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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license |
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import ast |
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import json |
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import platform |
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import zipfile |
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from collections import OrderedDict, namedtuple |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from PIL import Image |
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from ultralytics.utils import ARM64, IS_JETSON, IS_RASPBERRYPI, LINUX, LOGGER, PYTHON_VERSION, ROOT, yaml_load |
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from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml, is_rockchip |
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from ultralytics.utils.downloads import attempt_download_asset, is_url |
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def check_class_names(names): |
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""" |
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Check class names. |
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Map imagenet class codes to human-readable names if required. Convert lists to dicts. |
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""" |
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if isinstance(names, list): # names is a list |
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names = dict(enumerate(names)) # convert to dict |
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if isinstance(names, dict): |
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# Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True' |
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names = {int(k): str(v) for k, v in names.items()} |
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n = len(names) |
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if max(names.keys()) >= n: |
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raise KeyError( |
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f"{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices " |
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f"{min(names.keys())}-{max(names.keys())} defined in your dataset YAML." |
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) |
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if isinstance(names[0], str) and names[0].startswith("n0"): # imagenet class codes, i.e. 'n01440764' |
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names_map = yaml_load(ROOT / "cfg/datasets/ImageNet.yaml")["map"] # human-readable names |
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names = {k: names_map[v] for k, v in names.items()} |
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return names |
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def default_class_names(data=None): |
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"""Applies default class names to an input YAML file or returns numerical class names.""" |
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if data: |
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try: |
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return yaml_load(check_yaml(data))["names"] |
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except Exception: |
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pass |
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return {i: f"class{i}" for i in range(999)} # return default if above errors |
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class AutoBackend(nn.Module): |
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""" |
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Handles dynamic backend selection for running inference using Ultralytics YOLO models. |
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The AutoBackend class is designed to provide an abstraction layer for various inference engines. It supports a wide |
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range of formats, each with specific naming conventions as outlined below: |
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Supported Formats and Naming Conventions: |
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| Format | File Suffix | |
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| --------------------- | ----------------- | |
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| PyTorch | *.pt | |
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| TorchScript | *.torchscript | |
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| ONNX Runtime | *.onnx | |
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| ONNX OpenCV DNN | *.onnx (dnn=True) | |
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| OpenVINO | *openvino_model/ | |
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| CoreML | *.mlpackage | |
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| TensorRT | *.engine | |
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| TensorFlow SavedModel | *_saved_model/ | |
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| TensorFlow GraphDef | *.pb | |
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| TensorFlow Lite | *.tflite | |
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| TensorFlow Edge TPU | *_edgetpu.tflite | |
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| PaddlePaddle | *_paddle_model/ | |
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| MNN | *.mnn | |
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| NCNN | *_ncnn_model/ | |
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| IMX | *_imx_model/ | |
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| RKNN | *_rknn_model/ | |
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This class offers dynamic backend switching capabilities based on the input model format, making it easier to deploy |
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models across various platforms. |
