EVOLUTION-MANAGER

Edit File: tensor_format.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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http://www.apache.org/licenses/LICENSE-2.0

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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_UTIL_TENSOR_FORMAT_H_
#define TENSORFLOW_CORE_UTIL_TENSOR_FORMAT_H_

#include <array>
#include <vector>

#include "absl/strings/string_view.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {

// Tensor format for input/output activations used in convolution operations.
// The mnemonics specify the meaning of each tensor dimension sorted from
// largest to smallest memory stride.
// N = Batch, H = Image Height, W = Image Width, C = Number of Channels.
// TODO(pauldonnelly): It would probably be better to switch to a registration
// process for tensor formats, so specialized formats could be defined more
// locally to where they are used.
enum TensorFormat {
  // FORMAT_NHWC is the default format in TensorFlow.
  FORMAT_NHWC = 0,

// FORMAT_NCHW often improves performance on GPUs.
  FORMAT_NCHW = 1,

// NCHW_VECT_C is the most performant tensor format for cudnn6's quantized
  // int8 convolution and fused convolution. It is laid out in the same order
  // as NCHW, except that the size of the Channels dimension is divided by 4,
  // and a new dimension of size 4 is appended, which packs 4 adjacent channel
  // activations for the same pixel into an int32. Thus an NCHW format tensor
  // with dimensions [N, C, H, W] would have dimensions [N, C/4, H, W, 4] in
  // NCHW_VECT_C format.
  // A pre-condition of this format is that C must be a multiple of 4.
  FORMAT_NCHW_VECT_C = 2,

// Similar to NHWC, but the size of the W dimension is divided by 4, and a
  // new dimension of size 4 is appended, which packs 4 adjacent activations
  // in the width dimension.
  FORMAT_NHWC_VECT_W = 3,

// Note: although the current code in this file assumes VECT_C and VECT_W
  // enums imply int8x4 vectors, this should not be relied upon.
  // In the future we may change the meaning of these enums to include vectors
  // of other types such as int16x2, with op implementations automatically
  // determining which format is implied based on the datatype.

// FORMAT_HWNC is for TPUs.
  FORMAT_HWNC = 4,

// FORMAT_HWCN is for TPUs.
  FORMAT_HWCN = 5,
};

// Tensor format for convolutional filters.
// The mnemonics specify the meaning of each tensor dimension sorted
// from largest to smallest memory stride.
// H = Kernel Height, W = Kernel Width, I = Input Channels, O = Output Channels.
// Note: In cudnnGetFilter4dDescriptor(), 'O' is called 'K', 'I' is called 'C'.
enum FilterTensorFormat {
  // FORMAT_HWIO is the default filter format in TensorFlow.
  // Ops that do not have a 'filter_format' attribute will assume this format.
  FORMAT_HWIO = 0,

// FORMAT_OIHW often improves performance on GPUs.
  FORMAT_OIHW = 1,

// FORMAT_OHWI used by cuDNN for NHWC convolutions.
  FORMAT_OHWI = 2,

// OIHW_VECT_I is the most performant tensor format for cudnn6's quantized
  // int8 convolution and fused convolution. It is analogous to the NCHW_VECT_C
  // data format. It is laid out in the same order as OIHW, except that the size
  // of the Input Channels dimension is divided by 4, and a new dimension of
  // size 4 is appended, which packs 4 adjacent input channel weights into an
  // int32. Thus an OIHW format filter with dimensions [O, I, H, W] would have
  // dimensions [O, I/4, H, W, 4] in OIHW_VECT_I format.
  // A pre-condition of this format is that I must be a multiple of 4.
  FORMAT_OIHW_VECT_I = 3,
};

// Parse tensor format from the given string.
// Return true if the parsing succeeds, and false if it fails.
bool FormatFromString(absl::string_view format_str, TensorFormat* format);

// Parse tensor format from the given string.
// Return true if the parsing succeeds, and false if it fails.
bool FilterFormatFromString(absl::string_view format_str,
                            FilterTensorFormat* format);

// Convert a tensor format into string.
std::string ToString(TensorFormat format);

// Convert a filter tensor format into string.
std::string ToString(FilterTensorFormat format);

// Returns the number of spatial dims of a tensor of rank 'num_dims' and tensor
// format 'format'.
inline int GetTensorSpatialDims(int num_dims, TensorFormat format) {
  switch (format) {
    case FORMAT_NHWC:
    case FORMAT_NCHW:
    case FORMAT_HWNC:
    case FORMAT_HWCN:
      return num_dims - 2;  // Exclude N,C.
    case FORMAT_NCHW_VECT_C:
    case FORMAT_NHWC_VECT_W:
      // Note: the VECT_W is not counted as an independent spatial dim here,
      // since it just a component of the width dimension.
      return num_dims - 3;  // Exclude N,C,VectDim.
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

inline int GetFilterTensorSpatialDims(int num_dims, FilterTensorFormat format) {
  if (format == FORMAT_OIHW_VECT_I) {
    return num_dims - 3;  // Exclude O,I,InnerI.
  } else {
    return num_dims - 2;  // Exclude O,I.
  }
}

// Returns the rank of a tensor with 'num_spatial_dims' spatial dimensions and
// tensor format 'format'. This is the inverse of GetTensorSpatialDims.
inline int GetTensorDimsFromSpatialDims(int num_spatial_dims,
                                        TensorFormat format) {
  switch (format) {
    case FORMAT_NHWC:
    case FORMAT_NCHW:
    case FORMAT_HWNC:
    case FORMAT_HWCN:
      return num_spatial_dims + 2;  // Include N,C.
    case FORMAT_NCHW_VECT_C:
    case FORMAT_NHWC_VECT_W:
      return num_spatial_dims + 3;  // Include N,C,VectDim.
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

// Returns the rank of a tensor with 'num_spatial_dims' spatial dimensions and
// filter tensor format 'format'.
inline int GetFilterTensorDimsFromSpatialDims(int num_spatial_dims,
                                              FilterTensorFormat format) {
  if (format == FORMAT_OIHW_VECT_I) {
    return num_spatial_dims + 3;  // Include O,I,InnerI.
  } else {
    return num_spatial_dims + 2;  // Include O,I.
  }
}

// Returns the index of the batch dimension.
inline int GetTensorBatchDimIndex(int num_dims, TensorFormat format) {
  switch (format) {
    case FORMAT_NHWC:
    case FORMAT_NCHW:
    case FORMAT_NCHW_VECT_C:
    case FORMAT_NHWC_VECT_W:
      return 0;
    case FORMAT_HWNC:
      return num_dims - 2;
    case FORMAT_HWCN:
      return num_dims - 1;
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

// Returns the index of the feature dimension. If format is NCHW_VECT_C, returns
// the index of the outer feature dimension (i.e. dimension 1, whose size would
// be num_features / 4 in this case).
inline int GetTensorFeatureDimIndex(int num_dims, TensorFormat format) {
  switch (format) {
    case FORMAT_NHWC:
    case FORMAT_HWNC:
      return num_dims - 1;
    case FORMAT_NHWC_VECT_W:
    case FORMAT_HWCN:
      return num_dims - 2;
    case FORMAT_NCHW:
    case FORMAT_NCHW_VECT_C:
      return 1;
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

// Returns the index of the inner feature dimension.
inline int GetTensorInnerFeatureDimIndex(int num_dims, TensorFormat format) {
  DCHECK_EQ(format, FORMAT_NCHW_VECT_C);
  return num_dims - 1;
}

// Returns the index of the inner width dimension.
inline int GetTensorInnerWidthDimIndex(int num_dims, TensorFormat format) {
  DCHECK_EQ(format, FORMAT_NHWC_VECT_W);
  return num_dims - 1;
}

// Returns the dimension index of the specified 'spatial_dim' within an
// activation tensor. If format is NHWC_VECT_W and spatial_dim is 1, returns
// the index of the outer width dimension (i.e. dimension 2, whose size would
// be width / 4 in this case).
inline int GetTensorSpatialDimIndex(int num_dims, TensorFormat format,
                                    int spatial_dim) {
  CHECK(spatial_dim >= 0 &&
        spatial_dim < GetTensorSpatialDims(num_dims, format))
      << spatial_dim << " " << num_dims << " " << ToString(format);
  switch (format) {
    case FORMAT_NHWC:
    case FORMAT_NHWC_VECT_W:
      return spatial_dim + 1;
    case FORMAT_NCHW:
    case FORMAT_NCHW_VECT_C:
      return spatial_dim + 2;
    case FORMAT_HWNC:
    case FORMAT_HWCN:
      return spatial_dim;
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

inline int GetFilterTensorSpatialDimIndex(int num_dims,
                                          FilterTensorFormat format, int dim) {
  CHECK(dim >= 0 && dim < GetFilterTensorSpatialDims(num_dims, format))
      << dim << " " << num_dims << " " << ToString(format);
  switch (format) {
    case FORMAT_HWIO:
      return dim;
    case FORMAT_OIHW:
    case FORMAT_OIHW_VECT_I:
      return dim + 2;
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

