CNN Face Detection with tensor as input

[hamedato]

I am generating some face images using Conv2DTranspose. I'd like to output the 68 landmarks as the final output rather than the generated image. So, I used lambda layer to define my custom function. However, cnn_face_detection_model_v1 says it only accepts either list or array and not tensors. How Can I bring cnn_face_detection_model_v1 into my custom function? Here's the full error description:

TypeError: call(): incompatible function arguments. The following argument types are supported:
1. (self: dlib.cnn_face_detection_model_v1, imgs: list, upsample_num_times: int=0, batch_size: int=128) -> std::vector<std::vector<dlib::mmod_rect,std::allocatordlib::mmod_rect >,std::allocator<std::vector<dlib::mmod_rect,std::allocatordlib::mmod_rect > > >
2. (self: dlib.cnn_face_detection_model_v1, img: array, upsample_num_times: int=0) -> std::vector<dlib::mmod_rect,std::allocatordlib::mmod_rect >

Invoked with: <dlib.cnn_face_detection_model_v1 object at 0x000001D00FC0FF48>, <tf.Tensor 'conv2d/BiasAdd:0' shape=(?, 160, 160, 3) dtype=float32>, 1

Did you forget to #include <pybind11/stl.h>? Or <pybind11/complex.h>,
<pybind11/functional.h>, <pybind11/chrono.h>, etc. Some automatic
conversions are optional and require extra headers to be included
when compiling your pybind11 module.