<
   ir_version: 10,
   opset_import: ["" : 20]
>
"PaddlePaddle Graph in PIR mode" (uint8[batch,height,width,3] image) => (float[batch,height,width] dbnet_probs) 
   <
      float[batch,24,unk__0,unk__1] "Add.1"
      float[batch,48,1,1] "Add.101"
      float[batch,192,1,1] "Add.103"
      float[batch,384,unk__50,unk__51] "Add.105"
      float[batch,192,unk__50,unk__51] "Add.109"
      float[batch,48,unk__8,unk__9] "Add.11"
      float[batch,192,unk__50,unk__51] "Add.111"
      float[batch,192,unk__50,unk__51] "Add.113"
      float[batch,384,unk__50,unk__51] "Add.115"
      float[batch,192,unk__50,unk__51] "Add.119"
      float[batch,192,unk__50,unk__51] "Add.121"
      float[batch,192,unk__88,unk__89] "Add.123"
      float[batch,384,unk__88,unk__89] "Add.125"
      float[batch,384,unk__88,unk__89] "Add.129"
      float[batch,12,1,1] "Add.13"
      float[batch,384,unk__88,unk__89] "Add.131"
      float[batch,96,1,1] "Add.133"
      float[batch,384,1,1] "Add.135"
      float[batch,768,unk__88,unk__89] "Add.137"
      float[batch,384,unk__88,unk__89] "Add.141"
      float[batch,384,unk__88,unk__89] "Add.143"
      float[batch,384,unk__88,unk__89] "Add.145"
      float[batch,768,unk__88,unk__89] "Add.147"
      float[batch,48,1,1] "Add.15"
      float[batch,384,unk__88,unk__89] "Add.151"
      float[batch,384,unk__88,unk__89] "Add.153"
      float[batch,96,unk__88,unk__89] "Add.159"
      float[batch,96,unk__50,unk__51] "Add.165"
      float[batch,96,unk__8,unk__9] "Add.17"
      float[batch,96,unk__28,unk__29] "Add.171"
      float[batch,96,unk__8,unk__9] "Add.177"
      float[batch,96,unk__50,unk__51] "Add.179"
      float[batch,96,unk__28,unk__29] "Add.181"
      float[batch,96,unk__8,unk__9] "Add.183"
      float[batch,96,unk__88,unk__89] "Add.185"
      float[batch,24,unk__88,unk__89] "Add.191"
      float[batch,96,unk__50,unk__51] "Add.193"
      float[batch,24,unk__50,unk__51] "Add.199"
      float[batch,96,unk__28,unk__29] "Add.201"
      float[batch,24,unk__28,unk__29] "Add.207"
      float[batch,96,unk__8,unk__9] "Add.209"
      float[batch,48,unk__8,unk__9] "Add.21"
      float[batch,24,unk__8,unk__9] "Add.215"
      float[batch,24,unk__8,unk__9] "Add.217"
      float[batch,24,unk__0,unk__1] "Add.219"
      float[batch,1,height,width] "Add.221"
      float[batch,48,unk__8,unk__9] "Add.23"
      float[batch,48,unk__8,unk__9] "Add.25"
      float[batch,96,unk__8,unk__9] "Add.27"
      float[batch,12,unk__0,unk__1] "Add.3"
      float[batch,48,unk__8,unk__9] "Add.31"
      float[batch,48,unk__8,unk__9] "Add.33"
      float[batch,48,unk__28,unk__29] "Add.35"
      float[batch,96,unk__28,unk__29] "Add.37"
      float[batch,96,unk__28,unk__29] "Add.41"
      float[batch,96,unk__28,unk__29] "Add.43"
      float[batch,24,1,1] "Add.45"
      float[batch,96,1,1] "Add.47"
      float[batch,192,unk__28,unk__29] "Add.49"
      float[batch,24,unk__0,unk__1] "Add.5"
      float[batch,96,unk__28,unk__29] "Add.53"
      float[batch,96,unk__28,unk__29] "Add.55"
      float[batch,96,unk__28,unk__29] "Add.57"
      float[batch,192,unk__28,unk__29] "Add.59"
      float[batch,96,unk__28,unk__29] "Add.63"
      float[batch,96,unk__28,unk__29] "Add.65"
      float[batch,96,unk__50,unk__51] "Add.67"
      float[batch,192,unk__50,unk__51] "Add.69"
      float[batch,24,unk__8,unk__9] "Add.7"
      float[batch,192,unk__50,unk__51] "Add.73"
      float[batch,192,unk__50,unk__51] "Add.75"
      float[batch,48,1,1] "Add.77"
      float[batch,192,1,1] "Add.79"
      float[batch,384,unk__50,unk__51] "Add.81"
      float[batch,192,unk__50,unk__51] "Add.85"
      float[batch,192,unk__50,unk__51] "Add.87"
      float[batch,192,unk__50,unk__51] "Add.89"
      float[batch,48,unk__8,unk__9] "Add.9"
      float[batch,384,unk__50,unk__51] "Add.91"
      float[batch,192,unk__50,unk__51] "Add.95"
      float[batch,192,unk__50,unk__51] "Add.97"
      float[batch,192,unk__50,unk__51] "Add.99"
      float[batch,48,unk__0,unk__1] "Concat.1"
      float[batch,96,unk__8,unk__9] "Concat.3"
      float[batch,48,unk__8,unk__9] "Mul.1"
      float[batch,96,unk__28,unk__29] "Mul.12"
      float[batch,192,unk__50,unk__51] "Mul.23"
      float[batch,192,unk__50,unk__51] "Mul.31"
      float[batch,384,unk__88,unk__89] "Mul.42"
      float[batch,48,1,1] "ReduceMean.1"
      float[batch,96,1,1] "ReduceMean.3"
