diff options
20 files changed, 631 insertions, 257 deletions
diff --git a/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h b/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h index dbb25dd7c7..8015f08d1b 100644 --- a/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h +++ b/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h @@ -58,12 +58,12 @@ public: * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. */ - void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon, + void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta = nullptr, const ICLTensor *gamma = nullptr, float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); /** Static function to check if given info will lead to a valid configuration of @ref CLBatchNormalizationLayerKernel * @@ -73,17 +73,17 @@ public: * @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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, - const ITensorInfo *beta, const ITensorInfo *gamma, - float epsilon, ActivationLayerInfo act_info); + const ITensorInfo *beta = nullptr, const ITensorInfo *gamma = nullptr, + float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; diff --git a/arm_compute/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.h b/arm_compute/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.h index 754268a348..bf971a2729 100644 --- a/arm_compute/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.h +++ b/arm_compute/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.h @@ -55,13 +55,32 @@ public: * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (optional) Small value to avoid division with zero. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. */ - void configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *var, const IGCTensor *beta, const IGCTensor *gamma, float epsilon, + void configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *var, const IGCTensor *beta = nullptr, const IGCTensor *gamma = nullptr, float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); + /** Static function to check if given info will lead to a valid configuration of @ref GCBatchNormalizationLayerKernel + * + * @param[in] input Source tensor info. In case of @p output tensor info = 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 (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *mean, const ITensorInfo *var, + const ITensorInfo *beta = nullptr, const ITensorInfo *gamma = nullptr, + float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); // Inherited methods overridden: void run(const Window &window) override; diff --git a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h index 2408a665e4..ae6b8634b3 100644 --- a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h @@ -61,13 +61,12 @@ public: * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. - * Data types supported: F32 */ - void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, + void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta = nullptr, const ITensor *gamma = nullptr, float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); /** Static function to check if given info will lead to a valid configuration of @ref NEBatchNormalizationLayerKernel * @@ -77,18 +76,17 @@ public: * @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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. - * Data types supported: F32 * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, - const ITensorInfo *beta, const ITensorInfo *gamma, - float epsilon, ActivationLayerInfo act_info); + const ITensorInfo *beta = nullptr, const ITensorInfo *gamma = nullptr, + float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; diff --git a/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h b/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h index 39f567d6a3..9386a86ae5 100644 --- a/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h +++ b/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h @@ -54,12 +54,12 @@ public: * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. */ - void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon, + void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta = nullptr, const ICLTensor *gamma = nullptr, float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); /** Static function to check if given info will lead to a valid configuration of @ref CLBatchNormalizationLayer * @@ -69,17 +69,17 @@ public: * @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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, - const ITensorInfo *beta, const ITensorInfo *gamma, - float epsilon, ActivationLayerInfo act_info); + const ITensorInfo *beta = nullptr, const ITensorInfo *gamma = nullptr, + float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); // Inherited methods overridden: void run() override; diff --git a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h index 85c62663ab..feb2087aa0 100644 --- a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h +++ b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h @@ -54,13 +54,12 @@ public: * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] gamma Gamma values tensor. 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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. - * Data types supported: F32 */ - void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, + void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta = nullptr, const ITensor *gamma = nullptr, float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); /** Static function to check if given info will lead to a valid configuration of @ref NEBatchNormalizationLayer * @@ -70,18 +69,17 @@ public: * @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. + * @param[in] beta (Optional) Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma (Optional) Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon (Optional) Small value to avoid division with zero. Default value is 0.001f. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. - * Data types supported: F32 * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, - const ITensorInfo *beta, const ITensorInfo *gamma, - float epsilon, ActivationLayerInfo act_info); + const ITensorInfo *beta = nullptr, const ITensorInfo *gamma = nullptr, + float epsilon = 0.001f, ActivationLayerInfo act_info = ActivationLayerInfo()); // Inherited methods overridden: void run() override; diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl index 0b61b5638c..29b62d3d92 100644 --- a/src/core/CL/cl_kernels/batchnormalization_layer.cl +++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl @@ -93,8 +93,12 @@ __kernel void batchnormalization_layer(TENSOR3D_DECLARATION(input), #endif /* not IN_PLACE */ VECTOR_DECLARATION(mean), VECTOR_DECLARATION(var), +#ifndef USE_DEFAULT_BETA VECTOR_DECLARATION(beta), +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA VECTOR_DECLARATION(gamma), +#endif /* USE_DEFAULT_GAMMA */ float epsilon) { Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(input); @@ -103,10 +107,14 @@ __kernel void batchnormalization_layer(TENSOR3D_DECLARATION(input), #else /* IN_PLACE */ Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ - Vector mean = CONVERT_TO_VECTOR_STRUCT(mean); - Vector var = CONVERT_TO_VECTOR_STRUCT(var); - Vector beta = CONVERT_TO_VECTOR_STRUCT(beta); + Vector mean = CONVERT_TO_VECTOR_STRUCT(mean); + Vector var = CONVERT_TO_VECTOR_STRUCT(var); +#ifndef USE_DEFAULT_BETA + Vector beta = CONVERT_TO_VECTOR_STRUCT(beta); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA