def test_stop_layers1(self):
from dlpy.applications import MobileNetV1
backbone = MobileNetV1(self.s, width=1248, height=1248)
backbone_pure = backbone.to_functional_model(
stop_layers=backbone.layers[-2])
# expect last layer to be a bn layer right before global average pooling
self.assertEqual(backbone_pure.output_layers[0].name, 'conv_pw_13_bn')
def test_mobilenetv1(self):
from dlpy.applications import MobileNetV1
model = MobileNetV1(self.s, n_classes=2, n_channels=3, depth_multiplier=10, alpha=2)
self.assertTrue(len(model.layers) == 57)
self.assertTrue(model.layers[49]._output_size == (7, 7, 2048))
model.print_summary()
self.assertEqual(model.summary.iloc[7, -1], 36126720)
def test_multiple_stop_layers2(self):
from dlpy.applications import MobileNetV1
resnet50 = MobileNetV1(self.s, "MobileNetV1")
stop_layers = [resnet50.layers[x] for x in [-2, 4, -8] ]
feature_extractor1 = resnet50.to_functional_model(stop_layers=stop_layers)