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""" |
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@torch.no_grad() |
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def __init__( |
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self, |
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weights="yolo11n.pt", |
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device=torch.device("cpu"), |
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dnn=False, |
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data=None, |
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fp16=False, |
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batch=1, |
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fuse=True, |
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verbose=True, |
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): |
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""" |
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Initialize the AutoBackend for inference. |
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Args: |
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weights (str | torch.nn.Module): Path to the model weights file or a module instance. Defaults to 'yolo11n.pt'. |
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device (torch.device): Device to run the model on. Defaults to CPU. |
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dnn (bool): Use OpenCV DNN module for ONNX inference. Defaults to False. |
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data (str | Path | optional): Path to the additional data.yaml file containing class names. Optional. |
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fp16 (bool): Enable half-precision inference. Supported only on specific backends. Defaults to False. |
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batch (int): Batch-size to assume for inference. |
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fuse (bool): Fuse Conv2D + BatchNorm layers for optimization. Defaults to True. |
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verbose (bool): Enable verbose logging. Defaults to True. |
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""" |
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super().__init__() |
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w = str(weights[0] if isinstance(weights, list) else weights) |
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nn_module = isinstance(weights, torch.nn.Module) |
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( |
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pt, |
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jit, |
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onnx, |
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xml, |
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engine, |
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coreml, |
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saved_model, |
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pb, |
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tflite, |
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edgetpu, |
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tfjs, |
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paddle, |
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mnn, |
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ncnn, |
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imx, |
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rknn, |
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triton, |
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) = self._model_type(w) |
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fp16 &= pt or jit or onnx or xml or engine or nn_module or triton # FP16 |
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nhwc = coreml or saved_model or pb or tflite or edgetpu or rknn # BHWC formats (vs torch BCWH) |
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stride = 32 # default stride |
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end2end = False # default end2end |
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model, metadata, task = None, None, None |
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# Set device |
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cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA |
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if cuda and not any([nn_module, pt, jit, engine, onnx, paddle]): # GPU dataloader formats |
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device = torch.device("cpu") |
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cuda = False |
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# Download if not local |
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if not (pt or triton or nn_module): |
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w = attempt_download_asset(w) |
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# In-memory PyTorch model |
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if nn_module: |
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model = weights.to(device) |
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if fuse: |
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model = model.fuse(verbose=verbose) |
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if hasattr(model, "kpt_shape"): |
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kpt_shape = model.kpt_shape # pose-only |
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stride = max(int(model.stride.max()), 32) # model stride |
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names = model.module.names if hasattr(model, "module") else model.names # get class names |
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model.half() if fp16 else model.float() |
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self.model = model # explicitly assign for to(), cpu(), cuda(), half() |
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pt = True |
