Update .pre-commit-config.yaml (#1026)
This commit is contained in:
Glenn Jocher
GitHub
@ -10,10 +10,10 @@ from ultralytics.yolo.engine.model import YOLO
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from ultralytics.yolo.utils import LOGGER, PREFIX, emojis
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# Define all export formats
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EXPORT_FORMATS_HUB = EXPORT_FORMATS_LIST + ["ultralytics_tflite", "ultralytics_coreml"]
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EXPORT_FORMATS_HUB = EXPORT_FORMATS_LIST + ['ultralytics_tflite', 'ultralytics_coreml']
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def start(key=""):
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def start(key=''):
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"""
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Start training models with Ultralytics HUB. Usage: from src.ultralytics import start; start('API_KEY')
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"""
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@ -34,7 +34,7 @@ def start(key=""):
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session.register_callbacks(trainer)
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trainer.train(**session.train_args)
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except Exception as e:
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LOGGER.warning(f"{PREFIX}{e}")
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LOGGER.warning(f'{PREFIX}{e}')
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def request_api_key(auth, max_attempts=3):
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@ -43,56 +43,56 @@ def request_api_key(auth, max_attempts=3):
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"""
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import getpass
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for attempts in range(max_attempts):
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LOGGER.info(f"{PREFIX}Login. Attempt {attempts + 1} of {max_attempts}")
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input_key = getpass.getpass("Enter your Ultralytics HUB API key:\n")
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LOGGER.info(f'{PREFIX}Login. Attempt {attempts + 1} of {max_attempts}')
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input_key = getpass.getpass('Enter your Ultralytics HUB API key:\n')
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auth.api_key, model_id = split_key(input_key)
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if auth.authenticate():
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LOGGER.info(f"{PREFIX}Authenticated ✅")
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LOGGER.info(f'{PREFIX}Authenticated ✅')
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return model_id
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LOGGER.warning(f"{PREFIX}Invalid API key ⚠️\n")
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LOGGER.warning(f'{PREFIX}Invalid API key ⚠️\n')
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raise ConnectionError(emojis(f"{PREFIX}Failed to authenticate ❌"))
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raise ConnectionError(emojis(f'{PREFIX}Failed to authenticate ❌'))
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def reset_model(key=""):
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def reset_model(key=''):
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# Reset a trained model to an untrained state
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api_key, model_id = split_key(key)
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r = requests.post("https://api.ultralytics.com/model-reset", json={"apiKey": api_key, "modelId": model_id})
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r = requests.post('https://api.ultralytics.com/model-reset', json={'apiKey': api_key, 'modelId': model_id})
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if r.status_code == 200:
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LOGGER.info(f"{PREFIX}model reset successfully")
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LOGGER.info(f'{PREFIX}model reset successfully')
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return
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LOGGER.warning(f"{PREFIX}model reset failure {r.status_code} {r.reason}")
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LOGGER.warning(f'{PREFIX}model reset failure {r.status_code} {r.reason}')
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def export_model(key="", format="torchscript"):
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def export_model(key='', format='torchscript'):
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# Export a model to all formats
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assert format in EXPORT_FORMATS_HUB, f"Unsupported export format '{format}', valid formats are {EXPORT_FORMATS_HUB}"
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api_key, model_id = split_key(key)
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r = requests.post("https://api.ultralytics.com/export",
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r = requests.post('https://api.ultralytics.com/export',
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json={
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"apiKey": api_key,
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"modelId": model_id,
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"format": format})
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assert (r.status_code == 200), f"{PREFIX}{format} export failure {r.status_code} {r.reason}"
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LOGGER.info(f"{PREFIX}{format} export started ✅")
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'apiKey': api_key,
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'modelId': model_id,
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'format': format})
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assert (r.status_code == 200), f'{PREFIX}{format} export failure {r.status_code} {r.reason}'
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LOGGER.info(f'{PREFIX}{format} export started ✅')
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def get_export(key="", format="torchscript"):
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def get_export(key='', format='torchscript'):
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# Get an exported model dictionary with download URL
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assert format in EXPORT_FORMATS_HUB, f"Unsupported export format '{format}', valid formats are {EXPORT_FORMATS_HUB}"
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api_key, model_id = split_key(key)
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r = requests.post("https://api.ultralytics.com/get-export",
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r = requests.post('https://api.ultralytics.com/get-export',
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json={
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"apiKey": api_key,
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"modelId": model_id,
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"format": format})
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assert (r.status_code == 200), f"{PREFIX}{format} get_export failure {r.status_code} {r.reason}"
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'apiKey': api_key,
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'modelId': model_id,
