| /aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/tests/ |
| H A D | convolution_variants_test.cc | 56 const Array4D<float> input_array(1, 1, 1, 1, {2}); in XLA_TEST_F() local
57 auto input = ConstantR4FromArray4D<float>(&builder, input_array); in XLA_TEST_F()
71 const Array4D<float> input_array(5, 1, 1, 1, {1, 2, 3, 4, 5}); in XLA_TEST_F() local
72 auto input = ConstantR4FromArray4D<float>(&builder, input_array); in XLA_TEST_F()
86 Array4D<float> input_array(2, 1, 3, 4); in XLA_TEST_F() local
87 input_array.FillWithMultiples(1); in XLA_TEST_F()
88 auto input = ConstantR4FromArray4D<float>(&builder, input_array); in XLA_TEST_F()
103 Array4D<float> input_array(1, 2, 1, 1, {10, 1}); in XLA_TEST_F() local
104 auto input = ConstantR4FromArray4D<float>(&builder, input_array); in XLA_TEST_F()
118 Array4D<float> input_array(1, 1, 1, 2, {1, 2}); in XLA_TEST_F() local
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| H A D | reduce_window_test.cc | 163 Array4D<float> input_array(1, 0, 2, 1); in XLA_TEST_P() local
164 const auto input = CreateConstantFromArray(input_array, &builder_); in XLA_TEST_P()
168 auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 2, 1}, in XLA_TEST_P()
176 Array4D<float> input_array(1, 2, 2, 1); in XLA_TEST_P() local
177 input_array.FillRandom(2.f, 2.f); in XLA_TEST_P()
178 const auto input = CreateConstantFromArray(input_array, &builder_); in XLA_TEST_P()
183 auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 2, 1}, in XLA_TEST_P()
191 Array4D<float> input_array(1, 3, 3, 1); in XLA_TEST_P() local
192 input_array.FillRandom(2.f, 2.f); in XLA_TEST_P()
193 const auto input = CreateConstantFromArray(input_array, &builder_); in XLA_TEST_P()
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| H A D | batch_normalization_test.cc | 516 Array4D<float> input_array(bounds[0], bounds[1], bounds[2], bounds[3]); in XLA_TEST_P() local
517 input_array.FillRandom(GetParam().random_value_var, in XLA_TEST_P()
528 ReferenceUtil::MapArray4D(input_array, [](float a) { return a * a; }); in XLA_TEST_P()
537 ReferenceUtil::Reduce4DTo1D(input_array, /*init=*/0.0f, reduce_dims, in XLA_TEST_P()
572 auto normalized = *ReferenceUtil::BatchNorm4D(input_array, mean4D, var4D, in XLA_TEST_P()
580 auto input_literal = LiteralUtil::CreateR4FromArray4D<float>(input_array); in XLA_TEST_P()
617 Array4D<float> input_array(bounds[0], bounds[1], bounds[2], bounds[3]); in XLA_TEST_P() local
618 input_array.FillRandom(GetParam().random_value_var, in XLA_TEST_P()
629 ReferenceUtil::MapArray4D(input_array, [](float a) { return a * a; }); in XLA_TEST_P()
638 ReferenceUtil::Reduce4DTo1D(input_array, /*init=*/0.0f, reduce_dims, in XLA_TEST_P()
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| /aosp_15_r20/external/tensorflow/tensorflow/lite/toco/graph_transformations/ |
| H A D | propagate_fixed_sizes.cc | 126 const auto& input_array = model->GetArray(op->inputs[0]); in ProcessConvOperator() local
128 if (!input_array.has_shape()) { in ProcessConvOperator()
131 const auto& input_shape = input_array.shape(); in ProcessConvOperator()
222 const auto& input_array = in ProcessTransposeConvOperator() local
224 if (!input_array.has_shape()) { in ProcessTransposeConvOperator()
228 const auto& input_shape = input_array.shape(); in ProcessTransposeConvOperator()
253 const auto& input_array = model->GetArray(op->inputs[0]); in ProcessDepthwiseConvOperator() local
255 if (!input_array.has_shape()) { in ProcessDepthwiseConvOperator()
258 const auto& input_shape = input_array.shape(); in ProcessDepthwiseConvOperator()
295 const auto& input_array = model->GetArray(op->inputs[0]); in ProcessDepthToSpaceOperator() local
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| H A D | resolve_constant_reshape.cc | 55 const Array& input_array = model->GetArray(op->inputs[0]); in Run() local
56 if (!ShapesAgreeUpToExtending(input_array.shape(), output_array.shape())) { in Run()
58 ShapeToString(input_array.shape()), in Run()
64 switch (input_array.data_type) { 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()
78 CopyArrayBuffer<ArrayDataType::kInt16>(input_array, &output_array); in Run()
81 CopyArrayBuffer<ArrayDataType::kUint16>(input_array, &output_array); in Run()
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| H A D | make_initial_dequantize_operator.cc | 56 auto& input_array = model->GetArray(input_name); in AddDequantizeOperatorToInput() local
