o #Õjwã@sLddlZddlmZddlmZddlmZddlmZGdd„deƒZ dS) éN)Úfleet)ÚLayeré)Ú QuantConfig)Ú Quantizationcs>eZdZdZdef‡fdd„ Zdd„Zd defd d „Z‡Z S) ÚPTQz; Applying post training quantization to the model. Úconfigcstƒ |¡dS)N)ÚsuperÚ__init__)Úselfr©Ú __class__©úX/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/quantization/ptq.pyr sz PTQ.__init__cCs.z t ¡dkr WdSWdStyYdSw)NéTF)rZ worker_numÚ Exception)r rrrÚ_is_parallel_training s  ÿzPTQ._is_parallel_trainingFÚmodelcCsd|}|s| ¡r Jdƒ‚t |¡}| ¡|jrJdƒ‚|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().) rÚcopyÚdeepcopyÚevalZtrainingÚ_configZ_specifyZ_convert_to_quant_layersZ_insert_activation_observers)r rZinplaceZ_modelrrrÚquantize)s Gÿþ ÿþ z PTQ.quantize)F) Ú__name__Ú __module__Ú __qualname__Ú__doc__rr rrrÚ __classcell__rrr rrs  r) rZpaddle.distributedrZ paddle.nnrrrrrrrrrrÚs