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/external/tensorflow/tensorflow/lite/toco/graph_transformations/
Dpropagate_array_data_types.cc30 model->GetArray(output).data_type = data_type; in SetDataTypeForAllOutputs()
45 model->GetArray(input).data_type == ArrayDataType::kNone) { in Run()
53 old_output_data_types[output] = model->GetArray(output).data_type; in Run()
90 const ArrayDataType data_type = model->GetArray(op->inputs[1]).data_type; in Run()
97 const ArrayDataType data_type = model->GetArray(op->inputs[0]).data_type; in Run()
104 const ArrayDataType data_type = model->GetArray(op->inputs[2]).data_type; in Run()
112 model->GetArray(op->outputs[0]).data_type = cast_op->dst_data_type; in Run()
119 model->GetArray(op->outputs[0]).data_type = argmax_op->output_data_type; in Run()
126 model->GetArray(op->outputs[0]).data_type = argmin_op->output_data_type; in Run()
139 data_type = model->GetArray(op->inputs[0]).data_type; in Run()
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Dpropagate_fixed_sizes.cc126 const auto& input_array = model->GetArray(op->inputs[0]); in ProcessConvOperator()
136 const auto& weights_array = model->GetArray(op->inputs[1]); in ProcessConvOperator()
144 auto& output_array = model->GetArray(op->outputs[0]); in ProcessConvOperator()
159 auto& im2col_array = model->GetArray(op->outputs[1]); in ProcessConvOperator()
178 model->GetArray(op->inputs[TransposeConvOperator::OUTPUT_SHAPE]); in ProcessTransposeConvOperator()
198 model->GetArray(op->inputs[TransposeConvOperator::WEIGHTS]); in ProcessTransposeConvOperator()
225 model->GetArray(op->inputs[TransposeConvOperator::DATA_INPUT]); in ProcessTransposeConvOperator()
241 auto& output_array = model->GetArray(op->outputs[0]); in ProcessTransposeConvOperator()
247 auto& im2col_array = model->GetArray(op->outputs[1]); in ProcessTransposeConvOperator()
255 const auto& input_array = model->GetArray(op->inputs[0]); in ProcessDepthwiseConvOperator()
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Dhardcode_min_max.cc33 auto& im2col_array = model->GetArray(op->outputs[1]); in HardcodeMinMaxForIm2colArray()
37 const auto& input_array = model->GetArray(op->inputs[0]); in HardcodeMinMaxForIm2colArray()
50 auto& output_array = model->GetArray(op->outputs[0]); in HardcodeMinMaxForL2Normalization()
54 const auto& input_array = model->GetArray(op->inputs[0]); in HardcodeMinMaxForL2Normalization()
67 auto& input = model->GetArray(op->inputs[0]); in HardcodeInputMinMaxFromOutput()
74 auto& output = model->GetArray(op->outputs[0]); in HardcodeInputMinMaxFromOutput()
76 const auto* minmax = model->GetArray(op->outputs[0]).minmax.get(); in HardcodeInputMinMaxFromOutput()
92 if (model->GetArray(input).minmax) { in HardcodeMinMaxForConcatenation()
94 const auto* minmax = model->GetArray(input).minmax.get(); in HardcodeMinMaxForConcatenation()
101 auto& output = model->GetArray(op->outputs[0]); in HardcodeMinMaxForConcatenation()
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Didentify_lstm_merge_inputs.cc60 int num_cell = model->GetArray(src_op->inputs[kInputToInputWeightsTensor]) in Run()
63 int num_input = model->GetArray(src_op->inputs[kInputToInputWeightsTensor]) in Run()
67 model->GetArray(src_op->inputs[kRecurrentToInputWeightsTensor]) in Run()
90 model->GetArray(src_op->inputs[kInputToInputWeightsTensor]), 0, 0); in Run()
93 model->GetArray(src_op->inputs[kInputToCellWeightsTensor]), num_cell, 0); in Run()
96 model->GetArray(src_op->inputs[kInputToForgetWeightsTensor]), in Run()
100 model->GetArray(src_op->inputs[kInputToOutputWeightsTensor]), in Run()
104 model->GetArray(src_op->inputs[kRecurrentToInputWeightsTensor]), 0, in Run()
108 model->GetArray(src_op->inputs[kRecurrentToCellWeightsTensor]), num_cell, in Run()
112 model->GetArray(src_op->inputs[kRecurrentToForgetWeightsTensor]), in Run()
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Dmerge_reshape_into_preceding_transpose.cc39 if (!model.GetArray(op->inputs[0]).has_shape() || in OperatorReady()
40 !model.GetArray(op->outputs[0]).has_shape()) { in OperatorReady()
45 if (!model.GetArray(op->inputs[1]).buffer) { in OperatorReady()
60 const auto& input_array = model.GetArray(op->inputs[0]); in ReshapeToTranspose()
61 const auto& output_array = model.GetArray(op->outputs[0]); in ReshapeToTranspose()
