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Add test cases for antialias.
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‎test/TorchSharpTest/NN.cs‎

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Original file line numberDiff line numberDiff line change
@@ -6643,6 +6643,117 @@ public void TestInterpolateBilinear2D()
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}
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}
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[Fact]
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public void TestInterpolateBilinear2DNoAntialias()
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{
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foreach (var device in TestUtils.AvailableDevices()) {
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using Tensor input = torch.tensor(rawArray: new float[] {
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f
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}, new long[] { 1, 1, 9, 9 }, float32, device: device);
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using var res = torch.nn.functional.interpolate(input, new long[] { 6, 6 }, mode: InterpolationMode.Bilinear, antialias: false);
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using Tensor expect = torch.tensor(rawArray: new float[] {
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0.7500f, 0.0000f, 0.7500f, 0.0000f, 0.7500f, 0.0000f,
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0.7500f, 0.0000f, 0.7500f, 0.0000f, 0.7500f, 0.0000f,
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0.2500f, 0.2500f, 0.2500f, 0.2500f, 0.2500f, 0.2500f,
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0.2500f, 0.2500f, 0.2500f, 0.2500f, 0.2500f, 0.2500f,
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0.0000f, 0.7500f, 0.0000f, 0.7500f, 0.0000f, 0.7500f,
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0.0000f, 0.7500f, 0.0000f, 0.7500f, 0.0000f, 0.7500f
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}, new long[] { 1, 1, 6, 6 }, float32, device: device);
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Assert.True(torch.allclose(res, expect, rtol: 0.0, atol: 1E-04));
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}
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}
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[Fact]
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public void TestInterpolateBilinear2DAntialias()
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{
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foreach (var device in TestUtils.AvailableDevices()) {
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using Tensor input = torch.tensor(rawArray: new float[] {
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f
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}, new long[] { 1, 1, 9, 9 }, float32, device: device);
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using var res = torch.nn.functional.interpolate(input, new long[] { 6, 6 }, mode: InterpolationMode.Bilinear, antialias: true);
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using Tensor expect = torch.tensor(rawArray: new float[] {
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0.6250f, 0.1111f, 0.5556f, 0.1111f, 0.5556f, 0.0000f,
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0.5972f, 0.1358f, 0.5309f, 0.1358f, 0.5309f, 0.0417f,
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0.4028f, 0.3086f, 0.3580f, 0.3086f, 0.3580f, 0.3333f,
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0.3333f, 0.3580f, 0.3086f, 0.3580f, 0.3086f, 0.4028f,
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0.0417f, 0.5309f, 0.1358f, 0.5309f, 0.1358f, 0.5972f,
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0.0000f, 0.5556f, 0.1111f, 0.5556f, 0.1111f, 0.6250f
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}, new long[] { 1, 1, 6, 6 }, float32, device: device);
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Assert.True(torch.allclose(res, expect, rtol: 0.0, atol: 1E-04));
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}
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}
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[Fact]
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public void TestInterpolateBicubic2DNoAntialias()
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{
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foreach (var device in TestUtils.AvailableDevices()) {
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using Tensor input = torch.tensor(rawArray: new float[] {
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f
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}, new long[] { 1, 1, 9, 9 }, float32, device: device);
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using var res = torch.nn.functional.interpolate(input, new long[] { 6, 6 }, mode: InterpolationMode.Bicubic, antialias: true);
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using Tensor expect = torch.tensor(rawArray: new float[] {
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0.6493f, 0.0467f, 0.6196f, 0.0471f, 0.6226f, -0.0440f,
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0.6356f, 0.0619f, 0.6042f, 0.0624f, 0.6069f, -0.0205f,
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0.4083f, 0.3155f, 0.3487f, 0.3180f, 0.3464f, 0.3712f,
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0.3712f, 0.3464f, 0.3180f, 0.3487f, 0.3155f, 0.4083f,
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-0.0205f, 0.6069f, 0.0624f, 0.6042f, 0.0619f, 0.6356f,
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-0.0440f, 0.6226f, 0.0471f, 0.6196f, 0.0467f, 0.6493f
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}, new long[] { 1, 1, 6, 6 }, float32, device: device);
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Assert.True(torch.allclose(res, expect, rtol: 0.0, atol: 1E-04));
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}
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}
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[Fact]
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public void TestInterpolateBicubic2DAntialias()
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{
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foreach (var device in TestUtils.AvailableDevices()) {
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using Tensor input = torch.tensor(rawArray: new float[] {
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f,
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0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 1.0f
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}, new long[] { 1, 1, 9, 9 }, float32, device: device);
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using var res = torch.nn.functional.interpolate(input, new long[] { 6, 6 }, mode: InterpolationMode.Bicubic, antialias: false);
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using Tensor expect = torch.tensor(rawArray: new float[] {
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0.7734f, -0.1406f, 0.8789f, -0.1406f, 0.8789f, -0.0352f,
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0.8274f, -0.1831f, 0.9440f, -0.1831f, 0.9440f, -0.0665f,
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0.2077f, 0.3042f, 0.1966f, 0.3042f, 0.1966f, 0.2930f,
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0.2930f, 0.1966f, 0.3042f, 0.1966f, 0.3042f, 0.2077f,
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-0.0665f, 0.9440f, -0.1831f, 0.9440f, -0.1831f, 0.8274f,
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-0.0352f, 0.8789f, -0.1406f, 0.8789f, -0.1406f, 0.7734f
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}, new long[] { 1, 1, 6, 6 }, float32, device: device);
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Assert.True(torch.allclose(res, expect, rtol: 0.0, atol: 1E-04));
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}
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}
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[Fact]
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public void TestInterpolateArea()

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