网络编译

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Q: 编译时报错“’self.xx’ should be initialized as a ‘Parameter’ type in the ‘__init__’ function”怎么办?

A: 在 construct 函数内,如果想对类成员 self.xx 赋值,那么 self.xx 必须已经在 __init__ 函数中被定义为 Parameter 类型,其他类型则不支持。局部变量 xx 不受这个限制。


Q: 编译时报错“For syntax like ‘a is not b’, b supports True, False and None”怎么办?

A: 对于语法 isis not 而言,当前 MindSpore 仅支持与 TrueFalseNone 的比较。暂不支持其他类型,如字符串等。


Q: 编译时报错“Only support comparison with 1 operator, but got 2”怎么办?

A: 对于比较语句,MindSpore 最多支持一个操作数。例如不支持语句 1 < x < 3,请使用 1 < x and x < 3 的方式代替。


Q: 编译时报错“TypeError: For ‘Cell’, the function construct requires 1 positional argument and 0 default argument, total 1, but got 2”怎么办?

A: 网络的实例被调用时,会执行 construct 方法,然后会检查 construct 方法需要的参数个数和实际传入的参数个数,如果不一致则会抛出以上异常。 请检查脚本中调用网络实例时传入的参数个数,和定义的网络中 construct 函数需要的参数个数是否一致。


Q: 编译时报错“Unsupported expression ‘Yield’”怎么办?

A: MindSpore在静态图模式下不支持 yield 语法。


Q: 编译时报错“Type Join Failed”怎么办?

A: 在前端编译的推理阶段,会对节点的抽象类型(包含 typeshape 等)进行推导,常见抽象类型包括 AbstractScalarAbstractTensorAbstractFunctionAbstractTupleAbstractList 等。在一些场景比如多分支场景,会对不同分支返回值的抽象类型进行 join 合并,推导出返回结果的抽象类型。如果抽象类型不匹配,或者 type/shape 不一致,则会抛出以上异常。

当出现类似“Type Join Failed: dtype1 = Float32, dtype2 = Float16”的报错时,说明数据类型不一致,导致抽象类型合并失败。根据提供的数据类型和代码行信息,可以快速定位出错范围。此外,报错信息中提供了具体的抽象类型信息、节点信息,可以通过 analyze_fail.ir 文件查看MindIR信息,定位解决问题。关于MindIR的具体介绍,可以参考MindSpore IR(MindIR)。代码样例如下:

import numpy as np
import mindspore as ms
import mindspore.ops as ops
from mindspore import nn

ms.set_context(mode=ms.GRAPH_MODE)
class Net(nn.Cell):
    def __init__(self):
        super().__init__()
        self.relu = ops.ReLU()
        self.cast = ops.Cast()

    def construct(self, x, a, b):
        if a > b:    # if的两个分支返回值的type不一致
            return self.relu(x)    # shape: (2, 3, 4, 5), dtype:Float32
        else:
            return self.cast(self.relu(x), ms.float16)    # shape: (2, 3, 4, 5), dtype:Float16

input_x = ms.Tensor(np.random.rand(2, 3, 4, 5).astype(np.float32))
input_a = ms.Tensor(2, ms.float32)
input_b = ms.Tensor(6, ms.float32)
net = Net()
out_me = net(input_x, input_a, input_b)

执行结果如下:

TypeError: Cannot join the return values of different branches, perhaps you need to make them equal.
Type Join Failed: dtype1 = Float32, dtype2 = Float16.
For more details, please refer to https://www.mindspore.cn/search?inputValue=Type%20Join%20Failed.

Inner Message:
The abstract type of the return value of the current branch is AbstractTensor(shape: (2, 3, 4, 5), element: AbstractScalar(Type: Float16, Value: AnyValue, Shape: NoShape), value_ptr: 0x55b9f289d090, value: AnyValue), and that of the previous branch is AbstractTensor(shape: (2, 3, 4, 5), element: AbstractScalar(Type: Float32, Value: AnyValue, Shape: NoShape), value_ptr: 0x55b9f289d090, value: AnyValue).
The node is construct.6:[CNode]13{[0]: construct.6:[CNode]12{[0]: ValueNode<Primitive> Switch, [1]: [CNode]11, [2]: ValueNode<FuncGraph> ✓construct.4, [3]: ValueNode<FuncGraph> ✗construct.5}}, true branch: ✓construct.4, false branch: ✗construct.5

