How to use test_sample method in Slash

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test.py

Source:test.py

1import unittest2import numpy as np3from decimal import *4class LinearRegressionTestCase(unittest.TestCase):5    """Test for linear regression project"""6    def test_calc_Norm(self):7        test_sample=[1,2,3,4,5]8        self.assertEqual(Decimal(calc_Norm(test_sample,p=0,infty=False)),5.0,'L0 Wrong answer')9        self.assertEqual(calc_Norm(test_sample,p=1,infty=False),15.0,'L1 Wrong answer')10        self.assertEqual(calc_Norm(test_sample,p=2,infty=False),7.416198487095663,'L2 Wrong answer')11        self.assertEqual(calc_Norm(test_sample,p=2,infty=True),5.0,'L_max Wrong answer')12        13        test_sample=np.array([1.2])14        self.assertEqual(Decimal(calc_Norm(test_sample,p=0,infty=False)),1.0,'L0 Wrong answer')15        self.assertEqual(calc_Norm(test_sample,p=1,infty=False),1.2,'L1 Wrong answer')16        self.assertEqual(calc_Norm(test_sample,p=2,infty=False),1.2,'L2 Wrong answer')17        self.assertEqual(calc_Norm(test_sample,p=2,infty=True),1.2,'L_max Wrong answer')        18        19    def test_calc_Frobenius_Norm(self):20        test_sample=[[1,2,3],[4,5,6]]21        self.assertEqual(Decimal(calc_Frobenius_Norm(test_sample)),9.539392014169456,'Wrong answer')22        23    24    def test_calc_Condition_Number(self):25        test_sample=[[1,2],[2,4.0001]]26        self.assertEqual(Decimal(calc_Condition_Number(test_sample)),250008.00010058272,'Wrong answer')27        test_sample=[[1,2],[3,4]]28        self.assertEqual(Decimal(calc_Condition_Number(test_sample)),14.999999999999998,'Wrong answer')29    def test_calc_svd(self):30        test_sample =[[1,2],[3,4]]31        result = (np.array([[-0.40455358, -0.9145143 ],32                         [-0.9145143 ,  0.40455358]]),33                 np.array([5.4649857 , 0.36596619]),34                 np.array([[-0.57604844, -0.81741556],35                        [ 0.81741556, -0.57604844]]))36        self.assertEqual(calc_svd(test_sample)[0].any()==result[0].any(),True,'Wrong answer U')37        self.assertEqual(calc_svd(test_sample)[1].any()==result[1].any(),True,'Wrong answer D')38        self.assertEqual(calc_svd(test_sample)[2].any()==result[2].any(),True,'Wrong answer VT')   39        40    def test_calc_svd_decompostion(self):41        test_sample =[[1,2,3,4,5,6,7,8],42                       [2,3,4,5,6,7,8,9],43                       [3,4,5,6,7,8,9,10],44                       [4,5,6,7,8,9,10,11],45                       [5,6,7,8,9,10,11,12]]46        result = np.array([[-14.13331129,  -2.06143446],47                       [-16.81265405,  -1.15527645],48                       [-19.49199682,  -0.24911844],49                       [-22.17133959,   0.65703957],50                       [-24.85068235,   1.56319758]])51        52        self.assertEqual(calc_svd_decompostion(test_sample).any()==result.any(),True,'Wrong answer')        53        54    def test_calc_svd_reconsitution(self):55        test_sample =[[1,2,3,4,5,6,7,8],56                       [2,3,4,5,6,7,8,9],57                       [3,4,5,6,7,8,9,10],58                       [4,5,6,7,8,9,10,11],59                       [5,6,7,8,9,10,11,12]]60        result = np.array([[ 2.28811868,  2.98679854,  3.68547839,  4.38415824,  5.0828381 ,61                         5.78151795,  6.4801978 ,  7.17887766],62                           [ 2.72189206,  3.55302515,  4.38415824,  5.21529133,  6.04642442,63                         6.87755751,  7.7086906 ,  8.53982369],64                           [ 3.15566545,  4.11925177,  5.0828381 ,  6.04642442,  7.01001075,65                         7.97359707,  8.9371834 ,  9.90076972],66                           [ 3.58943883,  4.68547839,  5.78151795,  6.87755751,  7.97359707,67                         9.06963663, 10.16567619, 11.26171575],68                           [ 4.02321221,  5.25170501,  6.4801978 ,  7.7086906 ,  8.9371834 ,69                         10.16567619, 11.39416899, 12.62266179]])70        71        self.assertEqual(calc_svd_reconsitution(test_sample,topk=1).any()==result.any(),True,'Wrong answer') 72            73        74if __name__ == '__main__':...

