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authorGian Marco Iodice <gianmarco.iodice@arm.com>2018-05-14 14:21:39 +0100
committerAnthony Barbier <anthony.barbier@arm.com>2018-11-02 16:52:35 +0000
commita8aef2916379402e241d9f2c5e0faf3f99c860f7 (patch)
treeaccf1f74bb836766260dbdb90aad7b6048c675d2
parentcb0010b02281245c66d5c996fa9ef8b22f036a2d (diff)
downloadComputeLibrary-a8aef2916379402e241d9f2c5e0faf3f99c860f7.tar.gz
COMPMID-792 - Re-enabled Winograd on NEON in all graph examples.
Since now the input transform can be multi-threaded, I re-ebaled Winograd in all graph examples Change-Id: I39ef78243bb47fdae135e18dcae2102af0675b3b Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/131048 Reviewed-by: Anthony Barbier <anthony.barbier@arm.com> Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat
-rw-r--r--arm_compute/core/NEON/NEFixedPoint.h4
-rw-r--r--examples/graph_alexnet.cpp3
-rw-r--r--examples/graph_googlenet.cpp8
-rw-r--r--examples/graph_inception_v3.cpp8
-rw-r--r--examples/graph_inception_v4.cpp8
-rw-r--r--examples/graph_resnet50.cpp8
-rw-r--r--examples/graph_squeezenet.cpp8
-rw-r--r--examples/graph_squeezenet_v1_1.cpp8
-rw-r--r--src/runtime/CL/functions/CLConvolutionLayer.cpp4
-rw-r--r--src/runtime/NEON/functions/NEConvolutionLayer.cpp8
-rw-r--r--tests/validation/CL/ConvolutionLayer.cpp14
-rw-r--r--tests/validation/CL/DilatedConvolutionLayer.cpp4
-rw-r--r--tests/validation/NEON/ConvolutionLayer.cpp6
13 files changed, 43 insertions, 48 deletions
diff --git a/arm_compute/core/NEON/NEFixedPoint.h b/arm_compute/core/NEON/NEFixedPoint.h
index 5719b63618..504ec6c444 100644
--- a/arm_compute/core/NEON/NEFixedPoint.h
+++ b/arm_compute/core/NEON/NEFixedPoint.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -1222,4 +1222,4 @@ qint16x8_t vqpowq_qs16(qint16x8_t a, qint16x8_t b, int fixed_point_position);
float32x4x2_t vmax2q_f32(float32x4x2_t a, float32x4x2_t b);
} // namespace arm_compute
#include "arm_compute/core/NEON/NEFixedPoint.inl"
-#endif /* __ARM_COMPUTE_NEFIXEDPOINT_H__ */
+#endif /* __ARM_COMPUTE_NEFIXEDPOINT_H__ */ \ No newline at end of file
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index b97ca54c78..ffcd8b8411 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -56,10 +56,9 @@ public:
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
- // TODO (geopin01) : Get GPU target somehow and set gemm also for midgard ?
const bool is_neon = (target_hint == Target::NEON);
ConvolutionMethod convolution_5x5_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DIRECT;
- ConvolutionMethod convolution_3x3_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ ConvolutionMethod convolution_3x3_hint = ConvolutionMethod::DEFAULT;
FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index bac3c7c6d5..a47fc9d588 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -52,10 +52,9 @@ public:
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
if(argc < 2)
@@ -107,7 +106,6 @@ public:
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
PadStrideInfo(2, 2, 3, 3))
- << convolution_hint
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index 8e30fd97f2..1d4eb53233 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -51,10 +51,9 @@ public:
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
if(argc < 2)
@@ -111,7 +110,6 @@ public:
get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << convolution_hint
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"),
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index 827370ec5e..ed95baa99e 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -54,10 +54,9 @@ public:
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
if(argc < 2)
@@ -114,7 +113,6 @@ public:
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << convolution_hint
// Conv2d_2a_3x3
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_weights.npy"),
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index 6fac372550..7332bb8b15 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -52,10 +52,9 @@ public:
false /* Do not convert to BGR */);
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
if(argc < 2)
@@ -108,7 +107,6 @@ public:
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 3, 3))
.set_name("conv1/convolution")
- << convolution_hint
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"),
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 2627c96774..4d7bcf1ca8 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -53,10 +53,9 @@ public:
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
if(argc < 2)
@@ -110,7 +109,6 @@ public:
PadStrideInfo(2, 2, 0, 0))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
- << convolution_hint
<< ConvolutionLayer(
1U, 1U, 16U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index c4a5433352..f5fede2f70 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -56,10 +56,9 @@ public:
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Parse arguments
if(argc < 2)
@@ -112,7 +111,6 @@ public:
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_b.npy"),
