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Diese Datei ist verfügbar unter https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/utils/benchmarks .py. Wenn du ein Problem entdeckst, hilf bitte mit, es zu beheben, indem du einen Pull Request 🛠️ einreichst. Vielen Dank 🙏!



ultralytics.utils.benchmarks.RF100Benchmark

Quellcode in ultralytics/utils/benchmarks.py
class RF100Benchmark:
    def __init__(self):
        """Function for initialization of RF100Benchmark."""
        self.ds_names = []
        self.ds_cfg_list = []
        self.rf = None
        self.val_metrics = ["class", "images", "targets", "precision", "recall", "map50", "map95"]

    def set_key(self, api_key):
        """
        Set Roboflow API key for processing.

        Args:
            api_key (str): The API key.
        """

        check_requirements("roboflow")
        from roboflow import Roboflow

        self.rf = Roboflow(api_key=api_key)

    def parse_dataset(self, ds_link_txt="datasets_links.txt"):
        """
        Parse dataset links and downloads datasets.

        Args:
            ds_link_txt (str): Path to dataset_links file.
        """

        (shutil.rmtree("rf-100"), os.mkdir("rf-100")) if os.path.exists("rf-100") else os.mkdir("rf-100")
        os.chdir("rf-100")
        os.mkdir("ultralytics-benchmarks")
        safe_download("https://ultralytics.com/assets/datasets_links.txt")

        with open(ds_link_txt, "r") as file:
            for line in file:
                try:
                    _, url, workspace, project, version = re.split("/+", line.strip())
                    self.ds_names.append(project)
                    proj_version = f"{project}-{version}"
                    if not Path(proj_version).exists():
                        self.rf.workspace(workspace).project(project).version(version).download("yolov8")
                    else:
                        print("Dataset already downloaded.")
                    self.ds_cfg_list.append(Path.cwd() / proj_version / "data.yaml")
                except Exception:
                    continue

        return self.ds_names, self.ds_cfg_list

    @staticmethod
    def fix_yaml(path):
        """
        Function to fix YAML train and val path.

        Args:
            path (str): YAML file path.
        """

        with open(path, "r") as file:
            yaml_data = yaml.safe_load(file)
        yaml_data["train"] = "train/images"
        yaml_data["val"] = "valid/images"
        with open(path, "w") as file:
            yaml.safe_dump(yaml_data, file)

    def evaluate(self, yaml_path, val_log_file, eval_log_file, list_ind):
        """
        Model evaluation on validation results.

        Args:
            yaml_path (str): YAML file path.
            val_log_file (str): val_log_file path.
            eval_log_file (str): eval_log_file path.
            list_ind (int): Index for current dataset.
        """
        skip_symbols = ["🚀", "⚠️", "💡", "❌"]
        with open(yaml_path) as stream:
            class_names = yaml.safe_load(stream)["names"]
        with open(val_log_file, "r", encoding="utf-8") as f:
            lines = f.readlines()
            eval_lines = []
            for line in lines:
                if any(symbol in line for symbol in skip_symbols):
                    continue
                entries = line.split(" ")
                entries = list(filter(lambda val: val != "", entries))
                entries = [e.strip("\n") for e in entries]
                eval_lines.extend(
                    {
                        "class": entries[0],
                        "images": entries[1],
                        "targets": entries[2],
                        "precision": entries[3],
                        "recall": entries[4],
                        "map50": entries[5],
                        "map95": entries[6],
                    }
                    for e in entries
                    if e in class_names or (e == "all" and "(AP)" not in entries and "(AR)" not in entries)
                )
        map_val = 0.0
        if len(eval_lines) > 1:
            print("There's more dicts")
            for lst in eval_lines:
                if lst["class"] == "all":
                    map_val = lst["map50"]
        else:
            print("There's only one dict res")
            map_val = [res["map50"] for res in eval_lines][0]

        with open(eval_log_file, "a") as f:
            f.write(f"{self.ds_names[list_ind]}: {map_val}\n")

__init__()

Funktion zur Initialisierung von RF100Benchmark.

Quellcode in ultralytics/utils/benchmarks.py
def __init__(self):
    """Function for initialization of RF100Benchmark."""
    self.ds_names = []
    self.ds_cfg_list = []
    self.rf = None
    self.val_metrics = ["class", "images", "targets", "precision", "recall", "map50", "map95"]

evaluate(yaml_path, val_log_file, eval_log_file, list_ind)

Modellbewertung anhand der Validierungsergebnisse.

