/external/tensorflow/tensorflow/lite/toco/graph_transformations/ |
D | resolve_constant_reshape.cc | 45 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 46 if (output_array.data_type == ArrayDataType::kNone) { in Run() 50 if (!output_array.has_shape()) { in Run() 56 if (!ShapesAgreeUpToExtending(input_array.shape(), output_array.shape())) { in Run() 59 ShapeToString(output_array.shape())); in Run() 63 CHECK(!output_array.buffer); in Run() 66 CopyArrayBuffer<ArrayDataType::kBool>(input_array, &output_array); in Run() 69 CopyArrayBuffer<ArrayDataType::kFloat>(input_array, &output_array); in Run() 72 CopyArrayBuffer<ArrayDataType::kInt8>(input_array, &output_array); in Run() 75 CopyArrayBuffer<ArrayDataType::kUint8>(input_array, &output_array); in Run() [all …]
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D | resolve_constant_tile.cc | 73 Array* output_array) { in Tile() argument 75 auto& output_data = output_array->GetMutableBuffer<Type>().data; in Tile() 76 output_data.resize(RequiredBufferSizeForShape(output_array->shape())); in Tile() 83 output_array->GetMutableBuffer<Type>().data.data(), 0); in Tile() 89 output_array->GetMutableBuffer<Type>().data.data(), 0); in Tile() 113 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 114 if (output_array.data_type == ArrayDataType::kNone) { in Run() 118 if (!output_array.has_shape()) { in Run() 134 CopyMinMaxAndQuantizationRelatedFields(input_array, &output_array); in Run() 136 CHECK(!output_array.buffer); in Run() [all …]
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D | resolve_constant_gather.cc | 29 Array* output_array) { in Gather() argument 37 const Shape& output_shape = output_array->shape(); in Gather() 39 output_array->GetMutableBuffer<Type>().data; in Gather() 42 CHECK_EQ(coords_shape.dims(0), output_array->shape().dims(0)); in Gather() 80 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 81 if (output_array.data_type == ArrayDataType::kNone) { in Run() 85 if (!output_array.has_shape()) { in Run() 116 auto& output_minmax = output_array.GetOrCreateMinMax(); in Run() 121 CHECK(!output_array.buffer); in Run() 122 switch (output_array.data_type) { in Run() [all …]
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D | resolve_constant_strided_slice.cc | 29 Array* output_array) { in StridedSlice() argument 36 CHECK(output_array->data_type == Type); in StridedSlice() 47 output_array->GetMutableBuffer<Type>().data; in StridedSlice() 48 output_data.resize(RequiredBufferSizeForShape(output_array->shape())); in StridedSlice() 115 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 116 if (output_array.data_type == ArrayDataType::kNone) { in Run() 121 if (!output_array.has_shape()) { in Run() 142 CHECK(!output_array.buffer); in Run() 143 switch (output_array.data_type) { in Run() 145 StridedSlice<ArrayDataType::kFloat>(*op, input_array, &output_array); in Run() [all …]
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D | resolve_constant_slice.cc | 28 Array* output_array) { in Slice() argument 32 CHECK(output_array->data_type == Type); in Slice() 37 output_array->GetMutableBuffer<Type>().data; in Slice() 38 output_data.resize(RequiredBufferSizeForShape(output_array->shape())); in Slice() 102 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 103 if (output_array.data_type == ArrayDataType::kNone) { in Run() 108 if (!output_array.has_shape()) { in Run() 128 CHECK(!output_array.buffer); in Run() 129 switch (output_array.data_type) { in Run() 131 if (!Slice<ArrayDataType::kFloat>(*op, input_array, &output_array)) { in Run() [all …]
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D | resolve_reorder_axes.cc | 58 const Array& input_array, Array* output_array) { in ReorderAxes() argument 60 DCHECK(!output_array->buffer); in ReorderAxes() 62 auto& output_data = output_array->GetMutableBuffer<DataType>().data; in ReorderAxes() 63 output_data.resize(RequiredBufferSizeForShape(output_array->shape())); in ReorderAxes() 66 Shape output_shape = output_array->shape(); in ReorderAxes() 74 output_array->GetOrCreateMinMax() = input_array.GetMinMax(); in ReorderAxes() 77 output_array->narrow_range = true; in ReorderAxes() 96 auto& output_array = model->GetArray(output_array_name); in Run() local 101 if (!output_array.has_shape()) { in Run() 108 input_array, &output_array); in Run() [all …]
