FATE —— 二.2.4 Homo

FATE —— 二.2.4 Homo

前言

FATE版本为1.10.0单机部署版，win10+centos7

构建模型

在FATE 1.10.0中，您可以使用管道提交PyTorch Sequential模型。然而，Sequential模型结合PyTorch的内置层可能不足以表示更复杂的模型。例如，当构建与ResNet中发现的模块类似的剩余模块时，需要重用某些模块的输出，这可能无法使用Sequential模型。

为了解决这个问题，FATE 1.10.0中引入了model_zoo模块，该模块位于del_goo下。该模块允许您自定义自己的PyTorch模型，前提是它是基于dule开发的并实现了转发接口。有关更多信息，请参阅自定义模块上的PyTorch文档PyTorch模块。要在联合任务中使用自定义模型，只需将其放置在federatedml/nn/model_zoo目录中，并在提交任务时通过接口指定模块和模型类。Homo NN将自动搜索并导入您已实现的模型。

例如，考虑MNIST手写识别的任务。我们可以先在本地编写一个带有残余连接的简单神经网络模块，然后在联合任务中使用它。

定制模型

将模型代码命名为image_net.py，您可以将其直接放在fedratedml/nn/model_zoo下，也可以使用jupyter笔记本的快捷界面将其直接保存到fedratedml/nn/model_zoo

importsave_to_fate

%%save_to_fate model image_net.py
import torch as t
from torch import nn
 import Module# the residual component
class Residual(Module):def __init__(self, ch, kernel_size=3, padding=1):super(Residual, self).__init__()vs = t.nn.ModuleList([nn.Conv2d(ch, ch, kernel_size=kernel_size, padding=padding) for i in range(2)])self.act = nn.ReLU()def forward(self, x):x = self.vs[0](x))x_ = vs[1](x)return self.act(x + x_)# we call it image net
class ImgNet(nn.Module):def __init__(self, class_num=10):super(ImgNet, self).__init__()self.seq = t.nn.Sequential(nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5),Residual(12),nn.MaxPool2d(kernel_size=3),nn.Conv2d(in_channels=12, out_channels=12, kernel_size=3),Residual(12),nn.AvgPool2d(kernel_size=3))self.fc = t.nn.Sequential(nn.Linear(48, 32),nn.ReLU(),nn.Linear(32, class_num))self.softmax = nn.Softmax(dim=1)def forward(self, x):x = self.seq(x)x = x.flatten(start_dim=1)x = self.fc(x)aining:return xelse:return self.softmax(x)

img_model = ImgNet(10)
img_model

ImgNet((seq): Sequential((0): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))(1): Residual((convs): ModuleList((0): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(act): ReLU())(2): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)(3): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1))(4): Residual((convs): ModuleList((0): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(act): ReLU())(5): AvgPool2d(kernel_size=3, stride=3, padding=0))(fc): Sequential((0): Linear(in_features=48, out_features=32, bias=True)(1): ReLU()(2): Linear(in_features=32, out_features=10, bias=True))(softmax): Softmax(dim=1)
)

dataset.image import ImageDatasetds = ImageDataset()
ds.load('/mnt/hgfs/mnist/')

ds.get_sample_ids()[0]  # 数据展示
ds[0]

运行本地测试

我们可以使用我们的数据集、自定义模型和Trainer进行本地调试，以测试程序是否可以运行。在本地测试的情况下，将跳过所有联合过程，并且模型将不执行fed平均。

import torch as t
ainer.fedavg_trainer import FedAVGTrainer
trainer = FedAVGTrainer(epochs=3, batch_size=256, shuffle=True, data_loader_worker=8, pin_memory=False) 
trainer.set_model(img_model) # set model

trainer.local_mode() # !! use local mode to skip federation process !!

optimizer = t.optim.Adam(img_model.parameters(), lr=0.01)
loss = t.nn.CrossEntropyLoss()
ain(train_set=ds, optimizer=optimizer, loss=loss)

它起作用了！现在我们可以提交联合任务了。

提交具有自定义模型的Homo NN任务

import torch as t
from torch import nn
from pipeline import fate_torch_hook
from pipelineponent import HomoNN
from pipeline.backend.pipeline import PipeLine
from pipelineponent import Reader, Evaluation, DataTransform
from pipeline.interface import Data, Modelt = fate_torch_hook(t)

import os
# bind data path to name & namespace
# fate_project_path = os.path.abspath('../../../../')
host = 10000
guest = 9999
arbiter = 10000
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host,arbiter=arbiter)data_0 = {"name": "mnist_guest", "namespace": "experiment"}
data_1 = {"name": "mnist_host", "namespace": "experiment"}# 路径根据自己得文件位置及名称进行调整，这里以FATE 1.10.0 版本为例
data_path_0 = '/mnt/hgfs/mnist/'
data_path_1 = '/mnt/hgfs/mnist/'
pipeline.bind_table(name=data_0['name'], namespace=data_0['namespace'], path=data_path_0)
pipeline.bind_table(name=data_1['name'], namespace=data_1['namespace'], path=data_path_1)

{'namespace': 'experiment', 'table_name': 'mnist_host'}

# 定义reader
reader_0 = Reader(name="reader_0")
_party_instance(role='guest', party_id=guest)ponent_param(table=data_0)
_party_instance(role='host', party_id=host)ponent_param(table=data_1)

nn.CustModel

在fate_arch_hook之后，我们可以使用t.nn.CustModel来指定模型。您应该在此处指定模块名和类名。也可以在此设置模型初始化参数。初始化参数必须是JSON可序列化的，否则无法提交此PipeLine。

from pipelineponent.homo_nn import DatasetParam, TrainerParammodel = t.nn.Sequential(# the class_num=10 is the initialzation parameter for CustModel(module_name='image_net', class_name='ImgNet', class_num=10) 
)nn_component = HomoNN(name='nn_0',model=model, # your cust modelloss&#CrossEntropyLoss(),optimizer=t.optim.Adam(model.parameters(), lr=0.01),dataset=DatasetParam(dataset_name='image'),  # use image datasettrainer=TrainerParam(trainer_name='fedavg_trainer', epochs=3, batch_size=1024, validation_freqs=1),torch_seed=100 # global random seed)

pipeline.add_component(reader_0)
pipeline.add_component(nn_component, data=Data(train_data=reader_0.output.data))
pipeline.add_component(Evaluation(name='eval_0', eval_type='multi'), data=Data(data=nn_component.output.data))

pipelinepile()
pipeline.fit()