def test_backbone(backbone, alpha):
# ignore warnings in this test
warnings.simplefilter('ignore')
num_classes = 10
inputs = np.zeros((1, 1024, 363, 3), dtype=np.float32)
targets = [
np.zeros((1, 68760, 5), dtype=np.float32),
np.zeros((1, 68760, num_classes + 1))
]
inp = keras.layers.Input(inputs[0].shape)
mobilenet_backbone = MobileNetBackbone(
backbone='{}_{}'.format(backbone, format(alpha)))
training_model = mobilenet_backbone.retinanet(num_classes=num_classes,
inputs=inp)
training_model.summary()
# compile model
training_model.compile(loss={
'regression': losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.Adam(lr=1e-5,
clipnorm=0.001))
training_model.fit(inputs, targets, batch_size=1)
def test_backbone(backbone, alpha):
# ignore warnings in this test
warnings.simplefilter('ignore')
num_classes = 10
inputs = np.zeros((1, 1024, 363, 3), dtype=np.float32)
targets = [np.zeros((1, 68760, 5), dtype=np.float32), np.zeros((1, 68760, num_classes + 1))]
inp = keras.layers.Input(inputs[0].shape)
mobilenet_backbone = MobileNetBackbone(backbone='{}_{}'.format(backbone, format(alpha)))
training_model = mobilenet_backbone.retinanet(num_classes=num_classes, inputs=inp)
training_model.summary()
# compile model
training_model.compile(
loss={
'regression': losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001))
training_model.fit(inputs, targets, batch_size=1)