सामग्री पर जाएं

के लिए संदर्भ ultralytics/utils/benchmarks.py

नोट

यह फ़ाइल यहाँ उपलब्ध है https://github.com/ultralytics/ultralytics/बूँद/मुख्य/ultralytics/utils/benchmarks.py का उपयोग करें। यदि आप कोई समस्या देखते हैं तो कृपया पुल अनुरोध का योगदान करके इसे ठीक करने में मदद करें 🛠️। 🙏 धन्यवाद !



ultralytics.utils.benchmarks.RF100Benchmark

में स्रोत कोड 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__()

RF100Benchmark के आरंभीकरण के लिए कार्य।

में स्रोत कोड 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)

सत्यापन परिणामों पर मॉडल मूल्यांकन।

पैरामीटर:

नाम प्रकार या क़िस्‍म चूक
yaml_path str

YAML फ़ाइल पथ।

आवश्यक
val_log_file str

val_log_file रास्ता।

आवश्यक
eval_log_file str

eval_log_file रास्ता।

आवश्यक
list_ind int

वर्तमान डेटासेट के लिए अनुक्रमणिका।

आवश्यक
में स्रोत कोड 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.

पैरामीटर:

नाम प्रकार या क़िस्‍म चूक
path str

YAML फ़ाइल पथ।

आवश्यक
में स्रोत कोड 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')

डेटासेट लिंक पार्स करें और डेटासेट डाउनलोड करें।

पैरामीटर:

नाम प्रकार या क़िस्‍म चूक
ds_link_txt str

फ़ाइल dataset_links पथ.

'datasets_links.txt'
में स्रोत कोड 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)

अस्त हो Roboflow प्रसंस्करण के लिए एपीआई कुंजी।

पैरामीटर:

नाम प्रकार या क़िस्‍म चूक
api_key str

एपीआई कुंजी।

आवश्यक
में स्रोत कोड 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 क्लास पर विभिन्न मॉडलों की रूपरेखा के लिए ONNX और TensorRT.

यह वर्ग विभिन्न मॉडलों के प्रदर्शन को प्रोफाइल करता है, मॉडल की गति और FLOPs जैसे परिणाम लौटाता है।

विशेषताएँ:

नाम प्रकार या क़िस्‍म
paths list

प्रोफ़ाइल के लिए मॉडल के पथ।

num_timed_runs int

प्रोफाइलिंग के लिए समयबद्ध रन की संख्या। डिफ़ॉल्ट 100 है।

num_warmup_runs int

प्रोफाइलिंग से पहले वार्मअप रन की संख्या। डिफ़ॉल्ट 10 है।

min_time float

प्रोफ़ाइल के लिए सेकंड की न्यूनतम संख्या। डिफ़ॉल्ट 60 है।

imgsz int

मॉडल में उपयोग की जाने वाली छवि का आकार। डिफ़ॉल्ट 640 है।

विधियाँ:

नाम या क़िस्‍म
profile

मॉडल को प्रोफाइल करता है और परिणाम प्रिंट करता है।

उदाहरण
from ultralytics.utils.benchmarks import ProfileModels

ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'], imgsz=640).profile()
में स्रोत कोड 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)

मॉडल की रूपरेखा के लिए ProfileModels वर्ग प्रारंभ करें।

पैरामीटर:

नाम प्रकार या क़िस्‍म चूक
paths list

प्रोफाइल किए जाने वाले मॉडलों के रास्तों की सूची।

आवश्यक
num_timed_runs int

प्रोफाइलिंग के लिए समयबद्ध रन की संख्या। डिफ़ॉल्ट 100 है।

100
num_warmup_runs int

वास्तविक प्रोफाइलिंग शुरू होने से पहले वार्मअप की संख्या चलती है। डिफ़ॉल्ट 10 है।

10
min_time float

एक मॉडल की रूपरेखा के लिए सेकंड में न्यूनतम समय। डिफ़ॉल्ट 60 है।

60
imgsz int

प्रोफाइलिंग के दौरान उपयोग की गई छवि का आकार। डिफ़ॉल्ट 640 है।

640
half bool

यह इंगित करने के लिए ध्वजांकित करें कि प्रोफाइलिंग के लिए अर्ध-सटीक फ़्लोटिंग पॉइंट का उपयोग करना है या नहीं।

True
trt bool

यह इंगित करने के लिए ध्वजांकित करें कि प्रोफ़ाइल का उपयोग करना है या नहीं TensorRT. डिफ़ॉल्ट True है.

