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2275 lines
87 KiB
2275 lines
87 KiB
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <stdbool.h>
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#include <stddef.h>
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#include <stdint.h>
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#include <pthreadpool.h>
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#ifdef __cplusplus
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extern "C" {
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#endif
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/// The number of bytes XNNPACK may read beyond array bounds.
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/// The caller must allocate at least this many extra bytes after the tensor data passed to XNNPACK.
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///
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/// Note: XNNPACK reads, but never writes beyond array bounds.
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#define XNN_EXTRA_BYTES 16
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/// Maximum number of dimensions in tensor shape.
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#define XNN_MAX_TENSOR_DIMS 6
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/// Allow sparse inference in a Runtime.
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///
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/// Note: this flag forces XNNPACK to consider sparse inference, but does not guarantee it.
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#define XNN_FLAG_SPARSE_INFERENCE 0x00000001
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/// The convolution operator represents a depthwise convolution, and use HWGo layout for filters.
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#define XNN_FLAG_DEPTHWISE_CONVOLUTION 0x00000001
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/// Assume transposed weights in a fully connected operator.
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#define XNN_FLAG_TRANSPOSE_WEIGHTS 0x00000001
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/// The operator assumes NHWC layout for the input, regardless of the output layout.
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#define XNN_FLAG_INPUT_NHWC 0x00000002
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/// Match "SAME" padding in TensorFlow. Exact padding values are computed dynamically depending on input size.
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#define XNN_FLAG_TENSORFLOW_SAME_PADDING 0x00000004
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/// Implicitly flatten and reshape input of a Fully Connected operator into a 2D
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/// tensor.
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#define XNN_FLAG_TENSORFLOW_RESHAPE_2D 0x00000004
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/// Match behaviour of TensorFlow 1.x.
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#define XNN_FLAG_TENSORFLOW_LEGACY_MODE 0x00000004
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/// Align corners of input and output images in resize operations.
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#define XNN_FLAG_ALIGN_CORNERS 0x00000008
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/// Status code for any XNNPACK function call.
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enum xnn_status {
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/// The call succeeded, and all output arguments now contain valid data.
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xnn_status_success = 0,
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xnn_status_uninitialized = 1,
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xnn_status_invalid_parameter = 2,
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xnn_status_invalid_state = 3,
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xnn_status_unsupported_parameter = 4,
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xnn_status_unsupported_hardware = 5,
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xnn_status_out_of_memory = 6,
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};
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struct xnn_allocator {
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/// User-specified pointer that will be passed as-is to all functions in this structure.
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void* context;
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/// Pointer to a function to be called for general memory allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param size - The size of the memory block to allocate, in bytes.
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///
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/// @returns Pointer to the allocated memory block of at least @ref size bytes.
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/// If allocation fails, the function must return NULL.
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void* (*allocate)(void* context, size_t size);
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/// Pointer to a function to be called for general memory re-allocation, i.e. to increase or shrink a previously
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/// allocated memory block. The content of the old memory block is copied to the new memory block.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
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/// If the pointer is NULL, the @ref reallocate call is equivalent to an @ref allocate call.
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/// @param size - The new size of the memory block to allocate, in bytes.
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///
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/// @returns Pointer to the newly allocated memory block of at least @ref size bytes with the content of the previous
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/// memory block.
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/// If allocation fails, the function must return NULL, but must not release the previous memory block.
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void* (*reallocate)(void* context, void* pointer, size_t size);
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/// Pointer to a function to be called for general memory de-allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
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/// If the pointer is NULL, the @ref deallocate call is a no-op.
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void (*deallocate)(void* context, void* pointer);
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/// Pointer to a function to be called for aligned memory allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param alignment - The alignment of the memory block to allocate, in bytes. Alignment is always a power-of-2.
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/// @param size - The size of the memory block to allocate, in bytes.
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///
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/// @returns Pointer to the allocated memory block of at least @ref size bytes.
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/// If allocation fails, the function must return NULL.
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void* (*aligned_allocate)(void* context, size_t alignment, size_t size);
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/// Pointer to a function to be called for aligned memory de-allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param pointer - Pointer to a memory block allocated by @ref aligned_allocate function. Can be NULL.
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/// If the pointer is NULL, the @ref aligned_deallocate call is a no-op.
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void (*aligned_deallocate)(void* context, void* pointer);
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};
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/// Initialize XNNPACK library.
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///
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/// XNNPACK must be successfully initialized before use.
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/// During initialization, XNNPACK populates internal structures depending on host processor. It can be time-consuming.
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///
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/// @param[in] allocator - structure with function pointers to be use for memory allocation and de-allocation.
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/// If this argument is NULL, system-provided memory management functions (e.g. malloc/free)
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/// will be used.
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///
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/// @retval xnn_status_success - XNNPACK is succesfully initialized and ready to use.
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/// @retval xnn_status_out_of_memory - initialization failed due to out-of-memory condition.
