COMPMID-1590: Memory corruption in arm_compute_validation.
RSH code only support padding SAME|VALID, this means we cannot
call it with padx=1 for kernel size 5x5. The supporting padding
values are 2 and 0.
Fixed the problem by modifying the test shapes and added some
asserts in NEWinogradConvolutionLayer.
Change-Id: I4b73fa9d13c2200a47002965dc3b471d0f2cafba
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/149883
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/tests/datasets/LargeConvolutionLayerDataset.h b/tests/datasets/LargeConvolutionLayerDataset.h
index 170d562..ca38abb 100644
--- a/tests/datasets/LargeConvolutionLayerDataset.h
+++ b/tests/datasets/LargeConvolutionLayerDataset.h
@@ -46,15 +46,15 @@
// Batch size 1
add_config(TensorShape(224U, 222U, 64U), TensorShape(3U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 1, 1));
add_config(TensorShape(112U, 113U, 64U), TensorShape(3U, 3U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U), PadStrideInfo(1, 1, 1, 1));
- add_config(TensorShape(112U, 112U, 128U), TensorShape(3U, 3U, 128U, 129U), TensorShape(129U), TensorShape(112U, 110U, 129U), PadStrideInfo(1, 1, 1, 0));
- add_config(TensorShape(53U, 56U, 125U), TensorShape(3U, 3U, 125U, 256U), TensorShape(256U), TensorShape(51U, 56U, 256U), PadStrideInfo(1, 1, 0, 1));
- add_config(TensorShape(56U, 56U, 256U), TensorShape(3U, 3U, 256U, 256U), TensorShape(256U), TensorShape(56U, 54U, 256U), PadStrideInfo(1, 1, 1, 0));
- add_config(TensorShape(28U, 28U, 257U), TensorShape(3U, 3U, 257U, 512U), TensorShape(512U), TensorShape(26U, 28U, 512U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(112U, 112U, 128U), TensorShape(3U, 3U, 128U, 129U), TensorShape(129U), TensorShape(112U, 112U, 129U), PadStrideInfo(1, 1, 1, 1));
+ add_config(TensorShape(53U, 56U, 125U), TensorShape(3U, 3U, 125U, 256U), TensorShape(256U), TensorShape(51U, 54U, 256U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(56U, 56U, 256U), TensorShape(3U, 3U, 256U, 256U), TensorShape(256U), TensorShape(54U, 54U, 256U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(28U, 28U, 257U), TensorShape(3U, 3U, 257U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 1, 1));
add_config(TensorShape(28U, 28U, 512U), TensorShape(3U, 3U, 512U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 1, 1));
add_config(TensorShape(14U, 14U, 512U), TensorShape(3U, 3U, 512U, 512U), TensorShape(512U), TensorShape(12U, 12U, 512U), PadStrideInfo(1, 1, 0, 0));
// Batch size 3, 2 and 4
add_config(TensorShape(224U, 222U, 64U, 3U), TensorShape(3U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 3U), PadStrideInfo(1, 1, 1, 1));
- add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(3U, 3U, 64U, 128U), TensorShape(128U), TensorShape(110U, 113U, 128U, 2U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(3U, 3U, 64U, 128U), TensorShape(128U), TensorShape(110U, 111U, 128U, 2U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(111U, 112U, 127U, 4U), TensorShape(3U, 3U, 127U, 128U), TensorShape(128U), TensorShape(111U, 112U, 128U, 4U), PadStrideInfo(1, 1, 1, 1));
}
};
@@ -110,14 +110,14 @@
{
// Kernel size 5
// Batch size 1
- add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 5U, 3U, 64U), TensorShape(64U), TensorShape(222U, 222U, 64U), PadStrideInfo(1, 1, 1, 1));
- add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 5U, 16U, 7U), TensorShape(7U), TensorShape(121U, 130U, 7U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 5U, 3U, 64U), TensorShape(64U), TensorShape(220U, 220U, 64U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U), PadStrideInfo(1, 1, 2, 2));
add_config(TensorShape(181U, 152U, 42U), TensorShape(5U, 5U, 42U, 100U), TensorShape(100U), TensorShape(177U, 148U, 100U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(200U, 201U, 24U), TensorShape(5U, 5U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 2, 2));
// Batch size 2, 3 and 4
- add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 5U, 3U, 64U), TensorShape(64U), TensorShape(222U, 222U, 64U, 2U), PadStrideInfo(1, 1, 1, 1));
- add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 5U, 16U, 7U), TensorShape(7U), TensorShape(121U, 130U, 7U, 3U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 5U, 3U, 64U), TensorShape(64U), TensorShape(220U, 220U, 64U, 2U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U, 3U), PadStrideInfo(1, 1, 2, 2));
add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(5U, 5U, 42U, 100U), TensorShape(100U), TensorShape(177U, 148U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
}
};
@@ -128,14 +128,14 @@
LargeWinogradConvolutionLayer5x1Dataset()
{
// Batch size 1
- add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(222U, 224U, 64U), PadStrideInfo(1, 1, 1, 0));
- add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(121U, 134U, 7U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U), PadStrideInfo(1, 1, 2, 0));
+ add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U), PadStrideInfo(1, 1, 2, 0));
add_config(TensorShape(181U, 152U, 42U), TensorShape(5U, 1U, 42U, 100U), TensorShape(100U), TensorShape(177U, 152U, 100U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(200U, 201U, 24U), TensorShape(5U, 1U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 2, 0));
// Batch size 2, 3 and 4
- add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(222U, 224U, 64U, 2U), PadStrideInfo(1, 1, 1, 0));
- add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(121U, 134U, 7U, 3U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U, 2U), PadStrideInfo(1, 1, 2, 0));
+ add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U, 3U), PadStrideInfo(1, 1, 2, 0));
add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(5U, 1U, 42U, 100U), TensorShape(100U), TensorShape(177U, 152U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
}
};
@@ -146,13 +146,13 @@
LargeWinogradConvolutionLayer1x5Dataset()
{
// Batch size 1
- add_config(TensorShape(224U, 224U, 3U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(224U, 224U, 3U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U), PadStrideInfo(1, 1, 0, 2));
add_config(TensorShape(123U, 134U, 16U), TensorShape(1U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 130U, 7U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(181U, 152U, 42U), TensorShape(1U, 5U, 42U, 100U), TensorShape(100U), TensorShape(181U, 148U, 100U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(200U, 201U, 24U), TensorShape(1U, 5U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 0, 2));
// Batch size 2, 3 and 4
- add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 2U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U, 2U), PadStrideInfo(1, 1, 0, 2));
add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(1U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 130U, 7U, 3U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(1U, 5U, 42U, 100U), TensorShape(100U), TensorShape(181U, 148U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
}