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'format': format})
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assert (r.status_code == 200), f'{PREFIX}{format} get_export failure {r.status_code} {r.reason}'
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return r.json()
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# temp. For checking
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if __name__ == "__main__":
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if __name__ == '__main__':
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start()
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@ -5,7 +5,7 @@ import requests
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from ultralytics.hub.utils import HUB_API_ROOT, request_with_credentials
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from ultralytics.yolo.utils import is_colab
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API_KEY_PATH = "https://hub.ultralytics.com/settings?tab=api+keys"
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API_KEY_PATH = 'https://hub.ultralytics.com/settings?tab=api+keys'
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class Auth:
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@ -18,7 +18,7 @@ class Auth:
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@staticmethod
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def _clean_api_key(key: str) -> str:
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"""Strip model from key if present"""
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separator = "_"
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separator = '_'
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return key.split(separator)[0] if separator in key else key
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def authenticate(self) -> bool:
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@ -26,11 +26,11 @@ class Auth:
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try:
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header = self.get_auth_header()
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if header:
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r = requests.post(f"{HUB_API_ROOT}/v1/auth", headers=header)
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r = requests.post(f'{HUB_API_ROOT}/v1/auth', headers=header)
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if not r.json().get('success', False):
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raise ConnectionError("Unable to authenticate.")
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raise ConnectionError('Unable to authenticate.')
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return True
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raise ConnectionError("User has not authenticated locally.")
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raise ConnectionError('User has not authenticated locally.')
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except ConnectionError:
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self.id_token = self.api_key = False # reset invalid
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return False
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@ -43,21 +43,21 @@ class Auth:
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if not is_colab():
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return False # Currently only works with Colab
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try:
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authn = request_with_credentials(f"{HUB_API_ROOT}/v1/auth/auto")
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if authn.get("success", False):
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self.id_token = authn.get("data", {}).get("idToken", None)
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authn = request_with_credentials(f'{HUB_API_ROOT}/v1/auth/auto')
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if authn.get('success', False):
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self.id_token = authn.get('data', {}).get('idToken', None)
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self.authenticate()
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return True
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raise ConnectionError("Unable to fetch browser authentication details.")
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raise ConnectionError('Unable to fetch browser authentication details.')
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except ConnectionError:
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self.id_token = False # reset invalid
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return False
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def get_auth_header(self):
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if self.id_token:
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return {"authorization": f"Bearer {self.id_token}"}
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return {'authorization': f'Bearer {self.id_token}'}
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elif self.api_key:
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return {"x-api-key": self.api_key}
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return {'x-api-key': self.api_key}
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else:
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return None
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@ -11,7 +11,7 @@ from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_
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from ultralytics.yolo.utils import LOGGER, PREFIX, __version__, emojis, is_colab, threaded
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from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
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AGENT_NAME = f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local"
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AGENT_NAME = f'python-{__version__}-colab' if is_colab() else f'python-{__version__}-local'
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session = None
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@ -20,9 +20,9 @@ class HubTrainingSession:
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def __init__(self, model_id, auth):
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self.agent_id = None # identifies which instance is communicating with server
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self.model_id = model_id
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self.api_url = f"{HUB_API_ROOT}/v1/models/{model_id}"
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self.api_url = f'{HUB_API_ROOT}/v1/models/{model_id}'
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self.auth_header = auth.get_auth_header()
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self._rate_limits = {"metrics": 3.0, "ckpt": 900.0, "heartbeat": 300.0} # rate limits (seconds)
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self._rate_limits = {'metrics': 3.0, 'ckpt': 900.0, 'heartbeat': 300.0} # rate limits (seconds)
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self._timers = {} # rate limit timers (seconds)
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self._metrics_queue = {} # metrics queue
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self.model = self._get_model()
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@ -40,7 +40,7 @@ class HubTrainingSession:
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passed by signal.