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/// @retval xnn_status_unsupported_hardware - initialization failed because the host processor does not satisfy the
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/// minimum hardware requirements for XNNPACK. E.g. this may happen on x86
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/// processors without SSE2 extension, or on 32-bit ARM processors without
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/// the NEON SIMD extension.
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enum xnn_status xnn_initialize(const struct xnn_allocator* allocator);
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/// Deinitialize XNNPACK library.
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///
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/// To avoid memory and resource leaks, users must call xnn_deinitialize once for each successful xnn_initialize call.
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///
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/// @retval xnn_status_success - deinitialization call succeeded.
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enum xnn_status xnn_deinitialize(void);
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/// Subgraph is an abstract representation of a neural network model.
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/// Subgraph objects are used to define Values (tensors) and Nodes (operators) comprising the model.
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typedef struct xnn_subgraph* xnn_subgraph_t;
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/// Create a empty Subgraph object.
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///
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/// @param external_value_ids - number of Value IDs to reserve for communication with external graph representation.
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/// The Subgraph object would avoid creating internal Value IDs in the
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/// [0, reserved_value_ids-1] range.
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/// @param flags - binary features of the subgraph. No supported flags are currently defined.
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/// @param subgraph_out - pointer to the variable that will be initialized with a handle to the Subgraph object upon
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/// successful return.
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enum xnn_status xnn_create_subgraph(
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uint32_t external_value_ids,
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uint32_t flags,
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xnn_subgraph_t* subgraph_out);
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/// Destroy a Subgraph object, as well as Values, and Nodes associated with the subgraph.
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///
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/// @param subgraph - the Subgraph object to destroy.
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enum xnn_status xnn_delete_subgraph(
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xnn_subgraph_t subgraph);
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#define XNN_VALUE_FLAG_EXTERNAL_INPUT 0x00000001
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#define XNN_VALUE_FLAG_EXTERNAL_OUTPUT 0x00000002
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#define XNN_INVALID_VALUE_ID UINT32_MAX
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/// Type of elements in a Value object.
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enum xnn_datatype {
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/// Invalid data type. Valid Values never have this datatype.
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xnn_datatype_invalid = 0,
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/// IEEE754 single-precision floating-point.
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xnn_datatype_fp32 = 1,
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/// IEEE754 half-precision floating-point.
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xnn_datatype_fp16 = 2,
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};
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/// Define a tensor-type Value and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Value.
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/// @param datatype - type of the tensor elements.
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/// @param num_dims - number of dimensions in the shape.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
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/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
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/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
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/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
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/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
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/// created for the Value.
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/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
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/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
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/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
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/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
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enum xnn_status xnn_define_tensor_value(
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xnn_subgraph_t subgraph,
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enum xnn_datatype datatype,
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size_t num_dims,
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const size_t* dims,
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const void* data,
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uint32_t external_id,
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uint32_t flags,
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uint32_t* id_out);
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/// Define a 2D Convolution Node and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Node.
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/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
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/// flag is specified.
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/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
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/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
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/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
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/// @param kernel_height - kernel (filter) height.
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/// @param kernel_width - kernel (filter) width.
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/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
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/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
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/// @param dilation_height - dilation of kernel elements along the height dimension.
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/// @param dilation_width - dilation of kernel elements along the width dimension.
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/// @param groups - number of convolution groups.
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/// @param group_input_channels - number of input channels per group.
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/// @param group_output_channels - number of output channels per group.
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/// @param output_min - lower bound for clipping output values.
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/// @param output_max - upper bound for clipping output values.
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/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
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/// with [N, IH, IW, groups * group_input_channels] dimensions
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/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
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/// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
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/// dimensions.
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/// @param bias_id - Value ID for the bias tensor. The bias tensor must be a 1D tensor defined in the @a subgraph with
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/// [groups * group_output_channels] dimensions.
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/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
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/// with [N, OH, OW, groups * group_output_channels] dimensions.
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/// @param flags - binary features of the 2D Convolution Node. The only currently supported values is
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
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enum xnn_status xnn_define_convolution_2d(
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xnn_subgraph_t subgraph,
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uint32_t input_padding_top,
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uint32_t input_padding_right,
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uint32_t input_padding_bottom,
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uint32_t input_padding_left,
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uint32_t kernel_height,
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uint32_t kernel_width,
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uint32_t subsampling_height,
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uint32_t subsampling_width,
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uint32_t dilation_height,
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uint32_t dilation_width,
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uint32_t groups,
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size_t group_input_channels,
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size_t group_output_channels,
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float output_min,
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float output_max,
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uint32_t input_id,
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uint32_t filter_id,
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uint32_t bias_id,
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uint32_t output_id,
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uint32_t flags);
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/// Define a 2D Deconvolution (Transposed Convolution) Node and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Node.
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/// @param padding_top - implicit padding above 2D output data.
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/// @param padding_right - implicit padding to the right of 2D output data.
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/// @param padding_bottom - implicit padding below 2D output data.
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/// @param padding_left - implicit padding to the left of 2D output data.
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/// @param adjustment_height - additional elements in the bottom of the 2D output data.
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/// @param adjustment_width - additional elements to the right of the 2D output data.
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/// @param kernel_height - kernel (filter) height.
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/// @param kernel_width - kernel (filter) width.
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/// @param upsampling_height - height of upsampling region for deconvolution input (deconvolution height stride).
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/// @param upsampling_width - width of upsampling region for deconvolution input (deconvolution width stride).
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/// @param dilation_height - dilation of kernel elements along the height dimension.
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/// @param dilation_width - dilation of kernel elements along the width dimension.
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/// @param groups - number of convolution groups.
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/// @param group_input_channels - number of input channels per group.
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/// @param group_output_channels - number of output channels per group.
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/// @param output_min - lower bound for clipping output values.
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/// @param output_max - upper bound for clipping output values.
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/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
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/// with [N, IH, IW, groups * group_input_channels] dimensions
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/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
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/// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
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/// dimensions.
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/// @param bias_id - Value ID for the bias tensor. The bias tensor must be a 1D tensor defined in the @a subgraph with
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/// [groups * group_output_channels] dimensions.
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/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
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/// with [N, OH, OW, groups * group_output_channels] dimensions.
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/// @param flags - binary features of the 2D Deconvolution Node. No supported flags are currently defined.
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enum xnn_status xnn_define_deconvolution_2d(
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xnn_subgraph_t subgraph,
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uint32_t padding_top,
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uint32_t padding_right,
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uint32_t padding_bottom,
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uint32_t padding_left,
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uint32_t adjustment_height,
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uint32_t adjustment_width,
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uint32_t kernel_height,
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uint32_t kernel_width,
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uint32_t upsampling_height,
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uint32_t upsampling_width,
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uint32_t dilation_height,
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uint32_t dilation_width,
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uint32_t groups,
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size_t group_input_channels,
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size_t group_output_channels,
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float output_min,
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float output_max,
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uint32_t input_id,
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uint32_t filter_id,
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uint32_t bias_id,
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uint32_t output_id,
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uint32_t flags);
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/// Define a 2D Depthwise Convolution Node and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Node.
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/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
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/// flag is specified.
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/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
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/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
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/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
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/// @param kernel_height - kernel (filter) height.
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/// @param kernel_width - kernel (filter) width.
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/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
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/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
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/// @param dilation_height - dilation of kernel elements along the height dimension.
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/// @param dilation_width - dilation of kernel elements along the width dimension.
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/// @param depth_multiplier - ratio of output channels to input channels.
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/// @param input_channels - number of input channels.
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/// @param output_min - lower bound for clipping output values.
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/// @param output_max - upper bound for clipping output values.
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/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
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/// with [N, IH, IW, input_channels] dimensions
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/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
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/// with [1, kernel_height, kernel_width, input_channels * depth_multiplier] dimensions.
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/// @param bias_id - Value ID for the bias tensor. The bias tensor must be a 1D tensor defined in the @a subgraph with
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/// [input_channels * depth_multiplier] dimensions.
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/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
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/// with [N, OH, OW, input_channels * depth_multiplier] dimensions.
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/// @param flags - binary features of the 2D Depthwise Convolution Node. The only currently supported values is
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/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
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enum xnn_status xnn_define_depthwise_convolution_2d(
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xnn_subgraph_t subgraph,
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uint32_t input_padding_top,
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uint32_t input_padding_right,
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uint32_t input_padding_bottom,
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uint32_t input_padding_left,
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uint32_t kernel_height,
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uint32_t kernel_width,
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uint32_t subsampling_height,
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uint32_t subsampling_width,
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uint32_t dilation_height,
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uint32_t dilation_width,
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uint32_t depth_multiplier,
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size_t input_channels,
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float output_min,
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float output_max,
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uint32_t input_id,
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uint32_t filter_id,
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uint32_t bias_id,
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uint32_t output_id,
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uint32_t flags);
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/// Define a DepthToSpace Node and add it to a Subgraph.
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///
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/// The DepthToSpace Node rearranges data from depth into blocks of spatial data (a reverse transform for SpaceToDepth).
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/// For a given input pixel, an output square of pixels with side @a block_size is formed from values in the corresponding
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/// number of its channels. The output depth is therefore @a block_size x @a block_size times smaller than that of the input.
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///
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/// @param subgraph - a Subgraph object that will own the created Node.
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/// @param input_id - Value ID for the input tensor. The input tensor must be divisible by @a block_size in the channel dimension.
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/// @param output_id - Value ID for the output tensor.
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/// @param block_size - the size of the spatial block.
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/// @param flags - binary features of the DepthToSpace Node. No supported flags are currently defined.
