Vh GdZddlZddlmZmZddlZddlmcmcm Z ddl mZddl mcm ZddlmZddlmZddlmZddlmZddlmZmZmZgd ZGd d e j6ZGd d e j8ZGdde j:ZGdde j<ZGdde j>ZGdde j@Z y)z*Dynamically quantized convolution modules.N)ClassVarOptional)Tensor)ops)_reverse_repeat_padding) _size_1_t)_pair_single_triple)Conv1dConv2dConv3dConvTranspose1dConvTranspose2dConvTranspose3dc$eZdZUdZej Zeeej e d<dZ ee eeje d<dZ ee eeje d< ddededed ed ed ed ed edeffd ZdZddededefdZxZS)r aA dynamically quantized conv module with floating point tensors as inputs and outputs. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv1d` and :class:`~torch.ao.nn.quantized.dynamic.Conv1d` and Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv1d` for other attributes. Examples:: >>> # xdoctest: +SKIP >>> m = nn.quantized.dynamic.Conv1d(16, 33, 3, stride=2) >>> input = torch.randn(20, 16, 100) >>> output = m(input) _FLOAT_MODULEN_NNIQAT_CONV_BN_MODULE_NNI_CONV_RELU_MODULE in_channels out_channels kernel_sizestridepaddingdilationgroupsbias padding_modec  tjd|jd| | d} t|}t|}t |t r|n t|}t|}t |||||||||| f i| yN"The current implementation of the B module has poor numerical accuracy and its use is not recommendeddevicedtype)warningswarn _get_namer isinstancestrsuper__init__)selfrrrrrrrrrr$r% reduce_rangefactory_kwargs __class__s Z/home/dcms/DCMS/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.pyr,zConv1d.__init__7s  01A0BCE F %+U;k* '5'77;K8$            cy)NDynamicQuantizedConv1dr-s r1r(zConv1d._get_name\'r2inputr.returnc0t|jdk7r td|jdk7r:t |j dd}t j|||j}tjj||j|S)N Input shape must be `(N, C, L)`!zerosmode) lenshape ValueErrorrrrFpadr quantizedconv1d_dynamic_packed_paramsr-r8r. _reversed_padding_repeated_twices r1forwardzConv1d.forward_s u{{ q ?@ @    '/Ft||TVUVGW/X ,EE7d>O>OE}}++E43F3F UUr2) r>rr>r>Tr=NNTT)__name__ __module__ __qualname____doc__nnr rrtype__annotations__rrModulerintrboolr*r,r(rrK __classcell__r0s@r1r r s,02yyM8DO,8BFHXd299o%>?FAE8HT"))_$=>E## # #  #  #  # # # # # J( VV V4 V6 Vr2r ceZdZUdZej Zeeej e d<dZ ee eeje d<dZ ee eeje d< d fd ZdZd ded ed efd ZxZS)r aA dynamically quantized conv module with floating point tensors as inputs and outputs. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv2d` and :class:`~torch.ao.nn.quantized.dynamic.Conv2d` and Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv2d` for other attributes. Examples:: >>> # xdoctest: +SKIP >>> # With square kernels and equal stride >>> m = nn.quantized.dynamic.Conv2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input) rNrrc tjd|jd| | d} t|}t|}t|}t|}t |||||||||| f i| yr )r&r'r(r r+r,r-rrrrrrrrrr$r%r/r0s r1r,zConv2d.__init__s  01A0BCI I %+U;K( v.?            r2cy)NDynamicQuantizedConv2dr5r6s r1r(zConv2d._get_namer7r2r8r.r9c*t|jdk7r td|jdk7r7t |j }t j|||j}tjj||j|S)N#Input shape must be `(N, C, H, W)`!r=r?) rArBrCrrrrDrErrFconv2d_dynamicrHrIs r1rKzConv2d.forwards{ u{{ q BC C    '/Ft||/T ,EE7d>O>OE}}++E43F3F UUr2r>rr>r>Tr=NNrL)rMrNrOrPrQr rrrRrSrrrTrr,r(rrVrKrWrXs@r1r r ms402yyM8DO,8BFHXd299o%>?FAE8HT"))_$=>E # J( VV V4 V6 Vr2r ceZdZUdZej Zeeej e d<dZ ee eeje d<dZ ee eeje d< d fd ZdZd ded ed efd ZxZS)raA dynamically quantized conv module with floating point tensors as inputs and outputs. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv3d` and :class:`~torch.ao.nn.quantized.dynamic.Conv3d` and Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv3d` for other attributes. Examples:: >>> # xdoctest: +SKIP >>> # With square kernels and equal stride >>> m = nn.quantized.dynamic.Conv3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2)) >>> input = torch.randn(20, 16, 56, 56, 56) >>> output = m(input) rNrrc tjd|jd| dk7sJd| | d} t|}t|}t|}t|}t |||||||dtd||| f i| y)Nr!r"reflectz*Conv3d does not support reflection paddingr#Fr)r&r'r(r r+_initr[s r1r,zConv3d.