/*
-// Copyright (c) 2016 Intel Corporation
+// Copyright (c) 2016-2018 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
size_t feature_num;
/// @brief Number of spatial (x,y) dimensions in a format.
size_t spatial_num;
+ /// @brief Number of local (x,y) dimensions in a format.
+ size_t local_num;
/// @brief Dimensions changing order from rare to often.
std::string order;
/// @brief Dimensions order for internal storage.
static const char* feature_chars() { return "fioc"; }
/// @brief Characters representing spatial dimensions in an order.
static const char* spatial_chars() { return "xyzhsw"; }
+ /// @brief Characters representing local dimensions in an order.
+ static const char* local_chars() { return "kl"; }
/// @brief Checks if @p c represents batch dimension.
static bool is_batch_char(char c) { return std::string(batch_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents feature map/channel dimension.
static bool is_feature_char(char c) { return std::string(feature_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents spatial dimension.
static bool is_spatial_char(char c) { return std::string(spatial_chars()).find_first_of(c) != std::string::npos; }
+ /// @brief Checks if @p c represents local dimensions.
+ static bool is_local_char(char c) { return std::string(local_chars()).find_first_of(c) != std::string::npos; }
};
/// @brief Represents memory formats (orders).
fyxb = cldnn_format_fyxb, ///< format not used inside clDNN, but supported in reorder as extension for user provided formats.
os_iyx_osv16 = cldnn_format_os_iyx_osv16, ///< format used only for convolution weights: os - output feature maps slice, i - input feature maps, yx - spatials, sv16 - 16 values of single slice.
///< \n \image html os_iyx_osv16.jpg
+ os_iyx_osv32 = cldnn_format_os_iyx_osv32, ///< format used only for convolution weights: os - output feature maps slice, i - input feature maps, yx - spatials, sv32 - 32 values of single slice.
+ os_iyx_osv64 = cldnn_format_os_iyx_osv64, ///< format used only for convolution weights: os - output feature maps slice, i - input feature maps, yx - spatials, sv64 - 64 values of single slice.
bs_xs_xsv8_bsv8 = cldnn_format_bs_xs_xsv8_bsv8, ///< format used only for fully connected weights: bs - batch slice, xs - x slice, bsv8 - 8 values of single slice.
///< \n \image html bs_xs_xsv8_bsv8.jpg
bs_xs_xsv8_bsv16 = cldnn_format_bs_xs_xsv8_bsv16,///< format used only for fully connected weights: bs - batch slice, xs - x slice, bsv16 - 16 values of single slice.
image_2d_weights_winograd_6x3_s1_fbxyb, ///< image format used for weights for winograd fused convolution, F(6,3) -- filter 3x3 with stride 1
image_2d_weights_winograd_6x3_s1_xfbyb, ///< image format used for weights for winograd fused convolution, F(6,3) -- filter 3x3 with stride 1
os_is_yx_isa8_osv8_isv4, /// format for weights for MMAD convolution
+ os_is_yx_isa8_osv8_isv4_swizzled_by_4, /// format for weights for MMAD convolution
is_o_yx_isv32, /// format for weights for 1x1 MMAD convolutions
+ is_o32_yx_isv32_swizzled_by_4, /// format for weights for 1x1 MMAD convolutions
