class ConfigTest(unittest.TestCase):
def setUp(self):
self.config = Config()
def test_config_values(self):
self.assertEqual(self.config.GPU_COUNT, 1)
self.assertEqual(self.config.IMAGES_PER_GPU, 2)
self.assertEqual(self.config.STEPS_PER_EPOCH, 1000)
self.assertEqual(self.config.BACKBONE, "resnet101")
def test_config_value_types(self):
self.assertIsInstance(self.config.BACKBONE, str)
self.assertIsInstance(self.config.BACKBONE_STRIDES, list)
self.assertIsInstance(self.config.MEAN_PIXEL, np.ndarray)
self.assertIsInstance(self.config.LOSS_WEIGHTS, dict)
self.assertIsInstance(self.config.USE_RPN_ROIS, bool)
self.assertIsInstance(self.config.TRAIN_BN, bool)
self.assertIsInstance(self.config.IMAGE_META_SIZE, int)
def test_config_value_shapes(self):
self.assertEqual(len(self.config.BACKBONE_STRIDES), 5)
self.assertEqual(len(self.config.MINI_MASK_SHAPE), 2)
self.assertEqual(self.config.MEAN_PIXEL.shape, (3, ))
self.assertEqual(self.config.IMAGE_SHAPE.shape, (3, ))
def test_config_value_calc(self):
self.assertEqual(self.config.BATCH_SIZE,
self.config.IMAGES_PER_GPU * self.config.GPU_COUNT)
def test_display_func(self):
capturedOutput = io.StringIO()
sys.stdout = capturedOutput
self.config.display()
self.assertTrue('Configurations:' in capturedOutput.getvalue())
print("Weights: ", args.weights)
print("Dataset: ", args.dataset)
print("Logs: ", args.logs)
# Configurations
if args.command == "train":
config = Config()
else:
class InferenceConfig(Config):
# Set batch size to 1 since we'll be running inference on
# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
GPU_COUNT = 1
IMAGES_PER_GPU = 1
config = InferenceConfig()
config.display()
# Create model
if args.command == "train":
model = modellib.MaskRCNN(mode="training", config=config,
model_dir=args.logs)
else:
model = modellib.MaskRCNN(mode="inference", config=config,
model_dir=args.logs)
# Select weights file to load
if args.weights.lower() == "coco":
weights_path = COCO_WEIGHTS_PATH
# Download weights file
if not os.path.exists(weights_path):
utils.download_trained_weights(weights_path)
def test_config_display(capsys):
config = Config()
config.display()
captured = capsys.readouterr()
assert "LEARNING_MOMENTUM 0.9" in captured.out
