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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Paula DerrengerUltralyticsAssistantGlenn Jocher
 authored and GitHub committed 2024-01-08 01:29:14 +01:00
1 parent f56355d321
commit ad97dc6a6d
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utils/loggers/__init__.py
+103 -92
View File
@@ -1,7 +1,5 @@
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Logging utils
"""
"""Logging utils."""
import json
import os
import warnings
@@ -16,8 +14,8 @@ from utils.loggers.wandb.wandb_utils import WandbLogger
from utils.plots import plot_images, plot_labels, plot_results
from utils.torch_utils import de_parallel
LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML
RANK = int(os.getenv('RANK', -1))
LOGGERS = ("csv", "tb", "wandb", "clearml", "comet") # *.csv, TensorBoard, Weights & Biases, ClearML
RANK = int(os.getenv("RANK", -1))
try:
from torch.utils.tensorboard import SummaryWriter
@@ -27,8 +25,8 @@ except ImportError:
try:
import wandb
assert hasattr(wandb, '__version__') # verify package import not local dir
if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
assert hasattr(wandb, "__version__") # verify package import not local dir
if pkg.parse_version(wandb.__version__) >= pkg.parse_version("0.12.2") and RANK in {0, -1}:
try:
wandb_login_success = wandb.login(timeout=30)
except wandb.errors.UsageError: # known non-TTY terminal issue
@@ -41,7 +39,7 @@ except (ImportError, AssertionError):
try:
import clearml
assert hasattr(clearml, '__version__') # verify package import not local dir
assert hasattr(clearml, "__version__") # verify package import not local dir
except (ImportError, AssertionError):
clearml = None
@@ -49,7 +47,7 @@ try:
if RANK in {0, -1}:
import comet_ml
assert hasattr(comet_ml, '__version__') # verify package import not local dir
assert hasattr(comet_ml, "__version__") # verify package import not local dir
from utils.loggers.comet import CometLogger
else:
@@ -59,7 +57,11 @@ except (ImportError, AssertionError):
def _json_default(value):
"""Format `value` for JSON serialization (e.g. unwrap tensors). Fall back to strings."""
"""
Format `value` for JSON serialization (e.g. unwrap tensors).
Fall back to strings.
"""
if isinstance(value, torch.Tensor):
try:
value = value.item()
@@ -70,7 +72,7 @@ def _json_default(value):
return str(value)
class Loggers():
class Loggers:
# YOLOv5 Loggers class
def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
self.save_dir = save_dir
@@ -81,62 +83,65 @@ class Loggers():
self.logger = logger # for printing results to console
self.include = include
self.keys = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95', # metrics
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'] # params
self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
"train/box_loss",
"train/obj_loss",
"train/cls_loss", # train loss
"metrics/precision",
"metrics/recall",
"metrics/mAP_0.5",
"metrics/mAP_0.5:0.95", # metrics
"val/box_loss",
"val/obj_loss",
"val/cls_loss", # val loss
"x/lr0",
"x/lr1",
"x/lr2",
] # params
self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"]
for k in LOGGERS:
setattr(self, k, None) # init empty logger dictionary
self.csv = True # always log to csv
self.ndjson_console = ('ndjson_console' in self.include) # log ndjson to console
self.ndjson_file = ('ndjson_file' in self.include) # log ndjson to file
self.ndjson_console = "ndjson_console" in self.include # log ndjson to console
self.ndjson_file = "ndjson_file" in self.include # log ndjson to file
# Messages
if not comet_ml:
prefix = colorstr('Comet: ')
prefix = colorstr("Comet: ")
s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
self.logger.info(s)
# TensorBoard
s = self.save_dir
if 'tb' in self.include and not self.opt.evolve:
prefix = colorstr('TensorBoard: ')
if "tb" in self.include and not self.opt.evolve:
prefix = colorstr("TensorBoard: ")
self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
self.tb = SummaryWriter(str(s))
# W&B
if wandb and 'wandb' in self.include:
if wandb and "wandb" in self.include:
self.opt.hyp = self.hyp # add hyperparameters
self.wandb = WandbLogger(self.opt)
else:
self.wandb = None
# ClearML
if clearml and 'clearml' in self.include:
if clearml and "clearml" in self.include:
try:
self.clearml = ClearmlLogger(self.opt, self.hyp)
except Exception:
self.clearml = None
prefix = colorstr('ClearML: ')
LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.'