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# PyTorch |
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elif pt: |
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from ultralytics.nn.tasks import attempt_load_weights |
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model = attempt_load_weights( |
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weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse |
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) |
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if hasattr(model, "kpt_shape"): |
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kpt_shape = model.kpt_shape # pose-only |
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stride = max(int(model.stride.max()), 32) # model stride |
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names = model.module.names if hasattr(model, "module") else model.names # get class names |
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model.half() if fp16 else model.float() |
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self.model = model # explicitly assign for to(), cpu(), cuda(), half() |
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# TorchScript |
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elif jit: |
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LOGGER.info(f"Loading {w} for TorchScript inference...") |
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extra_files = {"config.txt": ""} # model metadata |
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model = torch.jit.load(w, _extra_files=extra_files, map_location=device) |
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model.half() if fp16 else model.float() |
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if extra_files["config.txt"]: # load metadata dict |
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metadata = json.loads(extra_files["config.txt"], object_hook=lambda x: dict(x.items())) |
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# ONNX OpenCV DNN |
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elif dnn: |
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LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...") |
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check_requirements("opencv-python>=4.5.4") |
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net = cv2.dnn.readNetFromONNX(w) |
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# ONNX Runtime and IMX |
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elif onnx or imx: |
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LOGGER.info(f"Loading {w} for ONNX Runtime inference...") |
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check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime")) |
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if IS_RASPBERRYPI or IS_JETSON: |
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# Fix 'numpy.linalg._umath_linalg' has no attribute '_ilp64' for TF SavedModel on RPi and Jetson |
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check_requirements("numpy==1.23.5") |
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import onnxruntime |
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providers = ["CPUExecutionProvider"] |
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if cuda: |
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if "CUDAExecutionProvider" in onnxruntime.get_available_providers(): |
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providers.insert(0, "CUDAExecutionProvider") |
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else: # Only log warning if CUDA was requested but unavailable |
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LOGGER.warning("WARNING ⚠️ Failed to start ONNX Runtime with CUDA. Using CPU...") |
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device = torch.device("cpu") |
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cuda = False |
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LOGGER.info(f"Using ONNX Runtime {providers[0]}") |
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if onnx: |
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session = onnxruntime.InferenceSession(w, providers=providers) |
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else: |
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check_requirements( |
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["model-compression-toolkit==2.1.1", "sony-custom-layers[torch]==0.2.0", "onnxruntime-extensions"] |
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) |
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w = next(Path(w).glob("*.onnx")) |
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LOGGER.info(f"Loading {w} for ONNX IMX inference...") |
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import mct_quantizers as mctq |
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from sony_custom_layers.pytorch.object_detection import nms_ort # noqa |
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session = onnxruntime.InferenceSession( |
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w, mctq.get_ort_session_options(), providers=["CPUExecutionProvider"] |
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) |
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task = "detect" |
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output_names = [x.name for x in session.get_outputs()] |
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metadata = session.get_modelmeta().custom_metadata_map |
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dynamic = isinstance(session.get_outputs()[0].shape[0], str) |
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fp16 = "float16" in session.get_inputs()[0].type |
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if not dynamic: |
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io = session.io_binding() |
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bindings = [] |