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enum xnn_status xnn_define_depth_to_space(
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xnn_subgraph_t subgraph,
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uint32_t input_id,
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uint32_t output_id,
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uint32_t block_size,
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uint32_t flags);
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/// Define a 2D Global Average Pooling Node and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Node.
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/// @param output_min - lower bound for clipping output values.
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/// @param output_max - upper bound for clipping output values.
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/// @param input_id - Value ID for the input tensor. The input tensor must be a
|
|
/// 4D tensor defined in the @a subgraph with [N, H, W, C]
|
|
/// dimensions
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be
|
|
/// a 4D tensor defined in the @a subgraph with [N, 1, 1, C]
|
|
/// dimensions.
|
|
/// @param flags - binary features of the 2D Global Average Pooling Node. No
|
|
/// supported flags are currently defined.
|
|
enum xnn_status xnn_define_global_average_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Average Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
|
|
/// flag is specified.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param pooling_height - pooling (kernel) height.
|
|
/// @param pooling_width - pooling (kernel) width.
|
|
/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
|
|
/// to vertically adjacent output pixels.
|
|
/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
|
|
/// to horizontally adjacent output pixels.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, channels] dimensions
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, channels] dimensions.
|
|
/// @param flags - binary features of the 2D Average Pooling Node. The only currently supported values is
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
|
|
enum xnn_status xnn_define_average_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Fully Connected Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an
|
|
/// N-dimensional tensor defined in the @a
|
|
/// subgraph.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the
|
|
/// input tensor must be at least 1D and its last dimension
|
|
/// must match the last dimension of the filter tensor. In
|
|
/// particular, if input is a 2D tensor, it must have
|
|
/// [batch_size, input_channels] dimensions. If
|
|
/// XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of
|
|
/// elements in the input tensor must be divisible by the
|
|
/// input_channels. The tensor will be first flattened into a
|
|
/// 1D tensor of [num_input_elements] dimensions, then
|
|
/// reshaped into a 2D tensor of [num_input_elements /
|
|
/// input_channels, input_channels] dimensions where
|
|
/// num_input_elements is the total number of elements in the
|
|
/// input tensor.
|
|
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge
|
|
/// a 2D tensor defined in the @a subgraph
|
|
/// with [output_channels, input_channels] dimensions.
|
|
/// @param bias_id - Value ID for the bias tensor. The bias tensor must be a 1D
|
|
/// tensor defined in the @a subgraph with
|
|
/// [output_channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be
|
|
/// defined in the @a subgraph.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the
|
|
/// output tensor must have the same dimensionality as the
|
|
/// input tensor, all its dimensions but the last one must
|
|
/// match the corresponding dimensions of the input tensor,
|
|
/// and the last dimensions of the output tensor must match
|
|
/// the first dimension of the filter tensor. In particular,
|
|
/// if input is a 2D tensor, output must be a 2D tensor of
|
|
/// [batch_size, output_channels] dimensions. If
|
|
/// XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must
|
|
/// be a 2D tensor of [num_input_elements / input_channels,
|
|
/// output_channels] dimensions where num_input_elements is
|
|
/// the total number of elements in the input tensor.
|
|
/// @param flags - binary features of the Fully Connected Node. The only
|
|
/// currently supported value is XNN_FLAG_TENSORFLOW_RESHAPE_2D.
|
|
enum xnn_status xnn_define_fully_connected(xnn_subgraph_t subgraph,
|
|
float output_min, float output_max,
|
|
uint32_t input_id,
|
|
uint32_t filter_id, uint32_t bias_id,
|
|
uint32_t output_id, uint32_t flags);
|
|
|
|
/// Define a 2D Max Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
|
|
/// flag is specified.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param pooling_height - pooling (kernel) height.
|
|
/// @param pooling_width - pooling (kernel) width.
|
|
/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
|
|
/// to vertically adjacent output pixels.
|
|
/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
|
|
/// to horizontally adjacent output pixels.
|
|
/// @param dilation_height - dilation of pooling elements along the height dimension.
|
|
/// @param dilation_width - dilation of pooling elements along the width dimension.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, channels] dimensions
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, channels] dimensions.
|
|
/// @param flags - binary features of the 2D Max Pooling Node. The only currently supported values is
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
|
|
enum xnn_status xnn_define_max_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D ArgMax Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data.
|
|
/// @param pooling_height - pooling (kernel) height. Vertical stride between pooling regions match this value.
|
|
/// @param pooling_width - pooling (kernel) width. Horizontal stride between pooling regions match this value.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, channels] dimensions
|
|
/// @param output_value_id - Value ID for the output tensor with the maximum values in the pools. The output tensor must
|
|
/// be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels] dimensions.
|
|
/// @param output_index_id - Value ID for the output tensor with the indexes of the maximum values in the pools. The
|
|
/// output tensor must be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels]
|
|
/// dimensions.
