def calculate_conv_dimensions(W_in, H_in, K, S, P):
    """
    Calculate the output dimensions of a convolutional layer.

    Parameters:
    W_in (int): Width of the input image
    H_in (int): Height of the input image
    K (int): Size of the filter (assumed to be square)
    S (int): Stride
    P (int): Padding

    Returns:
    (int, int): Width and height of the output activation map
    """
    W_out = (W_in - K + (2 * P)) / S + 1
    H_out = (H_in - K + (2 * P)) / S + 1

    return W_out, H_out


print(f"w, h = {calculate_conv_dimensions(W_in=2048, H_in=32, K=11, S=4, P=2)=}")
# w, h = calculate_conv_dimensions(W_in=2048, H_in=32, K=11, S=4, P=2)
# print(f"{calculate_conv_dimensions(W_in=w, H_in=h, K=11, S=4, P=2)=}")