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yolov5
Unverified 提交 d5966c93 authored 作者: Glenn Jocher's avatar Glenn Jocher 提交者： GitHub
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Namespace `VERBOSE` env variable to `YOLOv5_VERBOSE` (#6428)

* Verbose updates * Verbose updates
上级 ff8646cd
隐藏空白字符变更
内嵌 并排
正在显示 包含 36 行增加 和 36 行删除
hubconf.py
浏览文件 @ d5966c93
......@@ -12,10 +12,10 @@ import torch
def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
"""Creates a specified YOLOv5 model
"""Creates or loads a YOLOv5 model
Arguments:
name (str): name of model, i.e. 'yolov5s'
name (str): model name 'yolov5s' or path 'path/to/best.pt'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
......@@ -24,19 +24,19 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
device (str, torch.device, None): device to use for model parameters
Returns:
YOLOv5 pytorch model
YOLOv5 model
"""
from pathlib import Path
from models.common import AutoShape, DetectMultiBackend
from models.yolo import Model
from utils.downloads import attempt_download
from utils.general import check_requirements, intersect_dicts, set_logging
from utils.general import LOGGER, check_requirements, intersect_dicts, logging
from utils.torch_utils import select_device
if not verbose:
LOGGER.setLevel(logging.WARNING)
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
set_logging(verbose=verbose)
name = Path(name)
path = name.with_suffix('.pt') if name.suffix == '' else name # checkpoint path
try:
......
utils/general.py
浏览文件 @ d5966c93
......@@ -36,7 +36,7 @@ from utils.metrics import box_iou, fitness
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
VERBOSE = str(os.getenv('VERBOSE', True)).lower() == 'true' # global verbose mode
VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode
torch.set_printoptions(linewidth=320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
......@@ -241,20 +241,20 @@ def check_online():
def check_git_status():
# Recommend 'git pull' if code is out of date
msg = ', for updates see https://github.com/ultralytics/yolov5'
print(colorstr('github: '), end='')
assert Path('.git').exists(), 'skipping check (not a git repository)' + msg
assert not is_docker(), 'skipping check (Docker image)' + msg
assert check_online(), 'skipping check (offline)' + msg
s = colorstr('github: ') # string
assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg
assert not is_docker(), s + 'skipping check (Docker image)' + msg
assert check_online(), s + 'skipping check (offline)' + msg
cmd = 'git fetch && git config --get remote.origin.url'
url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git') # git fetch
branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
if n > 0:
s = f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
else:
s = f'up to date with {url} ✅'
print(emojis(s)) # emoji-safe
s += f'up to date with {url} ✅'
LOGGER.info(emojis(s)) # emoji-safe
def check_python(minimum='3.6.2'):
......@@ -294,21 +294,21 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
s = f"{prefix} {r} not found and is required by YOLOv5"
if install:
print(f"{s}, attempting auto-update...")
LOGGER.info(f"{s}, attempting auto-update...")
try:
assert check_online(), f"'pip install {r}' skipped (offline)"
print(check_output(f"pip install '{r}'", shell=True).decode())
LOGGER.info(check_output(f"pip install '{r}'", shell=True).decode())
n += 1
except Exception as e:
print(f'{prefix} {e}')
LOGGER.warning(f'{prefix} {e}')
else:
print(f'{s}. Please install and rerun your command.')
LOGGER.info(f'{s}. Please install and rerun your command.')
if n: # if packages updated
source = file.resolve() if 'file' in locals() else requirements
s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
print(emojis(s))
LOGGER.info(emojis(s))
def check_img_size(imgsz, s=32, floor=0):
......@@ -318,7 +318,7 @@ def check_img_size(imgsz, s=32, floor=0):
else: # list i.e. img_size=[640, 480]
new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
if new_size != imgsz:
print(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
LOGGER.warning(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
return new_size
......@@ -333,7 +333,7 @@ def check_imshow():
cv2.waitKey(1)
return True
except Exception as e:
print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
LOGGER.warning(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
return False
......@@ -363,9 +363,9 @@ def check_file(file, suffix=''):
url = str(Path(file)).replace(':/', '://') # Pathlib turns :// -> :/
file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
if Path(file).is_file():
print(f'Found {url} locally at {file}') # file already exists
LOGGER.info(f'Found {url} locally at {file}') # file already exists
else:
print(f'Downloading {url} to {file}...')