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"""
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if self.alive is True:
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LOGGER.info(f"{PREFIX}Kill signal received! ❌")
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LOGGER.info(f'{PREFIX}Kill signal received! ❌')
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self._stop_heartbeat()
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sys.exit(signum)
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@ -49,23 +49,23 @@ class HubTrainingSession:
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self.alive = False
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def upload_metrics(self):
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payload = {"metrics": self._metrics_queue.copy(), "type": "metrics"}
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smart_request(f"{self.api_url}", json=payload, headers=self.auth_header, code=2)
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payload = {'metrics': self._metrics_queue.copy(), 'type': 'metrics'}
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smart_request(f'{self.api_url}', json=payload, headers=self.auth_header, code=2)
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def upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
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# Upload a model to HUB
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file = None
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if Path(weights).is_file():
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with open(weights, "rb") as f:
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with open(weights, 'rb') as f:
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file = f.read()
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if final:
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smart_request(
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f"{self.api_url}/upload",
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f'{self.api_url}/upload',
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data={
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"epoch": epoch,
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"type": "final",
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"map": map},
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files={"best.pt": file},
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'epoch': epoch,
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'type': 'final',
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'map': map},
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files={'best.pt': file},
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headers=self.auth_header,
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retry=10,
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timeout=3600,
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@ -73,66 +73,66 @@ class HubTrainingSession:
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)
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else:
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smart_request(
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f"{self.api_url}/upload",
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f'{self.api_url}/upload',
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data={
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"epoch": epoch,
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"type": "epoch",
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"isBest": bool(is_best)},
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'epoch': epoch,
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'type': 'epoch',
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'isBest': bool(is_best)},
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headers=self.auth_header,
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files={"last.pt": file},
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files={'last.pt': file},
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code=3,
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)
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def _get_model(self):
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# Returns model from database by id
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api_url = f"{HUB_API_ROOT}/v1/models/{self.model_id}"
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api_url = f'{HUB_API_ROOT}/v1/models/{self.model_id}'
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headers = self.auth_header
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try:
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response = smart_request(api_url, method="get", headers=headers, thread=False, code=0)
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data = response.json().get("data", None)
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response = smart_request(api_url, method='get', headers=headers, thread=False, code=0)
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data = response.json().get('data', None)
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if data.get("status", None) == "trained":
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if data.get('status', None) == 'trained':
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raise ValueError(
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emojis(f"Model is already trained and uploaded to "
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f"https://hub.ultralytics.com/models/{self.model_id} 🚀"))
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emojis(f'Model is already trained and uploaded to '
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f'https://hub.ultralytics.com/models/{self.model_id} 🚀'))
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if not data.get("data", None):
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raise ValueError("Dataset may still be processing. Please wait a minute and try again.") # RF fix
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self.model_id = data["id"]
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if not data.get('data', None):
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raise ValueError('Dataset may still be processing. Please wait a minute and try again.') # RF fix
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self.model_id = data['id']
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# TODO: restore when server keys when dataset URL and GPU train is working
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self.train_args = {
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"batch": data["batch_size"],
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"epochs": data["epochs"],
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"imgsz": data["imgsz"],
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"patience": data["patience"],
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"device": data["device"],
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"cache": data["cache"],
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"data": data["data"]}
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'batch': data['batch_size'],
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'epochs': data['epochs'],
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'imgsz': data['imgsz'],
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'patience': data['patience'],
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'device': data['device'],
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'cache': data['cache'],
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'data': data['data']}
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self.input_file = data.get("cfg", data["weights"])
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self.input_file = data.get('cfg', data['weights'])
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# hack for yolov5 cfg adds u
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if "cfg" in data and "yolov5" in data["cfg"]:
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self.input_file = data["cfg"].replace(".yaml", "u.yaml")
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if 'cfg' in data and 'yolov5' in data['cfg']:
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self.input_file = data['cfg'].replace('.yaml', 'u.yaml')
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return data
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except requests.exceptions.ConnectionError as e:
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raise ConnectionRefusedError("ERROR: The HUB server is not online. Please try again later.") from e
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raise ConnectionRefusedError('ERROR: The HUB server is not online. Please try again later.') from e
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except Exception:
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raise
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def check_disk_space(self):
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if not check_dataset_disk_space(self.model["data"]):
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raise MemoryError("Not enough disk space")
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if not check_dataset_disk_space(self.model['data']):
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raise MemoryError('Not enough disk space')
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def register_callbacks(self, trainer):
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trainer.add_callback("on_pretrain_routine_end", self.on_pretrain_routine_end)
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trainer.add_callback("on_fit_epoch_end", self.on_fit_epoch_end)
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trainer.add_callback("on_model_save", self.on_model_save)
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trainer.add_callback("on_train_end", self.on_train_end)
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trainer.add_callback('on_pretrain_routine_end', self.on_pretrain_routine_end)
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trainer.add_callback('on_fit_epoch_end', self.on_fit_epoch_end)
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trainer.add_callback('on_model_save', self.on_model_save)
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trainer.add_callback('on_train_end', self.on_train_end)
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def on_pretrain_routine_end(self, trainer):
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"""
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@ -140,57 +140,57 @@ class HubTrainingSession:
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This method does not use trainer. It is passed to all callbacks by default.