The function call stack (See file 'analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir):
# 0 In file test.py(14)
        if a > b:
        ^

当出现如“Type Join Failed: abstract type AbstractTensor can not join with AbstractTuple”的报错时,说明抽象类型不匹配,导致抽象类型合并失败,代码样例如下:

import mindspore.ops as ops
import mindspore as ms

x = ms.Tensor([1.0])
y = ms.Tensor([2.0])
grad = ops.GradOperation(get_by_list=False, sens_param=True)
sens = 1.0

def test_net(a, b):
    return a, b

@ms.jit()
def join_fail():
    sens_i = ops.Fill()(ops.DType()(x), ops.Shape()(x), sens)    # sens_i 是一个标量shape: (1), dtype:Float64, value:1.0
    # sens_i = (sens_i, sens_i)
    a = grad(test_net)(x, y, sens_i)    # 对输出类型为tuple(Tensor, Tensor)的test_net求梯度需要sens_i的类型同输出保持一致,但sens_i是个Tensor; 在grad前设置sens_i = (sens_i, sens_i)可以修复问题。
    return a

join_fail()

执行结果如下:

TypeError: Type Join Failed: abstract type AbstractTensor cannot join with AbstractTuple.
For more details, please refer to https://www.mindspore.cn/search?inputValue=Type%20Join%20Failed.

Inner Message:
This: AbstractTensor(shape: (1), element: AbstractScalar(Type: Float32, Value: AnyValue, Shape: NoShape), value_ptr: 0x55c969c44c60, value: Tensor(shape=[1], dtype=Float32, value=[ 1.00000000e+00])), other: AbstractTuple{element[0]: AbstractTensor(shape: (1), element: AbstractScalar(Type: Float32, Value: AnyValue, Shape: NoShape), value_ptr: 0x55c96a9a3bd0, value: Tensor(shape=[1], dtype=Float32, value=[ 1.00000000e+00])), element[1]: AbstractTensor(shape: (1), element: AbstractScalar(Type: Float32, Value: AnyValue, Shape: NoShape), value_ptr: 0x55c96a5f06a0, value: Tensor(shape=[1], dtype=Float32, value=[ 2.00000000e+00])), sequence_nodes: {test_net.3:[CNode]4{[0]: ValueNode<PrimitivePy> MakeTuple, [1]: a, [2]: b}, elements_use_flags: {ptr: 0x55c96ae83400, value: [const vector][1, 1]}}}. Please check the node: test_net.5:a{[0]: a, [1]: test_net}

The function call stack (See file 'analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir):

The function call stack:
# 0 In file test.py(17)
    a = grad(test_net)(x, y, sens_i)
        ^

Q: 编译时报错“The params of function ‘bprop’ of Primitive or Cell requires the forward inputs as well as the ‘out’ and ‘dout’”怎么办?

A: 用户自定义的Cell的反向传播函数 bprop,它的输入需要包含正向网络的输入,以及 outdout,代码样例如下:

import mindspore as ms
from mindspore import nn, ops, Tensor
from mindspore import dtype as mstype

class BpropUserDefinedNet(nn.Cell):
        def __init__(self):
            super(BpropUserDefinedNet, self).__init__()
            self.zeros_like = ops.ZerosLike()

        def construct(self, x, y):
            return x + y

        # def bprop(self, x, y, out, dout):    # 正确写法
        def bprop(self, x, y, out):
            return self.zeros_like(out), self.zeros_like(out)

ms.set_context(mode=ms.GRAPH_MODE)
net = BpropUserDefinedNet()
x = Tensor(2, mstype.float32)
y = Tensor(6, mstype.float32)
grad_fn = ms.grad(net, grad_position=(0, 1))
output = grad_fn(x, y)
print(output)

执行结果如下:

TypeError: The params of function 'bprop' of Primitive or Cell requires the forward inputs as well as the 'out' and 'dout'.
In file test.py(13)
        def bprop(self, x, y, out):

Q: 编译时报错“There isn’t any branch that can be evaluated”怎么办?

A: 当出现There isn’t any branch that can be evaluated 时,说明代码中可能出现了无穷递归或者死循环,导致if条件的每一个分支都无法推导出正确的类型和维度信息。


Q: 编译时报错”Exceed function call depth limit 1000”怎么办?

