`ultralytics 8.0.69` HUB CI and ClearML fixes (#1888)

Co-authored-by: Victor Sonck <victor.sonck@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
single_channel
Glenn Jocher 2 years ago committed by GitHub
parent d3f097314f
commit c2cd3fd20e
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@ -46,17 +46,14 @@ jobs:
- name: Test HUB training
shell: python
env:
APIKEY: ${{ secrets.ULTRALYTICS_HUB_APIKEY }}
API_KEY: ${{ secrets.ULTRALYTICS_HUB_API_KEY }}
MODEL_ID: ${{ secrets.ULTRALYTICS_HUB_MODEL_ID }}
run: |
import os
from pathlib import Path
from ultralytics import YOLO, hub
from ultralytics.yolo.utils import USER_CONFIG_DIR
Path(USER_CONFIG_DIR / 'settings.yaml').unlink()
key = os.environ['APIKEY']
hub.reset_model(key)
key, model_id = key.split('_')
hub.login(key)
api_key, model_id = os.environ['API_KEY'], os.environ['MODEL_ID']
hub.login(api_key)
hub.reset_model(model_id)
model = YOLO('https://hub.ultralytics.com/models/' + model_id)
model.train()

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = '8.0.68'
__version__ = '8.0.69'
from ultralytics.hub import start
from ultralytics.yolo.engine.model import YOLO

@ -2,7 +2,8 @@
import requests
from ultralytics.hub.utils import PREFIX, split_key
from ultralytics.hub.auth import Auth
from ultralytics.hub.utils import PREFIX
from ultralytics.yolo.utils import LOGGER, SETTINGS, USER_CONFIG_DIR, yaml_save
@ -17,7 +18,6 @@ def login(api_key=''):
from ultralytics import hub
hub.login('API_KEY')
"""
from ultralytics.hub.auth import Auth
Auth(api_key)
@ -42,20 +42,20 @@ def start(key=''):
key (str, optional): A string containing either the API key and model ID combination (apikey_modelid),
or the full model URL (https://hub.ultralytics.com/models/apikey_modelid).
"""
api_key, model_id = key.split('_')
LOGGER.warning(f"""
WARNING ultralytics.start() is deprecated in 8.0.60. Updated usage to train your Ultralytics HUB model is below:
WARNING ultralytics.start() is deprecated after 8.0.60. Updated usage to train Ultralytics HUB models is:
from ultralytics import YOLO
from ultralytics import YOLO, hub
model = YOLO('https://hub.ultralytics.com/models/{key}')
hub.login('{api_key}')
model = YOLO('https://hub.ultralytics.com/models/{model_id}')
model.train()""")
def reset_model(key=''):
def reset_model(model_id=''):
# 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': Auth().api_key, 'modelId': model_id})
if r.status_code == 200:
LOGGER.info(f'{PREFIX}Model reset successfully')
return
@ -68,26 +68,24 @@ def export_fmts_hub():
return list(export_formats()['Argument'][1:]) + ['ultralytics_tflite', 'ultralytics_coreml']
def export_model(key='', format='torchscript'):
def export_model(model_id='', format='torchscript'):
# Export a model to all formats
assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
api_key, model_id = split_key(key)
r = requests.post('https://api.ultralytics.com/export',
json={
'apiKey': api_key,
'apiKey': Auth().api_key,
'modelId': model_id,
'format': format})
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(model_id='', format='torchscript'):
# Get an exported model dictionary with download URL
assert format in export_fmts_hub, f"Unsupported export format '{format}', valid formats are {export_fmts_hub}"
api_key, model_id = split_key(key)
r = requests.post('https://api.ultralytics.com/get-export',
json={
'apiKey': api_key,
'apiKey': Auth().api_key,
'modelId': model_id,
'format': format})
assert r.status_code == 200, f'{PREFIX}{format} get_export failure {r.status_code} {r.reason}'

@ -13,7 +13,7 @@ import requests
from tqdm import tqdm
from ultralytics.yolo.utils import (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM_BAR_FORMAT,
TryExcept, __version__, colorstr, emojis, get_git_origin_url, is_colab, is_git_dir,
TryExcept, __version__, colorstr, get_git_origin_url, is_colab, is_git_dir,
is_pip_package)
PREFIX = colorstr('Ultralytics HUB: ')
@ -80,29 +80,6 @@ def request_with_credentials(url: str) -> any:
return output.eval_js('_hub_tmp')
def split_key(key=''):
"""
Verify and split a 'api_key[sep]model_id' string, sep is one of '.' or '_'
Args:
key (str): The model key to split. If not provided, the user will be prompted to enter it.
Returns:
Tuple[str, str]: A tuple containing the API key and model ID.
"""
import getpass
error_string = emojis(f'{PREFIX}Invalid API key ⚠️\n') # error string
if not key:
key = getpass.getpass('Enter model key: ')
sep = '_' if '_' in key else None # separator
assert sep, error_string
api_key, model_id = key.split(sep)
assert len(api_key) and len(model_id), error_string
return api_key, model_id
def requests_with_progress(method, url, **kwargs):
"""
Make an HTTP request using the specified method and URL, with an optional progress bar.

@ -27,14 +27,16 @@ def _log_debug_samples(files, title='Debug Samples'):
files (List(PosixPath)) a list of file paths in PosixPath format
title (str) A title that groups together images with the same values
"""
for f in files:
if f.exists():
it = re.search(r'_batch(\d+)', f.name)
iteration = int(it.groups()[0]) if it else 0
Task.current_task().get_logger().report_image(title=title,
series=f.name.replace(it.group(), ''),
local_path=str(f),
iteration=iteration)
task = Task.current_task()
if task:
for f in files:
if f.exists():
it = re.search(r'_batch(\d+)', f.name)
iteration = int(it.groups()[0]) if it else 0
task.get_logger().report_image(title=title,
series=f.name.replace(it.group(), ''),
local_path=str(f),
iteration=iteration)
def _log_plot(title, plot_path):
@ -54,11 +56,9 @@ def _log_plot(title, plot_path):
def on_pretrain_routine_start(trainer):
# TODO: reuse existing task
try:
if Task.current_task():
task = Task.current_task()
task = Task.current_task()
if task:
# Make sure the automatic pytorch and matplotlib bindings are disabled!
# We are logging these plots and model files manually in the integration
PatchPyTorchModelIO.update_current_task(None)
@ -80,43 +80,46 @@ def on_pretrain_routine_start(trainer):
def on_train_epoch_end(trainer):
if trainer.epoch == 1:
if trainer.epoch == 1 and Task.current_task():
_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
def on_fit_epoch_end(trainer):
# You should have access to the validation bboxes under jdict
Task.current_task().get_logger().report_scalar(title='Epoch Time',
series='Epoch Time',
value=trainer.epoch_time,
iteration=trainer.epoch)
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['inference'], 3)}
for k, v in model_info.items():
Task.current_task().get_logger().report_single_value(k, v)
task = Task.current_task()
if task:
# You should have access to the validation bboxes under jdict
task.get_logger().report_scalar(title='Epoch Time',
series='Epoch Time',
value=trainer.epoch_time,
iteration=trainer.epoch)
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['inference'], 3)}
for k, v in model_info.items():
task.get_logger().report_single_value(k, v)
def on_val_end(validator):
# Log val_labels and val_pred
_log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')
if Task.current_task():
# Log val_labels and val_pred
_log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')
def on_train_end(trainer):
# Log final results, CM matrix + PR plots
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
for f in files:
_log_plot(title=f.stem, plot_path=f)
# Report final metrics
for k, v in trainer.validator.metrics.results_dict.items():
Task.current_task().get_logger().report_single_value(k, v)
# Log the final model
Task.current_task().update_output_model(model_path=str(trainer.best),
model_name=trainer.args.name,
auto_delete_file=False)
task = Task.current_task()
if task:
# Log final results, CM matrix + PR plots
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
for f in files:
_log_plot(title=f.stem, plot_path=f)
# Report final metrics
for k, v in trainer.validator.metrics.results_dict.items():
task.get_logger().report_single_value(k, v)
# Log the final model
task.update_output_model(model_path=str(trainer.best), model_name=trainer.args.name, auto_delete_file=False)
callbacks = {

@ -337,6 +337,10 @@ def git_describe(path=ROOT): # path must be a directory
def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
# Print function arguments (optional args dict)
def strip_auth(v):
# Clean longer Ultralytics HUB URLs by stripping potential authentication information
return clean_url(v) if (isinstance(v, str) and v.startswith('http') and len(v) > 100) else v
x = inspect.currentframe().f_back # previous frame
file, _, func, _, _ = inspect.getframeinfo(x)
if args is None: # get args automatically
@ -347,4 +351,4 @@ def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
except ValueError:
file = Path(file).stem
s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')
LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
LOGGER.info(colorstr(s) + ', '.join(f'{k}={strip_auth(v)}' for k, v in args.items()))

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