Renamed option with the prefix EXPERIMENTAL_.
Change-Id: Idb84736507d5c07ebdf182b8a15d55906d0d7fc0
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@ -98,7 +98,7 @@ class OpsSet(enum.Enum):
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# and int16 activations.
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# and int16 activations.
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# Specifying this will throw an error for operations that do not yet have
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# Specifying this will throw an error for operations that do not yet have
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# quantized implementations.
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# quantized implementations.
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TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 = "TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8"
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EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 = "EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8"
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def __str__(self):
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def __str__(self):
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return self.value
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return self.value
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@ -356,7 +356,7 @@ class QuantizationMode(object):
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def _is_int16x8_target_required(self):
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def _is_int16x8_target_required(self):
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return bool(
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return bool(
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set(self._target_spec.supported_ops).intersection([
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set(self._target_spec.supported_ops).intersection([
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OpsSet.TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
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OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
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]))
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]))
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def _is_allow_float(self):
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def _is_allow_float(self):
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@ -885,7 +885,7 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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# Quantize model to Int8: with disable mlir
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# Quantize model to Int8: with disable mlir
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('UseTfliteBuiltinsIntDisableMLIR', [lite.OpsSet.TFLITE_BUILTINS_INT8], False),
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('UseTfliteBuiltinsIntDisableMLIR', [lite.OpsSet.TFLITE_BUILTINS_INT8], False),
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# Quantize model to Int16: with disable mlir
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# Quantize model to Int16: with disable mlir
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('UseTfliteBuiltinsInt16DisableMLIR', [lite.OpsSet.TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8], False))
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('UseTfliteBuiltinsInt16DisableMLIR', [lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8], False))
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def testCalibrateAndQuantizeBuiltinInt(self, supported_ops, enable_mlir):
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def testCalibrateAndQuantizeBuiltinInt(self, supported_ops, enable_mlir):
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with ops.Graph().as_default():
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with ops.Graph().as_default():
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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