      float[batch,192,1,1] "ReduceMean.5"
      float[batch,192,1,1] "ReduceMean.7"
      float[batch,384,1,1] "ReduceMean.9"
      float[batch,96,unk__88,unk__89] "p2o.pd_op.conv2d.41.0"
      float[batch,96,unk__50,unk__51] "p2o.pd_op.conv2d.44.0"
      float[batch,96,unk__28,unk__29] "p2o.pd_op.conv2d.47.0"
      float[batch,96,unk__8,unk__9] "p2o.pd_op.conv2d.50.0"
      float[batch,24,unk__88,unk__89] "p2o.pd_op.conv2d.53.0"
      float[batch,24,unk__50,unk__51] "p2o.pd_op.conv2d.56.0"
      float[batch,24,unk__28,unk__29] "p2o.pd_op.conv2d.59.0"
      float[batch,24,unk__8,unk__9] "p2o.pd_op.conv2d.62.0"
      float[batch,96,unk__8,unk__9] "p2o.pd_op.gelu.0.0"
      float[batch,96,unk__8,unk__9] "p2o.pd_op.gelu.1.0"
      float[batch,384,unk__88,unk__89] "p2o.pd_op.gelu.10.0"
      float[batch,768,unk__88,unk__89] "p2o.pd_op.gelu.11.0"
      float[batch,768,unk__88,unk__89] "p2o.pd_op.gelu.12.0"
      float[batch,96,unk__28,unk__29] "p2o.pd_op.gelu.2.0"
      float[batch,192,unk__28,unk__29] "p2o.pd_op.gelu.3.0"
      float[batch,192,unk__28,unk__29] "p2o.pd_op.gelu.4.0"
      float[batch,192,unk__50,unk__51] "p2o.pd_op.gelu.5.0"
      float[batch,384,unk__50,unk__51] "p2o.pd_op.gelu.6.0"
      float[batch,384,unk__50,unk__51] "p2o.pd_op.gelu.7.0"
      float[batch,384,unk__50,unk__51] "p2o.pd_op.gelu.8.0"
      float[batch,384,unk__50,unk__51] "p2o.pd_op.gelu.9.0"
      float[batch,48,1,1] "p2o.pd_op.hardsigmoid.0.0"
      float[batch,96,1,1] "p2o.pd_op.hardsigmoid.1.0"
      float[batch,192,1,1] "p2o.pd_op.hardsigmoid.2.0"
      float[batch,192,1,1] "p2o.pd_op.hardsigmoid.3.0"
      float[batch,384,1,1] "p2o.pd_op.hardsigmoid.4.0"
      float[batch,96,unk__50,unk__51] "p2o.pd_op.nearest_interp.0.0"
      float[batch,96,unk__28,unk__29] "p2o.pd_op.nearest_interp.1.0"
      float[batch,96,unk__8,unk__9] "p2o.pd_op.nearest_interp.2.0"
      float[batch,24,unk__8,unk__9] "p2o.pd_op.nearest_interp.3.0"
      float[batch,24,unk__8,unk__9] "p2o.pd_op.nearest_interp.4.0"
      float[batch,24,unk__8,unk__9] "p2o.pd_op.nearest_interp.5.0"
      float[batch,24,unk__0,unk__1] "p2o.pd_op.pool2d.0.0"
      float[batch,96,1,1] "p2o.pd_op.pool2d.1.0"
      float[batch,96,1,1] "p2o.pd_op.pool2d.2.0"
      float[batch,96,1,1] "p2o.pd_op.pool2d.3.0"
      float[batch,96,1,1] "p2o.pd_op.pool2d.4.0"
      float[batch,24,1,1] "p2o.pd_op.pool2d.5.0"
      float[batch,24,1,1] "p2o.pd_op.pool2d.6.0"
      float[batch,24,1,1] "p2o.pd_op.pool2d.7.0"
      float[batch,24,1,1] "p2o.pd_op.pool2d.8.0"
      float[batch,24,unk__0,unk__1] "p2o.pd_op.relu.0.0"
      float[batch,12,unk__0,unk__1] "p2o.pd_op.relu.1.0"
      float[batch,24,unk__8,unk__9] "p2o.pd_op.relu.18.0"
      float[batch,24,unk__0,unk__1] "p2o.pd_op.relu.19.0"
      float[batch,24,unk__0,unk__1] "p2o.pd_op.relu.2.0"
      float[batch,24,unk__8,unk__9] "p2o.pd_op.relu.3.0"
      float[batch,48,unk__8,unk__9] "p2o.pd_op.relu.4.0"
      float[batch,12,1,1] "p2o.pd_op.relu.5.0"
      float[batch,24,1,1] "p2o.pd_op.relu.6.0"
      float[batch,48,1,1] "p2o.pd_op.relu.7.0"
      float[batch,48,1,1] "p2o.pd_op.relu.8.0"
      float[batch,96,1,1] "p2o.pd_op.relu.9.0"
      float[batch,96,1,1] "se_group0_p2o.pd_op.hardsigmoid.5.0"
      float[batch,96,1,1] "se_group0_p2o.pd_op.hardsigmoid.6.0"
      float[batch,96,1,1] "se_group0_p2o.pd_op.hardsigmoid.7.0"
      float[batch,96,1,1] "se_group0_p2o.pd_op.hardsigmoid.8.0"
      float[batch,24,1,1] "se_group1_p2o.pd_op.hardsigmoid.10.0"
      float[batch,24,1,1] "se_group1_p2o.pd_op.hardsigmoid.11.0"
      float[batch,24,1,1] "se_group1_p2o.pd_op.hardsigmoid.12.0"
      float[batch,24,1,1] "se_group1_p2o.pd_op.hardsigmoid.9.0"
      float[batch,1,height,width] fetch_name_0
      float[batch,96,1,1] se_group0_down
      float[batch,384,1,1] se_group0_gates
      float[batch,384,1,1] se_group0_pooled
      float[batch,96,1,1] se_group0_relu
      float[batch,384,1,1] se_group0_up
      float[batch,24,1,1] se_group1_down
      float[batch,96,1,1] se_group1_gates
      float[batch,96,1,1] se_group1_pooled
      float[batch,24,1,1] se_group1_relu
      float[batch,96,1,1] se_group1_up
      float[batch,height,width,3] tmp
      float[batch,3,height,width] tmp_0
      float[batch,96,1,1] val_0
      float[batch,96,1,1] val_1
      float[batch,96,1,1] val_2