|
|
/// @param flags - binary features of the 2D ArgMax Pooling Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_argmax_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t input_id,
|
|
uint32_t output_value_id,
|
|
uint32_t output_index_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D UnPooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param padding_top - implicit padding above 2D output data.
|
|
/// @param padding_right - implicit padding to the right of 2D output data.
|
|
/// @param padding_bottom - implicit padding below 2D output data.
|
|
/// @param padding_left - implicit padding to the left of 2D output data.
|
|
/// @param pooling_height - height of the pooling window.
|
|
/// @param pooling_width - width of the pooling window.
|
|
/// @param input_value_id - Value ID for the input tensor with the max-pooling values to invert. The input value tensor
|
|
/// must be a 4D tensor defined in the @a subgraph with [N, IH, IW, channels] dimensions.
|
|
/// @param input_index_id - Value ID for the input tensor with the indices of the per-pool maximum values produced by
|
|
/// a 2D UnPooling Node. The input tensor must be a 4D tensor defined in the @a subgraph with
|
|
/// [N, IH, IW, channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, channels] dimensions.
|
|
/// @param flags - binary features of the 2D UnPooling Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_unpooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t padding_top,
|
|
uint32_t padding_right,
|
|
uint32_t padding_bottom,
|
|
uint32_t padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t input_value_id,
|
|
uint32_t input_index_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Add Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Add Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_add2(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Multiply Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Multiply Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_multiply2(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Subtract Node and add it to a Subgraph.
|
|
///
|
|
/// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Subtract Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_subtract(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Divide Node and add it to a Subgraph.
|
|
///
|
|
/// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Divide Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_divide(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Maximum Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Maximum Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_maximum2(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Minimum Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Minimum Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_minimum2(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Squared Difference Node and add it to a Subgraph.
|
|
///
|
|
/// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting
|
|
/// rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_squared_difference(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Constant Pad Node with static padding specification and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param pre_paddings - number of padding elements to insert before input elements for every dimension. This array
|
|
/// must have as many elements as the the number of dimensions in the input tensor.
|
|
/// @param post_paddings - number of padding elements to insert after input elements for every dimension. This array
|
|
/// must have as many elements as the the number of dimensions in the input tensor.
|
|
/// @param padding_value - constant value used to initialize padding elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor with padding.
|
|
/// @param flags - binary features of the Constant Pad Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_static_constant_pad(
|
|
xnn_subgraph_t subgraph,
|
|
const size_t* pre_paddings,
|
|
const size_t* post_paddings,
|
|
float padding_value,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Reshape Node with static shape specification and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param num_dims - number of shape dimensions in the output tensor.
|
|
/// @param new_shape - shape dimensions of the output tensor.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor with padding.
|
|
/// @param flags - binary features of the Reshape Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_static_reshape(
|
|
xnn_subgraph_t subgraph,
|
|
size_t num_dims,
|
|
const size_t* new_shape,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Resize Bilinear Node with static output height & width specification and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param new_height - height dimension of the output tensor.
|
|
/// @param new_width - width dimension of the output tensor.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, H, W, C] dimensions
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, new_height, new_width, C] dimensions.
|
|
/// @param flags - binary features of the 2D Resize Bilinear Node. The only currently supported values are
|
|
/// XNN_FLAG_TENSORFLOW_LEGACY_MODE and XNN_FLAG_ALIGN_CORNERS, which are mutually exclusive.
|
|
enum xnn_status xnn_define_static_resize_bilinear_2d(
|
|
xnn_subgraph_t subgraph,
|
|
size_t new_height,
|
|
size_t new_width,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, H, W, channels] dimensions
|
|
/// @param slope_id - Value ID for the bias tensor. The bias tensor must be a 1D tensor defined in the @a subgraph with
|
|
/// [channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, H, W, channels] dimensions.
|
|
/// @param flags - binary features of the PReLU Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_prelu(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t slope_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Abs Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Abs Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_abs(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Bankers' Rounding Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_bankers_rounding(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Ceiling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Ceiling Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_ceiling(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Clamp Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Clamp Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_clamp(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param alpha - scale factor for negative output elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the ELU Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_elu(
|
|
xnn_subgraph_t subgraph,
|
|
float alpha,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Floor Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Floor Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_floor(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a HardSwish Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the HardSwish Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_hardswish(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Leaky ReLU Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param negative_slope - scale factor for negative input elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_leaky_relu(
|
|
xnn_subgraph_t subgraph,
|
|
float negative_slope,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Negate Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Negate Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_negate(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Sigmoid Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_sigmoid(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a SoftMax Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph, and have at
|
|
/// least one dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the SoftMax Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_softmax(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Square Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Square Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_square(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Square Root Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Square Root Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_square_root(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Runtime is a combination of an execution plan for subgraph Nodes and a memory manager for subgraph Values.
|
|
typedef struct xnn_runtime* xnn_runtime_t;
|
|
|
|
/// Create a Runtime object from a subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object with all Values and Nodes that would be handled by the runtime. No Values or
|
|
/// Nodes can be added to the runtime once it is constructed.
