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.NIH_Chest import NIHChestBinaryTrainSplit
if __name__ == "__main__":
dataset = NIHChestBinaryTrainSplit(root_path=os.path.join(args.root_path, "NIHCC"), binary=True, expand_channels=False, downsample=64)
model = Generic_VAE(dims=(1, 64, 64), max_channels=512, depth=12, n_hidden=512)
#model = ALILikeVAE(dims=(1, 64, 64))
AESetup.train_variational_autoencoder(args, model=model, dataset=dataset.get_D1_train(), BCE_Loss=False)
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 Generic_VAE, Generic_AE, Residual_AE
from datasets.NIH_Chest import NIHChestBinaryTrainSplit
if __name__ == "__main__":
dataset = NIHChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, "NIHCC"),
binary=True,
expand_channels=False,
downsample=64)
model = Residual_AE(dims=(1, 64, 64))
AESetup.train_autoencoder(args,
model=model,
dataset=dataset.get_D1_train(),
BCE_Loss=False)
currentdir = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0, parentdir)
import models as Models
import global_vars as Global
from utils.args import args
import categories.classifier_setup as CLSetup
from models.classifiers import NIHDenseBinary, NIHChestVGG
from datasets.NIH_Chest import NIHChestBinaryTrainSplit
if __name__ == "__main__":
dataset = NIHChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, "NIHCC"),
binary=True)
model = NIHChestVGG()
CLSetup.train_classifier(args, model=model, dataset=dataset.get_D1_train())
# task_list = [
# # The list of models, The function that does the training, Can I skip-test?, suffix of the operation.
# # The procedures that can be skip-test are the ones that we can determine
# # whether we have done them before without instantiating the network architecture or dataset.
# # saves quite a lot of time when possible.
# (Global.dataset_reference_classifiers, CLSetup.train_classifier, True, ['base0']),
# (Global.dataset_reference_classifiers, KLogisticSetup.train_classifier, True, ['KLogistic']),
# (Global.dataset_reference_classifiers, DeepEnsembleSetup.train_classifier, True, ['DE.%d'%i for i in range(5)]),
# (Global.dataset_reference_autoencoders, AESetup.train_BCE_AE, False, []),
# (Global.dataset_reference_autoencoders, AESetup.train_MSE_AE, False, []),
# (Global.dataset_reference_vaes, AESetup.train_variational_autoencoder, False, []),
'bceaeknn/1',
'vaemseaeknn/2',
'vaebceaeknn/2',
'mseaeknn/2',
'bceaeknn/2',
'vaemseaeknn/4',
'vaebceaeknn/4',
'mseaeknn/4',
'bceaeknn/4',
'vaemseaeknn/8',
'vaebceaeknn/8',
'mseaeknn/8',
'bceaeknn/8',
]
D1 = NIHChestBinaryTrainSplit(
root_path=os.path.join(args.root_path, 'NIHCC'))
D164 = NIHChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, 'NIHCC'),
downsample=64)
args.D1 = 'NIHCC'
#Usecase 1 Evaluation
d2s = [
'CIFAR10',
'UniformNoise',
'MURAHAND',
]
D2s = []
for d2 in d2s:
dataset = Global.all_datasets[d2]
if 'dataset_path' in dataset.__dict__:
from __future__ import print_function
import os, sys, inspect
from termcolor import colored
import torch
currentdir = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0, parentdir)
import models as Models
import global_vars as Global
from utils.args import args
import categories.classifier_setup as CLSetup
from models.classifiers import NIHDenseBinary
from datasets.NIH_Chest import NIHChestBinaryTrainSplit
if __name__ == "__main__":
dataset = NIHChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, "NIHCC"),
binary=True)
model = NIHDenseBinary("mono_model.pth.tar")
CLSetup.train_classifier(args,
model=model,
dataset=dataset.get_D1_train(),
balanced=True)
