mirrors/yolov5
1
0
Fork 0
mirror of https://github.com/ultralytics/yolov5.git synced 2025-06-03 14:49:29 +08:00
Code Issues Projects Releases Wiki Activity

Add --noplots flag to suppress figures and images logging (#7534)

Browse Source 
* support nomedia

* support nomedia for validation

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update train.py

* Revert no plot evolve

evolve plots do not contain any images

* Revert plot_results

contains no media

* Update wandb_utils.py

* sync-bn cleanup

* Cleanup

* Rename nomedia -> noplots

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia 2022-04-23 03:34:08 +05:30 committed by GitHub
parent f62609eb52
commit 4cac1cfc8f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 7 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
train.py
View File

@ -100,7 +100,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
callbacks.register_action(k, callback=getattr(loggers, k))
# Config
plots = not evolve # create plots
plots = not evolve and not opt.noplots # create plots
cuda = device.type != 'cpu'
init_seeds(1 + RANK)
with torch_distributed_zero_first(LOCAL_RANK):
@ -373,7 +373,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
(f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots)
if callbacks.stop_training:
return
# end batch ------------------------------------------------------------------------------------------------
@ -488,6 +488,7 @@ def parse_opt(known=False):
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
parser.add_argument('--noplots', action='store_true', help='save no plot files')
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')

4
utils/loggers/__init__.py
View File

@ -99,11 +99,11 @@ class Loggers():
if self.wandb:
self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn):
def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
# Callback runs on train batch end
if plots:
if ni == 0:
if not sync_bn: # tb.add_graph() --sync known issue https://github.com/ultralytics/yolov5/issues/3754
if not self.opt.sync_bn: # --sync known issue https://github.com/ultralytics/yolov5/issues/3754
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress jit trace warning
self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])

4
utils/loggers/wandb/wandb_utils.py
View File

@ -250,8 +250,8 @@ class WandbLogger():
self.map_val_table_path()
if opt.bbox_interval == -1:
self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
if opt.evolve:
self.bbox_interval = opt.bbox_interval = opt.epochs + 1
if opt.evolve or opt.noplots:
self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
# Update the the data_dict to point to local artifacts dir
if train_from_artifact: