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test.py
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test.py
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import os
import os.path
import argparse
import numpy as np
import torch
import time
import h5py
from utils import utils_image
import PIL
from PIL import Image
import utils.save_image as save_img
from network.oscnet import OSCNet
from network.oscnetplus import OSCNetplus
parser = argparse.ArgumentParser(description="OSCNet_Test")
#for model_selection
parser.add_argument('--model', type=str, default="osc", help='osc or oscplus')
parser.add_argument("--model_dir", type=str, default="model_osc/net_latest.pt", help='path to model file')
parser.add_argument("--data_path", type=str, default="data/test/", help='path to test data')
parser.add_argument("--use_GPU", type=bool, default=True, help='use GPU or not')
parser.add_argument("--gpu_id", type=str, default="0", help='GPU id')
parser.add_argument("--save_path", type=str, default="save_results/", help='path to testing results')
#for filter parameterization
parser.add_argument('--padding', type=int, default=4, help='the number of padding during convolution')
parser.add_argument('--inP', type=int, default=5, help='control the basis for filter parameterization')
parser.add_argument('--sizeP', type=int, default=9, help='control the basis for filter parameterization')
parser.add_argument('--ifini', type=float, default=1, help='indicator for filter parameterization')
parser.add_argument('--cdiv', type=float, default=1, help='controlling the updating rate of filter for oscnetplus. For oscnet, it is fixed as 1') # oscnet: default as 1
#for network and dictionary model
parser.add_argument('--num_M', type=int, default=4, help='the number of feature maps at every rotation angle')
parser.add_argument('--num_Q', type=int, default=32, help='the number of channel concatenation')
parser.add_argument('--num_rot', type=int, default=8, help='the number of rotation angles')
parser.add_argument('--S', type=int, default=10, help='Stage number S')
parser.add_argument('--T', type=int, default=3, help='Resblocks number in each ProxNet')
parser.add_argument('--etaM', type=float, default=1, help='stepsize for updating M')
parser.add_argument('--etaX', type=float, default=5, help='stepsize for updating B')
opt = parser.parse_args()
if opt.use_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
def mkdir(path):
folder = os.path.exists(path)
if not folder:
os.makedirs(path)
print("--- new folder... ---")
print("--- " + path + " ---")
else:
print("--- There exsits folder " + path + " ! ---")
out_dir = opt.save_path+ opt.model +'/'
mkdir(out_dir)
input_dir = opt.save_path+'/input/'
mkdir(input_dir)
gt_dir = opt.save_path+'/gt/'
mkdir(gt_dir)
def normalized(X):
maxX = np.max(X)
minX = np.min(X)
X = (X - minX) / (maxX - minX)
return X
def print_network(net):
num_params = 0
for param in net.parameters():
num_params += param.numel()
print('Total number of parameters: %d' % num_params)
def image_get_minmax():
return 0.0, 1.0
def normalize(data, minmax):
data_min, data_max = minmax
data = np.clip(data, data_min, data_max)
data = data * 255.0
data = data.astype(np.float32)
data = np.expand_dims(np.transpose(np.expand_dims(data, 2), (2, 0, 1)),0)
return data
test_mask = np.load(os.path.join(opt.data_path, 'testmask.npy'))
def test_image(data_path, imag_idx, mask_idx):
txtdir = os.path.join(data_path, 'test_640geo_dir.txt')
mat_files = open(txtdir, 'r').readlines()
gt_dir = mat_files[imag_idx]
file_dir = gt_dir[:-6]
data_file = file_dir + str(mask_idx) + '.h5'
abs_dir = os.path.join(data_path, 'test_640geo/', data_file)
gt_absdir = os.path.join(data_path, 'test_640geo/', gt_dir[:-1])
gt_file = h5py.File(gt_absdir, 'r')
Xgt = gt_file['image'][()]
gt_file.close()
file = h5py.File(abs_dir, 'r')
Xma= file['ma_CT'][()]
XLI =file['LI_CT'][()]
file.close()
M512 = test_mask[:,:,mask_idx]
M = np.array(Image.fromarray(M512).resize((416, 416), PIL.Image.BILINEAR))
Xma = normalize(Xma, image_get_minmax())
Xgt = normalize(Xgt, image_get_minmax())
XLI = normalize(XLI, image_get_minmax())
Mask = M.astype(np.float32)
Mask = np.expand_dims(np.transpose(np.expand_dims(Mask, 2), (2, 0, 1)),0)
non_mask = 1 - Mask
return torch.Tensor(Xma).cuda(), torch.Tensor(Xgt).cuda(), torch.Tensor(XLI).cuda(), torch.Tensor(non_mask).cuda()
def main():
# Build model
print('Loading model ...\n')
if "plus" not in opt.model:
net= OSCNet(opt).cuda()
else:
net= OSCNetplus(opt).cuda()
net.eval()
net.load_state_dict(torch.load(opt.model_dir))
print_network(net)
time_test = 0
count = 0
for imag_idx in range(200): # for original testing, 200 clean CT images
print("imag_idx:",imag_idx)
for mask_idx in range(10): # for original testing, 10 testing metal masks
Xma, X, XLI, M = test_image(opt.data_path, imag_idx, mask_idx)
with torch.no_grad():
if opt.use_GPU:
torch.cuda.synchronize()
start_time = time.time()
X0, ListX, ListA = net(Xma, XLI, M)
end_time = time.time()
dur_time = end_time - start_time
time_test += dur_time
Xoutclip = torch.clamp(ListX[-1] / 255.0, 0, 0.5)
Xgtclip = torch.clamp(X / 255.0, 0, 0.5)
Xmaclip = torch.clamp(Xma / 255.0, 0, 0.5)
Xoutnorm = Xoutclip / 0.5
Xgtnorm = Xgtclip / 0.5
Xmanorm = Xmaclip / 0.5
idx = imag_idx *10+ mask_idx + 1
Xnorm = [Xoutnorm, Xmanorm, Xgtnorm]
dir = [out_dir, input_dir, gt_dir]
save_img.imwrite(idx, dir, Xnorm)
print('Times: ', dur_time)
count += 1
print(100*'*')
if __name__ == "__main__":
main()