updates
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							|  | @ -93,11 +93,11 @@ def train( | |||
|     scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1) | ||||
|     # scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch - 1) | ||||
| 
 | ||||
|     # y = [] | ||||
|     # for epoch in range(epochs): | ||||
|     #     scheduler.step() | ||||
|     #     y.append(optimizer.param_groups[0]['lr']) | ||||
|     # plt.plot(y) | ||||
|     y = [] | ||||
|     for _ in range(epochs): | ||||
|         scheduler.step() | ||||
|         y.append(optimizer.param_groups[0]['lr']) | ||||
|     plt.plot(y) | ||||
| 
 | ||||
|     # Dataset | ||||
|     dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True) | ||||
|  | @ -246,11 +246,11 @@ def train( | |||
| def print_mutation(hyp, results): | ||||
|     # Write mutation results | ||||
|     a = '%11s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys | ||||
|     b = '%11.3g' * len(results) % results  # results (P, R, mAP, F1, test_loss) | ||||
|     c = '%11.4g' * len(hyp) % tuple(hyp.values())  # hyperparam values | ||||
|     b = '%11.4g' * len(hyp) % tuple(hyp.values())  # hyperparam values | ||||
|     c = '%11.3g' * len(results) % results  # results (P, R, mAP, F1, test_loss) | ||||
|     print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) | ||||
|     with open('evolve.txt', 'a') as f: | ||||
|         f.write(a + b + '\n') | ||||
|         f.write(c + a + '\n') | ||||
| 
 | ||||
| 
 | ||||
| if __name__ == '__main__': | ||||
|  |  | |||
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