+ os_is_y_x8_osv8_isv4, /// format for weights for 1x1 MMAD convolutions
byxf_af32, /// < \n format for input for primitives using MMAD
+ byx8_f4, /// < \n format for input for MMAD convolutions
fs_bs_yx_bsv4_fsv32, /// < \n format for batched input for primitives using MMAD
+ bf_lyx_yx = cldnn_bf_lyx_yx, /// < \n format for local convolution weights
+ b_fs_yx_fsv4, /// < \n format for input for IMAD convolutions
+ os_is_yx_osv16_isv4, /// < \n format for weights for IMAD convolutions
format_num = cldnn_format_format_num, ///< number of format types
any = cldnn_format_any
};
{
static const std::map<type, format_traits> traits
{
- { yxfb,{ 1, 1, 2, "yxfb", "bfxy" } },
- { byxf,{ 1, 1, 2, "byxf", "bfxy" } },
- { bfyx,{ 1, 1, 2, "bfyx", "bfxy" } },
- { fyxb,{ 1, 1, 2, "fyxb", "bfxy" } },
- { os_iyx_osv16, { 1, 1, 2, "bfyx", "bfxy" } },
- { bs_xs_xsv8_bsv8, { 1, 1, 1, "bx", "b?x?" } },
- { bs_xs_xsv8_bsv16,{ 1, 1, 1, "bx", "b?x?" } },
- { bs_x_bsv16, { 1, 1, 1, "bx", "b?x?" } },
- { bf8_xy16, { 1, 1, 2, "bfyx", "bfxy" }},
- { image_2d_weights_c4_fyx_b, { 1, 1, 2, "bfyx", "bfxy" } },
- { image_2d_weights_c1_b_fyx, { 1, 1, 2, "bfyx", "bfxy" } },
- { winograd_2x3_s1_data, { 1, 1, 2, "bxyf", "bfxy" } },
- { winograd_2x3_s1_weights, { 1, 1, 2, "bfyx", "bfxy" } },
- { winograd_2x3_s1_fused_weights, { 1, 1, 2, "xyfb", "bfxy" } },
- { winograd_6x3_s1_fused_weights,{ 1, 1, 2, "xyfb", "bfxy" } },
- { image_2d_weights_winograd_6x3_s1_fbxyb,{ 1, 1, 2, "xyfb", "bfxy" } },
- { image_2d_weights_winograd_6x3_s1_xfbyb,{ 1, 1, 2, "xyfb", "bfxy" } },
- { os_is_yx_isa8_osv8_isv4, { 1, 1, 2, "bfyx", "bfxy" } },
- { is_o_yx_isv32 , {1, 1, 2, "byxf", "bfxy" } },
- { byxf_af32, { 1, 1, 2, "byxf", "bfxy" } },
- { fs_bs_yx_bsv4_fsv32 , { 1, 1, 2, "fbyx", "bfxy" }}
+ { yxfb,{ 1, 1, 2, 0, "yxfb", "bfxy" } },
+ { byxf,{ 1, 1, 2, 0, "byxf", "bfxy" } },
+ { bfyx,{ 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { fyxb,{ 1, 1, 2, 0, "fyxb", "bfxy" } },
+ { os_iyx_osv16, { 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { os_iyx_osv32,{ 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { os_iyx_osv64,{ 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { bs_xs_xsv8_bsv8, { 1, 1, 1, 0, "bx", "b?x?" } },
+ { bs_xs_xsv8_bsv16,{ 1, 1, 1, 0, "bx", "b?x?" } },
+ { bs_x_bsv16, { 1, 1, 1, 0, "bx", "b?x?" } },
+ { bf8_xy16, { 1, 1, 2, 0, "bfyx", "bfxy" }},
+ { image_2d_weights_c4_fyx_b, { 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { image_2d_weights_c1_b_fyx, { 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { winograd_2x3_s1_data, { 1, 1, 2, 0, "bxyf", "bfxy" } },
+ { winograd_2x3_s1_weights, { 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { winograd_2x3_s1_fused_weights, { 1, 1, 2, 0, "xyfb", "bfxy" } },
+ { winograd_6x3_s1_fused_weights,{ 1, 1, 2, 0, "xyfb", "bfxy" } },
+ { image_2d_weights_winograd_6x3_s1_fbxyb,{ 1, 1, 2, 0, "xyfb", "bfxy" } },
+ { image_2d_weights_winograd_6x3_s1_xfbyb,{ 1, 1, 2, 0, "xyfb", "bfxy" } },
+ { os_is_yx_isa8_osv8_isv4, { 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { os_is_yx_isa8_osv8_isv4_swizzled_by_4,{ 1, 1, 2, 0, "bfyx", "bfxy" } },
+ { byxf_af32, { 1, 1, 2, 0, "byxf", "bfxy" } },
