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/external/tensorflow/tensorflow/lite/kernels/
Dcomparisons.cc36 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in ComparisonPrepare() local
42 input1->type != kTfLiteString || input1->type != kTfLiteBool); in ComparisonPrepare()
44 TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); in ComparisonPrepare()
47 bool requires_broadcast = !HaveSameShapes(input1, input2); in ComparisonPrepare()
52 context, input1, input2, &output_size)); in ComparisonPrepare()
54 output_size = TfLiteIntArrayCopy(input1->dims); in ComparisonPrepare()
64 const TfLiteTensor* input1, \
67 if (input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8) { \
68 auto input1_offset = -input1->params.zero_point; \
74 QuantizeMultiplierSmallerThanOneExp(input1->params.scale, \
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Dfloor_mod.cc49 T FloorMod(T input1, T input2) { in FloorMod() argument
54 T trunc_mod = mod_func(input1, input2); in FloorMod()
55 return (input1 < T(0)) == (input2 < T(0)) in FloorMod()
77 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
81 TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); in Prepare()
83 const TfLiteType type = input1->type; in Prepare()
91 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
96 context, input1, input2, &output_size)); in Prepare()
98 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
106 const TfLiteTensor* input1, const TfLiteTensor* input2, in EvalImpl() argument
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Dmul.cc70 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
74 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
76 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
81 context, input1, input2, &output_size)); in Prepare()
83 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
100 input1->params.scale * input2->params.scale / output->params.scale; in Prepare()
110 const OpData* data, const TfLiteTensor* input1, in EvalMul() argument
119 type::opname(op_params, GetTensorShape(input1), \ in EvalMul()
120 GetTensorData<data_type>(input1), GetTensorShape(input2), \ in EvalMul()
159 const TfLiteTensor* input1, in EvalQuantized() argument
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Dfloor_div.cc38 T FloorDiv(T input1, T input2) { in FloorDiv() argument
39 return std::floor(std::divides<double>()(static_cast<double>(input1), in FloorDiv()
60 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
64 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
66 const TfLiteType type = input1->type; in Prepare()
73 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
78 context, input1, input2, &output_size)); in Prepare()
80 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
88 const TfLiteTensor* input1, const TfLiteTensor* input2, in EvalImpl() argument
101 GetTensorShape(input1), GetTensorData<T>(input1), in EvalImpl()
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Dsquared_difference.cc38 T SquaredDifference(T input1, T input2) { in SquaredDifference() argument
39 const T difference = input1 - input2; in SquaredDifference()
59 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
63 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
66 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
71 context, input1, input2, &output_size)); in Prepare()
73 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
81 const OpData* data, const TfLiteTensor* input1, in EvalSquaredDifference() argument
85 GetTensorShape(input1), GetTensorData<T>(input1), in EvalSquaredDifference()
90 GetTensorShape(input1), GetTensorData<T>(input1), in EvalSquaredDifference()
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Dcomparisons_test.cc38 ComparisonOpModel(const TensorData& input1, const TensorData& input2, in ComparisonOpModel() argument
40 input1_ = AddInput(input1); in ComparisonOpModel()
47 int input1() { return input1_; } in input1() function in tflite::__anon92c241bf0111::ComparisonOpModel
98 model.PopulateTensor<bool>(model.input1(), {true, false, true, false}); in TEST()
109 model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3}); in TEST()
120 model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3}); in TEST()
131 model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3}); in TEST()
142 model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8}); in TEST()
154 model.PopulateTensor<bool>(model.input1(), {true, false, true, false}); in TEST()
165 model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3}); in TEST()
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Dpow.cc53 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
57 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
