B d&@s|dZddlZddlmZddlmZddlmZddlmZddl m Z ddl m Z e d e d Zd d ZGd ddZdS)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_wrappercCs t|S)z0Adds intermediate tensors to fused op if needed.)rZAddIntermediateTensors) model_contentr \/var/www/html/venv/lib/python3.7/site-packages/tensorflow/lite/python/optimize/calibrator.pyadd_intermediate_tensors"sr c@s|eZdZdZdddZddZddZeej e j e j e jd d fd d Zeej e j dd dZeej e jddZdS) CalibratorzqCalibrates a floating point model and then quantizes it. This is an internal class, not a public interface. Nc Cs|s td|dkrg}|dkr$g}yt||||_||_Wn.tk rl}ztd|Wdd}~XYnX|js|tdd|_dS)aConstructor. 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Unsupported value from dataset: {}.TcSsg|]}t|jqSr )listshape).0sr r r ~sz,Calibrator._feed_tensors..cSsg|]}t|jqSr )r$r%)r&r'r r r r(s) isinstancetupledictrrrrr#r$formatrZPrepareZ FeedTensor)r dataset_gen resize_inputZ initializedsamplerr!r r r _feed_tensorsYsB         zCalibrator._feed_tensorsTFc CsR||||jt|jt|j|t|jt|j|S)ayCalibrates 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. )r0r QuantizeModelnpdtypeas_numpy_dtypenum) rr- input_type output_type allow_floatZactivations_typeZ bias_typer.Zdisable_per_channelr r r calibrate_and_quantizes # z!Calibrator.calibrate_and_quantizecCs6||||jt|jt|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. )r0rr1r2r3r4r5)rr-r6r7r8Zop_output_namer.r r r calibrate_and_quantize_singles z(Calibrator.calibrate_and_quantize_singlecCs|j|dd|jS)zCalibrates the model with specified generator. Returns: A model with min and max calibration stats. Args: dataset_gen: A generator that generates calibration samples. 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