57 if (input_array.data_type != ArrayDataType::kFloat) { in AddDequantizeOperatorToInput()
61 if (input_array.final_data_type == input_array.data_type || in AddDequantizeOperatorToInput()
62 input_array.final_data_type == ArrayDataType::kNone) { in AddDequantizeOperatorToInput()
84 const auto& input_minmax = input_array.GetMinMax(); in AddDequantizeOperatorToInput()
87 auto& input_qparams = input_array.GetOrCreateQuantizationParams(); in AddDequantizeOperatorToInput()
88 input_array.data_type = input_array.final_data_type; in AddDequantizeOperatorToInput()
90 input_array, input_array.data_type, &input_qparams); in AddDequantizeOperatorToInput()
111 for (auto& input_array : *model->flags.mutable_input_arrays()) { in Run()
112 if (input_array.name() == input) { in Run()
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| H A D | resolve_reorder_axes.cc | 59 const Array& input_array, Array* output_array) { in ReorderAxes() argument
60 DCHECK(input_array.buffer->type == DataType); in ReorderAxes()
62 const auto& input_data = input_array.GetBuffer<DataType>().data; in ReorderAxes()
66 Shape input_shape = input_array.shape(); in ReorderAxes()
74 if (input_array.minmax) { in ReorderAxes()
75 output_array->GetOrCreateMinMax() = input_array.GetMinMax(); in ReorderAxes()
77 if (input_array.narrow_range) { in ReorderAxes()
96 auto& input_array = model->GetArray(input_array_name); in Run() local
98 if (!input_array.buffer) { in Run()
106 if (input_array.buffer->type == ArrayDataType::kFloat) { in Run()
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| H A D | resolve_constant_unary.cc | 105 const auto& input_array = model->GetArray(op.inputs[0]); in CopyMinMaxFromFirstInput() local
106 if (!input_array.minmax) { in CopyMinMaxFromFirstInput()
109 const auto& input_minmax = input_array.GetMinMax(); in CopyMinMaxFromFirstInput()
183 const auto& input_array = model->GetArray(unary_op->inputs[0]); in Run() local
186 CHECK(input_array.buffer); in Run()
198 if (cast_op->src_data_type != input_array.buffer->type) { in Run()
205 if (input_array.buffer->type != ArrayDataType::kFloat) { in Run()
208 input_float_data = &(input_array.GetBuffer<ArrayDataType::kFloat>().data); in Run()
219 const Shape& input_shape = input_array.shape(); in Run()
224 if (input_array.buffer->type == ArrayDataType::kFloat) { in Run()
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| H A D | hardcode_min_max.cc | 37 const auto& input_array = model->GetArray(op->inputs[0]); in HardcodeMinMaxForIm2colArray() local
38 if (!input_array.minmax) { in HardcodeMinMaxForIm2colArray()
41 const auto& input_minmax = input_array.GetMinMax(); in HardcodeMinMaxForIm2colArray()
54 const auto& input_array = model->GetArray(op->inputs[0]); in HardcodeMinMaxForL2Normalization() local
55 if (!input_array.minmax) { in HardcodeMinMaxForL2Normalization()
58 const auto& input_minmax = input_array.GetMinMax(); in HardcodeMinMaxForL2Normalization()
156 auto& input_array = model->GetArray(op->inputs[1]); in HardcodeMinMaxForSplit() local
157 if (!input_array.minmax) { in HardcodeMinMaxForSplit()
163 if (!array.minmax || !(array.GetMinMax() == input_array.GetMinMax())) { in HardcodeMinMaxForSplit()
165 array.GetOrCreateMinMax() = *input_array.minmax; in HardcodeMinMaxForSplit()
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| H A D | resolve_constant_strided_slice.cc | 28 void StridedSlice(StridedSliceOperator const& op, Array const& input_array, in StridedSlice() argument
35 CHECK(input_array.data_type == Type); in StridedSlice()
51 Shape const& input_shape = input_array.shape(); in StridedSlice()
52 Buffer<Type> const& input_buffer = input_array.GetBuffer<Type>(); in StridedSlice()
62 strided_slice_params, ToRuntimeShape(input_array.shape()), axis); in StridedSlice()
65 strided_slice_params, ToRuntimeShape(input_array.shape()), axis, in StridedSlice()
132 const auto& input_array = model->GetArray(op->inputs[0]); in Run() local
133 if (!input_array.has_shape()) { in Run()
145 StridedSlice<ArrayDataType::kFloat>(*op, input_array, &output_array); in Run()
148 StridedSlice<ArrayDataType::kUint8>(*op, input_array, &output_array); in Run()
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| H A D | resolve_constant_tile.cc | 73 inline void Tile(const Array& input_array, const Array& multiples_array, in Tile() argument
