def symbolic(g, x, weight, l2NormParams):
return g.op(add_domain("Normalize"),
x,
weight,
eps_f=l2NormParams.eps,
across_spatial_i=l2NormParams.across_spatial,
channel_shared_i=l2NormParams.channel_shared)
def symbolic(g, x):
zero = g.constant(0, [1], 'float')
zero = g.op("Unsqueeze", zero, axes_i=[1, 2, 3])
scale = g.op("Abs", x)
scale = g.op("ReduceMean", scale, axes_i=[0, 1, 2, 3])
scale_neg = g.op("Neg", scale)
return g.op(add_domain("FakeQuantize"), x, zero, zero, scale_neg, scale, levels_i=2)
def symbolic(g, input_fm, img_tensor, priorbox_params):
return g.op(add_domain("PriorBox"),
input_fm,
img_tensor,
min_size_f=[priorbox_params.min_size],
max_size_f=[priorbox_params.max_size],
aspect_ratio_f=priorbox_params.aspect_ratio,
flip_i=priorbox_params.flip,
clip_i=priorbox_params.clip,
variance_f=priorbox_params.variance,
step_f=priorbox_params.step,
offset_f=priorbox_params.offset,
step_h_f=priorbox_params.step_h,
step_w_f=priorbox_params.step_w,
img_size_i=priorbox_params.img_size,
img_h_i=priorbox_params.img_h,
img_w_i=priorbox_params.img_w)
def symbolic(g, input_, levels, input_low, input_high, output_low, output_high):
return g.op(add_domain("FakeQuantize"), input_, input_low, input_high, output_low, output_high, levels_i=levels)
def symbolic(g, x, scale, threshold):
zero = g.constant(0, [1], 'float')
zero = g.op("Unsqueeze", zero, axes_i=[0, 2, 3])
threshold = g.op("Mul", threshold, scale)
scale = g.op("Unsqueeze", scale, axes_i=[0, 2, 3])
return g.op(add_domain("FakeQuantize"), x, threshold, threshold, zero, scale, levels_i=2)