Fixed #1402

Author: NiklasGustafsson

RELEASENOTES.md

@@ -2,6 +2,17 @@
 
 Releases, starting with 9/2/2021, are listed with the most recent release at the top.
 
+# NuGet Version 0.104.0
+
+__Breaking Changes__:
+
+The argument defaults for `torch.diagonal()` and `Tensor.diagonal()` arguments have been changed.
+
+__Bug Fixes__:
+
+#1400 There may be an error in torchvision.transforms.GaussianBlur
+#1402 diagonal() has incorrect default
+
 # NuGet Version 0.103.1
 
 __Breaking Changes__:

src/TorchSharp/LinearAlgebra.cs

@@ -136,8 +136,8 @@ public static (Tensor, Tensor) slogdet(Tensor input)
             /// </summary>
             /// <param name="input">The input tensor</param>
             /// <param name="offset">Which diagonal to consider. Default: 0 (main diagonal).</param>
-            /// <param name="dim1">First dimension with respect to which to take diagonal. Default: -1.</param>
-            /// <param name="dim2">Second dimension with respect to which to take diagonal. Default: -2.</param>
+            /// <param name="dim1">First dimension with respect to which to take diagonal. Default: -2.</param>
+            /// <param name="dim2">Second dimension with respect to which to take diagonal. Default: -1.</param>
             /// <remarks>
             /// Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input.
             /// However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.

src/TorchSharp/Tensor/Tensor.cs

@@ -3329,8 +3329,9 @@ public Tensor diagflat(long offset = 0)
             /// Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input.
             /// However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.
             /// </remarks>
-            public Tensor diagonal(long offset = 0, long dim1 = 0, long dim2 = 0)
+            public Tensor diagonal(long offset = 0L, long dim1 = 0L, long dim2 = 1L)
             {
+                if (dim1 == dim2) throw new ArgumentException($"Diagonal dimensions cannot be identical {dim1}, {dim2}");
                 var res = NativeMethods.THSTensor_diagonal(Handle, offset, dim1, dim2);
                 if (res == IntPtr.Zero) { CheckForErrors(); }
                 return new Tensor(res);

src/TorchSharp/Tensor/torch.OtherOperations.cs

@@ -313,7 +313,7 @@ public static Tensor diag_embed(Tensor input, long offset = 0L, long dim1 = -2L,
         /// Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input.
         /// However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.
         /// </remarks>
-        public static Tensor diagonal(Tensor input, long offset = 0, long dim1 = 0, long dim2 = 0) => input.diagonal(offset, dim1, dim2);
+        public static Tensor diagonal(Tensor input, long offset = 0L, long dim1 = 0L, long dim2 = 1L) => input.diagonal(offset, dim1, dim2);
 
         // https://pytorch.org/docs/stable/generated/torch.diff
         /// <summary>

test/TorchSharpTest/TestTorchTensorBugs.cs

@@ -1692,5 +1692,15 @@ public void Validate1400()
             var img = torch.rand(1,3,256,256);
             var t = trans.call(img);
         }
+
+        [Fact]
+        public void Validate1402()
+        {
+            var t = torch.arange(100).reshape(10,10);
+
+            var d = t.diagonal();
+
+            Assert.Equal(new long[]{0, 11, 22, 33, 44, 55, 66, 77, 88, 99}, d.data<long>().ToArray());
+        }
     }
 }