\;jRddlZddlmZddlmZddlmZddlmZGddeZ y) N)fleet)Layer) QuantConfig) Quantizationc<eZdZdZdeffd ZdZddefdZxZ S)PTQz; Applying post training quantization to the model. configc$t||y)N)super__init__)selfr __class__s `G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/quantization/ptq.pyr z PTQ.__init__s  cT tjdkDryy#t$rYywxYw)NTF)r worker_num Exception)rs r_is_parallel_trainingzPTQ._is_parallel_training s0 !A%  s  ''modelcR|}|s<|jrJdtj|}|j|jrJd|j j ||j||j |j||j |S)a; Create a model for post-training quantization. The quantization configuration will be propagated in the model. And it will insert observers into the model to collect and compute quantization parameters. Args: model(Layer) - The model to be quantized. inplace(bool) - Whether to modify the model in-place. Return: The prepared model for post-training quantization. Examples: .. code-block:: python >>> from paddle.quantization import PTQ, QuantConfig >>> from paddle.quantization.observers import AbsmaxObserver >>> from paddle.vision.models import LeNet >>> observer = AbsmaxObserver() >>> q_config = QuantConfig(activation=observer, weight=observer) >>> ptq = PTQ(q_config) >>> model = LeNet() >>> model.eval() >>> quant_model = ptq.quantize(model) >>> print(quant_model) LeNet( (features): Sequential( (0): QuantedConv2D( (weight_quanter): AbsmaxObserverLayer() (activation_quanter): AbsmaxObserverLayer() ) (1): ObserveWrapper( (_observer): AbsmaxObserverLayer() (_observed): ReLU() ) (2): ObserveWrapper( (_observer): AbsmaxObserverLayer() (_observed): MaxPool2D(kernel_size=2, stride=2, padding=0) ) (3): QuantedConv2D( (weight_quanter): AbsmaxObserverLayer() (activation_quanter): AbsmaxObserverLayer() ) (4): ObserveWrapper( (_observer): AbsmaxObserverLayer() (_observed): ReLU() ) (5): ObserveWrapper( (_observer): AbsmaxObserverLayer() (_observed): MaxPool2D(kernel_size=2, stride=2, padding=0) ) ) (fc): Sequential( (0): QuantedLinear( (weight_quanter): AbsmaxObserverLayer() (activation_quanter): AbsmaxObserverLayer() ) (1): QuantedLinear( (weight_quanter): AbsmaxObserverLayer() (activation_quanter): AbsmaxObserverLayer() ) (2): QuantedLinear( (weight_quanter): AbsmaxObserverLayer() (activation_quanter): AbsmaxObserverLayer() ) ) ) z3'inplace' is not compatible with parallel training.ziPost-Training Quantization shoud not work on training models. Please set evaluation mode by model.eval().) rcopydeepcopyevaltraining_config_specify_convert_to_quant_layers_insert_activation_observers)rrinplace_models rquantizez PTQ.quantize)sN..0 ED E0]]5)F KKM w v w  f% %%fdll; ))&$,,? r)F) __name__ __module__ __qualname____doc__rr rrr# __classcell__)rs@rr r s'!{!TeTrr ) rpaddle.distributedr paddle.nnrr rr#rr rrr,s$ $"e,er