def test_noFilenameMatches_ReturnDatasetWithTrainOnly(self):
items = ["001_%d.png" % (i) for i in range(15)]
alwaysFalse = lambda x:False
target = PredicateDatasetSplitter(alwaysFalse, alwaysFalse)
result = target.split(items)
self.assertEqual(items, result[0])
self.assertEqual(0, len(result[1]))
self.assertEqual(0, len(result[2]))
def test_noFilenameMatches_ReturnDatasetWithTrainOnly(self):
items = ["001_%d.png" % (i) for i in range(15)]
alwaysFalse = lambda x: False
target = PredicateDatasetSplitter(alwaysFalse, alwaysFalse)
result = target.split(items)
self.assertEqual(items, result[0])
self.assertEqual(0, len(result[1]))
self.assertEqual(0, len(result[2]))
def test_filenameMatches_ReturnImagesPartitionedInTrainValidTest(self):
items = ["001_%d" % (i) for i in range(15)]
isInValidation = createMatcherWithTrueFor(["001_2","001_4","001_9"])
isInTest = createMatcherWithTrueFor(["001_3","001_5"])
target = PredicateDatasetSplitter(isInValidation, isInTest)
train, valid, test = target.split(items)
self.assertEqual(10, len(train))
self.assertEqual(["001_2","001_4","001_9"], sorted(valid))
self.assertEqual(["001_3","001_5"], sorted(test))
def test_filenameMatches_ReturnImagesPartitionedInTrainValidTest(self):
items = ["001_%d" % (i) for i in range(15)]
isInValidation = createMatcherWithTrueFor(["001_2", "001_4", "001_9"])
isInTest = createMatcherWithTrueFor(["001_3", "001_5"])
target = PredicateDatasetSplitter(isInValidation, isInTest)
train, valid, test = target.split(items)
self.assertEqual(10, len(train))
self.assertEqual(["001_2", "001_4", "001_9"], sorted(valid))
self.assertEqual(["001_3", "001_5"], sorted(test))
def CreateWithPredicateSplitter(imageSource, imgNumbersInValid, imgNumbersInTest, preprocessor = None):
return DatasetCreator(imageSource = imageSource,
datasetSplitter = PredicateDatasetSplitter(
shouldBeInValid = FileNumberRegexMatcher(imgNumbersInValid),
shouldBeInTest = FileNumberRegexMatcher(imgNumbersInTest)),
preprocessor = preprocessor)
