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Results 1 - 10 of 10 for input_dtype (0.35 sec)
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tensorflow/compiler/mlir/tf2xla/internal/utils/test_metadata_config.cc
for (auto input_type : func_type.getInputs()) { tensorflow::TensorShape tensor_shape; xla::Shape xla_shape = xla::TypeToShape(input_type); TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape( xla_shape.dimensions(), &tensor_shape)); arg_shapes.emplace_back(tensor_shape); DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 13 23:59:33 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
// accumulation over the given input type. Type GetSumAccumulationType(Type input_type) { MLIRContext *ctx = input_type.getContext(); if (input_type.isBF16() || input_type.isF16()) return FloatType::getF32(ctx); if (input_type.isSignlessInteger(8) || input_type.isSignlessInteger(16)) return IntegerType::get(ctx, 32); return input_type; } // Returns axis in HLO format from TF elements attr with exactly one element or
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model.h
// Quantizes the input model represented as `model_buffer` and writes the result // to the `output_buffer`. Both `model_buffer` and `output_buffer` should be a // valid FlatBuffer format for Model supported by TFLite. // // The `input_type`, `output_type` and `inference_type` can be float32 / qint8 / // int8 / int16. // // Returns a partially quantized model if `fully_quantize` is false. Returns a // non-OK status if the quantization fails. //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 2.8K 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 =
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/tensorflow/translate/tf_mlir_translate_registration.cc
enable_shape_inference, unconditionally_use_set_output_shapes, enable_soft_placement, set_original_tf_func_name}; auto module_or = tensorflow::GraphdefToMlirTranslateFunction( input, input_arrays, input_dtypes, input_shapes, output_arrays, control_output_arrays, options, context); if (!module_or.status().ok()) return nullptr; return std::move(module_or).value(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 22:19:26 UTC 2024 - 7.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model_test.cc
TfLiteStatus QuantizeModel(ModelT* model, const TensorType& input_type, const TensorType& output_type, bool allow_float, std::string& output_buffer) { return QuantizeModel(model, input_type, output_type, allow_float, /*operator_names=*/{}, TensorType_INT8, output_buffer); } TfLiteStatus QuantizeModel(ModelT* model, const TensorType& input_type,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/converter_python_api.cc
const tflite::TensorType input_type = FromTocoDataTypeToTflitToTensorType(input_data_type); const tflite::TensorType output_type = FromTocoDataTypeToTflitToTensorType(output_data_type); std::string output_model; const absl::string_view input_model_buffer(buf, length); auto status = mlir::lite::QuantizeModel( input_model_buffer, input_type, output_type, inference_tensor_type,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 19.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
TypeRange input_types, ArrayRef<func::FuncOp> functions, int64_t max_iterations); // Propagates shapes to regions given the shapes of the inputs of the regions. // All regions provided in `regions` are assumed to have inputs of type // `input_types`.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
platforms/core-configuration/configuration-cache/src/integTest/groovy/org/gradle/internal/cc/impl/ConfigurationCacheBuildServiceIntegrationTest.groovy
} static String convertingValueSourceImpl(String valueSourceClassName, String inputType, String returnType, String conversion) { """ abstract class $valueSourceClassName implements ${ValueSource.name}<$returnType, Params> { interface Params extends ${ValueSourceParameters.name} { Property<$inputType> getInput() } @Override $returnType obtain() {
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Mon Jun 10 11:47:23 UTC 2024 - 29.1K bytes - Viewed (0) -
pkg/workloadapi/workload.pb.go
11, // 20: istio.workload.Workload.ServicesEntry.value:type_name -> istio.workload.PortList 21, // [21:21] is the sub-list for method output_type 21, // [21:21] is the sub-list for method input_type 21, // [21:21] is the sub-list for extension type_name 21, // [21:21] is the sub-list for extension extendee 0, // [0:21] is the sub-list for field type_name }
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Wed Jun 12 18:02:35 UTC 2024 - 65.9K bytes - Viewed (0)