o
    #Õj(  ã                   @   s@   d dl Z d dlmZ ddlmZ ddlmZ G dd„ deƒZdS )é    N)ÚLayeré   )ÚQuantConfig)ÚQuantizationc                       s6   e Zd ZdZdef‡ fdd„Zd	defdd„Z‡  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                    s   t ƒ  |¡ d S )N)ÚsuperÚ__init__)Úselfr   ©Ú	__class__© úX/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/quantization/qat.pyr	   '   s   zQAT.__init__FÚmodelc                 C   sL   |j sJ dƒ‚|r|nt |¡}| 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().)ZtrainingÚcopyÚdeepcopyÚ_configZ_specifyZ_convert_to_quant_layersZ_insert_activation_observers)r
   r   ZinplaceZ_modelr   r   r   Úquantize*   s   FÿþzQAT.quantize)F)	Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   r	   r   r   Ú__classcell__r   r   r   r   r      s    r   )r   Z	paddle.nnr   r   r   r   r   r   r   r   r   r   Ú<module>   s
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