Fix Pooling Layer Bug when input is 1xN size
Return error in pooling layer when any calculated output dimension is less than 1.
Simplify use of pooling layer output dimension values in
CpuPoolingKernel.cpp.
Remove some invalid tests in cpu/gpu pooling layers.
Resolves COMPMID-4358.
Signed-off-by: Freddie Liardet <frederick.liardet@arm.com>
Change-Id: If8f8ffec579d3eca1c27a45e5b0b684a77103cff
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5559
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/datasets/PoolingLayerDataset.h b/tests/datasets/PoolingLayerDataset.h
index 01b2491..1557240 100644
--- a/tests/datasets/PoolingLayerDataset.h
+++ b/tests/datasets/PoolingLayerDataset.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -106,7 +106,7 @@
PoolingLayerDatasetSpecial()
{
// Special cases
- add_config(TensorShape(2U, 3U, 4U, 1U), PoolingLayerInfo(PoolingType::AVG, Size2D(3, 3), DataLayout::NCHW, PadStrideInfo(3, 3, 0, 0), true));
+ add_config(TensorShape(2U, 3U, 4U, 1U), PoolingLayerInfo(PoolingType::AVG, Size2D(2, 2), DataLayout::NCHW, PadStrideInfo(3, 3, 0, 0), true));
add_config(TensorShape(60U, 52U, 3U, 2U), PoolingLayerInfo(PoolingType::AVG, Size2D(100, 100), DataLayout::NCHW, PadStrideInfo(5, 5, 50, 50), true));
// Asymmetric padding
add_config(TensorShape(112U, 112U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, DataLayout::NCHW, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp
index 0153e65..63dec39 100644
--- a/tests/validation/CL/PoolingLayer.cpp
+++ b/tests/validation/CL/PoolingLayer.cpp
@@ -101,6 +101,7 @@
TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), // Invalid output Global Pooling
TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 16U, 1U), 1, DataType::F32),
}),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32),
@@ -110,6 +111,7 @@
TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32),
TensorInfo(TensorShape(12U, 12U, 5U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 15U, 1U), 1, DataType::F32),
})),
framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)),
PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 2, 0)),
@@ -119,8 +121,9 @@
PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW),
PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NHWC, PadStrideInfo(), false),
PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW),
+ PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NHWC, PadStrideInfo(1, 1, 0, 0), false),
})),
- framework::dataset::make("Expected", { false, false, false, false, true, false, true, true })),
+ framework::dataset::make("Expected", { false, false, false, false, true, false, true, true , false})),
input_info, output_info, pool_info, expected)
{
ARM_COMPUTE_EXPECT(bool(CLPoolingLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pool_info)) == expected, framework::LogLevel::ERRORS);
diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp
index acc9c3e..b70a189 100644
--- a/tests/validation/NEON/PoolingLayer.cpp
+++ b/tests/validation/NEON/PoolingLayer.cpp
@@ -97,6 +97,7 @@
TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), // Invalid output Global Pooling
TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::QASYMM8), // Invalid exclude_padding = false with quantized type, no actual padding and NHWC
TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 16U, 1U), 1, DataType::F32),
}),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32),
@@ -106,6 +107,7 @@
TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32),
TensorInfo(TensorShape(12U, 12U, 5U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 15U, 1U), 1, DataType::F32),
})),
framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)),
PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)),
@@ -115,8 +117,9 @@
PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW),
PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NHWC, PadStrideInfo(), false),
PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW),
+ PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NHWC, PadStrideInfo(1, 1, 0, 0), false),
})),
- framework::dataset::make("Expected", { false, false, false, false, true, false, false, false, true })),
+ framework::dataset::make("Expected", { false, false, false, false, true, false, true, false, false})),
input_info, output_info, pool_info, expected)
{
bool is_valid = bool(NEPoolingLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pool_info));
@@ -145,15 +148,12 @@
FIXTURE_DATA_TEST_CASE(RunIndices, NEPoolingLayerIndicesFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), combine(PoolingLayerIndicesDatasetFPSmall,
framework::dataset::make("DataType",
DataType::F32))),
- framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })
-
- ))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
validate(Accessor(_target_indices), _ref_indices);
}
-
FIXTURE_DATA_TEST_CASE(RunSpecial, NESpecialPoolingLayerFixture<float>, framework::DatasetMode::ALL, datasets::PoolingLayerDatasetSpecial() * framework::dataset::make("DataType", DataType::F32))
{
// Validate output