.h,$ddlmZddlZddlZddlmZddlZddlmZm Z ddl m Z ddl m Z ddlmZGdd ej j"Zdd Zdd ZGd d ej j"Z d ddZy)) annotationsN)Path)DetectPose)LOGGER) make_anchors) copy_attrc*eZdZdZdfd ZdZxZS)FXModela} A custom model class for torch.fx compatibility. This class extends `torch.nn.Module` and is designed to ensure compatibility with torch.fx for tracing and graph manipulation. It copies attributes from an existing model and explicitly sets the model attribute to ensure proper copying. Attributes: model (nn.Module): The original model's layers. cjt|t|||j|_||_y)z Initialize the FXModel. Args: model (nn.Module): The original model to wrap for torch.fx compatibility. imgsz (tuple[int, int]): The input image size (height, width). Default is (640, 640). N)super__init__r modelimgsz)selfrr __class__s Z/var/www/html/ai-service/venv/lib/python3.12/site-packages/ultralytics/utils/export/imx.pyrzFXModel.__init__s, $[[  c g}|jD]K}|jdk7rMt|jtr||jn#|jDcgc]}|dk(r|n||c}}t|trt j t||_dttj|jDcgc] }||jjdz "c}d|jdD\|_|_t!|t"urt j t$||_||}|j)|N|Scc}wcc}w)aa Forward pass through the model. This method performs the forward pass through the model, handling the dependencies between layers and saving intermediate outputs. Args: x (torch.Tensor): The input tensor to the model. Returns: (torch.Tensor): The output tensor from the model. c3@K|]}|jddyw)rN) transpose).0xs r z"FXModel.forward..?s#(KK1%(sr)dimg?)rf isinstanceintrtypes MethodType _inferencertorchcatrstride unsqueezeanchorsstridestyper pose_forwardforwardappend)rrymjss rr,zFXModel.forward+s&  Assby(c2AaccFYZY\Y\8]TUa2g1Q49O8]!V$$// A> () tzz"R!1qxx'9'9"'=#="RXYZ\]\d\dfi($ 19 Aw$!,,\1= !A HHQK  9^ #Ss E %E%))r2__name__ __module__ __qualname____doc__rr, __classcell__rs@rr r s  rr c tj|Dcgc]/}|j|djd|jd1c}d}|j |j dz|jfd\}}|j|j||jjd|jz}|jdd|jjdddfScc}w)z5Decode boxes and cls scores for imx object detection.rrr)r$r%viewshapenosplitreg_maxnc decode_bboxesdflr(r'r)rsigmoidpermute)rrxix_catboxclsdboxs rr#r#Ls IIQGrrwwqtzz!}dggr:G KE{{DLL1,dgg6:HC   dhhsmT\\-C-CA-F G$,, VD >>!Q !6!6q!Q!? ??Hs4C8c |djd}tjt|jDcgc]5}|j |||j ||jd7c}d}tj||}|j||}g||jdddScc}w)zBForward pass for imx pose estimation, including keypoint decoding.rrr;r) r>r$r%rangenlcv4r=nkrr, kpts_decoderF)rrbsikptpred_kpts rr+r+Ts 1AB ))eDGGnU[TXXa[1&++B<UWY ZCtQAC(H *Q *  Aq) **Vs:B;cFeZdZdZ d dfd ZdZxZS) NMSWrapperzFWrap PyTorch Module with multiclass_nms layer from sony_custom_layers.cht|||_||_||_||_||_y)a Initialize NMSWrapper with PyTorch Module and NMS parameters. Args: model (torch.nn.Module): Model instance. score_threshold (float): Score threshold for non-maximum suppression. iou_threshold (float): Intersection over union threshold for non-maximum suppression. max_detections (int): The number of detections to return. task (str): Task type, either 'detect' or 'pose'. N)r rrscore_threshold iou_thresholdmax_detectionstask)rrrYrZr[r\rs rrzNMSWrapper.__init__`s6$  .*, rc ddlm}|j|}|d|d}}||||j|j|j }|j dk(ry|d}tj|d|jjdjdd|jd}|j|j|j|fS|j|j|j|j fS)z:Forward pass with model inference and NMS post-processing.r)multiclass_nms_with_indicesr)boxesscoresrYrZr[poser;r)sony_custom_layers.pytorchr^rrYrZr[r\r$gatherindicesr'expandsizer_r`labelsn_valid) rimagesr^outputsr_r` nms_outputskptsout_kptss rr,zNMSWrapper.forwardysJ**V$ GAJv1 00,,..   99 1:D||D![-@-@-J-J2-N-U-UVXZ\^b^g^ghj^k-lmH$$k&8&8+:L:LhV V  +"4"4k6H6H+J]J]]]r)MbP?gffffff?i,detect) rtorch.nn.ModulerYfloatrZrqr[r r\strr3r9s@rrWrW]sOP "'"!      2^rrWc ddl} ddl} ddlm} t j d|d| j d|fd} | dd } | jj}d |jvr0|jd k(r gd }d}d}nG|jdk(r8gd}d}d}n/|jd k(r gd}d}d}n|jdk(rgd}d}d}tt|jk7r tdD]B}|j| jj j"j%|gdD| jj'| jj)d| jj+d| }| jj-!}|r@| j.j1|| || j.j3d"d#d#$|| %dn"| j4j7|| ||| &d}t9||xsd'|||j(}t;t=|j?|j@d)}|jCd*|t;t=|jDj?|j@d+z }| jFjI||| ,| jK|}|jMD]7\}}|jNjQ}|t=|c|_)|_*9| jW||tYjZd-d.t=|d/t=|d0d1gd2t]|d3z d4d565}|j_|j`jMDcgc] \}}|d c}}ddd|Scc}}w#1swY|SxYw)7a Export YOLO model to IMX format for deployment on Sony IMX500 devices. This function quantizes a YOLO model using Model Compression Toolkit (MCT) and exports it to IMX format compatible with Sony IMX500 edge devices. It supports both YOLOv8n and YOLO11n models for detection and pose estimation tasks. Args: model (torch.nn.Module): The YOLO model to export. Must be YOLOv8n or YOLO11n. file (Path | str): Output file path for the exported model. conf (float): Confidence threshold for NMS post-processing. iou (float): IoU threshold for NMS post-processing. max_det (int): Maximum number of detections to return. metadata (dict | None, optional): Metadata to embed in the ONNX model. Defaults to None. gptq (bool, optional): Whether to use Gradient-Based Post Training Quantization. If False, uses standard Post Training Quantization. Defaults to False. dataset (optional): Representative dataset for quantization calibration. Defaults to None. prefix (str, optional): Logging prefix string. Defaults to "". Returns: f (Path): Path to the exported IMX model directory Raises: ValueError: If the model is not a supported YOLOv8n or YOLO11n variant. Example: >>> from ultralytics import YOLO >>> model = YOLO("yolo11n.pt") >>> path, _ = export_imx(model, "model.imx", conf=0.25, iou=0.45, max_det=300) Note: - Requires model_compression_toolkit, onnx, edgemdt_tpc, and sony_custom_layers packages - Only supports YOLOv8n and YOLO11n models (detection and pose tasks) - Output includes quantized ONNX model, IMX binary, and labels.txt file rN) get_target_platform_capabilities z0 starting export with model_compression_toolkit z...c38K|D]}|d}|dz }|gyw)Nimggo@) dataloaderbatchrws rrepresentative_dataset_genz-torch2imx..representative_dataset_gens. E,C+C%K sz4.0imx500) tpc_version device_typeC2PSAro)submul_2add_14cat_21g~8CAra)rrrcat_22cat_23mul_4add_15g\EBAi)rmuladd_6cat_17gffffuCA)add_7rcat_19rrrcat_18gBAz9IMX export only supported for YOLOv8n and YOLO11n models. ) num_of_imagesT)concat_threshold_update)mixed_precision_configquantization_configbit_width_config)weights_memoryiF)n_epochsuse_hessian_based_weightsuse_hessian_sample_attention)rrepresentative_data_gentarget_resource_utilization gptq_config core_configtarget_platform_capabilities) in_modulerrrrrn)rrYrZr[r\ _imx_model)exist_okz _imx.onnx)rsave_model_path repr_datasetz imxconv-ptz-iz-oz--no-input-persistencyz--overwrite-output)checkz labels.txtwzutf-8)encoding)1model_compression_toolkitonnx edgemdt_tpcrtrinfo __version__coreBitWidthConfig__str__r\lenlistmodules ValueErrorset_manual_activation_bit_widthcommonnetwork_editorsNodeNameFilter CoreConfig MixedPrecisionQuantizationConfigQuantizationConfigResourceUtilizationgptq+pytorch_gradient_post_training_quantizationget_pytorch_gptq_configptq"pytorch_post_training_quantizationrWrrrreplacesuffixmkdirnameexporterpytorch_export_modelloaditemsmetadata_propsaddkeyvaluesave subprocessrunopen writelinesnames)rfileconfioumax_detmetadatardatasetprefixmctrrtr{tpcbit_cfg layer_namesrn_layers layer_nameconfigresource_utilization quant_modelr onnx_model model_onnxkvmeta_rs r torch2imxrs\,< KK"VHLS__L]]`ab.5 +u( SChh%%'G%--/! :: !>K)NH ZZ6 ![K'NH :: !;K&NH ZZ6 !WK'NH 4  !X-TUU!r //1P1P1_1_`j1k0lnpqrXX "xxHHWYHZHH77PT7U !F 8877~7V  <<$>(<88]b9), =   WW 7 7$>(<), 8  *  ZZ K SY  t{{L 9:AGGTGT#dii.00kJKKJLL%%:D^&:&J )1((,,. #a&$*) IIj*%NN tS_dCF)O'O 6OOO)rlist[torch.Tensor]returnztuple[torch.Tensor])rrrz/tuple[torch.Tensor, torch.Tensor, torch.Tensor])NFN)rrprz Path | strrrqrrqrr rz dict | Nonerboolrrr) __future__rrr!pathlibrr$ultralytics.nn.modulesrrultralytics.utilsrultralytics.utils.talrultralytics.utils.torch_utilsr nnModuler r#r+rWrrxrrrs#  /$.38ehhoo8v@+.^.^n! 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