| /external/eigen/unsupported/Eigen/CXX11/src/Tensor/ |
| TensorDimensionList.h | 20 * \brief Special case of tensor index list used to list all the dimensions of a tensor of rank n.
25 template <typename Index, std::size_t Rank> struct DimensionList {
32 template<typename Index, std::size_t Rank> struct array_size<DimensionList<Index, Rank> > {
33 static const size_t value = Rank;
35 template<typename Index, std::size_t Rank> struct array_size<const DimensionList<Index, Rank> > {
36 static const size_t value = Rank;
39 template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(DimensionList<Index, Rank>&)
[all...] |
| TensorIO.h | 18 template <typename Tensor, int Rank>
69 static const int rank = internal::array_size<Dimensions>::value; local
70 internal::TensorPrinter<Evaluator, rank>::run(os, tensor);
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| /external/tensorflow/tensorflow/core/ops/ |
| ragged_math_ops.cc | 45 // Check that all inputs (starts, limits, and deltas) have rank 0 or 1.
53 // For the inputs with rank 1, make sure shapes match.
55 if (c->Rank(starts) == 1) {
58 if (c->Rank(limits) == 1) {
61 if (c->Rank(deltas) == 1) {
69 } else if (c->Rank(starts) == 0 && c->Rank(limits) == 0 &&
70 c->Rank(deltas) == 0) {
75 // `rt_dense_values` is rank 1, but size can't be calculated statically.
|
| rpc_ops.cc | 30 if (c->Rank(address) == 1) {
34 if (c->Rank(method) == 1) {
38 if (c->Rank(request) == 1) {
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| logging_ops.cc | 55 if (c->Rank(c->input(0)) != 0) {
56 return errors::InvalidArgument("input must be a scalar, but has rank: ",
57 c->Rank(c->input(0)));
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| ragged_conversion_ops.cc | 55 // Check that all rt_nested_splits have rank 1.
63 ? c->MakeDim(c->Rank(rt_dense_values) + num_splits)
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| set_ops.cc | 57 const int32 input0_rank = c->Rank(input0_shape);
63 const int32 rank = c->Rank(input1_shape);
66 c->Subshape(input0_shape, 0, rank - 1, &group0_shape));
69 c->Subshape(input1_shape, 0, rank - 1, &group1_shape));
79 output_rank = c->MakeDim(c->Rank(input1_shape));
119 const int32 input0_rank = c->Rank(input0_shape);
168 return errors::InvalidArgument("Input 0, expected rank >= 2, got ",
177 return errors::InvalidArgument("Input 1, expected rank >= 2, got ",
|
| tpu_cross_replica_ops.cc | 35 int64 rank;
37 rank = c->Rank(input);
39 return errors::InvalidArgument("input's rank is unknown.");
46 if (concat_dimension < 0 || concat_dimension >= rank) {
48 " is out of range of input rank ", rank);
52 if (split_dimension < 0 || split_dimension >= rank) {
54 " is out of range of input rank ", rank);
[all...] |
| control_flow_ops.cc | 96 int32 rank = c->Rank(out); local
99 if (!c->RankKnown(input) || c->Rank(input) != rank) {
104 for (int d = 0; d < rank; ++d) {
|
| batch_ops.cc | 101 c->set_output(0, c->UnknownShapeOfRank(c->Rank(c->input(2))));
|
| string_ops.cc | 152 if (c->Rank(c->input(i)) != 0) all_scalar = false;
161 // Don't merge inputs with unknown rank, as they can actually be scalars
165 if (c->RankKnown(c->input(i)) && c->Rank(c->input(i)) != 0) {
242 // Check that pos/len have same rank
243 TF_RETURN_IF_ERROR(c->WithRank(pos_shape, c->Rank(len_shape), &unused));
245 for (int32 i = 0; i < c->Rank(pos_shape); ++i) {
272 // Check rank of inner values
277 // Check rank of input_splits
|
| /external/llvm/include/llvm/Transforms/Scalar/ |
| Reassociate.h | 15 // In the implementation of this algorithm, constants are assigned rank = 0,
16 // function arguments are rank = 1, and other values are assigned ranks
18 // (starting at 2), which effectively gives values in deep loops higher rank
38 unsigned Rank;
40 ValueEntry(unsigned R, Value *O) : Rank(R), Op(O) {}
43 return LHS.Rank > RHS.Rank; // Sort so that highest rank goes to start.
|
| /external/swiftshader/third_party/llvm-7.0/llvm/include/llvm/Transforms/Scalar/ |
| Reassociate.h | 15 // In the implementation of this algorithm, constants are assigned rank = 0,
16 // function arguments are rank = 1, and other values are assigned ranks
18 // (starting at 2), which effectively gives values in deep loops higher rank
48 unsigned Rank;
51 ValueEntry(unsigned R, Value *O) : Rank(R), Op(O) {}
55 return LHS.Rank > RHS.Rank; // Sort so that highest rank goes to start.
