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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import numpy as np
import torch
from mmengine.dataset import default_collate, pseudo_collate
from mmengine.structures import BaseDataElement
from mmengine.utils import is_list_of
class TestDataUtils(TestCase):
def test_pseudo_collate(self):
# Test with list of dict tensor inputs.
input1 = torch.randn(1, 3, 5)
input2 = torch.randn(1, 3, 5)
label1 = torch.randn(1)
label2 = torch.randn(1)
data_batch = [
dict(inputs=input1, data_sample=label1),
dict(inputs=input2, data_sample=label2)
]
data_batch = pseudo_collate(data_batch)
self.assertTrue(torch.allclose(input1, data_batch['inputs'][0]))
self.assertTrue(torch.allclose(input2, data_batch['inputs'][1]))
self.assertTrue(torch.allclose(label1, data_batch['data_sample'][0]))
self.assertTrue(torch.allclose(label2, data_batch['data_sample'][1]))
# Test with list of dict, and each element contains `data_sample`
# inputs
data_sample1 = BaseDataElement(label=torch.tensor(1))
data_sample2 = BaseDataElement(label=torch.tensor(1))
data = [
dict(inputs=input1, data_sample=data_sample1),
dict(inputs=input2, data_sample=data_sample2),
]
data_batch = pseudo_collate(data)
batch_inputs, batch_data_sample = (data_batch['inputs'],
data_batch['data_sample'])
# check batch_inputs
self.assertTrue(is_list_of(batch_inputs, torch.Tensor))
self.assertIs(input1, batch_inputs[0])
self.assertIs(input2, batch_inputs[1])
# check data_sample
self.assertIs(batch_data_sample[0], data_sample1)
self.assertIs(batch_data_sample[1], data_sample2)
# Test with list of tuple, each tuple is a nested dict instance
data_batch = [(dict(
inputs=input1,
data_sample=data_sample1,
value=1,
name='1',
nested=dict(data_sample=data_sample1)),
dict(
inputs=input2,
data_sample=data_sample2,
value=2,
name='2',
nested=dict(data_sample=data_sample2))),
(dict(
inputs=input1,
data_sample=data_sample1,
value=1,
name='1',
nested=dict(data_sample=data_sample1)),
dict(
inputs=input2,
data_sample=data_sample2,
value=2,
name='2',
nested=dict(data_sample=data_sample2)))]
data_batch = pseudo_collate(data_batch)
batch_inputs_0 = data_batch[0]['inputs']
batch_inputs_1 = data_batch[1]['inputs']
batch_data_sample_0 = data_batch[0]['data_sample']
batch_data_sample_1 = data_batch[1]['data_sample']
batch_value_0 = data_batch[0]['value']
batch_value_1 = data_batch[1]['value']
batch_name_0 = data_batch[0]['name']
batch_name_1 = data_batch[1]['name']
batch_nested_0 = data_batch[0]['nested']
batch_nested_1 = data_batch[1]['nested']
self.assertTrue(is_list_of(batch_inputs_0, torch.Tensor))
self.assertTrue(is_list_of(batch_inputs_1, torch.Tensor))
self.assertIs(batch_inputs_0[0], input1)
self.assertIs(batch_inputs_0[1], input1)
self.assertIs(batch_inputs_1[0], input2)
self.assertIs(batch_inputs_1[1], input2)
self.assertIs(batch_data_sample_0[0], data_sample1)
self.assertIs(batch_data_sample_0[1], data_sample1)
self.assertIs(batch_data_sample_1[0], data_sample2)
self.assertIs(batch_data_sample_1[1], data_sample2)
self.assertEqual(batch_value_0, [1, 1])
self.assertEqual(batch_value_1, [2, 2])
self.assertEqual(batch_name_0, ['1', '1'])
self.assertEqual(batch_name_1, ['2', '2'])
self.assertIs(batch_nested_0['data_sample'][0], data_sample1)
self.assertIs(batch_nested_0['data_sample'][1], data_sample1)
self.assertIs(batch_nested_1['data_sample'][0], data_sample2)
self.assertIs(batch_nested_1['data_sample'][1], data_sample2)
def test_default_collate(self):
# `default_collate` has comment logic with `pseudo_collate`, therefore
# only test it cam stack batch tensor, convert int or float to tensor.
input1 = torch.randn(1, 3, 5)
input2 = torch.randn(1, 3, 5)
data_batch = [(
dict(inputs=input1, value=1, array=np.array(1)),
dict(inputs=input2, value=2, array=np.array(2)),
),
(
dict(inputs=input1, value=1, array=np.array(1)),
dict(inputs=input2, value=2, array=np.array(2)),
)]
data_batch = default_collate(data_batch)
batch_inputs_0 = data_batch[0]['inputs']
batch_inputs_1 = data_batch[1]['inputs']
batch_value_0 = data_batch[0]['value']
batch_value_1 = data_batch[1]['value']
batch_array_0 = data_batch[0]['array']
batch_array_1 = data_batch[1]['array']
self.assertEqual(tuple(batch_inputs_0.shape), (2, 1, 3, 5))
self.assertEqual(tuple(batch_inputs_1.shape), (2, 1, 3, 5))
self.assertTrue(
torch.allclose(batch_inputs_0, torch.stack([input1, input1])))
self.assertTrue(
torch.allclose(batch_inputs_1, torch.stack([input2, input2])))
target1 = torch.stack([torch.tensor(1), torch.tensor(1)])
target2 = torch.stack([torch.tensor(2), torch.tensor(2)])
self.assertTrue(
torch.allclose(batch_value_0.to(target1.dtype), target1))
self.assertTrue(
torch.allclose(batch_value_1.to(target2.dtype), target2))
self.assertTrue(
torch.allclose(batch_array_0.to(target1.dtype), target1))
self.assertTrue(
torch.allclose(batch_array_1.to(target2.dtype), target2))
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