A: 当出现Exceed function call depth limit 1000 时,说明代码中出现了无穷递归死循环,或者是代码过于复杂,类型推导过程中导致栈深度超过设置的最大深度。 此时可以通过设置 set_context(max_call_depth = value) 更改栈的最大深度,并考虑简化代码逻辑或者检查代码中是否存在无穷递归或死循环。 需要注意的是,设置max_call_depth虽然可以改变MindSpore的递归深度,但是可能会超过系统栈的最大深度,进而出现段错误。此时可能还需要设置系统栈深度。


Q: 编译时报错“could not get source code”以及“Mindspore can not compile temporary source code in terminal. Please write source code to a python file and run the file.”是什么原因?

A: MindSpore编译网络时通过 inspect.getsourcelines(self.fn) 获取网络代码所在的文件,如果网络是编辑在命令行中的临时代码,那么会出现如标题所示的报错,需要将网络写在Python文件中去执行才能避免该错误。


Q: 报错提示中的“Corresponding forward node candidate:”或“Corresponding code candidate:”是什么意思?

A: “Corresponding forward node candidate:”为关联的正向网络中的代码,表示该反向传播算子与该正向代码对应。“Corresponding code candidate:”表示该算子是由这些代码融合而来,其中分符“-”用以区分不同的代码。

例如:

  • 算子FusionOp_BNTrainingUpdate_ReLUV2报错,打印了如下的代码行:

    Corresponding code candidate:
     - In file /home/workspace/mindspore/build/package/mindspore/nn/layer/normalization.py(212)/                return self.bn_train(x,/
       In file /home/workspace/mindspore/tests/st/tbe_networks/resnet.py(265)/        x = self.bn1(x)/
       In file /home/workspace/mindspore/build/package/mindspore/nn/wrap/cell_wrapper.py(109)/        out = self._backbone(data)/
       In file /home/workspace/mindspore/build/package/mindspore/nn/wrap/cell_wrapper.py(356)/        loss = self.network(*inputs)/
       In file /home/workspace/mindspore/build/package/mindspore/train/dataset_helper.py(98)/        return self.network(*outputs)/
     - In file /home/workspace/mindspore/tests/st/tbe_networks/resnet.py(266)/        x = self.relu(x)/
       In file /home/workspace/mindspore/build/package/mindspore/nn/wrap/cell_wrapper.py(109)/        out = self._backbone(data)/
       In file /home/workspace/mindspore/build/package/mindspore/nn/wrap/cell_wrapper.py(356)/        loss = self.network(*inputs)/
       In file /home/workspace/mindspore/build/package/mindspore/train/dataset_helper.py(98)/        return self.network(*outputs)/
    

    第一个分隔符的代码调用栈指向了网络脚本文件中第265行的“x = self.bn1(x)”,第二个分隔符的代码调用栈指向了网络脚本文件中第266行的“x = self.relu(x)”。可知,该算子FusionOp_BNTrainingUpdate_ReLUV2由这两行代码融合而来。

  • 算子Conv2DBackpropFilter报错,打印了如下的代码行:

    In file /home/workspace/mindspore/build/package/mindspore/ops/_grad_experimental/grad_nn_ops.py(65)/        dw = filter_grad(dout, x, w_shape)/
    Corresponding forward node candidate:
     - In file /home/workspace/mindspore/build/package/mindspore/nn/layer/conv.py(266)/        output = self.conv2d(x, self.weight)/
       In file /home/workspace/mindspore/tests/st/tbe_networks/resnet.py(149)/        out = self.conv1(x)/
       In file /home/workspace/mindspore/tests/st/tbe_networks/resnet.py(195)/        x = self.a(x)/
       In file /home/workspace/mindspore/tests/st/tbe_networks/resnet.py(270)/        x = self.layer2(x)/
       In file /home/workspace/mindspore/build/package/mindspore/nn/wrap/cell_wrapper.py(109)/        out = self._backbone(data)/
       In file /home/workspace/mindspore/build/package/mindspore/nn/wrap/cell_wrapper.py(356)/        loss = self.network(*inputs)/
       In file /home/workspace/mindspore/build/package/mindspore/train/dataset_helper.py(98)/        return self.network(*outputs)/
    

    第一行是该算子的相应源码,该算子是反向算子,故由MindSpore实现。第二行提示此算子有关联的正向节点,第四行则指向了网络脚本文件第149行的“out = self.conv1(x)”。综上可知,算子Conv2DBackpropFilter是一个反向算子,相应的正向节点是一个卷积算子。


Q: 为什么运行代码时屏幕中会出现“Start compiling and it will take a while. Please wait…”和“End compiling.”的打印?

