Method Compute

tensorflow/core/kernels/sparse_concat_op.cc:41–151

  }

  void Compute(OpKernelContext* context) override {
    OpInputList inds;
    OP_REQUIRES_OK(context, context->input_list("indices", &inds));
    const int N = inds.size();
    for (int i = 0; i < N; i++) {
      OP_REQUIRES(context, TensorShapeUtils::IsMatrix(inds[i].shape()),
                  errors::InvalidArgument(
                      "Input indices should be a matrix but received shape ",
                      inds[i].shape().DebugString(), " at position ", i));
    }

    OpInputList vals;
    OP_REQUIRES_OK(context, context->input_list("values", &vals));
    OP_REQUIRES(context, vals.size() == N,
                errors::InvalidArgument("Expected ", N, " input values, got ",
                                        vals.size()));
    for (int i = 0; i < N; i++) {
      OP_REQUIRES(context, TensorShapeUtils::IsVector(vals[i].shape()),
                  errors::InvalidArgument(
                      "Input values should be a vector but received shape ",
                      vals[i].shape().DebugString(), " at position ", i));
    }

    OpInputList shapes;
    OP_REQUIRES_OK(context, context->input_list("shapes", &shapes));
    OP_REQUIRES(context, shapes.size() == N,
                errors::InvalidArgument("Expected ", N, " input shapes, got ",
                                        shapes.size()));
    for (int i = 0; i < N; i++) {
      OP_REQUIRES(context, TensorShapeUtils::IsVector(shapes[i].shape()),
                  errors::InvalidArgument(
                      "Input shapes should be a vector but received shape ",
                      shapes[i].shape().DebugString(), " at position ", i));
    }

    const TensorShape input_shape(shapes[0].vec<int64>());
    const int input_rank = input_shape.dims();
    const int concat_dim = (concat_dim_attr_ < 0)
                               ? input_rank + concat_dim_attr_
                               : concat_dim_attr_;
    OP_REQUIRES(context, concat_dim >= 0 && concat_dim < input_rank,
                errors::InvalidArgument("Concat dimension must be in range [",
                                        -input_rank, ", ", input_rank,
                                        "), got ", concat_dim_attr_));
    for (int i = 1; i < N; ++i) {
      const TensorShape current_shape(shapes[i].vec<int64>());
      OP_REQUIRES(
          context, current_shape.dims() == input_rank,
          errors::InvalidArgument(
              "Ranks of all input tensors must match: expected ", input_rank,
              " but got ", current_shape.dims(), " at position ", i));
      for (int j = 0; j < input_rank; ++j) {
        if (j != concat_dim) {
          OP_REQUIRES(
              context, input_shape.dim_size(j) == current_shape.dim_size(j),
              errors::InvalidArgument(
                  "Input shapes must match: expected ", input_shape.dim_size(j),
                  " for dimension ", j, " but got ", current_shape.dim_size(j),