Parameter:

Name Typ Beschreibung Standard
yaml_path str

YAML-Dateipfad.

erforderlich
val_log_file str

val_log_file Pfad.

erforderlich
eval_log_file str

eval_log_file Pfad.

erforderlich
list_ind int

Index für den aktuellen Datensatz.

erforderlich
Quellcode in ultralytics/utils/benchmarks.py
def evaluate(self, yaml_path, val_log_file, eval_log_file, list_ind):
    """
    Model evaluation on validation results.

    Args:
        yaml_path (str): YAML file path.
        val_log_file (str): val_log_file path.
        eval_log_file (str): eval_log_file path.
        list_ind (int): Index for current dataset.
    """
    skip_symbols = ["🚀", "⚠️", "💡", "❌"]
    with open(yaml_path) as stream:
        class_names = yaml.safe_load(stream)["names"]
    with open(val_log_file, "r", encoding="utf-8") as f:
        lines = f.readlines()
        eval_lines = []
        for line in lines:
            if any(symbol in line for symbol in skip_symbols):
                continue
            entries = line.split(" ")
            entries = list(filter(lambda val: val != "", entries))
            entries = [e.strip("\n") for e in entries]
            eval_lines.extend(
                {
                    "class": entries[0],
                    "images": entries[1],
                    "targets": entries[2],
                    "precision": entries[3],
                    "recall": entries[4],
                    "map50": entries[5],
                    "map95": entries[6],
                }
                for e in entries
                if e in class_names or (e == "all" and "(AP)" not in entries and "(AR)" not in entries)
            )
    map_val = 0.0
    if len(eval_lines) > 1:
        print("There's more dicts")
        for lst in eval_lines:
            if lst["class"] == "all":
                map_val = lst["map50"]
    else:
        print("There's only one dict res")
        map_val = [res["map50"] for res in eval_lines][0]

    with open(eval_log_file, "a") as f:
        f.write(f"{self.ds_names[list_ind]}: {map_val}\n")

fix_yaml(path) staticmethod

Function to fix YAML train and val path.

Parameter:

Name Typ Beschreibung Standard
path str

YAML-Dateipfad.

erforderlich
Quellcode in ultralytics/utils/benchmarks.py
@staticmethod
def fix_yaml(path):
    """
    Function to fix YAML train and val path.

    Args:
        path (str): YAML file path.
    """

    with open(path, "r") as file:
        yaml_data = yaml.safe_load(file)
    yaml_data["train"] = "train/images"
    yaml_data["val"] = "valid/images"
    with open(path, "w") as file:
        yaml.safe_dump(yaml_data, file)

parse_dataset(ds_link_txt='datasets_links.txt')

Analysiert Datensatz-Links und lädt Datensätze herunter.

Parameter:

Name Typ Beschreibung Standard
ds_link_txt str

Pfad zur Datei dataset_links.

'datasets_links.txt'
Quellcode in ultralytics/utils/benchmarks.py
def parse_dataset(self, ds_link_txt="datasets_links.txt"):
    """
    Parse dataset links and downloads datasets.

    Args:
        ds_link_txt (str): Path to dataset_links file.
    """

    (shutil.rmtree("rf-100"), os.mkdir("rf-100")) if os.path.exists("rf-100") else os.mkdir("rf-100")
    os.chdir("rf-100")
    os.mkdir("ultralytics-benchmarks")
    safe_download("https://ultralytics.com/assets/datasets_links.txt")

    with open(ds_link_txt, "r") as file:
        for line in file:
            try:
                _, url, workspace, project, version = re.split("/+", line.strip())
                self.ds_names.append(project)
                proj_version = f"{project}-{version}"
                if not Path(proj_version).exists():
                    self.rf.workspace(workspace).project(project).version(version).download("yolov8")
                else:
                    print("Dataset already downloaded.")
                self.ds_cfg_list.append(Path.cwd() / proj_version / "data.yaml")
            except Exception:
                continue

    return self.ds_names, self.ds_cfg_list

set_key(api_key)

Lege den Roboflow API-Schlüssel für die Verarbeitung fest.

Parameter:

Name Typ Beschreibung Standard
api_key str

Der API-Schlüssel.

erforderlich
Quellcode in ultralytics/utils/benchmarks.py
def set_key(self, api_key):
    """
    Set Roboflow API key for processing.

    Args:
        api_key (str): The API key.
    """

    check_requirements("roboflow")
    from roboflow import Roboflow

    self.rf = Roboflow(api_key=api_key)



ultralytics.utils.benchmarks.ProfileModels

ProfileModels Klasse für die Profilierung verschiedener Modelle auf ONNX und TensorRT.

Diese Klasse erstellt ein Profil der Leistung verschiedener Modelle und liefert Ergebnisse wie die Modellgeschwindigkeit und FLOPs.

Attribute:

Name Typ Beschreibung
paths list

Pfade der zu profilierenden Modelle.

num_timed_runs int

Anzahl der zeitlich begrenzten Durchläufe für die Profilerstellung. Der Standardwert ist 100.

num_warmup_runs int

Anzahl der Aufwärmläufe vor der Profilerstellung. Standard ist 10.

min_time float

Mindestanzahl von Sekunden, für die ein Profil erstellt werden soll. Der Standardwert ist 60.

imgsz int

Bildgröße, die in den Modellen verwendet wird. Standard ist 640.

Methoden:

Name Beschreibung
profile

Profiliert die Modelle und druckt das Ergebnis aus.

Beispiel
from ultralytics.utils.benchmarks import ProfileModels

ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'], imgsz=640).profile()
Quellcode in ultralytics/utils/benchmarks.py
class ProfileModels:
    """
    ProfileModels class for profiling different models on ONNX and TensorRT.

    This class profiles the performance of different models, returning results such as model speed and FLOPs.

    Attributes:
        paths (list): Paths of the models to profile.
        num_timed_runs (int): Number of timed runs for the profiling. Default is 100.
        num_warmup_runs (int): Number of warmup runs before profiling. Default is 10.
        min_time (float): Minimum number of seconds to profile for. Default is 60.
        imgsz (int): Image size used in the models. Default is 640.

    Methods:
        profile(): Profiles the models and prints the result.

    Example:
        ```python
        from ultralytics.utils.benchmarks import ProfileModels

        ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'], imgsz=640).profile()
        ```
    """

    def __init__(
        self,
        paths: list,
        num_timed_runs=100,
        num_warmup_runs=10,
        min_time=60,
        imgsz=640,
        half=True,
        trt=True,
        device=None,
    ):
        """
        Initialize the ProfileModels class for profiling models.

        Args:
            paths (list): List of paths of the models to be profiled.
            num_timed_runs (int, optional): Number of timed runs for the profiling. Default is 100.
            num_warmup_runs (int, optional): Number of warmup runs before the actual profiling starts. Default is 10.
            min_time (float, optional): Minimum time in seconds for profiling a model. Default is 60.
            imgsz (int, optional): Size of the image used during profiling. Default is 640.
            half (bool, optional): Flag to indicate whether to use half-precision floating point for profiling.
            trt (bool, optional): Flag to indicate whether to profile using TensorRT. Default is True.
            device (torch.device, optional): Device used for profiling. If None, it is determined automatically.
        """
        self.paths = paths
        self.num_timed_runs = num_timed_runs
        self.num_warmup_runs = num_warmup_runs
        self.min_time = min_time
        self.imgsz = imgsz
        self.half = half
        self.trt = trt  # run TensorRT profiling
        self.device = device or torch.device(0 if torch.cuda.is_available() else "cpu")

    def profile(self):
        """Logs the benchmarking results of a model, checks metrics against floor and returns the results."""
        files = self.get_files()

        if not files:
            print("No matching *.pt or *.onnx files found.")
            return

        table_rows = []
        output = []
        for file in files:
            engine_file = file.with_suffix(".engine")
            if file.suffix in {".pt", ".yaml", ".yml"}:
                model = YOLO(str(file))
                model.fuse()  # to report correct params and GFLOPs in model.info()
                model_info = model.info()
                if self.trt and self.device.type != "cpu" and not engine_file.is_file():
                    engine_file = model.export(
                        format="engine", half=self.half, imgsz=self.imgsz, device=self.device, verbose=False
                    )
                onnx_file = model.export(
                    format="onnx", half=self.half, imgsz=self.imgsz, simplify=True, device=self.device, verbose=False
                )
            elif file.suffix == ".onnx":
                model_info = self.get_onnx_model_info(file)
                onnx_file = file
            else:
                continue

            t_engine = self.profile_tensorrt_model(str(engine_file))
            t_onnx = self.profile_onnx_model(str(onnx_file))
            table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, model_info))
            output.append(self.generate_results_dict(file.stem, t_onnx, t_engine, model_info))

        self.print_table(table_rows)
        return output

    def get_files(self):
        """Returns a list of paths for all relevant model files given by the user."""
        files = []
        for path in self.paths:
            path = Path(path)
            if path.is_dir():
                extensions = ["*.pt", "*.onnx", "*.yaml"]
                files.extend([file for ext in extensions for file in glob.glob(str(path / ext))])
            elif path.suffix in {".pt", ".yaml", ".yml"}:  # add non-existing
                files.append(str(path))
            else:
                files.extend(glob.glob(str(path)))

        print(f"Profiling: {sorted(files)}")
        return [Path(file) for file in sorted(files)]

    def get_onnx_model_info(self, onnx_file: str):
        """Retrieves the information including number of layers, parameters, gradients and FLOPs for an ONNX model
        file.
        """
        return 0.0, 0.0, 0.0, 0.0  # return (num_layers, num_params, num_gradients, num_flops)

