def test_011_success_download(self):
name = 'r941_min_high_g351'
model_file = models.resolve_model(name)
tmp_file = "{}.tmp".format(model_file)
os.rename(model_file, tmp_file)
new_file = models.resolve_model(name)
self.assertTrue(os.path.isfile(new_file))
os.remove(new_file)
os.rename(tmp_file, model_file)
def test_000_total_bundled_size(self):
total = 0
for name in medaka.options.current_models:
model_file = models.resolve_model(name)
total += os.path.getsize(model_file)
self.assertLess(total / 1024 / 1024, 45,
"Bundled model file size too large")
def test_999_load_all_models(self):
for name in medaka.options.allowed_models:
model_file = models.resolve_model(name)
with medaka.models.open_model(model_file) as ds:
model = ds.load_model()
self.assertIsInstance(model, tensorflow.keras.models.Model)
feature_encoder = ds.get_meta('feature_encoder')
self.assertIsInstance(feature_encoder, BaseFeatureEncoder)
label_scheme = ds.get_meta('label_scheme')
self.assertIsInstance(label_scheme, BaseLabelScheme)
def test_999_load_all_models(self):
for name in medaka.options.allowed_models:
model_file = models.resolve_model(name)
model = medaka.models.open_model(model_file).load_model()
self.assertIsInstance(model, tensorflow.keras.models.Model)
# Check we can get necessary functions for inference
with DataStore(model_file) as ds:
feature_encoder = ds.get_meta('feature_encoder')
self.assertIsInstance(feature_encoder, BaseFeatureEncoder)
label_scheme = ds.get_meta('label_scheme')
self.assertIsInstance(label_scheme, BaseLabelScheme)
def test_010_failed_download(self):
name = 'garbage'
medaka.options.allowed_models.append(name)
with self.assertRaises(medaka.models.DownloadError):
models.resolve_model(name)
medaka.options.allowed_models.pop()