82 input_array.shape(), input_array.GetBuffer<Type>().data.data(), in Tile()
88 input_array.shape(), input_array.GetBuffer<Type>().data.data(), in Tile()
129 const Array& input_array = model->GetArray(op->inputs[0]); in Run() local
135 CopyMinMaxAndQuantizationRelatedFields(input_array, &output_array); in Run()
140 Tile<ArrayDataType::kFloat>(input_array, multiples_array, &output_array); in Run()
143 Tile<ArrayDataType::kUint8>(input_array, multiples_array, &output_array); in Run()
146 Tile<ArrayDataType::kInt16>(input_array, multiples_array, &output_array); in Run()
149 Tile<ArrayDataType::kInt32>(input_array, multiples_array, &output_array); in Run()
152 Tile<ArrayDataType::kInt64>(input_array, multiples_array, &output_array); in Run()
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| H A D | resolve_constant_slice.cc | 27 bool Slice(SliceOperator const& op, Array const& input_array, in Slice() argument
31 CHECK(input_array.data_type == Type); in Slice()
33 const auto& input_data = input_array.GetBuffer<Type>().data; in Slice()
57 dim_size = input_array.shape().dims()[i] - begin[i]; in Slice()
66 Shape padded_shape = input_array.shape(); in Slice()
118 const auto& input_array = model->GetArray(op->inputs[0]); in Run() local
119 if (!input_array.has_shape()) { in Run()
131 if (!Slice<ArrayDataType::kFloat>(*op, input_array, &output_array)) { in Run()
136 if (!Slice<ArrayDataType::kUint8>(*op, input_array, &output_array)) { in Run()
141 if (!Slice<ArrayDataType::kInt32>(*op, input_array, &output_array)) { in Run()
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| H A D | dequantize.cc | 58 for (auto& input_array : *model->flags.mutable_input_arrays()) { in ClearArrayQuantizationParams()
59 if (input_array.name() == array_name) { in ClearArrayQuantizationParams()
63 if (input_array.has_std_value()) { in ClearArrayQuantizationParams()
64 CHECK_LE(std::abs(new_std_value - input_array.std_value()), 0.001); in ClearArrayQuantizationParams()
66 input_array.set_std_value(new_std_value); in ClearArrayQuantizationParams()
68 if (input_array.has_mean_value()) { in ClearArrayQuantizationParams()
69 CHECK_LE(std::abs(new_mean_value - input_array.mean_value()), 0.001); in ClearArrayQuantizationParams()
71 input_array.set_mean_value(new_mean_value); in ClearArrayQuantizationParams()
196 auto& input_array = model->GetArray(op->inputs[0]); in Run() local
197 if (input_array.data_type == ArrayDataType::kFloat) { in Run()
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| H A D | resolve_constant_gather.cc | 28 inline void Gather(const Array& input_array, const Array& coords_array, in Gather() argument
30 const Shape& input_shape = input_array.shape(); in Gather()
32 input_array.GetBuffer<Type>().data; in Gather()
106 const Array& input_array = model->GetArray(op->inputs[0]); in Run() local
114 if (input_array.minmax) { in Run()
115 const auto& input_minmax = input_array.GetMinMax(); in Run()
124 Gather<ArrayDataType::kFloat>(input_array, coords_array, &output_array); in Run()
127 Gather<ArrayDataType::kUint8>(input_array, coords_array, &output_array); in Run()
130 Gather<ArrayDataType::kInt32>(input_array, coords_array, &output_array); in Run()
133 Gather<ArrayDataType::kInt64>(input_array, coords_array, &output_array); in Run()
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| H A D | shuffle_fc_weights.cc | 40 const Array& input_array = model->GetArray(fc_op->inputs[0]); in Run() local
47 if (input_array.data_type != ArrayDataType::kUint8 || in Run()
50 !input_array.quantization_params || !weights_array.quantization_params || in Run()
55 if (!input_array.has_shape() || !weights_array.has_shape()) { in Run()
60 const Shape& input_shape = input_array.shape(); in Run()
152 shuffled_input_workspace_array.data_type = input_array.data_type; in Run()
153 *shuffled_input_workspace_array.mutable_shape() = input_array.shape(); in Run()
154 shuffled_input_workspace_array.GetOrCreateMinMax() = input_array.GetMinMax(); in Run()
156 input_array.GetQuantizationParams(); in Run()
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| H A D | resolve_constant_transpose.cc | 29 void Transpose(Model* model, const Array& input_array, in Transpose() argument
31 const Shape& input_shape = input_array.shape(); in Transpose()