A: 当需要加速执行时,MindSpore会将Python源码转换成一种基于图表示的函数式IR,并进行相关的优化。这个过程也被称为编译流程。 当出现“Start compiling and it will take a while. Please wait…”的打印时,MindSpore开始了图编译流程;当出现“End compiling.”则表明图编译流程结束。

当前主要有以下两种场景会有该打印:

  • 静态图模式下运行网络。

  • 动态图下执行被@jit装饰的函数(例如优化器nn.Momentum)。

一次任务中有可能会触发多次编译流程。

Q: 编译时报出告警:“On the Ascend platform, if you read-only access to the parameter, you can take the value of the parameter, so that the system can do more optimization.”,是什么意思?

A: 由于Ascend平台不能真正返回一个内存地址,导致在整图下沉模式下,对于控制流场景中返回值存在参数的情况,会存在一些问题。为了避免出现问题,会对这种场景切换到统一运行时模式,从整图下沉模式切换到统一运行时模式,网络性能可能会劣化。如果控制流子图的返回值仅使用参数的值,可以通过参数的value接口获取参数的值,从而避免模式切换导致的性能劣化。

例如下面的用例,在网络“Net”中仅使用“InnerNet”中的“self.param1”和“self.param2”的值,没有使用参数的属性,所以可以使用value接口来避免模式切换导致的性能劣化。

import mindspore.nn as nn
import mindspore as ms
import mindspore.ops as ops
from mindspore import Tensor, Parameter

ms.set_context(mode=ms.GRAPH_MODE, device_target="Ascend")

class InnerNet(nn.Cell):
    def __init__(self):
        super().__init__()
        self.param1 = Parameter(Tensor(1), name="param1")
        self.param2 = Parameter(Tensor(2), name="param2")

    def construct(self, x):
        if x > 0:
            return self.param1.value(), self.param2.value()
        return self.param2.value(), self.param1.value()

class Net(nn.Cell):
    def __init__(self):
        super().__init__()
        self.inner_net = InnerNet()
        self.addn = ops.AddN()

    def construct(self, x, y):
        inner_params = self.inner_net(x)
        out_res = self.addn(inner_params) + y
        return out_res, inner_params[0] + inner_params[1]

input_x = Tensor(3)
input_y = Tensor(5)
net = Net()
out = net(input_x, input_y)
print("out:", out)

执行结果如下:

out: (Tensor(shape=[], dtype=Int64, value=8), Tensor(shape=[], dtype=Int64, value=3))

Q: load MindIR 时,出现 “The input number of parameters is not Compatible.” 该怎么办?

A: 首先检查导出参数和导入执行的参数个数是否是匹配的。如果是匹配的,则需要检查一下导出时候的参数是不是存在非Tensor的场景。

因为导出数据输入为非Tensor时,该导出的输入将会变成常量固化到MindIR中,使MindIR中的输入要少于网络构建的Construct入参。

如果是标量类型,可以将标量转成Tensor类型导出。如果是Tuple或者List类型.可以使用mutable接口进行包装后及进行导出。


Q: 编译时报错”ValueError: The shape of sense must not be dynamic shape.”怎么办?

A: 在图模式中,当调用GradOperation接口且参数sens_param=True时,通过nn.Cell.set_inputs传入动态shape的sense参数时会导致报错。代码样例如下:

import numpy as np
import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor

ms.set_context(mode=ms.GRAPH_MODE, device_target="CPU")

class Net(nn.Cell):
    """ReLU Net"""
    def __init__(self):
        super(Net, self).__init__()
        self.relu = ops.ReLU()

    def construct(self, x):
        return self.relu(x)

class GradWithSense(nn.Cell):
    """Grad Net"""
    def __init__(self, network):
        super(GradWithSense, self).__init__()
        self.grad = ops.GradOperation(get_all=True, sens_param=True)
        self.network = network

    def construct(self, input_, sense):
        return self.grad(self.network)(input_, sense)

x = np.array([[1, 1], [1, -1]]).astype(np.float32)
sense = np.array([[2, 3], [4, 5]]).astype(np.float32)
dynamic_x = Tensor(shape=[2, None], dtype=ms.float32)
sense_x = Tensor(shape=[1, None], dtype=ms.float32)
net = GradWithSense(Net())
net.set_inputs(dynamic_x, sense_x)
print(net(Tensor(x), Tensor(sense_x))) # ValueError: The shape of sense must not be dynamic shape.

