Function ShapeFromFormat

tensorflow/core/util/tensor_format.h:521–548

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.siz

// 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,