33 input_array.GetBuffer<Type>().data; in Transpose()
132 const Array& input_array = model->GetArray(op->inputs[0]); in Run() local
134 CopyMinMaxAndQuantizationRelatedFields(input_array, &output_array); in Run()
147 Transpose<ArrayDataType::kFloat>(model, input_array, op->perm, in Run()
151 Transpose<ArrayDataType::kUint8>(model, input_array, op->perm, in Run()
155 Transpose<ArrayDataType::kInt32>(model, input_array, op->perm, in Run()
159 Transpose<ArrayDataType::kInt64>(model, input_array, op->perm, in Run()
163 Transpose<ArrayDataType::kComplex64>(model, input_array, op->perm, in Run()
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| H A D | unroll_batch_matmul.cc | 41 const Array& input_array, Model* model, in SliceInput() argument
43 int rank = input_array.shape().dimensions_count(); in SliceInput()
44 int num_rows = input_array.shape().dims(rank - 2); in SliceInput()
45 int num_cols = input_array.shape().dims(rank - 1); in SliceInput()
55 reshape_op_output.data_type = input_array.data_type; in SliceInput()
74 slice_op_output.data_type = input_array.data_type; in SliceInput()
87 slice_reshape_op_output.data_type = input_array.data_type; in SliceInput()
95 std::vector<int32> GetTransposePerm(const Array& input_array) { in GetTransposePerm() argument
96 const int32_t dims = input_array.shape().dimensions_count(); in GetTransposePerm()
117 const auto& input_array = model->GetArray(input); in TransposeInput() local
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| H A D | resolve_constant_concatenation.cc | 41 for (Array* input_array : input_arrays) { in CopyTensorSegments()
42 if (!input_array->buffer) { in CopyTensorSegments()
61 for (Array* input_array : input_arrays) { in CopyTensorSegments()
62 src_ptr.push_back(input_array->GetBuffer<A>().data.data()); in CopyTensorSegments()
91 for (Array* input_array : input_arrays) { in ConcatenateTensorBuffers()
92 const Shape array_shape = input_array->shape(); in ConcatenateTensorBuffers()
120 for (Array* input_array : input_arrays) { in SetMinMaxForConcatenedArray()
123 if (!input_array->minmax) return; in SetMinMaxForConcatenedArray()
124 const MinMax& input_minmax = input_array->GetMinMax(); in SetMinMaxForConcatenedArray()
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| /aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/strings_ops/ |
| H A D | reduce_join_op_test.py | 97 input_array, argument
115 inputs=input_array,
124 def _testMultipleReduceJoin(self, input_array, axis, separator=" "): argument
138 inputs=input_array, axis=axis, keep_dims=False, separator=separator)
140 inputs=input_array, axis=axis, keep_dims=True, separator=separator)
142 truth = input_array
159 input_array = ["this", "is", "a", "test"]
162 self._testReduceJoin(input_array, truth, truth_shape, axis=0)
165 input_array = [["this", "is", "a", "test"],
172 input_array, truth_dim_zero, truth_shape_dim_zero, axis=0)
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| /aosp_15_r20/external/tensorflow/tensorflow/lite/python/optimize/ |
| H A D | calibrator.py | 80 input_array = []
86 input_array.append(inputs[input_name])
87 return input_array
103 input_array = self._create_input_array_from_dict(
110 input_array = self._create_input_array_from_dict(None, sample)
113 input_array = sample
126 self._calibrator.Prepare([list(s.shape) for s in input_array],
129 self._calibrator.Prepare([list(s.shape) for s in input_array])
136 self._calibrator.FeedTensor(input_array, signature_key)
138 self._calibrator.FeedTensor(input_array)
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| /aosp_15_r20/frameworks/rs/toolkit/ |
| H A D | JniEntryPoints.cpp | 225 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, jint vectorSize, in Java_android_renderscript_toolkit_Toolkit_nativeBlur() argument
229 ByteArrayGuard input{env, input_array}; in Java_android_renderscript_toolkit_Toolkit_nativeBlur()
248 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, in Java_android_renderscript_toolkit_Toolkit_nativeColorMatrix() argument
253 ByteArrayGuard input{env, input_array}; in Java_android_renderscript_toolkit_Toolkit_nativeColorMatrix()
277 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, jint vectorSize, in Java_android_renderscript_toolkit_Toolkit_nativeConvolve() argument