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"""
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# Start timer for upload rate limit
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LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
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self._timers = {"metrics": time(), "ckpt": time()} # start timer on self.rate_limit
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LOGGER.info(f'{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀')
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self._timers = {'metrics': time(), 'ckpt': time()} # start timer on self.rate_limit
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def on_fit_epoch_end(self, trainer):
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# Upload metrics after val end
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all_plots = {**trainer.label_loss_items(trainer.tloss, prefix="train"), **trainer.metrics}
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all_plots = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics}
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if trainer.epoch == 0:
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model_info = {
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"model/parameters": get_num_params(trainer.model),
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"model/GFLOPs": round(get_flops(trainer.model), 3),
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"model/speed(ms)": round(trainer.validator.speed[1], 3)}
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'model/parameters': get_num_params(trainer.model),
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'model/GFLOPs': round(get_flops(trainer.model), 3),
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'model/speed(ms)': round(trainer.validator.speed[1], 3)}
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all_plots = {**all_plots, **model_info}
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self._metrics_queue[trainer.epoch] = json.dumps(all_plots)
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if time() - self._timers["metrics"] > self._rate_limits["metrics"]:
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if time() - self._timers['metrics'] > self._rate_limits['metrics']:
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self.upload_metrics()
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self._timers["metrics"] = time() # reset timer
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self._timers['metrics'] = time() # reset timer
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self._metrics_queue = {} # reset queue
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def on_model_save(self, trainer):
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# Upload checkpoints with rate limiting
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is_best = trainer.best_fitness == trainer.fitness
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if time() - self._timers["ckpt"] > self._rate_limits["ckpt"]:
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LOGGER.info(f"{PREFIX}Uploading checkpoint {self.model_id}")
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if time() - self._timers['ckpt'] > self._rate_limits['ckpt']:
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LOGGER.info(f'{PREFIX}Uploading checkpoint {self.model_id}')
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self._upload_model(trainer.epoch, trainer.last, is_best)
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self._timers["ckpt"] = time() # reset timer
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self._timers['ckpt'] = time() # reset timer
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def on_train_end(self, trainer):
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# Upload final model and metrics with exponential standoff
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LOGGER.info(f"{PREFIX}Training completed successfully ✅")
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LOGGER.info(f"{PREFIX}Uploading final {self.model_id}")
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LOGGER.info(f'{PREFIX}Training completed successfully ✅')
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LOGGER.info(f'{PREFIX}Uploading final {self.model_id}')
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# hack for fetching mAP
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mAP = trainer.metrics.get("metrics/mAP50-95(B)", 0)
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mAP = trainer.metrics.get('metrics/mAP50-95(B)', 0)
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self._upload_model(trainer.epoch, trainer.best, map=mAP, final=True) # results[3] is mAP0.5:0.95
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self.alive = False # stop heartbeats
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LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
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LOGGER.info(f'{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀')
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def _upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
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# Upload a model to HUB
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file = None
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if Path(weights).is_file():
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with open(weights, "rb") as f:
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with open(weights, 'rb') as f:
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file = f.read()
|
||||
file_param = {"best.pt" if final else "last.pt": file}
|
||||
endpoint = f"{self.api_url}/upload"
|
||||
data = {"epoch": epoch}
|
||||
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})
|
||||
data.update({'type': 'final', 'map': map})
|
||||
else:
|
||||
data.update({"type": "epoch", "isBest": bool(is_best)})
|
||||
data.update({'type': 'epoch', 'isBest': bool(is_best)})
|
||||
|
||||
smart_request(
|
||||
endpoint,
|
||||
@ -207,14 +207,14 @@ class HubTrainingSession:
|
||||
self.alive = True
|
||||
while self.alive:
|
||||
r = smart_request(
|
||||
f"{HUB_API_ROOT}/v1/agent/heartbeat/models/{self.model_id}",
|
||||
f'{HUB_API_ROOT}/v1/agent/heartbeat/models/{self.model_id}',
|
||||
json={
|
||||
"agent": AGENT_NAME,
|
||||
"agentId": self.agent_id},
|
||||
'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"])
|
||||
self.agent_id = r.json().get('data', {}).get('agentId', None)
|
||||
sleep(self._rate_limits['heartbeat'])
|
||||
|
||||
@ -18,14 +18,14 @@ from ultralytics.yolo.utils.checks import check_online
|
||||
|
||||
PREFIX = colorstr('Ultralytics: ')
|
||||
HELP_MSG = 'If this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.'