|
|
/// @param threadpool - the thread pool to be used for parallelisation of computations in the runtime. If the thread
|
|
/// pool is NULL, the computation would run on the caller thread without parallelization.
|
|
/// @param flags - binary features of the runtime. The only currently supported value is XNN_FLAG_SPARSE_INFERENCE.
|
|
/// @param runtime_out - pointer to the variable that will be initialized with a handle to the Runtime object upon
|
|
/// successful return. Once constructed, the Runtime object is independent of the Subgraph object
|
|
/// used to create it.
|
|
enum xnn_status xnn_create_runtime_v2(
|
|
xnn_subgraph_t subgraph,
|
|
pthreadpool_t threadpool,
|
|
uint32_t flags,
|
|
xnn_runtime_t* runtime_out);
|
|
|
|
enum xnn_status xnn_create_runtime(
|
|
xnn_subgraph_t subgraph,
|
|
xnn_runtime_t* runtime_out);
|
|
|
|
struct xnn_external_value {
|
|
uint32_t id;
|
|
void* data;
|
|
};
|
|
|
|
/// Setup data pointers for external inputs and outputs in a Runtime object.
|
|
///
|
|
/// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2.
|
|
/// @param num_external_values - the number of external inputs and outputs specified in this call. This number must
|
|
/// match the number of external inputs and outputs in the runtime, i.e. all external
|
|
/// inputs and outputs in the runtime must be specified in one call.
|
|
/// @param external_values - array with location information for all external inputs and outputs in the runtime.
|
|
enum xnn_status xnn_setup_runtime(
|
|
xnn_runtime_t runtime,
|
|
size_t num_external_values,
|
|
const struct xnn_external_value* external_values);
|
|
|
|
/// Execute forward pass for all operators in the runtime.
|
|
///
|
|
/// @param runtime - the Runtime object with the execution plan to invoke.
|
|
enum xnn_status xnn_invoke_runtime(
|
|
xnn_runtime_t runtime);
|
|
|
|
/// Destroy a Runtime object, as well as operators and memory associated with it.
|
|
///
|
|
/// @param runtime - the Runtime object to destroy.
|
|
enum xnn_status xnn_delete_runtime(
|
|
xnn_runtime_t runtime);
|
|
|
|
typedef struct xnn_operator* xnn_operator_t;
|
|
|
|
enum xnn_status xnn_run_operator(
|
|
xnn_operator_t op,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_delete_operator(
|
|
xnn_operator_t op);
|
|
|
|
#ifndef XNN_NO_F32_OPERATORS
|
|
|
|
enum xnn_status xnn_create_abs_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* abs_op_out);
|
|
|
|
enum xnn_status xnn_setup_abs_nc_f32(
|
|
xnn_operator_t abs_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_add_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_setup_add_nd_f32(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_argmax_pooling2d_nhwc_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* argmax_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32(
|
|
xnn_operator_t argmax_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t* index,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_average_pooling2d_nhwc_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_average_pooling2d_nhwc_f32(
|
|
xnn_operator_t average_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_bankers_rounding_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* rounding_op_out);
|
|
|
|
enum xnn_status xnn_setup_bankers_rounding_nc_f32(
|
|
xnn_operator_t rounding_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_ceiling_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* ceiling_op_out);
|
|
|
|
enum xnn_status xnn_setup_ceiling_nc_f32(
|
|
xnn_operator_t ceiling_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_clamp_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* clamp_op_out);
|
|
|
|
enum xnn_status xnn_setup_clamp_nc_f32(
|
|
xnn_operator_t clamp_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_f32(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_deconvolution2d_nhwc_f32(
|
|
uint32_t output_padding_top,
|
|
uint32_t output_padding_right,
|
|
uint32_t output_padding_bottom,
|
|
uint32_t output_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* deconvolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_deconvolution2d_nhwc_f32(
|
|
xnn_operator_t deconvolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_divide_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* divide_op_out);
|
|
|
|
enum xnn_status xnn_setup_divide_nd_f32(
|
|
xnn_operator_t divide_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_elu_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float alpha,
|
|
uint32_t flags,
|
|
xnn_operator_t* elu_op_out);
|
|
|
|
enum xnn_status xnn_setup_elu_nc_f32(
|
|
xnn_operator_t elu_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_f32(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_f32(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_floor_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* floor_op_out);
|
|
|
|
enum xnn_status xnn_setup_floor_nc_f32(
|
|
xnn_operator_t floor_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_f32(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_hardswish_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* hardswish_op_out);
|
|
|
|
enum xnn_status xnn_setup_hardswish_nc_f32(
|
|
xnn_operator_t hardswish_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_leaky_relu_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float negative_slope,
|
|
uint32_t flags,
|
|
xnn_operator_t* leaky_relu_op_out);
|
|
|
|
enum xnn_status xnn_setup_leaky_relu_nc_f32(
|
|
xnn_operator_t leaky_relu_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_max_pooling2d_nhwc_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* max_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_max_pooling2d_nhwc_f32(
|
|
xnn_operator_t max_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_maximum_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* maximum_op_out);
|
|
|
|
enum xnn_status xnn_setup_maximum_nd_f32(
|
|
xnn_operator_t maximum_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_minimum_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* minimum_op_out);
|
|
|
|
enum xnn_status xnn_setup_minimum_nd_f32(
|
|
xnn_operator_t minimum_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_multiply_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* multiply_op_out);
|
|
|
|
enum xnn_status xnn_setup_multiply_nd_f32(
|
|
xnn_operator_t multiply_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_negate_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* negate_op_out);
|
|
|
|
enum xnn_status xnn_setup_negate_nc_f32(
|
|
