def inference_module(_data_path, _save_path, _pth_path):
model = Network(channel=32, n_class=1)
model.load_state_dict(torch.load(_pth_path))
model.cuda()
model.eval()
os.makedirs(_save_path, exist_ok=True)
# FIXME
image_root = '{}/'.format(_data_path)
# gt_root = '{}/mask/'.format(data_path)
test_loader = test_dataset(image_root, image_root, 352)
for i in range(test_loader.size):
image, name = test_loader.load_data()
#gt = np.asarray(gt, np.float32)
#gt /= (gt.max() + 1e-8)
image = image.cuda()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(
image)
res = lateral_map_2 # final segmentation
#res = F.upsample(res, size=gt.shape, mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
misc.imsave(_save_path + '/' + name, res)
def inference():
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--data_path', type=str, default='./Dataset/TestingSet/LungSegmentation/',
help='Path to test data')
parser.add_argument('--pth_path', type=str, default='./snapshots/save_weights/Semi-Inf-Net/Inf-Net-100.pth',
help='Path to weights file. If `semi-sup`, edit it to `Semi-Inf-Net/Semi-Inf-Net-100.pth`')
parser.add_argument('--save_path', type=str, default='./Results/LungSegmentation/Inf-Net/',
help='Path to save the predictions. if `semi-sup`, edit it to `Semi-Inf-Net`')
parser.add_argument('--backbone', type=str, default='Res2Net50',
help='change different backbone, choice: VGGNet16, ResNet50, Res2Net50')
opt = parser.parse_args()
print("#" * 20, "\nStart Testing (Inf-Net)\n{}\nThis code is written for 'Inf-Net: Automatic COVID-19 Lung "
"Infection Segmentation from CT Scans', 2020, arXiv.\n"
"----\nPlease cite the paper if you use this code and dataset. "
"And any questions feel free to contact me "
"via E-mail ([email protected])\n----\n".format(opt), "#" * 20)
if opt.backbone == 'Res2Net50':
print('Backbone loading: Res2Net50')
from Code.model_lung_infection.InfNet_Res2Net import Inf_Net as Network
elif opt.backbone == 'ResNet50':
print('Backbone loading: ResNet50')
from Code.model_lung_infection.InfNet_ResNet import Inf_Net as Network
elif opt.backbone == 'VGGNet16':
print('Backbone loading: VGGNet16')
from Code.model_lung_infection.InfNet_VGGNet import Inf_Net as Network
else:
raise ValueError('Invalid backbone parameters: {}'.format(opt.backbone))
model = Network()
# model = torch.nn.DataParallel(model, device_ids=[0, 1]) # uncomment it if you have multiply GPUs.
model.load_state_dict(torch.load(opt.pth_path, map_location={'cuda:1':'cuda:0'}))
model.cuda()
model.eval()
image_root = '{}/Imgs/'.format(opt.data_path)
# gt_root = '{}/GT/'.format(opt.data_path)
test_loader = test_dataset(image_root, opt.testsize)
os.makedirs(opt.save_path, exist_ok=True)
for i in range(test_loader.size):
image, name = test_loader.load_data()
image = image.cuda()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(image)
res = lateral_map_2
#res = F.upsample(res, size=(ori_size[1],ori_size[0]), mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
imsave(opt.save_path + name, res)
print('Test Done!')
def inference_module(_data_path, _save_path, _pth_path):
model = Network(channel=32, n_class=1)
model.load_state_dict(torch.load(_pth_path))
model.cuda()
model.eval()
os.makedirs(_save_path, exist_ok=True)
image_root = '{}/'.format(_data_path)
# gt_root = '{}/GT/'.format(_data_path)
test_loader = test_dataset(image_root, 352)
# initially test_loader = test_dataset(image_root, image_root, 352)
# but test_dataset class from Code.utils.dataloader_LungInf have folloving:
# def __init__(self, image_root, testsize) ...
# because self doesn't have to be in input, one image_root is need to be removed.
for i in range(test_loader.size):
