Python DRDの例

コード例 #1

ファイル: DRDTrainALILikeVAEBCE.py プロジェクト: caotians1/OD-test-master

from __future__ import print_function
import os,sys,inspect

currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0,parentdir)

from utils.args import args

import setup.categories.ae_setup as AESetup
from models.autoencoders import *
from datasets.DRD import DRD

if __name__ == "__main__":
    dataset = DRD(root_path=os.path.join(args.root_path, "diabetic-retinopathy-detection"), downsample=64)
    model = ALILikeVAE(dims=(3, 64, 64))
    AESetup.train_variational_autoencoder(args, model=model, dataset=dataset.get_D1_train(), BCE_Loss=True)

コード例 #2

ファイル: DRDTrain2.py プロジェクト: caotians1/OD-test-master

import global_vars as Global
from utils.args import args
import torchvision
from torch.utils.data.sampler import WeightedRandomSampler
import numpy as np
from torch import optim
import setup.categories.classifier_setup as CLSetup
from models.classifiers import DRDDense
from datasets.DRD import DRD

class DRDDenseCustom(DRDDense):
    def train_config(self):
        config = {}
        if self.train_features:
            config['optim'] = optim.Adam(
                [{'params':self.densenet121.classifier.parameters(), 'lr':1e-3}, {'params':self.densenet121.features.parameters()}],
                lr=1e-3)
        else:
            config['optim'] = optim.Adam(self.densenet121.classifier.parameters(), lr=1e-3, )
        config['scheduler'] = optim.lr_scheduler.StepLR(config['optim'], 30, gamma=0.5)
        config['max_epoch'] = 100
        return config

if __name__ == "__main__":
    dataset = DRD(root_path=os.path.join(args.root_path, "diabetic-retinopathy-detection"))
    model = DRDDenseCustom(train_features=True, pretrained_weights_path="./model.pth.tar")
    args.num_classes = 2
    CLSetup.train_classifier(args, model=model, dataset=dataset.get_D1_train(), balanced=True)

コード例 #3

ファイル: DRDTrainALILikeVAEMSE.py プロジェクト: caotians1/OD-test-master

from __future__ import print_function
import os, sys, inspect

currentdir = os.path.dirname(
    os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0, parentdir)

from utils.args import args

import setup.categories.ae_setup as AESetup
from models.autoencoders import *
from datasets.DRD import DRD

if __name__ == "__main__":
    dataset = DRD(root_path=os.path.join(args.root_path,
                                         "diabetic-retinopathy-detection"),
                  downsample=64)
    model = ALILikeVAE(dims=(3, 64, 64))
    AESetup.train_variational_autoencoder(args,
                                          model=model,
                                          dataset=dataset.get_D1_train(),
                                          BCE_Loss=False)

コード例 #4

ファイル: DRD_eval_rand_seeds.py プロジェクト: medical-projects/OD-test-master

        'knn/4',
        'knn/8',
        'vaemseaeknn/1',
        'vaebceaeknn/1',
        'mseaeknn/1',
        'bceaeknn/1',
        'vaemseaeknn/8',
        'vaebceaeknn/8',
        'mseaeknn/8',
        'bceaeknn/8',
        #'alivaemseaeknn/1', 'alivaebceaeknn/1', 'alimseaeknn/1', 'alibceaeknn/1',
        #'alivaemseaeknn/8', 'alivaebceaeknn/8', 'alimseaeknn/8', 'alibceaeknn/8',
    ]

    D1 = DRD(root_path=os.path.join(args.root_path,
                                    'diabetic-retinopathy-detection'),
             downsample=224)
    D164 = DRD(root_path=os.path.join(args.root_path,
                                      "diabetic-retinopathy-detection"),
               downsample=64)

    args.D1 = 'DRD'

    All_ODs = [
        'UniformNoise',
        'NormalNoise',
        'MNIST',
        'FashionMNIST',
        'NotMNIST',
        'CIFAR100',
        'CIFAR10',