add caffe2paddle.py #58
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qingqing01
reviewed
Jun 1, 2017
image_classification/caffe2paddle.py
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| def convert_BatchNorm_layer(self, params, name=None): | ||
| scale = np.array(params[-1].data) | ||
| for i in range(2): | ||
| data = np.array(params[i].data) * scale |
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参考代码https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cpp#L100 ， scale不为0时，应该需要 1/scale 吧
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文档需说明parameter name生成需要注意，对一些paddle配置和caffe配置顺序不一样，会导致生成的参数名字不对应问题等，需要加以说明~ |
qingqing01
reviewed
Jun 8, 2017
| @@ -0,0 +1,223 @@ | |||
| import numpy as np | |||
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这个文件不用引入，安装paddle之后，import即可~
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done，删除了image.py，caffe2paddle.py中加入了简单的预处理
qingqing01
reviewed
Jun 8, 2017
| converter.convert(name_map={}) | ||
| #进行预测并输出预测概率以便对比验证模型转换结果 | ||
| converter.caffe_predict(img='./caffe/examples/images/cat.jpg') | ||
| ``` |
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文档觉得需要简洁明了告诉用户怎么用，比如配置好参数之后，运行：
python caffe2paddle.py
然后再把code相关解释，注意事项列出来。
| converter.caffe_predict(img='./caffe/examples/images/cat.jpg') | ||
| ``` | ||
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| 为验证并使用转换得到的模型，需基于PaddlePaddle API编写对应的网络结构配置文件，具体可参照PaddlePaddle使用文档，我们这里附上ResNet的配置以供使用。需要注意，上文给出的模型转换在调用`ModelConverter.convert`时传入了空的`name_map`，这将在遍历每一个layer进行参数保存时使用PaddlePaddle默认的layer和参数命名规则：以`wrap_name_default`中的值和调用计数构造layer name，并以此为前缀构造参数名（比如第一个InnerProduct层的bias参数将被命名为`___fc_layer_0__.wbias`）；为此，在编写PaddlePaddle网络配置时要保证和Caffe端模型使用同样的拓扑顺序，尤其是对于ResNet这种有分支的网络结构，要保证两分支在PaddlePaddle和Caffe中先后顺序一致，这样才能够使得模型参数正确加载。如果不希望使用默认的layer name，可以使用一种更为精细的方法：建立Caffe和PaddlePaddle网络配置间layer name对应关系的`dict`并在调用`ModelConverter.convert`时作为`name_map`传入，这样在命名保存layer中的参数时将使用相应的layer name，另外这里只针对Caffe网络配置中Convolution、InnerProduct和BatchNorm类别的layer建立`name_map`即可（一方面，对于Pooling等无需训练的layer不需要保存，故这里没有提供转换接口；另一方面，对于Caffe中的Scale类别的layer，由于Caffe和PaddlePaddle在实现上的一些差别，PaddlePaddle中的batch_norm层同时包含BatchNorm和Scale层的复合，故这里对Scale进行了特殊处理）。 |
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resolve #27