def test_krakentrainer_rec_box_path(self):
"""
Tests recognition trainer constructor with legacy path training data.
"""
training_data = self.box_lines
evaluation_data = self.box_lines
module = RecognitionModel(format_type='path',
training_data=training_data,
evaluation_data=evaluation_data)
module.setup()
self.assertEqual(module.nn.seg_type, 'bbox')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.GroundTruthDataset)
trainer = KrakenTrainer(max_steps=1)
def test_krakentrainer_rec_bl_load_both(self):
training_data = [self.xml]
evaluation_data = [self.xml]
module = RecognitionModel(format_type='xml',
model=self.model,
training_data=training_data,
evaluation_data=evaluation_data,
resize='both')
module.setup()
self.assertEqual(module.nn.seg_type, 'baselines')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.PolygonGTDataset)
trainer = KrakenTrainer(max_steps=1)
self.assertEqual(module.nn.named_spec[-1].split("c")[-1], '60')
def test_krakentrainer_rec_bl_xml(self):
"""
Tests recognition trainer constructor with XML training data.
"""
training_data = [self.xml]
evaluation_data = [self.xml]
module = RecognitionModel(format_type='xml',
training_data=training_data,
evaluation_data=evaluation_data)
module.setup()
self.assertEqual(module.nn.seg_type, 'baselines')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.PolygonGTDataset)
self.assertEqual(len(module.train_set.dataset), 44)
self.assertEqual(len(module.val_set.dataset), 44)
trainer = KrakenTrainer(max_steps=1)
def test_krakentrainer_rec_box_load_both(self):
"""
Tests that adaptation works in both mode.
"""
training_data = self.box_lines
evaluation_data = self.box_lines
module = RecognitionModel(format_type='path',
model=self.model,
training_data=training_data,
evaluation_data=evaluation_data,
resize='both')
module.setup()
self.assertEqual(module.nn.seg_type, 'bbox')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.GroundTruthDataset)
trainer = KrakenTrainer(max_steps=1)
self.assertEqual(module.nn.named_spec[-1].split("c")[-1], '16')
def test_krakentrainer_rec_bl_append(self):
training_data = [self.xml]
evaluation_data = [self.xml]
module = RecognitionModel(format_type='xml',
model=self.model,
append=1,
spec='[Cr4,4,32]',
training_data=training_data,
evaluation_data=evaluation_data)
module.setup()
self.assertEqual(module.nn.seg_type, 'baselines')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.PolygonGTDataset)
self.assertTrue(
module.nn.spec.startswith(
'[1,48,0,1 Cr{C_0}4,2,1,4,2 Cr{C_1}4,4,32 O{O_2}'))
trainer = KrakenTrainer(max_steps=1)
def test_krakentrainer_rec_box_append(self):
"""
Tests that appending new layers onto a loaded model works.
"""
training_data = self.box_lines
evaluation_data = self.box_lines
module = RecognitionModel(format_type='path',
model=self.model,
append=1,
spec='[Cr4,4,32]',
training_data=training_data,
evaluation_data=evaluation_data)
module.setup()
self.assertEqual(module.nn.seg_type, 'bbox')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.GroundTruthDataset)
self.assertTrue(
module.nn.spec.startswith(
'[1,48,0,1 Cr{C_0}4,2,1,4,2 Cr{C_1}4,4,32 O{O_2}'))
trainer = KrakenTrainer(max_steps=1)
def test_krakentrainer_rec_bl_dict(self):
"""
Tests recognition trainer constructor with dictionary style training data.
"""
training_data = [{
'image': resources / 'bw.png',
'text': 'foo',
'baseline': [[10, 10], [300, 10]],
'boundary': [[10, 5], [300, 5], [300, 15], [10, 15]]
}]
evaluation_data = [{
'image': resources / 'bw.png',
'text': 'foo',
'baseline': [[10, 10], [300, 10]],
'boundary': [[10, 5], [300, 5], [300, 15], [10, 15]]
}]
module = RecognitionModel(format_type=None,
training_data=training_data,
evaluation_data=evaluation_data)
module.setup()
self.assertEqual(module.nn.seg_type, 'baselines')
self.assertIsInstance(module.train_set.dataset,
kraken.lib.dataset.PolygonGTDataset)
trainer = KrakenTrainer(max_steps=1)