      float[batch,96,1,1] val_3
      float[batch,24,1,1] val_4
      float[batch,24,1,1] val_5
      float[batch,24,1,1] val_6
      float[batch,24,1,1] val_7
      float[batch,3,height,width] x
   >
{
   [n0] tmp = Cast <to: int = 1> (image)
   [n1] tmp_0 = Transpose <perm: ints = [0, 3, 1, 2]> (tmp)
   [n4] x = Sub (tmp_0, const_cast)
   "Add.1" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> (x, "conv2d_112.w_0", "p2o.pd_op.conv2d.0.0_bias")
   ["Relu.0"] "p2o.pd_op.relu.0.0" = Relu ("Add.1")
   "Add.3" = Conv <auto_pad: string = "SAME_UPPER", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [2, 2], strides: ints = [1, 1]> ("p2o.pd_op.relu.0.0", "conv2d_113.w_0", "p2o.pd_op.conv2d.1.0_bias")
   ["Relu.1"] "p2o.pd_op.relu.1.0" = Relu ("Add.3")
   "Add.5" = Conv <auto_pad: string = "SAME_UPPER", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [2, 2], strides: ints = [1, 1]> ("p2o.pd_op.relu.1.0", "conv2d_114.w_0", "p2o.pd_op.conv2d.2.0_bias")
   ["Relu.2"] "p2o.pd_op.relu.2.0" = Relu ("Add.5")
   ["MaxPool.0"] "p2o.pd_op.pool2d.0.0" = MaxPool <auto_pad: string = "SAME_UPPER", ceil_mode: int = 0, kernel_shape: ints = [2, 2], strides: ints = [1, 1]> ("p2o.pd_op.relu.0.0")
   ["Concat.0"] "Concat.1" = Concat <axis: int = 1> ("p2o.pd_op.pool2d.0.0", "p2o.pd_op.relu.2.0")
   "Add.7" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("Concat.1", "conv2d_115.w_0", "p2o.pd_op.conv2d.3.0_bias")
   ["Relu.3"] "p2o.pd_op.relu.3.0" = Relu ("Add.7")
   "Add.9" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.3.0", "conv2d_116.w_0", "p2o.pd_op.conv2d.4.0_bias")
   ["Relu.4"] "p2o.pd_op.relu.4.0" = Relu ("Add.9")
   "Add.11" = Conv <dilations: ints = [1, 1], group: int = 48, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("p2o.pd_op.relu.4.0", "conv2d_118.w_0", "p2o.pd_op.depthwise_conv2d.0.0_bias")
   ["ReduceMean.0"] "ReduceMean.1" = ReduceMean <keepdims: int = 1> ("Add.11", _v_680)
   "Add.13" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("ReduceMean.1", "conv2d_7.w_0", "p2o.pd_op.conv2d.5.0_bias")
   ["Relu.5"] "p2o.pd_op.relu.5.0" = Relu ("Add.13")
   "Add.15" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.5.0", "conv2d_8.w_0", "p2o.pd_op.conv2d.6.0_bias")
   ["HardSigmoid.0"] "p2o.pd_op.hardsigmoid.0.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.15")
   ["Mul.0"] "Mul.1" = Mul ("Add.11", "p2o.pd_op.hardsigmoid.0.0")
   "Add.17" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.1", "conv2d_119.w_0", "p2o.pd_op.conv2d.7.0_bias")
   "p2o.pd_op.gelu.0.0" = Gelu <approximate: string = "none"> ("Add.17")
   "Add.21" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.0.0", "conv2d_120.w_0", "p2o.pd_op.conv2d.8.0_bias")
   ["Add.22"] "Add.23" = Add ("Mul.1", "Add.21")
   "Add.25" = Conv <dilations: ints = [1, 1], group: int = 48, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.23", "conv2d_122.w_0", "p2o.pd_op.depthwise_conv2d.1.0_bias")
   "Add.27" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.25", "conv2d_123.w_0", "p2o.pd_op.conv2d.9.0_bias")
   "p2o.pd_op.gelu.1.0" = Gelu <approximate: string = "none"> ("Add.27")
   "Add.31" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.1.0", "conv2d_124.w_0", "p2o.pd_op.conv2d.10.0_bias")
   ["Add.32"] "Add.33" = Add ("Add.25", "Add.31")
   "Add.35" = Conv <dilations: ints = [1, 1], group: int = 48, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("Add.33", "conv2d_125.w_0", "p2o.pd_op.depthwise_conv2d.2.0_bias")
   "Add.37" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.35", "conv2d_126.w_0", "p2o.pd_op.conv2d.11.0_bias")
   "p2o.pd_op.gelu.2.0" = Gelu <approximate: string = "none"> ("Add.37")
   "Add.41" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.2.0", "conv2d_127.w_0", "p2o.pd_op.conv2d.12.0_bias")
   "Add.43" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.41", "conv2d_129.w_0", "p2o.pd_op.depthwise_conv2d.3.0_bias")
   ["ReduceMean.2"] "ReduceMean.3" = ReduceMean <keepdims: int = 1> ("Add.43", _v_680)
   "Add.45" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("ReduceMean.3", "conv2d_20.w_0", "p2o.pd_op.conv2d.13.0_bias")
   ["Relu.6"] "p2o.pd_op.relu.6.0" = Relu ("Add.45")
   "Add.47" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.6.0", "conv2d_21.w_0", "p2o.pd_op.conv2d.14.0_bias")