+ { byx8_f4 , { 1, 1, 2, 0, "byxf", "bfyx"} },
+ { fs_bs_yx_bsv4_fsv32 , { 1, 1, 2, 0, "fbyx", "bfxy" }},
+ { is_o_yx_isv32 , {1, 1, 2, 0, "byxf", "bfxy" } },
+ { is_o32_yx_isv32_swizzled_by_4 , {1,1,2,0,"byxf", "bfxy" } },
+ { os_is_y_x8_osv8_isv4 , { 1, 1, 2, 0, "byxf", "bfxy" } },
+ { bf_lyx_yx,{ 1, 1, 2, 2, "bfklyx", "bfklxy" } },
+ { b_fs_yx_fsv4,{ 1, 1, 1, 0, "bfyx", "bfxy" } },
+ { os_is_yx_osv16_isv4,{ 1, 1, 1, 0, "bfxy", "bfxy?" } },
};
return traits.at(fmt);
}
static size_t feature_num(type fmt) { return traits(fmt).feature_num; }
/// @brief Returns number of spatial dimensions for a @p format.
static size_t spatial_num(type fmt) { return traits(fmt).spatial_num; }
+ /// @brief Returns number of local dimensions for a @p format.
+ static size_t local_num(type fmt) { return traits(fmt).local_num; }
/// @brief Returns an order of dimensions for a @ format.
static const std::string& order(type fmt) { return traits(fmt).order; }
/// @brief Returns an internal orders of dimensions for a @p format.
size_t feature_num() const { return traits(value).feature_num; }
/// @brief Returns number of spatial dimensions.
size_t spatial_num() const { return traits(value).spatial_num; }
+ /// @brief Returns number of local dimensions.
+ size_t local_num() const { return traits(value).local_num; }
/// @brief Returns an order of dimensions in form of string.
const std::string& order() const { return traits(value).order; }
/// @brief Returns an internal orders of dimensions form of string.
{
batch,
feature,
- spatial
+ spatial,
+ local
};
/// @brief template class with max_dimensionalities and dimension offset for dimension kinds
static constexpr int32_t dim_offset = CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX;
};
+template <>
+struct dim_vec_limits<dim_vec_kind::local>
+{
+ static constexpr int32_t max_dimentionality = CLDNN_TENSOR_LOCAL_DIM_MAX;
+ static constexpr int32_t dim_offset = CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX;
+};
+
/// @brief Template class used in tensor constructor using dim_vec_kinds
template <dim_vec_kind Kind>
class dim_vec_kind_init
return details::dim_vec_kind_init<details::dim_vec_kind::spatial>(std::forward<InitTys>(inits) ...);
}
+template <typename ... InitTys>
+details::dim_vec_kind_init<details::dim_vec_kind::local> local(InitTys&& ... inits)
+{
+ return details::dim_vec_kind_init<details::dim_vec_kind::local>(std::forward<InitTys>(inits) ...);
+}
+
/// @brief N-dimensional vector. Mostly used to represent memory size.
struct tensor
{
friend class details::dim_vec_kind_init<details::dim_vec_kind::batch>;
friend class details::dim_vec_kind_init<details::dim_vec_kind::feature>;
friend class details::dim_vec_kind_init<details::dim_vec_kind::spatial>;
+ friend class details::dim_vec_kind_init<details::dim_vec_kind::local>;
typedef int32_t value_type; ///< Values type stored in tensor.
//TODO find the way to prevent direct change of following fields.
mutable_array_ref<value_type> batch; ///< Batch dimensions.
mutable_array_ref<value_type> feature; ///< Feature maps.
mutable_array_ref<value_type> spatial; ///< Spatial dimensions.
