Fix DeconvolutionLayer tolerance issues in FP16 tests

This patch increases the tolerance value used for FP16 tests in Neon(TM) backend. The tolerance number means 0.01f means it is ok to have 1% mismatch in the resulting tensor between the reference and the target. The value adopts a slightly stricter threshold compared to ConvolutionLayer (which is currently at 7%). This increase makes sense because Deconvolution layer uses convolution under the hood.

Resolves: COMPMID-5841
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Change-Id: Ie0ebf5cce1e9753dc641a947d84128dd6da402d4
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9120
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-by: Sang Won Ha
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 21 insertions, 20 deletions

diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp
index 19bd742a61..a42042bcd1 100644
--- a/tests/validation/NEON/DeconvolutionLayer.cpp
+++ b/tests/validation/NEON/DeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2021, 2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -47,8 +47,9 @@ constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f);    /**< Tolerance for
 constexpr AbsoluteTolerance<float> tolerance_quantized(1.0f); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 const RelativeTolerance<half_float::half> tolerance_fp16(half_float::half(0.2f)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType::F16 */
+constexpr float                           tolerance_num_fp16 = 0.01f;             /**< Tolerance number for FP16 tests -- follows a slightly stricter approach compared to ConvolutionLayer tests */
 #endif                                                                            /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/
-constexpr float tolerance_num = 0.07f;                                            /**< Tolerance number */
+constexpr float tolerance_num_quant = 0.07f;                                      /**< Tolerance number for quantized types */
 
 const auto data4x4 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 3)
                      * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("NumKernels", { 3 });
@@ -231,7 +232,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerFixture4x4<half>, framework::Dat
                                                                                                            add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_fp16);
+    validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16);
 }
 TEST_SUITE_END() // W4x4
 TEST_SUITE(W3x3)
@@ -241,14 +242,14 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDeconvolutionLayerFixture3x3<half>, framework
                                                                                                                   add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_fp16);
+    validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16);
 }
 FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerFixture3x3<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data3x3, framework::dataset::make("DataType", DataType::F16)),
                                                                                                                         data_layouts_dataset),
                                                                                                                 add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_fp16);
+    validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16);
 }
 TEST_SUITE_END() // W3x3
 TEST_SUITE(W1x1)
@@ -257,7 +258,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerFixture1x1<half>, framework::Dat
                                                                                                            add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_fp16);
+    validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16);
 }
 TEST_SUITE_END() // W1x1
 TEST_SUITE_END() // FP16
@@ -295,7 +296,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture4x4<uint8_t>, fr
                                                                                                                        add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W4x4
 
@@ -309,7 +310,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDeconvolutionLayerQuantizedFixture3x3<uint8_t
                        add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data3x3,
                        framework::dataset::make("DataType",
@@ -320,7 +321,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<uint8_t
                        add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W3x3
 
@@ -333,7 +334,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture1x1<uint8_t>, fr
                                                                                                                        add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W1x1
 
@@ -350,7 +351,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture4x4<int8_t>, fra
                                                                                                                       add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W4x4
 
@@ -364,7 +365,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDeconvolutionLayerQuantizedFixture3x3<int8_t>
                        add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data3x3,
                        framework::dataset::make("DataType",
@@ -375,7 +376,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<int8_t>
                        add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W3x3
 
@@ -389,7 +390,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture1x1<int8_t>, fra
                                                                                                                       add_bias_dataset))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W1x1
 
@@ -412,7 +413,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture4x4<ui
                        framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture4x4<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data4x4,
                        framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
@@ -423,7 +424,7 @@ FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture
                        framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W4x4
 
@@ -437,7 +438,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture3x3<ui
                        framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture3x3<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data3x3,
                        framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
@@ -448,7 +449,7 @@ FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture
                        framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W3x3
 
@@ -462,7 +463,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture1x1<ui
                        framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture1x1<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data1x1,
                        framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
@@ -473,7 +474,7 @@ FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture
                        framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
 {
     // Validate output
-    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num);
+    validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant);
 }
 TEST_SUITE_END() // W1x1