updates
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16
train.py
16
train.py
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@ -93,11 +93,11 @@ def train(
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scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
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scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
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# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch - 1)
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# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch - 1)
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# y = []
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y = []
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# for epoch in range(epochs):
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for _ in range(epochs):
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# scheduler.step()
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scheduler.step()
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# y.append(optimizer.param_groups[0]['lr'])
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y.append(optimizer.param_groups[0]['lr'])
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# plt.plot(y)
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plt.plot(y)
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# Dataset
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# Dataset
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dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
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dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
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@ -246,11 +246,11 @@ def train(
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def print_mutation(hyp, results):
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def print_mutation(hyp, results):
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# Write mutation results
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# Write mutation results
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a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
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a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
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b = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
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b = '%11.4g' * len(hyp) % tuple(hyp.values()) # hyperparam values
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c = '%11.4g' * len(hyp) % tuple(hyp.values()) # hyperparam values
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c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
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print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
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print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
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with open('evolve.txt', 'a') as f:
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with open('evolve.txt', 'a') as f:
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f.write(a + b + '\n')
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f.write(c + a + '\n')
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if __name__ == '__main__':
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if __name__ == '__main__':
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