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# 映射数据文件到对应的脚本源码
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## 文档功能与适用场景
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在MindSpore进行计算调试，怀疑遇到精度问题时可以选择dump文件进行对比。此时用户希望知道dump文件夹下的每个数据文件对应的Python源码。
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本文的主要目的为指导用户使用该工具进行数据文件到python源码的映射。
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此指导文档适合运行在 **Ascend硬件** 环境下的计算。
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## 辅助工具使用
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1. 使用脚本的3步操作：
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① 用户在训练脚本里设置context.set_context(mode=context.GRAPH_MODE, save_graphs=True)，进行图文件的保存。
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② 用户开启dump数据功能，参考<https://www.mindspore.cn/tutorial/training/zh-CN/r1.0/advanced_use/custom_debugging_info.html>
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③ 获取dump数据文件的op_num，然后通过辅助脚本进行解析。如数据文件：Default--network-TrainOneStepCell--network-WithLossCell--_backbone-
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ResNet--layer2-SequentialCell--0-ResidualBlock--conv2-Conv2d--Cast-op954_input_0_shape_128_128_3_3_kNumberTypeFloat32_DefaultFormat.bin.
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可观察到Cast-op954，说明该算子的op_num为op954。
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脚本名： **map_file_to_code.py**; 执行方式:
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```ruby
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python3 map_file_to_code.py
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--graph_path(-p) [the graph path, default is the current path](option)
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--dump_op(-o) [Dump operator id, case insensitive, such as 'op954'.](required)
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For example:
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python3 map_file_to_code.py -p graph_path -o op954
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```
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2. 解析效果
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解析文件时通常有2种情况：
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① 匹配时会显示出调用栈过程，需要用户在调用栈中查找自己的源码：
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```ruby
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[INFO] Start to map the dump file to source code.
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[INFO] Find operation 'Cast'.
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In file /data1/jzg/mindspore/mindspore/nn/layer/conv.py(253)/
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output = self.conv2d(x, self.weight)
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In file /data1/jzg/dump_to_code/resnet/scripts/train/src/resnet.py(166)/
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out = self.conv2(out)
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In file /data1/jzg/mindspore/mindspore/nn/layer/container.py(173)/
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for cell in self.cell_list:
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In file /data1/jzg/dump_to_code/resnet/scripts/train/src/resnet.py(323)/ # 用户代码行
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c3 = self.layer2(c2)
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In file /data1/jzg/mindspore/mindspore/train/amp.py(101)/
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out = self._backbone(data)
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In file /data1/jzg/mindspore/mindspore/nn/wrap/cell_wrapper.py(247)/
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loss = self.network(*inputs)
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In file /data1/jzg/mindspore/mindspore/train/dataset_helper.py(87)/
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return self.network(*outputs)
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```
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② 未匹配，在图中未找对应节点的调用栈:
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```ruby
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[INFO] Start to map the dump file to source code.
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[WARNING] Cannot find cast's source code in ir file. # 未找到cast算子的信息
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```
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3. 手动代码查找
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这里还会存在些特殊情况，需要用户进行自行查找。通过将dump的数据文件名中的'--'替换为'/'可获取到算子的full_name。input和output文件名shape后面的数据为对应算子的输入输出shape信息。然后利用算子的full_name和输入输出信息回到源码中进行对应代码的查找。
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举个例子说明如何手动在代码中查找指定full_name和shape的算子，例如full_name为: Default/network/network/aspp/aspp_pooling/ResizeNearestNeighbor，输入的shape为[8, 256, 1, 1]， dtype为float32。
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可以观察到其scope为: Default/network/network/aspp/aspp_pooling，算子名为: ResizeNearestNeighbor。注意：scope中会存在Default、network自动填充，Default表示正向，network为网络名。
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查看以下用户定义的代码，首先我们先分析scope: Default/network/network/aspp/aspp_pooling。由network/aspp可定位到算子的定义与调用处分别为26行与31行，继续由network/aspp/aspp_pooling，可以定位到定义与调用处分别为4行与8行，然后通过算子名ResizeNearestNeighbor可以定位至定义与调用处分别为16行与19行。最后若存在相同scope下存在相同的算子名时，需要通过输入的shape进行进一步判断。
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```ruby
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1 class ASPP(nn.Cell):
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2 def __init__(self):
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3 super(ASPP, self).__init__()
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4 self.aspp_pooling = ASPPPooling()
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5 self.drop = nn.Dropout(0.3)
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6
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7 def construct(self, x):
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8 x5 = self.aspp_pooling(x)
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9 x = self.drop(x)
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10 return x
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11
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12 class ASPPPooling(nn.Cell):
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13 def __init__(self):
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14 super(ASPPPooling, self).__init__()
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15 self.shape = P.Shape()
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16 self.resizenearestneighbor = P.ResizeNearestNeighbor((size[2], size[3]), True)
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17 def construct(self, x):
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18 size = self.shape(x)
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19 out = self.resizenearestneighbor(out)
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20 return out
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21
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22 # 主结构
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23 class DeepLabV3(nn.Cell):
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24 def __init__(self, phase='train', num_classes=21, output_stride=16, freeze_bn=False):
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25 super(DeepLabV3, self).__init__()
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26 self.aspp = ASPP()
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27 self.shape = P.Shape()
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28
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29 def construct(self, x):
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30 size = self.shape(x)
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31 out = self.aspp(out)
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32 return out
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```
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