// Returns the index of the inner input channels dimension.
inline int GetFilterTensorInnerInputChannelsDimIndex(
    int num_dims, FilterTensorFormat format) {
  DCHECK_EQ(format, FORMAT_OIHW_VECT_I);
  return num_dims - 1;
}

// Returns the index of the input channels dimension.
// If 'format' is FORMAT_OIHW_VECT_I, returns the dimension index of the
// outer input channel (i.e. 1), which holds num_input_channels / 4.
inline int GetFilterTensorInputChannelsDimIndex(int num_dims,
                                                FilterTensorFormat format) {
  switch (format) {
    case FORMAT_HWIO:
      return num_dims - 2;
    case FORMAT_OIHW:
    case FORMAT_OIHW_VECT_I:
      return 1;
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

// Returns the index of the output channels dimension.
inline int GetFilterTensorOutputChannelsDimIndex(int num_dims,
                                                 FilterTensorFormat format) {
  switch (format) {
    case FORMAT_HWIO:
      return num_dims - 1;
    case FORMAT_OIHW:
    case FORMAT_OIHW_VECT_I:
      return 0;
    default:
      LOG(FATAL) << "Unknown format " << format;
      return -1;  // Avoid compiler warning about missing return value
  }
}

// TODO(pauldonnelly): Replace these tensor dimension index functions with
// constant structs to improve performance and reduce code size in Compute()
// functions.

// Return the dimension index for the specified 'dimension' of the specified
// data 'tensor_format'.  'dimension' is a char that can be 'N' (batch size),
// 'C' (channels), 'H' (height), 'W' (width),  or a numbered spatial dimension:
// '0',  .. (NUM_SPATIAL_DIMS-1)..
// If 'format' is NCHW_VECT_C and 'dimension' is 'C', returns the index of
// the outer channel dimension (i.e. 1).
template <int NUM_SPATIAL_DIMS>
inline int32 GetTensorDimIndex(TensorFormat format, char dimension) {
  if (format == FORMAT_NHWC || format == FORMAT_NHWC_VECT_W) {
    // clang-format off
    switch (dimension) {
      case 'N': return 0;
      case '0': return 1;
      case '1': return 2;
      case '2': return 3;
      case 'H': return NUM_SPATIAL_DIMS - 1;
      case 'W': return NUM_SPATIAL_DIMS;
      case 'C': return NUM_SPATIAL_DIMS + 1;
      default:
        LOG(FATAL) << "Invalid dimension: " << dimension;
        return -1;  // Avoid compiler warning about missing return value
    }
  } else if (format == FORMAT_NCHW || format == FORMAT_NCHW_VECT_C) {
    switch (dimension) {
      case 'N': return 0;
      case 'C': return 1;
      case '0': return 2;
      case '1': return 3;
      case '2': return 4;
      case 'H': return NUM_SPATIAL_DIMS;
      case 'W': return NUM_SPATIAL_DIMS + 1;
      default:
        LOG(FATAL) << "Invalid dimension: " << dimension;
        return -1;  // Avoid compiler warning about missing return value
    }
  } else if (format == FORMAT_HWNC) {
    switch (dimension) {
      case '0': return 0;
      case '1': return 1;
      case '2': return 2;
      case 'H': return NUM_SPATIAL_DIMS - 2;
      case 'W': return NUM_SPATIAL_DIMS - 1;
      case 'N': return NUM_SPATIAL_DIMS;
      case 'C': return NUM_SPATIAL_DIMS + 1;
      default:
        LOG(FATAL) << "Invalid dimension: " << dimension;
        return -1;  // Avoid compiler warning about missing return value
    }
  } else if (format == FORMAT_HWCN) {
    switch (dimension) {
      case '0': return 0;
      case '1': return 1;
      case '2': return 2;
      case 'H': return NUM_SPATIAL_DIMS - 2;
      case 'W': return NUM_SPATIAL_DIMS - 1;
      case 'C': return NUM_SPATIAL_DIMS;
      case 'N': return NUM_SPATIAL_DIMS + 1;
      default:
        LOG(FATAL) << "Invalid dimension: " << dimension;
        return -1;  // Avoid compiler warning about missing return value
    }
  } else {
    LOG(FATAL) << "Invalid format: " << static_cast<int>(format);
    return -1;  // Avoid compiler warning about missing return value
  }
  // clang-format on
}

