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
