Fix evaluation when using multiple batch sizes
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@ -37,6 +37,7 @@ class Trainer:
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self.val_compute_fea = opt_get(opt, ['eval', 'compute_fea'], False)
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self.current_step = 0
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self.total_training_data_encountered = 0
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self.next_eval_step = 0
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#### loading resume state if exists
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if opt['path'].get('resume_state', None):
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@ -253,7 +254,9 @@ class Trainer:
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val_freq = opt['train']['val_freq'] * batch_size
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else:
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val_freq = int(opt['train']['val_freq_megasamples'] * 1000000)
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if opt_get(opt, ['eval', 'pure'], False) and self.total_training_data_encountered % val_freq == 0:
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if opt_get(opt, ['eval', 'pure'], False) and self.total_training_data_encountered > self.next_eval_step:
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self.next_eval_step = self.total_training_data_encountered + val_freq
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metrics = []
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for val_data in tqdm(self.val_loader):
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self.model.feed_data(val_data, self.current_step, perform_micro_batching=False)
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