Weights and Bias error on Convolution layers

Using the Keras to NNoM converter, I got following compiler error(s):

In file included from ../src/main.c:19:
../include/model.h:55:70: error: 'CONV2D_OUTPUT_RSHIFT' undeclared here (not in a function); did you mean 'CONV2D_OUTPUT_SHIFT'?
   55 | static const nnom_weight_t conv2d_w = { (const void*)conv2d_weights, CONV2D_OUTPUT_RSHIFT};
      |                                                                      ^~~~~~~~~~~~~~~~~~~~
      |                                                                      CONV2D_OUTPUT_SHIFT
../include/model.h:57:90: error: 'DEPTHWISE_CONV2D_OUTPUT_RSHIFT' undeclared here (not in a function); did you mean 'DEPTHWISE_CONV2D_OUTPUT_SHIFT'?
   57 | static const nnom_weight_t depthwise_conv2d_w = { (const void*)depthwise_conv2d_weights, DEPTHWISE_CONV2D_OUTPUT_RSHIFT};
      |                                                                                          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      |                                                                                          DEPTHWISE_CONV2D_OUTPUT_SHIFT
../include/model.h:59:94: error: 'DEPTHWISE_CONV2D_1_OUTPUT_RSHIFT' undeclared here (not in a function); did you mean 'DEPTHWISE_CONV2D_1_OUTPUT_SHIFT'?
   59 | static const nnom_weight_t depthwise_conv2d_1_w = { (const void*)depthwise_conv2d_1_weights, DEPTHWISE_CONV2D_1_OUTPUT_RSHIFT};
      |                                                                                              ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      |                                                                                              DEPTHWISE_CONV2D_1_OUTPUT_SHIFT
In file included from ../src/main.c:19:
../include/model.h: In function 'nnom_model_create':
../include/model.h:75:25: error: expected expression before ']' token
   75 |  layer[0] = Input(shape[], nnom_input_data);
      |                         ^
../include/model.h:76:90: error: 'conv2d_b' undeclared (first use in this function); did you mean 'conv2d_w'?
   76 |  layer[1] = model.hook(Conv2D(8, kernel(1, 128), stride(1, 1), PADDING_SAME, &conv2d_w, &conv2d_b), layer[0]);
      |                                                                                          ^~~~~~~~
      |                                                                                          conv2d_w
../include/model.h:76:90: note: each undeclared identifier is reported only once for each function it appears in
../include/model.h:77:103: error: 'depthwise_conv2d_b' undeclared (first use in this function); did you mean 'depthwise_conv2d_w'?
   77 |  layer[2] = model.hook(DW_Conv2D(1, kernel(64, 1), stride(1, 1), PADDING_VALID, &depthwise_conv2d_w, &depthwise_conv2d_b), layer[1]);
      |                                                                                                       ^~~~~~~~~~~~~~~~~~
      |                                                                                                       depthwise_conv2d_w
../include/model.h:79:104: error: 'depthwise_conv2d_1_b' undeclared (first use in this function); did you mean 'depthwise_conv2d_1_w'?
   79 |  layer[5] = model.hook(DW_Conv2D(1, kernel(1, 16), stride(1, 1), PADDING_SAME, &depthwise_conv2d_1_w, &depthwise_conv2d_1_b), layer[4]);
      |                                                                                                        ^~~~~~~~~~~~~~~~~~~~
      |                                                                                                        depthwise_conv2d_1_w

The nnom_model_create function looks like this:

static nnom_model_t* nnom_model_create(void)
{
    static nnom_model_t model;
    nnom_layer_t* layer[11];

    new_model(&model);

    layer[0] = Input(shape[], nnom_input_data);
    layer[1] = model.hook(Conv2D(8, kernel(1, 128), stride(1, 1), PADDING_SAME, &conv2d_w, &conv2d_b), layer[0]);
    layer[2] = model.hook(DW_Conv2D(1, kernel(64, 1), stride(1, 1), PADDING_VALID, &depthwise_conv2d_w, &depthwise_conv2d_b), layer[1]);
    layer[4] = model.hook(AvgPool(kernel(1, 8), stride(1, 8), PADDING_VALID), layer[3]);
    layer[5] = model.hook(DW_Conv2D(1, kernel(1, 16), stride(1, 1), PADDING_SAME, &depthwise_conv2d_1_w, &depthwise_conv2d_1_b), layer[4]);
    layer[7] = model.hook(AvgPool(kernel(1, 8), stride(1, 8), PADDING_VALID), layer[6]);
    layer[8] = model.hook(Dense(4, &dense_w, &dense_b), layer[7]);
    layer[9] = model.hook(Softmax(), layer[8]);
    layer[10] = model.hook(Output(shape(4,1,1), nnom_output_data), layer[9]);
    model_compile(&model, layer[0], layer[10]);
    return &model;
}

Where does this error comes from? How can I avoid them? Is it because of the use_bias = False which I set in both Conv2D and DepthwiseConv2D layer?

majianjia / nnom

Weights and Bias error on Convolution layers #74