ESP32ML - MicroPython Wrapper for C Optimized Conv Layer

model.py

class convLayer(Module):
    def __init__(self, in_channels, out_channels, kernel_size: tuple, stride):
        self.kernels = [[None for _ in range(in_channels)] for _2
                        in range(out_channels)]# Start with no kernels
        self.bias = [None for _ in range(out_channels)]# Start with no kernels
        self.stride = stride
        self.kernel_size = kernel_size
    @micropython.native
    def __call__(self, images):
        conv_func = mlops.conv2d
        output_data = []
        for out_channel in range(len(self.kernels)):
            channel_output = []
            for in_channel, image in enumerate(images):
                channel_output.append(conv_func(image, self.kernels[out_channel][in_channel],self.stride))
                biased_channel_output = sumMatricesAddBias(channel_output, self.bias[out_channel])
            output_data.append(biased_channel_output)
        return output_data
    def load_state_dict(self, weights, bias):
        for i, in_channels in enumerate(weights):
            for j, kernels in enumerate(in_channels):
                kernel_data = []
                for row in kernels:
                    kernel_data.append(row)
                self.kernels[i][j] = kernel_data
        for i, b in enumerate(bias):
            self.bias[i] = b

@micropython.native
def sumMatricesAddBias(matrices, bias=0.0):
    return mlops.sum_matrices(matrices, bias)