def complexity(cx, w_in, head_width, num_classes): if head_width > 0: cx = conv2d_cx(cx, w_in, head_width, 1) cx = norm2d_cx(cx, head_width) w_in = head_width cx = gap2d_cx(cx, w_in) cx = linear_cx(cx, w_in, num_classes, bias=True) return cx
def complexity(cx, w_in, mlp_d, seq_len): cx = linear_cx(cx, w_in, mlp_d, bias=True, num_locations=seq_len) cx = linear_cx(cx, mlp_d, w_in, bias=True, num_locations=seq_len) return cx
def complexity(cx, w_in, num_classes): return linear_cx(cx, w_in, num_classes, bias=True)
def complexity(cx, w_in, w_out, num_classes): cx = conv2d_cx(cx, w_in, w_out, 1) cx = norm2d_cx(cx, w_out) cx = gap2d_cx(cx, w_out) cx = linear_cx(cx, w_out, num_classes, bias=True) return cx
def complexity(cx, w_in, num_classes): cx = gap2d_cx(cx, w_in) cx = linear_cx(cx, w_in, num_classes, bias=True) return cx