Python PCAMの例

コード例 #1

ファイル: PCAMTrainALILikeVAEMSE.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.PCAM import PCAM

if __name__ == "__main__":
    dataset = PCAM(root_path=os.path.join(args.root_path, "pcam"), extract=True, 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 #2

ファイル: PCAMTrainALILikeVAEBCE.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.PCAM import PCAM

if __name__ == "__main__":
    dataset = PCAM(root_path=os.path.join(args.root_path, "pcam"),
                   extract=True,
                   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 #3

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

        '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 = PCAM(root_path=os.path.join(args.root_path, 'pcam'), downsample=224)
    D164 = PCAMResize(D1, 64)

    args.D1 = 'PCAM'

    All_ODs = [
        'UniformNoise',
        'NormalNoise',
        'MNIST',
        'FashionMNIST',
        'NotMNIST',
        'CIFAR100',
        'CIFAR10',
        'STL10',
        'TinyImagenet',
        'Malaria',

コード例 #4

ファイル: PCAMTrain.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.classifier_setup as CLSetup
from models.classifiers import PCAMDense
from datasets.PCAM import PCAM


if __name__ == "__main__":
    dataset = PCAM(root_path=os.path.join(args.root_path, "pcam"), extract=True, downsample=224)
    model = PCAMDense("densenet121-a639ec97.pth", train_features=True)
    CLSetup.train_classifier(args, model=model, dataset=dataset.get_D1_train(), balanced=True)