U (d]C @sBddlZddlmZddlmZmZmZmZddlZddl m Z ddlm Z ddl m Z ddlmZdd lmZmZdd lmZdd lmZmZd d dddddddg ZdZGddde jZeeeeeeee jdddZd6eeeedddZ eeedd d!Z!Gd"d d ej jZ"d#ed$d%Z#Gd&d d eZ$Gd'ddeZ%Gd(ddeZ&Gd)ddeZ'eeee(ee"d*d+d,Z)ed-e$j*fd.dd/d0ee$e(ee"d1d2dZ+ed-e%j*fd.dd/d0ee%e(ee"d1d3dZ,ed-e&j*fd.dd/d0ee&e(ee"d1d4dZ-ed-e'j*fd.dd/d0ee'e(ee"d1d5dZ.dS)7N)partial)AnyDictListOptional)Tensor)ImageClassification)_log_api_usage_once) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_paramMNASNetMNASNet0_5_WeightsMNASNet0_75_WeightsMNASNet1_0_WeightsMNASNet1_3_Weights mnasnet0_5 mnasnet0_75 mnasnet1_0 mnasnet1_3g0U0*3?c s@eZdZd eeeeeeddfdd ZeedddZZS) _InvertedResidual皙?N)in_chout_ch kernel_sizestrideexpansion_factor bn_momentumreturncst|dkr td||dkr6td|||}||koL|dk|_ttj||dddtj||dtjd d tj||||d ||dd tj||dtjd d tj||dddtj||d|_ dS) Nr rz#stride should be 1 or 2 instead of )z(kernel_size should be 3 or 5 instead of r F)biasZmomentumTinplacerpaddingrgroupsr&) super__init__ ValueErrorapply_residualnn SequentialConv2d BatchNorm2dReLUlayers)selfrrrrr r!Zmid_ch __class__>/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/mnasnet.pyr.#s"      z_InvertedResidual.__init__)inputr"cCs"|jr|||S||SdS)N)r0r6)r7r<r:r:r;forward;sz_InvertedResidual.forward)r) __name__ __module__ __qualname__intfloatr.rr= __classcell__r:r:r8r;r"sr)rrrr exp_factorrepeatsr!r"c Csh|dkrtd|t||||||d}g}td|D]} |t|||d||dq8tj|f|S)z&Creates a stack of inverted residuals.r z$repeats should be >= 1, instead got )r!)r/rrangeappendr1r2) rrrrrDrEr!first remaining_r:r:r;_stackBsrK?)valdivisor round_up_biasr"cCsZd|krdks$ntd|t|t||d||}|||krR|S||S)zAsymmetric rounding to make `val` divisible by `divisor`. With default bias, will round up, unless the number is no more than 10% greater than the smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88.?zIround_up_bias should be greater than 0.0 and smaller than 1.0 instead of r)r/maxrA)rMrNrOnew_valr:r:r;_round_to_multiple_ofPsrT)alphar"cs&ddddddddg}fd d |DS) z]Scales tensor depths as in reference MobileNet code, prefers rouding up rather than down. (P`i@csg|]}t|dqS))rT).0depthrUr:r; ^sz_get_depths..r:)rUdepthsr:r`r; _get_depthsZsrcc sneZdZdZdZdeeeddfdd Zeed d d Z e e e e e e e e e e dd fd d ZZS)raMNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf. This implements the B1 variant of the model. >>> model = MNASNet(1.0, num_classes=1000) >>> x = torch.rand(1, 3, 224, 224) >>> y = model(x) >>> y.dim() 2 >>> y.nelement() 1000 r皙?N)rU num_classesdropoutr"cs~tt||dkr(td|||_||_t|}tjd|ddddddtj |dt d tj d d tj|d|dddd|ddd tj |dt d tj d d tj|d|ddddddtj |dt d t |d|dddddt t |d|dd dddt t |d|dd dddt t |d|d ddddt t |d |dd dddt t |d|dddddt tj|dddddddtj dt d tj d d g}tj ||_t tj|d dtd||_|D]}t|tjrtjj|jddd|jdk rvtj|jn\t|tj rFtj|jtj|jn0t|tjrtjj|jdddtj|jqdS)NrPz,alpha should be greater than 0.0 instead of r$rr rFr+rr&r'Tr(r*r%i)pr)Zfan_outZrelu)modeZ nonlinearityZsigmoid)r-r.r r/rUrfrcr1r3r4 _BN_MOMENTUMr5rKr2r6ZDropoutZLinear classifiermodules isinstanceinitZkaiming_normal_Zweightr&Zzeros_Zones_Zkaiming_uniform_)r7rUrfrgrbr6mr8r:r;r.psL  "       zMNASNet.__init__)xr"cCs"||}|ddg}||S)Nrr$)r6Zmeanro)r7rtr:r:r;r=s zMNASNet.forward) state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsr"c s|dd}|dkr"td||dkr|jdkst|j} tjdddddd d tjdtd tjd d tjddddddd dtjdtd tjd d tjdddddd d tjdtd t d| dddddtg } t | D]\} } | |j | <qd|_ t dtt|||||||dS)Nversionr#z+version shluld be set to 1 or 2 instead of r rQr$rVrFrhr'Tr(r*rWraA new version of MNASNet model has been implemented. Your checkpoint was saved using the previous version. This checkpoint will load and work as before, but you may want to upgrade by training a newer model or transfer learning from an updated ImageNet checkpoint.)getr/rUrcr1r3r4rnr5rK enumerater6_versionwarningswarn UserWarningr-_load_from_state_dict) r7rurvrwrxryrzr{r|rbZv1_stemidxZlayerr8r:r;rs>          zMNASNet._load_from_state_dict)rdre)r>r?r@__doc__rrBrAr.rr=rstrboolrrrCr:r:r8r;ras .)r r z(https://github.com/1e100/mnasnet_trainer)Zmin_size categoriesrecipec @s<eZdZedeeddedddddid d d ZeZd S) rzIhttps://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth crop_sizei! 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See :class:`~torchvision.models.MNASNet0_5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet0_5_Weights :members: g?)rverifyrrrrr:r:r;r=s cKst|}td||f|S)aMNASNet with depth multiplier of 0.75 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet0_75_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet0_75_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet0_75_Weights :members: g?)rrrrr:r:r;rXs cKst|}td||f|S)aMNASNet with depth multiplier of 1.0 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet1_0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet1_0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet1_0_Weights :members: rQ)rrrrr:r:r;rss cKst|}td||f|S)aMNASNet with depth multiplier of 1.3 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet1_3_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet1_3_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet1_3_Weights :members: g?)rrrrr:r:r;rs )rL)/r functoolsrtypingrrrrZtorchZtorch.nnr1rZtransforms._presetsr utilsr Z_apir r Z_metar_utilsrr__all__rnModulerrArBr2rKrTrcrrrrrrrrrrrrrr:r:r:r;sf      !  v """