test_convert_records_to_fo.py

Source:test_convert_records_to_fo.py

1import imp2from venv import create3import pytest4from PIL import Image5from fiftyone import list_datasets, load_dataset, Detection, Sample, Dataset6from icevision import tfms7from icevision import data8from icevision.data import Prediction9from icevision.data.convert_records_to_fo import (10    _convert_bbox_to_fo_bbox,11    record_to_fo_detections,12)13# Test subject14from icevision.data import (15    convert_record_to_fo_sample,16    convert_prediction_to_fo_sample,17    create_fo_dataset,18)19from tests.conftest import object_detection_record20# Fixtures21@pytest.fixture(scope="function")22def cleanup_fo_dataset():23    dataset_test_names = ["_iv_test", "_iv_test_1"]24    yield25    for dataset_name in dataset_test_names:26        if dataset_name in list_datasets():27            ds = load_dataset(dataset_name)28            ds.delete()29            del ds30# Test from low to high abstraction31def test_record_to_fo_detections(object_detection_record):32    detections = record_to_fo_detections(33        object_detection_record, lambda bbox: _convert_bbox_to_fo_bbox(bbox, 500, 500)34    )35    for fo_bbox in detections:36        assert isinstance(fo_bbox, Detection)37def test_convert_record_to_fo_sample(object_detection_record):38    # Test create new sample39    test_sample = convert_record_to_fo_sample(40        object_detection_record, "test", None, False, None, lambda x: x41    )42    assert isinstance(test_sample, Sample)43    assert hasattr(test_sample, "test")44    assert len(test_sample["test"]) > 045    # Test add to existing sample  and autosave46    sample = Sample(object_detection_record.common.filepath)47    test_sample = convert_record_to_fo_sample(48        object_detection_record, "test", sample, False, None, lambda x: x49    )50    assert isinstance(test_sample, Sample)51    assert hasattr(test_sample, "test")52    assert len(test_sample["test"]) > 053    # Test iv postprocess bbox54    test_tfms = [55        tfms.A.Adapter([*tfms.A.resize_and_pad([1000, 1000]), tfms.A.Normalize()])56    ]57    test_sample = convert_record_to_fo_sample(58        object_detection_record, "test", None, False, test_tfms, None59    )60    assert isinstance(test_sample, Sample)61    assert hasattr(test_sample, "test")62    assert len(test_sample["test"]) > 063def test_convert_prediction_to_fo_sample(object_detection_record):64    # Create Prediction65    object_detection_record.original_img_size = [1000, 1000]66    object_detection_prediction = Prediction(67        object_detection_record, object_detection_record68    )69    test_sample = convert_prediction_to_fo_sample(70        object_detection_prediction, undo_bbox_tfms_fn=lambda x: x71    )72    del (73        object_detection_record.original_img_size74    )  # Cleanup record, that has scope session75    assert isinstance(test_sample, Sample)76    assert hasattr(test_sample, "ground_truth")77    assert len(test_sample["ground_truth"]) > 078    assert hasattr(test_sample, "prediction")79    assert len(test_sample["prediction"]) > 080def test_create_fo_dataset(object_detection_record, cleanup_fo_dataset):81    # Create Prediction82    object_detection_record.original_img_size = [1000, 1000]83    object_detection_prediction = Prediction(84        object_detection_record, object_detection_record85    )86    # Test for record and with existing dataset87    dataset = Dataset("_iv_test")88    dataset = create_fo_dataset(89        [object_detection_prediction], dataset_name="_iv_test", exist_ok=True90    )91    del (92        object_detection_record.original_img_size93    )  # Cleanup record, that has scope session94    assert isinstance(dataset, Dataset)95    assert len(list(dataset.iter_samples())) > 096    # Test for prediction and create new dataset97    dataset = create_fo_dataset([object_detection_record], "_iv_test_1")98    assert isinstance(dataset, Dataset)...

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