f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme')
prefix = colorstr("ClearML: ")
LOGGER.warning(
f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme"
)
else:
self.clearml = None
# Comet
if comet_ml and 'comet' in self.include:
if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'):
run_id = self.opt.resume.split('/')[-1]
if comet_ml and "comet" in self.include:
if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"):
run_id = self.opt.resume.split("/")[-1]
self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)
else:
@@ -170,9 +175,9 @@ class Loggers():
# Callback runs on pre-train routine end
if self.plots:
plot_labels(labels, names, self.save_dir)
paths = self.save_dir.glob('*labels*.jpg') # training labels
paths = self.save_dir.glob("*labels*.jpg") # training labels
if self.wandb:
self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]})
self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
if self.comet_logger:
self.comet_logger.on_pretrain_routine_end(paths)
if self.clearml:
@@ -185,16 +190,16 @@ class Loggers():
# ni: number integrated batches (since train start)
if self.plots:
if ni < 3:
f = self.save_dir / f'train_batch{ni}.jpg' # filename
f = self.save_dir / f"train_batch{ni}.jpg" # filename
plot_images(imgs, targets, paths, f)
if ni == 0 and self.tb and not self.opt.sync_bn:
log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
if ni == 10 and (self.wandb or self.clearml):
files = sorted(self.save_dir.glob('train*.jpg'))
files = sorted(self.save_dir.glob("train*.jpg"))
if self.wandb:
self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
if self.clearml:
self.clearml.log_debug_samples(files, title='Mosaics')
self.clearml.log_debug_samples(files, title="Mosaics")
if self.comet_logger:
self.comet_logger.on_train_batch_end(log_dict, step=ni)
@@ -225,11 +230,11 @@ class Loggers():
def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
# Callback runs on val end
if self.wandb or self.clearml:
files = sorted(self.save_dir.glob('val*.jpg'))
files = sorted(self.save_dir.glob("val*.jpg"))
if self.wandb:
self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]})
self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
if self.clearml:
self.clearml.log_debug_samples(files, title='Validation')
self.clearml.log_debug_samples(files, title="Validation")
if self.comet_logger:
self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
@@ -238,18 +243,18 @@ class Loggers():
# Callback runs at the end of each fit (train+val) epoch
x = dict(zip(self.keys, vals))
if self.csv:
file = self.save_dir / 'results.csv'
file = self.save_dir / "results.csv"
n = len(x) + 1 # number of cols
s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header
with open(file, 'a') as f:
f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
s = "" if file.exists() else (("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",") + "\n") # add header
with open(file, "a") as f:
f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
if self.ndjson_console or self.ndjson_file:
json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default)
if self.ndjson_console:
print(json_data)
if self.ndjson_file:
file = self.save_dir / 'results.ndjson'
with open(file, 'a') as f:
file = self.save_dir / "results.ndjson"
with open(file, "a") as f:
print(json_data, file=f)
if self.tb:
@@ -279,9 +284,9 @@ class Loggers():
if self.wandb:
self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
if self.clearml:
self.clearml.task.update_output_model(model_path=str(last),
model_name='Latest Model',
auto_delete_file=False)
self.clearml.task.update_output_model(
model_path=str(last), model_name="Latest Model", auto_delete_file=False
)
if self.comet_logger:
self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
@@ -289,31 +294,34 @@ class Loggers():
def on_train_end(self, last, best, epoch, results):
# Callback runs on training end, i.e. saving best model
if self.plots:
plot_results(file=self.save_dir / 'results.csv') # save results.png
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
plot_results(file=self.save_dir / "results.csv") # save results.png
files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))]
files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles
for f in files:
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC")
if self.wandb:
self.wandb.log(dict(zip(self.keys[3:10], results)))
self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]})
self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
# Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
if not self.opt.evolve:
wandb.log_artifact(str(best if best.exists() else last),
