BVhd]tdZddlZddlZddlZddlmZmZmZmZm Z m Z m Z m Z m Z mZddlZddlmZddlmZddlmZddlmZeZdej6ej8d d d Zd Zd e eefde ee e!ffdZ"ej2dGddZ#ej2dGddZ$y)z$Python TF-Lite QuantizationDebugger.N) AnyCallableDictIOIterableListMappingOptionalSequenceTuple)convert) interpreter)metrics) tf_exportc|jSN)sizediffss h/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/lite/tools/optimize/debugging/python/debugger.pyr#s %**cRtjtj|Sr)npmaxabsrs rrr&s266"&&-#8rc2tj|dzS)N)raveragers rrr's 5!8(<r) num_elementsstddev mean_error max_abs_errormean_squared_error NumericVerify tensor_detailreturncH|d}|sy|dr|dr|dd|ddfSy)zBReturns first scale and zero point from tensor detail, if present.quantization_parametersNscales zero_pointsr)r& quant_paramss r_get_quant_paramsr.-sF89,  ( ] ;  "1 %|M'B1'E FF rz*lite.experimental.QuantizationDebugOptionsc2eZdZdZ d deeeeejge ffdeeeee eje ejge ffdeeeee eje eje e ge ffdee edee eded dfd Zy) QuantizationDebugOptionsz5Debug options to set up a given QuantizationDebugger.Nlayer_debug_metricsmodel_debug_metricslayer_direct_compare_metricsdenylisted_opsdenylisted_nodesfully_quantizer'c ||_||_||_g}|||fD]$}||j|j &t |t t |k7r td||_||_ ||_ y)a<Initializes debugger options. Args: layer_debug_metrics: a dict to specify layer debug functions {function_name_str: function} where the function accepts result of NumericVerify Op, which is value difference between float and dequantized op results. The function returns single scalar value. model_debug_metrics: a dict to specify model debug functions {function_name_str: function} where the function accepts outputs from two models, and returns single scalar value for a metric. (e.g. accuracy, IoU) layer_direct_compare_metrics: a dict to specify layer debug functions {function_name_str: function}. The signature is different from that of `layer_debug_metrics`, and this one gets passed (original float value, original quantized value, scale, zero point). The function's implementation is responsible for correctly dequantize the quantized value to compare. Use this one when comparing diff is not enough. (Note) quantized value is passed as int8, so cast to int32 is needed. denylisted_ops: a list of op names which is expected to be removed from quantization. denylisted_nodes: a list of op's output tensor names to be removed from quantization. fully_quantize: Bool indicating whether to fully quantize the model. Besides model body, the input/output will be quantized as well. Corresponding to mlir_quantize's fully_quantize parameter. Raises: ValueError: when there are duplicate keys Nz%Provided metrics have duplicate keys.) r1r2r3extendkeyslenset ValueErrorr4r5r6) selfr1r2r3r4r5r6r9rs r__init__z!QuantizationDebugOptions.__init__<sT 3D2D(DD% D02N$   GLLN# $  4yCD N" > ??(D,D(Dr)NNNNNF)__name__ __module__ __qualname____doc__r r strrrndarrayfloatr intrboolr>r,rrr0r08s'= JN-1#597;&+9)$,WS5=rzzl>C?D6E6E.F%G9)%-W(2::"68L!M!&"'((.)%* 9).6gc8RZZ((2::*>sKD?7.9) (S 29)"*$s)!49) $9)159)rr0z&lite.experimental.QuantizationDebuggerc`eZdZdZ d&deedeedeedeedeegee e jfdee d ee d dfd Zed e fd Zej"d e d dfdZdZded efdZd efdZd efdZ d'd e d e deedeed df dZd e d e fdZd e d e fdZd(dZd eeeeefffdZd eeeffdZdej@de e jded dfdZ!dej@d e"e jfd Z#d e"efd!Z$d"ed e%ee&ffd#Z'd$e(ed dfd%Z)y))QuantizationDebuggeraDebugger for Quantized TensorFlow Lite debug mode models. This can run the TensorFlow Lite converted models equipped with debug ops and collect debug information. This debugger calculates statistics from user-defined post-processing functions as well as default ones. Nquant_debug_model_pathquant_debug_model_contentfloat_model_pathfloat_model_content debug_dataset debug_options converterr'c||_|xs t|_d|_d|_d|_d|_||jjrH|j}|j||_|jj|_||_|j||_|jj|_|j|j|j|j|j ndtj|||jjdu|_|jjrtj|||_|j#y)aHRuns the TFLite debugging model with given debug options. Args: quant_debug_model_path: Path to the quantized debug TFLite model file. quant_debug_model_content: Content of the quantized debug TFLite model. float_model_path: Path to float TFLite model file. float_model_content: Content of the float TFLite model. debug_dataset: a factory function that returns dataset generator which is used to generate input samples (list of np.ndarray) for the model. The generated elements must have same types and shape as inputs to the model. debug_options: Debug options to debug the given model. converter: Optional, use converter instead of quantized model. Raises: ValueError: If the debugger was unable to be created. Attributes: layer_statistics: results of error metrics for each NumericVerify op results. in {layer_name: {metric_name: metric}} format. model_statistics: results of error metrics for difference between float and quantized models. in {metric_name: metric} format. N float_model)!experimental_preserve_all_tensors) _data_genr0_debug_optionsrPcalibrated_modelrS_float_interpreterr2 optimizations _set_converter_options_for_floatr &_set_converter_options_for_calibration_init_from_converter _interpreter Interpreterr3_quant_interpreter_initialize_stats) r=rJrKrLrMrNrOrPold_optimizationss rr>zQuantizationDebugger.__init__sN@#DN'E+C+EDDN DD"D   0 0%33>>yI>>113"3 BB9Mdn"nn446d     ..   &&  ( !- 8 8 #!!>>dJ !Md    0 0".":": 1#3rc|jSr)rVr=s roptionszQuantizationDebugger.optionss   rrdc||_|jr |jsy|j|j|j|j|j|j y)NrR)rVrPrWr\rSr`)r=rds rrdzQuantizationDebugger.optionss_!D >>!6!6    $$ &  rc Zt|_|jjD]2}|jj |dDcic]}||d c}4d|_d|_|js tdtj|_ |jjr/|jj |jjd|_d|_t!j"|_|j$j'ycc}w)z"Helper function initializes stats.outputsindexNz4Please check if the quantized model is in debug mode)dict _defining_opr__get_ops_detailsupdate_numeric_verify_tensor_details_numeric_verify_op_details"_get_numeric_verify_tensor_detailsr<_DEFAULT_LAYER_DEBUG_METRICScopy_layer_debug_metricsrVr1layer_statisticsmodel_statistics metrics_stub TFLiteMetrics_metrics"increase_counter_debugger_creation)r=op_info tensor_idxs rr`z&QuantizationDebugger._initialize_statss D**;;=O :A):L MJ:ww' ' MOO+/D'&*D#  2 2 4 M NN < A A CD .. &&t':':'N'NO D D ..0DMMM446 Ns D( is_debugc|js tdtj|j|jj |j j||j j|j jS)NzSNo converter found, use this function with the converter option in the constructor.disable_per_channelr6enable_numeric_verifyr4r5) rPr<r mlir_quantizerW!_experimental_disable_per_channelrVr6r4r5)r=r{s r_get_quantized_modelz)QuantizationDebugger._get_quantized_modelsv >> > ??    NNLL**99&**99,,==  ??rc&|jdS)alReturns a non-instrumented quantized model. Convert the quantized model with the initialized converter and return bytes for nondebug model. The model will not be instrumented with numeric verification operations. Returns: Model bytes corresponding to the model. Raises: ValueError: if converter is not passed to the debugger. Fr{rrcs rget_nondebug_quantized_modelz1QuantizationDebugger.get_nondebug_quantized_models  $ $e $ 44rc&|jdS)aReturns an instrumented quantized model. Convert the quantized model with the initialized converter and return bytes for model. The model will be instrumented with numeric verification operations and should only be used for debugging. Returns: Model bytes corresponding to the model. Raises: ValueError: if converter is not passed to the debugger. Trrrcs rget_debug_quantized_modelz.QuantizationDebugger.get_debug_quantized_models  $ $d $ 33rrWrSc(tj||j|jd|j|j |_tj|j |_ d|_ |tj||_ yy)aConvert the model and apply options. Converts the quantized model and initializes a quantized model interpreter with the quantized model. Returns a float model interpreter if float model is provided. Args: options: a QuantizationDebugOptions object. converter: an initialized tf.lite.TFLiteConverter. calibrated_model: Calibrated model bytes. float_model: Float model bytes. Tr}) model_contentN) r rrr6r4r5 quant_modelr]r^r_rX)r=rdrPrWrSs rr\z)QuantizationDebugger._init_from_converters",,%GG--"-- 11 3D+66&&(D"D , 8 8#!