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@ -205,8 +205,10 @@ class BenchmarkRunner:
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self.num_classes = self.model.num_classes
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self.param_count = count_params(self.model)
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_logger.info('Model %s created, param count: %d' % (model_name, self.param_count))
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self.scripted = False
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if torchscript:
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self.model = torch.jit.script(self.model)
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self.scripted = True
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data_config = resolve_data_config(kwargs, model=self.model, use_test_size=not use_train_size)
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self.input_size = data_config['input_size']
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@ -275,7 +277,7 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
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img_size=self.input_size[-1],
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param_count=round(self.param_count / 1e6, 2),
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)
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if not self.scripted:
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if has_deepspeed_profiling:
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macs, _ = profile_deepspeed(self.model, self.input_size)
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results['gmacs'] = round(macs / 1e9, 2)
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