From: Spandan Tiwari <sptiwari@microsoft.com>
Date: Wed, 6 Mar 2019 19:03:32 +0000 (-0800)
Subject: Temporarily disable Upsample operator tests in pytorch-onnx tests (#17696)
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~972
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=c658d9b21bd453904ff9c485e8a9e65608b1eabb;p=platform%2Fupstream%2Fpytorch.git

Temporarily disable Upsample operator tests in pytorch-onnx tests (#17696)

Summary:
In discussion with houseroad, because Upsample op is being updated in ONNX https://github.com/onnx/onnx/pull/1773 and these tests are blocking it. These tests will be updated once the ONNX PR goes in.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17696

Differential Revision: D14338845

Pulled By: houseroad

fbshipit-source-id: cfaf8cf1ab578ae69dd3bf21b1c0681b572b9b6f
---

diff --git a/test/onnx/expect/TestOperators.test_upsample.expect b/test/onnx/expect/TestOperators.test_upsample.expect
deleted file mode 100644
index 40fa9d5..0000000
--- a/test/onnx/expect/TestOperators.test_upsample.expect
+++ /dev/null
@@ -1,77 +0,0 @@
-ir_version: 4
-producer_name: "pytorch"
-producer_version: "1.1"
-graph {
-  node {
-    output: "1"
-    op_type: "Constant"
-    attribute {
-      name: "value"
-      t {
-        dims: 4
-        data_type: 1
-        raw_data: "\000\000\200?\000\000\200?\000\000\000@\000\000\000@"
-      }
-      type: TENSOR
-    }
-  }
-  node {
-    input: "input"
-    input: "1"
-    output: "2"
-    op_type: "Upsample"
-    attribute {
-      name: "mode"
-      s: "linear"
-      type: STRING
-    }
-  }
-  name: "torch-jit-export"
-  input {
-    name: "input"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 1
-          }
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 3
-          }
-          dim {
-            dim_value: 4
-          }
-        }
-      }
-    }
-  }
-  output {
-    name: "2"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 1
-          }
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 6
-          }
-          dim {
-            dim_value: 8
-          }
-        }
-      }
-    }
-  }
-}
-opset_import {
-  version: 10
-}
diff --git a/test/onnx/test_models.py b/test/onnx/test_models.py
index f3cbc44..f9bbc10 100644
--- a/test/onnx/test_models.py
+++ b/test/onnx/test_models.py
@@ -138,6 +138,7 @@ class TestModels(TestCase):
         sqnet_v1_1 = SqueezeNet(version=1.1)
         self.exportTest(toC(sqnet_v1_1), toC(x))
 
+    @unittest.skip("Temporary - waiting for https://github.com/onnx/onnx/pull/1773.")
     def test_densenet(self):
         # Densenet-121 model
         x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
diff --git a/test/onnx/test_operators.py b/test/onnx/test_operators.py
index 1a680a9..d8d0c42 100644
--- a/test/onnx/test_operators.py
+++ b/test/onnx/test_operators.py
@@ -455,6 +455,7 @@ class TestOperators(TestCase):
         x = torch.randn(1, 2, 3, 4, requires_grad=True)
         self.assertONNX(lambda x: x.norm(p=2, dim=2), (x))
 
+    @unittest.skip("Temporary - waiting for https://github.com/onnx/onnx/pull/1773.")
     def test_upsample(self):
         x = torch.randn(1, 2, 3, 4, requires_grad=True)
         self.assertONNX(lambda x: nn.functional.interpolate(x, scale_factor=2., mode='bilinear'), x)
diff --git a/test/onnx/test_pytorch_onnx_caffe2.py b/test/onnx/test_pytorch_onnx_caffe2.py
index 1d9fc26..89a8923 100644
--- a/test/onnx/test_pytorch_onnx_caffe2.py
+++ b/test/onnx/test_pytorch_onnx_caffe2.py
@@ -808,6 +808,7 @@ class TestCaffe2Backend(unittest.TestCase):
         x = torch.randn(4, 3, 2, 1, requires_grad=True)
         self.run_model_test(MyModel(), train=False, input=(x), batch_size=BATCH_SIZE, use_gpu=False)
 
+    @unittest.skip("Temporary - waiting for https://github.com/onnx/onnx/pull/1773.")
     def test_upsample(self):
         x = torch.randn(1, 2, 3, 4, requires_grad=True)
         model = nn.Upsample(scale_factor=2, mode='nearest')