ultralytics 8.0.46 TFLite and Benchmarks updates (#1141)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/ci.yaml

@@ -55,18 +55,20 @@ jobs:
       - name: Benchmark DetectionModel
         shell: python
         run: |
-          from ultralytics.yolo.utils.benchmarks import run_benchmarks
+          from ultralytics.yolo.utils.benchmarks import benchmark
-          run_benchmarks(model='${{ matrix.model }}.pt', imgsz=160, half=False, hard_fail=False)
+          benchmark(model='${{ matrix.model }}.pt', imgsz=160, half=False, hard_fail=0.20)
       - name: Benchmark SegmentationModel
         shell: python
         run: |
-          from ultralytics.yolo.utils.benchmarks import run_benchmarks
+          from ultralytics.yolo.utils.benchmarks import benchmark
-          run_benchmarks(model='${{ matrix.model }}-seg.pt', imgsz=160, half=False, hard_fail=False)
+          benchmark(model='${{ matrix.model }}-seg.pt', imgsz=160, half=False, hard_fail=0.14)
       - name: Benchmark ClassificationModel
         shell: python
         run: |
-          from ultralytics.yolo.utils.benchmarks import run_benchmarks
+          from ultralytics.yolo.utils.benchmarks import benchmark
-          run_benchmarks(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=False)
+          benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.70)
+      - name: Benchmark Summary
+        run: cat benchmarks.log
 
   Tests:
     timeout-minutes: 60
@@ -88,10 +90,10 @@ jobs:
       - uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Get cache dir
+      - name: Get cache dir  # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
-        # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
         id: pip-cache
-        run: echo "::set-output name=dir::$(pip cache dir)"
+        run: echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT
+        shell: bash  # for Windows compatibility
       - name: Cache pip
         uses: actions/cache@v3
         with:

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.45'
+__version__ = '8.0.46'
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks

ultralytics/yolo/cfg/__init__.py

@@ -254,8 +254,8 @@ def entrypoint(debug=''):
         else:
             check_cfg_mismatch(full_args_dict, {a: ''})
 
-    # Defaults
+    # Check keys
-    task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='imagenet100')
+    check_cfg_mismatch(full_args_dict, overrides)
 
     # Mode
     mode = overrides.get('mode', None)
@@ -279,8 +279,9 @@ def entrypoint(debug=''):
     model = YOLO(model)
 
     # Task
-    task = overrides.get('task', None)
+    task = overrides.get('task', model.task)
-    if task is not None and task not in TASKS:
+    if task is not None:
+        if task not in TASKS:
             raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
         else:
             model.task = task
@@ -292,8 +293,9 @@ def entrypoint(debug=''):
         LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
     elif mode in ('train', 'val'):
         if 'data' not in overrides:
-            overrides['data'] = task2data.get(overrides['task'], DEFAULT_CFG.data)
+            task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='imagenet100')
-            LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using {model.task} default 'data={overrides['data']}'.")
+            overrides['data'] = task2data.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
+            LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
     elif mode == 'export':
         if 'format' not in overrides:
             overrides['format'] = DEFAULT_CFG.format or 'torchscript'

ultralytics/yolo/data/dataset.py

@@ -16,10 +16,28 @@ from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image
 class YOLODataset(BaseDataset):
     cache_version = '1.0.1'  # dataset labels *.cache version, >= 1.0.0 for YOLOv8
     rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
-    """YOLO Dataset.
+    """
+    Dataset class for loading images object detection and/or segmentation labels in YOLO format.
+
     Args:
-        img_path (str): image path.
+        img_path (str): path to the folder containing images.
-        prefix (str): prefix.
+        imgsz (int): image size (default: 640).
+        cache (bool): if True, a cache file of the labels is created to speed up future creation of dataset instances
+        (default: False).
+        augment (bool): if True, data augmentation is applied (default: True).
+        hyp (dict): hyperparameters to apply data augmentation (default: None).
+        prefix (str): prefix to print in log messages (default: '').
+        rect (bool): if True, rectangular training is used (default: False).
+        batch_size (int): size of batches (default: None).
+        stride (int): stride (default: 32).
+        pad (float): padding (default: 0.0).
+        single_cls (bool): if True, single class training is used (default: False).
+        use_segments (bool): if True, segmentation masks are used as labels (default: False).
+        use_keypoints (bool): if True, keypoints are used as labels (default: False).
+        names (list): class names (default: None).
+
+    Returns:
+        A PyTorch dataset object that can be used for training an object detection or segmentation model.
     """
 
     def __init__(self,
@@ -44,7 +62,12 @@ class YOLODataset(BaseDataset):
         super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
 
     def cache_labels(self, path=Path('./labels.cache')):
-        # Cache dataset labels, check images and read shapes
+        """Cache dataset labels, check images and read shapes.
+        Args:
+            path (Path): path where to save the cache file (default: Path('./labels.cache')).
+        Returns:
+            (dict): labels.
+        """
         x = {'labels': []}
         nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
         desc = f'{self.prefix}Scanning {path.parent / path.stem}...'
@@ -119,9 +142,8 @@ class YOLODataset(BaseDataset):
         self.im_files = [lb['im_file'] for lb in labels]  # update im_files
 
         # Check if the dataset is all boxes or all segments
-        len_cls = sum(len(lb['cls']) for lb in labels)
+        lengths = ((len(lb['cls']), len(lb['bboxes']), len(lb['segments'])) for lb in labels)
-        len_boxes = sum(len(lb['bboxes']) for lb in labels)
+        len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
-        len_segments = sum(len(lb['segments']) for lb in labels)
         if len_segments and len_boxes != len_segments:
             LOGGER.warning(
                 f'WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, '

ultralytics/yolo/engine/exporter.py

@@ -294,7 +294,7 @@ class Exporter:
         # YOLOv8 ONNX export
         requirements = ['onnx>=1.12.0']
         if self.args.simplify:
-            requirements += ['onnxsim', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
+            requirements += ['onnxsim>=0.4.17', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
         check_requirements(requirements)
         import onnx  # noqa
 
@@ -513,8 +513,8 @@ class Exporter:
             cuda = torch.cuda.is_available()
             check_requirements(f"tensorflow{'-macos' if MACOS else '-aarch64' if ARM64 else '' if cuda else '-cpu'}")
             import tensorflow as tf  # noqa
-        check_requirements(('onnx', 'onnx2tf', 'sng4onnx', 'onnxsim', 'onnx_graphsurgeon', 'tflite_support',
+        check_requirements(('onnx', 'onnx2tf>=1.7.7', 'sng4onnx>=1.0.1', 'onnxsim>=0.4.17', 'onnx_graphsurgeon>=0.3.26',
-                            'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
+                            'tflite_support', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
                            cmds='--extra-index-url https://pypi.ngc.nvidia.com')
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
@@ -529,7 +529,7 @@ class Exporter:
 
         # Export to TF
         int8 = '-oiqt -qt per-tensor' if self.args.int8 else ''
-        cmd = f'onnx2tf -i {f_onnx} -o {f} --non_verbose {int8}'
+        cmd = f'onnx2tf -i {f_onnx} -o {f} -nuo --non_verbose {int8}'
         LOGGER.info(f'\n{prefix} running {cmd}')
         subprocess.run(cmd, shell=True)
         yaml_save(f / 'metadata.yaml', self.metadata)  # add metadata.yaml

ultralytics/yolo/engine/model.py

@@ -9,8 +9,9 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
                                   guess_model_task, nn)
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, callbacks, yaml_load
+from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
-from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml
+                                    is_git_dir, is_pip_package, yaml_load)
+from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update, check_yaml
 from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
 from ultralytics.yolo.utils.torch_utils import smart_inference_mode
 
@@ -150,6 +151,13 @@ class YOLO:
                             f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
                             f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
 
+    def _check_pip_update(self):
+        """
+        Inform user of ultralytics package update availability
+        """
+        if is_pip_package():
+            check_pip_update()
+
     def reset(self):
         """
         Resets the model modules.
@@ -189,6 +197,10 @@ class YOLO:
         Returns:
             (List[ultralytics.yolo.engine.results.Results]): The prediction results.
         """
+        if source is None:
+            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
+            LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
+
         overrides = self.overrides.copy()
         overrides['conf'] = 0.25
         overrides.update(kwargs)  # prefer kwargs
@@ -251,11 +263,12 @@ class YOLO:
         Args:
             **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
         """
-        from ultralytics.yolo.utils.benchmarks import run_benchmarks
+        self._check_is_pytorch_model()
+        from ultralytics.yolo.utils.benchmarks import benchmark
         overrides = self.model.args.copy()
         overrides.update(kwargs)
         overrides = {**DEFAULT_CFG_DICT, **overrides}  # fill in missing overrides keys with defaults
-        return run_benchmarks(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
+        return benchmark(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
 
     def export(self, **kwargs):
         """
@@ -283,6 +296,7 @@ class YOLO:
             **kwargs (Any): Any number of arguments representing the training configuration.
         """
         self._check_is_pytorch_model()
+        self._check_pip_update()
         overrides = self.overrides.copy()
         overrides.update(kwargs)
         if kwargs.get('cfg'):

ultralytics/yolo/engine/predictor.py

@@ -178,7 +178,12 @@ class BasePredictor:
             self.run_callbacks('on_predict_postprocess_end')
 
             # visualize, save, write results
-            for i in range(len(im)):
+            n = len(im)
+            for i in range(n):
+                self.results[i].speed = {
+                    'preprocess': self.dt[0].dt * 1E3 / n,
+                    'inference': self.dt[1].dt * 1E3 / n,
+                    'postprocess': self.dt[2].dt * 1E3 / n}
                 p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \
                     else (path, im0s.copy())
                 p = Path(p)

ultralytics/yolo/utils/__init__.py

@@ -354,22 +354,6 @@ def get_git_branch():
     return None  # if not git dir or on error
 
 
-def get_latest_pypi_version(package_name='ultralytics'):
-    """
-    Returns the latest version of a PyPI package without downloading or installing it.
-
-    Parameters:
-        package_name (str): The name of the package to find the latest version for.
-
-    Returns:
-        str: The latest version of the package.
-    """
-    response = requests.get(f'https://pypi.org/pypi/{package_name}/json')
-    if response.status_code == 200:
-        return response.json()['info']['version']
-    return None
-
-
 def get_default_args(func):
     """Returns a dictionary of default arguments for a function.
 
@@ -611,7 +595,7 @@ def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
 # Run below code on yolo/utils init ------------------------------------------------------------------------------------
 
 # Set logger
-set_logging(LOGGING_NAME)  # run before defining LOGGER
+set_logging(LOGGING_NAME, verbose=VERBOSE)  # run before defining LOGGER
 LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
 if WINDOWS:
     for fn in LOGGER.info, LOGGER.warning:

ultralytics/yolo/utils/benchmarks.py

@@ -37,11 +37,7 @@ from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.torch_utils import select_device
 
 
-def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
+def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, half=False, device='cpu', hard_fail=0.30):
-                   imgsz=640,
-                   half=False,
-                   device='cpu',
-                   hard_fail=False):
     device = select_device(device, verbose=False)
     if isinstance(model, (str, Path)):
         model = YOLO(model)
@@ -52,6 +48,7 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
         try:
             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
+            assert i != 11 or model.task != 'classify', 'paddle-classify bug'
 
             if 'cpu' in device.type:
                 assert cpu, 'inference not supported on CPU'
@@ -85,26 +82,28 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
             y.append([name, '✅', round(file_size(filename), 1), round(metric, 4), round(speed, 2)])
         except Exception as e:
             if hard_fail:
-                assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
+                assert type(e) is AssertionError, f'Benchmark hard_fail for {name}: {e}'
             LOGGER.warning(f'ERROR ❌️ Benchmark failure for {name}: {e}')
             y.append([name, '❌', None, None, None])  # mAP, t_inference
 