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for output in session.get_outputs(): |
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out_fp16 = "float16" in output.type |
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y_tensor = torch.empty(output.shape, dtype=torch.float16 if out_fp16 else torch.float32).to(device) |
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io.bind_output( |
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name=output.name, |
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device_type=device.type, |
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device_id=device.index if cuda else 0, |
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element_type=np.float16 if out_fp16 else np.float32, |
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shape=tuple(y_tensor.shape), |
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buffer_ptr=y_tensor.data_ptr(), |
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) |
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bindings.append(y_tensor) |
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# OpenVINO |
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elif xml: |
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LOGGER.info(f"Loading {w} for OpenVINO inference...") |
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check_requirements("openvino>=2024.0.0,<2025.0.0") |
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import openvino as ov |
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core = ov.Core() |
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w = Path(w) |
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if not w.is_file(): # if not *.xml |
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w = next(w.glob("*.xml")) # get *.xml file from *_openvino_model dir |
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ov_model = core.read_model(model=str(w), weights=w.with_suffix(".bin")) |
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if ov_model.get_parameters()[0].get_layout().empty: |
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ov_model.get_parameters()[0].set_layout(ov.Layout("NCHW")) |
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# OpenVINO inference modes are 'LATENCY', 'THROUGHPUT' (not recommended), or 'CUMULATIVE_THROUGHPUT' |
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inference_mode = "CUMULATIVE_THROUGHPUT" if batch > 1 else "LATENCY" |
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LOGGER.info(f"Using OpenVINO {inference_mode} mode for batch={batch} inference...") |
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ov_compiled_model = core.compile_model( |
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ov_model, |
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device_name="AUTO", # AUTO selects best available device, do not modify |
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config={"PERFORMANCE_HINT": inference_mode}, |
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) |
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input_name = ov_compiled_model.input().get_any_name() |
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metadata = w.parent / "metadata.yaml" |
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# TensorRT |
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elif engine: |
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LOGGER.info(f"Loading {w} for TensorRT inference...") |
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if IS_JETSON and PYTHON_VERSION <= "3.8.0": |
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# fix error: `np.bool` was a deprecated alias for the builtin `bool` for JetPack 4 with Python <= 3.8.0 |
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check_requirements("numpy==1.23.5") |
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try: |
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import tensorrt as trt # noqa https://developer.nvidia.com/nvidia-tensorrt-download |
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except ImportError: |
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if LINUX: |
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check_requirements("tensorrt>7.0.0,!=10.1.0") |
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import tensorrt as trt # noqa |
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check_version(trt.__version__, ">=7.0.0", hard=True) |
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check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239") |
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if device.type == "cpu": |
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device = torch.device("cuda:0") |
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Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) |
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logger = trt.Logger(trt.Logger.INFO) |
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# Read file |
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with open(w, "rb") as f, trt.Runtime(logger) as runtime: |
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try: |
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meta_len = int.from_bytes(f.read(4), byteorder="little") # read metadata length |
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metadata = json.loads(f.read(meta_len).decode("utf-8")) # read metadata |
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except UnicodeDecodeError: |
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f.seek(0) # engine file may lack embedded Ultralytics metadata |
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dla = metadata.get("dla", None) |
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if dla is not None: |
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runtime.DLA_core = int(dla) |
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model = runtime.deserialize_cuda_engine(f.read()) # read engine |
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# Model context |
