`ultralytics 8.0.31` updates and fixes (#857)

Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Kalen Michael <kalenmike@gmail.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.30"
+__version__ = "8.0.31"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/hub/__init__.py

@@ -4,67 +4,62 @@ import requests
 
 from ultralytics.hub.auth import Auth
 from ultralytics.hub.session import HubTrainingSession
-from ultralytics.hub.utils import PREFIX, split_key
-from ultralytics.yolo.utils import LOGGER, emojis
-from ultralytics.yolo.v8.detect import DetectionTrainer
+from ultralytics.hub.utils import split_key
+from ultralytics.yolo.engine.exporter import export_formats
+from ultralytics.yolo.engine.model import YOLO
+from ultralytics.yolo.utils import LOGGER, emojis, PREFIX
 
+# Define all export formats
+EXPORT_FORMATS = list(export_formats()['Argument'][1:]) + ["ultralytics_tflite", "ultralytics_coreml"]
 
-def start(key=''):
-    # Start training models with Ultralytics HUB. Usage: from src.ultralytics import start; start('API_KEY')
-    def request_api_key(attempts=0):
-        """Prompt the user to input their API key"""
-        import getpass
-
-        max_attempts = 3
-        tries = f"Attempt {str(attempts + 1)} of {max_attempts}" if attempts > 0 else ""
-        LOGGER.info(f"{PREFIX}Login. {tries}")
-        input_key = getpass.getpass("Enter your Ultralytics HUB API key:\n")
-        auth.api_key, model_id = split_key(input_key)
-        if not auth.authenticate():
-            attempts += 1
-            LOGGER.warning(f"{PREFIX}Invalid API key ⚠️\n")
-            if attempts < max_attempts:
-                return request_api_key(attempts)
-            raise ConnectionError(emojis(f"{PREFIX}Failed to authenticate ❌"))
-        else:
-            return model_id
 
+def start(key=""):
+    """
+    Start training models with Ultralytics HUB. Usage: from src.ultralytics import start; start('API_KEY')
+    """
+    auth = Auth(key)
     try:
-        api_key, model_id = split_key(key)
-        auth = Auth(api_key)  # attempts cookie login if no api key is present
-        attempts = 1 if len(key) else 0
         if not auth.get_state():
-            if len(key):
-                LOGGER.warning(f"{PREFIX}Invalid API key ⚠️\n")
-            model_id = request_api_key(attempts)
-        LOGGER.info(f"{PREFIX}Authenticated ✅")
+            model_id = request_api_key(auth)
+        else:
+            _, model_id = split_key(key)
+
         if not model_id:
             raise ConnectionError(emojis('Connecting with global API key is not currently supported. ❌'))
+
         session = HubTrainingSession(model_id=model_id, auth=auth)
         session.check_disk_space()
 
-        # TODO: refactor, hardcoded for v8
-        args = session.model.copy()
-        args.pop("id")
-        args.pop("status")
-        args.pop("weights")
-        args["data"] = "coco128.yaml"
-        args["model"] = "yolov8n.yaml"
-        args["batch_size"] = 16
-        args["imgsz"] = 64
-
-        trainer = DetectionTrainer(overrides=args)
+        trainer = YOLO(session.input_file)
         session.register_callbacks(trainer)
-        setattr(trainer, 'hub_session', session)
-        trainer.train()
+        trainer.train(**session.train_args)
     except Exception as e:
         LOGGER.warning(f"{PREFIX}{e}")
 
 
-def reset_model(key=''):
+def request_api_key(auth, max_attempts=3):
+    """
+    Prompt the user to input their API key. Returns the model ID.
+    """
+    import getpass
+    for attempts in range(max_attempts):
+        LOGGER.info(f"{PREFIX}Login. Attempt {attempts + 1} of {max_attempts}")
+        input_key = getpass.getpass("Enter your Ultralytics HUB API key:\n")
+        auth.api_key, model_id = split_key(input_key)
+
+        if auth.authenticate():
+            LOGGER.info(f"{PREFIX}Authenticated ✅")
+            return model_id
+
+        LOGGER.warning(f"{PREFIX}Invalid API key ⚠️\n")
+
+    raise ConnectionError(emojis(f"{PREFIX}Failed to authenticate ❌"))
+
+
+def reset_model(key=""):
     # Reset a trained model to an untrained state
     api_key, model_id = split_key(key)
-    r = requests.post('https://api.ultralytics.com/model-reset', json={"apiKey": api_key, "modelId": model_id})
+    r = requests.post("https://api.ultralytics.com/model-reset", json={"apiKey": api_key, "modelId": model_id})
 
     if r.status_code == 200:
         LOGGER.info(f"{PREFIX}model reset successfully")
@@ -72,38 +67,32 @@ def reset_model(key=''):
     LOGGER.warning(f"{PREFIX}model reset failure {r.status_code} {r.reason}")
 
 
-def export_model(key='', format='torchscript'):
+def export_model(key="", format="torchscript"):
     # Export a model to all formats
+    assert format in EXPORT_FORMATS, f"Unsupported export format '{format}' passed, valid formats are {EXPORT_FORMATS}"
     api_key, model_id = split_key(key)
-    formats = ('torchscript', 'onnx', 'openvino', 'engine', 'coreml', 'saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs',
-               'ultralytics_tflite', 'ultralytics_coreml')
-    assert format in formats, f"ERROR: Unsupported export format '{format}' passed, valid formats are {formats}"
-
-    r = requests.post('https://api.ultralytics.com/export',
+    r = requests.post("https://api.ultralytics.com/export",
                       json={
                           "apiKey": api_key,
                           "modelId": model_id,
                           "format": format})
-    assert r.status_code == 200, f"{PREFIX}{format} export failure {r.status_code} {r.reason}"
+    assert (r.status_code == 200), f"{PREFIX}{format} export failure {r.status_code} {r.reason}"
     LOGGER.info(f"{PREFIX}{format} export started ✅")
 
 
-def get_export(key='', format='torchscript'):
+def get_export(key="", format="torchscript"):
     # Get an exported model dictionary with download URL
+    assert format in EXPORT_FORMATS, f"Unsupported export format '{format}' passed, valid formats are {EXPORT_FORMATS}"
     api_key, model_id = split_key(key)
-    formats = ('torchscript', 'onnx', 'openvino', 'engine', 'coreml', 'saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs',
-               'ultralytics_tflite', 'ultralytics_coreml')
-    assert format in formats, f"ERROR: Unsupported export format '{format}' passed, valid formats are {formats}"
-
-    r = requests.post('https://api.ultralytics.com/get-export',
+    r = requests.post("https://api.ultralytics.com/get-export",
                       json={
                           "apiKey": api_key,
                           "modelId": model_id,
                           "format": format})
-    assert r.status_code == 200, f"{PREFIX}{format} get_export failure {r.status_code} {r.reason}"
+    assert (r.status_code == 200), f"{PREFIX}{format} get_export failure {r.status_code} {r.reason}"
     return r.json()
 
 
 # temp. For checking
 if __name__ == "__main__":
-    start(key="b3fba421be84a20dbe68644e14436d1cce1b0a0aaa_HeMfHgvHsseMPhdq7Ylz")
+    start()

ultralytics/hub/session.py

@@ -1,16 +1,18 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
+import json
 import signal
+import sys
 from pathlib import Path
-from time import sleep
+from time import sleep, time
 
 import requests
 
 from ultralytics import __version__
 from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_request
-from ultralytics.yolo.utils import is_colab, threaded
-
-AGENT_NAME = f'python-{__version__}-colab' if is_colab() else f'python-{__version__}-local'
+from ultralytics.yolo.utils import is_colab, threaded, LOGGER, emojis, PREFIX
+from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
 
+AGENT_NAME = (f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local")
 session = None
 
