diff options
Diffstat (limited to 'arm_compute')
4 files changed, 67 insertions, 11 deletions
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index d72547ed07..cb04182c21 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -275,6 +275,38 @@ inline TensorShape compute_flatten_shape(const ITensorInfo *input) return output_shape; } +inline TensorShape compute_softmax_shape(const ITensorInfo *input, size_t axis = 1) +{ + // The output shape will be a 2D version of the input. For instance: + // - [x,y,z] and axis 1 will return [x, y*z] + // - [x,y,z,w] and axis 2 will return [x*y, w*z] + // - [x,y,z,w] and axis 3 will return [x*y*z, w] + TensorShape shape2D = input->tensor_shape(); + + if(axis < input->num_dimensions()) + { + // Collapse from axis onward (this changes the shape) + shape2D.collapse_from(axis); + + // Collapse the rest (collapse is inclusive) + shape2D.collapse(shape2D.num_dimensions() - 1); + } + else + { + // Collapse everything + shape2D.collapse(shape2D.num_dimensions()); + } + + if(axis == 0) + { + // If axis is zero the first dim should be one. Since + // collapse is an inclusive operation we need to shift + shape2D.shift_right(1); + } + + return shape2D; +} + inline TensorShape compute_interleave_custom_shape(const TensorShape &input, const int x_interleave, const int y_interleave) { TensorShape output_shape{ input }; diff --git a/arm_compute/runtime/CL/functions/CLSoftmaxLayer.h b/arm_compute/runtime/CL/functions/CLSoftmaxLayer.h index 90c99d6569..8d2c03f930 100644 --- a/arm_compute/runtime/CL/functions/CLSoftmaxLayer.h +++ b/arm_compute/runtime/CL/functions/CLSoftmaxLayer.h @@ -58,16 +58,22 @@ public: * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32 * @param[out] output Destination tensor. Data types supported: same as @p input * @param[in] beta (Optional) A scaling factor for the exponent. Defaults to 1.f + * @param[in] axis (Optional) Reduction axis. It has the purpose of squashing the first @p axis + * dimensions together. For instance, given a [4x4x4x4] image, + * when @p axis is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image. */ - void configure(const ICLTensor *input, ICLTensor *output, float beta = 1.0f); + void configure(const ICLTensor *input, ICLTensor *output, float beta = 1.0f, size_t axis = 1); /** Static function to check if given info will lead to a valid configuration of @ref CLSoftmaxLayer * * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32 * @param[in] output Destination tensor. Data types supported: same as @p input - * + * @param[in] beta (Optional) A scaling factor for the exponent. Defaults to 1.f + * @param[in] axis (Optional) Reduction axis. It has the purpose of squashing the first @p axis + * dimensions together. For instance, given a [4x4x4x4] image, + * when @p axis is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image. * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output); + static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta = 1.0f, size_t axis = 1); // Inherited methods overridden: void run() override; @@ -82,19 +88,22 @@ private: * * @param[in] input Original source tensor. * @param[in] output Original destination tensor. + * @param[in] axis (Optional) Reduction axis. It has the purpose of squashing the first @p axis + * dimensions together. For instance, given a [4x4x4x4] image, + * when @p axis is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image. */ - void configure_flatten_kernel(const ICLTensor *input, const ICLTensor *output); + void configure_reshape_input_kernel(const ICLTensor *input, const ICLTensor *output, size_t axis); CLMemoryGroup _memory_group; CLLogits1DMaxShiftExpSumKernel _max_shift_exp_sum_kernel; CLLogits1DNormKernel _norm_kernel; - CLFlattenLayerKernel _flatten_kernel; + std::unique_ptr<ICLKernel> _flatten_kernel_ptr; CLReshapeLayerKernel _reshape_kernel; CLTensor _max; CLTensor _sum; CLTensor _tmp; - CLTensor _input_flat; - CLTensor _output_flat; + CLTensor _input_flattened; + CLTensor _output_flattened; bool _needs_flattening; }; } diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCSoftmaxLayer.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCSoftmaxLayer.h index 1011c9a2ef..f6c6edb6a1 100644 --- a/arm_compute/runtime/GLES_COMPUTE/functions/GCSoftmaxLayer.h +++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCSoftmaxLayer.h @@ -52,9 +52,14 @@ public: * * @param[in] input Source tensor. Data types supported: F16/F32 * @param[out] output Destination tensor. Data types supported: same as @p input - * @param[in] beta (Optional) A scaling factor for the exponent. Only beta = 1 is supported. + * @param[in] beta (Optional) A scaling factor for the exponent. Only beta = 1 is supported + * @param[in] axis (Optional) Reduction axis. It has the purpose of squashing the first @p axis + * dimensions together. For instance, given a [4x4x4x4] image, + * when @p axis is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image. + * + * @note The value of @p axis must be always 1 for GLES */ - void configure(const IGCTensor *input, IGCTensor *output, float beta = 1.0f); + void configure(const IGCTensor *input, IGCTensor *output, float beta = 1.0f, size_t axis = 1); // Inherited methods overridden: void run() override; diff --git a/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h b/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h index 61f46004d6..3f5ec8e820 100644 --- a/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h +++ b/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h @@ -56,17 +56,27 @@ public: * last value of each row to the nearest multiple. * @param[out] output Destination tensor. Data types supported: same as @p input. * @param[in] beta (Optional) A scaling factor for the exponent. + * @param[in] axis (Optional) Reduction axis. It has the purpose of squashing the first @p axis + * dimensions together. For instance, given a [4x4x4x4] image, + * when @p axis is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image. + * + * @note The value of @p axis must be always 1 for NEON */ - void configure(ITensor *input, ITensor *output, float beta = 1.0f); + void configure(ITensor *input, ITensor *output, float beta = 1.0f, size_t axis = 1); /** Static function to check if given info will lead to a valid configuration of @ref NESoftmaxLayer * * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32. * @param[in] output Destination tensor. Data types supported: same as @p input * @param[in] beta (Optional) A scaling factor for the exponent. + * @param[in] axis (Optional) Reduction axis. It has the purpose of squashing the first @p axis + * dimensions together. For instance, given a [4x4x4x4] image, + * when @p axis is 2, the Softmax reduction will be applied on each of the [4x4] planes of the input image. + * + * @note The value of @p axis must be always 1 for NEON * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta = 1.0f); + static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta = 1.0f, size_t axis = 1); // Inherited methods overridden: void run() override; |