// Return the dimension index for the specified 'dimension' of the specified
// 'filter_tensor_format'.  'dimension' is a char that can be 'O' (num output
// channels), 'I' (num input channels), 'H' (height), 'W' (width), or a
// numbered spatial dimension: '0',  .. (NUM_SPATIAL_DIMS-1).
// If 'format' is OIHW_VECT_I and 'dimension' is 'I', returns the index of the
// outer input channels dimension (i.e. 1).
template <int NUM_SPATIAL_DIMS>
inline int GetFilterDimIndex(FilterTensorFormat filter_tensor_format,
                             char dimension) {
  // clang-format off
  if (filter_tensor_format == FORMAT_HWIO) {
    switch (dimension) {
      case '0': return 0;
      case '1': return 1;
      case '2': return 2;
      case 'H': return NUM_SPATIAL_DIMS - 2;
      case 'W': return NUM_SPATIAL_DIMS - 1;
      case 'I': return NUM_SPATIAL_DIMS;
      case 'O': return NUM_SPATIAL_DIMS + 1;
      default:
        LOG(FATAL) << "Invalid dimension: " << dimension;
        return -1;  // Avoid compiler warning about missing return value
    }
  } else if (filter_tensor_format == FORMAT_OIHW ||
             filter_tensor_format == FORMAT_OIHW_VECT_I) {
    switch (dimension) {
      case 'O': return 0;
      case 'I': return 1;
      case '0': return 2;
      case '1': return 3;
      case '2': return 4;
      case 'H': return NUM_SPATIAL_DIMS;
      case 'W': return NUM_SPATIAL_DIMS + 1;
      default:
        LOG(FATAL) << "Invalid dimension: " << dimension;
        return -1;  // Avoid compiler warning about missing return value
    }
  } else {
    LOG(FATAL) << "Invalid format: " << static_cast<int>(filter_tensor_format);
    return -1;  // Avoid compiler warning about missing return value
  }
  // clang-format on
}

inline int32 GetTensorDimIndex(TensorFormat format, char dimension) {
  return GetTensorDimIndex<2>(format, dimension);
}

inline int32 GetTensorDimIndex(TensorFormat format, char dimension,
                               int num_total_dims) {
  int32 index = (GetTensorSpatialDims(num_total_dims, format) == 3)
                    ? GetTensorDimIndex<3>(format, dimension)
                    : GetTensorDimIndex<2>(format, dimension);
  CHECK(index >= 0 && index < num_total_dims)  // Crash OK.
      << "Invalid index from the dimension: " << index << ", " << format << ", "
      << dimension;
  return index;
}

// Return the element from 'dimension_attributes' that corresponds to the
// specified 'dimension' according to 'tensor_format'.
template <typename T>
T GetTensorDim(gtl::ArraySlice<T> dimension_attributes,
               TensorFormat tensor_format, char dimension) {
  int index =
      GetTensorDimIndex(tensor_format, dimension, dimension_attributes.size());
  return dimension_attributes[index];
}

// Return the element from 'dimension_attribute' that corresponds to the
// specified 'dimension' according to 'filter_tensor_format'.
template <typename T>
T GetFilterDim(gtl::ArraySlice<T> dimension_attribute,
               FilterTensorFormat filter_tensor_format, char dimension) {
  int index = (GetFilterTensorSpatialDims(dimension_attribute.size(),
                                          filter_tensor_format) == 3)
                  ? GetFilterDimIndex<3>(filter_tensor_format, dimension)
                  : GetFilterDimIndex<2>(filter_tensor_format, dimension);
  using size_type = typename gtl::ArraySlice<T>::size_type;
  CHECK(index >= 0 &&
        static_cast<size_type>(index) < dimension_attribute.size())
      << "Invalid index from the dimension: " << index << ", "
      << filter_tensor_format << ", " << dimension;
  return dimension_attribute[index];
}

template <typename T>
T GetTensorDim(const std::vector<T>& attributes, TensorFormat format,
               char dimension) {
  return GetTensorDim(gtl::ArraySlice<T>(attributes), format, dimension);
}

