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test.py
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test.py
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import argparse
import lib
import torch
import torchvision
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.utils import save_image
from torchvision.datasets import MNIST
import torch.nn.functional as F
import os
import matplotlib.pyplot as plt
import torch.utils.data as data
from PIL import Image
import numpy as np
from torchvision.utils import save_image
import torch
import torch.nn.init as init
from utils import JointTransform2D, ImageToImage2D, Image2D
from metrics import jaccard_index, f1_score, LogNLLLoss,classwise_f1
from utils import chk_mkdir, Logger, MetricList
import cv2
from functools import partial
from random import randint
parser = argparse.ArgumentParser(description='MedT')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
help='number of data loading workers (default: 8)')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
help='number of total epochs to run(default: 1)')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch_size', default=1, type=int,
metavar='N', help='batch size (default: 8)')
parser.add_argument('--learning_rate', default=1e-3, type=float,
metavar='LR', help='initial learning rate (default: 0.01)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-5, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--train_dataset', type=str)
parser.add_argument('--val_dataset', type=str)
parser.add_argument('--save_freq', type=int,default = 5)
parser.add_argument('--modelname', default='off', type=str,
help='name of the model to load')
parser.add_argument('--cuda', default="on", type=str,
help='switch on/off cuda option (default: off)')
parser.add_argument('--direc', default='./results', type=str,
help='directory to save')
parser.add_argument('--crop', type=int, default=None)
parser.add_argument('--device', default='cuda', type=str)
parser.add_argument('--loaddirec', default='load', type=str)
parser.add_argument('--imgsize', type=int, default=None)
parser.add_argument('--gray', default='no', type=str)
args = parser.parse_args()
direc = args.direc
gray_ = args.gray
aug = args.aug
direc = args.direc
modelname = args.modelname
imgsize = args.imgsize
loaddirec = args.loaddirec
if gray_ == "yes":
from utils_gray import JointTransform2D, ImageToImage2D, Image2D
imgchant = 1
else:
from utils import JointTransform2D, ImageToImage2D, Image2D
imgchant = 3
if args.crop is not None:
crop = (args.crop, args.crop)
else:
crop = None
tf_train = JointTransform2D(crop=crop, p_flip=0.5, color_jitter_params=None, long_mask=True)
tf_val = JointTransform2D(crop=crop, p_flip=0, color_jitter_params=None, long_mask=True)
train_dataset = ImageToImage2D(args.train_dataset, tf_val)
val_dataset = ImageToImage2D(args.val_dataset, tf_val)
predict_dataset = Image2D(args.val_dataset)
dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
valloader = DataLoader(val_dataset, 1, shuffle=True)
device = torch.device("cuda")
if modelname == "axialunet":
model = lib.models.axialunet(img_size = imgsize, imgchan = imgchant)
elif modelname == "MedT":
model = lib.models.axialnet.MedT(img_size = imgsize, imgchan = imgchant)
elif modelname == "gatedaxialunet":
model = lib.models.axialnet.gated(img_size = imgsize, imgchan = imgchant)
elif modelname == "logo":
model = lib.models.axialnet.logo(img_size = imgsize, imgchan = imgchant)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model = nn.DataParallel(model,device_ids=[0,1]).cuda()
model.to(device)
model.load_state_dict(torch.load(loaddirec))
model.eval()
for batch_idx, (X_batch, y_batch, *rest) in enumerate(valloader):
# print(batch_idx)
if isinstance(rest[0][0], str):
image_filename = rest[0][0]
else:
image_filename = '%s.png' % str(batch_idx + 1).zfill(3)
X_batch = Variable(X_batch.to(device='cuda'))
y_batch = Variable(y_batch.to(device='cuda'))
y_out = model(X_batch)
tmp2 = y_batch.detach().cpu().numpy()
tmp = y_out.detach().cpu().numpy()
tmp[tmp>=0.5] = 1
tmp[tmp<0.5] = 0
tmp2[tmp2>0] = 1
tmp2[tmp2<=0] = 0
tmp2 = tmp2.astype(int)
tmp = tmp.astype(int)
# print(np.unique(tmp2))
yHaT = tmp
yval = tmp2
epsilon = 1e-20
del X_batch, y_batch,tmp,tmp2, y_out
yHaT[yHaT==1] =255
yval[yval==1] =255
fulldir = direc+"/"
if not os.path.isdir(fulldir):
os.makedirs(fulldir)
cv2.imwrite(fulldir+image_filename, yHaT[0,1,:,:])