%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)
+ %input.9 : 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 = prim::Constant[value=6](), scope: AlexNet/AdaptiveAvgPool2d[avgpool]
+ %47 : int[] = prim::ListConstruct(%46, %46), scope: AlexNet/AdaptiveAvgPool2d[avgpool]
+ %48 : Double(1, 256, 6, 6) = aten::adaptive_avg_pool2d(%input.9, %47), scope: AlexNet/AdaptiveAvgPool2d[avgpool]
+ %49 : int = aten::size(%48, %24), scope: AlexNet
+ %50 : Long() = prim::NumToTensor(%49), scope: AlexNet
+ %51 : int = prim::Int(%50), scope: AlexNet
+ %52 : int = prim::Constant[value=9216](), scope: AlexNet
+ %53 : int[] = prim::ListConstruct(%51, %52), scope: AlexNet
+ %input.10 : Double(1, 9216) = aten::view(%48, %53), scope: AlexNet
+ %55 : float = prim::Constant[value=0.5](), scope: AlexNet/Sequential[classifier]/Dropout[0]
+ %input.11 : Double(1, 9216) = aten::dropout(%input.10, %55, %26), scope: AlexNet/Sequential[classifier]/Dropout[0]
+ %57 : Double(9216!, 4096!) = aten::t(%11), scope: AlexNet/Sequential[classifier]/Linear[1]
+ %input.12 : Double(1, 4096) = aten::addmm(%12, %input.11, %57, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[1]
+ %input.13 : Double(1, 4096) = aten::threshold_(%input.12, %28, %28), scope: AlexNet/Sequential[classifier]/ReLU[2]
+ %input.14 : Double(1, 4096) = aten::dropout(%input.13, %55, %26), scope: AlexNet/Sequential[classifier]/Dropout[3]
+ %61 : Double(4096!, 4096!) = aten::t(%13), scope: AlexNet/Sequential[classifier]/Linear[4]
+ %input.15 : Double(1, 4096) = aten::addmm(%14, %input.14, %61, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[4]
+ %input : Double(1, 4096) = aten::threshold_(%input.15, %28, %28), scope: AlexNet/Sequential[classifier]/ReLU[5]
+ %64 : Double(4096!, 1000!) = aten::t(%15), scope: AlexNet/Sequential[classifier]/Linear[6]
+ %65 : Double(1, 1000) = aten::addmm(%16, %input, %64, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[6]
+ return (%65)