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try: |
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context = model.create_execution_context() |
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except Exception as e: # model is None |
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LOGGER.error(f"ERROR: TensorRT model exported with a different version than {trt.__version__}\n") |
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raise e |
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bindings = OrderedDict() |
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output_names = [] |
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fp16 = False # default updated below |
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dynamic = False |
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is_trt10 = not hasattr(model, "num_bindings") |
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num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings) |
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for i in num: |
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if is_trt10: |
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name = model.get_tensor_name(i) |
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dtype = trt.nptype(model.get_tensor_dtype(name)) |
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is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT |
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if is_input: |
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if -1 in tuple(model.get_tensor_shape(name)): |
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dynamic = True |
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context.set_input_shape(name, tuple(model.get_tensor_profile_shape(name, 0)[1])) |
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if dtype == np.float16: |
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fp16 = True |
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else: |
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output_names.append(name) |
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shape = tuple(context.get_tensor_shape(name)) |
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else: # TensorRT < 10.0 |
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name = model.get_binding_name(i) |
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dtype = trt.nptype(model.get_binding_dtype(i)) |
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is_input = model.binding_is_input(i) |
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if model.binding_is_input(i): |
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if -1 in tuple(model.get_binding_shape(i)): # dynamic |
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dynamic = True |
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context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[1])) |
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if dtype == np.float16: |
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fp16 = True |
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else: |
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output_names.append(name) |
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shape = tuple(context.get_binding_shape(i)) |
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im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) |
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bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) |
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binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) |
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batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size |
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# CoreML |
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elif coreml: |
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LOGGER.info(f"Loading {w} for CoreML inference...") |
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import coremltools as ct |
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model = ct.models.MLModel(w) |
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metadata = dict(model.user_defined_metadata) |
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# TF SavedModel |
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elif saved_model: |
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LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...") |
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import tensorflow as tf |
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keras = False # assume TF1 saved_model |
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model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) |
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metadata = Path(w) / "metadata.yaml" |
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# TF GraphDef |
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elif pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt |
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LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...") |
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import tensorflow as tf |
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from ultralytics.engine.exporter import gd_outputs |
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def wrap_frozen_graph(gd, inputs, outputs): |
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"""Wrap frozen graphs for deployment.""" |
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x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped |
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ge = x.graph.as_graph_element |
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return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) |
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gd = tf.Graph().as_graph_def() # TF GraphDef |
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with open(w, "rb") as f: |