|
||||
HUB_API_ROOT = os.environ.get("ULTRALYTICS_HUB_API", "https://api.ultralytics.com")
|
||||
HUB_API_ROOT = os.environ.get('ULTRALYTICS_HUB_API', 'https://api.ultralytics.com')
|
||||
|
||||
|
||||
def check_dataset_disk_space(url='https://ultralytics.com/assets/coco128.zip', sf=2.0):
|
||||
# Check that url fits on disk with safety factor sf, i.e. require 2GB free if url size is 1GB with sf=2.0
|
||||
gib = 1 << 30 # bytes per GiB
|
||||
data = int(requests.head(url).headers['Content-Length']) / gib # dataset size (GB)
|
||||
total, used, free = (x / gib for x in shutil.disk_usage("/")) # bytes
|
||||
total, used, free = (x / gib for x in shutil.disk_usage('/')) # bytes
|
||||
LOGGER.info(f'{PREFIX}{data:.3f} GB dataset, {free:.1f}/{total:.1f} GB free disk space')
|
||||
if data * sf < free:
|
||||
return True # sufficient space
|
||||
@ -57,7 +57,7 @@ def request_with_credentials(url: str) -> any:
|
||||
});
|
||||
});
|
||||
""" % url))
|
||||
return output.eval_js("_hub_tmp")
|
||||
return output.eval_js('_hub_tmp')
|
||||
|
||||
|
||||
# Deprecated TODO: eliminate this function?
|
||||
@ -84,7 +84,7 @@ def split_key(key=''):
|
||||
return api_key, model_id
|
||||
|
||||
|
||||
def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post", verbose=True, **kwargs):
|
||||
def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method='post', verbose=True, **kwargs):
|
||||
"""
|
||||
Makes an HTTP request using the 'requests' library, with exponential backoff retries up to a specified timeout.
|
||||
|
||||
@ -128,7 +128,7 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
|
||||
m = f"Rate limit reached ({h['X-RateLimit-Remaining']}/{h['X-RateLimit-Limit']}). " \
|
||||
f"Please retry after {h['Retry-After']}s."
|
||||
if verbose:
|
||||
LOGGER.warning(f"{PREFIX}{m} {HELP_MSG} ({r.status_code} #{code})")
|
||||
LOGGER.warning(f'{PREFIX}{m} {HELP_MSG} ({r.status_code} #{code})')
|
||||
if r.status_code not in retry_codes:
|
||||
return r
|
||||
time.sleep(2 ** i) # exponential standoff
|
||||
@ -149,17 +149,17 @@ class Traces:
|
||||
self.rate_limit = 3.0 # rate limit (seconds)
|
||||
self.t = 0.0 # rate limit timer (seconds)
|
||||
self.metadata = {
|
||||
"sys_argv_name": Path(sys.argv[0]).name,
|
||||
"install": 'git' if is_git_dir() else 'pip' if is_pip_package() else 'other',
|
||||
"python": platform.python_version(),
|
||||
"release": __version__,
|
||||
"environment": ENVIRONMENT}
|
||||
'sys_argv_name': Path(sys.argv[0]).name,
|
||||
'install': 'git' if is_git_dir() else 'pip' if is_pip_package() else 'other',
|
||||
'python': platform.python_version(),
|
||||
'release': __version__,
|
||||
'environment': ENVIRONMENT}
|
||||
self.enabled = SETTINGS['sync'] and \
|
||||
RANK in {-1, 0} and \
|
||||
check_online() and \
|
||||
not is_pytest_running() and \
|
||||
not is_github_actions_ci() and \
|
||||
(is_pip_package() or get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git")
|
||||
(is_pip_package() or get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git')
|
||||
|
||||
def __call__(self, cfg, all_keys=False, traces_sample_rate=1.0):
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user