180 model->GetArray(transpose_op->inputs[1]) in Run()
186 model->GetArray(transpose_op->outputs[0]).clear_shape(); in Run()
Dmove_binary_operator_before_reshape.cc99 model->GetArray(binary_op->inputs[variable_input_idx]); in Run()
107 model->GetArray(binary_op->inputs[constant_input_idx]).shape(), in Run()
108 model->GetArray(binary_op->inputs[variable_input_idx]).shape())) { in Run()
127 const auto& reshape_input_array = model->GetArray(reshape_op->inputs[0]); in Run()
136 model->GetArray(binary_op->inputs[constant_input_idx]).shape(), in Run()
137 model->GetArray(reshape_op->outputs[0]).shape())) { in Run()
176 model->GetArray(binary_op->outputs[0]).clear_shape(); in Run()
Dfuse_binary_into_following_affine.cc45 const auto& weights = model->GetArray(following_op->inputs[1]); in FuseAddOrSubParamsIntoFollowingAffine()
46 auto& bias = model->GetArray(following_op->inputs[2]); in FuseAddOrSubParamsIntoFollowingAffine()
49 model->GetArray(add_or_sub_op->inputs[index_of_constant_input]); in FuseAddOrSubParamsIntoFollowingAffine()
125 auto& weights = model->GetArray(weights_name); in FuseMulOrDivParamsIntoFollowingAffine()
129 model->GetArray(mul_or_div_op->inputs[index_of_constant_input]); in FuseMulOrDivParamsIntoFollowingAffine()
203 model->GetArray(binary_op->inputs[index_of_constant_input]).shape(); in Run()
252 const auto& weights = model->GetArray(following_op->inputs[1]); in Run()
253 const auto& bias = model->GetArray(following_op->inputs[2]); in Run()
Dresolve_constant_binary.cc71 const auto& input0_array = model->GetArray(binary_op->inputs[0]); in EvaluateBinaryOperatorOnConstantInputs()
72 const auto& input1_array = model->GetArray(binary_op->inputs[1]); in EvaluateBinaryOperatorOnConstantInputs()
74 auto& output_array = model->GetArray(output_name); in EvaluateBinaryOperatorOnConstantInputs()
170 const auto inputs_data_type = model->GetArray(binary_op->inputs[0]).data_type; in EvaluateBinaryOperatorOnConstantInputs()
172 model->GetArray(binary_op->outputs[0]).data_type; in EvaluateBinaryOperatorOnConstantInputs()
214 const auto& input0_array = model->GetArray(binary_op->inputs[0]); in Run()
215 const auto& input1_array = model->GetArray(binary_op->inputs[1]); in Run()
221 auto& output_array = model->GetArray(binary_op->outputs[0]); in Run()
Dconvert_matrix_diag_v2_or_v3_to_v1.cc41 const auto& input_k = model->GetArray(op->inputs[1]); in Run()
42 const auto& input_num_rows = model->GetArray(op->inputs[2]); in Run()
43 const auto& input_num_cols = model->GetArray(op->inputs[3]); in Run()
44 const auto& input_padding_value = model->GetArray(op->inputs[4]); in Run()
Dresolve_constant_fill.cc26 const auto& val_array = model->GetArray(op->inputs[1]); in ComputeFillArray()
27 auto& output_array = model->GetArray(op->outputs[0]); in ComputeFillArray()
58 auto& output_array = model->GetArray(op->outputs[0]); in Run()
69 const auto& val_array = model->GetArray(op->inputs[1]); in Run()
Dresolve_constant_pack.cc28 auto& output_array = model->GetArray(op.outputs[0]); in Pack()
41 const auto& input_array = model->GetArray(op.inputs[i]); in Pack()
65 auto& output_array = model->GetArray(op->outputs[0]); in Run()
86 axis += model->GetArray(op->inputs[0]).shape().dims().size(); in Run()
Dreorder_reshape_transpose.cc36 if (!model.GetArray(op->inputs[0]).has_shape() || in OperatorReady()
37 !model.GetArray(op->outputs[0]).has_shape()) { in OperatorReady()
42 if (!model.GetArray(op->inputs[1]).buffer) { in OperatorReady()
161 const auto& input_array = model->GetArray(input_name); in Run()
162 const auto& intermediate_array = model->GetArray(intermediate_name); in Run()
163 const auto& output_array = model->GetArray(output_name); in Run()
Didentify_lstm_split_inputs.cc56 if (!model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) in Run()
65 int num_input = model->GetArray(curr_op->inputs[LstmCellOperator::DATA_INPUT]) in Run()
71 model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) in Run()
88 model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]); in Run()
136 model->GetArray(curr_op->inputs[LstmCellOperator::BIASES_INPUT]); in Run()
Dresolve_constant_range.cc55 const auto& start_array = model->GetArray(op->inputs[0]); in Run()
60 const auto& limit_array = model->GetArray(op->inputs[1]); in Run()