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gd.ParseFromString(f.read()) |
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frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) |
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try: # find metadata in SavedModel alongside GraphDef |
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metadata = next(Path(w).resolve().parent.rglob(f"{Path(w).stem}_saved_model*/metadata.yaml")) |
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except StopIteration: |
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pass |
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# TFLite or TFLite Edge TPU |
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elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python |
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try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu |
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from tflite_runtime.interpreter import Interpreter, load_delegate |
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except ImportError: |
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import tensorflow as tf |
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Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate |
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if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime |
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device = device[3:] if str(device).startswith("tpu") else ":0" |
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LOGGER.info(f"Loading {w} on device {device[1:]} for TensorFlow Lite Edge TPU inference...") |
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delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[ |
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platform.system() |
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] |
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interpreter = Interpreter( |
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model_path=w, |
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experimental_delegates=[load_delegate(delegate, options={"device": device})], |
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) |
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device = "cpu" # Required, otherwise PyTorch will try to use the wrong device |
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else: # TFLite |
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LOGGER.info(f"Loading {w} for TensorFlow Lite inference...") |
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interpreter = Interpreter(model_path=w) # load TFLite model |
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|
interpreter.allocate_tensors() # allocate |
|
|
input_details = interpreter.get_input_details() # inputs |
|
|
output_details = interpreter.get_output_details() # outputs |
|
|
# Load metadata |
|
|
try: |
|
|
with zipfile.ZipFile(w, "r") as model: |
|
|
meta_file = model.namelist()[0] |
|
|
metadata = ast.literal_eval(model.read(meta_file).decode("utf-8")) |
|
|
except zipfile.BadZipFile: |
|
|
pass |
|
|
|
|
|
# TF.js |
|
|
elif tfjs: |
|
|
raise NotImplementedError("YOLOv8 TF.js inference is not currently supported.") |
|
|
|
|
|
# PaddlePaddle |
|
|
elif paddle: |
|
|
LOGGER.info(f"Loading {w} for PaddlePaddle inference...") |
|
|
check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle") |
|
|
import paddle.inference as pdi # noqa |
|
|
|
|
|
w = Path(w) |
|
|
if not w.is_file(): # if not *.pdmodel |
|
|
w = next(w.rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir |
|
|
config = pdi.Config(str(w), str(w.with_suffix(".pdiparams"))) |
|
|
if cuda: |
|
|
config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) |
|
|
predictor = pdi.create_predictor(config) |
|
|
input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) |
|
|
output_names = predictor.get_output_names() |
|
|
metadata = w.parents[1] / "metadata.yaml" |
|
|
|
|
|
# MNN |
|
|
elif mnn: |
|
|
LOGGER.info(f"Loading {w} for MNN inference...") |
|
|
check_requirements("MNN") # requires MNN |
|
|
import os |
|
|
|
|
|
import MNN |
|
|
|
|
|
config = {"precision": "low", "backend": "CPU", "numThread": (os.cpu_count() + 1) // 2} |
|
|
rt = MNN.nn.create_runtime_manager((config,)) |
|
|
net = MNN.nn.load_module_from_file(w, [], [], runtime_manager=rt, rearrange=True) |
|
|
|
|
|
def torch_to_mnn(x): |
|
|
return MNN.expr.const(x.data_ptr(), x.shape) |
|
|
|
|
|
metadata = json.loads(net.get_info()["bizCode"]) |
|
|
|
|
|
# NCNN |
|
|
elif ncnn: |
|
|
LOGGER.info(f"Loading {w} for NCNN inference...") |
|
|
check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn") # requires NCNN |
|
|
import ncnn as pyncnn |
|
|
|
|
|
net = pyncnn.Net() |
|
|
net.opt.use_vulkan_compute = cuda |
|
|
w = Path(w) |
|
|
if not w.is_file(): # if not *.param |
|
|
w = next(w.glob("*.param")) # get *.param file from *_ncnn_model dir |
|
|
net.load_param(str(w)) |
|
|
net.load_model(str(w.with_suffix(".bin"))) |
|
|
metadata = w.parent / "metadata.yaml" |
|
|
|
|
|
# NVIDIA Triton Inference Server |
|
|
elif triton: |
|
|
check_requirements("tritonclient[all]") |
|
|
from ultralytics.utils.triton import TritonRemoteModel |
|
|
|
|
|
model = TritonRemoteModel(w) |
|
|
metadata = model.metadata |
|
|
|
|
|
# RKNN |
|
|
elif rknn: |
|
|
if not is_rockchip(): |
|
|
raise OSError("RKNN inference is only supported on Rockchip devices.") |
|
|
LOGGER.info(f"Loading {w} for RKNN inference...") |
|
|
check_requirements("rknn-toolkit-lite2") |
|
|
from rknnlite.api import RKNNLite |
|
|
|
|
|
w = Path(w) |
|
|
if not w.is_file(): # if not *.rknn |
|
|
w = next(w.rglob("*.rknn")) # get *.rknn file from *_rknn_model dir |
|
|
rknn_model = RKNNLite() |
|
|
rknn_model.load_rknn(w) |
|
|
rknn_model.init_runtime() |
|
|
metadata = Path(w).parent / "metadata.yaml" |
|
|
|
|
|
# Any other format (unsupported) |
|
|
else: |
|
|
from ultralytics.engine.exporter import export_formats |
|
|
|
|
|
raise TypeError( |
|
|