     # Print results
-    LOGGER.info('\n')
     check_yolo(device=device)  # print system info
-    c = ['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)'] if map else ['Format', 'Export', '', '']
+    c = ['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)']
     df = pd.DataFrame(y, columns=c)
-    LOGGER.info(f'\nBenchmarks complete for {Path(model.ckpt_path).name} on {data} at imgsz={imgsz} '
-                f'({time.time() - t0:.2f}s)')
-    LOGGER.info(str(df if map else df.iloc[:, :2]))
 
-    if hard_fail and isinstance(hard_fail, str):
+    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') as f:
+        f.write(s)
+
+    if hard_fail and isinstance(hard_fail, float):
         metrics = df[key].array  # values to compare to floor
-        floor = eval(hard_fail)  # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
+        floor = hard_fail  # 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'HARD FAIL: metric < floor {floor}'
+        assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: one or more metric(s) < floor {floor}'
 
     return df
 
 
 if __name__ == '__main__':
-    run_benchmarks()
+    benchmark()

ultralytics/yolo/utils/checks.py

@@ -16,6 +16,7 @@ import cv2
 import numpy as np
 import pkg_resources as pkg
 import psutil
+import requests
 import torch
 from matplotlib import font_manager
 
@@ -117,6 +118,31 @@ def check_version(current: str = '0.0.0',
     return result
 
 
+def check_latest_pypi_version(package_name='ultralytics'):
+    """
+    Returns the latest version of a PyPI package without downloading or installing it.
+
+    Parameters:
+        package_name (str): The name of the package to find the latest version for.
+
+    Returns:
+        str: The latest version of the package.
+    """
+    response = requests.get(f'https://pypi.org/pypi/{package_name}/json')
+    if response.status_code == 200:
+        return response.json()['info']['version']
+    return None
+
+
+def check_pip_update():
+    from ultralytics import __version__
+    latest = check_latest_pypi_version()
+    latest = '9.0.0'
+    if pkg.parse_version(__version__) < pkg.parse_version(latest):
+        LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
+                    f"Update with 'pip install -U ultralytics'")
+
+
 def check_font(font='Arial.ttf'):
     """
     Find font locally or download to user's configuration directory if it does not already exist.

ultralytics/yolo/utils/dist.py

@@ -1,10 +1,12 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import os
+import re
 import shutil
 import socket
 import sys
 import tempfile
+from pathlib import Path
 
 from . import USER_CONFIG_DIR
 from .torch_utils import TORCH_1_9
@@ -22,12 +24,12 @@ def find_free_network_port() -> int:
 
 
 def generate_ddp_file(trainer):
-    import_path = '.'.join(str(trainer.__class__).split('.')[1:-1])
+    module, name = f'{trainer.__class__.__module__}.{trainer.__class__.__name__}'.rsplit('.', 1)
 
     content = f'''cfg = {vars(trainer.args)} \nif __name__ == "__main__":
-    from ultralytics.{import_path} import {trainer.__class__.__name__}
+    from {module} import {name}
 
-    trainer = {trainer.__class__.__name__}(cfg=cfg)
+    trainer = {name}(cfg=cfg)
     trainer.train()'''
     (USER_CONFIG_DIR / 'DDP').mkdir(exist_ok=True)
     with tempfile.NamedTemporaryFile(prefix='_temp_',
@@ -41,12 +43,12 @@ def generate_ddp_file(trainer):
 
 
 def generate_ddp_command(world_size, trainer):
-    import __main__  # local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
+    import __main__  # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
-    file = os.path.abspath(sys.argv[0])
-    using_cli = not file.endswith('.py')
     if not trainer.resume:
         shutil.rmtree(trainer.save_dir)  # remove the save_dir
-    if using_cli:
+    file = str(Path(sys.argv[0]).resolve())
+    safe_pattern = re.compile(r'^[a-zA-Z0-9_. /\\-]{1,128}$')  # allowed characters and maximum of 100 characters
+    if not (safe_pattern.match(file) and Path(file).exists() and file.endswith('.py')):  # using CLI
         file = generate_ddp_file(trainer)
     dist_cmd = 'torch.distributed.run' if TORCH_1_9 else 'torch.distributed.launch'
     port = find_free_network_port()