xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ImageNet names, classify inference, resume fixes (#712)

Browse Source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-01-30 22:34:28 +01:00
committed by GitHub
parent aecd17d455
commit 522f1937ed
16 changed files with 1121 additions and 115 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
ultralytics/yolo/utils/__init__.py
View File

@ -479,7 +479,7 @@ def set_sentry():
if SETTINGS['sync'] and \
not is_pytest_running() and \
not is_github_actions_ci() and \
(is_pip_package() or
((is_pip_package() and not is_git_dir()) or
(get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git" and get_git_branch() == "main")):
import sentry_sdk # noqa
@ -493,6 +493,10 @@ def set_sentry():
before_send=before_send,
ignore_errors=[KeyboardInterrupt])
# Disable all sentry logging
for logger in "sentry_sdk", "sentry_sdk.errors":
logging.getLogger(logger).setLevel(logging.CRITICAL)
def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
"""

35
ultralytics/yolo/utils/autobatch.py
View File

@ -52,21 +52,22 @@ def autobatch(model, imgsz=640, fraction=0.7, batch_size=16):
try:
img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
results = profile(img, model, n=3, device=device)
# Fit a solution
y = [x[2] for x in results if x] # memory [2]
p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
if None in results: # some sizes failed
i = results.index(None) # first fail index
if b >= batch_sizes[i]: # y intercept above failure point
b = batch_sizes[max(i - 1, 0)] # select prior safe point
if b < 1 or b > 1024: # b outside of safe range
b = batch_size
LOGGER.info(f'{prefix}WARNING ⚠️ CUDA anomaly detected, using default batch-size {batch_size}.')
fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
return b
except Exception as e:
LOGGER.warning(f'{prefix}{e}')
# Fit a solution
y = [x[2] for x in results if x] # memory [2]
p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
if None in results: # some sizes failed
i = results.index(None) # first fail index
if b >= batch_sizes[i]: # y intercept above failure point
b = batch_sizes[max(i - 1, 0)] # select prior safe point
if b < 1 or b > 1024: # b outside of safe range
b = batch_size
LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
return b
LOGGER.warning(f'{prefix}WARNING ⚠️ error detected: {e}, using default batch-size {batch_size}.')
return batch_size