset, shuffle=True, seed=0)
sampler.set_epoch(10)
g = torch.Generator()
g.manual_seed(10)
self.assertEqual(
list(sampler),
torch.randperm(len(self.dataset), generator=g).tolist())
sampler = DefaultSampler(self.dataset, shuffle=True, seed=42)
sampler.set_epoch(10)
g = torch.Generator()
g.manual_seed(42 + 10)
self.assertEqual(
list(sampler),
torch.randperm(len(self.dataset), generator=g).tolist())
class TestInfiniteSampler(TestCase):
def setUp(self):
self.data_length = 100
self.dataset = list(range(self.data_length))
@patch('mmengine.dataset.sampler.get_dist_info', return_value=(0, 1))
def test_non_dist(self, mock):
sampler = InfiniteSampler(self.dataset)
self.assertEqual(sampler.world_size, 1)
self.assertEqual(sampler.rank, 0)
# test iteration
sampler = InfiniteSampler(self.dataset, shuffle=False)
self.assertEqual(len(sampler), self.data_length)
self.assertEqual(sampler.size, self.data_length)
sampler_iter = iter(sampler)
items = [next(sampler_iter) for _ in range(self.data_length * 2)]
self.assertEqual(items, list(range(self.data_length)) * 2)
@patch('mmengine.dataset.sampler.get_dist_info', return_value=(2, 3))
def test_dist(self, mock):
sampler = InfiniteSampler(self.dataset)
self.assertEqual(sampler.world_size, 3)
self.assertEqual(sampler.rank, 2)
# test iteration
sampler = InfiniteSampler(self.dataset, shuffle=False)
self.assertEqual(len(sampler), self.data_length)
self.assertEqual(sampler.size, self.data_length)
targets = (list(range(self.data_length)) * 2)[2::3]
sampler_iter = iter(sampler)
samples = [next(sampler_iter) for _ in range(len(targets))]
print(samples)
self.assertEqual(samples, targets)
@patch('mmengine.dataset.sampler.get_dist_info', return_value=(0, 1))
@patch('mmengine.dataset.sampler.sync_random_seed', return_value=7)
def test_shuffle(self, mock1, mock2):
# test seed=None
sampler = InfiniteSampler(self.dataset, seed=None)
self.assertEqual(sampler.seed, 7)
# test the random seed
sampler = InfiniteSampler(self.dataset, shuffle=True, seed=42)
sampler_iter = iter(sampler)
samples = [next(sampler_iter) for _ in range(self.data_length)]
g = torch.Generator()
g.manual_seed(42)
self.assertEqual(
samples,
torch.randperm(self.data_length, generator=g).tolist())
def test_set_epoch(self):
sampler = InfiniteSampler(self.dataset)
sampler.set_epoch(10)
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