59 const TfLiteType type = input1->type; in Prepare()
66 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
71 context, input1, input2, &output_size)); in Prepare()
73 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
80 void PowImpl(const TfLiteTensor* input1, const TfLiteTensor* input2, in PowImpl() argument
84 GetTensorShape(input1), GetTensorData<T>(input1), in PowImpl()
88 reference_ops::Pow(GetTensorShape(input1), GetTensorData<T>(input1), in PowImpl()
110 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Eval() local
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Dadd.cc79 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
83 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
86 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
91 context, input1, input2, &output_size)); in Prepare()
93 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
98 data->input1_offset = -input1->params.zero_point; in Prepare()
103 2 * std::max(input1->params.scale, input2->params.scale); in Prepare()
105 input1->params.scale / twice_max_input_scale; in Prepare()
139 TF_LITE_ENSURE_EQ(context, input1->params.zero_point, 0); in Prepare()
145 CheckedLog2(input1->params.scale, &input1_scale_log2_rounded); in Prepare()
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Dlogical.cc54 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
58 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
60 const TfLiteType type = input1->type; in Prepare()
67 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
72 context, input1, input2, &output_size)); in Prepare()
74 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
84 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in LogicalImpl() local
90 GetTensorShape(input1), GetTensorData<bool>(input1), in LogicalImpl()
94 reference_ops::Logical(GetTensorShape(input1), GetTensorData<bool>(input1), in LogicalImpl()
Dfloor_mod_test.cc29 FloorModModel(const TensorData& input1, const TensorData& input2, in FloorModModel() argument
31 input1_ = AddInput(input1); in FloorModModel()
39 int input1() { return input1_; } in input1() function in tflite::__anon60c92a720111::FloorModModel
55 model.PopulateTensor<int32_t>(model.input1(), {10, 9, 11, 3}); in TEST()
66 model.PopulateTensor<int32_t>(model.input1(), {10, -9, -11, 7}); in TEST()
76 model.PopulateTensor<int32_t>(model.input1(), {10, -9, -11, 7}); in TEST()
86 model.PopulateTensor<int64_t>(model.input1(), {10, -9, -11, (1LL << 34) + 9}); in TEST()
98 model.PopulateTensor<float>(model.input1(), {10, 9, 11, 3}); in TEST()
109 model.PopulateTensor<float>(model.input1(), {10, -9, -11, 7}); in TEST()
120 model.PopulateTensor<float>(model.input1(), {10, -9, -11, 7}); in TEST()
Dsub.cc145 const TfLiteTensor* input1, in PrepareInt16SubOp() argument
156 TF_LITE_ENSURE_EQ(context, input1->params.zero_point, 0); in PrepareInt16SubOp()
162 CheckedLog2(input1->params.scale, &input1_scale_log2_rounded); in PrepareInt16SubOp()
197 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
201 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
204 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
209 context, input1, input2, &output_size)); in Prepare()
211 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
215 TF_LITE_ENSURE_OK(context, Prepare8BitSubOp(context, input1, input2, output, in Prepare()
218 TF_LITE_ENSURE_OK(context, PrepareInt16SubOp(context, input1, input2, in Prepare()
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Ddiv_test.cc28 BaseDivOpModel(const TensorData& input1, const TensorData& input2, in BaseDivOpModel() argument
31 input1_ = AddInput(input1); in BaseDivOpModel()
39 int input1() { return input1_; } in input1() function in tflite::__anon86027ab40111::BaseDivOpModel
66 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8}); in TEST()
77 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8}); in TEST()
91 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0}); in TEST()
108 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, 0.07, 0.08, 0.11, -0.123}); in TEST()
122 m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -15, 8}); in TEST()
132 m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -12, 8}); in TEST()
145 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 3, 8, 11, -20}); in TEST()
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Dmul_test.cc28 BaseMulOpModel(const TensorData& input1, const TensorData& input2, in BaseMulOpModel() argument
31 input1_ = AddInput(input1); in BaseMulOpModel()
39 int input1() { return input1_; } in input1() function in tflite::__anond9e73d7f0111::BaseMulOpModel
92 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8}); in TEST()
