j4ddlZddlmZddlmZddlmZmZmZm Z m Z m Z ddl Z ddl m Z mZddlmZddlmZdd lmZd d lmZd d lmZmZmZd d lmZd dlmZmZd dl m!Z!m"Z"ddlmZ#ddl$m%Z%ddl&m'Z'ddl(m)Z)m*Z*ddgZ+de,de,de,dede j-fde j.f dZ/de,de,dede j-fde j.fdZ0de j-fd Z1Gd!d"e j-Z2Gd#d$e*Z3Gd%d&e*Z4Gd'd(e j-Z5d)e ej6ej7fd*e,dede j-ffd+Z8Gd,deZ9eed-e9j:fd.e"j;f/dd0de"j;ddd1d2e e9d3e!> a a Hg   /+:OQRT^__ ` ` ` `Z    [11111r0 rXrYrZrr[rr r\rLr^r_s@r.rMrM`st2923792KN2\dehjljses\t2222222222r0rMc^eZdZdeedeededejfffd ZxZ S)rOr#rGr&.c  tj}t||D]-\}}|t |d|zd|.t |t |ddS)Nr rb)rQr#rGr&bbox_regrfrgrRs r.rLzSSDLiteRegressionHead.__init__ls=??!$[+!>!> U U Hg OO-hG Q SS T T T TX 1%%%%%r0rhr_s@r.rOrOksj&DI&DI&S[\_acaj\jSk&&&&&&&&&&r0rOc ~eZdZ d dejdededejfdedef fd Zd e d e e e ffd Z xZ S) SSDLiteFeatureExtractorMobileNet?backbonec4_posr&. width_mult min_depthc tt|||jrt dt jt jg|d|||jdRt j||jddg||dzdR|_fd}t j t|dj |d|t|d|d|t|d|d|t|d|d|g}t|||_ dS) Nz0backbone[c4_pos].use_res_connect should be FalserrcDtt|zSrJ)maxr[)drtrss r.z;SSDLiteFeatureExtractorMobileNet.__init__..sc)SZ-@-@AAr0i)rKrLruse_res_connect ValueErrorr r+blockfeaturesrcr5r$rDextra) rQrqrrr&rsrt get_depthrrRs `` r.rLz)SSDLiteFeatureExtractorMobileNet.__init__us D!!! F  + QOPP P M H8GVG, Hhv.>.DQ.G H H H M(6*04 Nx 7M N N N   BAAAA  Xb\6 # SSYYs^^YYs^^ZHHYYs^^YYs^^ZHHYYs^^YYs^^ZHH     U r0rSr'cg}|jD]"}||}||#|jD]"}||}||#tdt |DS)Nc6g|]\}}t||fS)r]).0ivs r. z.s&FFFDAqSVVQKFFFr0)rrerr enumerate)rQrSoutputrs r.rWz(SSDLiteFeatureExtractorMobileNet.forwards]  EaA MM!    Z  EaA MM!    FFIf4E4EFFFGGGr0)rorp)rXrYrZr r\r[rfloatrLr rr]rWr^r_s@r.rnrnts   )S")^,     B H HDf$5 H H H H H H H Hr0rnrqtrainable_layersc|j}dgdt|Dzt|dz gz}t|}d|cxkr|ksntd|dkrt|n |||z }|d|D].}|D]}|d/t ||d|S)Nrc:g|]\}}t|dd|S)_is_cnF)getattr)rrbs r.rz(_mobilenet_extractor..s.\\\A8UZ@[@[\1\\\r0rzYtrainable_layers should be in the range [0, {num_stages}], instead got {trainable_layers}F)rrlenr~ parametersrequires_grad_rn)rqrr& stage_indices num_stages freeze_beforer parameters r._mobilenet_extractorrs  HC\\8)<)<\\\\`cdl`m`mpq`q_rrM]##J  . . . .J . . . .tuuu%5%:%:CMMM j[kNk@lM n}n %,, , ,I  $ $U + + + + , ,HmB6G T TTr0c HeZdZedededddddiidd d d  ZeZd S)r!zShttps://download.pytorch.org/models/ssdlite320_mobilenet_v3_large_coco-a79551df.pthi}4)rrz]https://github.com/pytorch/vision/tree/main/references/detection#ssdlite320-mobilenetv3-largez COCO-val2017box_mapgL5@g-?gt*@zSThese weights were produced by following a similar training recipe as on the paper.) num_params categoriesmin_sizerecipe_metrics_ops _file_size_docs)url transformsmetaN)rXrYrZrrrCOCO_V1DEFAULTrr0r.r!r!sdg a"!