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Results 11 - 20 of 286 for TfLite (0.12 sec)
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tensorflow/compiler/mlir/lite/flatbuffer_operator.cc
.Case("TANH", tflite::ActivationFunctionType_TANH) .Case("SIGN_BIT", tflite::ActivationFunctionType_SIGN_BIT); } static tflite::TensorType ConvertDerivedTFLiteTypeAttrForOptionWriter( tflite::TensorType type, flatbuffers::FlatBufferBuilder* builder) { if (type == tflite::TensorType_INT64) { return tflite::TensorType_INT64; } else if (type == tflite::TensorType_INT32) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 38K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tac_filter.proto
// A list of filters for TAC users to run ops/functions on ML hardwares. The // intuition is that, for ops/functions that can be run on ML hardware (e.g. // EdgeTPU) and TFLite CPU, TAC users give a hint that they're more performant // to run on TFLite CPU. These filters give the TAC users freedom to specify the // parts that they want to use other hardware to accelerate. message TacFilters {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/mlir_tflite_runner.cc
return 1; // Create TFLite interpreter & invoke converted program. std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromBuffer(serialized_flatbuffer.c_str(), serialized_flatbuffer.size()); tflite::ops::builtin::BuiltinOpResolver builtins; std::unique_ptr<tflite::Interpreter> interpreter;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 03 00:14:05 UTC 2023 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/importer_test_min_max.cc
#include "tensorflow/lite/model.h" using llvm::cl::opt; // RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \ // RUN: | %p/importer_test_min_max - \ // RUN: | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - \ // RUN: | FileCheck %s // RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \ // RUN: | %p/importer_test_min_max - \ // RUN: | flatbuffer_to_string - \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/converter_python_api_wrapper.cc
return tensorflow::PyoOrThrow( tflite::MlirSparsifyModel(input_contents_txt_raw.ptr())); }, py::arg("input_contents_txt_raw"), R"pbdoc( Returns a sparsified model. )pbdoc"); m.def( "RegisterCustomOpdefs", [](py::object custom_opdefs_txt_raw) { return tensorflow::PyoOrThrow( tflite::RegisterCustomOpdefs(custom_opdefs_txt_raw.ptr())); },
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 18:18:30 UTC 2024 - 5.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model.cc
<< ", input_inference_type: " << tflite::EnumNameTensorType(input_type) << ", output_inference_type: " << tflite::EnumNameTensorType(output_type) << "\n"; mlir::Builder mlir_builder(&context); mlir::Type input_mlir_type = tflite::ConvertElementType(input_type, mlir_builder); mlir::Type output_mlir_type = tflite::ConvertElementType(output_type, mlir_builder);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/const_tensor_utils.h
#include "tensorflow/core/framework/tensor.pb.h" #include "tensorflow/core/framework/tensor_shape.pb.h" namespace mlir { namespace TFL { bool IsQuantized(const tflite::TensorT& tensor); absl::StatusOr<mlir::quant::QuantizedType> GetQuantizedType( const tflite::TensorT& tensor, mlir::Builder builder, bool is_constant = false, mlir::Type storage_type = {});
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 07 23:04:40 UTC 2024 - 2.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test.cc
namespace { TEST(SparsifyModelTest, MetadataIsAddedToOutputModel) { std::string expected_key = tflite::optimize::kTfLiteReducedPrecisionKey; std::string expected_value = "test_data"; // Load input model auto input_fbm = tflite::FlatBufferModel::BuildFromFile( "tensorflow/lite/testdata/sparse_tensor.bin"); tflite::ModelT input_model; input_fbm->GetModel()->UnPackTo(&input_model); // Populate input metadata
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jun 10 20:16:40 UTC 2024 - 2.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/sparsity/sparsify_model.cc
namespace lite { absl::Status SparsifyModel(const tflite::ModelT& input_model, flatbuffers::FlatBufferBuilder* builder) { MLIRContext context; StatusScopedDiagnosticHandler statusHandler(&context, /*propagate=*/true); // Import input_model to a MLIR module flatbuffers::FlatBufferBuilder input_builder; flatbuffers::Offset<tflite::Model> input_model_location =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jun 10 20:16:40 UTC 2024 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/converter_gen.cc
} } } os << " tflite::" << tflite_option_name << "Builder b(*fbb);\n"; for (const auto &option : options) os << formatv(" b.add_{0}(std::move({0}));\n", option); os << " return b.Finish();\n}\n"; } } // For each TFLite op, emits a builder function that packs the TFLite op into // the corresponding FlatBuffer object. //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Dec 19 15:05:28 UTC 2023 - 23.7K bytes - Viewed (0)