103 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8}); in TEST()
117 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); in TEST()
134 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); in TEST()
148 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8}); in TEST()
158 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8}); in TEST()
171 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20}); in TEST()
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Dadd_test.cc28 BaseAddOpModel(const TensorData& input1, const TensorData& input2, in BaseAddOpModel() argument
31 input1_ = AddInput(input1); in BaseAddOpModel()
39 int input1() { return input1_; } in input1() function in tflite::__anonb0f7933a0111::BaseAddOpModel
93 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8}); in TEST()
103 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8}); in TEST()
116 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); in TEST()
132 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); in TEST()
146 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8}); in TEST()
156 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8}); in TEST()
169 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20}); in TEST()
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Dmaximum_minimum.cc40 input1 = GetInput(context, node, kInputTensor1); in OpContext()
44 const TfLiteTensor* input1; member
54 TF_LITE_ENSURE_EQ(context, op_context.input1->type, op_context.input2->type); in Prepare()
55 op_context.output->type = op_context.input1->type; in Prepare()
58 !HaveSameShapes(op_context.input1, op_context.input2); in Prepare()
63 context, CalculateShapeForBroadcast(context, op_context.input1, in Prepare()
66 output_size = TfLiteIntArrayCopy(op_context.input1->dims); in Prepare()
90 GetTensorShape(op_context.input1), in TFLiteOperation()
91 GetTensorData<data_type>(op_context.input1), in TFLiteOperation()
Ddiv.cc60 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
64 TF_LITE_ENSURE_EQ(context, input1->type, input2->type); in Prepare()
67 data->requires_broadcast = !HaveSameShapes(input1, input2); in Prepare()
72 context, input1, input2, &output_size)); in Prepare()
74 output_size = TfLiteIntArrayCopy(input1->dims); in Prepare()
82 const OpData* data, const TfLiteTensor* input1, in EvalDiv() argument
91 type::opname(op_params, GetTensorShape(input1), \ in EvalDiv()
92 GetTensorData<data_type>(input1), GetTensorShape(input2), \ in EvalDiv()
132 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Eval() local
137 EvalDiv<kernel_type>(context, node, params, data, input1, input2, output); in Eval()
Dsub_test.cc28 BaseSubOpModel(const TensorData& input1, const TensorData& input2, in BaseSubOpModel() argument
31 input1_ = AddInput(input1); in BaseSubOpModel()
39 int input1() { return input1_; } in input1() function in tflite::__anon6d3712fb0111::BaseSubOpModel
93 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5}); in TEST()
104 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5}); in TEST()
118 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0}); in TEST()
135 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0}); in TEST()
149 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8}); in TEST()
159 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8}); in TEST()
172 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20}); in TEST()
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Dpow_test.cc30 PowOpModel(const TensorData& input1, const TensorData& input2, in PowOpModel() argument
32 input1_ = AddInput(input1); in PowOpModel()
40 int input1() { return input1_; } in input1() function in tflite::__anon5316d1870111::PowOpModel
56 model.PopulateTensor<int32_t>(model.input1(), {12, 2, 7, 8}); in TEST()
67 model.PopulateTensor<int32_t>(model.input1(), {0, 2, -7, 8}); in TEST()
78 model.PopulateTensor<float>(model.input1(), {0.3, 0.4, 0.7, 5.8}); in TEST()
91 model.PopulateTensor<float>(model.input1(), {0.3, 0.4, 0.7, 5.8}); in TEST()
103 model.PopulateTensor<int32_t>(model.input1(), {12, 2, 7, 8}); in TEST()
Dsquared_difference_test.cc28 BaseSquaredDifferenceOpModel(const TensorData& input1, in BaseSquaredDifferenceOpModel() argument
31 input1_ = AddInput(input1); in BaseSquaredDifferenceOpModel()
40 int input1() { return input1_; } in input1() function in tflite::__anonbd4518900111::BaseSquaredDifferenceOpModel
67 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8}); in TEST()
81 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0}); in TEST()
99 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, 0.5, 0.8, 0.11, 1.1}); in TEST()
113 m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -15, 8}); in TEST()