65 const auto& delta_array = model->GetArray(op->inputs[2]); in Run()
79 auto& output_array = model->GetArray(op->outputs[0]); in Run()
Dconvert_squeeze_to_reshape.cc45 const auto& input_array = model->GetArray(squeeze_op->inputs[0]); in Run()
55 !model->GetArray(squeeze_op->outputs[0]).has_shape()) { in Run()
61 const auto& output_shape = model->GetArray(squeeze_op->outputs[0]).shape(); in Run()
Dresolve_strided_slice_attributes.cc56 const auto& input_array = model->GetArray(op->inputs[0]); in Run()
62 auto& start_array = model->GetArray(op->inputs[1]); in Run()
69 auto& stop_array = model->GetArray(op->inputs[2]); in Run()
72 auto& stride_array = model->GetArray(op->inputs[3]); in Run()
Dconvert_trivial_tile_to_concat.cc34 const auto& input_array = model->GetArray(tile_op->inputs[0]); in Run()
35 const auto& multiples_array = model->GetArray(tile_op->inputs[1]); in Run()
36 const auto& output_array = model->GetArray(tile_op->outputs[0]); in Run()
Dquantize.cc42 const auto& array = model->GetArray(op.outputs[0]); in SupportsQuantization()
125 auto& array = model->GetArray(array_name); in GetOrComputeMinMax()
229 auto& array = model->GetArray(input); in ChooseQuantizationForOperatorInput()
259 model->GetArray(op.inputs[activations_input_index]); in ChooseQuantizationForOperatorInput()
260 const auto& input_weights = model->GetArray(op.inputs[weights_input_index]); in ChooseQuantizationForOperatorInput()
385 auto& array = model->GetArray(output); in ChooseQuantizationForOperatorOutput()
392 *quantized_data_type = model->GetArray(op.inputs[0]).data_type; in ChooseQuantizationForOperatorOutput()
415 const auto& input_array = model->GetArray(op.inputs[data_input_index]); in ChooseQuantizationForOperatorOutput()
526 const auto& input_array = model->GetArray(input); in Run()
543 const auto& array = model->GetArray(input); in Run()
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Dfuse_binary_into_preceding_affine.cc39 auto& bias = model->GetArray(preceding_op->inputs[2]); in FuseAddOrSubParamsIntoPrecedingAffine()
42 model->GetArray(add_or_sub_op->inputs[index_of_constant_input]); in FuseAddOrSubParamsIntoPrecedingAffine()
105 auto& weights = model->GetArray(weights_name); in FuseMulOrDivParamsIntoPrecedingAffine()
107 auto& bias = model->GetArray(bias_name); in FuseMulOrDivParamsIntoPrecedingAffine()
110 model->GetArray(mul_or_div_op->inputs[index_of_constant_input]); in FuseMulOrDivParamsIntoPrecedingAffine()
282 const auto& weights = model->GetArray(weights_name); in Run()
283 const auto& bias = model->GetArray(bias_name); in Run()
Dremove_trivial_passthrough.cc45 const Array& from_array = model->GetArray(from); in Reroute()
86 if (!model->GetArray(passthru_op->inputs[i]).buffer) { in RemoveTrivialPassthroughOp()
116 model->GetArray(main_input_name).has_shape()) { in RemoveTrivialPassthroughOp()
Dresolve_batch_normalization.cc39 auto& mean_array = model->GetArray(bn_op->inputs[1]); in Run()
40 const auto& multiplier_array = model->GetArray(bn_op->inputs[2]); in Run()
41 const auto& offset_array = model->GetArray(bn_op->inputs[3]); in Run()
80 intermediate_array.data_type = model->GetArray(bn_op->inputs[0]).data_type; in Run()
Dremove_trivial_binary.cc86 const auto& input_array_0 = model->GetArray(binary_op->inputs[0]); in Run()
87 const auto& input_array_1 = model->GetArray(binary_op->inputs[1]); in Run()
106 model->GetArray(binary_op->inputs[index_of_constant_input]); in Run()
Didentify_nearest_upsample.cc107 ? model->GetArray(lhs) in Run()
108 : model->GetArray(rhs); in Run()
110 ? model->GetArray(rhs) in Run()
111 : model->GetArray(lhs); in Run()
112 Array& output_array = model->GetArray(output); in Run()
Ddequantize.cc56 auto* array = &model->GetArray(array_name); in ClearArrayQuantizationParams()
80 auto* array = &model->GetArray(array_name); in DequantizeArray()
196 auto& input_array = model->GetArray(op->inputs[0]); in Run()
205 auto& output_array = model->GetArray(op->outputs[0]); in Run()
/external/tensorflow/tensorflow/lite/toco/graph_transformations/tests/
Dunpack_quantize_test.cc122 const auto& unpack_input_array = model.GetArray("unpack_op_input"); in TEST_F()
123 const auto& unpack_array0 = model.GetArray("unpack_out0"); in TEST_F()
124 const auto& unpack_array1 = model.GetArray("unpack_out1"); in TEST_F()

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