   ["HardSigmoid.1"] "p2o.pd_op.hardsigmoid.1.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.47")
   ["Mul.11"] "Mul.12" = Mul ("Add.43", "p2o.pd_op.hardsigmoid.1.0")
   "Add.49" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.12", "conv2d_130.w_0", "p2o.pd_op.conv2d.15.0_bias")
   "p2o.pd_op.gelu.3.0" = Gelu <approximate: string = "none"> ("Add.49")
   "Add.53" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.3.0", "conv2d_131.w_0", "p2o.pd_op.conv2d.16.0_bias")
   ["Add.54"] "Add.55" = Add ("Mul.12", "Add.53")
   "Add.57" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.55", "conv2d_133.w_0", "p2o.pd_op.depthwise_conv2d.4.0_bias")
   "Add.59" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.57", "conv2d_134.w_0", "p2o.pd_op.conv2d.17.0_bias")
   "p2o.pd_op.gelu.4.0" = Gelu <approximate: string = "none"> ("Add.59")
   "Add.63" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.4.0", "conv2d_135.w_0", "p2o.pd_op.conv2d.18.0_bias")
   ["Add.64"] "Add.65" = Add ("Add.57", "Add.63")
   "Add.67" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("Add.65", "conv2d_136.w_0", "p2o.pd_op.depthwise_conv2d.5.0_bias")
   "Add.69" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.67", "conv2d_137.w_0", "p2o.pd_op.conv2d.19.0_bias")
   "p2o.pd_op.gelu.5.0" = Gelu <approximate: string = "none"> ("Add.69")
   "Add.73" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.5.0", "conv2d_138.w_0", "p2o.pd_op.conv2d.20.0_bias")
   "Add.75" = Conv <dilations: ints = [1, 1], group: int = 192, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.73", "conv2d_140.w_0", "p2o.pd_op.depthwise_conv2d.6.0_bias")
   ["ReduceMean.4"] "ReduceMean.5" = ReduceMean <keepdims: int = 1> ("Add.75", _v_680)
   "Add.77" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("ReduceMean.5", "conv2d_33.w_0", "p2o.pd_op.conv2d.21.0_bias")
   ["Relu.7"] "p2o.pd_op.relu.7.0" = Relu ("Add.77")
   "Add.79" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.7.0", "conv2d_34.w_0", "p2o.pd_op.conv2d.22.0_bias")
   ["HardSigmoid.2"] "p2o.pd_op.hardsigmoid.2.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.79")
   ["Mul.22"] "Mul.23" = Mul ("Add.75", "p2o.pd_op.hardsigmoid.2.0")
   "Add.81" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.23", "conv2d_141.w_0", "p2o.pd_op.conv2d.23.0_bias")
   "p2o.pd_op.gelu.6.0" = Gelu <approximate: string = "none"> ("Add.81")
   "Add.85" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.6.0", "conv2d_142.w_0", "p2o.pd_op.conv2d.24.0_bias")
   ["Add.86"] "Add.87" = Add ("Mul.23", "Add.85")
   "Add.89" = Conv <dilations: ints = [1, 1], group: int = 192, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.87", "conv2d_144.w_0", "p2o.pd_op.depthwise_conv2d.7.0_bias")
   "Add.91" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.89", "conv2d_145.w_0", "p2o.pd_op.conv2d.25.0_bias")
   "p2o.pd_op.gelu.7.0" = Gelu <approximate: string = "none"> ("Add.91")
   "Add.95" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.7.0", "conv2d_146.w_0", "p2o.pd_op.conv2d.26.0_bias")
   ["Add.96"] "Add.97" = Add ("Add.89", "Add.95")
   "Add.99" = Conv <dilations: ints = [1, 1], group: int = 192, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.97", "conv2d_148.w_0", "p2o.pd_op.depthwise_conv2d.8.0_bias")
   ["ReduceMean.6"] "ReduceMean.7" = ReduceMean <keepdims: int = 1> ("Add.99", _v_680)
   "Add.101" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("ReduceMean.7", "conv2d_43.w_0", "p2o.pd_op.conv2d.27.0_bias")
   ["Relu.8"] "p2o.pd_op.relu.8.0" = Relu ("Add.101")
   "Add.103" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.8.0", "conv2d_44.w_0", "p2o.pd_op.conv2d.28.0_bias")
   ["HardSigmoid.3"] "p2o.pd_op.hardsigmoid.3.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.103")
   ["Mul.30"] "Mul.31" = Mul ("Add.99", "p2o.pd_op.hardsigmoid.3.0")
   "Add.105" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.31", "conv2d_149.w_0", "p2o.pd_op.conv2d.29.0_bias")
   "p2o.pd_op.gelu.8.0" = Gelu <approximate: string = "none"> ("Add.105")
   "Add.109" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.8.0", "conv2d_150.w_0", "p2o.pd_op.conv2d.30.0_bias")
   ["Add.110"] "Add.111" = Add ("Mul.31", "Add.109")
   "Add.113" = Conv <dilations: ints = [1, 1], group: int = 192, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.111", "conv2d_152.w_0", "p2o.pd_op.depthwise_conv2d.9.0_bias")
