o + iïã@shddlmZddlZddlmZddlmZddlmZer*ddl m Z ddl m Z Gd d „d eƒZ dS) é)Ú annotationsN)Ú TYPE_CHECKING)Úfleeté)Ú Quantization)ÚLayer)Ú QuantConfigcs6eZdZdZd‡fdd„ Zdd „Zdddd„Z‡ZS)ÚPTQz; Applying post training quantization to the model. ÚconfigrÚreturnÚNonecstƒ |¡dS)N)ÚsuperÚ__init__)Úselfr ©Ú __class__©úc/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/quantization/ptq.pyr"sz PTQ.__init__cCs.z t ¡dkr WdSWdStyYdSw)NéTF)rZ worker_numÚ Exception)rrrrÚ_is_parallel_training%s  ÿzPTQ._is_parallel_trainingFÚmodelrÚinplaceÚboolcCsd|}|s| ¡r Jdƒ‚t |¡}| ¡|jrJdƒ‚|j |¡| ||j¡| ||j¡|S)a9 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.zjPost-Training Quantization should not work on training models. Please set evaluation mode by model.eval().) rÚcopyÚdeepcopyÚevalZtrainingÚ_configZ_specifyZ_convert_to_quant_layersZ_insert_activation_observers)rrrÚ_modelrrrÚquantize.sG ÿ ÿ z PTQ.quantize)r rr r )F)rrrrr r)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrÚ __classcell__rrrrr s  r )Ú __future__rrÚtypingrZpaddle.distributedrrrZ paddle.nnrr rr rrrrÚs