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D | resolve_constant_transpose.cc | 30 const std::vector<int>& perm, Array* output_array) { in Transpose() argument 35 const Shape& output_shape = output_array->shape(); in Transpose() 37 output_array->GetMutableBuffer<Type>().data; in Transpose() 117 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 118 if (output_array.data_type == ArrayDataType::kNone) { in Run() 122 if (!output_array.has_shape()) { in Run() 134 CopyMinMaxAndQuantizationRelatedFields(input_array, &output_array); in Run() 144 CHECK(!output_array.buffer); in Run() 145 switch (output_array.data_type) { in Run() 148 &output_array); in Run() [all …]
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D | propagate_fixed_sizes.cc | 85 Array* output_array) { in ComputeBinaryOperatorOutputSize() argument 93 std::vector<int>* dims_out = output_array->mutable_shape()->mutable_dims(); in ComputeBinaryOperatorOutputSize() 121 CHECK(output_array->has_shape()); in ComputeBinaryOperatorOutputSize() 143 auto& output_array = model->GetArray(op->outputs[0]); in ProcessConvOperator() local 150 output_array.mutable_shape(), in ProcessConvOperator() 152 CHECK_EQ(output_array.shape().dimensions_count(), 4); in ProcessConvOperator() 156 const auto& output_shape = output_array.shape(); in ProcessConvOperator() 240 auto& output_array = model->GetArray(op->outputs[0]); in ProcessTransposeConvOperator() local 241 *(output_array.mutable_shape()->mutable_dims()) = specified_output_shape; in ProcessTransposeConvOperator() 344 auto& output_array = model->GetArray(op->outputs[0]); in ProcessOpWithShapeInput() local [all …]
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D | resolve_multiply_by_zero.cc | 64 auto& output_array = model->GetArray(output_array_name); in Run() local 70 if (output_array.data_type == ArrayDataType::kNone) { in Run() 76 if (!output_array.has_shape()) { in Run() 104 CHECK(constant_input_array.data_type == output_array.data_type); in Run() 105 switch (output_array.data_type) { in Run() 113 FillArrayWithZeros<ArrayDataType::kFloat>(&output_array); in Run() 122 FillArrayWithZeros<ArrayDataType::kUint8>(&output_array); in Run() 131 FillArrayWithZeros<ArrayDataType::kInt32>(&output_array); in Run() 140 FillArrayWithZeros<ArrayDataType::kInt64>(&output_array); in Run() 149 FillArrayWithZeros<ArrayDataType::kComplex64>(&output_array); in Run()
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D | resolve_constant_pack.cc | 28 auto& output_array = model->GetArray(op.outputs[0]); in Pack() local 29 CHECK(output_array.data_type == Type); in Pack() 33 output_array.GetMutableBuffer<Type>().data; in Pack() 34 output_data.resize(RequiredBufferSizeForShape(output_array.shape())); in Pack() 65 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 66 if (output_array.data_type == ArrayDataType::kNone) { in Run() 71 if (!output_array.has_shape()) { in Run() 90 CHECK(!output_array.buffer); in Run() 91 switch (output_array.data_type) { in Run()
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D | resolve_constant_random_uniform.cc | 35 auto& output_array = model->GetArray(op->outputs[0]); in ComputeRandomUniformArray() local 36 CHECK(output_array.data_type == Type); in ComputeRandomUniformArray() 38 output_array.GetMutableBuffer<Type>().data; in ComputeRandomUniformArray() 39 data.resize(RequiredBufferSizeForShape(output_array.shape())); in ComputeRandomUniformArray() 76 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 77 if (output_array.data_type == ArrayDataType::kNone) { in Run() 82 if (!output_array.has_shape()) { in Run() 95 switch (output_array.data_type) { in Run()
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D | hardcode_min_max.cc | 50 auto& output_array = model->GetArray(op->outputs[0]); in HardcodeMinMaxForL2Normalization() local 51 if (output_array.minmax) { in HardcodeMinMaxForL2Normalization() 59 CHECK(!output_array.minmax); in HardcodeMinMaxForL2Normalization() 60 auto& output_minmax = output_array.GetOrCreateMinMax(); in HardcodeMinMaxForL2Normalization() 173 auto& output_array = model->GetArray(op->outputs[0]); in HardcodeMinMaxForAverageOrMaxPool() local 174 if (output_array.minmax) { in HardcodeMinMaxForAverageOrMaxPool() 182 CHECK(!output_array.minmax); in HardcodeMinMaxForAverageOrMaxPool() 183 auto& output_minmax = output_array.GetOrCreateMinMax(); in HardcodeMinMaxForAverageOrMaxPool() 190 auto& output_array = model->GetArray(op->outputs[0]); in HardcodeMinMaxFromFirstInput() local 191 if (output_array.minmax) { in HardcodeMinMaxFromFirstInput() [all …]
