def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
logger.info("***** Validation results *****")
if pl_module.is_logger():
metrics = trainer.callback_metrics
# Log results
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
logger.info("{} = {}\n".format(key, str(metrics[key])))
def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
logger.info("***** Test results *****")
if pl_module.is_logger():
metrics = trainer.callback_metrics
# Log and save results to file
output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
with open(output_test_results_file, "w") as writer:
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
logger.info("{} = {}\n".format(key, str(metrics[key])))
writer.write("{} = {}\n".format(key, str(metrics[key])))
def _write_logs(self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
type_path: str,
save_generations=True) -> None:
logger.info(
f"***** {type_path} results at step {trainer.global_step:05d} *****"
)
if not pl_module.is_logger():
return
metrics = trainer.callback_metrics
trainer.logger.log_metrics({
k: v
for k, v in metrics.items()
if k not in ["log", "progress_bar", "preds"]
})
# Log results
od = Path(pl_module.hparams.output_dir)
if type_path == "test":
results_file = od / "test_results.txt"
generations_file = od / "test_generations.txt"
else:
results_file = od / f"{type_path}_results_{trainer.global_step:05d}.txt"
generations_file = od / f"{type_path}_generations_{trainer.global_step:05d}.txt"
with open(results_file, "a+") as writer:
for key in sorted(metrics):
if key in ["log", "progress_bar", "preds"]:
continue
val = metrics[key]
if isinstance(val, torch.Tensor):
val = val.item()
msg = f"{key}: {val:.6f}\n"
writer.write(msg)
if not save_generations:
return
if "preds" in metrics:
content = "\n".join(metrics["preds"])
generations_file.open("w+").write(content)