__init__s  01A0BCE F y(V*VV($*U;k* '"8$         AJ     r2cy)NDynamicQuantizedConv3dr5r6s r1r(zConv3d._get_namer7r2r8r.r9c*t|jdk7r td|jdk7r7t |j }t j|||j}tjj||j|S)Nz&Input shape must be `(N, C, D, H, W)`!r=r?) rArBrCrrrrDrErrFconv3d_dynamicrHrIs r1rKzConv3d.forward s{ u{{ q EF F    '/Ft||/T ,EE7d>O>OE}}++E43F3F UUr2rbrL)rMrNrOrPrQrrrrRrSrrrTrr,r(rrVrKrWrXs@r1rrs402yyM8DO,8BFHXd299o%>?FAE8HT"))_$=>E $ L( VV V4 V6 Vr2rceZdZUdZej Zeeej e d< d fd Z dZ d de de de fdZxZS) rabA dynamically quantized transposed convolution module with floating point tensors as inputs and outputs. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose1d`. For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv1d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose1d` for other attributes. Examples:: >>> # xdoctest: +SKIP >>> # With square kernels and equal stride >>> m = nndq.ConvTranspose1d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nndq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> downsample = nndq.Conv1d(16, 16, 3, stride=2, padding=1) >>> upsample = nndq.ConvTranspose1d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12]) rc tjd|jd| | d} t|||||||||| | f i| yr r&r'r(r+r,r-rrrrroutput_paddingrrrrr$r%r/r0s r1r,zConvTranspose1d.__init__:n  01A0BCE F %+U;             r2cy)NDynamicQuantizedConvTranspose1dr5r6s r1r(zConvTranspose1d._get_name[0r2r8r.r9ct|jdk7r tdtjj j ||j|S)Nr;r<)rArBrCtorchrrFconv_transpose1d_dynamicrHr-r8r.s r1rKzConvTranspose1d.forward^sK u{{ q ?@ @yy"";; 4&&   r2 r>rrr>Tr>r=NNrL)rMrNrOrPrQrrrrRrSr,r(rrVrKrWrXs@r1rrmB9;8J8JM8D!3!345J  B1 V 4 6 r2rceZdZUdZej Zeeej e d< d fd Z dZ d de de de fdZxZS) raeA dynamically quantized transposed convolution module with floating point tensors as inputs and outputs. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose2d`. For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv2d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose2d` for other attributes. Examples:: >>> # xdoctest: +SKIP >>> # With square kernels and equal stride >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12]) rc tjd|jd| | d} t|||||||||| | f i| yr rnros r1r,zConvTranspose2d.__init__rqr2cy)NDynamicQuantizedConvTranspose2dr5r6s r1r(zConvTranspose2d._get_namertr2r8r.r9ct|jdk7r tdtjj ||j |S)Nr_r`)rArBrCrrFconv_transpose2d_dynamicrHrxs r1rKzConvTranspose2d.forwardsE u{{ q BC C}}55 4&&   r2ryrL)rMrNrOrPrQrrrrRrSr,r(rrVrKrWrXs@r1rrhrzr2rceZdZUdZej Zeeej e d< d fd Z dZ d de de de fdZxZS) rasA dynamically quantized transposed convolution module with floating point tensors as inputs and outputs. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose3d`. For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv3d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose3d` for other attributes. Examples:: >>> # xdoctest: +SKIP >>> # With cubic kernels and equal stride >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2) >>> # non-cubic kernels and unequal stride and with padding >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2)) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12, 12]) rc tjd|jd| | d} t|||||||||| | f i| yr rnros r1r,zConvTranspose3d.__init__rqr2cy)NDynamicQuantizedConvTranspose3dr5r6s r1r(zConvTranspose3d._get_namertr2r8r.r9ct|jdk7r tdtjj ||j |S)Nrjz&Input shape must be `(N, C, T, H, W)`!)rArBrCrrFconv_transpose3d_dynamicrHrxs r1rKzConvTranspose3d.forwardsE u{{ q EF F}}55 4&&   r2ryrL)rMrNrOrPrQrrrrRrSr,r(rrVrKrWrXs@r1rrrzr2r)!rPr&typingrrrvtorch.ao.nn.quantizedaorQrFnnqtorch.nntorch.nn.functional functionalrDr torch._opsr"torch.ao.nn.quantized.modules.convrtorch.nn.common_typesrtorch.nn.modules.utilsr r r __all__r r rrrrr5r2r1rs1% ##F+:: NVSZZNVbQVSZZQVhRVSZZRVjO c))O dO c))O dO c))O r2