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/external/tensorflow/tensorflow/lite/kernels/internal/reference/
Dlegacy_reference_ops.h36 inline void ShapeFromDims(const tflite::Dims<4>& dims, RuntimeShape* shape) { in ShapeFromDims()
41 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv()
42 const float* filter_data, const Dims<4>& filter_dims, in DepthwiseConv()
43 const float* bias_data, const Dims<4>& bias_dims, in DepthwiseConv()
49 const Dims<4>& output_dims) { in DepthwiseConv()
68 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv()
69 const float* filter_data, const Dims<4>& filter_dims, in DepthwiseConv()
70 const float* bias_data, const Dims<4>& bias_dims, in DepthwiseConv()
75 const Dims<4>& output_dims) { in DepthwiseConv()
84 void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv()
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Dprocess_broadcast_shapes.h57 if (extended_shape0.Dims(i) == extended_shape1.Dims(i)) { in ProcessBroadcastShapes()
59 } else if (extended_shape0.Dims(i) == 1) { in ProcessBroadcastShapes()
63 } else if (extended_shape1.Dims(i) == 1) { in ProcessBroadcastShapes()
99 while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { in ProcessBroadcastShapes()
100 params->broadcast_shape[4] *= shape_b->Dims(i); in ProcessBroadcastShapes()
105 while (i >= 0 && shape_a->Dims(i) == 1) { in ProcessBroadcastShapes()
106 params->broadcast_shape[3] *= shape_b->Dims(i); in ProcessBroadcastShapes()
109 while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { in ProcessBroadcastShapes()
110 params->broadcast_shape[2] *= shape_a->Dims(i); in ProcessBroadcastShapes()
114 while (i >= 0 && shape_b->Dims(i) == 1) { in ProcessBroadcastShapes()
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Dpooling.h34 const int input_height = input_shape.Dims(1); in AveragePool()
35 const int input_width = input_shape.Dims(2); in AveragePool()
36 const int output_height = output_shape.Dims(1); in AveragePool()
37 const int output_width = output_shape.Dims(2); in AveragePool()
89 const int input_height = input_shape.Dims(1); in AveragePool()
90 const int input_width = input_shape.Dims(2); in AveragePool()
91 const int output_height = output_shape.Dims(1); in AveragePool()
92 const int output_width = output_shape.Dims(2); in AveragePool()
142 const int input_height = input_shape.Dims(1); in L2Pool()
143 const int input_width = input_shape.Dims(2); in L2Pool()
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Dconv.h54 const int input_height = input_shape.Dims(1); in Conv()
55 const int input_width = input_shape.Dims(2); in Conv()
56 const int filter_height = filter_shape.Dims(1); in Conv()
57 const int filter_width = filter_shape.Dims(2); in Conv()
58 const int output_height = output_shape.Dims(1); in Conv()
59 const int output_width = output_shape.Dims(2); in Conv()
134 const int input_height = input_shape.Dims(1); in Conv()
135 const int input_width = input_shape.Dims(2); in Conv()
136 const int filter_height = filter_shape.Dims(1); in Conv()
137 const int filter_width = filter_shape.Dims(2); in Conv()
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Dconcatenation.h45 concat_size += input_shapes[i]->Dims(axis); in Concatenation()
47 TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); in Concatenation()
50 outer_size *= output_shape.Dims(i); in Concatenation()
56 base_inner_size *= output_shape.Dims(i); in Concatenation()
62 const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; in Concatenation()
97 concat_size += input_shapes[i]->Dims(axis); in ConcatenationWithScaling()
99 TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); in ConcatenationWithScaling()
102 outer_size *= output_shape.Dims(i); in ConcatenationWithScaling()
108 base_inner_size *= output_shape.Dims(i); in ConcatenationWithScaling()
115 const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; in ConcatenationWithScaling()
Darg_min_max.h35 const int axis_size = input1_shape.Dims(axis); in ArgMinMax()
39 TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i)); in ArgMinMax()
40 outer_size *= input1_shape.Dims(i); in ArgMinMax()
46 TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i - 1)); in ArgMinMax()
47 inner_size *= input1_shape.Dims(i); in ArgMinMax()