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D | resolve_constant_fill.cc | 27 auto& output_array = model->GetArray(op->outputs[0]); in ComputeFillArray() local 30 CHECK(output_array.data_type == Type); in ComputeFillArray() 34 output_array.GetMutableBuffer<Type>().data; in ComputeFillArray() 35 data.resize(RequiredBufferSizeForShape(output_array.shape())); in ComputeFillArray() 58 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 59 if (output_array.data_type == ArrayDataType::kNone) { in Run() 64 if (!output_array.has_shape()) { in Run() 80 switch (output_array.data_type) { in Run()
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D | resolve_constant_shape_or_rank.cc | 33 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 34 if (output_array.data_type == ArrayDataType::kNone) { in Run() 45 if (!output_array.has_shape()) { in Run() 51 CHECK(!output_array.buffer); in Run() 52 auto& output_buffer = output_array.GetMutableBuffer<ArrayDataType::kInt32>(); in Run() 61 output_array.mutable_shape()->ReplaceDims( in Run()
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D | resolve_constant_fake_quant.cc | 98 auto& output_array = model->GetArray(fakequant_op->outputs[0]); in Run() local 100 output_array.data_type = ArrayDataType::kFloat; in Run() 106 output_array.final_data_type = quantized_data_type; in Run() 109 CHECK(!output_array.buffer); in Run() 111 output_array.GetOrCreateMinMax() = *fakequant_op->minmax; in Run() 112 auto& output_buffer = output_array.GetMutableBuffer<ArrayDataType::kFloat>(); in Run() 117 output_array, quantized_data_type, &qparams); in Run() 123 output_array.narrow_range = true; in Run()
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D | propagate_default_min_max.cc | 60 auto& output_array = model->GetArray(output); in Run() local 61 if (!output_array.minmax && !output_array.buffer && in Run() 62 SupportsMinMax(output_array)) { in Run() 63 did_change |= SetArrayMinMax(output, &output_array); in Run()
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D | resolve_constant_range.cc | 24 const Array& delta_array, Array* output_array) { in FillRangeOutput() argument 29 auto& buffer = output_array->GetMutableBuffer<A>(); in FillRangeOutput() 39 CHECK_EQ(buffer.data.size(), output_array->shape().dims()[0]); in FillRangeOutput() 78 auto& output_array = model->GetArray(op->outputs[0]); in Run() local 79 if (output_array.data_type == ArrayDataType::kNone) { in Run() 101 delta_array, &output_array); in Run() 104 delta_array, &output_array); in Run()
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D | remove_trivial_reshape.cc | 38 const auto& output_array = model.GetArray(op.outputs[0]); in IsReshapeTrivial() local 39 if (input_array.has_shape() && output_array.has_shape()) { in IsReshapeTrivial() 41 ShapesAgreeUpToExtending(input_array.shape(), output_array.shape())) { in IsReshapeTrivial() 49 if (input_array.shape().dims() == output_array.shape().dims()) { in IsReshapeTrivial()
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D | propagate_fake_quant_num_bits.cc | 214 auto& output_array = model->GetArray(output); in RecursivelyForwardPropagateDataType() local 215 if (output_array.final_data_type == new_data_type) { in RecursivelyForwardPropagateDataType() 220 if (output_array.final_data_type == ArrayDataType::kNone || in RecursivelyForwardPropagateDataType() 221 output_array.final_data_type != new_data_type) { in RecursivelyForwardPropagateDataType() 224 ArrayDataTypeName(output_array.final_data_type), in RecursivelyForwardPropagateDataType() 226 did_change |= ChangeArrayDataType(transformation, &output_array, in RecursivelyForwardPropagateDataType()