   "Add.115" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.113", "conv2d_153.w_0", "p2o.pd_op.conv2d.31.0_bias")
   "p2o.pd_op.gelu.9.0" = Gelu <approximate: string = "none"> ("Add.115")
   "Add.119" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.9.0", "conv2d_154.w_0", "p2o.pd_op.conv2d.32.0_bias")
   ["Add.120"] "Add.121" = Add ("Add.113", "Add.119")
   "Add.123" = Conv <dilations: ints = [1, 1], group: int = 192, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("Add.121", "conv2d_155.w_0", "p2o.pd_op.depthwise_conv2d.10.0_bias")
   "Add.125" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.123", "conv2d_156.w_0", "p2o.pd_op.conv2d.33.0_bias")
   "p2o.pd_op.gelu.10.0" = Gelu <approximate: string = "none"> ("Add.125")
   "Add.129" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.10.0", "conv2d_157.w_0", "p2o.pd_op.conv2d.34.0_bias")
   "Add.131" = Conv <dilations: ints = [1, 1], group: int = 384, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.129", "conv2d_159.w_0", "p2o.pd_op.depthwise_conv2d.11.0_bias")
   ["ReduceMean.8"] "ReduceMean.9" = ReduceMean <keepdims: int = 1> ("Add.131", _v_680)
   "Add.133" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("ReduceMean.9", "conv2d_56.w_0", "p2o.pd_op.conv2d.35.0_bias")
   ["Relu.9"] "p2o.pd_op.relu.9.0" = Relu ("Add.133")
   "Add.135" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.9.0", "conv2d_57.w_0", "p2o.pd_op.conv2d.36.0_bias")
   ["HardSigmoid.4"] "p2o.pd_op.hardsigmoid.4.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.135")
   ["Mul.41"] "Mul.42" = Mul ("Add.131", "p2o.pd_op.hardsigmoid.4.0")
   "Add.137" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.42", "conv2d_160.w_0", "p2o.pd_op.conv2d.37.0_bias")
   "p2o.pd_op.gelu.11.0" = Gelu <approximate: string = "none"> ("Add.137")
   "Add.141" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.11.0", "conv2d_161.w_0", "p2o.pd_op.conv2d.38.0_bias")
   ["Add.142"] "Add.143" = Add ("Mul.42", "Add.141")
   "Add.145" = Conv <dilations: ints = [1, 1], group: int = 384, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Add.143", "conv2d_163.w_0", "p2o.pd_op.depthwise_conv2d.12.0_bias")
   "Add.147" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.145", "conv2d_164.w_0", "p2o.pd_op.conv2d.39.0_bias")
   "p2o.pd_op.gelu.12.0" = Gelu <approximate: string = "none"> ("Add.147")
   "Add.151" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.gelu.12.0", "conv2d_165.w_0", "p2o.pd_op.conv2d.40.0_bias")
   ["Add.152"] "Add.153" = Add ("Add.145", "Add.151")
   ["Conv.54"] "p2o.pd_op.conv2d.41.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.153", "conv2d_94.w_0")
   ["GlobalAveragePool.0"] "p2o.pd_op.pool2d.1.0" = GlobalAveragePool ("p2o.pd_op.conv2d.41.0")
   ["Conv.57"] "p2o.pd_op.conv2d.44.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.121", "conv2d_84.w_0")
   ["GlobalAveragePool.1"] "p2o.pd_op.pool2d.2.0" = GlobalAveragePool ("p2o.pd_op.conv2d.44.0")
   ["Conv.60"] "p2o.pd_op.conv2d.47.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.65", "conv2d_74.w_0")
   ["GlobalAveragePool.2"] "p2o.pd_op.pool2d.3.0" = GlobalAveragePool ("p2o.pd_op.conv2d.47.0")
   ["Conv.63"] "p2o.pd_op.conv2d.50.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.33", "conv2d_64.w_0")
   ["GlobalAveragePool.3"] "p2o.pd_op.pool2d.4.0" = GlobalAveragePool ("p2o.pd_op.conv2d.50.0")
   se_group0_pooled = Concat <axis: int = 1> ("p2o.pd_op.pool2d.1.0", "p2o.pd_op.pool2d.2.0", "p2o.pd_op.pool2d.3.0", "p2o.pd_op.pool2d.4.0")
   se_group0_down = Conv <dilations: ints = [1, 1], group: int = 4, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (se_group0_pooled, se_group0_down1, se_group0_down2)
   se_group0_relu = Relu (se_group0_down)
   se_group0_up = Conv <dilations: ints = [1, 1], group: int = 4, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (se_group0_relu, se_group0_up1, se_group0_up2)
   se_group0_gates = HardSigmoid <alpha: float = 0.2, beta: float = 0.5> (se_group0_up)
   "se_group0_p2o.pd_op.hardsigmoid.5.0", "se_group0_p2o.pd_op.hardsigmoid.6.0", "se_group0_p2o.pd_op.hardsigmoid.7.0", "se_group0_p2o.pd_op.hardsigmoid.8.0" = Split <axis: int = 1> (se_group0_gates, se_group0_sizes)
   val_0 = Add ("se_group0_p2o.pd_op.hardsigmoid.5.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.159" = Mul ("p2o.pd_op.conv2d.41.0", val_0)