    @staticmethod
    def iterative_sigma_clipping(data, sigma=2, max_iters=3):
        """Applies an iterative sigma clipping algorithm to the given data times number of iterations."""
        data = np.array(data)
        for _ in range(max_iters):
            mean, std = np.mean(data), np.std(data)
            clipped_data = data[(data > mean - sigma * std) & (data < mean + sigma * std)]
            if len(clipped_data) == len(data):
                break
            data = clipped_data
        return data

    def profile_tensorrt_model(self, engine_file: str, eps: float = 1e-3):
        """Profiles the TensorRT model, measuring average run time and standard deviation among runs."""
        if not self.trt or not Path(engine_file).is_file():
            return 0.0, 0.0

        # Model and input
        model = YOLO(engine_file)
        input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32)  # must be FP32

        # Warmup runs
        elapsed = 0.0
        for _ in range(3):
            start_time = time.time()
            for _ in range(self.num_warmup_runs):
                model(input_data, imgsz=self.imgsz, verbose=False)
            elapsed = time.time() - start_time

        # Compute number of runs as higher of min_time or num_timed_runs
        num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs * 50)

        # Timed runs
        run_times = []
        for _ in TQDM(range(num_runs), desc=engine_file):
            results = model(input_data, imgsz=self.imgsz, verbose=False)
            run_times.append(results[0].speed["inference"])  # Convert to milliseconds

        run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
        return np.mean(run_times), np.std(run_times)

    def profile_onnx_model(self, onnx_file: str, eps: float = 1e-3):
        """Profiles an ONNX model by executing it multiple times and returns the mean and standard deviation of run
        times.
        """
        check_requirements("onnxruntime")
        import onnxruntime as ort

        # Session with either 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
        sess_options = ort.SessionOptions()
        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        sess_options.intra_op_num_threads = 8  # Limit the number of threads
        sess = ort.InferenceSession(onnx_file, sess_options, providers=["CPUExecutionProvider"])

        input_tensor = sess.get_inputs()[0]
        input_type = input_tensor.type
        dynamic = not all(isinstance(dim, int) and dim >= 0 for dim in input_tensor.shape)  # dynamic input shape
        input_shape = (1, 3, self.imgsz, self.imgsz) if dynamic else input_tensor.shape

        # Mapping ONNX datatype to numpy datatype
        if "float16" in input_type:
            input_dtype = np.float16
        elif "float" in input_type:
            input_dtype = np.float32
        elif "double" in input_type:
            input_dtype = np.float64
        elif "int64" in input_type:
            input_dtype = np.int64
        elif "int32" in input_type:
            input_dtype = np.int32
        else:
            raise ValueError(f"Unsupported ONNX datatype {input_type}")

        input_data = np.random.rand(*input_shape).astype(input_dtype)
        input_name = input_tensor.name
        output_name = sess.get_outputs()[0].name

        # Warmup runs
        elapsed = 0.0
        for _ in range(3):
            start_time = time.time()
            for _ in range(self.num_warmup_runs):
                sess.run([output_name], {input_name: input_data})
            elapsed = time.time() - start_time

        # Compute number of runs as higher of min_time or num_timed_runs
        num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs)

        # Timed runs
        run_times = []
        for _ in TQDM(range(num_runs), desc=onnx_file):
            start_time = time.time()
            sess.run([output_name], {input_name: input_data})
            run_times.append((time.time() - start_time) * 1000)  # Convert to milliseconds

        run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=5)  # sigma clipping
        return np.mean(run_times), np.std(run_times)

    def generate_table_row(self, model_name, t_onnx, t_engine, model_info):
        """Generates a formatted string for a table row that includes model performance and metric details."""
        layers, params, gradients, flops = model_info
        return (
            f"| {model_name:18s} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± "
            f"{t_engine[1]:.2f} ms | {params / 1e6:.1f} | {flops:.1f} |"
        )

    @staticmethod
    def generate_results_dict(model_name, t_onnx, t_engine, model_info):
        """Generates a dictionary of model details including name, parameters, GFLOPS and speed metrics."""
        layers, params, gradients, flops = model_info
        return {
            "model/name": model_name,
            "model/parameters": params,
            "model/GFLOPs": round(flops, 3),
            "model/speed_ONNX(ms)": round(t_onnx[0], 3),
            "model/speed_TensorRT(ms)": round(t_engine[0], 3),
        }

    @staticmethod
    def print_table(table_rows):
        """Formats and prints a comparison table for different models with given statistics and performance data."""
        gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else "GPU"
        header = (
            f"| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | "
            f"Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |"
        )
        separator = (
            "|-------------|---------------------|--------------------|------------------------------|"
            "-----------------------------------|------------------|-----------------|"
        )

        print(f"\n\n{header}")
        print(separator)
        for row in table_rows:
            print(row)

__init__(paths, num_timed_runs=100, num_warmup_runs=10, min_time=60, imgsz=640, half=True, trt=True, device=None)

Initialisiere die Klasse ProfileModels für Profilierungsmodelle.