Vector gamma = CONVERT_TO_VECTOR_STRUCT(gamma); +#endif /* USE_DEFAULT_GAMMA */ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) data = 0; @@ -117,9 +125,7 @@ __kernel void batchnormalization_layer(TENSOR3D_DECLARATION(input), VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) x_bar = 0; VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - gamma_vec = 0; - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - beta_vec = 0; + res = 0; const int current_slice = get_global_id(2); @@ -132,11 +138,22 @@ __kernel void batchnormalization_layer(TENSOR3D_DECLARATION(input), numerator = SUB_OP(data, numerator); x_bar = MUL_OP(numerator, denominator); +#ifndef USE_DEFAULT_GAMMA + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) gamma_vec = *((__global DATA_TYPE *)(gamma.ptr + current_slice * gamma.stride_x)); - beta_vec = *((__global DATA_TYPE *)(beta.ptr + current_slice * beta.stride_x)); + res = MUL_OP(gamma_vec, x_bar); +#else /* USE_DEFAULT_GAMMA */ + // gamma is equal to 1, no need to perform multiplications + res = x_bar; +#endif /* USE_DEFAULT_GAMMA */ + +#ifndef USE_DEFAULT_BETA VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - res = ADD_OP(MUL_OP(gamma_vec, x_bar), beta_vec); + beta_vec = *((__global DATA_TYPE *)(beta.ptr + current_slice * beta.stride_x)); + // beta is not zero, hence we need to perform the addition + res = ADD_OP(res, beta_vec); +#endif /* USE_DEFAULT_BETA */ res = ACTIVATION_FUNC(res); @@ -144,4 +161,4 @@ __kernel void batchnormalization_layer(TENSOR3D_DECLARATION(input), (res, 0, (__global DATA_TYPE *)out.ptr); } -#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) */
\ No newline at end of file +#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) */ diff --git a/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp index 95c8250ee7..62f21eed96 100644 --- a/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp +++ b/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp @@ -46,9 +46,22 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, { ARM_COMPUTE_UNUSED(epsilon); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma); - 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); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var); + if(beta != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, beta); + } + if(gamma != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, gamma); + } + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != mean->dimension(0)); if(act_info.enabled()) { @@ -108,7 +121,7 @@ CLBatchNormalizationLayerKernel::CLBatchNormalizationLayerKernel() void CLBatchNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon, ActivationLayerInfo act_info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var, beta, gamma); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var); _input = input; _output = output; @@ -120,15 +133,9 @@ void CLBatchNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *out _run_in_place = (output == nullptr) || (output == input); - if(output != nullptr) - { - ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), output->info()); - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output->info(), *input->info()->clone()); - } - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr, - mean->info(), var->info(), beta->info(), gamma->info(), epsilon, act_info)); + mean->info(), var->info(), (beta != nullptr) ? beta->info() : nullptr, + (gamma != nullptr) ? gamma->info() : nullptr, epsilon, act_info)); const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size(); @@ -141,13 +148,23 @@ void CLBatchNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *out build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); build_opts.add_option_if(_run_in_place, "-DIN_PLACE"); build_opts.add_option_if(is_data_type_fixed_point(input->info()->data_type()), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); + build_opts.add_option_if(beta == nullptr, "-DUSE_DEFAULT_BETA"); + build_opts.add_option_if(gamma == nullptr, "-DUSE_DEFAULT_GAMMA"); // Create kernel _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("batchnormalization_layer", build_opts.options())); // Set kernel static arguments unsigned int include_output = (!_run_in_place) ? 1 : 0; - unsigned int idx = (1 + include_output) * num_arguments_per_3D_tensor() + 4 * num_arguments_per_1D_tensor(); // Skip the input and output parameters + unsigned int idx = (1 + include_output) * num_arguments_per_3D_tensor() + 2 * num_arguments_per_1D_tensor(); // Skip the input and output parameters + if(_beta != nullptr) + { + idx += num_arguments_per_1D_tensor(); // Skip beta parameter + } + if(_gamma != nullptr) + { + idx += num_arguments_per_1D_tensor(); // Skip gamma parameter + } _kernel.setArg<cl_float>(idx++, _epsilon); // Configure kernel window @@ -191,8 +208,14 @@ void CLBatchNormalizationLayerKernel::run(const Window &window, cl::CommandQueue unsigned int idx = (1 + include_output) * num_arguments_per_3D_tensor(); add_1D_tensor_argument(idx, _mean, vector_slice); add_1D_tensor_argument(idx, _var, vector_slice); - add_1D_tensor_argument(idx, _beta, vector_slice); - add_1D_tensor_argument(idx, _gamma, vector_slice); + if(_beta != nullptr) + { + add_1D_tensor_argument(idx, _beta, vector_slice); + } + if(_gamma != nullptr) + { + add_1D_tensor_argument(idx, _gamma, vector_slice); + } do { diff --git a/src/core/GLES_COMPUTE/cs_shaders/batchnormalization_layer.cs b/src/core/GLES_COMPUTE/cs_shaders/batchnormalization_layer.cs index 7629b255b7..81be9679b2 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/batchnormalization_layer.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/batchnormalization_layer.cs @@ -50,6 +50,8 @@ precision mediump float; * * @note The data type must be passed at compile time using "#define DATA_TYPE_NAME". e.g. "#define DATA_TYPE_FP32" * @note Epsilon parameter in the batch normalization equation should be given as a preprocessor argument using "#define EPSILON". e.g. "#define EPSILON 0.1" + * @note Beta is optional with default value of 0. If not provided, the preprocessor argument "USE_DEFAULT_BETA" should be given + * @note Gamma is optional with default value of 1. If not provided, the preprocessor argument "USE_DEFAULT_GAMMA" should be given * * @param[in] src_ptr Pointer to the first source tensor. Supported data types: F16/F32 * @param[in] src_attrs The attributes of the source tensor @@ -59,10 +61,10 @@ precision mediump float; * @param[in] mean_attrs The attributes of the mean tensor * @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p src_ptr * @param[in] var_attrs The attributes of the var tensor - * @param[in] beta_ptr Pointer to the beta source tensor. Supported data types: same as @p src_ptr - * @param[in] beta_attrs The attributes of the beta tensor - * @param[in] gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p src_ptr - * @param[in] gamma_attrs The attributes of the gamma tensor + * @param[in] beta_ptr (Optional) Pointer to the beta source tensor. If not provided, default value of beta is 0. Supported data types: same as @p src_ptr + * @param[in] beta_attrs (Optional) The attributes of the beta tensor + * @param[in] gamma_ptr (Optional) Pointer to the gamma source tensor. If not provided, default value of gamma is 1. Supported data types: same as @p src_ptr + * @param[in] gamma_attrs (Optional) The attributes of the gamma tensor */ SHADER_PARAMS_DECLARATION { @@ -70,8 +72,12 @@ SHADER_PARAMS_DECLARATION