updates
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train.py
4
train.py
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@ -363,14 +363,14 @@ if __name__ == '__main__':
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# Mutate hyperparameters
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# Mutate hyperparameters
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old_hyp = hyp.copy()
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old_hyp = hyp.copy()
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init_seeds(seed=int(time.time()))
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init_seeds(seed=int(time.time()))
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s = [.4, .4, .4, .4, .4, .4, .4, .4, .4, .04, .4] # fractional sigmas
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s = [.4, .4, .4, .4, .4, .4, .4, .4*0, .4*0, .04*0, .4*0] # fractional sigmas
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for i, k in enumerate(hyp.keys()):
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for i, k in enumerate(hyp.keys()):
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x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100)
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x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100)
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hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma
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hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma
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# Clip to limits
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# Clip to limits
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keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']
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keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']
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limits = [(1e-4, 1e-2), (0, 0.00), (0.70, 0.99), (0, 0.01)]
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limits = [(1e-4, 1e-2), (0, 0.70), (0.70, 0.98), (0, 0.01)]
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for k, v in zip(keys, limits):
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for k, v in zip(keys, limits):
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hyp[k] = np.clip(hyp[k], v[0], v[1])
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hyp[k] = np.clip(hyp[k], v[0], v[1])
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