 
@@ -19,23 +21,37 @@ class HubTrainingSession:
     def __init__(self, model_id, auth):
         self.agent_id = None  # identifies which instance is communicating with server
         self.model_id = model_id
-        self.api_url = f'{HUB_API_ROOT}/v1/models/{model_id}'
+        self.api_url = f"{HUB_API_ROOT}/v1/models/{model_id}"
         self.auth_header = auth.get_auth_header()
-        self.rate_limits = {'metrics': 3.0, 'ckpt': 900.0, 'heartbeat': 300.0}  # rate limits (seconds)
-        self.t = {}  # rate limit timers (seconds)
-        self.metrics_queue = {}  # metrics queue
-        self.alive = True  # for heartbeats
+        self._rate_limits = {"metrics": 3.0, "ckpt": 900.0, "heartbeat": 300.0}  # rate limits (seconds)
+        self._timers = {}  # rate limit timers (seconds)
+        self._metrics_queue = {}  # metrics queue
         self.model = self._get_model()
-        self._heartbeats()  # start heartbeats
-        signal.signal(signal.SIGTERM, self.shutdown)  # register the shutdown function to be called on exit
-        signal.signal(signal.SIGINT, self.shutdown)
+        self._start_heartbeat()  # start heartbeats
+        self._register_signal_handlers()
 
-    def shutdown(self, *args):  # noqa
-        self.alive = False  # stop heartbeats
+    def _register_signal_handlers(self):
+        signal.signal(signal.SIGTERM, self._handle_signal)
+        signal.signal(signal.SIGINT, self._handle_signal)
+
+    def _handle_signal(self, signum, frame):
+        """
+        Prevent heartbeats from being sent on Colab after kill.
+        This method does not use frame, it is included as it is
+        passed by signal.
+        """
+        if self.alive is True:
+            LOGGER.info(f"{PREFIX}Kill signal received! ❌")
+            self._stop_heartbeat()
+            sys.exit(signum)
+
+    def _stop_heartbeat(self):
+        """End the heartbeat loop"""
+        self.alive = False
 
     def upload_metrics(self):
-        payload = {"metrics": self.metrics_queue.copy(), "type": "metrics"}
-        smart_request(f'{self.api_url}', json=payload, headers=self.auth_header, code=2)
+        payload = {"metrics": self._metrics_queue.copy(), "type": "metrics"}
+        smart_request(f"{self.api_url}", json=payload, headers=self.auth_header, code=2)
 
     def upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
         # Upload a model to HUB
@@ -44,25 +60,29 @@ class HubTrainingSession:
             with open(weights, "rb") as f:
                 file = f.read()
         if final:
-            smart_request(f'{self.api_url}/upload',
-                          data={
-                              "epoch": epoch,
-                              "type": "final",
-                              "map": map},
-                          files={"best.pt": file},
-                          headers=self.auth_header,
-                          retry=10,
-                          timeout=3600,
-                          code=4)
+            smart_request(
+                f"{self.api_url}/upload",
+                data={
+                    "epoch": epoch,
+                    "type": "final",
+                    "map": map},
+                files={"best.pt": file},
+                headers=self.auth_header,
+                retry=10,
+                timeout=3600,
+                code=4,
+            )
         else:
-            smart_request(f'{self.api_url}/upload',
-                          data={
-                              "epoch": epoch,
-                              "type": "epoch",
-                              "isBest": bool(is_best)},
-                          headers=self.auth_header,
-                          files={"last.pt": file},
-                          code=3)
+            smart_request(
+                f"{self.api_url}/upload",
+                data={
+                    "epoch": epoch,
+                    "type": "epoch",
+                    "isBest": bool(is_best)},
+                headers=self.auth_header,
+                files={"last.pt": file},
+                code=3,
+            )
 
     def _get_model(self):
         # Returns model from database by id
@@ -70,31 +90,131 @@ class HubTrainingSession:
         headers = self.auth_header
 
