def test_mobilenet_network_creation(self, mobilenet_model_id,
filter_size_scale):
"""Test for creation of a MobileNet classifier."""
mobilenet_params = {
('MobileNetV1', 1.0): 4254889,
('MobileNetV1', 0.75): 2602745,
('MobileNetV2', 1.0): 3540265,
('MobileNetV2', 0.75): 2664345,
('MobileNetV3Large', 1.0): 5508713,
('MobileNetV3Large', 0.75): 4013897,
('MobileNetV3Small', 1.0): 2555993,
('MobileNetV3Small', 0.75): 2052577,
('MobileNetV3EdgeTPU', 1.0): 4131593,
('MobileNetV3EdgeTPU', 0.75): 3019569,
}
inputs = np.random.rand(2, 224, 224, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.MobileNet(
model_id=mobilenet_model_id, filter_size_scale=filter_size_scale)
num_classes = 1001
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
self.assertEqual(model.count_params(),
mobilenet_params[(mobilenet_model_id, filter_size_scale)])
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
def test_mobilenet_creation(self, model_id, filter_size_scale):
"""Test creation of Mobilenet models."""
network = backbones.MobileNet(model_id=model_id,
filter_size_scale=filter_size_scale,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(
type='mobilenet',
mobilenet=backbones_cfg.MobileNet(
model_id=model_id, filter_size_scale=filter_size_scale))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
backbone_config=backbone_config,
norm_activation_config=norm_activation_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def test_mobilenet_network_creation(self, mobilenet_model_id,
filter_size_scale):
"""Test for creation of a MobileNet classifier."""
inputs = np.random.rand(2, 224, 224, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.MobileNet(model_id=mobilenet_model_id,
filter_size_scale=filter_size_scale)
num_classes = 1001
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