Parameter:

Name Typ Beschreibung Standard
paths list

Liste der Pfade der Modelle, die profiliert werden sollen.

erforderlich
num_timed_runs int

Anzahl der zeitlich begrenzten Durchläufe für die Profilerstellung. Der Standardwert ist 100.

100
num_warmup_runs int

Anzahl der Aufwärmläufe, bevor die eigentliche Profilerstellung beginnt. Standard ist 10.

10
min_time float

Mindestzeit in Sekunden für die Profilerstellung eines Modells. Der Standardwert ist 60.

60
imgsz int

Größe des Bildes, das bei der Profilerstellung verwendet wird. Standard ist 640.

640
half bool

Flag, das angibt, ob für die Profilerstellung halbgenaue Fließkommazahlen verwendet werden sollen.

True
trt bool

Flagge, die angibt, ob das Profil mit TensorRT erstellt werden soll. Die Voreinstellung ist True.

True
device device

Das für die Profilerstellung verwendete Gerät. Wenn Keine, wird es automatisch ermittelt.

None
Quellcode in ultralytics/utils/benchmarks.py
def __init__(
    self,
    paths: list,
    num_timed_runs=100,
    num_warmup_runs=10,
    min_time=60,
    imgsz=640,
    half=True,
    trt=True,
    device=None,
):
    """
    Initialize the ProfileModels class for profiling models.

    Args:
        paths (list): List of paths of the models to be profiled.
        num_timed_runs (int, optional): Number of timed runs for the profiling. Default is 100.
        num_warmup_runs (int, optional): Number of warmup runs before the actual profiling starts. Default is 10.
        min_time (float, optional): Minimum time in seconds for profiling a model. Default is 60.
        imgsz (int, optional): Size of the image used during profiling. Default is 640.
        half (bool, optional): Flag to indicate whether to use half-precision floating point for profiling.
        trt (bool, optional): Flag to indicate whether to profile using TensorRT. Default is True.
        device (torch.device, optional): Device used for profiling. If None, it is determined automatically.
    """
    self.paths = paths
    self.num_timed_runs = num_timed_runs
    self.num_warmup_runs = num_warmup_runs
    self.min_time = min_time
    self.imgsz = imgsz
    self.half = half
    self.trt = trt  # run TensorRT profiling
    self.device = device or torch.device(0 if torch.cuda.is_available() else "cpu")

generate_results_dict(model_name, t_onnx, t_engine, model_info) staticmethod

Erzeugt ein Wörterbuch mit Modelldetails wie Name, Parameter, GFLOPS und Geschwindigkeitsmetriken.

Quellcode in ultralytics/utils/benchmarks.py
@staticmethod
def generate_results_dict(model_name, t_onnx, t_engine, model_info):
    """Generates a dictionary of model details including name, parameters, GFLOPS and speed metrics."""
    layers, params, gradients, flops = model_info
    return {
        "model/name": model_name,
        "model/parameters": params,
        "model/GFLOPs": round(flops, 3),
        "model/speed_ONNX(ms)": round(t_onnx[0], 3),
        "model/speed_TensorRT(ms)": round(t_engine[0], 3),
    }

generate_table_row(model_name, t_onnx, t_engine, model_info)

Erzeugt eine formatierte Zeichenkette für eine Tabellenzeile, die Details zur Modellleistung und Metrik enthält.

Quellcode in ultralytics/utils/benchmarks.py
def generate_table_row(self, model_name, t_onnx, t_engine, model_info):
    """Generates a formatted string for a table row that includes model performance and metric details."""
    layers, params, gradients, flops = model_info
    return (
        f"| {model_name:18s} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± "
        f"{t_engine[1]:.2f} ms | {params / 1e6:.1f} | {flops:.1f} |"
    )

get_files()

Gibt eine Liste der Pfade für alle relevanten Modelldateien zurück, die der Benutzer angegeben hat.

