* Add `on_fit_epoch_end` callback * Add results to train * Update __init__.pymodifyDataloader
@@ -423,8 +423,10 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary | |||
plots=True, | |||
callbacks=callbacks, | |||
compute_loss=compute_loss) # val best model with plots | |||
if is_coco: | |||
callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) | |||
callbacks.run('on_train_end', last, best, plots, epoch) | |||
callbacks.run('on_train_end', last, best, plots, epoch, results) | |||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") | |||
torch.cuda.empty_cache() |
@@ -131,7 +131,7 @@ class Loggers(): | |||
if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: | |||
self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) | |||
def on_train_end(self, last, best, plots, epoch): | |||
def on_train_end(self, last, best, plots, epoch, results): | |||
# Callback runs on training end | |||
if plots: | |||
plot_results(file=self.save_dir / 'results.csv') # save results.png |