Python PCAM.get_D1_train Exemples

Exemple #1

Fichier : PCAMTrainALILikeVAEMSE.py Projet : 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)

Exemple #2

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 = Generic_AE(dims=(3, 64, 64), max_channels=512, depth=12, n_hidden=512)
    AESetup.train_autoencoder(args, model=model, dataset=dataset.get_D1_train(), BCE_Loss=True)

Exemple #3

Fichier : PCAMTrainALILikeVAEBCE.py Projet : 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)

Exemple #4

Fichier : PCAMTrain.py Projet : 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)