单独文件中的FastAPI / Pydantic循环引用

如何解决单独文件中的FastAPI / Pydantic循环引用

我很想在FastAPI中使用类似于以下内容的模式：

from __future__ import annotations
from typing import List
from pydantic import BaseModel


class Project(BaseModel):
    members: List[User]


class User(BaseModel):
    projects: List[Project]


Project.update_forward_refs()

但是为了保持我的项目结构整洁，我会这样做。喜欢在单独的文件中定义它们。我该如何做而不创建循环引用？

使用FastAPI中的架构生成上面的代码可以正常工作，我只是不知道如何将其分离到单独的文件中。然后，在随后的步骤中，我将使用@property而不是使用属性来定义这些对象的子类中的这些对象的吸气剂。但是对于OpenAPI文档生成，我需要结合使用-我认为。

解决方法

在以下三种情况下，循环依赖关系可能在Python中起作用：

• 模块顶部：# Use ImageDataGenerator to create 3 lots of batches train_batches = ImageDataGenerator( rescale=1/255).flow_from_directory(directory=train_path,target_size=(80,80),classes=['cn','ad'],batch_size=100,color_mode="rgb") valid_batches = ImageDataGenerator( rescale=1/255).flow_from_directory(directory=valid_path,color_mode="rgb") # test_batches = ImageDataGenerator( # rescale=1/255).flow_from_directory(directory=test_path,# target_size=(224,224),batch_size=10,# color_mode="rgb") imgs,labels = next(train_batches) # Test to see normalisation has occurred properly print(imgs[1][8]) # Define method to plot MRIs def plotImages(images_arr): fig,axes = plt.subplots(1,10,figsize=(20,20)) axes = axes.flatten() for img,ax in zip( images_arr,axes): ax.imshow(img) ax.axis('off') plt.tight_layout() plt.show() # Plot a sample of MRIs plotImages(imgs) # # Define the model # # VGG16 # model = Sequential() # model.add(Conv2D(input_shape=(160,160,3),filters=64,kernel_size=(3,padding="same",activation="relu")) # model.add(Conv2D(filters=64,activation="relu")) # model.add(MaxPool2D(pool_size=(2,2),strides=(2,2))) # model.add(Conv2D(filters=128,activation="relu")) # model.add(Conv2D(filters=128,2))) # model.add(Conv2D(filters=256,activation="relu")) # model.add(Conv2D(filters=256,2))) # model.add(Conv2D(filters=512,activation="relu")) # model.add(Conv2D(filters=512,2))) # model.add(Flatten()) # model.add(Dense(units=1024,activation="relu")) # model.add(Dense(units=128,activation="relu")) # model.add(Dense(units=2,activation="softmax")) # # Model from the paper # model = Sequential([ # Conv2D(filters=32,activation='relu',padding = 'same',input_shape=(160,3)),# Conv2D(filters=32,padding='same'),# MaxPool2D(pool_size=(2,strides=2),# Flatten(),# Dense(units=2,activation='softmax') # ]) ## Model from Dr Paul # static_conv_layer=Conv2D(filters=16,kernel_size=(5,5),padding = 'same') # # model = Sequential([ # Conv2D(filters=16,input_shape=(32,32,# Dropout(0.1),# static_conv_layer,activation='softmax') # ]) # This model hits around 75% train acc,54% val acc model = Sequential([ Conv2D(filters=16,input_shape=(80,80,MaxPool2D(pool_size=(2,# Dropout(0.1),# Conv2D(filters=16,# MaxPool2D(pool_size=(2,Flatten(),Dense(units=2,activation='softmax') ]) # model = Sequential([ # Conv2D(filters=32,activation='softmax') # ]) ## Basic model with dropouts # model = Sequential([ # Conv2D(filters=32,input_shape=(224,224,# Conv2D(filters=64,# Dropout(0.2),# Conv2D(filters=128,# Dropout(0.3),# Dense(units=1,activation='sigmoid') # ]) # Summarise each layer of the model print(model.summary()) # Compile and train the model model.compile(optimizer=Adam(),loss='binary_crossentropy',metrics=['accuracy']) model.fit(x=train_batches,steps_per_epoch=len(train_batches),validation_data=valid_batches,validation_steps=len(valid_batches),epochs=20,verbose=1 )
• 模块底部：import package.module
• 功能顶层：可同时使用

根据您的情况，第二种情况“模块底部”会有所帮助。 因为您使用need to use from package.module import attribute函数来解决像这样的pydantic推迟的注释：

update_forward_refs

# project.py
from typing import List
from pydantic import BaseModel


class Project(BaseModel):
    members: "List[User]"


from user import User
Project.update_forward_refs()

不过，我强烈劝阻您不要故意引入循环依赖项