BVh &dZddlZddlmZddlmZddlmZddlmZddl m Z ddl m Z e d e d Zd ZGd d Zy)z?Python wrapper for post training quantization with calibration.N) Component) convert_phase) SubComponent) Interpreter)dtypes) LazyLoader_calibration_wrapperzKtensorflow.lite.python.optimize._pywrap_tensorflow_lite_calibration_wrapperc,tj|S)z0Adds intermediate tensors to fused op if needed.)r AddIntermediateTensors model_contents Z/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/lite/python/optimize/calibrator.pyadd_intermediate_tensorsr&s  4 4] CCc2eZdZdZ d dZdZdZeeje je je jdddfdZeeje j d d Zeeje j"d Zy) CalibratorzqCalibrates a floating point model and then quantizes it. This is an internal class, not a public interface. Nc|s td|g}|g} tj||||_||_|js tdd|_y#t $r}td|zd}~wwxYw)aConstructor. Args: model_content: Content of a TF-Lite Flatbuffer file. custom_op_registerers_by_name: List of str (symbol names) that take a pointer to a MutableOpResolver and register custom ops. custom_op_registerers_by_func: List of functions that take a pointer to a MutableOpResolver and register custom ops. Raises: ValueError: If the calibrator was unable to open the model. z"`model_content` must be specified.NzFailed to parse the model: %s.zFailed to parse the model.) ValueErrorr CalibrationWrapper _calibrator_model_content Exception _interpreter)selfr custom_op_registerers_by_namecustom_op_registerers_by_funces r__init__zCalibrator.__init__1s$  ; <<$,&(#$,&(#=-@@  ' 'd *d    3 44D = 7!; <<=s#A A5"A00A5cg}|jj|}t|jj d}|D]\}}|j |||S)Nc|ddS)Nindex)items rz:Calibrator._create_input_array_from_dict..[sa)r)key)rget_signature_runnersortedget_input_detailsitemsappend)r signature_keyinputs input_arraysignature_runner input_details input_name_s r_create_input_array_from_dictz(Calibrator._create_input_array_from_dictVsnK((==mL**,224 )M'- A +,- rc i}|D]}t|tr`t|dts td|jt |j |_|d}|j||d}n{t|tr<|jt |j |_d}|jd|}n/t|trd}|}ntdj|||vrd||<|r}|>|jj|Dcgc]}t|jc}|nu|jj|Dcgc]}t|jc}n8||jj|n|jj||jj|||jj|ycc}wcc}w)zFeed tensors to the calibrator.r!zgYou need to provide either a dictionary with input names and values in the second argument in the tupleNr ra"You need to provide either a dictionary with input names and values, a tuple with signature key and a dictionary with input names and values, or an array with input values in the order of input tensors of the graph in the representative_dataset function. Unsupported value from dataset: {}.T) isinstancetupledictrrrrr3listformatrPrepareshape FeedTensor)r dataset_gen resize_input initializedsampler,r.ss r _feed_tensorszCalibrator._feed_tensorsasK-31 FE "&)T*     $)8K8KL$ q 88 6!9  fd #    $)8K8KL$  88vF fd #   2 39&.    k )%) M"   &    $ $(341agg4m     $ $[%Id177m%I J  &    $ $] 3    $ $ &  " ##K? ##K0g31N5&Js G8 G= TFc |j|||jjtj|j j tj|j j |tj|j j tj|j j || S)aCalibrates the model with specified generator and then quantizes it. The input shapes of the calibrator are resized with the calibration data if `resize_input` is set. Returns: A quantized model. Args: dataset_gen: A generator that generates calibration samples. input_type: A tf.dtype representing the desired real-value input type. output_type: A tf.dtype representing the desired real-value output type. allow_float: A boolean. False if the resulting model cannot perform float computation, useful when targeting an integer-only backend. If False, an error will be thrown if an operation cannot be quantized, otherwise the model will fallback to float ops. activations_type: A tf.dtype representing the desired type for activations. bias_type: A tf.dtype representing the desired type for bias. resize_input: A boolean. True if the shape of the sample data is different from the input. disable_per_channel: A boolean. True if disabling per-channel quantization. disable_per_channel_quantization_for_dense_layers: A boolean. True if disabling per-channel quantization only in Dense layers. rBr QuantizeModelnpdtypeas_numpy_dtypenum) rr= input_type output_type allow_floatactivations_type bias_typer>disable_per_channel1disable_per_channel_quantization_for_dense_layerss rcalibrate_and_quantizez!Calibrator.calibrate_and_quantizesT {L1    ) ) **,-11 ++-.22 !002377 ))+,009 rc|j|||jjtj|j j tj|j j ||S)aCalibrates the model with specified generator and then quantizes it. Only the single op with output op_output_name will be quantized. The input shapes of the calibrator are resized with the calibration data. Returns: A quantized model. Args: dataset_gen: A generator that generates calibration samples. input_type: A tf.dtype representing the desired real-value input type. output_type: A tf.dtype representing the desired real-value output type. allow_float: A boolean. False if the resulting model cannot perform float computation, useful when targeting an integer-only backend. If False, an error will be thrown if an operation cannot be quantized, otherwise the model will fallback to float ops. op_output_name: A string, only this op will be quantized. resize_input: A boolean. True if the shape of the sample data is different from the input. rD)rr=rJrKrLop_output_namer>s rcalibrate_and_quantize_singlez(Calibrator.calibrate_and_quantize_singlesjB {L1    ) ) **,-11 ++-.22  rc\|j|d|jjS)zCalibrates the model with specified generator. Returns: A model with min and max calibration stats. Args: dataset_gen: A generator that generates calibration samples. T)r>)rBr Calibrate)rr=s r calibratezCalibrator.calibrates, {6    % % ''r)NN)T)__name__ __module__ __qualname____doc__rr3rBrrOPTIMIZE_TFLITE_MODELr#QUANTIZE_USING_DEPRECATED_QUANTIZERrint8int32rQrT CALIBRATErWr#rrrr+s%)$( #J 71r%%66{{ 8=/ /b%%66# #J00,2H2HI (J (rr)r[numpyrF$tensorflow.lite.python.convert_phaserrr"tensorflow.lite.python.interpreterrtensorflow.python.frameworkr"tensorflow.python.util.lazy_loaderrglobalsr rrr#rrrgsLF:>=:.9 " I 6 D X(X(r