|
| /external/tensorflow/tensorflow/core/grappler/utils/ |
| symbolic_shapes.h | 30 // Shape is symbolically defined, if it has a known rank, and each dimension is
35 // Returns the rank of the shape ir -1 if unknown
36 int Rank(const TensorShapeProto& shape);
43 // Shapes are symbolically equal, if they have the same rank, they are known or
|
| symbolic_shapes_test.cc | 114 EXPECT_EQ(2, Rank(MakeShape({32, 32})));
116 EXPECT_EQ(2, Rank(MakeShape({-2, 32})));
120 EXPECT_EQ(-1, Rank(shape));
|
| /external/tensorflow/tensorflow/contrib/reduce_slice_ops/ops/ |
| reduce_slice_ops.cc | 33 // "data" must have rank at least 1
35 // "indices" must have have rank 1 or rank 2 with the number of columns must
40 if (c->Rank(c->input(1)) == 1) {
46 } else { // c->Rank(c->input(1)) == 2
55 c->set_output(0, c->UnknownShapeOfRank(c->Rank(c->input(0))));
95 The data must be at least rank 1. The indices must be of shape (?,2) where the
140 The data must be at least rank 1. The indices can be of shape (?,2) where the
197 The data must be at least rank 1. The indices can be of shape (?,2) where the
254 The data must be at least rank 1. The indices can be of shape (?,2) where th
[all...] |
| /external/llvm/lib/Transforms/Instrumentation/ |
| CFGMST.h | 62 // Make the smaller rank tree a direct child or the root of high rank tree.
63 if (BB1G->Rank < BB2G->Rank)
68 if (BB1G->Rank == BB2G->Rank)
69 BB1G->Rank++;
|
| /external/swiftshader/third_party/llvm-7.0/llvm/lib/Transforms/Instrumentation/ |
| CFGMST.h | 69 // Make the smaller rank tree a direct child or the root of high rank tree.
70 if (BB1G->Rank < BB2G->Rank)
75 if (BB1G->Rank == BB2G->Rank)
76 BB1G->Rank++;
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/ops/ |
| model_ops.cc | 97 if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0 &&
132 if (c->RankKnown(c->input(1)) && c->Rank(c->input(1)) > 0 &&
|
| /external/swiftshader/third_party/LLVM/lib/Transforms/Scalar/ |
| Reassociate.cpp | 15 // In the implementation of this algorithm, constants are assigned rank = 0,
16 // function arguments are rank = 1, and other values are assigned ranks
18 // (starting at 2), which effectively gives values in deep loops higher rank
50 unsigned Rank;
52 ValueEntry(unsigned R, Value *O) : Rank(R), Op(O) {}
55 return LHS.Rank > RHS.Rank; // Sort so that highest rank goes to start.
69 dbgs() << ", #" << Ops[i].Rank << "] ";
174 return 0; // Otherwise it's a global or constant, rank 0
[all...] |
| /external/tensorflow/tensorflow/compiler/tf2xla/ops/ |
| xla_ops.cc | 27 // Helper shape function for operators that return an output with the same rank
31 c->set_output(0, c->UnknownShapeOfRank(c->Rank(c->input(0))));
50 Broadcasts `lhs` and `rhs` to the same rank, by adding size 1 dimensions to
51 whichever of `lhs` and `rhs` has the lower rank, using XLA's broadcasting rules
195 dimension -- [start, start + size). The shape of start_indices must have rank 1,
196 with dimension size equal to the rank of operand.
200 start_indices: Rank 1 tensor of N integers containing the starting indices of
225 of indices must be rank == 1, with dimension size equal to the rank of `input`.
230 indices: A vector of indices into `input`. Must have length equal to the rank o
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses.py | 39 labels: Rank 2 (N, 1) tensor of per-example labels.
40 weights: Rank 2 (N, 1) tensor of per-example weights.
41 predictions: Rank 2 (N, 1) tensor of per-example predictions.
44 loss: A Rank 2 (N, 1) tensor of per-example logistic loss.
69 labels: Rank 2 (N, D) tensor of per-example labels.
70 weights: Rank 2 (N, 1) tensor of per-example weights.
71 predictions: Rank 2 (N, D) tensor of per-example predictions.
75 loss: A Rank 2 (N, 1) tensor of per-example quantile loss.
104 labels: Rank 2 (N, 1) or Rank 1 (N) tensor of per-example labels
[all...] |
| /external/tensorflow/tensorflow/contrib/fused_conv/ops/ |
| fused_conv2d_bias_activation_op.cc | 64 const int rank =
67 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), rank, &filter_shape));
89 if (c->Rank(side_input_shape) > 1) {
|
| /external/tensorflow/tensorflow/contrib/periodic_resample/ops/ |
| array_ops.cc | 40 const int rank = c->Rank(input_tensor_shape);
41 std::vector<tensorflow::int64> target_dimensions(rank);
44 for (int i = 0; i < rank; ++i) {
56 for (int i = 0; i < rank; ++i) {
73 `T` = `values` tensor of rank `R`
77 `P` = `output` tensor of rank `R`
127 values: The tensor of rank `R` to periodic_resample
|
| /external/tensorflow/tensorflow/core/framework/ |
| shape_inference_testutil.cc | 147 return Unknown(err_prefix, " expected rank ", expected_dims.size(),
150 if (c.Rank(out) != expected_dims.size()) {
151 return Unknown(err_prefix, " expected rank ", expected_dims.size(),
152 " but was ", c.Rank(out), err_suffix);
162 for (int j = 0; j < c.Rank(in); ++j) {
|
