Update `on_train_end` callback (#7716)
Browse files- train.py +0 -1
- utils/loggers/__init__.py +1 -0
train.py
CHANGED
@@ -466,7 +466,6 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
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callbacks.run('on_train_end', last, best, plots, epoch, results)
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-
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
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torch.cuda.empty_cache()
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return results
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callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
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callbacks.run('on_train_end', last, best, plots, epoch, results)
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torch.cuda.empty_cache()
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return results
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utils/loggers/__init__.py
CHANGED
@@ -164,6 +164,7 @@ class Loggers():
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plot_results(file=self.save_dir / 'results.csv') # save results.png
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files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
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files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
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if self.tb:
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for f in files:
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plot_results(file=self.save_dir / 'results.csv') # save results.png
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files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
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files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
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+
self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
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if self.tb:
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for f in files:
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