图模式下,不支持动态shape的sense,建议修改为以下代码:

import numpy as np
import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor

ms.set_context(mode=ms.GRAPH_MODE, device_target="CPU")

class Net(nn.Cell):
    """ReLU Net"""
    def __init__(self):
        super(Net, self).__init__()
        self.relu = ops.ReLU()

    def construct(self, x):
        return self.relu(x)

class NetWithSense(nn.Cell):
    """ReLU Net"""
    def __init__(self, sense):
        super(NetWithSense, self).__init__()
        self.relu = ops.ReLU()
        self.sense = sense

    def construct(self, x):
        return self.relu(x) * self.sense  # 将sense加入正向计算网络中

class Grad(nn.Cell):
    """Grad Net"""
    def __init__(self, network):
        super(Grad, self).__init__()
        self.grad = ops.GradOperation(get_all=True)
        self.network = network

    def construct(self, input_):
        return self.grad(self.network)(input_)

x = np.array([[1, 1], [1, -1]]).astype(np.float32)
sense = np.array([[2, 3], [4, 5]]).astype(np.float32)
dynamic_x = Tensor(shape=[2, None], dtype=ms.float32)
net = Grad(NetWithSense(Tensor(sense)))
net.set_inputs(dynamic_x)
print(net(Tensor(x)))

执行结果如下:

(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 2.00000000e+00,  3.00000000e+00],
 [ 4.00000000e+00,  0.00000000e+00]]),)

Q: 编译时报错 “‘External’ TypeError” 怎么办?

A: “External” 类型表示在图模式中使用了无法原生支持的对象。例如:第三方库对象是 “External” 类型。


Q: 编译时报错”Nested execution during JIT execution for ‘xxx’ is not supported when ‘xxx’ compile and execute.”怎么办?

A: 当触发编译流程,即代码编译成静态计算图时,见Graph模式执行原理,同时在默认使用JIT Fallback特性时,再次进入编译流程时,则会抛出以上异常。

下面以JIT Fallback支持调用第三方库的对象和方法为例:

  1. 再次调用@jit装饰器修饰函数或者类的成员方法,所修饰的函数或方法将会被编译成静态计算图。

from mindspore import context, Tensor, jit, nn
import numpy as np
context.set_context(mode=context.GRAPH_MODE)

class UserDefinedNet: # 自定义普通Python类
    def __init__(self):
        self.value = 10

    @jit
    def func(self, x):  # jit装饰的方法
        return 2 * x + self.value

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.net = UserDefinedNet()

    def construct(self, x):
        x = self.net.value + self.net.func(x)
        return x

x = np.random.randn(2, 2, 3).astype(np.float32)
net = Net()
out = net(Tensor(x))

执行结果如下:

Nested execution during JIT execution for 'UserDefinedNet.func' is not supported when 'Net.construct' compile and execute.

当前场景建议去掉@jit装饰器。

  1. 使用Cell类并且在construct函数中编写执行代码,此时construct函数的代码将会被编译成静态计算图。

from mindspore import context, Tensor, jit, nn
import numpy as np
context.set_context(mode=context.GRAPH_MODE)

class InnerNet(nn.Cell):
    def __init__(self):
        super(InnerNet, self).__init__()

    def construct(self, x):
        return x

class UserDefinedNet: # 自定义普通Python类
    def __init__(self):
        self.value = 10
        self.inner_net = InnerNet()

    def func(self, x):
        return 2 * x * self.inner_net(x) + self.value

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.net = UserDefinedNet()

    def construct(self, x):
        x = self.net.value + self.net.func(x)
        return x

x = np.random.randn(2, 2, 3).astype(np.float32)
net = Net()
out = net(Tensor(x))

执行结果如下:

Nested execution during JIT execution for 'InnerNet.construct' is not supported when 'Net.construct' compile and execute.

