join(path, '%d_%s.png' % (image_id, lbl_A[b])))
if isinstance(fake_A, np.ndarray) & isinstance(lbl_B, list):
cv2.imwrite(join(path, '%d_%s.png' % (image_id, lbl_B[b])),
self.unnormalize(fake_A[b]))
print('wrote to ',
join(path, '%d_%s.png' % (image_id, lbl_B[b])))
def unnormalize(self, im):
#im = np.array(im)
im = np.array(255 * (0.5 * im + 0.5), dtype=np.uint8)
#print(im.shape)
#print(im)
return im
if __name__ == '__main__':
args = test_options()
gan = CycleGAN(args)
if args.direction == 'both':
gan.test_both(batch_size=args.batch,
iteration=args.iteration,
set=args.set)
elif args.direction == 'A2B':
gan.test_A2B(batch_size=args.batch,
iteration=args.iteration,
set=args.set)
elif args.direction == 'B2A':
gan.test_B2A(batch_size=args.batch,
iteration=args.iteration,
set=args.set)
import options
import vaetest
import numpy as np
import os
import random
random.seed(1000)
opt = options.test_options()
opt.istest = 1
text_file = open(opt.dataset + "_progress.txt", "w")
text_file.close()
#First read all classes one at a time and iterate through all
text_file = open(opt.dataset + "_folderlist.txt", "r")
folders = text_file.readlines()
text_file.close()
folders = [i.split('\n', 1)[0] for i in folders]
follist = range(0, 251, 10)
#folders = range(0,10)
for classname in folders: #[8,2,3,0,1,4,5,6,7,9]:#folders:
filelisttext = open(opt.dataset + '_trainlist.txt', 'w')
filelisttext.write(str(classname))
filelisttext.close()
filelisttext = open(opt.dataset + '_novellist.txt', 'w')
novellist = list(set(folders) - set([classname]))
print(novellist)
for novel in novellist:
filelisttext.write(str(novel) + '\n')
filelisttext.close()
from tqdm import tqdm
import argparse
from get_data import HumanAtlasDatasetTest, HumanAtlasDataset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from networks import DenseNet121
import os
import torch
from torch.autograd import Variable
import numpy as np
import pandas as pd
from options import test_options
opt = test_options()
# get DataLoader:
test_dataset = HumanAtlasDataset(data_dir=opt.data_dir, label_file=opt.image_list, n_class=opt.n_class,
transform = transforms.Compose([
transforms.ToTensor()
]))
# get dataloader
test_loader = DataLoader(dataset=test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=0, pin_memory=True)
model_0 = DenseNet121(opt.n_class).cuda()
model_1 = DenseNet121(opt.n_class).cuda()
model_2 = DenseNet121(opt.n_class).cuda()
checkpoint_0 = torch.load(f"{opt.chckpnt_dir}/{opt.chckpnt_folder}/model_0_{opt.model_type}.pth.tar")
checkpoint_1 = torch.load(f"{opt.chckpnt_dir}/{opt.chckpnt_folder}/model_1_{opt.model_type}.pth.tar")
checkpoint_2 = torch.load(f"{opt.chckpnt_dir}/{opt.chckpnt_folder}/model_2_{opt.model_type}.pth.tar")