   val_1 = Add ("se_group0_p2o.pd_op.hardsigmoid.6.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.165" = Mul ("p2o.pd_op.conv2d.44.0", val_1)
   val_2 = Add ("se_group0_p2o.pd_op.hardsigmoid.7.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.171" = Mul ("p2o.pd_op.conv2d.47.0", val_2)
   val_3 = Add ("se_group0_p2o.pd_op.hardsigmoid.8.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.177" = Mul ("p2o.pd_op.conv2d.50.0", val_3)
   ["Resize.0"] "p2o.pd_op.nearest_interp.0.0" = Resize <coordinate_transformation_mode: string = "asymmetric", mode: string = "nearest", nearest_mode: string = "floor"> ("Add.159", "helper.constant.40", "helper.constant.39")
   ["Add.178"] "Add.179" = Add ("Add.165", "p2o.pd_op.nearest_interp.0.0")
   ["Resize.1"] "p2o.pd_op.nearest_interp.1.0" = Resize <coordinate_transformation_mode: string = "asymmetric", mode: string = "nearest", nearest_mode: string = "floor"> ("Add.179", "helper.constant.40", "helper.constant.39")
   ["Add.180"] "Add.181" = Add ("Add.171", "p2o.pd_op.nearest_interp.1.0")
   ["Resize.2"] "p2o.pd_op.nearest_interp.2.0" = Resize <coordinate_transformation_mode: string = "asymmetric", mode: string = "nearest", nearest_mode: string = "floor"> ("Add.181", "helper.constant.40", "helper.constant.39")
   ["Add.182"] "Add.183" = Add ("Add.177", "p2o.pd_op.nearest_interp.2.0")
   "Add.185" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [7, 7], pads: ints = [3, 3, 3, 3], strides: ints = [1, 1]> ("Add.159", "conv2d_169.w_0", "p2o.pd_op.depthwise_conv2d.13.0_bias")
   ["Conv.67"] "p2o.pd_op.conv2d.53.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.185", "conv2d_101.w_0")
   ["GlobalAveragePool.4"] "p2o.pd_op.pool2d.5.0" = GlobalAveragePool ("p2o.pd_op.conv2d.53.0")
   "Add.193" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [7, 7], pads: ints = [3, 3, 3, 3], strides: ints = [1, 1]> ("Add.179", "conv2d_168.w_0", "p2o.pd_op.depthwise_conv2d.14.0_bias")
   ["Conv.71"] "p2o.pd_op.conv2d.56.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.193", "conv2d_91.w_0")
   ["GlobalAveragePool.5"] "p2o.pd_op.pool2d.6.0" = GlobalAveragePool ("p2o.pd_op.conv2d.56.0")
   "Add.201" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [7, 7], pads: ints = [3, 3, 3, 3], strides: ints = [1, 1]> ("Add.181", "conv2d_167.w_0", "p2o.pd_op.depthwise_conv2d.15.0_bias")
   ["Conv.75"] "p2o.pd_op.conv2d.59.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.201", "conv2d_81.w_0")
   ["GlobalAveragePool.6"] "p2o.pd_op.pool2d.7.0" = GlobalAveragePool ("p2o.pd_op.conv2d.59.0")
   "Add.209" = Conv <dilations: ints = [1, 1], group: int = 96, kernel_shape: ints = [7, 7], pads: ints = [3, 3, 3, 3], strides: ints = [1, 1]> ("Add.183", "conv2d_166.w_0", "p2o.pd_op.depthwise_conv2d.16.0_bias")
   ["Conv.79"] "p2o.pd_op.conv2d.62.0" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Add.209", "conv2d_71.w_0")
   ["GlobalAveragePool.7"] "p2o.pd_op.pool2d.8.0" = GlobalAveragePool ("p2o.pd_op.conv2d.62.0")
   se_group1_pooled = Concat <axis: int = 1> ("p2o.pd_op.pool2d.5.0", "p2o.pd_op.pool2d.6.0", "p2o.pd_op.pool2d.7.0", "p2o.pd_op.pool2d.8.0")
   se_group1_down = Conv <dilations: ints = [1, 1], group: int = 4, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (se_group1_pooled, se_group1_down1, se_group1_down2)
   se_group1_relu = Relu (se_group1_down)
   se_group1_up = Conv <dilations: ints = [1, 1], group: int = 4, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (se_group1_relu, se_group1_up1, se_group1_up2)
   se_group1_gates = HardSigmoid <alpha: float = 0.2, beta: float = 0.5> (se_group1_up)
   "se_group1_p2o.pd_op.hardsigmoid.9.0", "se_group1_p2o.pd_op.hardsigmoid.10.0", "se_group1_p2o.pd_op.hardsigmoid.11.0", "se_group1_p2o.pd_op.hardsigmoid.12.0" = Split <axis: int = 1> (se_group1_gates, se_group1_sizes)
   val_4 = Add ("se_group1_p2o.pd_op.hardsigmoid.9.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.191" = Mul ("p2o.pd_op.conv2d.53.0", val_4)
   val_5 = Add ("se_group1_p2o.pd_op.hardsigmoid.10.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.199" = Mul ("p2o.pd_op.conv2d.56.0", val_5)
   val_6 = Add ("se_group1_p2o.pd_op.hardsigmoid.11.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.207" = Mul ("p2o.pd_op.conv2d.59.0", val_6)
