MindSpore报错：The value parameter,it's name 'xxxx' already exsts. please set a unique name for the parameter .

经验分享 Tech Blogs 模型训练-Model Training
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1 系统环境

硬件环境(Ascend/GPU/CPU): GPU
软件环境:MindSpore 版本: 1.9.0
执行模式： 动态图 PYNATIVE
Python 版本: 3.7.5
操作系统平台: Linux

2 报错信息

2.1 问题描述

两个网络中的层有相同名称时报错

2.2 报错信息

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附件示例和图片示例感觉不太一样，以附件为准

2.3 脚本代码

从附件中获取

import mindspore.nn as nn
from mindspore.common.parameter import ParameterTuple, Parameter


class FullyConnectedNet(nn.Cell):
    def __init__(self, input_size, hidden_size, output_size):
        super(FullyConnectedNet, self).__init__(auto_prefix=False)
        self.linear1 = nn.Dense( input_size, hidden_size, weight_init="XavierUniform")
        self.linear2 = nn.Dense(hidden_size, output_size, weight_init="XavierUniform")
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.relu(self.linear1(x))
        x = self.linear2(x)
        return x

class EmaUpdate(nn.Cell):
    def __init__(self, policy_net, target_net):
        super(EmaUpdate, self).__init__()
        self.policy_param = ParameterTuple(policy_net.get_parameters())
        self.target_param = ParameterTuple(target_net.get_parameters())


    def construct(self, x):
        return x


policy_net = FullyConnectedNet(4, 100, 2)
target_net = FullyConnectedNet(4, 100, 2)
ema_update = EmaUpdate(policy_net, target_net)

3 根因分析

1. FullyConnectedNet网络中auto_prefix=False， 因此没有给网络参数的名称添加前缀， 两个参数的name都叫做’weight’， 重名

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2. 而当把参数给ParameterTuple的时候，ParameterTuple对名字的唯一性有要求，因此就会报错，说’weight’已经存在
921×419 19 KB

4 解决方案

解决方案说明：把FullyConnectedNet网络的auto_prefix=False改成auto_prefix=True, 或者不设置，因为默认就是True, 这样自动给网络参数的名称添加前缀，一个叫linear1.weight 一个叫linear2.weight， ParameterTuple就不会报错
修改后代码：

import mindspore.nn as nn
from mindspore.common.parameter import ParameterTuple, Parameter
from mindspore import context

context.set_context(mode=context.PYNATIVE_MODE)

class FullyConnectedNet(nn.Cell):
    def __init__(self, input_size, hidden_size, output_size):
        super(FullyConnectedNet, self).__init__(auto_prefix=True)
        self.linear1 = nn.Dense(input_size, hidden_size, weight_init="XavierUniform")
        self.linear2 = nn.Dense(hidden_size, output_size, weight_init="XavierUniform")
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.relu(self.linear1(x))
        x = self.linear2(x)
        return x

class EmaUpdate(nn.Cell):
    def __init__(self, policy_net, target_net):
        super(EmaUpdate, self).__init__()
        self.policy_param = ParameterTuple(policy_net.get_parameters())
        self.target_param = ParameterTuple(target_net.get_parameters())


    def construct(self, x):
        return x


policy_net = FullyConnectedNet(4, 100, 2)
target_net = FullyConnectedNet(4, 100, 2)
print("mmmmmmmmmmmmm", policy_net.trainable_params())
print("wwwwwwwww", target_net.trainable_params())
ema_update = EmaUpdate(policy_net, target_net)

执行后的结果
用例pass且打印想要的参数

mmmmmmmmmmmmm [Parameter (name=linear1.weight, shape=(100, 4), dtype=Float32, requires_grad=True), Parameter (name=linear1.bias, shape=(100,), dtype=Float32, requires_grad=True), Parameter (name=linear2.weight, shape=(2, 100), dtype=Float32, requires_grad=True), Parameter (name=linear2.bias, shape=(2,), dtype=Float32, requires_grad=True)]
wwwwwwwww [Parameter (name=linear1.weight, shape=(100, 4), dtype=Float32, requires_grad=True), Parameter (name=linear1.bias, shape=(100,), dtype=Float32, requires_grad=True), Parameter (name=linear2.weight, shape=(2, 100), dtype=Float32, requires_grad=True), Parameter (name=linear2.bias, shape=(2,), dtype=Float32, requires_grad=True)]
x_name mmmmmmmmmmmmmmmmmmmm linear1.weight
names wwwwwwwwwwwwww {'linear1.weight'}
x_name mmmmmmmmmmmmmmmmmmmm linear1.bias
names wwwwwwwwwwwwww {'linear1.bias', 'linear1.weight'}
x_name mmmmmmmmmmmmmmmmmmmm linear2.weight
names wwwwwwwwwwwwww {'linear1.bias', 'linear1.weight', 'linear2.weight'}
x_name mmmmmmmmmmmmmmmmmmmm linear2.bias
names wwwwwwwwwwwwww {'linear1.bias', 'linear1.weight', 'linear2.bias', 'linear2.weight'} ---ParameterTuple1
x_name mmmmmmmmmmmmmmmmmmmm linear1.weight
names wwwwwwwwwwwwww {'linear1.weight'}
x_name mmmmmmmmmmmmmmmmmmmm linear1.bias
names wwwwwwwwwwwwww {'linear1.bias', 'linear1.weight'}
x_name mmmmmmmmmmmmmmmmmmmm linear2.weight
names wwwwwwwwwwwwww {'linear1.bias', 'linear1.weight', 'linear2.weight'}
x_name mmmmmmmmmmmmmmmmmmmm linear2.bias
names wwwwwwwwwwwwww {'linear1.bias', 'linear1.weight', 'linear2.bias', 'linear2.weight'} ---ParameterTuple2