LOGGER.info(f'Downloading {url} to {file}...')
torch.hub.download_url_to_file(url, file)
assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
return file
......@@ -407,23 +407,23 @@ def check_dataset(data, autodownload=True):
if val:
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
if not all(x.exists() for x in val):
print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
LOGGER.info('\nDataset not found, missing paths: %s' % [str(x) for x in val if not x.exists()])
if s and autodownload: # download script
root = path.parent if 'path' in data else '..' # unzip directory i.e. '../'
if s.startswith('http') and s.endswith('.zip'): # URL
f = Path(s).name # filename
print(f'Downloading {s} to {f}...')
LOGGER.info(f'Downloading {s} to {f}...')
torch.hub.download_url_to_file(s, f)
Path(root).mkdir(parents=True, exist_ok=True) # create root
ZipFile(f).extractall(path=root) # unzip
Path(f).unlink() # remove zip
r = None # success
elif s.startswith('bash '): # bash script
print(f'Running {s} ...')
LOGGER.info(f'Running {s} ...')
r = os.system(s)
else: # python script
r = exec(s, {'yaml': data}) # return None
print(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")
LOGGER.info(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")
else:
raise Exception('Dataset not found.')
......@@ -445,13 +445,13 @@ def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
if Path(url).is_file(): # exists in current path
Path(url).rename(f) # move to dir
elif not f.exists():
print(f'Downloading {url} to {f}...')
LOGGER.info(f'Downloading {url} to {f}...')
if curl:
os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail
else:
torch.hub.download_url_to_file(url, f, progress=True) # torch download
if unzip and f.suffix in ('.zip', '.gz'):
print(f'Unzipping {f}...')
LOGGER.info(f'Unzipping {f}...')
if f.suffix == '.zip':
ZipFile(f).extractall(path=dir) # unzip
elif f.suffix == '.gz':
......@@ -744,7 +744,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
output[xi] = x[i]
if (time.time() - t) > time_limit:
print(f'WARNING: NMS time limit {time_limit}s exceeded')
LOGGER.warning(f'WARNING: NMS time limit {time_limit}s exceeded')
break # time limit exceeded
return output
......@@ -763,7 +763,7 @@ def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_op
p.requires_grad = False
torch.save(x, s or f)
mb = os.path.getsize(s or f) / 1E6 # filesize
print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
LOGGER.info(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
def print_mutation(results, hyp, save_dir, bucket):
......@@ -786,8 +786,8 @@ def print_mutation(results, hyp, save_dir, bucket):
f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
# Print to screen
print(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
print(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals), end='\n\n\n')
LOGGER.info(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
LOGGER.info(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n')
# Save yaml
with open(evolve_yaml, 'w') as f:
......
utils/plots.py
浏览文件 @ d5966c93
......@@ -57,7 +57,7 @@ def check_font(font='Arial.ttf', size=10):
return ImageFont.truetype(str(font) if font.exists() else font.name, size)
except Exception as e: # download if missing
url = "https://ultralytics.com/assets/" + font.name
print(f'Downloading {url} to {font}...')
LOGGER.info(f'Downloading {url} to {font}...')
torch.hub.download_url_to_file(url, str(font), progress=False)
try:
return ImageFont.truetype(str(font), size)
......@@ -143,7 +143,7 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
ax[i].axis('off')
print(f'Saving {f}... ({n}/{channels})')
LOGGER.info(f'Saving {f}... ({n}/{channels})')
plt.savefig(f, dpi=300, bbox_inches='tight')
plt.close()
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
......@@ -417,7 +417,7 @@ def plot_results(file='path/to/results.csv', dir=''):
# if j in [8, 9, 10]: # share train and val loss y axes
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
except Exception as e:
print(f'Warning: Plotting error for {f}: {e}')
LOGGER.info(f'Warning: Plotting error for {f}: {e}')
ax[1].legend()
fig.savefig(save_dir / 'results.png', dpi=200)
plt.close()
......
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