         try:
-            r = smart_request(api_url, method="get", headers=headers, thread=False, code=0)
-            data = r.json().get("data", None)
-            if not data:
-                return
-            assert data['data'], 'ERROR: Dataset may still be processing. Please wait a minute and try again.'  # RF fix
+            response = smart_request(api_url, method="get", headers=headers, thread=False, code=0)
+            data = response.json().get("data", None)
+
+            if data.get("status", None) == "trained":
+                raise ValueError(
+                    emojis(f"Model trained. View model at https://hub.ultralytics.com/models/{self.model_id} 🚀"))
+
+            if not data.get("data", None):
+                raise ValueError("Dataset may still be processing. Please wait a minute and try again.")  # RF fix
             self.model_id = data["id"]
 
+            # TODO: restore when server keys when dataset URL and GPU train is working
+
+            self.train_args = {
+                "batch": data["batch_size"],
+                "epochs": data["epochs"],
+                "imgsz": data["imgsz"],
+                "patience": data["patience"],
+                "device": data["device"],
+                "cache": data["cache"],
+                "data": data["data"]}
+
+            self.input_file = data.get("cfg", data["weights"])
+
+            # hack for yolov5 cfg adds u
+            if "cfg" in data and "yolov5" in data["cfg"]:
+                self.input_file = data["cfg"].replace(".yaml", "u.yaml")
+
             return data
         except requests.exceptions.ConnectionError as e:
-            raise ConnectionRefusedError('ERROR: The HUB server is not online. Please try again later.') from e
+            raise ConnectionRefusedError("ERROR: The HUB server is not online. Please try again later.") from e
+        except Exception:
+            raise
 
     def check_disk_space(self):
-        if not check_dataset_disk_space(self.model['data']):
+        if not check_dataset_disk_space(self.model["data"]):
             raise MemoryError("Not enough disk space")
 