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D | resolve_constant_unary.cc | 99 auto& output_array = model->GetArray(op.outputs[0]); in CopyMinMaxFromFirstInput() local 100 if (output_array.minmax) { in CopyMinMaxFromFirstInput() 108 CHECK(!output_array.minmax); in CopyMinMaxFromFirstInput() 109 auto& output_minmax = output_array.GetOrCreateMinMax(); in CopyMinMaxFromFirstInput() 157 auto& output_array = model->GetArray(unary_op->outputs[0]); in Run() local 158 if (!output_array.has_shape()) { in Run() 209 const Shape& output_shape = output_array.shape(); in Run() 213 output_array.GetMutableBuffer<ArrayDataType::kFloat>().data; in Run()
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/external/tensorflow/tensorflow/lite/toco/ |
D | tooling_util.cc | 109 for (const auto& output_array : model.flags.output_arrays()) { in IsOutputArray() local 110 if (array_name == output_array) { in IsOutputArray() 843 for (const string& output_array : model_flags.output_arrays()) { in CheckInputArraysAreNotOutputArrays() local 844 QCHECK_NE(input_array.name(), output_array) in CheckInputArraysAreNotOutputArrays() 845 << "The array " << output_array in CheckInputArraysAreNotOutputArrays() 886 for (const string& output_array : model_flags.output_arrays()) { in CheckNonAsciiIOArrays() local 887 QCHECK(IsAsciiPrintable(output_array)) in CheckNonAsciiIOArrays() 889 << output_array << ". Pass --allow_nonascii_arrays to allow that. " in CheckNonAsciiIOArrays() 891 << DumpAscii(output_array); in CheckNonAsciiIOArrays() 908 for (const string& output_array : model.flags.output_arrays()) { in CheckNonExistentIOArrays() local [all …]
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/external/tensorflow/tensorflow/lite/toco/tflite/ |
D | export_test.cc | 48 Array& output_array = input_model_.GetOrCreateArray(op->outputs[0]); in AddOperatorsByName() local 51 output_array.data_type = ArrayDataType::kFloat; in AddOperatorsByName() 59 Array& output_array = input_model_.GetOrCreateArray(op->outputs[0]); in AddOperatorsByName() local 62 output_array.data_type = ArrayDataType::kFloat; in AddOperatorsByName() 70 Array& output_array = input_model_.GetOrCreateArray(op->outputs[0]); in AddOperatorsByName() local 73 output_array.data_type = ArrayDataType::kFloat; in AddOperatorsByName() 129 Array& output_array = input_model_.GetOrCreateArray(op->outputs[0]); in BuildQuantizableTestModel() local 132 output_array.data_type = ArrayDataType::kFloat; in BuildQuantizableTestModel() 141 Array& output_array = input_model_.GetOrCreateArray(op->outputs[0]); in BuildQuantizableTestModel() local 144 output_array.data_type = ArrayDataType::kFloat; in BuildQuantizableTestModel() [all …]
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/external/tensorflow/tensorflow/compiler/xla/service/llvm_ir/ |
D | dynamic_update_slice_util.cc | 44 const IrArray& output_array, const gpu::LaunchDimensions* launch_dimensions, in EmitDynamicUpdateSliceInPlaceImpl() argument 46 const Shape& output_shape = output_array.GetShape(); in EmitDynamicUpdateSliceInPlaceImpl() 94 output_array.EmitWriteArrayElement(output_index, update_data, b); in EmitDynamicUpdateSliceInPlaceImpl() 107 const IrArray& output_array, in EmitDynamicUpdateSliceInPlace() argument 116 Shape output_shape = output_array.GetShape(); in EmitDynamicUpdateSliceInPlace() 130 output_array, /*launch_dimensions=*/nullptr, name, b); in EmitDynamicUpdateSliceInPlace()
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/external/tensorflow/tensorflow/python/kernel_tests/ |
D | reduce_join_op_test.py | 82 output_array = _input_array(num_dims).reshape([-1]) 83 self.assertAllEqualUnicode(truth, output_array) 123 output_array = self.evaluate(output) 125 self.assertAllEqualUnicode(truth, output_array) 153 output_array = self.evaluate(output) 158 self.assertAllEqualUnicode(truth_squeezed_array, output_array) 242 output_array = reduced.eval(feed_dict={placeholder.name: input_array}) 243 self.assertAllEqualUnicode(truth, output_array)
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/external/libjpeg-turbo/ |
D | jutils.c | 95 JSAMPARRAY output_array, int dest_row, int num_rows, in jcopy_sample_rows() argument 108 output_array += dest_row; in jcopy_sample_rows() 112 outptr = *output_array++; in jcopy_sample_rows()
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