xnn_operator_t negate_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_prelu_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* negative_slope,
|
|
uint32_t flags,
|
|
xnn_operator_t* prelu_op_out);
|
|
|
|
enum xnn_status xnn_setup_prelu_nc_f32(
|
|
xnn_operator_t prelu_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nchw_f32(
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nchw_f32(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t output_height,
|
|
size_t output_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nhwc_f32(
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f32(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t output_height,
|
|
size_t output_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_sigmoid_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* sigmoid_op_out);
|
|
|
|
enum xnn_status xnn_setup_sigmoid_nc_f32(
|
|
xnn_operator_t sigmoid_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_softmax_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* softmax_op_out);
|
|
|
|
enum xnn_status xnn_setup_softmax_nc_f32(
|
|
xnn_operator_t softmax_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_square_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* square_op_out);
|
|
|
|
enum xnn_status xnn_setup_square_nc_f32(
|
|
xnn_operator_t square_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_square_root_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* sqrt_op_out);
|
|
|
|
enum xnn_status xnn_setup_square_root_nc_f32(
|
|
xnn_operator_t sqrt_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_squared_difference_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* squared_difference_op_out);
|
|
|
|
enum xnn_status xnn_setup_squared_difference_nd_f32(
|
|
xnn_operator_t squared_difference_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_subtract_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* subtract_op_out);
|
|
|
|
enum xnn_status xnn_setup_subtract_nd_f32(
|
|
xnn_operator_t subtract_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_truncation_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* truncation_op_out);
|
|
|
|
enum xnn_status xnn_setup_truncation_nc_f32(
|
|
xnn_operator_t truncation_op,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
#ifndef XNN_NO_NCHW_OPERATORS
|
|
|
|
enum xnn_status xnn_create_convolution2d_nchw_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nchw_f32(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_ncw_f32(
|
|
size_t channels,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_ncw_f32(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
const float* input,
|
|
float* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
#endif // XNN_NO_NCHW_OPERATORS
|
|
|
|
#endif // XNN_NO_F32_OPERATORS
|
|
|
|
#ifndef XNN_NO_X32_OPERATORS
|
|
|
|
enum xnn_status xnn_create_channel_shuffle_nc_x32(
|
|
size_t groups,
|
|
size_t group_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* channel_shuffle_op_out);
|
|
|
|
enum xnn_status xnn_setup_channel_shuffle_nc_x32(
|
|
xnn_operator_t channel_shuffle_op,
|
|
size_t batch_size,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_constant_pad_nd_x32(
|
|
const void* padding_value,
|
|
uint32_t flags,
|
|
xnn_operator_t* constant_pad_op_out);
|
|
|
|
enum xnn_status xnn_setup_constant_pad_nd_x32(
|
|
xnn_operator_t constant_pad_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_padding,
|
|
const size_t* post_padding,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_copy_nc_x32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* copy_op_out);
|
|
|
|
enum xnn_status xnn_setup_copy_nc_x32(
|
|
xnn_operator_t copy_op,
|
|
size_t batch_size,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nhwc_x32(
|
|
size_t output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nhwc_x32(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x32(
|
|
size_t output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x32(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_unpooling2d_nhwc_x32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* unpooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_unpooling2d_nhwc_x32(
|
|
xnn_operator_t unpooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const void* input,
|
|
const uint32_t* index,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
#endif // XNN_NO_X32_OPERATORS
|
|
|
|
#ifndef XNN_NO_F16_OPERATORS
|
|
|
|
enum xnn_status xnn_create_add_nd_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_setup_add_nd_f16(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_f16(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const void* kernel,
|
|
const void* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_f16(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_f16(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_f16(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_hardswish_nc_f16(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* hardswish_op_out);
|
|
|
|
enum xnn_status xnn_setup_hardswish_nc_f16(
|
|
xnn_operator_t hardswish_op,
|
|
size_t batch_size,
|
|
const void* input,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_multiply_nd_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* multiply_op_out);
|
|
|
|
enum xnn_status xnn_setup_multiply_nd_f16(
|
|
xnn_operator_t multiply_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
#endif // XNN_NO_F16_OPERATORS
|
|
|
|
#ifndef XNN_NO_QS8_OPERATORS
|
|
|
|
enum xnn_status xnn_create_add_nd_qs8(
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_setup_add_nd_qs8(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qs8(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
float kernel_scale,
|
|
const int8_t* kernel,
|
|
const int32_t* bias,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qs8(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const int8_t* input,
|
|
int8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_qs8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_qs8(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
const int8_t* input,
|
|
int8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
#endif // XNN_NO_QS8_OPERATORS
|
|
|
|
#ifndef XNN_NO_QU8_OPERATORS
|
|
|
|
enum xnn_status xnn_create_average_pooling2d_nhwc_qu8(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_average_pooling2d_nhwc_qu8(
|
|
xnn_operator_t average_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qu8(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t kernel_zero_point,
|
|
float kernel_scale,
|
|
const uint8_t* kernel,
|
|
const int32_t* bias,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qu8(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_deconvolution2d_nhwc_qu8(
