U d$@sddlZddlmZddlmZmZmZddlmZddl m Z m Z m Z ddl mZmZmZe dejjjdZGdd d ejjjZGd d d eejZGd d d eejZGdddeejZdS)N)_single_pair_triple) _FusedModule)TupleTypeVarUnion) _size_1_t _size_2_t _size_3_tMOD)boundc@szeZdZeZd eeeedfeedfeedfeedfeeedfeeedd ddZ ddZ e dd Z d d Z dS) _ConvNdN.) in_channels out_channels kernel_sizestridepaddingdilation transposedoutput_paddinggroupsbias padding_modereturnc CsX| |d}tjjjj|||||||||| | | f || s@td| |_| j|d|_dS)N)devicedtypez'qconfig must be provided for QAT module)factory_kwargs) nnmodulesconvr__init__AssertionErrorqconfigweightweight_fake_quant)selfrrrrrrrrrrrr#rrrr'=/tmp/pip-unpacked-wheel-ua33x9lu/torch/nn/qat/modules/conv.pyr!s   z_ConvNd.__init__cCs||||j|jSNZ _conv_forwardr%r$rr&inputr'r'r(forward%sz_ConvNd.forwardc Cst||jks(td|jd|jjt|ds:td|jsHtdtt|tr^|d}|j}||j|j |j |j |j |j |j|jdk |j|d }|j|_|j|_|S) zCreate a qat module from a float module Args: `mod`: a float module, either produced by torch.ao.quantization utilities or directly from user zqat.z.from_float only works for r#z,Input float module must have qconfig definedz,Input float module must have a valid qconfigrN)rrrrrrr#)type _FLOAT_MODULEr"__name__hasattrr# issubclassrrrrrrrrrrr$)clsmodr#Zqat_convr'r'r( from_float(s4z_ConvNd.from_floatc Cst|}||j|j|j|j|j|j|j|j dk |j }t j |j|_|j dk rjt j |j |_ t|tr|g}t|dst|}|||j|}||j|S|SdS)z This works for both single qat conv, and the qat conv - relu modules to convert the qat module to a floating point module N_FLOAT_RELU_MODULE)r._FLOAT_CONV_MODULErrrrrrrrrtorchr Parameterr$detachr2rr1r"r6appendr/ZtrainZtraining)r&r3r rZreluZfusedr'r'r(to_floatCs0      z_ConvNd.to_float)NNN)r0 __module__ __qualname__r r/intrboolstrr!r- staticmethodr5r<r'r'r'r(r s,       rc s^eZdZdZejZejZd eee e e e e fe ee e dd fdd Z efd d ZZS) Conv1daZ A Conv1d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as :class:`~torch.nn.Conv1d` Similar to :class:`~torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight rTzerosN rrrrrrrrrrc s^t|} t|}t|tr|nt|}t|}tj||| |||dtd||| | | | ddSNFr) rrrrrrrrr#rr)r isinstancerAsuperr!r&rrrrrrrrrr#rrZ kernel_size_Zstride_Zpadding_Z dilation_ __class__r'r(r!qs( zConv1d.__init__cst||Sr)rIr5r3r4rKr'r(r5szConv1d.from_float) rDrrDrDTrENNN)r0r=r>__doc__rrCr/r7r?r rrAr@r! classmethodr5 __classcell__r'r'rKr(rCas4  !rCc sfeZdZdZejZejZdeee e e e e fe ee e dd fdd Z d d Zefd d ZZS)Conv2da A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv2d`, please see https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d for documentation. Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight rDrTrENrFc s^t|} t|}t|tr|nt|}t|}tj||| |||dtd||| | | | ddSrG)rrHrArIr!rJrKr'r(r!s( zConv2d.__init__cCs||||j|jSr)r*r+r'r'r(r-szConv2d.forwardcst||Sr)rMrNrKr'r(r5szConv2d.from_float) rDrrDrDTrENNN)r0r=r>rOrrRr/r7r?r rrAr@r!r-rPr5rQr'r'rKr(rRs6 !rRc sfeZdZdZejZejZdeee e e e e fe ee e dd fdd Z d d Zefd d ZZS)Conv3da A Conv3d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv3d`, please see https://pytorch.org/docs/stable/nn.html?highlight=conv3d#torch.nn.Conv3d for documentation. Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight rDrTrENrFc s^t|} t|}t|tr|nt|}t|}tj||| |||dtd||| | | | ddSrG)rrHrArIr!rJrKr'r(r!s( zConv3d.__init__cCs||||j|jSr)r*r+r'r'r(r-szConv3d.forwardcst||Sr)rMrNrKr'r(r5szConv3d.from_float) rDrrDrDTrENNN)r0r=r>rOrrSr/r7r?r rrAr@r!r-rPr5rQr'r'rKr(rSs6 !rS)r8Ztorch.nnrZtorch.nn.modules.utilsrrrZtorch.nn.intrinsicrtypingrrrZtorch.nn.common_typesr r r rr rr rCrRrSr'r'r'r(s  W5: