FiddlmZddlmZddlmZddlZddlmZddl m Z m Z m Z ddl mZGdd eZGd d eZy) ) annotations)Path)AnyN) IS_JETSONLOGGER is_jetson) BaseBackendcheZdZdZ d dfd ZddZ d d dZxZS) PyTorchBackendzPyTorch inference backend for native model execution. Loads and runs inference with native PyTorch models (.pt checkpoint files) or pre-loaded nn.Module instances. Supports model layer fusion, FP16 precision, and NVIDIA Jetson compatibility. cD||_||_t| |||y)aInitialize the PyTorch backend. Args: weight (str | Path | nn.Module): Path to the .pt model file or a pre-loaded nn.Module instance. device (torch.device): Device to run inference on (e.g., 'cpu', 'cuda:0'). fp16 (bool): Whether to use FP16 half-precision inference. fuse (bool): Whether to fuse Conv2D + BatchNorm layers for optimization. verbose (bool): Whether to print verbose model loading messages. N)fuseverbosesuper__init__)selfweightdevicefp16rr __class__s a/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/nn/backends/pytorch.pyrzPyTorchBackend.__init__s$"   .cddlm}t|tjj r}|j rUt|drItr'tdr|j|j}|j |j}|j|j}n"|||j|j \}}t|dr|j|_ t|d r-tt|j jd nd |_t|d r|j"j$n t'|d i|_t|d r|j(j+ddnd|_|j.r|j1n|j3|j5D] }d|_ ||_t'|dd|_y)zLoad a PyTorch model from a checkpoint file or nn.Module instance. Args: weight (str | torch.nn.Module): Path to the .pt checkpoint or a pre-loaded module. r)load_checkpointr)jetpack)r)rr kpt_shapestride modulenamesyamlchannelsFend2endN)ultralytics.nn.tasksr isinstancetorchnnModulerhasattrrrtorrrmaxintrr r!getattrr"getr#rhalffloat parameters requires_gradmodelr%)rrrr5_ps r load_modelzPyTorchBackend.load_model,sb 9 fehhoo .yyWVV41!5#YYt{{3FT\\:IIdkk*E&vdkk RHE1 5+ &"__DN:A%:Rc#ell..0126XZ +25(+CU\\''QVX_acId 9@9O z15UV   u{{}!!# $A#AO $ ui7 rc 0|j|f|||d|S)ayRun native PyTorch inference with support for augmentation, visualization, and embeddings. Args: im (torch.Tensor): Input image tensor in BCHW format, normalized to [0, 1]. augment (bool): Whether to apply test-time augmentation. visualize (bool): Whether to visualize intermediate feature maps. embed (list | None): List of layer indices to extract embeddings from, or None. **kwargs (Any): Additional keyword arguments passed to the model forward method. Returns: (torch.Tensor | list[torch.Tensor]): Model predictions as tensor(s). )augment visualizeembedr5)rimr:r;r<kwargss rforwardzPyTorchBackend.forwardKs$tzz"Zg%ZSYZZr)FTT) rzstr | Path | nn.Moduler torch.devicerboolrrBrrB)rzstr | torch.nn.ModulereturnNone)FFN) r> torch.Tensorr:rBr;rBr<z list | Noner?rrC!torch.Tensor | list[torch.Tensor]__name__ __module__ __qualname____doc__rr8r@ __classcell__rs@rr r s /&// /  /  /*8@fj[[)-[BF[Wb[ux[ *[rr c6eZdZdZddfd ZddZddZxZS) TorchScriptBackendaPyTorch TorchScript inference backend for serialized model execution. Loads and runs inference with TorchScript models (.torchscript files) created via torch.jit.trace or torch.jit.script. Supports FP16 precision and embedded metadata extraction. c(t||||y)aInitialize the TorchScript backend. Args: weight (str | Path): Path to the .torchscript model file. device (torch.device): Device to run inference on (e.g., 'cpu', 'cuda:0'). fp16 (bool): Whether to use FP16 half-precision inference. N)rr)rrrrrs rrzTorchScriptBackend.__init__ds .rcddl}ddl}tjd|dddi}tj j |||j|_|jr|jjn|jj|dr'|j|j|dd yy) zLoad a TorchScript model from a .torchscript file with optional embedded metadata. Args: weight (str): Path to the .torchscript model file. rNzLoading z for TorchScript inference...z config.txt) _extra_files map_locationc4t|jS)N)dictitems)xs rz/TorchScriptBackend.load_model..~s\`abahahaj\kr) object_hook)json torchvisionrinfor(jitloadrr5rr1r2apply_metadataloads)rrr[r\ extra_filess rr8zTorchScriptBackend.load_modelns  hvh&CDE#R( YY^^FSWS^S^^_ !YY DJJ,<,<,> | $    ;|+DRk l m %rc$|j|S)zRun TorchScript inference. Args: im (torch.Tensor): Input image tensor in BCHW format, normalized to [0, 1]. Returns: (torch.Tensor | list[torch.Tensor]): Model predictions as tensor(s). r=)rr>s rr@zTorchScriptBackend.forwardszz"~r)F)rz str | PathrrArrB)rstrrCrD)r>rErCrFrGrMs@rrOrO]s /n$ rrO) __future__rpathlibrtypingrr(torch.nnr)ultralytics.utilsrrrbaser r rOrrrls<# ::J[[J[Z,,r