mmclassification/tests/test_structures/test_datasample.py

# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase

import numpy as np
import torch

from mmpretrain.structures import DataSample, MultiTaskDataSample


class TestDataSample(TestCase):

    def _test_set_label(self, key):
        data_sample = DataSample()
        method = getattr(data_sample, 'set_' + key)
        # Test number
        method(1)
        self.assertIn(key, data_sample)
        label = getattr(data_sample, key)
        self.assertIsInstance(label, torch.LongTensor)

        # Test tensor with single number
        method(torch.tensor(2))
        self.assertIn(key, data_sample)
        label = getattr(data_sample, key)
        self.assertIsInstance(label, torch.LongTensor)

        # Test array with single number
        method(np.array(3))
        self.assertIn(key, data_sample)
        label = getattr(data_sample, key)
        self.assertIsInstance(label, torch.LongTensor)

        # Test tensor
        method(torch.tensor([1, 2, 3]))
        self.assertIn(key, data_sample)
        label = getattr(data_sample, key)
        self.assertIsInstance(label, torch.Tensor)
        self.assertTrue((label == torch.tensor([1, 2, 3])).all())

        # Test array
        method(np.array([1, 2, 3]))
        self.assertIn(key, data_sample)
        label = getattr(data_sample, key)
        self.assertTrue((label == torch.tensor([1, 2, 3])).all())

        # Test Sequence
        method([1, 2, 3])
        self.assertIn(key, data_sample)
        label = getattr(data_sample, key)
        self.assertTrue((label == torch.tensor([1, 2, 3])).all())

        # Test unavailable type
        with self.assertRaisesRegex(TypeError, "<class 'str'> is not"):
            method('hi')

    def test_set_gt_label(self):
        self._test_set_label('gt_label')

    def test_set_pred_label(self):
        self._test_set_label('pred_label')

    def test_set_gt_score(self):
        data_sample = DataSample()
        data_sample.set_gt_score(torch.tensor([0.1, 0.1, 0.6, 0.1, 0.1]))
        self.assertIn('gt_score', data_sample)
        torch.testing.assert_allclose(data_sample.gt_score,
                                      [0.1, 0.1, 0.6, 0.1, 0.1])

        # Test invalid length
        with self.assertRaisesRegex(AssertionError, 'should be equal to'):
            data_sample.set_gt_score([1, 2])

        # Test invalid dims
        with self.assertRaisesRegex(AssertionError, 'but got 2'):
            data_sample.set_gt_score(torch.tensor([[0.1, 0.1, 0.6, 0.1, 0.1]]))

    def test_set_pred_score(self):
        data_sample = DataSample()
        data_sample.set_pred_score(torch.tensor([0.1, 0.1, 0.6, 0.1, 0.1]))
        self.assertIn('pred_score', data_sample)
        torch.testing.assert_allclose(data_sample.pred_score,
                                      [0.1, 0.1, 0.6, 0.1, 0.1])

        # Test invalid length
        with self.assertRaisesRegex(AssertionError, 'should be equal to'):
            data_sample.set_gt_score([1, 2])

        # Test invalid dims
        with self.assertRaisesRegex(AssertionError, 'but got 2'):
            data_sample.set_pred_score(
                torch.tensor([[0.1, 0.1, 0.6, 0.1, 0.1]]))


class TestMultiTaskDataSample(TestCase):

    def test_multi_task_data_sample(self):
        gt_label = {'task0': {'task00': 1, 'task01': 1}, 'task1': 1}
        data_sample = MultiTaskDataSample()
        task_sample = DataSample().set_gt_label(gt_label['task1'])
        data_sample.set_field(task_sample, 'task1')
        data_sample.set_field(MultiTaskDataSample(), 'task0')
        for task_name in gt_label['task0']:
            task_sample = DataSample().set_gt_label(
                gt_label['task0'][task_name])
            data_sample.task0.set_field(task_sample, task_name)
        self.assertIsInstance(data_sample.task0, MultiTaskDataSample)
        self.assertIsInstance(data_sample.task1, DataSample)
        self.assertIsInstance(data_sample.task0.task00, DataSample)