Tensor3DAttributes dst_attrs; VectorAttributes mean_attrs; VectorAttributes var_attrs; - VectorAttributes beta_attrs; - VectorAttributes gamma_attrs; +#ifndef USE_DEFAULT_BETA + VectorAttributes beta_attrs; +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA + VectorAttributes gamma_attrs; +#endif /* USE_DEFAULT_GAMMA */ }; #ifdef DATA_TYPE_FP32 @@ -79,24 +85,34 @@ TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly); TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, writeonly); TENSOR_DECLARATION(3, meanBuffer, float, mean_ptr, mean_shift, 2, readonly); TENSOR_DECLARATION(4, varBuffer, float, var_ptr, var_shift, 2, readonly); +#ifndef USE_DEFAULT_BETA TENSOR_DECLARATION(5, betaBuffer, float, beta_ptr, beta_shift, 2, readonly); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA +#ifdef USE_DEFAULT_BETA +TENSOR_DECLARATION(5, gammaBuffer, float, gamma_ptr, gamma_shift, 2, readonly); +#else /* USE_DEFAULT_BETA */ TENSOR_DECLARATION(6, gammaBuffer, float, gamma_ptr, gamma_shift, 2, readonly); +#endif /* USE_DEFAULT_BETA */ +#endif /* USE_DEFAULT_GAMMA */ void main(void) { - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); - Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift); - VectorIterator mean_iter = CONVERT_TO_VECTOR_ITERATOR(mean_attrs, mean_shift); - VectorIterator var_iter = CONVERT_TO_VECTOR_ITERATOR(var_attrs, var_shift); - VectorIterator beta_iter = CONVERT_TO_VECTOR_ITERATOR(beta_attrs, beta_shift); - VectorIterator gamma_iter = CONVERT_TO_VECTOR_ITERATOR(gamma_attrs, gamma_shift); + Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); + Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift); + VectorIterator mean_iter = CONVERT_TO_VECTOR_ITERATOR(mean_attrs, mean_shift); + VectorIterator var_iter = CONVERT_TO_VECTOR_ITERATOR(var_attrs, var_shift); +#ifndef USE_DEFAULT_BETA + VectorIterator beta_iter = CONVERT_TO_VECTOR_ITERATOR(beta_attrs, beta_shift); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA + VectorIterator gamma_iter = CONVERT_TO_VECTOR_ITERATOR(gamma_attrs, gamma_shift); +#endif /* USE_DEFAULT_GAMMA */ float input_value = 0.f; float denominator = 0.f; float numerator = 0.f; float x_bar = 0.f; - float gamma_param = 0.f; - float beta_param = 0.f; uint current_slice = gl_GlobalInvocationID.z; @@ -109,10 +125,18 @@ void main(void) numerator = SUB_OP(input_value, numerator); x_bar = MUL_OP(numerator, denominator); - gamma_param = LOAD(gamma_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(gamma_iter, current_slice * beta_attrs.stride_x)); - beta_param = LOAD(beta_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(beta_iter, current_slice * beta_attrs.stride_x)); +#ifndef USE_DEFAULT_GAMMA + float gamma_param = LOAD(gamma_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(gamma_iter, current_slice * gamma_attrs.stride_x)); + + x_bar = MUL_OP(gamma_param, x_bar); +#endif /* USE_DEFAULT_GAMMA */ +#ifndef USE_DEFAULT_BETA + float beta_param = LOAD(beta_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(beta_iter, current_slice * beta_attrs.stride_x)); + + x_bar = ADD_OP(x_bar, beta_param); +#endif /* USE_DEFAULT_BETA */ - STORE_CURRENT_ITEM(dst_ptr, dst_iter, ACTIVATION_FUNC(ADD_OP(MUL_OP(gamma_param, x_bar), beta_param))); + STORE_CURRENT_ITEM(dst_ptr, dst_iter, ACTIVATION_FUNC(x_bar)); } #elif defined(DATA_TYPE_FP16) @@ -120,8 +144,16 @@ TENSOR_DECLARATION(1, srcBuffer, uvec2, src_ptr, src_shift, 3, readonly); TENSOR_DECLARATION(2, dstBuffer, uvec2, dst_ptr, dst_shift, 3, writeonly); TENSOR_DECLARATION(3, meanBuffer, uvec2, mean_ptr, mean_shift, 3, readonly); TENSOR_DECLARATION(4, varBuffer, uvec2, var_ptr, var_shift, 3, readonly); +#ifndef USE_DEFAULT_BETA TENSOR_DECLARATION(5, betaBuffer, uvec2, beta_ptr, beta_shift, 3, readonly); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA +#ifdef USE_DEFAULT_BETA +TENSOR_DECLARATION(5, gammaBuffer, uvec2, gamma_ptr, gamma_shift, 3, readonly); +#else /* USE_DEFAULT_BETA */ TENSOR_DECLARATION(6, gammaBuffer, uvec2, gamma_ptr, gamma_shift, 3, readonly); +#endif /* USE_DEFAULT_BETA */ +#endif /* USE_DEFAULT_GAMMA */ void main(void) { @@ -129,14 +161,18 @@ void main(void) Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift); VectorIterator mean_iter = CONVERT_TO_VECTOR_ITERATOR(mean_attrs, mean_shift); VectorIterator var_iter = CONVERT_TO_VECTOR_ITERATOR(var_attrs, var_shift); +#ifndef USE_DEFAULT_BETA VectorIterator beta_iter = CONVERT_TO_VECTOR_ITERATOR(beta_attrs, beta_shift); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA VectorIterator gamma_iter = CONVERT_TO_VECTOR_ITERATOR(gamma_attrs, gamma_shift); +#endif /* USE_DEFAULT_GAMMA */ vec4 unpacked_s[5]; float denominator; float numerator; - float gamma_param; - float beta_param; + float gamma_param = 1.f; + float beta_param = 0.f; vec4 x_bar; vec4 result; @@ -144,68 +180,87 @@ void main(void) unpacked_s[0] = LOAD_UNPACK4_CURRENT_ITEM_HALF(src_ptr, src_iter); unpacked_s[1] = LOAD_UNPACK4_HALF(var_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(var_iter, current_slice * var_attrs.stride_x)); unpacked_s[2] = LOAD_UNPACK4_HALF(mean_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(mean_iter, current_slice * mean_attrs.stride_x)); - unpacked_s[3] = LOAD_UNPACK4_HALF(gamma_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(gamma_iter, current_slice * beta_attrs.stride_x)); +#ifndef USE_DEFAULT_GAMMA + unpacked_s[3] = LOAD_UNPACK4_HALF(gamma_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(gamma_iter, current_slice * gamma_attrs.stride_x)); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_BETA unpacked_s[4] = LOAD_UNPACK4_HALF(beta_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(beta_iter, current_slice * beta_attrs.stride_x)); +#endif /* USE_DEFAULT_GAMMA */ if((current_slice % uint(4)) == uint(0)) { denominator = unpacked_s[1].x; denominator = INVSQRT_OP(ADD_OP(denominator, SQCVT_SAT(float(ESPILON)))); - //Calculate x bar and store results - numerator = unpacked_s[2].x; - x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); + // Calculate x bar + numerator = unpacked_s[2].x; + x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); +#ifndef USE_DEFAULT_GAMMA gamma_param = unpacked_s[3].x; +#endif /* USE_DEFAULT_GAMMA */ +#ifndef USE_DEFAULT_BETA beta_param = unpacked_s[4].x; - result = ACTIVATION_FUNC(ADD_OP(MUL_OP(gamma_param, x_bar), beta_param)); - - STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); +#endif /* USE_DEFAULT_BETA */ } else if((current_slice % uint(4)) == uint(1)) { denominator = unpacked_s[1].y; denominator = INVSQRT_OP(ADD_OP(denominator, SQCVT_SAT(float(ESPILON)))); - //Calculate x bar and store results - numerator = unpacked_s[2].y; - x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); + // Calculate x bar + numerator = unpacked_s[2].y; + x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); +#ifndef USE_DEFAULT_GAMMA gamma_param = unpacked_s[3].y; +#endif /* USE_DEFAULT_GAMMA */ +#ifndef USE_DEFAULT_BETA beta_param = unpacked_s[4].y; - result = ACTIVATION_FUNC(ADD_OP(MUL_OP(gamma_param, x_bar), beta_param)); - - STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); +#endif /* USE_DEFAULT_BETA */ } else if((current_slice % uint(4)) == uint(2)) { denominator = unpacked_s[1].z; denominator = INVSQRT_OP(ADD_OP(denominator, SQCVT_SAT(float(ESPILON)))); - //Calculate x bar and store results - numerator = unpacked_s[2].z; - x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); + // Calculate x bar + numerator = unpacked_s[2].z; + x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); +#ifndef USE_DEFAULT_GAMMA gamma_param = unpacked_s[3].z; +#endif /* USE_DEFAULT_GAMMA */ +#ifndef USE_DEFAULT_BETA beta_param = unpacked_s[4].z; - result = ACTIVATION_FUNC(ADD_OP(MUL_OP(gamma_param, x_bar), beta_param)); - - STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); +#endif /* USE_DEFAULT_BETA */ } else { denominator = unpacked_s[1].w; denominator = INVSQRT_OP(ADD_OP(denominator, SQCVT_SAT(float(ESPILON)))); - //Calculate x bar and store results - numerator = unpacked_s[2].w; - x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); + // Calculate x bar + numerator = unpacked_s[2].w; + x_bar = MUL_OP(SUB_OP(unpacked_s[0], numerator), denominator); +#ifndef USE_DEFAULT_GAMMA gamma_param = unpacked_s[3].w; +#endif /* USE_DEFAULT_GAMMA */ +#ifndef USE_DEFAULT_BETA beta_param = unpacked_s[4].w; - result = ACTIVATION_FUNC(ADD_OP(MUL_OP(gamma_param, x_bar), beta_param)); - - STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); +#endif /* USE_DEFAULT_BETA */ } + +#ifndef USE_DEFAULT_GAMMA + x_bar = MUL_OP(gamma_param, x_bar); +#endif /* USE_DEFAULT_GAMMA */ +#ifndef USE_DEFAULT_BETA + x_bar = ADD_OP(x_bar, beta_param); +#endif /* USE_DEFAULT_BETA */ + + result = ACTIVATION_FUNC(x_bar); + + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); } #endif /*DATA_TYPE_FP16*/ diff --git a/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp index cd93f6997e..9a592dfe00 100644 --- a/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp +++ b/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp @@ -36,32 +36,118 @@ using namespace arm_compute; -GCBatchNormalizationLayerKernel::GCBatchNormalizationLayerKernel() - : _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _beta(nullptr), _gamma(nullptr), _epsilon(0.0f) +namespace { -} - -void GCBatchNormalizationLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *var, const IGCTensor *beta, const IGCTensor *gamma, - float epsilon, ActivationLayerInfo act_info) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *mean, const ITensorInfo *var, + const ITensorInfo *beta, const ITensorInfo *gamma, + float epsilon, ActivationLayerInfo act_info) { + ARM_COMPUTE_UNUSED(epsilon); + ARM_COMPUTE_UNUSED(var); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_NULLPTR(output); - // 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_DATA_TYPES(input, mean, var); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var); + ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var); - 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(input, output); - ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma); + if(output->total_size() != 0) + { + ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); + } + + if(beta != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, beta); + } + if(gamma != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, gamma); + } if(act_info.enabled()) { - ARM_COMPUTE_ERROR_ON(input->info()->data_type() != DataType::F32 && input->info()->data_type() != DataType::F16); + ARM_COMPUTE_ERROR_ON(input->data_type() != DataType::F32 && input->data_type() != DataType::F16); ARM_COMPUTE_ERROR_ON(act_info.activation() != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU && act_info.activation() != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && act_info.activation() != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a()); } + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, + ITensorInfo *mean, ITensorInfo *var, + ITensorInfo *beta, ITensorInfo *gamma) +{ + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type(), input->fixed_point_position()); + + unsigned int num_elems_processed_per_iteration = 1; + if(input->data_type() == DataType::F16) + { + num_elems_processed_per_iteration = 4; + } + + // Configure kernel window + 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); + AccessWindowStatic mean_access(mean, 0, 0, mean->dimension(0) + 3, mean->dimension(1)); + AccessWindowStatic var_access(var, 0, 0, var->dimension(0) + 3, var->dimension(1)); + + bool window_changed = false; + if(beta != nullptr) + { + AccessWindowStatic beta_access(beta, 0, 0, beta->dimension(0) + 3, beta->dimension(1)); + if(gamma != nullptr) + { + AccessWindowStatic gamma_access(gamma, 0, 0, gamma->dimension(0) + 3, gamma->dimension(1)); + window_changed = update_window_and_padding(win, input_access, output_access, mean_access, var_access, beta_access, gamma_access); + } + else + { + window_changed = update_window_and_padding(win, input_access, output_access, mean_access, var_access, beta_access); + } + } + else + { + if(gamma != nullptr) + { + AccessWindowStatic gamma_access(gamma, 0, 0, gamma->dimension(0) + 3, gamma->dimension(1)); + window_changed = update_window_and_padding(win, input_access, output_access, mean_access, var_access, gamma_access); + } + else + { + window_changed = update_window_and_padding(win, input_access, output_access, mean_access, var_access); + } + } + output_access.set_valid_region(win, input->valid_region()); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +GCBatchNormalizationLayerKernel::GCBatchNormalizationLayerKernel() + : _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _beta(nullptr), _gamma(nullptr), _epsilon(0.0f) +{ +} + +void GCBatchNormalizationLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *var, const IGCTensor *beta, const IGCTensor *gamma, + float epsilon, ActivationLayerInfo act_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, mean, var); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), mean->info(), var->info(), + (beta != nullptr) ? beta->info() : nullptr, (gamma != nullptr) ? gamma->info() : nullptr, + epsilon, act_info)); _input = input; _output = output; @@ -71,12 +157,6 @@ void GCBatchNormalizationLayerKernel::configure(const IGCTensor *input, IGCTenso _gamma = gamma; _epsilon = epsilon; - unsigned int num_elems_processed_per_iteration = 1; - if(input->info()->data_type() == DataType::F16) - { - num_elems_processed_per_iteration = 4; - } - // Set build options std::set<std::string> build_opts; std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16"; @@ -85,6 +165,14 @@ void GCBatchNormalizationLayerKernel::configure(const IGCTensor *input, IGCTenso build_opts.emplace(("#define LOCAL_SIZE_X " + support::cpp11::to_string(1))); build_opts.emplace(("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1))); build_opts.emplace(("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1))); + if(beta == nullptr) + { + build_opts.emplace("#define USE_DEFAULT_BETA"); + } + if(gamma == nullptr) + { + build_opts.emplace("#define USE_DEFAULT_GAMMA"); + } if(act_info.enabled()) { @@ -97,19 +185,25 @@ void GCBatchNormalizationLayerKernel::configure(const IGCTensor *input, IGCTenso _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel("batchnormalization_layer", build_opts)); // Configure kernel window - Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); + auto win_config = validate_and_configure_window(input->info(), output->info(), mean->info(), var->info(), + (beta != nullptr) ? beta->info() : nullptr, (gamma != nullptr) ? gamma->info() : nullptr); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); - AccessWindowStatic mean_access(mean->info(), 0, 0, mean->info()->dimension(0) + 3, mean->info()->dimension(1)); - AccessWindowStatic var_access(var->info(), 0, 0, var->info()->dimension(0) + 3, var->info()->dimension(1)); - AccessWindowStatic