Dreference_ops.h80 const int input_depth = input_shape.Dims(3); in DepthToSpace()
81 const int input_width = input_shape.Dims(2); in DepthToSpace()
82 const int input_height = input_shape.Dims(1); in DepthToSpace()
83 const int input_batch = input_shape.Dims(0); in DepthToSpace()
85 const int output_depth = output_shape.Dims(3); in DepthToSpace()
86 const int output_width = output_shape.Dims(2); in DepthToSpace()
87 const int output_height = output_shape.Dims(1); in DepthToSpace()
88 const int output_batch = output_shape.Dims(0); in DepthToSpace()
133 const int input_depth = input_shape.Dims(3); in SpaceToDepth()
134 const int input_width = input_shape.Dims(2); in SpaceToDepth()
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Ddepthwiseconv_uint8.h120 const int input_height = input_shape.Dims(1); in Run()
121 const int input_width = input_shape.Dims(2); in Run()
122 const int input_depth = input_shape.Dims(3); in Run()
123 const int filter_height = filter_shape.Dims(1); in Run()
124 const int filter_width = filter_shape.Dims(2); in Run()
125 const int output_height = output_shape.Dims(1); in Run()
126 const int output_width = output_shape.Dims(2); in Run()
207 const int input_height = input_shape.Dims(1); in RunPerChannel()
208 const int input_width = input_shape.Dims(2); in RunPerChannel()
209 const int input_depth = input_shape.Dims(3); in RunPerChannel()
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Ddepthwiseconv_float.h46 const int input_height = input_shape.Dims(1); in DepthwiseConv()
47 const int input_width = input_shape.Dims(2); in DepthwiseConv()
48 const int input_depth = input_shape.Dims(3); in DepthwiseConv()
49 const int filter_height = filter_shape.Dims(1); in DepthwiseConv()
50 const int filter_width = filter_shape.Dims(2); in DepthwiseConv()
51 const int output_height = output_shape.Dims(1); in DepthwiseConv()
52 const int output_width = output_shape.Dims(2); in DepthwiseConv()
/external/tensorflow/tensorflow/lite/kernels/internal/
Dtypes.h124 struct Dims { struct
189 inline int32 Dims(int i) const { in Dims() function
302 inline tflite::Dims<4> ToRuntimeDims(const tflite::RuntimeShape& array_shape) { in ToRuntimeDims()
303 tflite::Dims<4> result; in ToRuntimeDims()
309 (i < dimensions_count) ? array_shape.Dims(dimensions_count - 1 - i) : 1; in ToRuntimeDims()
318 inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { in DimsToShape()
389 inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { in Offset()
398 inline int Offset(const Dims<4>& dims, int* index) { in Offset()
411 int ArraySize(const Dims<N>& array, int index) { in ArraySize()
434 TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); in MatchingDim()
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Dtranspose_utils.cc26 *dim0 = input_shape.Dims(0); in IsTranspose2DApplicable()
27 *dim1 = input_shape.Dims(1); in IsTranspose2DApplicable()
45 *dim0 *= input_shape.Dims(i); in IsTranspose2DApplicable()
47 *dim1 *= input_shape.Dims(i); in IsTranspose2DApplicable()
61 if (input_shape->Dims(i) == 1) { in RemoveOneSizeDimensions()
84 if (input_shape->Dims(i) == 1) { in RemoveOneSizeDimensions()
87 input_shape->SetDim(new_dims_cnt, input_shape->Dims(i)); in RemoveOneSizeDimensions()
96 if (output_shape->Dims(i) == 1) { in RemoveOneSizeDimensions()
100 output_shape->SetDim(new_dims_cnt, output_shape->Dims(i)); in RemoveOneSizeDimensions()
130 flat_size /= input_shape.Dims(i); in Flatten()
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Ddepthwiseconv_quantized_test.cc190 << " input_width = " << input_shape.Dims(2) in DispatchDepthwiseConvImpl()
191 << " input_height = " << input_shape.Dims(1) in DispatchDepthwiseConvImpl()
192 << " output_width = " << output_shape.Dims(2) in DispatchDepthwiseConvImpl()
193 << " output_height = " << output_shape.Dims(1); in DispatchDepthwiseConvImpl()
203 /*thread_end=*/output_shape.Dims(1), /*thread_dim=*/1); in DispatchDepthwiseConvImpl()
211 /*thread_end=*/output_shape.Dims(1), /*thread_dim=*/1); in DispatchDepthwiseConvImpl()
237 /*thread_end=*/output_shape.Dims(1), /*thread_dim=*/1); in DispatchDepthwiseConvImpl()
260 << " input_width = " << input_shape.Dims(2) in DispatchDepthwiseConvImpl()
261 << " input_height = " << input_shape.Dims(1) in DispatchDepthwiseConvImpl()
