retinanet.py

def load_retinanet(weights, n_classes, freeze=True):
    modifier = freeze_model if freeze else None

    model = resnet50_retinanet(num_classes=num_classes, modifier=modifier)
    model.load_weights(weights, by_name=True, skip_mismatch=True)

    return model
  
  
def compile(model):
    model.compile(
        loss={
            'regression'    : keras_retinanet.losses.smooth_l1(),
            'classification': keras_retinanet.losses.focal()
        },
        optimizer=optimizers.adam(lr=configs['lr'], clipnorm=0.001)
    )
    
    
def train(model, train_gen, val_gen, callbacks, n_epochs=20):
    """train_gen and val_gen are instances of DfGenerator."""
    model.fit_generator(
        train_gen,
        steps_per_epoch=len(train_gen),
        validation_data=val_gen,
        validation_steps=len(val_gen),
        callbacks=callbacks,
        epochs=n_epochs,
        verbose=2
    )
    

From line 1, it looks to me that line 4 should be:

model = resnet50_retinanet(num_classes=n_classes, modifier=modifier)