建议修改为以下代码:

from mindspore import context, Tensor, jit, nn
import numpy as np
context.set_context(mode=context.GRAPH_MODE)

class InnerNet(nn.Cell):
    def __init__(self):
        super(InnerNet, self).__init__()

    def construct(self, x):
        return x

class UserDefinedNet: # 自定义普通Python类
    def __init__(self):
        self.value = 10

    def func(self, x, y):
        return 2 * x * y + self.value

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.net = UserDefinedNet()
        self.inner_net = InnerNet()

    def construct(self, x):
        y = self.inner_net(x)
        x = self.net.value + self.net.func(x, y)
        return x

x = np.random.randn(2, 2, 3).astype(np.float32)
net = Net()
out = net(Tensor(x))

Q: 编译时报错 “ValueError: The value Parameter (name=name_a, shape=(1,), dtype=Float32, requires_grad=True) , its name ‘name_a’ already exists. Please set a unique name for the parameter.”,是什么含义?应该怎么处理?

A: 图模式下要求Parameter的name拥有唯一性,如果存在同名的两个或者多个Parameter,网络中区分不出不同的对象,将造成错误。我们可以从下面几个角度来排查脚本中的同名的Parameter,对其中的Parameter设置唯一的name。

import mindspore as ms
from mindspore.nn import Cell
from mindspore import Tensor, context, ParameterTuple, Parameter

context.set_context(mode=context.GRAPH_MODE)


class ParamNet(Cell):
    def __init__(self):
        super(ParamNet, self).__init__()
        self.res1 = ParameterTuple((Parameter(Tensor([2], ms.float32), name="name_a"),
                                    Parameter(Tensor([4], ms.float32), name="name_a")))
        self.param_tuple = (Parameter(Tensor([1], ms.float32), name="name_b"),
                            Parameter(Tensor([2], ms.float32)))
        self.param_list = [Parameter(Tensor([3], ms.float32), name="name_b"),
                           Parameter(Tensor([4], ms.float32))]

    def construct(self):
        out1 = self.res1[0] + self.res1[1]
        out2 = self.param_tuple[0] + self.param_tuple[1] + self.param_list[0] + self.param_listp[1]
        return out1, out2


net = ParamNet()
res = net()

如上脚本,ParameterTuple中定义了两个同名name_a的Parameter,是不允许的。param_tuple和param_list中定义了同名name_b的Parameter,也是不允许的。还有一种情况是脚本中在同一个Cell中实例化某个网络,如下面例子,也将报错“its name ‘name_a’ already exists.”。

import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context, ParameterTuple, Parameter


context.set_context(mode=context.GRAPH_MODE)


class InnerNet(nn.Cell):
    def __init__(self):
        super(InnerNet, self).__init__()
        self.param = Parameter(Tensor([1], ms.float32), name="name_a")

    def construct(self, x):
        return x + self.param


class OutNet1(nn.Cell):
    def __init__(self, net1, net2):
        super(OutNet1, self).__init__()
        self.param1 = ParameterTuple(net1.get_parameters())
        self.param2 = ParameterTuple(net2.get_parameters())

    def construct(self, x):
        return x + self.param1[0] + self.param2[0]


net1 = InnerNet()
net2 = InnerNet()
out_net = OutNet1(net1, net2)
res = out_net(Tensor([1], ms.float32))
print("res:", res)

针对这种情况,我们可以使用CellList来管理同一个网络的多个实例。

import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context, ParameterTuple, Parameter


context.set_context(mode=context.GRAPH_MODE)


class InnerNet(nn.Cell):
    def __init__(self):
        super(InnerNet, self).__init__()
        self.param = Parameter(Tensor([1], ms.float32), name="name_a")

    def construct(self, x):
        return x + self.param


class OutNet1(nn.Cell):
    def __init__(self, net1, net2):
        super(OutNet1, self).__init__()
        self.cell_list = nn.CellList()
        self.cell_list.append(net1)
        self.cell_list.append(net2)
        self.param1 = ParameterTuple(self.cell_list[0].get_parameters())
        self.param2 = ParameterTuple(self.cell_list[1].get_parameters())

    def construct(self, x):
        return x + self.param1[0] + self.param2[0]


net1 = InnerNet()
net2 = InnerNet()
out_net = OutNet1(net1, net2)
res = out_net(Tensor([1], ms.float32))
print("res:", res)