262 << " output_width = " << output_shape.Dims(2) in DispatchDepthwiseConvImpl()
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/external/tensorflow/tensorflow/core/kernels/
Dmirror_pad_op.h95 static constexpr int Dims = internal::array_size<PaddingDimensions>::value;
96 typedef DSizes<Index, Dims> Dimensions;
120 EIGEN_STATIC_ASSERT(Dims > 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
131 for (int dim = 0; dim < Dims; ++dim) {
141 for (int i = 0; i < Dims - 1; ++i) {
146 input_strides_[numext::maxi(0, Dims - 1)] = 1;
147 output_strides_[numext::maxi(0, Dims - 1)] = 1;
148 for (int i = Dims - 1; i > 0; --i) {
174 coeff(array<Index, Dims> coords) const {
175 for (int dim = 0; dim < Dims; ++dim) {
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Dreverse_sequence_op.h28 template <typename T, typename Tlen, size_t Dims>
32 ReverseGenerator(typename TTypes<T, Dims>::ConstTensor input, int32 batch_dim, in ReverseGenerator()
40 operator()(const Eigen::array<Eigen::DenseIndex, Dims>& coords) const { in operator()
41 Eigen::array<Eigen::DenseIndex, Dims> new_coords = coords; in operator()
51 typename TTypes<T, Dims>::ConstTensor input_;
61 template <typename Device, typename T, typename Tlen, size_t Dims>
64 const Device& d, typename TTypes<T, Dims>::ConstTensor input, in Compute()
67 typename TTypes<T, Dims>::Tensor output) { in Compute()
68 generator::ReverseGenerator<T, Tlen, Dims> generator(input, batch_dim, in Compute()
Dargmax_op.h30 #define DECLARE_COMPUTE_SPEC(Dims) \ argument
31 EIGEN_ALWAYS_INLINE static void Reduce##Dims( \
32 const Device& d, typename TTypes<T, Dims>::ConstTensor input, \
33 const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output) { \
50 #define DECLARE_COMPUTE_SPEC(Dims) \ argument
51 EIGEN_ALWAYS_INLINE static void Reduce##Dims( \
52 const Device& d, typename TTypes<T, Dims>::ConstTensor input, \
53 const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output) { \
Dpad_op.cc242 template <int Dims>
244 typename TTypes<T, Dims>::ConstTensor input, in Operate()
247 CHECK_EQ(Dims, paddings.dimension(0)); in Operate()
249 Eigen::array<Eigen::IndexPair<Tpadding>, Dims> paddings_array; in Operate()
250 for (int i = 0; i < Dims; ++i) { in Operate()
253 functor::Pad<Device, T, Tpadding, Dims> functor; in Operate()
254 functor(context->eigen_device<Device>(), output->tensor<T, Dims>(), input, in Operate()
295 #define DECLARE_GPU_SPEC(T, Dims) \ argument
297 void Pad<GPUDevice, T, int32, Dims>::operator()( \
298 const GPUDevice& d, typename TTypes<T, Dims>::Tensor output, \
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/external/tensorflow/tensorflow/lite/kernels/internal/optimized/
Dlegacy_optimized_ops.h80 VectorMap<Scalar> MapAsVector(Scalar* data, const Dims<N>& dims) { in MapAsVector()
87 const Dims<N>& dims) { in MapAsMatrixWithFirstDimAsRows()
98 const Dims<N>& dims) { in MapAsMatrixWithLastDimAsCols()
109 const Dims<N>& dims) { in MapAsArrayWithFirstDimAsRows()
122 const Dims<N>& dims, in MapAsMatrixWithGivenNumberOfRows()
130 inline bool AreSameDims(const Dims<4>& dims1, const Dims<4>& dims2) { in AreSameDims()
139 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv()
140 const float* filter_data, const Dims<4>& filter_dims, in DepthwiseConv()
141 const float* bias_data, const Dims<4>& bias_dims, in DepthwiseConv()
147 const Dims<4>& output_dims) { in DepthwiseConv()
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Dim2col_utils.h136 const int input_height = input_shape.Dims(1); in DilatedIm2col()
137 const int input_width = input_shape.Dims(2); in DilatedIm2col()
139 const int filter_height = filter_shape.Dims(1); in DilatedIm2col()
140 const int filter_width = filter_shape.Dims(2); in DilatedIm2col()
141 const int output_height = output_shape.Dims(1); in DilatedIm2col()
142 const int output_width = output_shape.Dims(2); in DilatedIm2col()
210 const int input_depth = input_shape.Dims(3); in Im2col()
211 const int input_width = input_shape.Dims(2); in Im2col()
212 const int input_height = input_shape.Dims(1); in Im2col()
213 const int output_depth = output_shape.Dims(3); in Im2col()
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/external/eigen/unsupported/Eigen/CXX11/src/Tensor/
