| /external/tensorflow/tensorflow/lite/tools/optimize/ |
| D | quantize_model.cc | 149 const TensorType& activations_type, bool disable_per_channel = false) { in GetOperatorProperty() argument
156 if (activations_type == TensorType_INT16 && !property.quantizable_int16) { in GetOperatorProperty()
214 const TensorType& activations_type) { in PopulateRealValueOpSet() argument
231 operator_name, activations_type); in PopulateRealValueOpSet()
412 const TensorType& activations_type) { in SetInputType() argument
419 activations_type == TensorType_INT16 ? "int16" : "int8"; in SetInputType()
476 const TensorType& activations_type) { in SetOutputType() argument
483 activations_type == TensorType_INT16 ? "int16" : "int8"; in SetOutputType()
539 const TensorType& activations_type, in SetInputAndOutputTypes() argument
563 model, subgraph, subgraph->inputs[i], input_type, activations_type); in SetInputAndOutputTypes()
[all …]
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| D | quantize_model.h | 76 const TensorType& activations_type,
86 bool allow_float, const TensorType& activations_type,
95 bool allow_float, const TensorType& activations_type,
108 const TensorType& activations_type,
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| D | quantization_utils.h | 151 TfLiteStatus GetQuantizationParams(TensorT* tensor, TensorType activations_type,
156 TfLiteStatus QuantizeActivation(TensorT* tensor, TensorType activations_type,
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| D | quantization_utils.cc | 106 TfLiteStatus GetQuantizationParams(TensorT* tensor, TensorType activations_type, in GetQuantizationParams() argument
109 if (activations_type == TensorType_INT8) { in GetQuantizationParams()
114 } else if (activations_type == TensorType_INT16) { in GetQuantizationParams()
123 activations_type); in GetQuantizationParams()
752 TfLiteStatus QuantizeActivation(TensorT* tensor, TensorType activations_type, in QuantizeActivation() argument
755 tensor, activations_type, tensor->quantization.get(), error_reporter)); in QuantizeActivation()
756 tensor->type = activations_type; in QuantizeActivation()
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| /external/tensorflow/tensorflow/lite/python/optimize/ |
| D | calibrator_test.py | 34 def test_calibration_with_quantization(self, activations_type): argument
49 activations_type)
57 def test_calibration_with_quantization_allow_float(self, activations_type): argument
72 activations_type)
111 self, activations_type): argument
128 activations_type)
168 activations_type=dtypes.int8,
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| D | calibrator.py | 147 activations_type=dtypes.int8, argument
179 np.dtype(activations_type.as_numpy_dtype()).num,
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| D | calibration_wrapper.cc | 578 TfLiteType activations_type = in QuantizeModel() local
595 TfLiteTypeToSchemaType(activations_type), in QuantizeModel()
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| /external/tensorflow/tensorflow/lite/python/ |
| D | lite.py | 338 def activations_type(self): member in QuantizationMode
351 if self.activations_type() == _dtypes.int16:
353 elif self.activations_type() == _dtypes.int8:
365 self.activations_type()),
446 if self.activations_type() == _dtypes.float32:
451 if self.activations_type() == _dtypes.int8 and bias_type != _dtypes.int32:
456 if self.activations_type(
590 def _quantize(self, result, input_type, output_type, activations_type, argument
619 activations_type != _dtypes.int16):
633 activations_type,
[all …]
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| /external/tensorflow/tensorflow/compiler/mlir/lite/quantization/lite/ |
| D | quantize_model_test.cc | 50 const TensorType& activations_type, ErrorReporter* error_reporter, in QuantizeModel() argument
54 TensorType inference_tensor_type = activations_type;
99 bool allow_float, const TensorType& activations_type, in QuantizeModelAllOperators() argument
102 /*operator_names=*/{}, activations_type, error_reporter, in QuantizeModelAllOperators()
111 const TensorType& activations_type, in QuantizeModelAllOperators() argument
114 /*operator_names=*/{}, activations_type, error_reporter); in QuantizeModelAllOperators()
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