| /external/tensorflow/tensorflow/core/kernels/ |
| spectrogram_convert_test_data.cc | 29 std::vector<std::vector<std::complex<double>>> input_data; local
30 ReadCSVFileToComplexVectorOrDie(input_filename, &input_data);
32 if (!WriteComplexVectorToRawFloatFile(output_filename, input_data)) {
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| colorspace_op.cc | 65 typename TTypes<T, 2>::ConstTensor input_data = input.flat_inner_dims<T>(); variable
71 TensorShape({input_data.dimension(0)}),
76 functor::RGBToHSV<Device, T>()(context->eigen_device<Device>(), input_data,
102 typename TTypes<T, 2>::ConstTensor input_data = input.flat_inner_dims<T>(); variable
105 functor::HSVToRGB<Device, T>()(context->eigen_device<Device>(), input_data,
129 const GPUDevice& d, TTypes<T, 2>::ConstTensor input_data, \
134 const GPUDevice& d, TTypes<T, 2>::ConstTensor input_data, \
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| cudnn_pooling_gpu.cc | 91 auto input_data = AsDeviceMemory(transformed_input.template flat<T>().data(), local
101 ->ThenPoolForward(pooling_desc, input_desc, input_data,
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| adjust_hue_op.cc | 211 auto input_data = input->shaped<float, 2>({channel_count, kChannelSize}); variable
219 [channel_count, &input_data, &output_data, delta_h](
221 const float* p = input_data.data() + start_channel * kChannelSize;
268 const float* input_data = input->flat<float>().data(); variable
271 functor::AdjustHueGPU()(&device, number_of_elements, input_data, delta_h,
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| /external/tensorflow/tensorflow/compiler/xla/service/gpu/ |
| convolution_thunk.cc | 66 se::DeviceMemoryBase input_data = local
79 convolution_kind_, input_shape_, filter_shape_, output_shape_, input_data,
90 return input_data.opaque();
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| /external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
| resolve_reorder_axes.cc | 37 auto& input_data = input_array->GetMutableBuffer<DataType>().data; local
48 input_data.data(), reordered_data.data());
49 input_data = reordered_data;
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| resolve_constant_transpose.cc | 32 const std::vector<DataType<Type>>& input_data = local
80 input_data.data() + i3 * input_stride_3;
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| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/ |
| reinterpret_string_to_float_op.cc | 38 void Evaluate(const Tensor& input_data, Tensor output_data, int32 start,
41 const auto in_data = input_data.unaligned_flat<string>();
54 const Tensor& input_data = context->input(0); variable
57 if (!CheckTensorBounds(context, input_data)) return;
61 context, context->allocate_output(0, input_data.shape(), &output_data));
64 const int32 num_data = static_cast<int32>(input_data.NumElements());
68 Evaluate(input_data, *output_data, 0, num_data);
70 auto work = [&input_data, output_data, num_data](int64 start, int64 end) {
73 Evaluate(input_data, *output_data, static_cast<int32>(start),
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| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/core/ops/ |
| routing_function_op.cc | 51 .Input("input_data: float")
68 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
88 const Tensor& input_data = context->input(0); variable
92 if (input_data.shape().dim_size(0) > 0) {
94 context, input_data.shape().dims() == 2,
95 errors::InvalidArgument("input_data should be two-dimensional"));
99 if (!CheckTensorBounds(context, input_data)) return;
101 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0));
103 static_cast<int32>(input_data.shape().dim_size(1))
[all...] |
| routing_gradient_op.cc | 46 .Input("input_data: float")
93 const Tensor& input_data = context->input(0); variable
100 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0));
102 static_cast<int32>(input_data.shape().dim_size(1));
117 const Tensor point = input_data.Slice(i, i + 1);
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| hard_routing_function_op.cc | 52 .Input("input_data: float")
69 Chooses a single path for each instance in `input_data` and returns the leaf
74 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
97 const Tensor& input_data = context->input(0); variable
101 if (input_data.shape().dim_size(0) > 0) {
103 context, input_data.shape().dims() == 2,
104 errors::InvalidArgument("input_data should be two-dimensional"));
108 if (!CheckTensorBounds(context, input_data)) return;
110 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0))
[all...] |
| k_feature_routing_function_op.cc | 54 .Input("input_data: float")
77 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
103 const Tensor& input_data = context->input(0); variable
107 if (input_data.shape().dim_size(0) > 0) {
109 context, input_data.shape().dims() == 2,
110 errors::InvalidArgument("input_data should be two-dimensional"));
114 if (!CheckTensorBounds(context, input_data)) return;
116 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0));
118 static_cast<int32>(input_data.shape().dim_size(1))
[all...] |
| stochastic_hard_routing_function_op.cc | 56 .Input("input_data: float")
73 Samples a path for each instance in `input_data` and returns the
79 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
108 const Tensor& input_data = context->input(0); variable
112 if (input_data.shape().dim_size(0) > 0) {
114 context, input_data.shape().dims() == 2,
115 errors::InvalidArgument("input_data should be two-dimensional"));
119 if (!CheckTensorBounds(context, input_data)) return;
121 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0))
[all...] |
| k_feature_gradient_op.cc | 41 .Input("input_data: float")
56 input_data: The training batch's features as a 2-d tensor;
57 `input_data[i][j]` gives the j-th feature of the i-th input.
