Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Torch] Fix classification sample metric dumps not in main process #2146

Merged
merged 2 commits into from
Sep 21, 2023
Merged

[Torch] Fix classification sample metric dumps not in main process #2146

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9dd78cf
Fix classification sample metric dumps not in main process
daniil-lyakhov Sep 18, 2023
c67e51c
Code style
daniil-lyakhov Sep 19, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions examples/torch/classification/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -380,14 +380,15 @@ def train(
is_best = is_best_by_accuracy or compression_stage > best_compression_stage
if is_best:
best_acc1 = acc1
config.mlflow.safe_call("log_metric", "best_acc1", best_acc1)
best_compression_stage = max(compression_stage, best_compression_stage)
acc = best_acc1 / 100
if config.metrics_dump is not None:
write_metrics(acc, config.metrics_dump)
if is_main_process():
logger.info(statistics.to_str())

if config.metrics_dump is not None:
acc = best_acc1 / 100
write_metrics(acc, config.metrics_dump)
config.mlflow.safe_call("log_metric", "best_acc1", best_acc1)

checkpoint_path = osp.join(config.checkpoint_save_dir, get_run_name(config) + "_last.pth")
checkpoint = {
"epoch": epoch + 1,
Expand Down Expand Up @@ -735,12 +736,11 @@ def validate(val_loader, model, criterion, config, epoch=0, log_validation_info=
config.mlflow.safe_call("log_metric", "val/top1", float(top1.avg), epoch)
config.mlflow.safe_call("log_metric", "val/top5", float(top5.avg), epoch)

if log_validation_info:
logger.info(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}\n".format(top1=top1, top5=top5))

if is_main_process() and config.metrics_dump is not None:
acc = top1.avg / 100
if config.metrics_dump is not None:
write_metrics(acc, config.metrics_dump)
write_metrics(acc, config.metrics_dump)

return top1.avg, top5.avg, losses.avg

Expand Down
4 changes: 2 additions & 2 deletions tests/torch/test_compression_training.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -442,7 +442,7 @@ def test_compression_train(self, desc: CompressionTrainingTestDescriptor, tmp_pa
self._validate_train_metric(desc)

@pytest.mark.dependency(depends=["train"])
def test_compression_eval(self, desc: LEGRTrainingTestDescriptor, tmp_path, mocker):
def test_compression_eval(self, desc: CompressionTrainingTestDescriptor, tmp_path, mocker):
validator = desc.get_validator()
args = validator.get_default_args(tmp_path)
metric_file_path = self._add_args_for_eval(args, desc, tmp_path)
Expand Down Expand Up @@ -497,7 +497,7 @@ def test_compression_nas_eval(self, nas_desc: NASTrainingTestDescriptor, tmp_pat
self._validate_eval_metric(nas_desc, metric_file_path)

@staticmethod
def _validate_eval_metric(desc, metric_file_path):
def _validate_eval_metric(desc: CompressionTrainingTestDescriptor, metric_file_path):
with open(str(metric_file_path), encoding="utf8") as metric_file:
metrics = json.load(metric_file)
ref_metric = metrics["Accuracy"]
Expand Down