COMPMID-3271: Add support for QASYMM8_SIGNED in CPPTopKVKernel/CPPTopKV
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: Ic34616fc3480ca85cc582e4e3db031d631ed5861
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2887
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/CPP/TopKV.cpp b/tests/validation/CPP/TopKV.cpp
index 0217819..e528c62 100644
--- a/tests/validation/CPP/TopKV.cpp
+++ b/tests/validation/CPP/TopKV.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -134,7 +134,7 @@
validate(Accessor(output), expected_output);
}
-TEST_CASE(Quantized, framework::DatasetMode::ALL)
+TEST_CASE(QASYMM8, framework::DatasetMode::ALL)
{
const unsigned int k = 5;
@@ -187,6 +187,59 @@
validate(Accessor(output), expected_output);
}
+TEST_CASE(QASYMM8_SIGNED, framework::DatasetMode::ALL)
+{
+ const unsigned int k = 5;
+
+ Tensor predictions = create_tensor<Tensor>(TensorShape(10, 20), DataType::QASYMM8_SIGNED, 1, QuantizationInfo());
+ Tensor targets = create_tensor<Tensor>(TensorShape(20), DataType::U32);
+
+ predictions.allocator()->allocate();
+ targets.allocator()->allocate();
+
+ // Fill the tensors with random pre-generated values
+ fill_tensor(Accessor(predictions), std::vector<int8_t>
+ {
+ 123, -34, 69, 118, 20, -45, 99, -98, 127, 117, //-34
+ -99, 1, -128, 90, 60, 25, 102, 76, 24, -110, //25
+ 99, 119, 49, 43, -40, 60, 43, 84, 29, 67, //84
+ 33, 109, 74, 90, 90, 44, 98, 90, 35, 123, //74
+ 62, 118, 24, -32, 34, 21, 114, 113, 124, 20, //124
+ 74, 98, 48, 107, 127, 125, 6, -98, 127, 59, //98
+ 75, 83, 75, -118, 56, 101, 85, 97, 49, 127, //75
+ 72, -20, 40, 14, 28, -30, 109, 43, 127, -31, //-20
+ 78, 121, 109, 66, 29, 90, 70, 21, 38, 48, //109
+ 18, 10, 115, 124, 17, 123, 51, 54, 15, 17, //17
+ 66, 46, -66, 125, 104, 90, 123, 113, -54, -126, //125
+ 58, -85, 74, 39, 115, 39, 111, -27, 44, 51, //51
+ 71, 122, -34, -123, 94, 113, 125, 111, 38, 64, //94
+ -17, 40, 42, 68, 96, 68, 101, 40, 79, 71, //40
+ 89, 88, 54, 82, 127, 12, 112, 52, 125, 22, //22
+ -128, 56, 82, 31, 98, 94, 102, 105, 127, 123, //123
+ 112, 50, 61, 41, 39, 63, -77, 92, 26, 70, //39
+ 2, 90, 31, 99, -34, 114, 112, 126, 127, 87, //90
+ 125, 63, 56, 123, 50, -77, 97, -93, 1, 29, //56
+ 100, -35, 116, 64, 36, 92, 56, 82, -22, -118 //36
+ });
+
+ fill_tensor(Accessor(targets), std::vector<int> { 1, 5, 7, 2, 8, 1, 2, 1, 2, 4, 3, 9, 4, 1, 9, 9, 4, 1, 2, 4 });
+
+ // Determine the output through the CPP kernel
+ Tensor output;
+ CPPTopKV topkv;
+ topkv.configure(&predictions, &targets, &output, k);
+
+ output.allocator()->allocate();
+
+ // Run the kernel
+ topkv.run();
+
+ // Validate against the expected values
+ SimpleTensor<int8_t> expected_output(TensorShape(20), DataType::U8);
+ fill_tensor(expected_output, std::vector<int8_t> { 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0 });
+ validate(Accessor(output), expected_output);
+}
+
TEST_SUITE_END() // TopKV
TEST_SUITE_END() // CPP
} // namespace validation