+    def register_callbacks(self, trainer):
+        trainer.add_callback("on_pretrain_routine_end", self.on_pretrain_routine_end)
+        trainer.add_callback("on_fit_epoch_end", self.on_fit_epoch_end)
+        trainer.add_callback("on_model_save", self.on_model_save)
+        trainer.add_callback("on_train_end", self.on_train_end)
+
+    def on_pretrain_routine_end(self, trainer):
+        """
+        Start timer for upload rate limit.
+        This method does not use trainer. It is passed to all callbacks by default.
+        """
+        # Start timer for upload rate limit
+        LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
+        self._timers = {"metrics": time(), "ckpt": time()}  # start timer on self.rate_limit
+
+    def on_fit_epoch_end(self, trainer):
+        # Upload metrics after val end
+        all_plots = {**trainer.label_loss_items(trainer.tloss, prefix="train"), **trainer.metrics}
+
+        if trainer.epoch == 0:
+            model_info = {
+                "model/parameters": get_num_params(trainer.model),
+                "model/GFLOPs": round(get_flops(trainer.model), 3),
+                "model/speed(ms)": round(trainer.validator.speed[1], 3)}
+            all_plots = {**all_plots, **model_info}
+        self._metrics_queue[trainer.epoch] = json.dumps(all_plots)
+        if time() - self._timers["metrics"] > self._rate_limits["metrics"]:
+            self.upload_metrics()
+            self._timers["metrics"] = time()  # reset timer
+            self._metrics_queue = {}  # reset queue
+
+    def on_model_save(self, trainer):
+        # Upload checkpoints with rate limiting
+        is_best = trainer.best_fitness == trainer.fitness
+        if time() - self._timers["ckpt"] > self._rate_limits["ckpt"]:
+            LOGGER.info(f"{PREFIX}Uploading checkpoint {self.model_id}")
+            self._upload_model(trainer.epoch, trainer.last, is_best)
+            self._timers["ckpt"] = time()  # reset timer
+
+    def on_train_end(self, trainer):
+        # Upload final model and metrics with exponential standoff
+        LOGGER.info(f"{PREFIX}Training completed successfully ✅")
+        LOGGER.info(f"{PREFIX}Uploading final {self.model_id}")
+
+        # hack for fetching mAP
+        mAP = trainer.metrics.get("metrics/mAP50-95(B)", 0)
+        self._upload_model(trainer.epoch, trainer.best, map=mAP, final=True)  # results[3] is mAP0.5:0.95
+        self.alive = False  # stop heartbeats
+        LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
+
+    def _upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
+        # Upload a model to HUB
+        file = None
+        if Path(weights).is_file():
+            with open(weights, "rb") as f:
+                file = f.read()
+        file_param = {"best.pt" if final else "last.pt": file}
+        endpoint = f"{self.api_url}/upload"
+        data = {"epoch": epoch}
+        if final:
+            data.update({"type": "final", "map": map})
+        else:
+            data.update({"type": "epoch", "isBest": bool(is_best)})
+
+        smart_request(
+            endpoint,
+            data=data,
+            files=file_param,
+            headers=self.auth_header,
+            retry=10 if final else None,
+            timeout=3600 if final else None,
+            code=4 if final else 3,
+        )
+
     @threaded
-    def _heartbeats(self):
+    def _start_heartbeat(self):
+        self.alive = True
         while self.alive:
-            r = smart_request(f'{HUB_API_ROOT}/v1/agent/heartbeat/models/{self.model_id}',
-                              json={
-                                  "agent": AGENT_NAME,
-                                  "agentId": self.agent_id},
-                              headers=self.auth_header,
-                              retry=0,
-                              code=5,
-                              thread=False)
-            self.agent_id = r.json().get('data', {}).get('agentId', None)
-            sleep(self.rate_limits['heartbeat'])
+            r = smart_request(
+                f"{HUB_API_ROOT}/v1/agent/heartbeat/models/{self.model_id}",
+                json={
+                    "agent": AGENT_NAME,
+                    "agentId": self.agent_id},
+                headers=self.auth_header,
+                retry=0,
+                code=5,
+                thread=False,
+            )
+            self.agent_id = r.json().get("data", {}).get("agentId", None)
+            sleep(self._rate_limits["heartbeat"])

ultralytics/nn/tasks.py

@@ -172,7 +172,7 @@ class DetectionModel(BaseModel):
         if nc and nc != self.yaml['nc']:
             LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
             self.yaml['nc'] = nc  # override yaml value
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], verbose=verbose)  # model, savelist
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
         self.names = {i: f'{i}' for i in range(self.yaml['nc'])}  # default names dict
         self.inplace = self.yaml.get('inplace', True)
 
@@ -282,7 +282,7 @@ class ClassificationModel(BaseModel):
         if nc and nc != self.yaml['nc']:
             LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
             self.yaml['nc'] = nc  # override yaml value
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], verbose=verbose)  # model, savelist
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
         self.names = {i: f'{i}' for i in range(self.yaml['nc'])}  # default names dict
         self.info()
 
@@ -421,7 +421,7 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
         Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
         if verbose:
             LOGGER.info(f"{colorstr('activation:')} {act}")  # print
-
+    ch = [ch]
     layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
     for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
         m = eval(m) if isinstance(m, str) else m  # eval strings

ultralytics/yolo/cfg/__init__.py

@@ -49,6 +49,20 @@ CLI_HELP_MSG = \
     GitHub: https://github.com/ultralytics/ultralytics
     """
 
+CFG_FLOAT_KEYS = {'warmup_epochs', 'box', 'cls', 'dfl'}
+CFG_FRACTION_KEYS = {
+    'dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr', 'fl_gamma',
+    'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'degrees', 'translate', 'scale', 'shear', 'perspective', 'flipud',
+    'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou'}
+CFG_INT_KEYS = {
+    'epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
+    'line_thickness', 'workspace', 'nbs'}
+CFG_BOOL_KEYS = {
+    'save', 'cache', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect',
+    'cos_lr', 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt',
+    'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks',
+    'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader'}
+
 
 def cfg2dict(cfg):
     """
@@ -88,11 +102,31 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG, override
         check_cfg_mismatch(cfg, overrides)
         cfg = {**cfg, **overrides}  # merge cfg and overrides dicts (prefer overrides)
 