Quellcode in ultralytics/utils/benchmarks.py
def get_files(self):
    """Returns a list of paths for all relevant model files given by the user."""
    files = []
    for path in self.paths:
        path = Path(path)
        if path.is_dir():
            extensions = ["*.pt", "*.onnx", "*.yaml"]
            files.extend([file for ext in extensions for file in glob.glob(str(path / ext))])
        elif path.suffix in {".pt", ".yaml", ".yml"}:  # add non-existing
            files.append(str(path))
        else:
            files.extend(glob.glob(str(path)))

    print(f"Profiling: {sorted(files)}")
    return [Path(file) for file in sorted(files)]

get_onnx_model_info(onnx_file)

Ruft die Informationen ab, einschließlich der Anzahl der Schichten, Parameter, Gradienten und FLOPs für eine ONNX Modell Datei.

Quellcode in ultralytics/utils/benchmarks.py
def get_onnx_model_info(self, onnx_file: str):
    """Retrieves the information including number of layers, parameters, gradients and FLOPs for an ONNX model
    file.
    """
    return 0.0, 0.0, 0.0, 0.0  # return (num_layers, num_params, num_gradients, num_flops)

iterative_sigma_clipping(data, sigma=2, max_iters=3) staticmethod

Wendet einen iterativen Sigma-Clipping-Algorithmus auf die angegebenen Daten mal Anzahl der Iterationen an.

Quellcode in ultralytics/utils/benchmarks.py
@staticmethod
def iterative_sigma_clipping(data, sigma=2, max_iters=3):
    """Applies an iterative sigma clipping algorithm to the given data times number of iterations."""
    data = np.array(data)
    for _ in range(max_iters):
        mean, std = np.mean(data), np.std(data)
        clipped_data = data[(data > mean - sigma * std) & (data < mean + sigma * std)]
        if len(clipped_data) == len(data):
            break
        data = clipped_data
    return data

print_table(table_rows) staticmethod

Formatiert und druckt eine Vergleichstabelle für verschiedene Modelle mit vorgegebenen Statistiken und Leistungsdaten.

Quellcode in ultralytics/utils/benchmarks.py
@staticmethod
def print_table(table_rows):
    """Formats and prints a comparison table for different models with given statistics and performance data."""
    gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else "GPU"
    header = (
        f"| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | "
        f"Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |"
    )
    separator = (
        "|-------------|---------------------|--------------------|------------------------------|"
        "-----------------------------------|------------------|-----------------|"
    )

    print(f"\n\n{header}")
    print(separator)
    for row in table_rows:
        print(row)

profile()

Protokolliert die Benchmarking-Ergebnisse eines Modells, prüft die Metriken gegen den Boden und gibt die Ergebnisse zurück.

Quellcode in ultralytics/utils/benchmarks.py
def profile(self):
    """Logs the benchmarking results of a model, checks metrics against floor and returns the results."""
    files = self.get_files()

    if not files:
        print("No matching *.pt or *.onnx files found.")
        return

    table_rows = []
    output = []
    for file in files:
        engine_file = file.with_suffix(".engine")
        if file.suffix in {".pt", ".yaml", ".yml"}:
            model = YOLO(str(file))
            model.fuse()  # to report correct params and GFLOPs in model.info()
            model_info = model.info()
            if self.trt and self.device.type != "cpu" and not engine_file.is_file():
                engine_file = model.export(
                    format="engine", half=self.half, imgsz=self.imgsz, device=self.device, verbose=False
                )
            onnx_file = model.export(
                format="onnx", half=self.half, imgsz=self.imgsz, simplify=True, device=self.device, verbose=False
            )
        elif file.suffix == ".onnx":
            model_info = self.get_onnx_model_info(file)
            onnx_file = file
        else:
            continue

        t_engine = self.profile_tensorrt_model(str(engine_file))
        t_onnx = self.profile_onnx_model(str(onnx_file))
        table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, model_info))
        output.append(self.generate_results_dict(file.stem, t_onnx, t_engine, model_info))

    self.print_table(table_rows)
    return output

profile_onnx_model(onnx_file, eps=0.001)

Profiliert ein ONNX Modell, indem es mehrfach ausgeführt wird, und gibt den Mittelwert und die Standardabweichung der Lauf Zeiten zurück.

Quellcode in ultralytics/utils/benchmarks.py
def profile_onnx_model(self, onnx_file: str, eps: float = 1e-3):
    """Profiles an ONNX model by executing it multiple times and returns the mean and standard deviation of run
    times.
    """
    check_requirements("onnxruntime")
    import onnxruntime as ort

    # Session with either 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
    sess_options = ort.SessionOptions()
    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
    sess_options.intra_op_num_threads = 8  # Limit the number of threads
    sess = ort.InferenceSession(onnx_file, sess_options, providers=["CPUExecutionProvider"])

    input_tensor = sess.get_inputs()[0]
    input_type = input_tensor.type
    dynamic = not all(isinstance(dim, int) and dim >= 0 for dim in input_tensor.shape)  # dynamic input shape
    input_shape = (1, 3, self.imgsz, self.imgsz) if dynamic else input_tensor.shape