|
|
uint32_t output_padding_top,
|
|
uint32_t output_padding_right,
|
|
uint32_t output_padding_bottom,
|
|
uint32_t output_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t kernel_zero_point,
|
|
float kernel_scale,
|
|
const uint8_t* kernel,
|
|
const int32_t* bias,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* deconvolution_op_out);
|
|
|
|
enum xnn_status xnn_setup_deconvolution2d_nhwc_qu8(
|
|
xnn_operator_t deconvolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qu8(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t kernel_zero_point,
|
|
float kernel_scale,
|
|
const uint8_t* kernel,
|
|
const int32_t* bias,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qu8(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_qu8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_qu8(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_leaky_relu_nc_qu8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float negative_slope,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* leaky_relu_op_out);
|
|
|
|
enum xnn_status xnn_setup_leaky_relu_nc_qu8(
|
|
xnn_operator_t leaky_relu_op,
|
|
size_t batch_size,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_sigmoid_nc_qu8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* sigmoid_op_out);
|
|
|
|
enum xnn_status xnn_setup_sigmoid_nc_qu8(
|
|
xnn_operator_t sigmoid_op,
|
|
size_t batch_size,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_softmax_nc_qu8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint32_t flags,
|
|
xnn_operator_t* softmax_op_out);
|
|
|
|
enum xnn_status xnn_setup_softmax_nc_qu8(
|
|
xnn_operator_t softmax_op,
|
|
size_t batch_size,
|
|
const uint8_t* input,
|
|
uint8_t* output,
|
|
pthreadpool_t threadpool);
|
|
|
|
#endif // XNN_NO_QU8_OPERATORS
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#ifndef XNN_NO_U8_OPERATORS
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enum xnn_status xnn_create_clamp_nc_u8(
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size_t channels,
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size_t input_stride,
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size_t output_stride,
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uint8_t output_min,
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uint8_t output_max,
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uint32_t flags,
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xnn_operator_t* clamp_op_out);
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enum xnn_status xnn_setup_clamp_nc_u8(
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xnn_operator_t clamp_op,
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size_t batch_size,
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const uint8_t* input,
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uint8_t* output,
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pthreadpool_t threadpool);
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enum xnn_status xnn_create_max_pooling2d_nhwc_u8(
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uint32_t input_padding_top,
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uint32_t input_padding_right,
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uint32_t input_padding_bottom,
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uint32_t input_padding_left,
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uint32_t pooling_height,
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uint32_t pooling_width,
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uint32_t stride_height,
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uint32_t stride_width,
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uint32_t dilation_height,
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uint32_t dilation_width,
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size_t channels,
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size_t input_pixel_stride,
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size_t output_pixel_stride,
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uint8_t output_min,
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uint8_t output_max,
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uint32_t flags,
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xnn_operator_t* max_pooling_op_out);
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enum xnn_status xnn_setup_max_pooling2d_nhwc_u8(
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xnn_operator_t max_pooling_op,
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size_t batch_size,
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size_t input_height,
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|
size_t input_width,
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const uint8_t* input,
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uint8_t* output,
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pthreadpool_t threadpool);
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#endif // XNN_NO_U8_OPERATORS
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#ifndef XNN_NO_X8_OPERATORS
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enum xnn_status xnn_create_channel_shuffle_nc_x8(
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size_t groups,
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size_t group_channels,
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size_t input_stride,
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|
size_t output_stride,
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|
uint32_t flags,
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|
xnn_operator_t* channel_shuffle_op_out);
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enum xnn_status xnn_setup_channel_shuffle_nc_x8(
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xnn_operator_t channel_shuffle_op,
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size_t batch_size,
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const void* input,
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void* output,
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pthreadpool_t threadpool);
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#endif // XNN_NO_X8_OPERATORS
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#ifdef __cplusplus
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} // extern "C"
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#endif
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