Add tf2paddle to convert models #483
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jacquesqiao
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Nov 28, 2017
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| 为使TensorFlow模型中的variable能够正确对应到PaddlePaddle模型中layer的参数，正确完成转换，模型转换具有如下约束： | ||
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| - 支持TensorFlow中conv2d，batchnorm，fc这三种带有trainable variable的Operator中参数的转换。- TensorFlow配置中同一Operator内的variable属于相同的scope，以此将variable划分到不同的layer。 |
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| ``` | ||
| 1. 目前仅支持将TensorFlow中 `conv2d`，`batchnorm`，`fc`这三种带有可学习`Variable`的Operator训练出的参数向PaddlePaddle模型参数转换。 | ||
| 1. TensorFlow网络配置中同一Operator内的`Variable`属于相同的scope，以此为依据将`Variable`划分到不同的`paddle.layer`。 | ||
| 1. `conv2d`、`batchnorm`、`fc`的scope需分别包含`conv`、`bn`、`fc`，以此获取对应`paddle.layer`的类型。也可以通过为`TFModelConverter`传入`layer_type_map`的`dict`，将scope映射到对应的`paddle.layer`的type来规避此项约束。 |
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这里的 conv2d、batchnorm、fc 能否换成精确的 TensorFlow 中的调用接口，比如 ：conv2d --> tf.nn.conv2d，方便使用者在看到文档时候，能够精确定位到自己配置中的 operator。
| 1. 目前仅支持将TensorFlow中 `conv2d`，`batchnorm`，`fc`这三种带有可学习`Variable`的Operator训练出的参数向PaddlePaddle模型参数转换。 | ||
| 1. TensorFlow网络配置中同一Operator内的`Variable`属于相同的scope，以此为依据将`Variable`划分到不同的`paddle.layer`。 | ||
| 1. `conv2d`、`batchnorm`、`fc`的scope需分别包含`conv`、`bn`、`fc`，以此获取对应`paddle.layer`的类型。也可以通过为`TFModelConverter`传入`layer_type_map`的`dict`，将scope映射到对应的`paddle.layer`的type来规避此项约束。 | ||
| 1. `conv2d`、`fc`中`Variable`的顺序为：先可学习`Weight`后`Bias`；`batchnorm`中`Variable`的顺序为：`scale`、`shift`、`mean`、`var`，请注意参数存储的顺序将`Variable`对应到`paddle.layer.batch_norm`相应位置的参数。 |
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同上，请把 conv2d、batchnorm、fc 换成精确的 TensorFlow 中的调用接口。
| 1. TensorFlow网络配置中同一Operator内的`Variable`属于相同的scope，以此为依据将`Variable`划分到不同的`paddle.layer`。 | ||
| 1. `conv2d`、`batchnorm`、`fc`的scope需分别包含`conv`、`bn`、`fc`，以此获取对应`paddle.layer`的类型。也可以通过为`TFModelConverter`传入`layer_type_map`的`dict`，将scope映射到对应的`paddle.layer`的type来规避此项约束。 | ||
| 1. `conv2d`、`fc`中`Variable`的顺序为：先可学习`Weight`后`Bias`；`batchnorm`中`Variable`的顺序为：`scale`、`shift`、`mean`、`var`，请注意参数存储的顺序将`Variable`对应到`paddle.layer.batch_norm`相应位置的参数。 | ||
| 1. TensorFlow网络拓扑顺序需和PaddlePaddle网络拓扑顺序一致，尤其注意具有分支时左右分支的顺序。这是针对模型转换和PaddlePaddle网络配置均使用PaddlePaddle默认参数命名的情况，此时将根据拓扑顺序进行参数命名。 |
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这句话我不是非常理解 “尤其注意具有分支时左右分支的顺序”，能否进一步解释一下。这里值的“左右分支”是什么呢？
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Fix #482