DTensorArgMax.h134 template<typename ReduceOp, typename Dims, typename XprType>
135 struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
143 static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
147 template<typename ReduceOp, typename Dims, typename XprType>
148 struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
150 typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>& type;
153 template<typename ReduceOp, typename Dims, typename XprType>
154 struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
155 typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
157 typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
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/external/tensorflow/tensorflow/lite/kernels/internal/reference/integer_ops/
Ddepthwise_conv.h51 const int input_height = input_shape.Dims(1); in DepthwiseConvPerChannel()
52 const int input_width = input_shape.Dims(2); in DepthwiseConvPerChannel()
53 const int input_depth = input_shape.Dims(3); in DepthwiseConvPerChannel()
54 const int filter_height = filter_shape.Dims(1); in DepthwiseConvPerChannel()
55 const int filter_width = filter_shape.Dims(2); in DepthwiseConvPerChannel()
56 const int output_height = output_shape.Dims(1); in DepthwiseConvPerChannel()
57 const int output_width = output_shape.Dims(2); in DepthwiseConvPerChannel()
145 const int input_height = input_shape.Dims(1); in DepthwiseConvHybridPerChannel()
146 const int input_width = input_shape.Dims(2); in DepthwiseConvHybridPerChannel()
147 const int input_depth = input_shape.Dims(3); in DepthwiseConvHybridPerChannel()
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Dfully_connected.h41 const int batches = output_shape.Dims(0); in FullyConnected()
42 const int output_depth = output_shape.Dims(1); in FullyConnected()
43 TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); in FullyConnected()
44 const int accum_depth = filter_shape.Dims(filter_dim_count - 1); in FullyConnected()
83 const int batches = output_shape.Dims(0); in FullyConnected()
84 const int output_depth = output_shape.Dims(1); in FullyConnected()
85 TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); in FullyConnected()
86 const int accum_depth = filter_shape.Dims(filter_dim_count - 1); in FullyConnected()
Dmean.h36 const int output_batch = output_shape.Dims(0); in Mean()
37 const int output_height = output_shape.Dims(1); in Mean()
38 const int output_width = output_shape.Dims(2); in Mean()
39 const int output_depth = output_shape.Dims(3); in Mean()
40 const int input_height = input_shape.Dims(1); in Mean()
41 const int input_width = input_shape.Dims(2); in Mean()
Dpooling.h33 const int input_height = input_shape.Dims(1); in AveragePool()
34 const int input_width = input_shape.Dims(2); in AveragePool()
35 const int output_height = output_shape.Dims(1); in AveragePool()
36 const int output_width = output_shape.Dims(2); in AveragePool()
94 const int input_height = input_shape.Dims(1); in MaxPool()
95 const int input_width = input_shape.Dims(2); in MaxPool()
96 const int output_height = output_shape.Dims(1); in MaxPool()
97 const int output_width = output_shape.Dims(2); in MaxPool()
/external/tensorflow/tensorflow/lite/kernels/internal/optimized/integer_ops/
Dconv.h54 const int filter_width = filter_shape.Dims(2); in ConvPerChannel()
55 const int filter_height = filter_shape.Dims(1); in ConvPerChannel()
84 const int gemm_input_rows = gemm_input_shape->Dims(3); in ConvPerChannel()
86 const int filter_rows = filter_shape.Dims(0); in ConvPerChannel()
88 const int output_rows = output_shape.Dims(3); in ConvPerChannel()
92 output_shape.Dims(0) * output_shape.Dims(1) * output_shape.Dims(2); in ConvPerChannel()
Dtranspose_conv.h34 const int batch_size = input_shape.Dims(0); in TransposeConvV2()
38 const int input_image_size = input_shape.Dims(1) * input_shape.Dims(2); in TransposeConvV2()
39 const int output_height = output_shape.Dims(1); in TransposeConvV2()
40 const int output_width = output_shape.Dims(2); in TransposeConvV2()
49 const int filter_height = hwoi_ordered_filter_shape.Dims(0); in TransposeConvV2()
50 const int filter_width = hwoi_ordered_filter_shape.Dims(1); in TransposeConvV2()

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