Add ndjson logging for training (#10970)
* Add ndjson logging for training This adds support for NDJSON (newline-delimited JSON) metrics logging, for both console (stdout) output and a file (like the current CSV file). NDJSON can be easily grepped from the output and/or parsed with e.g. `jq`. The feature is enabled with the `--ndjson-console` and `--ndjson-file` switches to `train.py`. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@@ -58,7 +58,7 @@ from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, ch
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get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
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labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer,
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yaml_save)
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from utils.loggers import Loggers
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from utils.loggers import LOGGERS, Loggers
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from utils.loggers.comet.comet_utils import check_comet_resume
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from utils.loss import ComputeLoss
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from utils.metrics import fitness
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@@ -98,7 +98,20 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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# Loggers
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data_dict = None
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if RANK in {-1, 0}:
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loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
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include_loggers = list(LOGGERS)
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if getattr(opt, 'ndjson_console', False):
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include_loggers.append('ndjson_console')
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if getattr(opt, 'ndjson_file', False):
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include_loggers.append('ndjson_file')
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loggers = Loggers(
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save_dir=save_dir,
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weights=weights,
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opt=opt,
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hyp=hyp,
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logger=LOGGER,
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include=tuple(include_loggers),
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)
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# Register actions
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for k in methods(loggers):
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@@ -482,6 +495,10 @@ def parse_opt(known=False):
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parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval')
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parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use')
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# NDJSON logging
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parser.add_argument('--ndjson-console', action='store_true', help='Log ndjson to console')
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parser.add_argument('--ndjson-file', action='store_true', help='Log ndjson to file')
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return parser.parse_known_args()[0] if known else parser.parse_args()
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@@ -2,7 +2,7 @@
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"""
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Logging utils
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"""
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import json
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import os
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import warnings
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from pathlib import Path
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@@ -58,6 +58,18 @@ except (ImportError, AssertionError):
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comet_ml = None
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def _json_default(value):
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"""Format `value` for JSON serialization (e.g. unwrap tensors). Fall back to strings."""
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if isinstance(value, torch.Tensor):
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try:
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value = value.item()
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except ValueError: # "only one element tensors can be converted to Python scalars"
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pass
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if isinstance(value, float):
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return value
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return str(value)
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class Loggers():
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# YOLOv5 Loggers class
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def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
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@@ -86,6 +98,8 @@ class Loggers():
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for k in LOGGERS:
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setattr(self, k, None) # init empty logger dictionary
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self.csv = True # always log to csv
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self.ndjson_console = ('ndjson_console' in self.include) # log ndjson to console
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self.ndjson_file = ('ndjson_file' in self.include) # log ndjson to file
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# Messages
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if not comet_ml:
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@@ -228,6 +242,14 @@ class Loggers():
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s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header
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with open(file, 'a') as f:
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f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
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if self.ndjson_console or self.ndjson_file:
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json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default)
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if self.ndjson_console:
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print(json_data)
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if self.ndjson_file:
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file = self.save_dir / 'results.ndjson'
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with open(file, 'a') as f:
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print(json_data, file=f)
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if self.tb:
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for k, v in x.items():
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