// Return the size of the specified 'dimension' within 'tensor_shape'
// according to 'tensor_format'.
inline int64 GetTensorDim(const TensorShape& tensor_shape,
                          TensorFormat tensor_format, char dimension) {
  return GetTensorDim(gtl::ArraySlice<int64>(tensor_shape.dim_sizes()),
                      tensor_format, dimension);
}

// Return the size of the specified 'dimension' within 'tensor_shape'
// according to 'tensor_filter_format'.
inline int64 GetFilterDim(const TensorShape& tensor_shape,
                          FilterTensorFormat tensor_filter_format,
                          char dimension) {
  return GetFilterDim(gtl::ArraySlice<int64>(tensor_shape.dim_sizes()),
                      tensor_filter_format, dimension);
}

// Return the size of the specified 'dimension' of 'tensor' according to
// 'tensor_format'.
inline int64 GetTensorDim(const Tensor& tensor, TensorFormat tensor_format,
                          char dimension) {
  return GetTensorDim(tensor.shape(), tensor_format, dimension);
}

// Return the size of the specified 'dimension' of 'tensor' according to
// 'filter_tensor_format'.
inline int64 GetFilterDim(const Tensor& tensor,
                          FilterTensorFormat filter_tensor_format,
                          char dimension) {
  return GetFilterDim(tensor.shape(), filter_tensor_format, dimension);
}

inline void GetExplicitPaddingForDim(
    const std::vector<int64>& explicit_paddings, TensorFormat tensor_format,
    char dimension, int64* padding_before, int64* padding_after) {
  int index =
      GetTensorDimIndex(tensor_format, dimension, explicit_paddings.size() / 2);
  *padding_before = explicit_paddings[2 * index];
  *padding_after = explicit_paddings[2 * index + 1];
}

// Return the string that specifies the data format for convnet operations.
std::string GetConvnetDataFormatAttrString();
std::string GetConvnet3dDataFormatAttrString();

// Return the string that specifies the filter format for convnet operations.
std::string GetConvnetFilterFormatAttrString();
std::string GetConvnet3dFilterFormatAttrString();
std::string GetConvnetDataFormat2D3DAttrString();

// Returns a tensor shape for the specified format and dimension sizes.
// Works for both 2D and 3D operations. The output shapes are as follows:
// FORMAT_NHWC:        (N, spatial, C); rank = spatial.size() + 2
// FORMAT_NCHW:        (N, C, spatial); rank = spatial.size() + 2
// FORMAT_NCHW_VECT_C: (N, C, spatial, InnerC); rank = spatial.size() + 3
// FORMAT_NHWC_VECT_W: (N, spatial, C, InnerW); rank = spatial.size() + 3
inline TensorShape ShapeFromFormat(TensorFormat format, int64 N,
                                   gtl::ArraySlice<int64> spatial, int64 C) {
  const int dims = GetTensorDimsFromSpatialDims(spatial.size(), format);
  gtl::InlinedVector<int64, 6> dim_sizes(dims);
  dim_sizes[GetTensorBatchDimIndex(dims, format)] = N;
  for (int dim = 0; static_cast<size_t>(dim) < spatial.size(); dim++) {
    auto dim_size = spatial[dim];
    if (format == FORMAT_NHWC_VECT_W &&
        static_cast<size_t>(dim) == spatial.size() - 1) {
      CHECK_EQ(0, dim_size % 4)
          << "FORMAT_NHWC_VECT_W requires W to be a multiple of 4, but W="
          << dim_size;
      dim_sizes[GetTensorInnerWidthDimIndex(dims, format)] = 4;
      dim_size /= 4;
    }
    dim_sizes[GetTensorSpatialDimIndex(dims, format, dim)] = dim_size;
  }

int feature_index = GetTensorFeatureDimIndex(dims, format);
  if (format == FORMAT_NCHW_VECT_C) {
    CHECK_EQ(0, C % 4) << "NCHW_VECT_C requires C to be a multiple of 4, but C="
                       << C;
    C /= 4;
    dim_sizes[GetTensorInnerFeatureDimIndex(dims, format)] = 4;
  }
  dim_sizes[feature_index] = C;
  return TensorShape(dim_sizes);
}

