Update README.md

tensorflow2fluid/README.md

@@ -75,3 +75,49 @@ save_var.list|模型载入过程中的变量list
 > 3. 模型需要加载的参数列表为save_var.list
 
 仍然以上面转换后的vgg_16为例，下面通过示例展示如何加载模型，并进行预测
+
+```
+#coding:utf-8
+# paddle_vgg为转换后模型存储路径
+from paddle_vgg.mymodel import KitModel
+import paddle.fluid as fluid
+import numpy
+
+def model_initialize():
+    # 构建模型结构，并初始化参数
+    result = KitModel()
+    exe = fluid.Executor(fluid.CPUPlace())
+    exe.run(fluid.default_startup_program())
+
+    # 根据save_var.list列表，加载模型参数
+    var_list = list()
+    global_block = fluid.default_main_program().global_block()
+    with open('paddle_vgg/save_var.list') as f:
+        for line in f:
+            try:
+                # 过滤部分不需要加载的参数（OP配置参数）
+                var = global_block.var(line.strip())
+                var_list.append(var)
+            except:
+                pass
+    fluid.io.load_vars(exe, 'paddle_vgg', vars=var_list)
+
+    prog = fluid.default_main_program()
+    return exe, prog, result
+    
+def test_case(exe, prog, result):
+    # 测试随机数据输入
+    numpy.random.seed(13)
+    img_data = numpy.random.rand(1, 224, 224, 3)
+    # tf中输入为NHWC，PaddlePaddle则为NCHW，需transpose
+    img_data = numpy.transpose(img_data, (0, 3, 1, 2))
+
+    # input_0为输入数据的张量名，张量名和数据类型须与my_model.py中定义一致
+    r, = exe.run(fluid.default_main_program(),
+                feed={'input_0':numpy.array(img_data, dtype='float32')},
+                fetch_list=[result])
+    
+if __name__ == "__main__":
+    exe, prog, result = model_initialize()
+    test_case(exe, prog, result)
+```