Add APi document for analysis package.

doc/analysis_api.md

+# 模型分析API文档
+
+## flops
+
+>paddleslim.analysis.flops(program, detail=False) [源代码]()
+
+获得指定网络的每秒浮点运算次数(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。
+
+**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：**
+
+```
+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
+
+>paddleslim.analysis.model_size(program) [源代码]()
+
+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：**
+
+```
+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)))
+```