137 const auto input_data = input_data_tensor.tensor<float, 2>(); variable
177 weights_grad(i, j, k) = input_data(i, feature_set[k]);
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| stochastic_hard_routing_gradient_op.cc | 46 .Input("input_data: float")
76 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
119 const Tensor& input_data = context->input(0); variable
126 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0));
128 static_cast<int32>(input_data.shape().dim_size(1));
171 const auto data = input_data.tensor<float, 2>();
178 const Tensor point = input_data.Slice(i, i + 1);
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| /system/extras/simpleperf/ |
| cmd_debug_unwind_test.cpp | 80 std::string input_data = GetTestData(PERF_DATA_NO_UNWIND); local
84 ASSERT_TRUE(DebugUnwindCmd()->Run({"-i", input_data, "-o", tmp_file.path}));
88 ASSERT_TRUE(DebugUnwindCmd()->Run({"-i", input_data, "-o", tmp_file.path, "--time",
94 std::string input_data = GetTestData(NATIVELIB_IN_APK_PERF_DATA); local
98 ASSERT_TRUE(DebugUnwindCmd()->Run({"-i", input_data, "-o", tmp_file.path, "--symfs",
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| /external/libbrillo/brillo/streams/ |
| fake_stream_unittest.cc | 274 std::string input_data = "foobar-baz"; local
275 size_t split_pos = input_data.find('-');
278 stream_->AddReadPacketString({}, input_data.substr(0, split_pos));
279 stream_->AddReadPacketString(one_sec_delay, input_data.substr(split_pos));
288 buffer.resize(input_data.size());
306 EXPECT_EQ(input_data, (std::string{buffer.begin(), buffer.end()}));
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| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| copy_test.cc | 251 auto input_data = client_->TransferToServer(*empty).ConsumeValueOrDie(); local
253 auto actual = ExecuteAndTransfer(&builder, {input_data.get()}, &out_shape)
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| reshape_test.cc | 730 auto input_data = CreateParameterAndTransferLiteral( local
752 auto input_data = CreateParameterAndTransferLiteral( local
775 auto input_data = CreateParameterAndTransferLiteral( local
802 auto input_data = CreateParameterAndTransferLiteral( local
884 auto input_data = CreateParameterAndTransferLiteral( local
913 auto input_data = CreateParameterAndTransferLiteral( local
942 auto input_data = CreateParameterAndTransferLiteral( local
972 auto input_data = CreateParameterAndTransferLiteral( local
1001 auto input_data = CreateParameterAndTransferLiteral( local
[all...] |
| /external/tensorflow/tensorflow/contrib/image/kernels/ |
| adjust_hsv_in_yiq_op.cc | 105 auto input_data = input->shaped<float, 2>({channel_count, kChannelSize}); variable
118 [channel_count, &input_data, &output_data, &tranformation_matrix](
121 const float* p = input_data.data() + start_channel * kChannelSize;
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| /external/webrtc/webrtc/common_audio/ |
| audio_util_unittest.cc | 182 const float input_data[kNumChannels][kNumFrames] = {{1.f, 2.f, -1.f, -3.f}}; local
185 input[i] = input_data[i];
192 EXPECT_THAT(downmixed, ElementsAreArray(input_data[0]));
197 const float input_data[kNumChannels][kNumFrames] = {{1.f, 2.f, -1.f}, local
201 input[i] = input_data[i];
214 const int16_t input_data[kNumChannels][kNumFrames] = { local
218 input[i] = input_data[i];
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| /frameworks/base/media/mca/filterfw/jni/ |
| jni_native_program.cpp | 147 const char* input_data = NULL; local
156 input_data = reinterpret_cast<const char*>(native_frame->Data());
159 input_buffers[i] = input_data;
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| /hardware/intel/common/libva/test/basic/ |
| test_11.c | 106 uint32_t *input_data[NUM_BUFFER_TYPES]; local
113 input_data[i] = malloc(buffer_sizes[i]+4);
114 ASSERT(input_data[i]);
119 input_data[i][j] = random();
125 memcpy(data, input_data[i], buffer_sizes[i]);
146 ASSERT( memcmp(input_data[i], data, buffer_sizes[i]) == 0 );
157 free(input_data[i]);
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| /external/protobuf/src/google/protobuf/compiler/ |
| subprocess.cc | 157 string input_data = input.SerializeAsString(); local
191 input_data.data() + input_pos,
192 input_data.size() - input_pos,
196 input_pos = input_data.size();
201 if (input_pos == input_data.size()) {
360 string input_data = input.SerializeAsString();
388 int n = write(child_stdin_, input_data.data() + input_pos,
389 input_data.size() - input_pos);
393 input_pos = input_data.size();
398 if (input_pos == input_data.size())
[all...] |
| /external/protobuf/src/google/protobuf/util/ |
| json_util_test.cc | 242 string input_data = "0123456789"; local
243 for (int input_pattern = 0; input_pattern < (1 << (input_data.size() - 1));
250 for (int j = 0; j < input_data.length() - 1; ++j) {
252 byte_sink.Append(&input_data[start], j - start + 1);
256 byte_sink.Append(&input_data[start], input_data.length() - start);
258 EXPECT_EQ(input_data, string(buffer, input_data.length()));
262 input_data = "012345678";
263 for (int input_pattern = 0; input_pattern < (1 << (input_data.size() - 1))
[all...] |