-    # Type checks
+    # Special handling for numeric project/names
     for k in 'project', 'name':
         if k in cfg and isinstance(cfg[k], (int, float)):
             cfg[k] = str(cfg[k])
 
+    # Type and Value checks
+    for k, v in cfg.items():
+        if v is not None:  # None values may be from optional args
+            if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
+                raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
+            elif k in CFG_FRACTION_KEYS:
+                if not isinstance(v, (int, float)):
+                    raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                    f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
+                if not (0.0 <= v <= 1.0):
+                    raise ValueError(f"'{k}={v}' is an invalid value. "
+                                     f"Valid '{k}' values are between 0.0 and 1.0.")
+            elif k in CFG_INT_KEYS and not isinstance(v, int):
+                raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                f"'{k}' must be an int (i.e. '{k}=0')")
+            elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
+                raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')")
+
     # Return instance
     return IterableSimpleNamespace(**cfg)
 
@@ -184,6 +218,7 @@ def entrypoint(debug=''):
             try:
                 re.sub(r' *= *', '=', a)  # remove spaces around equals sign
                 k, v = a.split('=', 1)  # split on first '=' sign
+                assert v, f"missing '{k}' value"
                 if k == 'cfg':  # custom.yaml passed
                     LOGGER.info(f"{PREFIX}Overriding {DEFAULT_CFG_PATH} with {v}")
                     overrides = {k: val for k, val in yaml_load(v).items() if k != 'cfg'}
@@ -198,7 +233,7 @@ def entrypoint(debug=''):
                         with contextlib.suppress(Exception):
                             v = eval(v)
                     overrides[k] = v
-            except (NameError, SyntaxError, ValueError) as e:
+            except (NameError, SyntaxError, ValueError, AssertionError) as e:
                 raise argument_error(a) from e
 
         elif a in tasks:
@@ -224,7 +259,7 @@ def entrypoint(debug=''):
     mode = overrides.get('mode', None)
     if mode is None:
         mode = DEFAULT_CFG.mode or 'predict'
-        LOGGER.warning(f"WARNING ⚠️ 'mode=' is missing. Valid modes are {modes}. Using default 'mode={mode}'.")
+        LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {modes}. Using default 'mode={mode}'.")
     elif mode not in modes:
         if mode != 'checks':
             raise ValueError(emojis(f"ERROR ❌ Invalid 'mode={mode}'. Valid modes are {modes}."))
@@ -237,7 +272,7 @@ def entrypoint(debug=''):
     task = overrides.pop('task', None)
     if model is None:
         model = task2model.get(task, 'yolov8n.pt')
-        LOGGER.warning(f"WARNING ⚠️ 'model=' is missing. Using default 'model={model}'.")
+        LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
     from ultralytics.yolo.engine.model import YOLO
     overrides['model'] = model
     model = YOLO(model)
@@ -251,15 +286,15 @@ def entrypoint(debug=''):
     if mode == 'predict' and 'source' not in overrides:
         overrides['source'] = DEFAULT_CFG.source or ROOT / "assets" if (ROOT / "assets").exists() \
             else "https://ultralytics.com/images/bus.jpg"
-        LOGGER.warning(f"WARNING ⚠️ 'source=' is missing. Using default 'source={overrides['source']}'.")
+        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(task, DEFAULT_CFG.data)
-            LOGGER.warning(f"WARNING ⚠️ 'data=' is missing. Using {model.task} default 'data={overrides['data']}'.")
+            LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using {model.task} default 'data={overrides['data']}'.")
     elif mode == 'export':
         if 'format' not in overrides:
             overrides['format'] = DEFAULT_CFG.format or 'torchscript'
-            LOGGER.warning(f"WARNING ⚠️ 'format=' is missing. Using default 'format={overrides['format']}'.")
+            LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
 
