| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| index_ops.cc | 49 const int input_dims = input_shape.dims(); local
50 const int axis = dim < 0 ? dim + input_dims : dim;
53 ctx, axis >= 0 && axis < input_dims,
54 errors::InvalidArgument("Expected dimension in the range [", -input_dims,
55 ", ", input_dims, "), but got ", dim));
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| slice_op.cc | 54 const int input_dims = input_shape.dims(); variable
61 for (int i = 0; i < input_dims; ++i) {
68 for (int i = 0; i < input_dims; ++i) {
99 for (int i = 0; i < input_dims; ++i) {
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| tile_ops.cc | 52 const int input_dims = input_shape.dims(); variable
56 if (input_dims == 0) {
66 std::vector<int64> multiples_array(input_dims);
70 for (int i = 0; i < input_dims; ++i) {
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| concat_op.cc | 61 const int input_dims = shapes[0].dims(); variable
64 int32 axis = concat_dim < 0 ? concat_dim + input_dims : concat_dim;
66 (0 <= axis && axis < input_dims) ||
71 -input_dims, ", ", input_dims, "), but got ", concat_dim));
84 in_shape.dims() == input_dims || (input_is_scalar && in_is_scalar),
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| /external/tensorflow/tensorflow/core/kernels/ |
| sparse_slice_op.cc | 58 const int input_dims = input_shape.NumElements(); variable
59 OP_REQUIRES(context, input_dims == input_start.NumElements(),
61 "Expected start to be a vector of length ", input_dims,
64 OP_REQUIRES(context, input_dims == input_size.NumElements(),
66 "Expected size to be a vector of length ", input_dims,
73 input_dims);
75 input_dims);
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| argmax_op.cc | 58 const int input_dims = input.dims(); variable
60 int axis = dim < 0 ? dim + input_dims : dim;
62 OP_REQUIRES(context, axis >= 0 && axis < input_dims,
64 -input_dims, ", ", input_dims,
73 for (int d = 0; d < input_dims - 1; ++d) {
86 switch (input_dims) {
96 "ArgOp : Unhandled input dimensions: ", input_dims));
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| reduce_join_op.cc | 77 int32 input_dims) {
84 reduced_indices[i] += reduced_indices[i] < 0 ? input_dims : 0;
92 int32 input_dims,
94 for (int32 index = 0; index < input_dims; ++index) {
127 const int32 input_dims = input_shape.dims(); local
133 gtl::InlinedVector<bool, 8> index_is_reduced(input_dims, false);
137 reduce_index < 0 ? reduce_index + input_dims : reduce_index;
139 context, reduce_index >= -input_dims && reduce_index < input_dims,
141 " for input with ", input_dims, " dimension(s)"))
[all...] |
| reverse_sequence_op.cc | 131 const int input_dims = input.dims(); variable
144 switch (input_dims) {
154 input_dims));
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| concat_op.cc | 70 const int input_dims = values[0].dims(); variable
73 int32 axis = concat_dim < 0 ? concat_dim + input_dims : concat_dim;
75 (0 <= axis && axis < input_dims) ||
80 -input_dims, ", ", input_dims, "), but got ", concat_dim));
98 c, in.dims() == input_dims || (input_is_scalar && in_is_scalar),
103 for (int j = 0; j < input_dims; ++j) {
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| mirror_pad_op.h | 131 const auto& input_dims = impl_.dimensions(); local
136 input_strides_[i + 1] = input_strides_[i] * input_dims[i];
143 input_strides_[i - 1] = input_strides_[i] * input_dims[i];
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| reverse_op.cc | 166 const int input_dims = input.dims(); variable
172 context, input_dims == dims.dim_size(0),
176 input_dims, "'dims' has ", dims.dim_size(0), " values"));
177 OP_REQUIRES(context, input_dims <= 8,
190 switch (input_dims) {
243 const int input_dims = input.dims(); variable
250 gtl::InlinedVector<bool, 8> axes_dense(input_dims, false);
253 Tidx canonical_axis = axis < 0 ? input_dims + axis : axis;
254 OP_REQUIRES(context, canonical_axis >= 0 && canonical_axis < input_dims,
257 input_dims - 1))
[all...] |
| strided_slice_op.cc | 129 const int input_dims = input.dims(); variable
140 input_dims == 2 && processing_shape.dims() == 2 &&
168 errors::Unimplemented("Unhandled input dimensions ", input_dims));
234 // const int input_dims = input.dims();
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| where_op.cc | 137 const int input_dims = input.dims(); variable
148 TensorShape output_shape({num_true_t(), input_dims});
166 switch (input_dims) {
176 "WhereOp : Unhandled input dimensions: ", input_dims));
256 const int input_dims = input.dims(); variable
259 ComputeAsyncType<int32>(input, input_dims, context, done);
261 ComputeAsyncType<int64>(input, input_dims, context, done);
266 void ComputeAsyncType(const Tensor& input, const int input_dims,