f"model='{w}' is not a supported model format. Ultralytics supports: {export_formats()['Format']}\n" |
|
|
f"See https://docs.ultralytics.com/modes/predict for help." |
|
|
) |
|
|
|
|
|
# Load external metadata YAML |
|
|
if isinstance(metadata, (str, Path)) and Path(metadata).exists(): |
|
|
metadata = yaml_load(metadata) |
|
|
if metadata and isinstance(metadata, dict): |
|
|
for k, v in metadata.items(): |
|
|
if k in {"stride", "batch"}: |
|
|
metadata[k] = int(v) |
|
|
elif k in {"imgsz", "names", "kpt_shape", "args"} and isinstance(v, str): |
|
|
metadata[k] = eval(v) |
|
|
stride = metadata["stride"] |
|
|
task = metadata["task"] |
|
|
batch = metadata["batch"] |
|
|
imgsz = metadata["imgsz"] |
|
|
names = metadata["names"] |
|
|
kpt_shape = metadata.get("kpt_shape") |
|
|
end2end = metadata.get("args", {}).get("nms", False) |
|
|
elif not (pt or triton or nn_module): |
|
|
LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'") |
|
|
|
|
|
# Check names |
|
|
if "names" not in locals(): # names missing |
|
|
names = default_class_names(data) |
|
|
names = check_class_names(names) |
|
|
|
|
|
# Disable gradients |
|
|
if pt: |
|
|
for p in model.parameters(): |
|
|
p.requires_grad = False |
|
|
|
|
|
self.__dict__.update(locals()) # assign all variables to self |
|
|
|
|
|
def forward(self, im, augment=False, visualize=False, embed=None): |
|
|
""" |
|
|
Runs inference on the YOLOv8 MultiBackend model. |
|
|
|
|
|
Args: |
|
|
im (torch.Tensor): The image tensor to perform inference on. |
|
|
augment (bool): whether to perform data augmentation during inference, defaults to False |
|
|
visualize (bool): whether to visualize the output predictions, defaults to False |
|
|
embed (list, optional): A list of feature vectors/embeddings to return. |
|
|
|
|
|
Returns: |
|
|
(tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True) |
|
|
""" |
|
|
b, ch, h, w = im.shape # batch, channel, height, width |
|
|
if self.fp16 and im.dtype != torch.float16: |
|
|
im = im.half() # to FP16 |
|
|
if self.nhwc: |
|
|
im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) |
|
|
|
|
|
# PyTorch |
|
|
if self.pt or self.nn_module: |
|
|
y = self.model(im, augment=augment, visualize=visualize, embed=embed) |
|
|
|
|
|
# TorchScript |
|
|
elif self.jit: |
|
|
y = self.model(im) |
|
|
|
|
|
# ONNX OpenCV DNN |
|
|
elif self.dnn: |
|
|
im = im.cpu().numpy() # torch to numpy |
|
|
self.net.setInput(im) |
|
|
y = self.net.forward() |
|
|
|
|
|
# ONNX Runtime |
|
|
elif self.onnx or self.imx: |
|
|
if self.dynamic: |
|
|
im = im.cpu().numpy() # torch to numpy |
|
|
y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) |
|
|
else: |
|
|
if not self.cuda: |
|
|
im = im.cpu() |
|
|
self.io.bind_input( |
|
|
name="images", |
|
|
device_type=im.device.type, |
|
|
device_id=im.device.index if im.device.type == "cuda" else 0, |
|
|
element_type=np.float16 if self.fp16 else np.float32, |
|
|
shape=tuple(im.shape), |
|
|
buffer_ptr=im.data_ptr(), |
|
|
) |
|
|
self.session.run_with_iobinding(self.io) |
|
|
y = self.bindings |
|
|
if self.imx: |
|
|
# boxes, conf, cls |
|
|
y = np.concatenate([y[0], y[1][:, :, None], y[2][:, :, None]], axis=-1) |
|
|
|
|
|
# OpenVINO |
|
|
elif self.xml: |
|
|
im = im.cpu().numpy() # FP32 |
|
|
|
|
|
if self.inference_mode in {"THROUGHPUT", "CUMULATIVE_THROUGHPUT"}: # optimized for larger batch-sizes |
|
|
n = im.shape[0] # number of images in batch |
|
|
results = [None] * n # preallocate list with None to match the number of images |
|
|
|
|
|
def callback(request, userdata): |
|
|
"""Places result in preallocated list using userdata index.""" |
|
|
results[userdata] = request.results |
|
|
|
|
|
# Create AsyncInferQueue, set the callback and start asynchronous inference for each input image |
|
|
async_queue = self.ov.AsyncInferQueue(self.ov_compiled_model) |
|
|
async_queue.set_callback(callback) |
|
|
for i in range(n): |
|
|
# Start async inference with userdata=i to specify the position in results list |
|
|
async_queue.start_async(inputs={self.input_name: im[i : i + 1]}, userdata=i) # keep image as BCHW |
|
|
async_queue.wait_all() # wait for all inference requests to complete |
|
|
y = np.concatenate([list(r.values())[0] for r in results]) |
|
|
|
|
|
else: # inference_mode = "LATENCY", optimized for fastest first result at batch-size 1 |
|
|
y = list(self.ov_compiled_model(im).values()) |
|
|
|
|
|
# TensorRT |
|
|
elif self.engine: |
|
|
if self.dynamic and im.shape != self.bindings["images"].shape: |
|
|
if self.is_trt10: |
|
|
self.context.set_input_shape("images", im.shape) |
|
|
self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) |
|
|
for name in self.output_names: |
|
|
self.bindings[name].data.resize_(tuple(self.context.get_tensor_shape(name))) |
|
|
else: |
|
|
i = self.model.get_binding_index("images") |
|
|
self.context.set_binding_shape(i, im.shape) |
|
|
self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) |
|
|
for name in self.output_names: |
|
|
i = self.model.get_binding_index(name) |
|
|
self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) |
|
|
|
|
|
s = self.bindings["images"].shape |
|
|
assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" |
|
|
self.binding_addrs["images"] = int(im.data_ptr()) |
|
|
self.context.execute_v2(list(self.binding_addrs.values())) |
|
|
y = [self.bindings[x].data for x in sorted(self.output_names)] |
|
|
|
|
|
# CoreML |
|
|
elif self.coreml: |
|
|
im = im[0].cpu().numpy() |
|
|
im_pil = Image.fromarray((im * 255).astype("uint8")) |
|
|
# im = im.resize((192, 320), Image.BILINEAR) |
|
|
y = self.model.predict({"image": im_pil}) # coordinates are xywh normalized |
|
|
if "confidence" in y: |
|
|
raise TypeError( |
|
|
"Ultralytics only supports inference of non-pipelined CoreML models exported with " |
|
|
f"'nms=False', but 'model={w}' has an NMS pipeline created by an 'nms=True' export." |
|
|
) |
|
|
# TODO: CoreML NMS inference handling |
|
|
# from ultralytics.utils.ops import xywh2xyxy |
|
|
# box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels |
|
|
# conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float32) |
|
