COMPMID-617: Add validate support for NEON BatchNormalizationLayer.
Change-Id: I037ec6df7eee06bdd1381e908677803426fa614c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110788
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h
index 1dfe075..f5f818c 100644
--- a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h
@@ -62,6 +62,24 @@
* @param[in] epsilon Small value to avoid division with zero.
*/
void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon);
+ /** Static function to check if given info will lead to a valid configuration of @ref NEBatchNormalizationLayerKernel
+ *
+ * @param[in] input Source tensor info. In case of @p output tensor = nullptr, this tensor will store the result.
+ * 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32.
+ * @param[in] output Destination tensor info. Output will have the same number of dimensions as input. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] var Variance values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] beta Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] gamma Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] epsilon Small value to avoid division with zero.
+ *
+ * @return an error status
+ */
+ static Error validate(const ITensorInfo *input, const ITensorInfo *output,
+ const ITensorInfo *mean, const ITensorInfo *var,
+ const ITensorInfo *beta, const ITensorInfo *gamma,
+ float epsilon);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
diff --git a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h
index b2de716..b311088 100644
--- a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h
@@ -58,6 +58,24 @@
* @param[in] epsilon Small value to avoid division with zero.
*/
void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon);
+ /** Static function to check if given info will lead to a valid configuration of @ref NEBatchNormalizationLayer
+ *
+ * @param[in] input Source tensor info. In case of @p output tensor = nullptr, this tensor will store the result.
+ * 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32.
+ * @param[in] output Destination tensor info. Output will have the same number of dimensions as input. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] var Variance values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] beta Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] gamma Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] epsilon Small value to avoid division with zero.
+ *
+ * @return an error status
+ */
+ static Error validate(const ITensorInfo *input, const ITensorInfo *output,
+ const ITensorInfo *mean, const ITensorInfo *var,
+ const ITensorInfo *beta, const ITensorInfo *gamma,
+ float epsilon);
// Inherited methods overridden:
void run() override;
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
index 1123f2c..4bbf67d 100644
--- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
@@ -33,9 +33,39 @@
using namespace arm_compute;
-NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
- : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon()
+namespace
{
+Error validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, const ITensorInfo *beta, const ITensorInfo *gamma, float epsilon)
+{
+ ARM_COMPUTE_UNUSED(epsilon);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+
+ if(nullptr != output)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var, beta, gamma);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var, beta, gamma);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != mean->dimension(0));
+
+ return Error{};
+}
+
+std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+{
+ unsigned int num_elems_processed_per_iteration = 16 / input->element_size();
+
+ Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+ AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+ bool window_changed = update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, input->valid_region());
+ Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{};
+ return std::make_pair(err, win);
}
void batch_normalization_q8(ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
@@ -213,10 +243,28 @@
input, output);
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+} // namespace
+
+NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
+ : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon()
+{
+}
void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var, beta, gamma);
+
+ ITensorInfo *output_info = nullptr;
+
+ if(nullptr != output)
+ {
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), *input->info());
+
+ output_info = output->info();
+ }
+
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output_info, mean->info(), var->info(), beta->info(), gamma->info(), epsilon));
_input = input;
_output = input;
@@ -228,39 +276,23 @@
if(output != nullptr)
{
- // Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
-
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
-
_output = output;
}
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != mean->info()->dimension(0));
-
- unsigned int num_elems_processed_per_iteration = 0;
-
switch(input->info()->data_type())
{
case DataType::QS8:
- _func = &batch_normalization_q8;
- num_elems_processed_per_iteration = 16;
+ _func = &batch_normalization_q8;
break;
case DataType::QS16:
- _func = &batch_normalization_q16;
- num_elems_processed_per_iteration = 8;
+ _func = &batch_normalization_q16;
break;
case DataType::F32:
- _func = &batch_normalization_fp32;
- num_elems_processed_per_iteration = 4;
+ _func = &batch_normalization_fp32;
break;
case DataType::F16:
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- _func = &batch_normalization_fp16;
- num_elems_processed_per_iteration = 8;
+ _func = &batch_normalization_fp16;
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
default:
@@ -268,19 +300,19 @@
break;
}
- Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
- AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
- if(output != nullptr)
- {
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
- update_window_and_padding(win, input_access, output_access);
- output_access.set_valid_region(win, input->info()->valid_region());
- }
- else
- {
- update_window_and_padding(win, input_access);
- }
- INEKernel::configure(win);
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), output_info);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ INEKernel::configure(win_config.second);
+}
+
+Error NEBatchNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, const ITensorInfo *beta, const ITensorInfo *gamma,
+ float epsilon)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output ? output->clone().get() : nullptr).first);
+
+ return Error{};
}
void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo &info)
diff --git a/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp b/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp
index ef79b02..cfab12c 100644
--- a/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp
+++ b/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp
@@ -43,6 +43,12 @@
_norm_kernel.configure(input, output, mean, var, beta, gamma, epsilon);
}
+Error NEBatchNormalizationLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, const ITensorInfo *beta, const ITensorInfo *gamma,
+ float epsilon)
+{
+ return NEBatchNormalizationLayerKernel::validate(input, output, mean, var, beta, gamma, epsilon);
+}
+
void NEBatchNormalizationLayer::run()
{
NEScheduler::get().schedule(&_norm_kernel, Window::DimY);
diff --git a/tests/validation/NEON/BatchNormalizationLayer.cpp b/tests/validation/NEON/BatchNormalizationLayer.cpp
index a1421d0..806d3b3 100644
--- a/tests/validation/NEON/BatchNormalizationLayer.cpp
+++ b/tests/validation/NEON/BatchNormalizationLayer.cpp
@@ -80,6 +80,52 @@
validate(dst.info()->valid_region(), valid_region);
}
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching data types
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching data types
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Invalid mean/var/beta/gamma shape
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), // Mismatching fixed point position
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
+ }),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 3),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
+ TensorInfo(),
+ })),
+ framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
+ TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
+ TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
+ })),
+ framework::dataset::make("Expected", { false, true, true, true, true, true, false, false})),
+ input_info, output_info, mvbg_info, expected)
+{
+ const auto &mean_info = mvbg_info;
+ const auto &var_info = mvbg_info;
+ const auto &beta_info = mvbg_info;
+ const auto &gamma_info = mvbg_info;
+ bool has_error = bool(NEBatchNormalizationLayer::validate(
+ &input_info.clone()->set_is_resizable(false), output_info.total_size() ? &output_info.clone()->set_is_resizable(false) : nullptr,
+ &mean_info.clone()->set_is_resizable(false), &var_info.clone()->set_is_resizable(false),
+ &beta_info.clone()->set_is_resizable(false), &gamma_info.clone()->set_is_resizable(false), 1.f));
+ ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
TEST_SUITE(Float)
FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::RandomBatchNormalizationLayerDataset(),
framework::dataset::make("DataType", DataType::F32)))