282 ByteArrayGuard input{env, input_array}; in Java_android_renderscript_toolkit_Toolkit_nativeConvolve()
320 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, in Java_android_renderscript_toolkit_Toolkit_nativeHistogram() argument
324 ByteArrayGuard input{env, input_array}; in Java_android_renderscript_toolkit_Toolkit_nativeHistogram()
343 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, in Java_android_renderscript_toolkit_Toolkit_nativeHistogramDot() argument
348 ByteArrayGuard input{env, input_array}; in Java_android_renderscript_toolkit_Toolkit_nativeHistogramDot()
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| /aosp_15_r20/external/renderscript-intrinsics-replacement-toolkit/renderscript-toolkit/src/main/cpp/ |
| H A D | JniEntryPoints.cpp | 225 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, jint vectorSize, in Java_com_google_android_renderscript_Toolkit_nativeBlur() argument
229 ByteArrayGuard input{env, input_array}; in Java_com_google_android_renderscript_Toolkit_nativeBlur()
248 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, in Java_com_google_android_renderscript_Toolkit_nativeColorMatrix() argument
253 ByteArrayGuard input{env, input_array}; in Java_com_google_android_renderscript_Toolkit_nativeColorMatrix()
277 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, jint vectorSize, in Java_com_google_android_renderscript_Toolkit_nativeConvolve() argument
282 ByteArrayGuard input{env, input_array}; in Java_com_google_android_renderscript_Toolkit_nativeConvolve()
320 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, in Java_com_google_android_renderscript_Toolkit_nativeHistogram() argument
324 ByteArrayGuard input{env, input_array}; in Java_com_google_android_renderscript_Toolkit_nativeHistogram()
343 JNIEnv* env, jobject /*thiz*/, jlong native_handle, jbyteArray input_array, in Java_com_google_android_renderscript_Toolkit_nativeHistogramDot() argument
348 ByteArrayGuard input{env, input_array}; in Java_com_google_android_renderscript_Toolkit_nativeHistogramDot()
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| /aosp_15_r20/external/tensorflow/tensorflow/lite/python/ |
| H A D | convert.py | 689 input_array = model_flags.input_arrays.add()
692 input_array.mean_value, input_array.std_value = (
699 input_array.name = name
700 input_array.shape.dims.extend(list(map(int, shape)))
749 input_array = model_flags.input_arrays.add()
751 input_array.name = input_tensor.name
753 input_array.name = util.get_tensor_name(input_tensor)
754 input_array.data_type = convert_tensor_tf_type_to_tflite_type(
759 input_array.mean_value, input_array.std_value = (
781 input_array.shape.dims.extend(dims)
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| /aosp_15_r20/external/tensorflow/tensorflow/compiler/tests/ |
| H A D | spacetobatch_op_test.py | 27 def space_to_batch_direct(input_array, block_shape, paddings): argument
40 input_array = np.array(input_array)
45 padded = np.pad(input_array,
47 (input_array.ndim - 1 - num_block_dims)),
49 reshaped_padded_shape = [input_array.shape[0]]
50 output_shape = [input_array.shape[0] * np.prod(block_shape)]
56 reshaped_padded_shape.extend(input_array.shape[num_block_dims + 1:])
57 output_shape.extend(input_array.shape[num_block_dims + 1:])
63 np.arange(input_array.ndim - num_block_dims - 1) + 1 + num_block_dims
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| /aosp_15_r20/external/tensorflow/tensorflow/lite/toco/ |
| H A D | tooling_util.cc | 102 for (const auto& input_array : model.flags.input_arrays()) { in IsInputArray() local
103 if (array_name == input_array.name()) { in IsInputArray()
872 for (const auto& input_array : model_flags.input_arrays()) { in CheckInputArraysAreNotOutputArrays() local
874 QCHECK_NE(input_array.name(), output_array) in CheckInputArraysAreNotOutputArrays()
908 for (const auto& input_array : model_flags.input_arrays()) { in CheckNonAsciiIOArrays() local
909 QCHECK(IsAsciiPrintable(input_array.name())) in CheckNonAsciiIOArrays()
911 << input_array.name() in CheckNonAsciiIOArrays()
914 << DumpAscii(input_array.name()); in CheckNonAsciiIOArrays()
1494 for (const auto& input_array : model->flags.input_arrays()) { in CreateOrCheckRnnStateArray() local
1497 if (input_array.name() == name || num_dims == -1) { in CreateOrCheckRnnStateArray()
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