|
# y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) |
|
|
y = list(y.values()) |
|
|
if len(y) == 2 and len(y[1].shape) != 4: # segmentation model |
|
|
y = list(reversed(y)) # reversed for segmentation models (pred, proto) |
|
|
|
|
|
# PaddlePaddle |
|
|
elif self.paddle: |
|
|
im = im.cpu().numpy().astype(np.float32) |
|
|
self.input_handle.copy_from_cpu(im) |
|
|
self.predictor.run() |
|
|
y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] |
|
|
|
|
|
# MNN |
|
|
elif self.mnn: |
|
|
input_var = self.torch_to_mnn(im) |
|
|
output_var = self.net.onForward([input_var]) |
|
|
y = [x.read() for x in output_var] |
|
|
|
|
|
# NCNN |
|
|
elif self.ncnn: |
|
|
mat_in = self.pyncnn.Mat(im[0].cpu().numpy()) |
|
|
with self.net.create_extractor() as ex: |
|
|
ex.input(self.net.input_names()[0], mat_in) |
|
|
# WARNING: 'output_names' sorted as a temporary fix for https://github.com/pnnx/pnnx/issues/130 |
|
|
y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())] |
|
|
|
|
|
# NVIDIA Triton Inference Server |
|
|
elif self.triton: |
|
|
im = im.cpu().numpy() # torch to numpy |
|
|
y = self.model(im) |
|
|
|
|
|
# RKNN |
|
|
elif self.rknn: |
|
|
im = (im.cpu().numpy() * 255).astype("uint8") |
|
|
im = im if isinstance(im, (list, tuple)) else [im] |
|
|
y = self.rknn_model.inference(inputs=im) |
|
|
|
|
|
# TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) |
|
|
else: |
|
|
im = im.cpu().numpy() |
|
|
if self.saved_model: # SavedModel |
|
|
y = self.model(im, training=False) if self.keras else self.model(im) |
|
|
if not isinstance(y, list): |
|
|
y = [y] |
|
|
elif self.pb: # GraphDef |
|
|
y = self.frozen_func(x=self.tf.constant(im)) |
|
|
else: # Lite or Edge TPU |
|
|
details = self.input_details[0] |
|
|
is_int = details["dtype"] in {np.int8, np.int16} # is TFLite quantized int8 or int16 model |
|
|
if is_int: |
|
|
scale, zero_point = details["quantization"] |
|
|
im = (im / scale + zero_point).astype(details["dtype"]) # de-scale |
|
|
self.interpreter.set_tensor(details["index"], im) |
|
|
self.interpreter.invoke() |
|
|
y = [] |
|
|
for output in self.output_details: |
|
|
x = self.interpreter.get_tensor(output["index"]) |
|
|
if is_int: |
|
|
scale, zero_point = output["quantization"] |
|
|
x = (x.astype(np.float32) - zero_point) * scale # re-scale |
|
|
if x.ndim == 3: # if task is not classification, excluding masks (ndim=4) as well |
|
|
# Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695 |
|
|
# xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models |
|
|
if x.shape[-1] == 6 or self.end2end: # end-to-end model |
|
|
x[:, :, [0, 2]] *= w |
|
|
x[:, :, [1, 3]] *= h |
|
|
if self.task == "pose": |
|
|
x[:, :, 6::3] *= w |
|
|
x[:, :, 7::3] *= h |
|
|
else: |
|
|
x[:, [0, 2]] *= w |
|
|
x[:, [1, 3]] *= h |
|
|
if self.task == "pose": |
|
|
x[:, 5::3] *= w |
|
|
x[:, 6::3] *= h |
|
|
y.append(x) |
|
|
# TF segment fixes: export is reversed vs ONNX export and protos are transposed |
|
|
if len(y) == 2: # segment with (det, proto) output order reversed |
|
|
if len(y[1].shape) != 4: |
|
|
y = list(reversed(y)) # should be y = (1, 116, 8400), (1, 160, 160, 32) |
|
|
if y[1].shape[-1] == 6: # end-to-end model |
|
|
y = [y[1]] |
|
|
else: |
|
|
y[1] = np.transpose(y[1], (0, 3, 1, 2)) # should be y = (1, 116, 8400), (1, 32, 160, 160) |
|
|
y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] |
|
|
|
|
|
# for x in y: |
|
|
# print(type(x), len(x)) if isinstance(x, (list, tuple)) else print(type(x), x.shape) # debug shapes |
|
|
if isinstance(y, (list, tuple)): |
|
|
if len(self.names) == 999 and (self.task == "segment" or len(y) == 2): # segments and names not defined |
|
|
nc = y[0].shape[1] - y[1].shape[1] - 4 # y = (1, 32, 160, 160), (1, 116, 8400) |
|
|
self.names = {i: f"class{i}" for i in range(nc)} |
|
|
return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] |
|
|
else: |
|
|
return self.from_numpy(y) |
|
|
|
|
|
def from_numpy(self, x): |
|
|
""" |
|
|
Convert a numpy array to a tensor. |
|
|
|
|
|
Args: |
|
|
x (np.ndarray): The array to be converted. |
|
|
|
|
|
Returns: |
|
|
(torch.Tensor): The converted tensor |
|
|
""" |
|
|
return torch.tensor(x).to(self.device) if isinstance(x, np.ndarray) else x |
|
|
|
|
|
def warmup(self, imgsz=(1, 3, 640, 640)): |
|
|
""" |
|
|
Warm up the model by running one forward pass with a dummy input. |
|
|
|
|
|
Args: |
|
|
imgsz (tuple): The shape of the dummy input tensor in the format (batch_size, channels, height, width) |
|
|
""" |
|
|
import torchvision # noqa (import here so torchvision import time not recorded in postprocess time) |
|
|
|
|
|
warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton, self.nn_module |
|
|
if any(warmup_types) and (self.device.type != "cpu" or self.triton): |
|
|
im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input |
|
|
for _ in range(2 if self.jit else 1): |
|
|
self.forward(im) # warmup |
|
|
|
|
|
@staticmethod |
|
|
def _model_type(p="path/to/model.pt"): |
|
|
""" |
|
|
Takes a path to a model file and returns the model type. Possibles types are pt, jit, onnx, xml, engine, coreml, |
|
|
saved_model, pb, tflite, edgetpu, tfjs, ncnn or paddle. |
|
|
|
|
|
Args: |
|
|
p (str): path to the model file. Defaults to path/to/model.pt |
|
|
|
|
|
Examples: |
|
|
>>> model = AutoBackend(weights="path/to/model.onnx") |
|
|
>>> model_type = model._model_type() # returns "onnx" |
|
|
""" |
|
|
from ultralytics.engine.exporter import export_formats |
|
|
|
|
|
sf = export_formats()["Suffix"] # export suffixes |
|
|
if not is_url(p) and not isinstance(p, str): |
|
|
check_suffix(p, sf) # checks |
|
|
name = Path(p).name |
|
|
types = [s in name for s in sf] |
|
|
types[5] |= name.endswith(".mlmodel") # retain support for older Apple CoreML *.mlmodel formats |
|
|
types[8] &= not types[9] # tflite &= not edgetpu |
|
|
if any(types): |
|
|
triton = False |
|
|
else: |
|
|
from urllib.parse import urlsplit |
|
|
|
|
|
url = urlsplit(p) |
|
|
triton = bool(url.netloc) and bool(url.path) and url.scheme in {"http", "grpc"} |
|
|
|
|
|
return types + [triton]
|
|
|
|