# initially image, gt, name = test_loader.load_data()
# but load_data() function of class test_dataset have following: return image, name
# this error have makes no possible to find shape from gt, so we find shape from image.
# this is made truth sh = image.shape[2:4]
image, name = test_loader.load_data()
image = image.cuda()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(
image)
sh = image.shape[2:4]
res = lateral_map_2 # final segmentation
res = F.upsample(res, size=sh, mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
# GT map is binary image, so we need threshold. 0.5 was chosen and because some resulting images need
# lower threshold, and some need higher, 0.5 good enough.
res = np.where(res < np.max(res) / 2, True, False)
imsave(_save_path + '/' + name, res, cmap='binary')
def inference():
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--data_path', type=str, default='./Dataset/TestingSet/LungSegmentation/',
help='Path to test data')
parser.add_argument('--pth_path', type=str, default='./Snapshots/save_weights/Inf-Net/Inf-Net-100.pth',
help='Path to weights file. If `semi-sup`, edit it to `Semi-Inf-Net/Semi-Inf-Net-100.pth`')
parser.add_argument('--save_path', type=str, default='./Results/LungSegmentation/Inf-Net/',
help='Path to save the predictions. if `semi-sup`, edit it to `Semi-Inf-Net`')
opt = parser.parse_args()
print("#" * 20, "\nStart Testing (Inf-Net)\n{}\n".format(opt), "#" * 20)
model = Network()
model.load_state_dict(torch.load(opt.pth_path, map_location={'cuda:1':'cuda:0'}))
model.cuda()
model.eval()
image_root = '{}/Imgs/'.format(opt.data_path)
test_loader = test_dataset(image_root, opt.testsize)
os.makedirs(opt.save_path, exist_ok=True)
for i in range(test_loader.size):
image, name = test_loader.load_data()
image = image.cuda()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(image)
res = lateral_map_2
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
res = np.where(res < np.max(res) / 2, True, False)
res = np.invert(res)
imsave(opt.save_path + name, res, cmap = 'gray')
print('Test Done!')
def inference():
parser = argparse.ArgumentParser()
parser.add_argument('--testsize',
type=int,
default=352,
help='testing size')
parser.add_argument('--data_path',
type=str,
default='/Users/vignavramesh/Documents/CT_Scans/',
help='Path to test data')
parser.add_argument(
'--pth_path',
type=str,
default='./Snapshots/save_weights/Inf-Net/Inf-Net-100.pth',
help=
'Path to weights file. If `semi-sup`, edit it to `Semi-Inf-Net/Semi-Inf-Net-100.pth`'
)
parser.add_argument(
'--save_path',
type=str,
default='/Users/vignavramesh/Documents/CT_Masks/',
help=
'Path to save the predictions. if `semi-sup`, edit it to `Semi-Inf-Net`'
)
opt = parser.parse_args()
print(
"#" * 20,
"\nStart Testing (Inf-Net)\n{}\nThis code is written for 'Inf-Net: Automatic COVID-19 Lung "
"Infection Segmentation from CT Scans', 2020, arXiv.\n"
"----\nPlease cite the paper if you use this code and dataset. "
"And any questions feel free to contact me "
"via E-mail ([email protected])\n----\n".format(opt), "#" * 20)
model = Network()
model.load_state_dict(
torch.load(opt.pth_path, map_location=torch.device('cpu')))
model.eval()
count = 0
list = glob.glob(opt.data_path + '*')
list = sort_list(list)
for image_root in list: #subs
count = image_root[(image_root.rindex('Volume') + 6):]
image_root += '/'
test_loader = test_dataset(image_root, opt.testsize)
os.makedirs(opt.save_path, exist_ok=True)
for i in range(test_loader.size):
image, name = test_loader.load_data()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(
image)
res = lateral_map_2
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
string = opt.save_path + 'Volume' + str(count)
if not os.path.exists(string):
os.makedirs(string)
imageio.imwrite(string + '/' + name, res)
print('Test Done!')