*ut!  n   G$GGGr0 pretrainedpretrained_backbone)weightsweights_backboneT)rprogressrHrtrainable_backbone_layersr&rrrHrrkwargsc t|}tj|}d|vrtjd|,d}t d|t |jd}n|d}t|dup|du|dd}|du}|ttj dd }td||||d |}|t|t|||}d } td t!dDdd} t#j|| } | } t | t | jkr4t+dt | dt | jddddgdgdd} i| |}t-|| | |fdt/| | ||i|}|*|||d|S)aSSDlite model architecture with input size 320x320 and a MobileNetV3 Large backbone, as described at `Searching for MobileNetV3 `__ and `MobileNetV2: Inverted Residuals and Linear Bottlenecks `__. .. betastatus:: detection module See :func:`~torchvision.models.detection.ssd300_vgg16` for more details. Example: >>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(weights=SSDLite320_MobileNet_V3_Large_Weights.DEFAULT) >>> model.eval() >>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)] >>> predictions = model(x) Args: weights (:class:`~torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.detection.SSDLite320_MobileNet_V3_Large_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. num_classes (int, optional): number of output classes of the model (including the background). weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the backbone. trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If ``None`` is passed (the default) this value is set to 6. norm_layer (callable, optional): Module specifying the normalization layer to use. **kwargs: parameters passed to the ``torchvision.models.detection.ssd.SSD`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights :members: sizez?The size of the model is already fixed; ignoring the parameter.NrHr[gMbP?r8)epsmomentum)rrr& reduced_tail)@rcg|]}ddgS)rr r)r_s r.rz1ssdlite320_mobilenet_v3_large..,s+E+E+EqQF+E+E+Er0g?gffffff?) min_ratio max_ratioz4The length of the output channels from the backbone z? do not match the length of the anchor generator aspect ratios g?i,)?rr) score_thresh nms_threshdetections_per_imgtopk_candidates image_mean image_stdheadT)r check_hashr)r!verifyrwarningswarnrrrrrr BatchNorm2drrDrrrange det_utilsretrieve_out_channelsnum_anchors_per_location aspect_ratiosr~rrFload_state_dictget_state_dict)rrrHrrr&r reduce_tailrqranchor_generatorr$rGdefaultsmodels r.r"r"sp4::7CCG189IJJ  WXXX+M;GLYeLfHgHghh   :t;/t;=VXY[\!! #d*KR^FFF ! 8 YdhnHX#!H D*+E+EE!HH+E+E+EQT`deee28TBBL";;==K <C 0 >???? {3|CTCT { {VYZjZxVyVy { {   !&oo$__  H)X((F      {K L L      E g44hSW4XXYYY Lr0)>r collectionsr functoolsrtypingrrrrr r r=r r ops.miscrtransforms._presetsrutilsrr_apirrr_metarrrr mobilenetv3rrr anchor_utilsrbackbone_utilsrssdrr __all__r[r\r+r/r5rDrFrMrOrn MobileNetV2 MobileNetV3rr!r IMAGENET1K_V1boolr"rr0r.rsq######================ ,,,,,,222222((((((7777777777$$$$$$CCCCCCCCHHHHHHHH!!!!!!------666666$$$$$$$$,# $'69GOPSUWU^P^G_]$c(3PRPY>BZ_a_l29ry9999      ")    22222222&&&&&N&&&-H-H-H-H-Hry-H-H-H`UI)9+@@AUUbi(UUUU.K, @H I+-G-UV @D!%=W=e/359uuu ; <uu# u 9: u (} u#ry.12uu uuu  uuur0