+ mutable_array_ref<value_type> local; ///< Local dimensions.
private:
value_type _sizes[CLDNN_TENSOR_DIM_MAX];
, batch(_sizes, CLDNN_TENSOR_BATCH_DIM_MAX)
, feature(_sizes + CLDNN_TENSOR_BATCH_DIM_MAX, CLDNN_TENSOR_FEATURE_DIM_MAX)
, spatial(_sizes + CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX, CLDNN_TENSOR_SPATIAL_DIM_MAX)
+ , local(_sizes + CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX +
+ CLDNN_TENSOR_SPATIAL_DIM_MAX, CLDNN_TENSOR_LOCAL_DIM_MAX)
{
std::fill_n(_sizes, CLDNN_TENSOR_DIM_MAX, default_size);
}
_sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + 1] = height;
}
+ /// @brief Constructs @p tensor.
+ /// @details Example:
+ /*! @code
+ *
+ tensor my_tensor( 2, 3, 4, 5, 6, 7 ); // b=2, f=3, x=4, y=5, lx= 6, ly =7
+ cout << my_tensor.batch[0] << endl; // 2
+ cout << my_tensor.feature[0] << endl; // 3
+ cout << "x=" << my_tensor.spatial[0] << endl; // x=4
+ cout << "y=" << my_tensor.spatial[1] << endl; // y=5
+ cout << "local x=" << my_tensor.local[0] << endl; // local x=6
+ cout << "loxal y=" << my_tensor.local[1] << endl; // local y=7
+ *
+ * @endcode
+ */
+ tensor(value_type batch_num, value_type feature_num, value_type width,
+ value_type height, value_type local_x, value_type local_y)
+ : tensor(1)
+ {
+ _sizes[0] = batch_num;
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX] = feature_num;
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX] = width;
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + 1] = height;
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX] = local_x;
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX + 1] = local_y;
+ }
+
/// @brief Constructs @p tensor using vector of sizes.
/// @param[in] sizes dimensions need to be provided in the following order {batch, feature, spatial_x, spatial_y}.
/// @param[in] default_size default_size for tensor dimensions.
_sizes[CLDNN_TENSOR_BATCH_DIM_MAX] = sizes[CLDNN_TENSOR_BATCH_DIM_MAX];
_sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX] = sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX];
_sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + 1] = sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + 1];
+ if (sizes.size() == 6)
+ {
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX] =
+ sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX];
+ _sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX + 1] =
+ sizes[CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX + CLDNN_TENSOR_SPATIAL_DIM_MAX + 1];
+ }
}
tensor(format fmt, const std::vector<value_type>& sizes, value_type default_size = 1)
result.batch_num = batch.size();
result.feature_num = feature.size();
result.spatial_num = spatial.size();
+ result.local_num = local.size();
std::copy_n(_sizes, CLDNN_TENSOR_DIM_MAX, result.sizes);
return result;
}
my_sizes[0] = align_to(my_sizes[0], 16);
adjusted_coords[0] = align_to(adjusted_coords[0], 16);
}
+ else if (fmt == cldnn::format::os_iyx_osv32 && !is_aligned_to(my_sizes[0], 32))
+ {
+ my_sizes[0] = align_to(my_sizes[0], 32);
+ adjusted_coords[0] = align_to(adjusted_coords[0], 32);
+ }