     # Run command in python
     # getattr(model, mode)(**vars(get_cfg(overrides=overrides)))  # default args using default.yaml

ultralytics/yolo/data/dataset.py

@@ -132,7 +132,7 @@ class YOLODataset(BaseDataset):
             for lb in labels:
                 lb["segments"] = []
         if len_cls == 0:
-            raise ValueError(f"{self.prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}")
+            raise ValueError(f"All labels empty in {cache_path}, can not start training without labels. {HELP_URL}")
         return labels
 
     # TODO: use hyp config to set all these augmentations

ultralytics/yolo/utils/__init__.py

@@ -131,7 +131,11 @@ def yaml_save(file='data.yaml', data=None):
 
     with open(file, 'w') as f:
         # Dump data to file in YAML format, converting Path objects to strings
-        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
+        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v
+                        for k, v in data.items()},
+                       f,
+                       sort_keys=False,
+                       allow_unicode=True)
 
 
 def yaml_load(file='data.yaml', append_filename=False):
@@ -164,7 +168,7 @@ def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
         None
     """
     yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
-    dump = yaml.dump(yaml_dict, default_flow_style=False)
+    dump = yaml.dump(yaml_dict, sort_keys=False, allow_unicode=True)
     LOGGER.info(f"Printing '{colorstr('bold', 'black', yaml_file)}'\n\n{dump}")
 
 

ultralytics/yolo/utils/dist.py

@@ -7,6 +7,7 @@ import sys
 import tempfile
 
 from . import USER_CONFIG_DIR
+from .torch_utils import TORCH_1_9
 
 
 def find_free_network_port() -> int:
@@ -47,8 +48,9 @@ def generate_ddp_command(world_size, trainer):
     using_cli = not file_name.endswith(".py")
     if using_cli:
         file_name = generate_ddp_file(trainer)
+    torch_distributed_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch"
     return [
-        sys.executable, "-m", "torch.distributed.run", "--nproc_per_node", f"{world_size}", "--master_port",
+        sys.executable, "-m", torch_distributed_cmd, "--nproc_per_node", f"{world_size}", "--master_port",
         f"{find_free_network_port()}", file_name] + sys.argv[1:]
 
 

ultralytics/yolo/utils/torch_utils.py

@@ -24,6 +24,10 @@ LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable
 RANK = int(os.getenv('RANK', -1))
 WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
 
+TORCH_1_9 = check_version(torch.__version__, '1.9.0')
+TORCH_1_11 = check_version(torch.__version__, '1.11.0')
+TORCH_1_12 = check_version(torch.__version__, '1.12.0')
+
 
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
@@ -36,10 +40,10 @@ def torch_distributed_zero_first(local_rank: int):
         dist.barrier(device_ids=[0])
 
 
-def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
+def smart_inference_mode():
     # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
     def decorate(fn):
-        return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
+        return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
 
     return decorate
 
@@ -49,7 +53,7 @@ def DDP_model(model):
     assert not check_version(torch.__version__, '1.12.0', pinned=True), \
         'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
         'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
-    if check_version(torch.__version__, '1.11.0'):
+    if TORCH_1_11:
         return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
     else:
         return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
@@ -267,7 +271,7 @@ def init_seeds(seed=0, deterministic=False):
     torch.cuda.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)  # for Multi-GPU, exception safe
     # torch.backends.cudnn.benchmark = True  # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
-    if deterministic and check_version(torch.__version__, '1.12.0'):  # https://github.com/ultralytics/yolov5/pull/8213
+    if deterministic and TORCH_1_12:  # https://github.com/ultralytics/yolov5/pull/8213
         torch.use_deterministic_algorithms(True)
         torch.backends.cudnn.deterministic = True
         os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'