Beispiel #1
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 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)
Beispiel #2
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 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)
Beispiel #3
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 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)
Beispiel #5
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 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)