%drc,|jrg|_|S)?Verify converter options and set required experimental options.)rYr=rPs rrZz5QuantizationDebugger._set_converter_options_for_float2s "i rc~|js td|js tdd|_d|_|S)rz@converter object must set optimizations to lite.Optimize.DEFAULTz0converter object must set representative_datasetT)rYr<representative_datasetexperimental_mlir_quantizer_experimental_calibrate_onlyrs rr[z;QuantizationDebugger._set_converter_options_for_calibration9sJ  " "  L NN  + + I JJ,0I)-1I* rc|j|_|jjr|j |_yy)zRuns models and gets metrics.N)_collect_layer_statisticsrsrVr2_collect_model_statisticsrtrcs rrunzQuantizationDebugger.runFs8 ::d/rc Xtjd}d}|jD]}|j|j||d}|jj |j D]d}|d}|jj|d}|jjD]"\}}|||j||$f|jj|j D]}|d}|j|d} |jj| } | d\} } |jj| d} |jjjD]i\}}|||j||jj| |jj| | d d d| d d dk|j!D]$}|D]}t#j$||||<&|S) aCollects layer statistics by applying layer debug metrics. For all data from the given RepresentativeDataset, collect statistics per example by getting the NumericVerify op results in _quant_interpreter and calculating layer debug metrics on the results. Returns: aggregated per-layer statistics of NumericVerify results. {layer_name: {metric_name: metric}} c4tjtSr) collections defaultdictlistr,rrrz@QuantizationDebugger._collect_layer_statistics..Xs ''-rTFnamerhinputsrsubgraph_indexr)r*r+)rrrU_set_input_tensorsr_invokero get_tensorrritemsappendrVr3rj_get_op_details_get_tensor_detailsvaluesrnanmean)r=rs initialize tensor_datar& tensor_namer metric_name metric_fnop_idx op_detailq_idxf_idxquant_input_detailrs rrz.QuantizationDebugger._collect_layer_statisticsLsf#..-/J~~'  d55{JOj $$& BBDN-#F+ ''22=3IJ&*&?&?&E&E&G N "K ; ' 4 ; ;Ie? @@ rctjt}d}|jD]}|j |j |||j j |j|j }g}|jrR|j |j|||jj |j|j}d}|jjjD] \}}||j|||"|jDcic]\}}|tj|c}}Scc}}w)aCollects model output metrics. For all data from the given RepresentativeDataset, collect all model output results from float model & quantized debug model, and calculate metrics by using model output functions. As a result, self.model_results is filled, where self.model_results[model_output_function_name] = `aggregated model output function value` (a scalar). Returns: aggregated per-model output discrepancy metrics. {metric_name: aggregated_metric} TF)rrrrUrr_r_get_output_tensorsrXrVr2rrrmean) r=rtrrquant_tensor_datafloat_tensor_datarrmetrics rrz.QuantizationDebugger._collect_model_statisticssT#..t4J~~'=  d55{JO $$&2243J3JK    # #[* > &&( 44T5L5LMj,,@@FFH= ; %,, '): ; ==#=2$4#9#9#;  K RWWV_$  s? E#rrrc|j}t|t|k7r-tdjt|t||rCt ||D]$\}}|j |d|j &|jt ||D]\}}|jtjk(r`|dtjk(rJt|}|r=|\}} tj||z | zjtj}|j|d|y)aSets input tensors into TFLite model Interpreter. Args: interpreter: a tf.lite.Interpreter object with allocated tensors. tensor_data: a list of Numpy array data. initialize: set to true when input is first set for the interpreter, to set input shapes and allocate tensors. Raises: ValueError: when inputs can't be set, or size of provided inputs does not match size of model inputs. zPNumber of inputs provided ({}) does not match number of inputs to the model ({})rhdtypeN)get_input_detailsr:r<formatzipresize_tensor_inputshapeallocate_tensorsrrfloat32int8r.roundastype set_tensor) r=rrr input_details input_detailtensorr-scale zero_points rrz'QuantizationDebugger._set_input_tensorss" 113M =S--  !6#k"2C 4FG II"%m["AM ,'' W(=v||LM""$ #M; ?< f  # W(=(H(6 * %88Ve^z9:AA"''J&\'2F; Y))==  q>2 - .x 8 ; - .} =a @ *W tL)  r)NNNNNNN)NN)r'N)*r?r@rArBr rCbytesrrr rrDr0TFLiteConverterr>propertyrdsetterr`rGrrrr\rZr[rrrErrr]r^rrrror rFrrrr,rrrIrIxsj8<<@156::>CG6:>'/}>*25/>"*#>%-UO > 'xx 455(78 >''?@>#?3>@D>@ /  >> 5 $  72 ?4 ?E ? 5E 5 4 4"@D:> %$<%&5%.6e_%)1 %DH %>&+: & +: ? 4c4U 3C.C)D4l,c5j)9,\ rs+ %%%*>B,-ff**8<  *38$)1%s 2C)DAB<)<)C<)~=>ll?lr