@@ -97,7 +97,7 @@ def obtain_model_file(self, model):
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src_model_file = "/" .join ([self .model_path , model , prototxt_file ])
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if not os .path .isfile (src_model_file ):
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logging .exception ("template model file {} doesn't exist." .format (src_model_file ))
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- batch_size_pattern = re .compile (".*shape:.*" ) if self .dummy_data_use else re .compile (".*batch size :.*" )
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+ batch_size_pattern = re .compile (".*shape:.*" ) if self .dummy_data_use else re .compile ("^\s+batch_size :.*" )
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# we only care about train phase batch size for benchmarking
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batch_size_cnt = 2 if self .dummy_data_use else 1
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if model not in self .bkm_batch_size or self .cpu_model not in self .bkm_batch_size [model ]:
@@ -200,7 +200,7 @@ def obtain_average_fwd_bwd_time(self):
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if re .match (average_fwd_bwd_time_pattern , line ):
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average_time = line .split ()[- 2 ]
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if average_time == "" :
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- logging .exception ("Error: running intelcaffe failed , please check logs under: {}" .format (result_file ))
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+ logging .exception ("Error: can't find average forward-backward time within logs , please check logs under: {}" .format (result_file ))
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average_time = float (average_time )
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else :
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start_iteration = 100
@@ -228,7 +228,7 @@ def obtain_batch_size(self):
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with open (log_file , 'r' ) as f :
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batch_size_pattern_time = re .compile (".*SetMinibatchSize.*" )
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batch_size_pattern_dummy = re .compile (".*dim:.*" )
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- batch_size_pattern_real = re .compile (".* batch_size:.*" )
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+ batch_size_pattern_real = re .compile ("^\s+ batch_size:.*" )
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batch_size = ''
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for line in f .readlines ():
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if re .match (batch_size_pattern_time , line ) or re .match (batch_size_pattern_real , line ) or re .match (batch_size_pattern_dummy , line ):
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