+ else if (fmt == cldnn::format::os_iyx_osv64 && !is_aligned_to(my_sizes[0], 64))
+ {
+ my_sizes[0] = align_to(my_sizes[0], 64);
+ adjusted_coords[0] = align_to(adjusted_coords[0], 64);
+ }
else if (fmt == cldnn::format::bs_xs_xsv8_bsv8 && !(is_aligned_to(my_sizes[0], 8) && is_aligned_to(my_sizes[1], 8)))
{
my_sizes[0] = align_to(my_sizes[0], 8);
adjusted_coords[0] = align_to(adjusted_coords[0], 8);
adjusted_coords[1] = align_to(adjusted_coords[1], 32);
}
+ else if (fmt == cldnn::format::os_is_yx_isa8_osv8_isv4_swizzled_by_4 && !(is_aligned_to(my_sizes[0], 32)) && !(is_aligned_to(my_sizes[1], 32)))
+ {
+ my_sizes[0] = align_to(my_sizes[0], 32);
+ my_sizes[1] = align_to(my_sizes[1], 32);
+ adjusted_coords[0] = align_to(adjusted_coords[0], 32);
+ adjusted_coords[1] = align_to(adjusted_coords[1], 32);
+ }
else if (fmt == cldnn::format::is_o_yx_isv32 && !(is_aligned_to(my_sizes[1], 32)))
{
my_sizes[1] = align_to(my_sizes[1], 32);
adjusted_coords[1] = align_to(adjusted_coords[1], 32);
}
+ else if (fmt == cldnn::format::is_o32_yx_isv32_swizzled_by_4 && (!is_aligned_to(my_sizes[1], 32) || !is_aligned_to(my_sizes[0], 32)))
+ {
+ my_sizes[0] = align_to(my_sizes[0], 32);
+ my_sizes[1] = align_to(my_sizes[1], 32);
+ adjusted_coords[0] = align_to(adjusted_coords[0], 32);
+ adjusted_coords[1] = align_to(adjusted_coords[1], 32);
+ }
+ else if (fmt == cldnn::format::os_is_y_x8_osv8_isv4)
+ {
+ my_sizes[1] = align_to(my_sizes[1], 4);
+ my_sizes[0] = align_to(my_sizes[0], 8);
+ my_sizes[2] = align_to(my_sizes[2], 8);
+ }
else if (fmt == cldnn::format::byxf_af32 && !(is_aligned_to(my_sizes[1], 32)))
{
my_sizes[1] = align_to(my_sizes[1], 32);
adjusted_coords[1] = align_to(adjusted_coords[1], 32);
}
+ else if (fmt == cldnn::format::byx8_f4 && (!(is_aligned_to(my_sizes[1], 4)) || !(is_aligned_to(my_sizes[2], 8))))
+ {
+ my_sizes[1] = align_to(my_sizes[1], 4);
+ my_sizes[2] = align_to(my_sizes[2], 8);
+ adjusted_coords[1] = align_to(adjusted_coords[1], 4);
+ adjusted_coords[2] = align_to(adjusted_coords[2], 8);
+ }
else if (fmt == cldnn::format::fs_bs_yx_bsv4_fsv32 && (!is_aligned_to(my_sizes[1], 32) || !is_aligned_to(my_sizes[0], 4) ))
{
my_sizes[1] = align_to(my_sizes[1], 32);
}
};
+#define TensorValue(val) static_cast<cldnn::tensor::value_type>(val)
template<details::dim_vec_kind Kind>
inline void details::dim_vec_kind_init<Kind>::init_tensor_values(cldnn::tensor & t)
/// @brief Divides a @p tensor by a @p scalar
inline tensor operator/(const tensor& lhs, tensor::value_type rhs) { return lhs.div(rhs); }
+///
+/// \brief Converts C API tensor_array to std::vector<tensor>
+///
+inline std::vector<tensor> tensor_arr_to_vector(const cldnn_tensor_arr& arr)
+{
+ std::vector<tensor> result(arr.size);
+ for (size_t i = 0; i < arr.size; i++)
+ result[i] = arr.data[i];
+
+ return result;
+}
+
+///
+/// \brief Converts std::vector<tensor> to std::vector of C API tensor
+///
+inline std::vector<cldnn_tensor> tensor_vector_to_cldnn_vector(const std::vector<tensor>& stor)
+{
+ return std::vector<cldnn_tensor>(stor.begin(), stor.end());
+}
+
/// @}
/// @}
}
projects / platform / upstream / dldt.git / blobdiff