paddle可以通过scope_guard来管理作用域，不同的scope可以拥有相同的参数名。
如：

import paddle.fluid as fluid
import numpy

new_scope = fluid.Scope()
with fluid.scope_guard(new_scope):
     fluid.global_scope().var("data").get_tensor().set(numpy.ones((1, 2)), fluid.CPUPlace())
data = numpy.array(new_scope.find_var("data").get_tensor())
print(data)  # [[1. 1.]]

网络定义如下，最终会报错，应该还是不同scope中即使名字相同，也会有冲突

    def net(self, input, class_dim=1000):
        scope_s = fluid.Scope(  )
        with fluid.scope_guard(scope_s):
            student = ResNet34_vd()
            out_student = student.net( input, class_dim=class_dim )
        
        scope_t = fluid.Scope(  )
        with fluid.scope_guard(scope_t):
            teacher = ResNet50_vd()
            out_teacher = teacher.net( input, class_dim=class_dim )
            out_teacher.stop_gradient = True

错误信息：

ValueError: Variable res2a_branch2a_weights has been created before. the previous shape is (64L, 64L, 3L, 3L); the new shape is (64, 64, 1, 1). They are not matched.

我简单组了个网络，如下：

import paddle.fluid as fluid

class Net1(object):
    def __init__(self):
        pass

    def net(self, input, class_num):
        fc1 = fluid.layers.fc(input, 100, act='tanh', name='fc1')
        fc2 = fluid.layers.fc(fc1, size=class_num, act='softmax', name='fc2')
        return fc2

class Net2(object):
    def __init__(self):
        pass

    def net(self, input, class_num):
        fc1 = fluid.layers.fc(input, 50, act='tanh', name='fc1')
        fc2 = fluid.layers.fc(fc1, size=class_num, act='softmax', name='fc2')
        return fc2

def final_net(input, class_num):
    scope_s = fluid.Scope()
    with fluid.scope_guard(scope_s):
        student = Net1()
        out_student = student.net(input, class_dim=class_num)

    scope_t = fluid.Scope()
    with fluid.scope_guard(scope_t):
        teacher = Net2()
        out_teacher = teacher.net(input, class_dim=class_num)
        out_teacher.stop_gradient = True

这个跟我组的网其实是一样的呀，你这个是因为不同scope内，相同名字参数的的shape相同，所以才没有报错，这也验证了不同scope内的参数其实共享的？
或者说要实现scope内不同参数的管理，必须要保证相同名字的参数的shape完全一致？

或者说要实现scope内不同参数的管理，必须要保证相同名字的参数的shape完全一致？

应该是没有这个限制的。我把上述fc层的shape改为不一致，也是可以的。想问下你的网络有什么特殊之处么？

或者提供一下简单的可复现代码也可以的

或者说要实现scope内不同参数的管理，必须要保证相同名字的参数的shape完全一致？

应该是没有这个限制的。我把上述fc层的shape改为不一致，也是可以的。想问下你的网络有什么特殊之处么？

或者提供一下简单的可复现代码也可以的

ResNet_vd调用的就是模型库里的ResNet50vd代码
https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/resnet_vd.py

整个网络的定义为

class ResNet50_vd_distill_ResNet34_vd():

    def __init__(self):
        self.params = train_parameters
        self.width = 16
        
    def net(self, input, class_dim=1000):
        scope_s = fluid.Scope(  )
        with fluid.scope_guard(scope_s):
            student = ResNet34_vd()
            out_student = student.net( input, class_dim=class_dim )
        
        scope_t = fluid.Scope(  )
        with fluid.scope_guard(scope_t):
            teacher = ResNet50_vd()
            out_teacher = teacher.net( input, class_dim=class_dim )
            out_teacher.stop_gradient = True
        return  out_teacher, out_student

建议在resNet网络里添加一个prefix的name参数，以区分参数名称

好的，多谢