True
device device

प्रोफाइलिंग के लिए उपयोग किया जाने वाला उपकरण। यदि कोई नहीं, तो यह स्वचालित रूप से निर्धारित होता है।

None
में स्रोत कोड 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

नाम, पैरामीटर, GFLOPS और गति मैट्रिक्स सहित मॉडल विवरण का एक शब्दकोश उत्पन्न करता है।

में स्रोत कोड 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)

तालिका पंक्ति के लिए एक स्वरूपित स्ट्रिंग जनरेट करता है जिसमें मॉडल प्रदर्शन और मीट्रिक विवरण शामिल होते हैं.

में स्रोत कोड 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()

उपयोगकर्ता द्वारा दी गई सभी प्रासंगिक मॉडल फ़ाइलों के लिए पथ की एक सूची देता है।

में स्रोत कोड 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)

एक के लिए परतों, पैरामीटरों, ग्रेडिएंट और FLOPs की संख्या सहित जानकारी प्राप्त करता है ONNX को गढ़ना रेती।

में स्रोत कोड 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

पुनरावृत्तियों के दिए गए डेटा समय संख्या के लिए एक पुनरावृत्त सिग्मा क्लिपिंग एल्गोरिथ्म लागू करता है।

में स्रोत कोड 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

दिए गए आंकड़ों और प्रदर्शन डेटा के साथ विभिन्न मॉडलों के लिए एक तुलना तालिका को प्रारूपित और प्रिंट करता है।

में स्रोत कोड 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()

किसी मॉडल के बेंचमार्किंग परिणामों को लॉग करता है, फ़्लोर के विरुद्ध मीट्रिक की जाँच करता है और परिणाम लौटाता है.

में स्रोत कोड 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)

प्रोफाइल एक ONNX इसे कई बार निष्पादित करके मॉडल और रन का माध्य और मानक विचलन देता है गुणा।

में स्रोत कोड 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)

प्रोफाइल TensorRT मॉडल, रनों के बीच औसत रन टाइम और मानक विचलन को मापता है।

में स्रोत कोड 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)

बेंचमार्क a YOLO गति और सटीकता के लिए विभिन्न स्वरूपों में मॉडल।

पैरामीटर:

नाम प्रकार या क़िस्‍म चूक
model str | Path | optional

मॉडल फ़ाइल या निर्देशिका के लिए पथ। डिफ़ॉल्ट है पथ (सेटिंग्स ['weights_dir']) / 'yolov8n.pt'।

WEIGHTS_DIR / 'yolov8n.pt'
data str

डेटासेट पर मूल्यांकन करने के लिए, यदि पारित नहीं किया गया है तो TASK2DATA से विरासत में मिला है। डिफ़ॉल्ट कोई नहीं है।

None
imgsz int

बेंचमार्क के लिए छवि का आकार। डिफ़ॉल्ट 160 है।

160
half bool

यदि True है तो मॉडल के लिए अर्ध-परिशुद्धता का उपयोग करें। डिफ़ॉल्ट ग़लत है.

False
int8 bool

यदि सही है तो मॉडल के लिए int8-precision का उपयोग करें। डिफ़ॉल्ट ग़लत है.

False
device str

बेंचमार्क को चलाने के लिए डिवाइस, या तो 'सीपीयू' या 'क्यूडा'। डिफ़ॉल्ट 'सीपीयू' है।

'cpu'
verbose bool | float | optional

यदि सही या फ्लोट है, तो बेंचमार्क दिए गए मीट्रिक के साथ पास होने पर जोर दें। डिफ़ॉल्ट ग़लत है.

False

देता:

नाम प्रकार या क़िस्‍म
df DataFrame

फ़ाइल आकार सहित प्रत्येक प्रारूप के लिए बेंचमार्क परिणामों के साथ एक पांडा डेटाफ़्रेम, मीट्रिक, और अनुमान समय।

उदाहरण
from ultralytics.utils.benchmarks import benchmark

benchmark(model='yolov8n.pt', imgsz=640)
में स्रोत कोड 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)
    is_end2end = getattr(model.model.model[-1], "end2end", False)

    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"
                assert not is_end2end, "End-to-end models not supported by CoreML and TF.js yet"
            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"
                assert not is_end2end, "End-to-end models not supported by onnx2tf yet"
            if i in {11}:  # Paddle
                assert not isinstance(model, YOLOWorld), "YOLOWorldv2 Paddle exports not supported yet"
                assert not is_end2end, "End-to-end models not supported by PaddlePaddle yet"
            if i in {12}:  # NCNN
                assert not isinstance(model, YOLOWorld), "YOLOWorldv2 NCNN exports not supported yet"
                assert not is_end2end, "End-to-end models not supported by NCNN 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)