output

gistfile1.txt

graph(%0 : Float(1, 3, 224, 224)
      %1 : Float(64, 3, 11, 11)
      %2 : Float(64)
      %3 : Float(192, 64, 5, 5)
      %4 : Float(192)
      %5 : Float(384, 192, 3, 3)
      %6 : Float(384)
      %7 : Float(256, 384, 3, 3)
      %8 : Float(256)
      %9 : Float(256, 256, 3, 3)
      %10 : Float(256)
      %11 : Float(4096, 9216)
      %12 : Float(4096)
      %13 : Float(4096, 4096)
      %14 : Float(4096)
      %15 : Float(1000, 4096)
      %16 : Float(1000)) {
  %17 : Float(1, 64, 55, 55) = Conv[dilations=[1, 1], group=1, kernel_shape=[11, 11], pads=[2, 2, 2, 2], strides=[4, 4]](%0, %1, %2), scope: AlexNet/Sequential[features]/Conv2d[0]
  %18 : Float(1, 64, 55, 55) = Relu(%17), scope: AlexNet/Sequential[features]/ReLU[1]
  %19 : Float(1, 64, 27, 27) = MaxPool[kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%18), scope: AlexNet/Sequential[features]/MaxPool2d[2]
  %20 : Float(1, 192, 27, 27) = Conv[dilations=[1, 1], group=1, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%19, %3, %4), scope: AlexNet/Sequential[features]/Conv2d[3]
  %21 : Float(1, 192, 27, 27) = Relu(%20), scope: AlexNet/Sequential[features]/ReLU[4]
  %22 : Float(1, 192, 13, 13) = MaxPool[kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%21), scope: AlexNet/Sequential[features]/MaxPool2d[5]
  %23 : Float(1, 384, 13, 13) = Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%22, %5, %6), scope: AlexNet/Sequential[features]/Conv2d[6]
  %24 : Float(1, 384, 13, 13) = Relu(%23), scope: AlexNet/Sequential[features]/ReLU[7]
  %25 : Float(1, 256, 13, 13) = Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%24, %7, %8), scope: AlexNet/Sequential[features]/Conv2d[8]
  %26 : Float(1, 256, 13, 13) = Relu(%25), scope: AlexNet/Sequential[features]/ReLU[9]
  %27 : Float(1, 256, 13, 13) = Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%26, %9, %10), scope: AlexNet/Sequential[features]/Conv2d[10]
  %28 : Float(1, 256, 13, 13) = Relu(%27), scope: AlexNet/Sequential[features]/ReLU[11]
  %29 : Float(1, 256, 6, 6) = MaxPool[kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%28), scope: AlexNet/Sequential[features]/MaxPool2d[12]
  %30 : Float(1, 9216) = Flatten[axis=1](%29), scope: AlexNet
  %31 : Float(1, 9216), %32 : UNKNOWN_TYPE = Dropout[is_test=1, ratio=0.5](%30), scope: AlexNet
  %33 : Float(1, 4096) = Gemm[alpha=1, beta=1, broadcast=1, transB=1](%31, %11, %12), scope: AlexNet/Linear[fc1]
  %34 : Float(1, 4096) = Relu(%33), scope: AlexNet/ReLU[relu1]
  %35 : Float(1, 4096), %36 : UNKNOWN_TYPE = Dropout[is_test=1, ratio=0.5](%34), scope: AlexNet
  %37 : Float(1, 4096) = Gemm[alpha=1, beta=1, broadcast=1, transB=1](%35, %13, %14), scope: AlexNet/Linear[fc2]
  %38 : Float(1, 4096) = Relu(%37), scope: AlexNet/ReLU[relu2]
  %39 : Float(1, 1000) = Gemm[alpha=1, beta=1, broadcast=1, transB=1](%38, %15, %16), scope: AlexNet/Linear[fc3]
  return (%39);
}