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pipeine.py
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pipeine.py
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# -*- coding: utf-8 -*-
# @Time : 2021/11/08 21:40:40
# @Author : Zhan Genze <[email protected]>
# @Project : gzzhan
# @Description: pipeline for experiment
import pytorch_lightning as pl
import torch
from Callbacks import loadCallbacks
from DataModule import MedicalDataModule
from MedDec import MedDec
from utils import load_from_ckpt, load_sources, read_config, load_pretrain
def train_valid_pipeline(**kwargs):
sources = load_sources(kwargs['gpus'], **kwargs['add_sources'])
if 'pretrain' in kwargs.keys(): # 加载预训练模型
# pretrain_configs = read_config(kwargs['pretrain']['config_path'])
# pretrain_model = MedDec(pretrain_configs, sources)
# load_from_ckpt(kwargs['pretrain']['save_path'], pretrain_model)
pretrain_model = load_pretrain(kwargs['pretrain'], sources)
kwargs['model']['pretrain'] = pretrain_model
model = MedDec(kwargs, sources) # 加入额外的知识源
callbacks = loadCallbacks(kwargs['Callbacks'])
# turn validation before training off
trainer = pl.Trainer(gpus=[kwargs['gpus']], auto_select_gpus=True, max_epochs=kwargs['epochs'], callbacks=list(
callbacks.values()), num_sanity_val_steps=0, val_check_interval=1.0)
datamodule = MedicalDataModule(kwargs['data'], sources)
trainer.fit(model, datamodule=datamodule)
kwargs['trainer'] = trainer
if "ModelCheckpoint" in callbacks.keys():
kwargs['model']['save_path'] = callbacks['ModelCheckpoint'].best_model_path
if 'pretrain' in kwargs.keys():
del kwargs['model']['pretrain']
def train_pipeline(**kwargs):
sources = load_sources(kwargs['gpus'], **kwargs['add_sources'])
if 'pretrain' in kwargs.keys(): # 加载预训练模型
# pretrain_configs = read_config(kwargs['pretrain']['config_path'])
# pretrain_model = MedDec(pretrain_configs, sources)
# load_from_ckpt(kwargs['pretrain']['save_path'], pretrain_model)
pretrain_model = load_pretrain(kwargs['pretrain'], sources)
kwargs['model']['pretrain'] = pretrain_model
model = MedDec(kwargs, sources) # 加入额外的知识源
callbacks = loadCallbacks(kwargs['Callbacks'])
# turn validation before training off
trainer = pl.Trainer(gpus=[kwargs['gpus']], auto_select_gpus=True, max_epochs=kwargs['epochs'], callbacks=list(
callbacks.values()), num_sanity_val_steps=0, check_val_every_n_epoch=1000000)
datamodule = MedicalDataModule(kwargs['data'], sources)
trainer.fit(model, datamodule=datamodule)
kwargs['trainer'] = trainer
if "ModelCheckpoint" in callbacks.keys():
kwargs['model']['save_path'] = callbacks['ModelCheckpoint'].best_model_path
if 'pretrain' in kwargs.keys():
del kwargs['model']['pretrain']
def test_pipeline(**kwargs):
sources = load_sources(kwargs['gpus'], **kwargs['add_sources'])
model = MedDec(kwargs, sources)
#!: important line
# checkpoint = torch.load(kwargs['model']['save_path'], map_location=lambda storage, loc: storage)
# model.load_state_dict(checkpoint['state_dict'])
load_from_ckpt(kwargs['model']['save_path'], model)
datamodule = MedicalDataModule(kwargs['data'], sources)
trainer = pl.Trainer(gpus=[kwargs['gpus']], max_epochs=kwargs['epochs'])
trainer.test(model, datamodule=datamodule)