   val_7 = Add ("se_group1_p2o.pd_op.hardsigmoid.12.0", "p2o.pd_op.hardsigmoid.5.0_one")
   "Add.215" = Mul ("p2o.pd_op.conv2d.62.0", val_7)
   ["Resize.3"] "p2o.pd_op.nearest_interp.3.0" = Resize <coordinate_transformation_mode: string = "asymmetric", mode: string = "nearest", nearest_mode: string = "floor"> ("Add.191", "helper.constant.40", "helper.constant.45")
   ["Resize.4"] "p2o.pd_op.nearest_interp.4.0" = Resize <coordinate_transformation_mode: string = "asymmetric", mode: string = "nearest", nearest_mode: string = "floor"> ("Add.199", "helper.constant.40", "helper.constant.47")
   ["Resize.5"] "p2o.pd_op.nearest_interp.5.0" = Resize <coordinate_transformation_mode: string = "asymmetric", mode: string = "nearest", nearest_mode: string = "floor"> ("Add.207", "helper.constant.40", "helper.constant.39")
   ["Concat.2"] "Concat.3" = Concat <axis: int = 1> ("p2o.pd_op.nearest_interp.3.0", "p2o.pd_op.nearest_interp.4.0", "p2o.pd_op.nearest_interp.5.0", "Add.215")
   "Add.217" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("Concat.3", "conv2d_170.w_0", "p2o.pd_op.conv2d.65.0_bias")
   ["Relu.18"] "p2o.pd_op.relu.18.0" = Relu ("Add.217")
   "Add.219" = ConvTranspose <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> ("p2o.pd_op.relu.18.0", "auto.cast.95", "ConvTranspose.1_bias")
   ["Relu.19"] "p2o.pd_op.relu.19.0" = Relu ("Add.219")
   "Add.221" = ConvTranspose <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> ("p2o.pd_op.relu.19.0", "auto.cast.98", "ConvTranspose.3_bias")
   ["Sigmoid.0"] fetch_name_0 = Sigmoid ("Add.221")
   [n1_2] dbnet_probs = Squeeze (fetch_name_0, int64_1_1d)
}

weights:
ConvTranspose.1_bias FLOAT[24] bb27bed0521e
ConvTranspose.3_bias FLOAT[1] 24a801dd7a74
_v_680 INT64[2] fe6d3d3bb5dd
auto.cast.95 FLOAT[24,24,2,2] a1ba09ff931c
auto.cast.98 FLOAT[24,1,2,2] 70fd5d859bf3
const_cast FLOAT[] 9fd754fbfd83
conv2d_101.w_0 FLOAT[24,96,1,1] ff50c2a77482
conv2d_112.w_0 FLOAT[24,3,3,3] 7ab66000d821
conv2d_113.w_0 FLOAT[12,24,2,2] 409f7b590cdb
conv2d_114.w_0 FLOAT[24,12,2,2] 3b8d53915569
conv2d_115.w_0 FLOAT[24,48,3,3] 699a14b3a672
conv2d_116.w_0 FLOAT[48,24,1,1] 18f9782fec66
conv2d_118.w_0 FLOAT[48,1,3,3] 14caca75eccb
conv2d_119.w_0 FLOAT[96,48,1,1] 25c2c00f5320
conv2d_120.w_0 FLOAT[48,96,1,1] a75cc3f79592
conv2d_122.w_0 FLOAT[48,1,3,3] ea65c8c5f547
conv2d_123.w_0 FLOAT[96,48,1,1] d2af3cf57510
conv2d_124.w_0 FLOAT[48,96,1,1] 1fa028e4834e
conv2d_125.w_0 FLOAT[48,1,3,3] 6ad656e70cd1
conv2d_126.w_0 FLOAT[96,48,1,1] 49c9091e1a21
conv2d_127.w_0 FLOAT[96,96,1,1] d53510264579
conv2d_129.w_0 FLOAT[96,1,3,3] c95c2e93cc9d
conv2d_130.w_0 FLOAT[192,96,1,1] c045f2fb0e83
conv2d_131.w_0 FLOAT[96,192,1,1] e8cda567ae96
conv2d_133.w_0 FLOAT[96,1,3,3] 82261f0aae85
conv2d_134.w_0 FLOAT[192,96,1,1] c0d1a950593f
conv2d_135.w_0 FLOAT[96,192,1,1] 149ec1202e77
conv2d_136.w_0 FLOAT[96,1,3,3] 79fa22938743
conv2d_137.w_0 FLOAT[192,96,1,1] e9ce0320b439
conv2d_138.w_0 FLOAT[192,192,1,1] 029941e4fa35
conv2d_140.w_0 FLOAT[192,1,3,3] 0dd3656caf60
conv2d_141.w_0 FLOAT[384,192,1,1] 2615ae625e80
conv2d_142.w_0 FLOAT[192,384,1,1] d8b832788bc6
conv2d_144.w_0 FLOAT[192,1,3,3] d0c9831a2e02
conv2d_145.w_0 FLOAT[384,192,1,1] ce7b1b88d243
conv2d_146.w_0 FLOAT[192,384,1,1] 44bbd068c954
conv2d_148.w_0 FLOAT[192,1,3,3] 0a0cbeec90cd
conv2d_149.w_0 FLOAT[384,192,1,1] d981ac094ea4
conv2d_150.w_0 FLOAT[192,384,1,1] bb435884b12f
conv2d_152.w_0 FLOAT[192,1,3,3] 0d6586c94498
conv2d_153.w_0 FLOAT[384,192,1,1] bf8dad61e8a7
conv2d_154.w_0 FLOAT[192,384,1,1] f42675b37d3c
conv2d_155.w_0 FLOAT[192,1,3,3] 701f163f38d5
conv2d_156.w_0 FLOAT[384,192,1,1] 66d4ccfc0880
conv2d_157.w_0 FLOAT[384,384,1,1] 3a6b8fea1944
conv2d_159.w_0 FLOAT[384,1,3,3] 10e6a66b2b48
conv2d_160.w_0 FLOAT[768,384,1,1] 084b1182bae5
conv2d_161.w_0 FLOAT[384,768,1,1] ec7a23a264a1
conv2d_163.w_0 FLOAT[384,1,3,3] 61d410320a15
conv2d_164.w_0 FLOAT[768,384,1,1] 2ae146ddf99e
conv2d_165.w_0 FLOAT[384,768,1,1] be7968dd5732
conv2d_166.w_0 FLOAT[96,1,7,7] e7ad3d975d57
conv2d_167.w_0 FLOAT[96,1,7,7] 99dfc040a6f8
conv2d_168.w_0 FLOAT[96,1,7,7] 271ea890b28a
conv2d_169.w_0 FLOAT[96,1,7,7] f097d3d79e11
conv2d_170.w_0 FLOAT[24,96,3,3] 0065a3f984c6
conv2d_20.w_0 FLOAT[24,96,1,1] a4f89b61bef6
conv2d_21.w_0 FLOAT[96,24,1,1] cf404c262872
conv2d_33.w_0 FLOAT[48,192,1,1] f30b28999f13
conv2d_34.w_0 FLOAT[192,48,1,1] ba16586b29d2
conv2d_43.w_0 FLOAT[48,192,1,1] 3a90e66ffc7a
