Ë
    –\;jž  ã                   óF   — d dl Z d dlmZ ddlmZ ddlmZ  G d„ de«      Zy)é    N)ÚLayeré   )ÚQuantConfig)ÚQuantizationc                   ó6   ‡ — e Zd ZdZdefˆ fd„Zddefd„Zˆ xZS )ÚQATa  
    Tools used to prepare model for quantization-aware training.
    Args:
        config(QuantConfig) - Quantization configuration

    Examples:
        .. code-block:: python

            >>> from paddle.quantization import QAT, QuantConfig
            >>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
            >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
            >>> q_config = QuantConfig(activation=quanter, weight=quanter)
            >>> qat = QAT(q_config)
    Úconfigc                 ó$   •— t         ‰| �  |«       y )N)ÚsuperÚ__init__)Úselfr	   Ú	__class__s     €ú`G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/quantization/qat.pyr   zQAT.__init__'   s   ø€ Ü‰Ñ˜Õ ó    Úmodelc                 ó  — |j                   sJ d«       ‚|r|nt        j                  |«      }| j                  j	                  |«       | j                  || j                  «       | j                  || j                  «       |S )a  
        Create a model for quantization-aware training.

        The quantization configuration will be propagated in the model.
        And it will insert fake quanters into the model to simulate the quantization.

        Args:
            model(Layer) - The model to be quantized.
            inplace(bool) - Whether to modify the model in-place.

        Return: The prepared model for quantization-aware training.

        Examples:
            .. code-block:: python

                >>> from paddle.quantization import QAT, QuantConfig
                >>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
                >>> from paddle.vision.models import LeNet

                >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
                >>> q_config = QuantConfig(activation=quanter, weight=quanter)
                >>> qat = QAT(q_config)
                >>> model = LeNet()
                >>> quant_model = qat.quantize(model)
                >>> print(quant_model)
                LeNet(
                  (features): Sequential(
                    (0): QuantedConv2D(
                      (weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
                      (activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
                    )
                    (1): ObserveWrapper(
                      (_observer): FakeQuanterWithAbsMaxObserverLayer()
                      (_observed): ReLU()
                    )
                    (2): ObserveWrapper(
                      (_observer): FakeQuanterWithAbsMaxObserverLayer()
                      (_observed): MaxPool2D(kernel_size=2, stride=2, padding=0)
                    )
                    (3): QuantedConv2D(
                      (weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
                      (activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
                    )
                    (4): ObserveWrapper(
                      (_observer): FakeQuanterWithAbsMaxObserverLayer()
                      (_observed): ReLU()
                    )
                    (5): ObserveWrapper(
                      (_observer): FakeQuanterWithAbsMaxObserverLayer()
                      (_observed): MaxPool2D(kernel_size=2, stride=2, padding=0)
                    )
                  )
                  (fc): Sequential(
                    (0): QuantedLinear(
                      (weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
                      (activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
                    )
                    (1): QuantedLinear(
                      (weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
                      (activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
                    )
                    (2): QuantedLinear(
                      (weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
                      (activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
                    )
                  )
                )
        zeQuantization-Aware Training shoud work on training models. Please set training mode by model.train().)ÚtrainingÚcopyÚdeepcopyÚ_configÚ_specifyÚ_convert_to_quant_layersÚ_insert_activation_observers)r   r   ÚinplaceÚ_models       r   ÚquantizezQAT.quantize*   so   € ðL �NŠNð	sàró	sØá!‘¤t§}¡}°UÓ';ˆØ�‰×Ñ˜fÔ%Ø×%Ñ% f¨d¯l©lÔ;Ø×)Ñ)¨&°$·,±,Ô?Øˆr   )F)	Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   r   r   r   Ú__classcell__)r   s   @r   r   r      s"   ø„ ñð!˜{õ !ñL˜e÷ Lr   r   )r   Ú	paddle.nnr   r	   r   r   r   r   © r   r   Ú<module>r$      s!   ðó å å Ý "ô_ˆ,õ _r   