    # Mapping ONNX datatype to numpy datatype
    if "float16" in input_type:
        input_dtype = np.float16
    elif "float" in input_type:
        input_dtype = np.float32
    elif "double" in input_type:
        input_dtype = np.float64
    elif "int64" in input_type:
        input_dtype = np.int64
    elif "int32" in input_type:
        input_dtype = np.int32
    else:
        raise ValueError(f"Unsupported ONNX datatype {input_type}")

    input_data = np.random.rand(*input_shape).astype(input_dtype)
    input_name = input_tensor.name
    output_name = sess.get_outputs()[0].name

    # Warmup runs
    elapsed = 0.0
    for _ in range(3):
        start_time = time.time()
        for _ in range(self.num_warmup_runs):
            sess.run([output_name], {input_name: input_data})
        elapsed = time.time() - start_time

    # Compute number of runs as higher of min_time or num_timed_runs
    num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs)

    # Timed runs
    run_times = []
    for _ in TQDM(range(num_runs), desc=onnx_file):
        start_time = time.time()
        sess.run([output_name], {input_name: input_data})
        run_times.append((time.time() - start_time) * 1000)  # Convert to milliseconds

    run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=5)  # sigma clipping
    return np.mean(run_times), np.std(run_times)

profile_tensorrt_model(engine_file, eps=0.001)

Erstelle ein Profil des Modells TensorRT und messe die durchschnittliche Laufzeit und die Standardabweichung zwischen den Läufen.

Quellcode in ultralytics/utils/benchmarks.py
def profile_tensorrt_model(self, engine_file: str, eps: float = 1e-3):
    """Profiles the TensorRT model, measuring average run time and standard deviation among runs."""
    if not self.trt or not Path(engine_file).is_file():
        return 0.0, 0.0

    # Model and input
    model = YOLO(engine_file)
    input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32)  # must be FP32

    # Warmup runs
    elapsed = 0.0
    for _ in range(3):
        start_time = time.time()
        for _ in range(self.num_warmup_runs):
            model(input_data, imgsz=self.imgsz, verbose=False)
        elapsed = time.time() - start_time

    # Compute number of runs as higher of min_time or num_timed_runs
    num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs * 50)

    # Timed runs
    run_times = []
    for _ in TQDM(range(num_runs), desc=engine_file):
        results = model(input_data, imgsz=self.imgsz, verbose=False)
        run_times.append(results[0].speed["inference"])  # Convert to milliseconds

    run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
    return np.mean(run_times), np.std(run_times)



ultralytics.utils.benchmarks.benchmark(model=WEIGHTS_DIR / 'yolov8n.pt', data=None, imgsz=160, half=False, int8=False, device='cpu', verbose=False)

Vergleiche ein YOLO Modell in verschiedenen Formaten auf Geschwindigkeit und Genauigkeit.

Parameter:

Name Typ Beschreibung Standard
model str | Path | optional

Pfad zu der Modelldatei oder dem Verzeichnis. Standard ist Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.

WEIGHTS_DIR / 'yolov8n.pt'
data str

Datensatz, der ausgewertet werden soll. Wird von TASK2DATA geerbt, wenn er nicht übergeben wird. Der Standardwert ist None.

None
imgsz int

Bildgröße für den Benchmark. Standard ist 160.

160
half bool

Verwende die halbe Genauigkeit für das Modell, wenn True. Standard ist False.

False
int8 bool

Verwende int8-Präzision für das Modell, wenn True. Standard ist False.

False
device str

Gerät, auf dem der Benchmark ausgeführt werden soll, entweder "cpu" oder "cuda". Standard ist "cpu".

'cpu'
verbose bool | float | optional

Wenn True oder eine Fließkommazahl, werden die Benchmarks mit der angegebenen Metrik bestätigt. Standard ist False.

False

Retouren:

Name Typ Beschreibung
df DataFrame

Ein Pandas DataFrame mit Benchmark-Ergebnissen für jedes Format, einschließlich Dateigröße, Metrik und Inferenzzeit.

Beispiel
from ultralytics.utils.benchmarks import benchmark

benchmark(model='yolov8n.pt', imgsz=640)
Quellcode in ultralytics/utils/benchmarks.py
def benchmark(
    model=WEIGHTS_DIR / "yolov8n.pt", data=None, imgsz=160, half=False, int8=False, device="cpu", verbose=False
):
    """
    Benchmark a YOLO model across different formats for speed and accuracy.

