From: Gregory Chanan <gchanan@fb.com>
Date: Thu, 14 Feb 2019 21:45:04 +0000 (-0800)
Subject: Update alexnet expect.
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~1284
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=0a5de6e9720eb9600c6424fd2d7a5df0b36d9703;p=platform%2Fupstream%2Fpytorch.git

Update alexnet expect.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17122

Reviewed By: colesbury

Differential Revision: D14090209

Pulled By: gchanan

fbshipit-source-id: 78c5961dd7d752b237782b6ed90c376bbd6d3145
---

diff --git a/test/expect/TestJit.test_alexnet.expect b/test/expect/TestJit.test_alexnet.expect
index 3808623..8e3b066 100644
--- a/test/expect/TestJit.test_alexnet.expect
+++ b/test/expect/TestJit.test_alexnet.expect
@@ -40,22 +40,25 @@ graph(%0 : Double(1, 3, 224, 224),
   %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)