type='model',
name=f'run_{self.wandb.wandb_run.id}_model',
aliases=['latest', 'best', 'stripped'])
wandb.log_artifact(
str(best if best.exists() else last),
type="model",
name=f"run_{self.wandb.wandb_run.id}_model",
aliases=["latest", "best", "stripped"],
)
self.wandb.finish_run()
if self.clearml and not self.opt.evolve:
self.clearml.log_summary(dict(zip(self.keys[3:10], results)))
[self.clearml.log_plot(title=f.stem, plot_path=f) for f in files]
self.clearml.log_model(str(best if best.exists() else last),
"Best Model" if best.exists() else "Last Model", epoch)
self.clearml.log_model(
str(best if best.exists() else last), "Best Model" if best.exists() else "Last Model", epoch
)
if self.comet_logger:
final_results = dict(zip(self.keys[3:10], results))
@@ -339,38 +347,41 @@ class GenericLogger:
include: loggers to include
"""
def __init__(self, opt, console_logger, include=('tb', 'wandb', 'clearml')):
def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")):
# init default loggers
self.save_dir = Path(opt.save_dir)
self.include = include
self.console_logger = console_logger
self.csv = self.save_dir / 'results.csv' # CSV logger
if 'tb' in self.include:
prefix = colorstr('TensorBoard: ')
self.csv = self.save_dir / "results.csv" # CSV logger
if "tb" in self.include:
prefix = colorstr("TensorBoard: ")
self.console_logger.info(
f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/"
)
self.tb = SummaryWriter(str(self.save_dir))
if wandb and 'wandb' in self.include:
self.wandb = wandb.init(project=web_project_name(str(opt.project)),
name=None if opt.name == 'exp' else opt.name,
config=opt)
if wandb and "wandb" in self.include:
self.wandb = wandb.init(
project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt
)
else:
self.wandb = None
if clearml and 'clearml' in self.include:
if clearml and "clearml" in self.include:
try:
# Hyp is not available in classification mode
if 'hyp' not in opt:
if "hyp" not in opt:
hyp = {}
else:
hyp = opt.hyp
self.clearml = ClearmlLogger(opt, hyp)
except Exception:
self.clearml = None
prefix = colorstr('ClearML: ')
LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.'
f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme')
prefix = colorstr("ClearML: ")
LOGGER.warning(
f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
f" See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme"
)
else:
self.clearml = None
@@ -379,9 +390,9 @@ class GenericLogger:
if self.csv:
keys, vals = list(metrics.keys()), list(metrics.values())
n = len(metrics) + 1 # number of cols
s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header
with open(self.csv, 'a') as f:
f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n") # header
with open(self.csv, "a") as f:
f.write(s + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
if self.tb:
for k, v in metrics.items():
@@ -393,20 +404,20 @@ class GenericLogger:
if self.clearml:
self.clearml.log_scalars(metrics, epoch)
def log_images(self, files, name='Images', epoch=0):
def log_images(self, files, name="Images", epoch=0):
# Log images to all loggers
files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path
files = [f for f in files if f.exists()] # filter by exists
if self.tb:
for f in files:
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC")
if self.wandb:
self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
if self.clearml:
if name == 'Results':
if name == "Results":
[self.clearml.log_plot(f.stem, f) for f in files]
else:
self.clearml.log_debug_samples(files, title=name)
@@ -419,7 +430,7 @@ class GenericLogger:
def log_model(self, model_path, epoch=0, metadata={}):
# Log model to all loggers
if self.wandb:
art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata)
art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
art.add_file(str(model_path))
wandb.log_artifact(art)
if self.clearml:
@@ -440,15 +451,15 @@ def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand
im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty)
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress jit trace warning
warnings.simplefilter("ignore") # suppress jit trace warning
tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')
LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}")
def web_project_name(project):
# Convert local project name to web project name
if not project.startswith('runs/train'):
if not project.startswith("runs/train"):
return project
suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else ''
return f'YOLOv5{suffix}'
suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else ""
return f"YOLOv5{suffix}"