301 auto create_and_check_output = [context, &d, &input, input_dims,
319 0, TensorShape({num_true, input_dims}), &output)
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| reverse_test.cc | 40 tensorflow::gtl::ArraySlice<int64> input_dims; member in struct:xla::__anon39099::ReverseSpec
47 tensorflow::str_util::Join(input_dims, "x").c_str(),
83 ShapeUtil::ElementsIn(ShapeUtil::MakeShape(F32, spec.input_dims)));
86 auto input_literal = r1_literal->Reshape(spec.input_dims).ConsumeValueOrDie();
93 std::vector<int64> output_indices(spec.input_dims.size());
101 output_indices[dim] = (spec.input_dims[dim] - 1) - indices[dim];
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| slice_test.cc | 376 std::array<int64, 4> input_dims; member in struct:xla::__anon39108::R4Spec
386 "input_", Join(spec.input_dims, "x"), //
398 Array4D<float> values(spec.input_dims[0], spec.input_dims[1],
399 spec.input_dims[2], spec.input_dims[3]);
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| convolution_test.cc | 421 std::vector<int64> input_dims = {1, 4, 2, 3, 3}; local
423 Shape input_shape = ShapeUtil::MakeShape(F32, input_dims);
454 auto input_r5 = input_r1->Reshape(input_dims).ConsumeValueOrDie();
488 std::vector<int64> input_dims = {1, 3, 3, 5}; local
490 Shape input_shape = MakeShapeWrapper<T>(input_dims);
518 auto input_r4 = input_r1->Reshape(input_dims).ConsumeValueOrDie();
611 std::vector<int64> input_dims = {batch, window_size + num_windows - 1, local
615 Shape input_shape = MakeShapeWrapper<T>(input_dims);
640 auto input_r3 = input_r1->Reshape(input_dims).ConsumeValueOrDie();
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| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| softmax_test.cc | 95 static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, local
97 tflite::reference_ops::Softmax(input_buffer, input_dims, beta,
98 output_buffer.get(), input_dims);
123 static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, local
125 tflite::reference_ops::Softmax(input_buffer, input_dims, beta,
126 output_buffer.get(), input_dims);
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| squeeze.cc | 48 const TfLiteIntArray* input_dims = op_context.input->dims; local
55 if (input_dims->data[idx] == 1) {
65 input_dims->data[current] == 1);
75 output_dims->data[out_idx++] = input_dims->data[in_idx];
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| transpose_test.cc | 55 Dims<4> input_dims = GetTensorDims(shape); local
58 output_dims.sizes[i] = input_dims.sizes[reversed_perms[i]];
66 reference_ops::Transpose<float>(input.data(), input_dims,
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| mean.cc | 71 const TfLiteIntArray* input_dims = op_context->input->dims; local
87 output_dims->data[idx] = input_dims->data[idx];
125 output_dims->data[idx - num_skip_axis] = input_dims->data[idx];
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| /external/eigen/unsupported/Eigen/CXX11/src/Tensor/ |
| TensorBroadcasting.h | 122 const InputDimensions& input_dims = m_impl.dimensions(); local
125 eigen_assert(input_dims[i] > 0);
126 m_dimensions[i] = input_dims[i] * broadcast[i];
133 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
140 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
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| TensorInflation.h | 112 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); local
118 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
125 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
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| TensorPatch.h | 105 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); local
110 num_patches *= (input_dims[i] - patch_dims[i] + 1);
117 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
118 m_patchStrides[i] = m_patchStrides[i-1] * (input_dims[i-1] - patch_dims[i-1] + 1);
127 num_patches *= (input_dims[i] - patch_dims[i] + 1);
134 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
135 m_patchStrides[i] = m_patchStrides[i+1] * (input_dims[i+1] - patch_dims[i+1] + 1);
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| TensorShuffling.h | 122 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); local
125 m_dimensions[i] = input_dims[shuffle[i]];
134 inputStrides[i] = inputStrides[i - 1] * input_dims[i - 1];
141 inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1];
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| TensorStriding.h | 127 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); local
133 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
142 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
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