beta_access(beta->info(), 0, 0, beta->info()->dimension(0) + 3, beta->info()->dimension(1)); - AccessWindowStatic gamma_access(gamma->info(), 0, 0, gamma->info()->dimension(0) + 3, gamma->info()->dimension(1)); + IGCKernel::configure(win_config.second); +} - update_window_and_padding(win, input_access, output_access, mean_access, var_access, beta_access, gamma_access); - output_access.set_valid_region(win, input->info()->valid_region()); +Status GCBatchNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *mean, const ITensorInfo *var, + const ITensorInfo *beta, const ITensorInfo *gamma, + float epsilon, ActivationLayerInfo act_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon, act_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), + mean->clone().get(), var->clone().get(), + beta->clone().get(), gamma->clone().get()) + .first); - IGCKernel::configure(win); + return Status{}; } void GCBatchNormalizationLayerKernel::run(const Window &window) @@ -127,11 +221,18 @@ void GCBatchNormalizationLayerKernel::run(const Window &window) Window vector_slice = window.first_slice_window_1D(); vector_slice.set(Window::DimX, Window::Dimension(0, 0, 0)); - unsigned int idx = 2 * num_arguments_per_3D_tensor(); - add_1D_tensor_argument(idx, _mean, 3, vector_slice); - add_1D_tensor_argument(idx, _var, 4, vector_slice); - add_1D_tensor_argument(idx, _beta, 5, vector_slice); - add_1D_tensor_argument(idx, _gamma, 6, vector_slice); + unsigned int idx = 2 * num_arguments_per_3D_tensor(); + unsigned int binding_point = 3; + add_1D_tensor_argument(idx, _mean, binding_point, vector_slice); + add_1D_tensor_argument(idx, _var, ++binding_point, vector_slice); + if(_beta != nullptr) + { + add_1D_tensor_argument(idx, _beta, ++binding_point, vector_slice); + } + if(_gamma != nullptr) + { + add_1D_tensor_argument(idx, _gamma, ++binding_point, vector_slice); + } slice.shift(Window::DimX, -(_output->info()->padding()).left); diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index 1f730a2c3c..d1bdfac2da 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -62,9 +62,21 @@ validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const IT 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_MISMATCHING_DATA_TYPES(input, mean, var); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var); + if(beta != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta); + } + if(gamma != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma); + } ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != mean->dimension(0)); return Status{}; @@ -72,6 +84,12 @@ validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const IT std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) { + if(output != nullptr) + { + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output, *input->clone()); + } + unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); @@ -99,13 +117,13 @@ void NEBatchNormalizationLayerKernel::batch_normalization_qs8(const Window &wind const int fixed_point_position = _input->info()->fixed_point_position(); const auto input_mean = reinterpret_cast<const qint8_t *>(_mean->ptr_to_element(Coordinates(0, 0))); const auto input_var = reinterpret_cast<const qint8_t *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = reinterpret_cast<const qint8_t *>(_gamma->ptr_to_element(Coordinates(0, 0))); - const auto input_beta = reinterpret_cast<const qint8_t *>(_beta->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const qint8_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const qint8_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; qint8x16_t mean_vec = vdupq_n_qs8(0); qint8x16_t var_vec = vdupq_n_qs8(0); - qint8x16_t gamma_vec = vdupq_n_qs8(0); - qint8x16_t beta_vec = vdupq_n_qs8(0); + qint8x16_t gamma_vec = vdupq_n_qs8(sqcvt_qs8_f32(1, fixed_point_position)); + qint8x16_t beta_vec = vdupq_n_qs8(sqcvt_qs8_f32(0, fixed_point_position)); qint8x16_t denominator = vdupq_n_qs8(0); const qint8x16_t epsilon_vec = vdupq_n_qs8(sqcvt_qs8_f32(_epsilon, fixed_point_position)); execute_window_loop(window, [&](const Coordinates & id) @@ -113,10 +131,16 @@ void NEBatchNormalizationLayerKernel::batch_normalization_qs8(const Window &wind if(slice != id.z()) { // Conctruct vectors - mean_vec = vdupq_n_qs8(*(input_mean + id.z())); - var_vec = vdupq_n_qs8(*(input_var + id.z())); - gamma_vec = vdupq_n_qs8(*(input_gamma + id.z())); - beta_vec = vdupq_n_qs8(*(input_beta + id.z())); + mean_vec = vdupq_n_qs8(*(input_mean + id.z())); + var_vec = vdupq_n_qs8(*(input_var + id.z())); + if(input_gamma != nullptr) + { + gamma_vec = vdupq_n_qs8(*(input_gamma + id.z())); + } + if(input_beta != nullptr) + { + beta_vec = vdupq_n_qs8(*(input_beta + id.z())); + } // Calculate denominator denominator = vqinvsqrtq_qs8(vqaddq_qs8(var_vec, epsilon_vec), fixed_point_position); @@ -146,13 +170,13 @@ void NEBatchNormalizationLayerKernel::batch_normalization_qs16(const Window &win const int fixed_point_position = _input->info()->fixed_point_position(); const auto input_mean = reinterpret_cast<const qint16_t *>(_mean->ptr_to_element(Coordinates(0, 0))); const auto input_var = reinterpret_cast<const qint16_t *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = reinterpret_cast<const qint16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))); - const auto input_beta = reinterpret_cast<const qint16_t *>(_beta->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const qint16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const qint16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; qint16x8_t mean_vec = vdupq_n_qs16(0); qint16x8_t var_vec = vdupq_n_qs16(0); - qint16x8_t gamma_vec = vdupq_n_qs16(0); - qint16x8_t beta_vec = vdupq_n_qs16(0); + qint16x8_t gamma_vec = vdupq_n_qs16(sqcvt_qs16_f32(1, fixed_point_position)); + qint16x8_t beta_vec = vdupq_n_qs16(sqcvt_qs16_f32(0, fixed_point_position)); qint16x8_t denominator = vdupq_n_qs16(0); const qint16x8_t epsilon_vec = vdupq_n_qs16(sqcvt_qs16_f32(_epsilon, fixed_point_position)); execute_window_loop(window, [&](const Coordinates & id) @@ -160,10 +184,16 @@ void NEBatchNormalizationLayerKernel::batch_normalization_qs16(const Window &win if(slice != id.z()) { // Conctruct vectors - mean_vec = vdupq_n_qs16(*(input_mean + id.z())); - var_vec = vdupq_n_qs16(*(input_var + id.z())); - gamma_vec = vdupq_n_qs16(*(input_gamma + id.z())); - beta_vec = vdupq_n_qs16(*(input_beta + id.z())); + mean_vec = vdupq_n_qs16(*(input_mean + id.z())); + var_vec = vdupq_n_qs16(*(input_var + id.z())); + if(input_gamma != nullptr) + { + gamma_vec = vdupq_n_qs16(*(input_gamma + id.z())); + } + if(input_beta != nullptr) + { + beta_vec = vdupq_n_qs16(*(input_beta + id.z())); + } // Calculate denominator denominator = vqinvsqrtq_qs16(vqaddq_qs16(var_vec, epsilon_vec), fixed_point_position); @@ -194,12 +224,12 @@ void NEBatchNormalizationLayerKernel::batch_normalization_fp16(const Window &win const auto input_mean = reinterpret_cast<const float16_t *>(_mean->ptr_to_element(Coordinates(0, 0))); const auto input_var = reinterpret_cast<const float16_t *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = reinterpret_cast<const float16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))); - const auto input_beta = reinterpret_cast<const float16_t *>(_beta->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; float16x8_t mean_vec = vdupq_n_f16(0.0); float16x8_t var_vec = vdupq_n_f16(0.0); - float16x8_t gamma_vec = vdupq_n_f16(0.0); + float16x8_t