fix paddle duplicate var problem

Author: Superjomn

python/tests/conv2d_utils.py

@@ -5,67 +5,70 @@
 
 
 def conv2d_native(inputs_data, input_shape, filter_size, attrs, is_depthwise):
-    padding = [0, 0]
-    stride = [1, 1]
-    dilation = [1, 1]
-    data_format = "NCHW"
-    groups = 1
-    for key in attrs.attr_store:
-        if key == "stride":
-            stride = attrs.get_attr("stride")
-        elif key == "padding":
-            padding = attrs.get_attr("padding")
-        elif key == "dilation":
-            dilation = attrs.get_attr("dilation")
-        elif key == "groups":
-            groups = attrs.get_attr("groups")
-        elif key == "data_format":
-            data_format = attrs.get_attr("data_format")
-        else:
-            raise ValueError("attr_store {} is not supported".format(key))
+    main_program = fluid.Program()
+    with fluid.program_guard(main_program, fluid.Program()):
+        padding = [0, 0]
+        stride = [1, 1]
+        dilation = [1, 1]
+        data_format = "NCHW"
+        groups = 1
+        for key in attrs.attr_store:
+            if key == "stride":
+                stride = attrs.get_attr("stride")
+            elif key == "padding":
+                padding = attrs.get_attr("padding")
+            elif key == "dilation":
+                dilation = attrs.get_attr("dilation")
+            elif key == "groups":
+                groups = attrs.get_attr("groups")
+            elif key == "data_format":
+                data_format = attrs.get_attr("data_format")
+            else:
+                raise ValueError("attr_store {} is not supported".format(key))
 
-    img = fluid.layers.data(name='img', shape=input_shape[1:], dtype='float32')
-    if is_depthwise:
-        if data_format == "NCHW":
-            cin_index = 1
+        img = fluid.layers.data(name='img', shape=input_shape[1:], dtype='float32')
+        if is_depthwise:
+            if data_format == "NCHW":
+                cin_index = 1
+            else:
+                cin_index = 3
+            filter_size_new = [
+                filter_size[1] * input_shape[cin_index], filter_size[0] // groups,
+                filter_size[2], filter_size[3]
+            ]
         else:
-            cin_index = 3
-        filter_size_new = [
-            filter_size[1] * input_shape[cin_index], filter_size[0] // groups,
-            filter_size[2], filter_size[3]
-        ]
-    else:
-        filter_size_new = filter_size
-    param = fluid.initializer.NumpyArrayInitializer(
-        np.array(inputs_data[1]).reshape(filter_size_new).astype("float32"))
-    # filter: (c_out, c_in // group, kernel_h, kernel_w)
-    filter_hw = list(filter_size_new[2:4])
-    if data_format == "NHWC":
-        filter_hw = list(filter_size_new[1:3])
-    if isinstance(stride, int):
-        stride = [stride.copy(), stride.copy()]
-    if isinstance(padding, int):
-        padding = [padding.copy(), padding.copy()]
-    if isinstance(dilation, int):
-        dilation = [dilation.copy(), dilation.copy()]
+            filter_size_new = filter_size
+        param = fluid.initializer.NumpyArrayInitializer(
+            np.array(inputs_data[1]).reshape(filter_size_new).astype("float32"))
+        # filter: (c_out, c_in // group, kernel_h, kernel_w)
+        filter_hw = list(filter_size_new[2:4])
+        if data_format == "NHWC":
+            filter_hw = list(filter_size_new[1:3])
+        if isinstance(stride, int):
+            stride = [stride.copy(), stride.copy()]
+        if isinstance(padding, int):
+            padding = [padding.copy(), padding.copy()]
+        if isinstance(dilation, int):
+            dilation = [dilation.copy(), dilation.copy()]
+
+        res = fluid.layers.conv2d(
+            input=img,
+            num_filters=filter_size_new[0],
+            filter_size=filter_hw,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            param_attr=param,
+            data_format=data_format)
+        exe = fluid.Executor(fluid.CPUPlace())
+        exe.run(fluid.default_startup_program())
 
-    res = fluid.layers.conv2d(
-        input=img,
-        num_filters=filter_size_new[0],
-        filter_size=filter_hw,
-        stride=stride,
-        padding=padding,
-        dilation=dilation,
-        groups=groups,
-        param_attr=param,
-        data_format=data_format)
-    exe = fluid.Executor(fluid.CPUPlace())
-    exe.run(fluid.default_startup_program())
+        x = np.array(inputs_data[0]).reshape(input_shape).astype("float32")
+        output = exe.run(feed={"img": x}, fetch_list=[res])
+        output = np.array(output)
+        print("output's shape is:", output.shape)
 
-    x = np.array(inputs_data[0]).reshape(input_shape).astype("float32")
-    output = exe.run(feed={"img": x}, fetch_list=[res])
-    output = np.array(output)
-    print("output's shape is:", output.shape)
     res_shape = output.shape[1:]
     pad_shape = list(input_shape)
     dialtion_shape = list(filter_size_new)