SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Improve notrainer examples (#17449)

Browse Source 
* improve no-trainer examples

* Trigger CI

* adding comment to clarify tracker init on main process

* Trigger CI

* Trigger CI

* Trigger CI
This commit is contained in:
Sourab Mangrulkar
2022-05-28 00:06:31 +05:30
committed by GitHub
parent 7999ec125f
commit d156898f3b
10 changed files with 310 additions and 118 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
View File

@@ -285,7 +285,17 @@ def parse_args():
"--with_tracking",
required=False,
action="store_true",
help="Whether to load in all available experiment trackers from the environment and use them for logging.",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
args = parser.parse_args()
@@ -306,8 +316,11 @@ def main():
args = parse_args()
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment
accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator = (
Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator()
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
@@ -482,11 +495,15 @@ def main():
# Instantiate metric
metric = load_metric("mean_iou")
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config)
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -615,10 +632,11 @@ def main():
"mean_iou": eval_metrics["mean_iou"],
"mean_accuracy": eval_metrics["mean_accuracy"],
"overall_accuracy": eval_metrics["overall_accuracy"],
"train_loss": total_loss,
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
if args.push_to_hub and epoch < args.num_train_epochs - 1: