break if your dataset size is smaller than your batch size
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@ -110,6 +110,8 @@ class Trainer:
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self.dataset_debugger = get_dataset_debugger(dataset_opt)
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if self.dataset_debugger is not None and resume_state is not None:
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self.dataset_debugger.load_state(opt_get(resume_state, ['dataset_debugger_state'], {}))
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if len(self.train_set) <= dataset_opt['batch_size']:
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raise Exception("dataset size is less than batch size, consider reducing your batch size, or increasing your dataset.")
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train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size']))
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total_iters = int(opt['train']['niter'])
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self.total_epochs = int(math.ceil(total_iters / train_size))
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