// Return a tensor shape of the specified 'format', and dimensions.
// Works for both 2D and 3D operations. If 'format' is OIHW_VECT_I,
// the output TensorShape has spatial.size() + 3 dimensions, otherwise
// it has spatial.size() + 2 dimensions.
inline TensorShape ShapeFromFilterTensorFormat(FilterTensorFormat format,
                                               gtl::ArraySlice<int64> spatial,
                                               int64 I, int64 O) {
  const int dims = GetFilterTensorDimsFromSpatialDims(spatial.size(), format);
  gtl::InlinedVector<int64, 6> dim_sizes(dims);
  dim_sizes[GetFilterTensorOutputChannelsDimIndex(dims, format)] = O;
  for (int dim = 0; static_cast<size_t>(dim) < spatial.size(); dim++) {
    dim_sizes[GetFilterTensorSpatialDimIndex(dims, format, dim)] = spatial[dim];
  }

if (format == FORMAT_OIHW_VECT_I) {
    CHECK_EQ(0, I % 4) << "OIHW_VECT_I requires I to be a multiple of 4, but I="
                       << I;
    I /= 4;
    dim_sizes[GetFilterTensorInnerInputChannelsDimIndex(dims, format)] = 4;
  }
  dim_sizes[GetFilterTensorInputChannelsDimIndex(dims, format)] = I;
  return TensorShape(dim_sizes);
}

// Return a tensor shape of the specified 'format', and dimensions.
inline TensorShape ShapeFromFormat(TensorFormat format, int64 N, int64 H,
                                   int64 W, int64 C) {
  return ShapeFromFormat(format, N, {H, W}, C);
}

// Return a filter tensor shape of the specified 'format', and dimensions.
inline TensorShape ShapeFromFilterTensorFormat(FilterTensorFormat format,
                                               int64 H, int64 W, int64 I,
                                               int64 O) {
  return ShapeFromFilterTensorFormat(format, {H, W}, I, O);
}

// Returns a copy of the specified tensor 'src_shape' converted from
// 'src_format' to 'dst_format'.
inline TensorShape ShapeFromFormat(TensorFormat dst_format,
                                   const TensorShape& src_shape,
                                   TensorFormat src_format) {
  if (src_format == dst_format) {
    return src_shape;
  }

const int64 batch = GetTensorDim(src_shape, src_format, 'N');
  const int64 channels = GetTensorDim(src_shape, src_format, 'C') *
                         (src_format == FORMAT_NCHW_VECT_C ? 4 : 1);
  const int num_src_spatial_dims =
      GetTensorSpatialDims(src_shape.dims(), src_format);
  std::vector<int64> spatial_dims(num_src_spatial_dims);
  for (int spatial_dim = 0; spatial_dim < num_src_spatial_dims; ++spatial_dim) {
    spatial_dims[spatial_dim] =
        gtl::ArraySlice<int64>(src_shape.dim_sizes())[GetTensorSpatialDimIndex(
            src_shape.dims(), src_format, spatial_dim)];
  }
  if (src_format == FORMAT_NHWC_VECT_W) {
    spatial_dims[num_src_spatial_dims - 1] *= 4;
  }
  return ShapeFromFormat(dst_format, batch, {spatial_dims}, channels);
}

// Returns a copy of the specified filter tensor 'src_shape' converted from
// 'src_filter_format' to 'dst_filter_format'.
inline TensorShape ShapeFromFilterFormat(FilterTensorFormat dst_filter_format,
                                         const TensorShape& src_shape,
                                         FilterTensorFormat src_filter_format) {
  if (src_filter_format == dst_filter_format) {
    return src_shape;
  }

const int64 output_channels = GetFilterDim(src_shape, src_filter_format, 'O');
  const int64 input_channels =
      GetFilterDim(src_shape, src_filter_format, 'I') *
      (src_filter_format == FORMAT_OIHW_VECT_I ? 4 : 1);

if (GetFilterTensorSpatialDims(src_shape.dims(), src_filter_format) == 3) {
    return ShapeFromFilterTensorFormat(
        dst_filter_format,
        {{GetFilterDim(src_shape, src_filter_format, '0'),
          GetFilterDim(src_shape, src_filter_format, '1'),
          GetFilterDim(src_shape, src_filter_format, '2')}},
        input_channels, output_channels);
  }

return ShapeFromFilterTensorFormat(
      dst_filter_format,
      {{GetFilterDim(src_shape, src_filter_format, 'H'),
        GetFilterDim(src_shape, src_filter_format, 'W')}},
      input_channels, output_channels);
}

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_UTIL_TENSOR_FORMAT_H_