    Args:
        model (str | Path | optional): Path to the model file or directory. Default is
            Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.
        data (str, optional): Dataset to evaluate on, inherited from TASK2DATA if not passed. Default is None.
        imgsz (int, optional): Image size for the benchmark. Default is 160.
        half (bool, optional): Use half-precision for the model if True. Default is False.
        int8 (bool, optional): Use int8-precision for the model if True. Default is False.
        device (str, optional): Device to run the benchmark on, either 'cpu' or 'cuda'. Default is 'cpu'.
        verbose (bool | float | optional): If True or a float, assert benchmarks pass with given metric.
            Default is False.

    Returns:
        df (pandas.DataFrame): A pandas DataFrame with benchmark results for each format, including file size,
            metric, and inference time.

    Example:
        ```python
        from ultralytics.utils.benchmarks import benchmark

        benchmark(model='yolov8n.pt', imgsz=640)
        ```
    """
    import pandas as pd  # scope for faster 'import ultralytics'

    pd.options.display.max_columns = 10
    pd.options.display.width = 120
    device = select_device(device, verbose=False)
    if isinstance(model, (str, Path)):
        model = YOLO(model)

    y = []
    t0 = time.time()
    for i, (name, format, suffix, cpu, gpu) in export_formats().iterrows():  # index, (name, format, suffix, CPU, GPU)
        emoji, filename = "❌", None  # export defaults
        try:
            # Checks
            if i == 7:  # TF GraphDef
                assert model.task != "obb", "TensorFlow GraphDef not supported for OBB task"
            elif i == 9:  # Edge TPU
                assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
            elif i in {5, 10}:  # CoreML and TF.js
                assert MACOS or LINUX, "CoreML and TF.js export only supported on macOS and Linux"
                assert not IS_RASPBERRYPI, "CoreML and TF.js export not supported on Raspberry Pi"
                assert not IS_JETSON, "CoreML and TF.js export not supported on NVIDIA Jetson"
            if i in {3, 5}:  # CoreML and OpenVINO
                assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
            if i in {6, 7, 8, 9, 10}:  # All TF formats
                assert not isinstance(model, YOLOWorld), "YOLOWorldv2 TensorFlow exports not supported by onnx2tf yet"
            if i in {11}:  # Paddle
                assert not isinstance(model, YOLOWorld), "YOLOWorldv2 Paddle exports not supported yet"
            if i in {12}:  # NCNN
                assert not isinstance(model, YOLOWorld), "YOLOWorldv2 NCNN exports not supported yet"
            if "cpu" in device.type:
                assert cpu, "inference not supported on CPU"
            if "cuda" in device.type:
                assert gpu, "inference not supported on GPU"

            # Export
            if format == "-":
                filename = model.ckpt_path or model.cfg
                exported_model = model  # PyTorch format
            else:
                filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device, verbose=False)
                exported_model = YOLO(filename, task=model.task)
                assert suffix in str(filename), "export failed"
            emoji = "❎"  # indicates export succeeded

            # Predict
            assert model.task != "pose" or i != 7, "GraphDef Pose inference is not supported"
            assert i not in {9, 10}, "inference not supported"  # Edge TPU and TF.js are unsupported
            assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13"  # CoreML
            exported_model.predict(ASSETS / "bus.jpg", imgsz=imgsz, device=device, half=half)

            # Validate
            data = data or TASK2DATA[model.task]  # task to dataset, i.e. coco8.yaml for task=detect
            key = TASK2METRIC[model.task]  # task to metric, i.e. metrics/mAP50-95(B) for task=detect
            results = exported_model.val(
                data=data, batch=1, imgsz=imgsz, plots=False, device=device, half=half, int8=int8, verbose=False
            )
            metric, speed = results.results_dict[key], results.speed["inference"]
            fps = round((1000 / speed), 2)  # frames per second
            y.append([name, "✅", round(file_size(filename), 1), round(metric, 4), round(speed, 2), fps])
        except Exception as e:
            if verbose:
                assert type(e) is AssertionError, f"Benchmark failure for {name}: {e}"
            LOGGER.warning(f"ERROR ❌️ Benchmark failure for {name}: {e}")
            y.append([name, emoji, round(file_size(filename), 1), None, None, None])  # mAP, t_inference

    # Print results
    check_yolo(device=device)  # print system info
    df = pd.DataFrame(y, columns=["Format", "Status❔", "Size (MB)", key, "Inference time (ms/im)", "FPS"])

    name = Path(model.ckpt_path).name
    s = f"\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df}\n"
    LOGGER.info(s)
    with open("benchmarks.log", "a", errors="ignore", encoding="utf-8") as f:
        f.write(s)

    if verbose and isinstance(verbose, float):
        metrics = df[key].array  # values to compare to floor
        floor = verbose  # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
        assert all(x > floor for x in metrics if pd.notna(x)), f"Benchmark failure: metric(s) < floor {floor}"

    return df





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (6), Burhan-Q (1)