PadStrideInfo(2, 2, 0, 0))
- << convolution_hint
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
<< ConvolutionMethod::DEFAULT
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 3d4fb113b2..faec4bbf5a 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -123,7 +123,9 @@ ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *
ARM_COMPUTE_UNUSED(weights_info);
ARM_COMPUTE_UNUSED(gpu_target);
- if(dilation != Size2D(1U, 1U))
+ const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
+ if(dilation != Size2D(1U, 1U) || (input->dimension(idx_c) < 16))
{
return ConvolutionMethod::GEMM;
}
diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 0ad4babedc..69fb948d3f 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -104,16 +104,16 @@ ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *
const ITensorInfo *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_ERROR_ON_NULLPTR(weights);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, weights);
ARM_COMPUTE_UNUSED(output);
ARM_COMPUTE_UNUSED(weights_info);
const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
- if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) && (weights->num_dimensions() <= 4)
+ if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (input->dimension(idx_c) > 16) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3)
+ && (weights->num_dimensions() <= 4)
&& (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled()))
{
//FIXME Until COMPMID-1041 is implemented Winograd is slower than GEMM on A53.
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index 935a6ebefa..0c40953524 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -77,22 +77,25 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(23U, 27U, 31U, 4U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32, 0),
TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0)
}),
framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 31U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32, 0),
TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0)
})),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0),
TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0),
TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0),
TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32, 0),
TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32, 0),
@@ -101,6 +104,7 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 2, 1, 1),
PadStrideInfo(1, 2, 1, 1),
PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(2, 1, 0, 0),
PadStrideInfo(3, 2, 1, 0),
PadStrideInfo(1, 1, 2, 2),
@@ -109,6 +113,7 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z
framework::dataset::make("GpuTarget", { GPUTarget::BIFROST,
GPUTarget::MIDGARD,
GPUTarget::G71,
+ GPUTarget::G71,
GPUTarget::MIDGARD,
GPUTarget::BIFROST,
GPUTarget::BIFROST,
@@ -122,12 +127,13 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z
Size2D(1U, 1U),
Size2D(1U, 1U),
Size2D(1U, 1U),
+ Size2D(1U, 1U),
Size2D(2U, 1U),
})),
-framework::dataset::make("EnableFastMath", { false, false, false, false, false, true, true })),
+framework::dataset::make("EnableFastMath", { false, false, false, false, false, false, true, true })),
framework::dataset::make("Expected",
{
- ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM,
+ ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM,
})),
input_info, weights_info, output_info, conv_info, gpu_target, dilation, enable_fast_math, expected)
{
diff --git a/tests/validation/CL/DilatedConvolutionLayer.cpp b/tests/validation/CL/DilatedConvolutionLayer.cpp
index 9da7c32d7c..25931c0f4c 100644
--- a/tests/validation/CL/DilatedConvolutionLayer.cpp
+++ b/tests/validation/CL/DilatedConvolutionLayer.cpp
@@ -66,13 +66,13 @@ TEST_SUITE(DilatedConvolutionLayer)
DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(23U, 27U, 23U, 4U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0)
}),
framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 23U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0)
})),
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 4f59345f6c..330480e4d8 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -78,12 +78,12 @@ TEST_SUITE(NEON)
TEST_SUITE(ConvolutionLayer)
DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 8U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(23U, 27U, 32U, 4U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0)
}),
framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0)
})),
@@ -97,7 +97,7 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z
PadStrideInfo(2, 1, 0, 0),
PadStrideInfo(3, 2, 1, 0)
})),
- framework::dataset::make("Expected", { ConvolutionMethod::WINOGRAD, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })),
+ framework::dataset::make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })),
input_info, weights_info, output_info, conv_info, expected)
{
ConvolutionMethod is_valid = NEConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(false),
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-16 08:18:32 +0000