在执行macOS Sierra (10.12.4)下Caffe通过Python接口加载binaryproto格式的均值文件的时候，最后报告错误：
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Traceback (most recent call last): File "analysis_memnet.py", line 29, in <module> detector = caffe.Detector(model_def, pretrained_model, mean=means) File "/Users/Source/caffe/distribute/python/caffe/detector.py", line 46, in __init__ self.transformer.set_mean(in_, mean) File "/Users/Source/caffe/distribute/python/caffe/io.py", line 259, in set_mean raise ValueError('Mean shape incompatible with input shape.') ValueError: Mean shape incompatible with input shape. |
这个错误发生的原因是由于memnet提供的均值文件是256*256的，但是提供的配置文件却是227*227的，导致在io.py里面的代码在进行判断的时候发生异常。调整源代码中的python/caffe/io.py里面的代码：
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def set_mean(self, in_, mean): """ Set the mean to subtract for centering the data. Parameters ---------- in_ : which input to assign this mean. mean : mean ndarray (input dimensional or broadcastable) """ self.__check_input(in_) ms = mean.shape if mean.ndim == 1: # broadcast channels if ms[0] != self.inputs[in_][1]: raise ValueError('Mean channels incompatible with input.') mean = mean[:, np.newaxis, np.newaxis] else: # elementwise mean if len(ms) == 2: ms = (1,) + ms if len(ms) != 3: raise ValueError('Mean shape invalid') if ms != self.inputs[in_][1:]: raise ValueError('Mean shape incompatible with input shape.') self.mean[in_] = mean |
调整为：
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def set_mean(self, in_, mean): """ Set the mean to subtract for centering the data. Parameters ---------- in_ : which input to assign this mean. mean : mean ndarray (input dimensional or broadcastable) """ self.__check_input(in_) ms = mean.shape if mean.ndim == 1: # broadcast channels if ms[0] != self.inputs[in_][1]: raise ValueError('Mean channels incompatible with input.') mean = mean[:, np.newaxis, np.newaxis] else: # elementwise mean if len(ms) == 2: ms = (1,) + ms if len(ms) != 3: raise ValueError('Mean shape invalid') if ms != self.inputs[in_][1:]: in_shape = self.inputs[in_][1:] m_min, m_max = mean.min(), mean.max() normal_mean = (mean - m_min) / (m_max - m_min) mean = resize_image(normal_mean.transpose((1,2,0)),in_shape[1:]).transpose((2,0,1)) * (m_max - m_min) + m_min #raise ValueError('Mean shape incompatible with input shape.') self.mean[in_] = mean |
调整完成后，需要重新编译Caffe:
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$ make clean $ make $ make pycaffe $ make distribute |