gamma_vec = vdupq_n_f16(1.0); float16x8_t beta_vec = vdupq_n_f16(0.0); float16x8_t denominator = vdupq_n_f16(0.0); const float16x8_t epsilon_vec = vdupq_n_f16(_epsilon); @@ -208,10 +238,16 @@ void NEBatchNormalizationLayerKernel::batch_normalization_fp16(const Window &win if(slice != id.z()) { // Conctruct vectors - mean_vec = vdupq_n_f16(*(input_mean + id.z())); - var_vec = vdupq_n_f16(*(input_var + id.z())); - gamma_vec = vdupq_n_f16(*(input_gamma + id.z())); - beta_vec = vdupq_n_f16(*(input_beta + id.z())); + mean_vec = vdupq_n_f16(*(input_mean + id.z())); + var_vec = vdupq_n_f16(*(input_var + id.z())); + if(input_gamma != nullptr) + { + gamma_vec = vdupq_n_f16(*(input_gamma + id.z())); + } + if(input_beta != nullptr) + { + beta_vec = vdupq_n_f16(*(input_beta + id.z())); + } // Calculate denominator denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec)); @@ -241,12 +277,12 @@ void NEBatchNormalizationLayerKernel::batch_normalization_fp32(const Window &win const auto input_mean = reinterpret_cast<const float *>(_mean->ptr_to_element(Coordinates(0, 0))); const auto input_var = reinterpret_cast<const float *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = reinterpret_cast<const float *>(_gamma->ptr_to_element(Coordinates(0, 0))); - const auto input_beta = reinterpret_cast<const float *>(_beta->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; float32x4_t mean_vec = vdupq_n_f32(0.0); float32x4_t var_vec = vdupq_n_f32(0.0); - float32x4_t gamma_vec = vdupq_n_f32(0.0); + float32x4_t gamma_vec = vdupq_n_f32(1.0); float32x4_t beta_vec = vdupq_n_f32(0.0); float32x4_t denominator = vdupq_n_f32(0.0); const float32x4_t epsilon_vec = vdupq_n_f32(_epsilon); @@ -255,10 +291,16 @@ void NEBatchNormalizationLayerKernel::batch_normalization_fp32(const Window &win if(slice != id.z()) { // Conctruct vectors - mean_vec = vdupq_n_f32(*(input_mean + id.z())); - var_vec = vdupq_n_f32(*(input_var + id.z())); - gamma_vec = vdupq_n_f32(*(input_gamma + id.z())); - beta_vec = vdupq_n_f32(*(input_beta + id.z())); + mean_vec = vdupq_n_f32(*(input_mean + id.z())); + var_vec = vdupq_n_f32(*(input_var + id.z())); + if(input_gamma != nullptr) + { + gamma_vec = vdupq_n_f32(*(input_gamma + id.z())); + } + if(input_beta != nullptr) + { + beta_vec = vdupq_n_f32(*(input_beta + id.z())); + } // Calculate denominator denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec)); @@ -335,21 +377,12 @@ void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, const ITensor *beta, const ITensor *gamma, float epsilon, ActivationLayerInfo act_info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var, beta, gamma); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var); - 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, + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr, mean->info(), var->info(), - beta->info(), gamma->info(), + (beta != nullptr) ? beta->info() : nullptr, + (gamma != nullptr) ? gamma->info() : nullptr, epsilon, act_info)); _input = input; @@ -361,7 +394,8 @@ void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, _epsilon = epsilon; _act_info = act_info; - if(output != nullptr) + const bool run_in_place = (output == nullptr) || (output == input); + if(!run_in_place) { _output = output; } @@ -377,7 +411,7 @@ void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, } // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output_info); + auto win_config = validate_and_configure_window(input->info(), (run_in_place) ? nullptr : output->info()); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } diff --git a/tests/AssetsLibrary.h b/tests/AssetsLibrary.h index ae88824298..4bbe4c56f6 100644 --- a/tests/AssetsLibrary.h +++ b/tests/AssetsLibrary.h @@ -352,6 +352,16 @@ public: template <typename T> void fill_layer_data(T &&tensor, std::string name) const; + /** Fill a tensor with a constant value + * + * @param[in, out] tensor To be filled tensor. + * @param[in] value Value to be assigned to all elements of the input tensor. + * + * @note @p value must be of the same type as the data type of @p tensor + */ + template <typename T, typename D> + void fill_tensor_value(T &&tensor, D value) const; + private: // Function prototype to convert between image formats. using Converter = void (*)(const RawTensor &src, RawTensor &dst); @@ -774,6 +784,12 @@ void AssetsLibrary::fill_layer_data(T &&tensor, std::string name) const }); } } + +template <typename T, typename D> +void AssetsLibrary::fill_tensor_value(T &&tensor, D value) const +{ + fill_tensor_uniform(tensor, 0, value, value); +} } // namespace test } // namespace arm_compute #endif /* __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__ */ diff --git a/tests/benchmark/CL/BatchNormalizationLayer.cpp b/tests/benchmark/CL/BatchNormalizationLayer.cpp index 82c780008b..3312319aac 100644 --- a/tests/benchmark/CL/BatchNormalizationLayer.cpp +++ b/tests/benchmark/CL/BatchNormalizationLayer.cpp @@ -51,19 +51,25 @@ using CLBatchNormalizationLayerFixture = BatchNormalizationLayerFixture<CLTensor TEST_SUITE(CL) REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), data_types), framework::dataset::make("Batches", 1))); REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2BatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); @@ -71,19 +77,25 @@ REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, CLB TEST_SUITE(NIGHTLY) REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), data_types), framework::dataset::make("Batches", { 4, 8 }))); REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2BatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); diff --git a/tests/benchmark/GLES_COMPUTE/BatchNormalizationLayer.cpp b/tests/benchmark/GLES_COMPUTE/BatchNormalizationLayer.cpp index 6e5836ef9e..9a2950b88d 100644 --- a/tests/benchmark/GLES_COMPUTE/BatchNormalizationLayer.cpp +++ b/tests/benchmark/GLES_COMPUTE/BatchNormalizationLayer.cpp @@ -51,19 +51,25 @@ using GCBatchNormalizationLayerFixture = BatchNormalizationLayerFixture<GCTensor TEST_SUITE(GC) REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, GCBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), data_types), framework::dataset::make("Batches", 1))); REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2BatchNormalizationLayer, GCBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo())), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), + framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, GCBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); @@ -71,19 +77,25 @@ REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, GCB TEST_SUITE(NIGHTLY) REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, GCBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), data_types), framework::dataset::make("Batches", { 4, 8 }))); REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2BatchNormalizationLayer, GCBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo())), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), + framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, GCBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); diff --git a/tests/benchmark/NEON/BatchNormalizationLayer.cpp b/tests/benchmark/NEON/BatchNormalizationLayer.cpp index 6d28318b2e..786a5b1701 100644 --- a/tests/benchmark/NEON/BatchNormalizationLayer.cpp +++ b/tests/benchmark/NEON/BatchNormalizationLayer.cpp @@ -56,36 +56,48 @@ using NEBatchNormalizationLayerFixture = BatchNormalizationLayerFixture<Tensor, TEST_SUITE(NEON) REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, NEBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2BatchNormalizationLayer, NEBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, NEBatchNormalizationLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", 1))); TEST_SUITE(NIGHTLY) REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, NEBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2BatchNormalizationLayer, NEBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4BatchNormalizationLayer, NEBatchNormalizationLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), + framework::dataset::combine(framework::dataset::make("UseGamma", { false, true }), + framework::dataset::make("UseBeta", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), data_types), framework::dataset::make("Batches", { 4, 8 }))); diff --git a/tests/benchmark/fixtures/BatchNormalizationLayerFixture.h b/tests/benchmark/fixtures/BatchNormalizationLayerFixture.h index fbb7700710..c55bb2acc9 100644 --- a/tests/benchmark/fixtures/BatchNormalizationLayerFixture.h +++ b/tests/benchmark/fixtures/BatchNormalizationLayerFixture.h @@ -42,7 +42,7 @@ class BatchNormalizationLayerFixture : public framework::Fixture { public: template <typename...> - void setup(TensorShape tensor_shape, TensorShape param_shape, float epsilon, ActivationLayerInfo act_info, DataType data_type, int batches) + void setup(TensorShape tensor_shape, TensorShape param_shape, float epsilon, bool use_gamma, bool use_beta, ActivationLayerInfo act_info, DataType data_type, int batches) { // Set batched in source and destination shapes const unsigned int fixed_point_position = 4; @@ -57,7 +57,9 @@ public: gamma = create_tensor<TensorType>(param_shape, data_type, 1, fixed_point_position); // Create and configure function - batch_norm_layer.configure(&src, &dst, &mean, &variance, &beta, &gamma, epsilon, act_info); + TensorType *beta_ptr = use_beta ? &beta : nullptr; + TensorType *gamma_ptr = use_gamma ? &gamma : nullptr; + batch_norm_layer.configure(&src, &dst, &mean, &variance, beta_ptr, gamma_ptr, epsilon, act_info); // Allocate tensors src.allocator()->allocate(); diff --git a/tests/validation/CL/BatchNormalizationLayer.cpp b/tests/validation/CL/BatchNormalizationLayer.cpp index ef535153f2..8c143060cb 100644 --- a/tests/validation/CL/BatchNormalizationLayer.cpp +++ b/tests/validation/CL/BatchNormalizationLayer.cpp @@ -61,8 +61,11 @@ TEST_SUITE(BatchNormalizationLayer) template <typename T> using CLBatchNormalizationLayerFixture = BatchNormalizationLayerValidationFixture<CLTensor, CLAccessor, CLBatchNormalizationLayer, T>; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::RandomBatchNormalizationLayerDataset(), framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F16, DataType::F32 })), - shape0, shape1, epsilon, dt) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), + framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F16, DataType::F32 })), + shape0, shape1, epsilon, use_gamma, use_beta, dt) { // Set fixed point position data type allowed const int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0; @@ -77,7 +80,9 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::Ran // Create and Configure function CLBatchNormalizationLayer norm; - norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon); + CLTensor *beta_ptr = use_beta ? &beta : nullptr; + CLTensor *gamma_ptr = use_gamma ? &gamma : nullptr; + norm.configure(&src, &dst, &mean, &var, beta_ptr, gamma_ptr, epsilon); // Validate valid region const ValidRegion valid_region = shape_to_valid_region(shape0); @@ -150,7 +155,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( TEST_SUITE(Float) TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), +FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), act_infos), framework::dataset::make("DataType", DataType::F32))) { @@ -160,7 +167,9 @@ FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<float>, framewor TEST_SUITE_END() TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), +FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), framework::dataset::make("DataType", DataType::F16))) { @@ -175,10 +184,13 @@ template <typename T> using CLBatchNormalizationLayerFixedPointFixture = BatchNormalizationLayerValidationFixedPointFixture<CLTensor, CLAccessor, CLBatchNormalizationLayer, T>; TEST_SUITE(QS8) -FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", DataType::QS8)), - framework::dataset::make("FractionalBits", 1, 6))) +FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + framework::dataset::make("UseBeta", false)), + framework::dataset::make("UseGamma", false)), + framework::dataset::make("ActivationInfo", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qs8, 0); @@ -186,10 +198,13 @@ FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixedPointFixture<int8_t TEST_SUITE_END() TEST_SUITE(QS16) -FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", DataType::QS16)), - framework::dataset::make("FractionalBits", 1, 14))) +FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + framework::dataset::make("UseBeta", false)), + framework::dataset::make("UseGamma", false)), + framework::dataset::make("ActivationInfo", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qs16, 0); diff --git a/tests/validation/GLES_COMPUTE/BatchNormalizationLayer.cpp b/tests/validation/GLES_COMPUTE/BatchNormalizationLayer.cpp index d817fc0e67..2dbb0e0fbb 100644 --- a/tests/validation/GLES_COMPUTE/BatchNormalizationLayer.cpp +++ b/tests/validation/GLES_COMPUTE/BatchNormalizationLayer.cpp @@ -59,8 +59,11 @@ TEST_SUITE(BatchNormalizationLayer) template <typename T> using GCBatchNormalizationLayerFixture = BatchNormalizationLayerValidationFixture<GCTensor, GCAccessor, GCBatchNormalizationLayer, T>; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::RandomBatchNormalizationLayerDataset(), framework::dataset::make("DataType", { DataType::F32 })), - shape0, shape1, epsilon, dt) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), + framework::dataset::make("DataType", { DataType::F32 })), + shape0, shape1, epsilon, use_beta, use_gamma, dt) { // Set fixed point position data type allowed int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0; @@ -75,7 +78,9 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::Ran // Create and Configure function GCBatchNormalizationLayer norm; - norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon); + GCTensor *beta_ptr = use_beta ? &beta : nullptr; + GCTensor *gamma_ptr = use_gamma ? &gamma : nullptr; + norm.configure(&src, &dst, &mean, &var, beta_ptr, gamma_ptr, epsilon); // Validate valid region const ValidRegion valid_region = shape_to_valid_region(shape0); @@ -84,7 +89,9 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::Ran TEST_SUITE(Float) TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(Random, GCBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), +FIXTURE_DATA_TEST_CASE(Random, GCBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), act_infos), framework::dataset::make("DataType", DataType::F16))) { @@ -94,7 +101,9 @@ FIXTURE_DATA_TEST_CASE(Random, GCBatchNormalizationLayerFixture<half>, framework TEST_SUITE_END() TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(Random, GCBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), +FIXTURE_DATA_TEST_CASE(Random, GCBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), act_infos), framework::dataset::make("DataType", DataType::F32))) { diff --git a/tests/validation/NEON/BatchNormalizationLayer.cpp b/tests/validation/NEON/BatchNormalizationLayer.cpp index 054ed278a2..7bf1f2633e 100644 --- a/tests/validation/NEON/BatchNormalizationLayer.cpp +++ b/tests/validation/NEON/BatchNormalizationLayer.cpp @@ -63,8 +63,10 @@ TEST_SUITE(BatchNormalizationLayer) template <typename T> using NEBatchNormalizationLayerFixture = BatchNormalizationLayerValidationFixture<Tensor, Accessor, NEBatchNormalizationLayer, T>; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::RandomBatchNormalizationLayerDataset(), framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F32 })), - shape0, shape1, epsilon, dt) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), framework::dataset::make("UseGamma", { false, true }))), + framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F32 })), + shape0, shape1, epsilon, use_beta, use_gamma, dt) { // Set fixed point position data type allowed const int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0; @@ -79,7 +81,9 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::Ran // Create and Configure function NEBatchNormalizationLayer norm; - norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon); + Tensor *beta_ptr = use_beta ? &beta : nullptr; + Tensor *gamma_ptr = use_gamma ? &gamma : nullptr; + norm.configure(&src, &dst, &mean, &var, beta_ptr, gamma_ptr, epsilon); // Validate valid region const ValidRegion valid_region = shape_to_valid_region(shape0); @@ -150,7 +154,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( // *INDENT-ON* TEST_SUITE(Float) -FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), +FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), act_infos), framework::dataset::make("DataType", DataType::F32))) { @@ -161,7 +167,9 @@ TEST_SUITE_END() #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(Float16) -FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(), +FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + combine(framework::dataset::make("UseBeta", { false, true }), + framework::dataset::make("UseGamma", { false, true }))), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), framework::dataset::make("DataType", DataType::F16))) { @@ -176,10 +184,13 @@ template <typename T> using NEBatchNormalizationLayerFixedPointFixture = BatchNormalizationLayerValidationFixedPointFixture<Tensor, Accessor, NEBatchNormalizationLayer, T>; TEST_SUITE(QS8) -FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", DataType::QS8)), - framework::dataset::make("FractionalBits", 1, 6))) +FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + framework::dataset::make("UseBeta", false)), + framework::dataset::make("UseGamma", false)), + framework::dataset::make("ActivationInfo", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) { // Validate output validate(Accessor(_target), _reference, tolerance_qs8, 0); @@ -187,10 +198,13 @@ FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int8_t TEST_SUITE_END() TEST_SUITE(QS16) -FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", DataType::QS16)), - framework::dataset::make("FractionalBits", 1, 14))) +FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), + framework::dataset::make("UseBeta", false)), + framework::dataset::make("UseGamma", false)), + framework::dataset::make("ActivationInfo", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) { // Validate output validate(Accessor(_target), _reference, tolerance_qs16, 0); diff --git a/tests/validation/fixtures/BatchNormalizationLayerFixture.h b/tests/validation/fixtures/BatchNormalizationLayerFixture.h index e02c619249..4a6ac1af7f 100644 --- a/tests/validation/fixtures/BatchNormalizationLayerFixture.h +++ b/tests/validation/fixtures/BatchNormalizationLayerFixture.h @@ -45,10 +45,12 @@ class BatchNormalizationLayerValidationFixedPointFixture : public framework::Fix { public: template <typename...> - void setup(TensorShape shape0, TensorShape shape1, float epsilon, ActivationLayerInfo act_info, DataType dt, int fractional_bits) + void setup(TensorShape shape0, TensorShape shape1, float epsilon, bool use_beta, bool use_gamma, ActivationLayerInfo act_info, DataType dt, int fractional_bits) { _fractional_bits = fractional_bits; _data_type = dt; + _use_beta = use_beta; + _use_gamma = use_gamma; _target = compute_target(shape0, shape1, epsilon, act_info, dt, fractional_bits); _reference = compute_reference(shape0, shape1, epsilon, act_info, dt, fractional_bits); } @@ -67,8 +69,24 @@ protected: library->fill(src_tensor, distribution, 0); library->fill(mean_tensor, distribution, 1); library->fill(var_tensor, distribution_var, 0); - library->fill(beta_tensor, distribution, 3); - library->fill(gamma_tensor, distribution, 4); + if(_use_beta) + { + library->fill(beta_tensor, distribution, 3); + } + else + { + // Fill with default value 0.f + library->fill_tensor_value(beta_tensor, 0.f); + } + if(_use_gamma) + { + library->fill(gamma_tensor, distribution, 4); + } + else + { + // Fill with default value 1.f + library->fill_tensor_value(gamma_tensor, 1.f); + } } else { @@ -80,8 +98,24 @@ protected: library->fill(src_tensor, distribution, 0); library->fill(mean_tensor, distribution, 1); library->fill(var_tensor, distribution_var, 0); - library->fill(beta_tensor, distribution, 3); - library->fill(gamma_tensor, distribution, 4); + if(_use_beta) + { + library->fill(beta_tensor, distribution, 3); + } + else + { + // Fill with default value 0 + library->fill_tensor_value(beta_tensor, static_cast<T>(0)); + } + if(_use_gamma) + { + library->fill(gamma_tensor, distribution, 4); + } + else + { + // Fill with default value 1 + library->fill_tensor_value(gamma_tensor, static_cast<T>(1 << (_fractional_bits))); + } } } @@ -97,7 +131,9 @@ protected: // Create and configure function FunctionType norm; - norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon, act_info); + TensorType *beta_ptr = _use_beta ? &beta : nullptr; + TensorType *gamma_ptr = _use_gamma ? &gamma : nullptr; + norm.configure(&src, &dst, &mean, &var, beta_ptr, gamma_ptr, epsilon, act_info); ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); @@ -149,6 +185,8 @@ protected: SimpleTensor<T> _reference{}; int _fractional_bits{}; DataType _data_type{}; + bool _use_beta{}; + bool _use_gamma{}; }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T> @@ -156,9 +194,9 @@ class BatchNormalizationLayerValidationFixture : public BatchNormalizationLayerV { public: template <typename...> - void setup(TensorShape shape0, TensorShape shape1, float epsilon, ActivationLayerInfo act_info, DataType dt) + void setup(TensorShape shape0, TensorShape shape1, float epsilon, bool use_beta, bool use_gamma, ActivationLayerInfo act_info, DataType dt) { - BatchNormalizationLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape1, epsilon, act_info, dt, 0); + BatchNormalizationLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape1, epsilon, use_beta, use_gamma, act_info, dt, 0); } }; } // namespace validation diff --git a/tests/validation/reference/BatchNormalizationLayer.cpp b/tests/validation/reference/BatchNormalizationLayer.cpp index a9d9f0320d..c8badacc79 100644 --- a/tests/validation/reference/BatchNormalizationLayer.cpp +++ b/tests/validation/reference/BatchNormalizationLayer.cpp @@ -106,7 +106,6 @@ SimpleTensor<T> batch_normalization_layer(const SimpleTensor<T> &src, const Simp const float numerator = src[pos] - mean[i]; const float x_bar = numerator / denominator; result[pos] = beta[i] + x_bar * gamma[i]; - ; } } } |