126 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 3, 8, 11, -20}); in TEST()
142 m.PopulateTensor<int32_t>(m.input1(), {-20, 10, 7, 3, 1, 13}); in TEST()
Dadd_n.cc34 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in Prepare() local
36 output->type = input1->type; in Prepare()
41 TF_LITE_ENSURE(context, HaveSameShapes(input1, input)); in Prepare()
42 TF_LITE_ENSURE_EQ(context, input1->type, input->type); in Prepare()
47 TfLiteIntArray* input1_dims = input1->dims; in Prepare()
58 const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); in EvalAddN() local
59 reference_ops::AddN<T>(GetTensorShape(input1), num_inputs, all_inputs.data(), in EvalAddN()
/external/deqp-deps/glslang/Test/baseResults/
Dhlsl.max.frag.out7 0:2 'input1' ( in 4-component vector of float)
12 0:3 'input1' ( in 4-component vector of float)
18 0:? 'input1' ( temp 4-component vector of float)
19 0:? 'input1' (layout( location=0) in 4-component vector of float)
26 0:? 'input1' ( temp 4-component vector of float)
30 0:? 'input1' (layout( location=0) in 4-component vector of float)
42 0:2 'input1' ( in 4-component vector of float)
47 0:3 'input1' ( in 4-component vector of float)
53 0:? 'input1' ( temp 4-component vector of float)
54 0:? 'input1' (layout( location=0) in 4-component vector of float)
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/external/tensorflow/tensorflow/contrib/quantize/python/
Dquantize_test.py75 input1 = array_ops.zeros((batch_size, height, width, depth))
77 conv = conv2d(input1, 32, [5, 5], stride=2, padding='SAME',
114 input1 = array_ops.zeros((batch_size, height, width, depth))
116 conv = separable_conv2d(input1, None, [5, 5], stride=2,
151 input1 = array_ops.zeros((batch_size, height, width, depth))
154 input1,
206 input1 = array_ops.zeros((batch_size, height, width, depth))
208 input1,
233 input1 = array_ops.zeros((batch_size, height, width, depth))
235 input1,
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/external/v8/src/compiler/
Dnode-matchers.cc40 Node* input1 = merge->InputAt(1); in DiamondMatcher() local
41 if (input1->InputCount() != 1) return; in DiamondMatcher()
43 if (branch != input1->InputAt(0)) return; in DiamondMatcher()
46 input1->opcode() == IrOpcode::kIfFalse) { in DiamondMatcher()
49 if_false_ = input1; in DiamondMatcher()
51 input1->opcode() == IrOpcode::kIfTrue) { in DiamondMatcher()
53 if_true_ = input1; in DiamondMatcher()
/external/deqp/external/vulkancts/modules/vulkan/shaderexecutor/
DvktAtomicOperationTests.cpp338 const T input1 = *reinterpret_cast<const T*>(&original.input[elementNdx + NUM_ELEMENTS / 2]); in checkOperation() local
347 …exp.push_back(Expected<T>(originalInout + input0 + input1, originalInout, originalInout + input0)); in checkOperation()
348 …exp.push_back(Expected<T>(originalInout + input0 + input1, originalInout + input1, originalInout)); in checkOperation()
354 …exp.push_back(Expected<T>(originalInout & input0 & input1, originalInout, originalInout & input0)); in checkOperation()
355 …exp.push_back(Expected<T>(originalInout & input0 & input1, originalInout & input1, originalInout)); in checkOperation()
361 …exp.push_back(Expected<T>(originalInout | input0 | input1, originalInout, originalInout | input0)); in checkOperation()
362 …exp.push_back(Expected<T>(originalInout | input0 | input1, originalInout | input1, originalInout)); in checkOperation()
368 …exp.push_back(Expected<T>(originalInout ^ input0 ^ input1, originalInout, originalInout ^ input0)); in checkOperation()
369 …exp.push_back(Expected<T>(originalInout ^ input0 ^ input1, originalInout ^ input1, originalInout)); in checkOperation()
375 …exp.push_back(Expected<T>(de::min(de::min(originalInout, input0), input1), originalInout, de::min(… in checkOperation()
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/external/eigen/unsupported/test/
Dcxx11_tensor_chipping.cpp186 Tensor<float, 5, DataLayout> input1(2,3,5,7,11); in test_chip_in_expr() local
187 input1.setRandom(); in test_chip_in_expr()
191 Tensor<float, 4, DataLayout> result = input1.template chip<0>(0) + input2; in test_chip_in_expr()
196 float expected = input1(0,i,j,k,l) + input2(i,j,k,l); in test_chip_in_expr()
205 Tensor<float, 3, DataLayout> result2 = input1.template chip<0>(0).template chip<1>(2) + input3; in test_chip_in_expr()
209 float expected = input1(0,i,2,j,k) + input3(i,j,k); in test_chip_in_expr()
219 Tensor<float, 5, DataLayout> input1(2,3,5,7,11); in test_chip_as_lvalue() local
220 input1.setRandom(); in test_chip_as_lvalue()
224 Tensor<float, 5, DataLayout> tensor = input1; in test_chip_as_lvalue()
232 VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); in test_chip_as_lvalue()
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