ёixSSKJr SSKrSSKJr SSKJr SSKJr \(a SSK J r SSK J r "S S \5r g) ) annotationsN) TYPE_CHECKING)fleet) Quantization)Layer) QuantConfigcD^\rSrSrSrSU4SjjrSrSS SjjrSrU=r $) PTQz3 Applying post training quantization to the model. c$>[TU]U5 g)N)super__init__)selfconfig __class__s W/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/quantization/ptq.pyr PTQ.__init__"s  cZ[R"5S:agg![a gf=f)NTF)r worker_num Exception)rs r_is_parallel_trainingPTQ._is_parallel_training%s0 !A%  s  **cnUnU(dBUR5(aS5e[R"U5nUR5 UR(aS5eUR R U5 URX0R 5 URX0R 5 U$)aA 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().) rcopydeepcopyevaltraining_config_specify_convert_to_quant_layers_insert_activation_observers)rmodelinplace_models rquantize PTQ.quantize.sN1133 E 3]]5)F KKM>> x ! f% %%fll; ))&,,? r)rr returnNone)F)r%rr&boolr+r) __name__ __module__ __qualname____firstlineno____doc__rrr(__static_attributes__ __classcell__)rs@rr r s!TTrr ) __future__rrtypingrpaddle.distributedrr(r paddle.nnrrr r r*rrr9s-# $"#e,er