Format zh_cn API docs (PaddlePaddle#94)

slf12 · Feb 10, 2020 · 6ac0720 · 6ac0720
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diff --git a/docs/README.md b/docs/README.md
@@ -1,4 +1,4 @@
-# PaddleSlim文档构建与发布教程
+# 文档构建与发布教程
 
 ## 1. 文档构成
 
@@ -70,7 +70,16 @@ make html
 
 以上命令生成`html`文件到路径`./build/html/`。
 
-## 2.2 编译英文文档
+
+## 2.2 预览文档
+
+进入`PaddleSlim/docs/zh_cn/build/html`路径下。
+执行`python -m SimpleHTTPServer 8883`。
+假设当前机器IP为`server_ip`。
+
+通过浏览器查看链接`server_ip:8883`即可预览文档。
+
+## 2.3 编译英文文档
 
 进入路径`PaddleSlim/docs/en`
 
@@ -123,6 +132,6 @@ git commit -m "update pages"
 git push origin gh-pages
 ```
 
-# 4. 其它
+## 4. 其它
 
 英文API文档格式请参考：https://wanghaoshuang.github.io/PaddleSlim/api_en/paddleslim.analysis.html
diff --git a/docs/en/Makefile b/docs/en/Makefile
@@ -17,3 +17,4 @@ help:
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	rm -rf $(BUILDDIR)/html/index.html
diff --git a/docs/zh_cn/Makefile b/docs/zh_cn/Makefile
@@ -17,3 +17,4 @@ help:
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	rm -rf $(BUILDDIR)/$@/index_en.html
diff --git a/docs/zh_cn/api_cn/analysis_api.md b/docs/zh_cn/api_cn/analysis_api.md
diff --git a/docs/zh_cn/api_cn/analysis_api.rst b/docs/zh_cn/api_cn/analysis_api.rst
@@ -0,0 +1,181 @@
+模型分析
+=======
+
+FLOPs
+-----
+
+.. py:function:: paddleslim.analysis.flops(program, detail=False)
+
+`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/flops.py>`_
+
+获得指定网络的浮点运算次数(FLOPs)。
+
+**参数：**
+
+- **program(paddle.fluid.Program)** - 待分析的目标网络。更多关于Program的介绍请参考：`Program概念介绍 <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program>`_。
+
+- **detail(bool)** - 是否返回每个卷积层的FLOPs。默认为False。
+
+- **only_conv(bool)** - 如果设置为True，则仅计算卷积层和全连接层的FLOPs，即浮点数的乘加（multiplication-adds）操作次数。如果设置为False，则也会计算卷积和全连接层之外的操作的FLOPs。
+
+**返回值：**
+
+- **flops(float)** - 整个网络的FLOPs。
+
+- **params2flops(dict)** - 每层卷积对应的FLOPs，其中key为卷积层参数名称，value为FLOPs值。
+
+**示例：**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+    from paddle.fluid.param_attr import ParamAttr
+    from paddleslim.analysis import flops
+    
+    def conv_bn_layer(input,
+                      num_filters,
+                      filter_size,
+                      name,
+                      stride=1,
+                      groups=1,
+                      act=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False,
+            name=name + "_out")
+        bn_name = name + "_bn"
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            name=bn_name + '_output',
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance', )
+    
+    main_program = fluid.Program()
+    startup_program = fluid.Program()
+    #   X       X              O       X              O
+    # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
+    #     |            ^ |                    ^
+    #     |____________| |____________________|
+    #
+    # X: prune output channels
+    # O: prune input channels
+    with fluid.program_guard(main_program, startup_program):
+        input = fluid.data(name="image", shape=[None, 3, 16, 16])
+        conv1 = conv_bn_layer(input, 8, 3, "conv1")
+        conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
+        sum1 = conv1 + conv2
+        conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
+        conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
+        sum2 = conv4 + sum1
+        conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
+        conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
+    
+    print("FLOPs: {}".format(flops(main_program)))
+
+model_size
+----------
+
+.. py:function:: paddleslim.analysis.model_size(program)
+
+`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/model_size.py>`_
+
+获得指定网络的参数数量。
+
+**参数：**
+
+- **program(paddle.fluid.Program)** - 待分析的目标网络。更多关于Program的介绍请参考：`Program概念介绍 <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program>`_。
+
+**返回值：**
+
+- **model_size(int)** - 整个网络的参数数量。
+
+**示例：**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+    from paddle.fluid.param_attr import ParamAttr
+    from paddleslim.analysis import model_size
+    
+    def conv_layer(input,
+                      num_filters,
+                      filter_size,
+                      name,
+                      stride=1,
+                      groups=1,
+                      act=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False,
+            name=name + "_out")
+        return conv
+    
+    main_program = fluid.Program()
+    startup_program = fluid.Program()
+    #   X       X              O       X              O
+    # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
+    #     |            ^ |                    ^
+    #     |____________| |____________________|
+    #
+    # X: prune output channels
+    # O: prune input channels
+    with fluid.program_guard(main_program, startup_program):
+        input = fluid.data(name="image", shape=[None, 3, 16, 16])
+        conv1 = conv_layer(input, 8, 3, "conv1")
+        conv2 = conv_layer(conv1, 8, 3, "conv2")
+        sum1 = conv1 + conv2
+        conv3 = conv_layer(sum1, 8, 3, "conv3")
+        conv4 = conv_layer(conv3, 8, 3, "conv4")
+        sum2 = conv4 + sum1
+        conv5 = conv_layer(sum2, 8, 3, "conv5")
+        conv6 = conv_layer(conv5, 8, 3, "conv6")
+    
+    print("FLOPs: {}".format(model_size(main_program)))
+
+TableLatencyEvaluator
+---------------------
+
+.. py:class:: paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",")
+
+`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/latency.py>`_
+
+基于硬件延时表的模型延时评估器。
+
+**参数：**
+
+- **table_file(str)** - 所使用的延时评估表的绝对路径。关于演示评估表格式请参考：PaddleSlim硬件延时评估表格式
+
+- **delimiter(str)** - 在硬件延时评估表中，操作信息之前所使用的分割符，默认为英文字符逗号。
+
+**返回值：**
+
+- **Evaluator** - 硬件延时评估器的实例。
+
+   .. py:method:: latency(graph)
+
+   获得指定网络的预估延时。
+
+   **参数：**
+
+   - **graph(Program)** - 待预估的目标网络。
+
+   **返回值：**
+
+   - **latency** - 目标网络的预估延时。