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TestJit.test_alexnet.expect
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TestJit.test_alexnet.expect
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graph(%0 : Double(1, 3, 224, 224)
%1 : Double(64, 3, 11, 11)
%2 : Double(64)
%3 : Double(192, 64, 5, 5)
%4 : Double(192)
%5 : Double(384, 192, 3, 3)
%6 : Double(384)
%7 : Double(256, 384, 3, 3)
%8 : Double(256)
%9 : Double(256, 256, 3, 3)
%10 : Double(256)
%11 : Double(4096, 9216)
%12 : Double(4096)
%13 : Double(4096, 4096)
%14 : Double(4096)
%15 : Double(1000, 4096)
%16 : Double(1000)) {
%17 : int = prim::Constant[value=4](), scope: AlexNet/Sequential[features]/Conv2d[0]
%18 : int[] = prim::ListConstruct(%17, %17), scope: AlexNet/Sequential[features]/Conv2d[0]
%19 : int = prim::Constant[value=2](), scope: AlexNet/Sequential[features]/Conv2d[0]
%20 : int[] = prim::ListConstruct(%19, %19), scope: AlexNet/Sequential[features]/Conv2d[0]
%21 : int = prim::Constant[value=1](), scope: AlexNet/Sequential[features]/Conv2d[0]
%22 : int[] = prim::ListConstruct(%21, %21), scope: AlexNet/Sequential[features]/Conv2d[0]
%23 : bool = prim::Constant[value=0](), scope: AlexNet/Sequential[features]/Conv2d[0]
%24 : int = prim::Constant[value=0](), scope: AlexNet/Sequential[features]/Conv2d[0]
%25 : int[] = prim::ListConstruct(%24, %24), scope: AlexNet/Sequential[features]/Conv2d[0]
%26 : bool = prim::Constant[value=1](), scope: AlexNet/Sequential[features]/Conv2d[0]
%input.1 : Double(1, 64, 55, 55) = aten::_convolution(%0, %1, %2, %18, %20, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[0]
%28 : float = prim::Constant[value=0](), scope: AlexNet/Sequential[features]/ReLU[1]
%input.2 : Double(1, 64, 55, 55) = aten::threshold_(%input.1, %28, %28), scope: AlexNet/Sequential[features]/ReLU[1]
%30 : int = prim::Constant[value=3](), scope: AlexNet/Sequential[features]/MaxPool2d[2]
%31 : int[] = prim::ListConstruct(%30, %30), scope: AlexNet/Sequential[features]/MaxPool2d[2]
%32 : Double(1, 64, 27, 27), %33 : Long(1, 64, 27, 27) = aten::max_pool2d_with_indices(%input.2, %31, %20, %25, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[2]
%input.3 : Double(1, 192, 27, 27) = aten::_convolution(%32, %3, %4, %22, %20, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[3]
%input.4 : Double(1, 192, 27, 27) = aten::threshold_(%input.3, %28, %28), scope: AlexNet/Sequential[features]/ReLU[4]
%36 : Double(1, 192, 13, 13), %37 : Long(1, 192, 13, 13) = aten::max_pool2d_with_indices(%input.4, %31, %20, %25, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[5]
%input.5 : Double(1, 384, 13, 13) = aten::_convolution(%36, %5, %6, %22, %22, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[6]
%39 : Double(1, 384, 13, 13) = aten::threshold_(%input.5, %28, %28), scope: AlexNet/Sequential[features]/ReLU[7]
%input.6 : Double(1, 256, 13, 13) = aten::_convolution(%39, %7, %8, %22, %22, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[8]
%41 : Double(1, 256, 13, 13) = aten::threshold_(%input.6, %28, %28), scope: AlexNet/Sequential[features]/ReLU[9]
%input.7 : Double(1, 256, 13, 13) = aten::_convolution(%41, %9, %10, %22, %22, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[10]
%input.8 : Double(1, 256, 13, 13) = aten::threshold_(%input.7, %28, %28), scope: AlexNet/Sequential[features]/ReLU[11]
%44 : Double(1, 256, 6, 6), %45 : Long(1, 256, 6, 6) = aten::max_pool2d_with_indices(%input.8, %31, %20, %25, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[12]
%46 : int = aten::size(%44, %24), scope: AlexNet
%47 : Long() = prim::NumToTensor(%46), scope: AlexNet
%48 : int = prim::Int(%47), scope: AlexNet
%49 : int = prim::Constant[value=9216](), scope: AlexNet
%50 : int[] = prim::ListConstruct(%48, %49), scope: AlexNet
%input.9 : Double(1, 9216) = aten::view(%44, %50), scope: AlexNet
%52 : float = prim::Constant[value=0.5](), scope: AlexNet/Sequential[classifier]/Dropout[0]
%input.10 : Double(1, 9216) = aten::dropout(%input.9, %52, %26), scope: AlexNet/Sequential[classifier]/Dropout[0]
%54 : Double(9216!, 4096!) = aten::t(%11), scope: AlexNet/Sequential[classifier]/Linear[1]
%input.11 : Double(1, 4096) = aten::addmm(%12, %input.10, %54, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[1]
%input.12 : Double(1, 4096) = aten::threshold_(%input.11, %28, %28), scope: AlexNet/Sequential[classifier]/ReLU[2]
%input.13 : Double(1, 4096) = aten::dropout(%input.12, %52, %26), scope: AlexNet/Sequential[classifier]/Dropout[3]
%58 : Double(4096!, 4096!) = aten::t(%13), scope: AlexNet/Sequential[classifier]/Linear[4]
%input.14 : Double(1, 4096) = aten::addmm(%14, %input.13, %58, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[4]
%input : Double(1, 4096) = aten::threshold_(%input.14, %28, %28), scope: AlexNet/Sequential[classifier]/ReLU[5]
%61 : Double(4096!, 1000!) = aten::t(%15), scope: AlexNet/Sequential[classifier]/Linear[6]
%62 : Double(1, 1000) = aten::addmm(%16, %input, %61, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[6]
return (%62);
}