auto strip optimizer from best.pt after training
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15
train.py
15
train.py
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@ -362,14 +362,13 @@ def train():
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n = opt.name
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if len(n):
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n = '_' + n if not n.isnumeric() else n
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fresults, flast, fbest = 'results%s.txt' % n, 'last%s.pt' % n, 'best%s.pt' % n
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os.rename('results.txt', fresults)
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os.rename(wdir + 'last.pt', wdir + flast) if os.path.exists(wdir + 'last.pt') else None
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os.rename(wdir + 'best.pt', wdir + fbest) if os.path.exists(wdir + 'best.pt') else None
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if opt.bucket: # save to cloud
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os.system('gsutil cp %s gs://%s/results' % (fresults, opt.bucket))
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os.system('gsutil cp %s gs://%s/weights' % (wdir + flast, opt.bucket))
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os.system('gsutil cp %s gs://%s/weights' % (wdir + fbest, opt.bucket))
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fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
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for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]):
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if os.path.exists(f1):
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os.rename(f1, f2) # rename
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ispt = f2.endswith('.pt') # is *.pt
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strip_optimizer(f2) if ispt else None # strip optimizer
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os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None # upload
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if not opt.evolve:
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plot_results() # save as results.png
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