def inference(image_root):
parser = argparse.ArgumentParser()
parser.add_argument('--testsize',
type=int,
default=352,
help='testing size')
parser.add_argument('--data_path',
type=str,
default='./Dataset/TestingSet/LungInfection-Test/',
help='Path to test data')
parser.add_argument(
'--pth_path',
type=str,
default='./Snapshots/save_weights/Inf-Net/Inf-Net-100.pth',
help=
'Path to weights file. If `semi-sup`, edit it to `Inf-Net/Inf-Net-100.pth`'
)
parser.add_argument(
'--save_path',
type=str,
default='./Results/Lung infection segmentation/Inf-Net/Labels/',
help='Path to save the predictions. if `semi-sup`, edit it to `Inf-Net`'
)
parser.add_argument(
'--save_path_append',
type=str,
default='./Results/Lung infection segmentation/Inf-Net/Append_result/',
help='Path to save the predictions. if `semi-sup`, edit it to `Inf-Net`'
)
parser.add_argument(
'--save_path_mask',
type=str,
default='./Results/Lung infection segmentation/Inf-Net/Mask/',
help='Path to save the predictions. if `semi-sup`, edit it to `Inf-Net`'
)
opt = parser.parse_args()
model = Network()
# model = torch.nn.DataParallel(model, device_ids=[0, 1]) # uncomment it if you have multiply GPUs.
model.load_state_dict(
torch.load(opt.pth_path, map_location={'cuda:1': 'cuda:0'}))
model.cuda()
model.eval()
# gt_root = '{}/GT/'.format(opt.data_path)
test_loader = test_dataset(image_root, opt.testsize)
os.makedirs(opt.save_path, exist_ok=True)
image, name = test_loader.load_data()
image = image.cuda()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(
image)
res = lateral_map_2
# res = F.upsample(res, size=(ori_size[1],ori_size[0]), mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
print(name)
misc.imsave(opt.save_path + name, res)
icon = show_label(res, opt.save_path_mask + name)
img = Image.open(image_root)
img = img.convert("RGBA")
img_w, img_h = img.size
icon = icon.resize((img_w, img_h), Image.ANTIALIAS)
layer = Image.new("RGBA", img.size, (0, 0, 0, 0))
layer.paste(icon, (0, 0))
out = Image.composite(layer, img, layer)
out.save(opt.save_path_append + name)
print('Test Done!')
def inference():
parser = argparse.ArgumentParser()
parser.add_argument('--testsize',
type=int,
default=352,
help='testing size')
parser.add_argument('--data_path',
type=str,
default='./Dataset/TestingSet/LungInfection-Test/',
help='Path to test data')
parser.add_argument(
'--pth_path',
type=str,
default='./Snapshots/save_weights/Semi-Inf-Net/Semi-Inf-Net-100.pth',
help=
'Path to weights fileif `semi-sup`, edit it to `Semi-Inf-Net/Semi-Inf-Net-100.pth`'
)
parser.add_argument(
'--save_path',
type=str,
default='./Results/Lung infection segmentation/Semi-Inf-Net/',
help=
'Path to save the predictions. if `semi-sup`, edit it to `Semi-Inf-Net`'
)
opt = parser.parse_args()
print(
"#" * 20,
"\nStart Testing (Inf-Net)\n{}\nThis code is written for 'Inf-Net: Automatic COVID-19 Lung "
"Infection Segmentation from CT Scans', 2020, arXiv.\n"
"----\nPlease cite the paper if you use this code and dataset. "
"And any questions feel free to contact me "
"via E-mail ([email protected])\n----\n".format(opt.backbone,
opt), "#" * 20)
model = Network()
# model = torch.nn.DataParallel(model, device_ids=[0, 1]) # uncomment it if you have multiply GPUs.
model.load_state_dict(torch.load(opt.pth_path))
model.cuda()
model.eval()
image_root = '{}/Imgs/'.format(opt.data_path)
gt_root = '{}/GT/'.format(opt.data_path)
test_loader = test_dataset(image_root, gt_root, opt.testsize)
os.makedirs(opt.save_path, exist_ok=True)
for i in range(test_loader.size):
image, gt, name = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
lateral_map_5, lateral_map_4, lateral_map_3, lateral_map_2, lateral_edge = model(
image)
res = lateral_map_2
res = F.upsample(res,
size=gt.shape,
mode='bilinear',
align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
misc.imsave(opt.save_path + name, res)
print('Test Done!')