conv2d_44.w_0 FLOAT[192,48,1,1] a5416b2a84aa
conv2d_56.w_0 FLOAT[96,384,1,1] 5593f987bb53
conv2d_57.w_0 FLOAT[384,96,1,1] c82a00253076
conv2d_64.w_0 FLOAT[96,48,1,1] ecd5a4ddcea7
conv2d_7.w_0 FLOAT[12,48,1,1] 9ea7335d0b78
conv2d_71.w_0 FLOAT[24,96,1,1] 9c60636dd501
conv2d_74.w_0 FLOAT[96,96,1,1] 0612ef7d2a72
conv2d_8.w_0 FLOAT[48,12,1,1] 28ee9876167c
conv2d_81.w_0 FLOAT[24,96,1,1] a82b6ac75f28
conv2d_84.w_0 FLOAT[96,192,1,1] 8449e9496fd9
conv2d_91.w_0 FLOAT[24,96,1,1] 8db11eb4074b
conv2d_94.w_0 FLOAT[96,384,1,1] df47d9bef3e2
helper.constant.39 FLOAT[4] aa5b3e0ef3e8
helper.constant.40 FLOAT[0] e3b0c44298fc
helper.constant.45 FLOAT[4] cf5451a623be
helper.constant.47 FLOAT[4] 1811eb557cd7
int64_1_1d INT64[1] 7c9fa136d441
p2o.pd_op.conv2d.0.0_bias FLOAT[24] 080ff131f1b4
p2o.pd_op.conv2d.1.0_bias FLOAT[12] fb44a3dbcec3
p2o.pd_op.conv2d.10.0_bias FLOAT[48] f03f5544013b
p2o.pd_op.conv2d.11.0_bias FLOAT[96] e8fc8842fce1
p2o.pd_op.conv2d.12.0_bias FLOAT[96] fafc821c2deb
p2o.pd_op.conv2d.13.0_bias FLOAT[24] 542b37776115
p2o.pd_op.conv2d.14.0_bias FLOAT[96] 43bf1d4f2934
p2o.pd_op.conv2d.15.0_bias FLOAT[192] e75d4c5dd209
p2o.pd_op.conv2d.16.0_bias FLOAT[96] b90f5f02ea7d
p2o.pd_op.conv2d.17.0_bias FLOAT[192] 970f74a8465e
p2o.pd_op.conv2d.18.0_bias FLOAT[96] 74b51174478c
p2o.pd_op.conv2d.19.0_bias FLOAT[192] d93512b7341b
p2o.pd_op.conv2d.2.0_bias FLOAT[24] ace178c37ca9
p2o.pd_op.conv2d.20.0_bias FLOAT[192] 46ead826ff45
p2o.pd_op.conv2d.21.0_bias FLOAT[48] dbb2745138ae
p2o.pd_op.conv2d.22.0_bias FLOAT[192] f685dfdc1cb0
p2o.pd_op.conv2d.23.0_bias FLOAT[384] fc93cf357922
p2o.pd_op.conv2d.24.0_bias FLOAT[192] 789a74fa92a8
p2o.pd_op.conv2d.25.0_bias FLOAT[384] f7da5c7e92ad
p2o.pd_op.conv2d.26.0_bias FLOAT[192] 80e854616b1d
p2o.pd_op.conv2d.27.0_bias FLOAT[48] 06050b48ae86
p2o.pd_op.conv2d.28.0_bias FLOAT[192] 1e6b6f3fa5d7
p2o.pd_op.conv2d.29.0_bias FLOAT[384] e22e1c63c711
p2o.pd_op.conv2d.3.0_bias FLOAT[24] 5e76358415dd
p2o.pd_op.conv2d.30.0_bias FLOAT[192] aba7f93b697e
p2o.pd_op.conv2d.31.0_bias FLOAT[384] aa359f729e08
p2o.pd_op.conv2d.32.0_bias FLOAT[192] 6152ba83d44a
p2o.pd_op.conv2d.33.0_bias FLOAT[384] 642e07251477
p2o.pd_op.conv2d.34.0_bias FLOAT[384] 8f2a7f18d892
p2o.pd_op.conv2d.35.0_bias FLOAT[96] 2dfacf20434a
p2o.pd_op.conv2d.36.0_bias FLOAT[384] 00cf8a271ea5
p2o.pd_op.conv2d.37.0_bias FLOAT[768] 8941b5e8768f
p2o.pd_op.conv2d.38.0_bias FLOAT[384] 13d024f6018e
p2o.pd_op.conv2d.39.0_bias FLOAT[768] e2a65643caf4
p2o.pd_op.conv2d.4.0_bias FLOAT[48] 6a5e84e06010
p2o.pd_op.conv2d.40.0_bias FLOAT[384] 11630665d435
p2o.pd_op.conv2d.5.0_bias FLOAT[12] 072bcd13597b
p2o.pd_op.conv2d.6.0_bias FLOAT[48] 27746617c6c0
p2o.pd_op.conv2d.65.0_bias FLOAT[24] 66ae74136e7d
p2o.pd_op.conv2d.7.0_bias FLOAT[96] 240e170586e4
p2o.pd_op.conv2d.8.0_bias FLOAT[48] 69179daadcc4
p2o.pd_op.conv2d.9.0_bias FLOAT[96] 5ba255267b85
p2o.pd_op.depthwise_conv2d.0.0_bias FLOAT[48] c7c2ddf18b26
p2o.pd_op.depthwise_conv2d.1.0_bias FLOAT[48] 875330127b20
p2o.pd_op.depthwise_conv2d.10.0_bias FLOAT[192] dc9604ab84cb
p2o.pd_op.depthwise_conv2d.11.0_bias FLOAT[384] 6519852ced86
p2o.pd_op.depthwise_conv2d.12.0_bias FLOAT[384] 8eaa2604be84
p2o.pd_op.depthwise_conv2d.13.0_bias FLOAT[96] d21bee60e6c4
p2o.pd_op.depthwise_conv2d.14.0_bias FLOAT[96] d0e2be8bf9d4
p2o.pd_op.depthwise_conv2d.15.0_bias FLOAT[96] dc764011e68e
p2o.pd_op.depthwise_conv2d.16.0_bias FLOAT[96] 020d5df9f122
p2o.pd_op.depthwise_conv2d.2.0_bias FLOAT[48] 6432a104355b
p2o.pd_op.depthwise_conv2d.3.0_bias FLOAT[96] f0247c5d388e
p2o.pd_op.depthwise_conv2d.4.0_bias FLOAT[96] e015265be367
p2o.pd_op.depthwise_conv2d.5.0_bias FLOAT[96] 698eaf9d5bd4
p2o.pd_op.depthwise_conv2d.6.0_bias FLOAT[192] 7a1a2157ce33
p2o.pd_op.depthwise_conv2d.7.0_bias FLOAT[192] 0f62183d7b8e
p2o.pd_op.depthwise_conv2d.8.0_bias FLOAT[192] 29bf164403a9
p2o.pd_op.depthwise_conv2d.9.0_bias FLOAT[192] 8c35a49e1a9a
p2o.pd_op.hardsigmoid.5.0_one FLOAT[] e00e5eb94441
se_group0_down1 FLOAT[96,96,1,1] 6fad60f053f8
se_group0_down2 FLOAT[96] b78e6150eaaa
se_group0_sizes INT64[4] 4bb249469bee
se_group0_up1 FLOAT[384,24,1,1] 932850ac08d0
se_group0_up2 FLOAT[384] f69393803114
se_group1_down1 FLOAT[24,24,1,1] 24ff7d4b4dac
se_group1_down2 FLOAT[24] 3844f028fb3e
se_group1_sizes INT64[4] 5c995dc6a0ef
se_group1_up1 FLOAT[96,6,1,1] d5eeb48adccd
se_group1_up2 FLOAT[96] 265ea2dc4538
