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test_ACDC.py
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test_ACDC.py
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import argparse
import os
import re
import shutil
import h5py
import numpy as np
import SimpleITK as sitk
import torch
from medpy import metric
from scipy.ndimage.interpolation import zoom
from tqdm import tqdm
import importlib
from tool import pyutils
from time import strftime
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str,
default='../data/ACDC', help='Name of Experiment')
parser.add_argument('--exp', type=str,
default='ACDC/scribbleVC', help='experiment_name')
parser.add_argument('--model', type=str,
default='scribbleVC', help='model_name')
parser.add_argument('--fold', type=str,
default='MAAGfold', help='fold')
parser.add_argument('--num_classes', type=int, default=4,
help='output channel of network')
parser.add_argument('--sup_type', type=str, default="scribble",
help='label')
parser.add_argument("--network", default="network.scribbleVC", type=str)
parser.add_argument("--train_epochs", default="best", type=str)
parser.add_argument('--linear_layer', action="store_true", help='linear layer')
parser.add_argument('--bilinear', action="store_false", help='use bilinear in Upsample layers')
parser.add_argument('--save_prediction', action="store_true", help='save predictions while testing')
parser.add_argument("--arch", default='ACDC', type=str)
def get_fold_ids(fold):
if fold == "MAAGfold":
training_set = ["patient{:0>3}".format(i) for i in
[37, 50, 53, 100, 38, 19, 61, 74, 97, 31, 91, 35, 56, 94, 26, 69, 46, 59, 4, 89,
71, 6, 52, 43, 45, 63, 93, 14, 98, 88, 21, 28, 99, 54, 90]]
testing_set = ["patient{:0>3}".format(i) for i in
[5, 39, 77, 82, 78, 10, 64, 24, 30, 73, 80, 41, 36, 60, 72]]
return [training_set, testing_set]
elif "MAAGfold" in fold:
training_set = ["patient{:0>3}".format(i) for i in
[37, 50, 53, 100, 38, 19, 61, 74, 97, 31, 91, 35, 56, 94, 26, 69, 46, 59, 4, 89,
71, 6, 52, 43, 45, 63, 93, 14, 98, 88, 21, 28, 99, 54, 90, 2, 76, 34, 85, 70, 86, 3, 8, 51, 40,
7, 13, 47, 55, 12, 58, 87, 9, 65, 62, 33, 42,
23, 92, 29, 11, 83, 68, 75, 67, 16, 48, 66, 20, 15]]
testing_set = ["patient{:0>3}".format(i) for i in
[5, 39, 77, 82, 78, 10, 64, 24, 30, 73, 80, 41, 36, 60, 72]]
return [training_set, testing_set]
else:
return "ERROR KEY"
def calculate_metric_percase(pred, gt, spacing):
pred[pred > 0] = 1
gt[gt > 0] = 1
dice = metric.binary.dc(pred, gt)
asd = metric.binary.asd(pred, gt, voxelspacing=spacing)
hd95 = metric.binary.hd95(pred, gt, voxelspacing=spacing)
return dice, hd95, asd
def test_single_volume(case, net, test_save_path, FLAGS):
h5f = h5py.File(FLAGS.root_path +
"/ACDC_training_volumes/{}".format(case), 'r')
image = h5f['image'][:]
label = h5f['label'][:]
prediction = np.zeros_like(label)
for ind in range(image.shape[0]):
slice = image[ind, :, :]
x, y = slice.shape[0], slice.shape[1]
slice = zoom(slice, (256 / x, 256 / y), order=0)
input = torch.from_numpy(slice).unsqueeze(
0).unsqueeze(0).float().cuda()
net.eval()
with torch.no_grad():
out_aux1, out_aux2 = net(input)[0], net(input)[1]
out_aux1_soft = torch.softmax(out_aux1, dim=1)
out_aux2_soft = torch.softmax(out_aux2, dim=1)
out = torch.argmax((out_aux1_soft+out_aux2_soft)*0.5, dim=1).squeeze(0)
out = out.cpu().detach().numpy()
pred = zoom(out, (x / 256, y / 256), order=0)
prediction[ind] = pred
case = case.replace(".h5", "")
org_img_path = "../data/ACDC_training/{}.nii.gz".format(case)
org_img_itk = sitk.ReadImage(org_img_path)
spacing = org_img_itk.GetSpacing()
first_metric = calculate_metric_percase(
prediction == 1, label == 1, (spacing[2], spacing[0], spacing[1]))
second_metric = calculate_metric_percase(
prediction == 2, label == 2, (spacing[2], spacing[0], spacing[1]))
third_metric = calculate_metric_percase(
prediction == 3, label == 3, (spacing[2], spacing[0], spacing[1]))
if FLAGS.save_prediction:
img_itk = sitk.GetImageFromArray(image.astype(np.float32))
img_itk.CopyInformation(org_img_itk)
prd_itk = sitk.GetImageFromArray(prediction.astype(np.float32))
prd_itk.CopyInformation(org_img_itk)
lab_itk = sitk.GetImageFromArray(label.astype(np.float32))
lab_itk.CopyInformation(org_img_itk)
sitk.WriteImage(prd_itk, test_save_path + case + "_pred.nii.gz")
sitk.WriteImage(img_itk, test_save_path + case + "_img.nii.gz")
sitk.WriteImage(lab_itk, test_save_path + case + "_gt.nii.gz")
return first_metric, second_metric, third_metric
def Inference(FLAGS):
train_ids, test_ids = get_fold_ids(FLAGS.fold)
all_volumes = os.listdir(
FLAGS.root_path + "/ACDC_training_volumes")
image_list = []
for ids in test_ids:
new_data_list = list(filter(lambda x: re.match(
'{}.*'.format(ids), x) != None, all_volumes))
image_list.extend(new_data_list)
snapshot_path = "../model/{}_{}/{}".format(
FLAGS.exp, FLAGS.fold, FLAGS.sup_type)
test_save_path = "../model/{}_{}/{}/{}_predictions/".format(
FLAGS.exp, FLAGS.fold, FLAGS.sup_type, FLAGS.model)
if FLAGS.save_prediction:
if os.path.exists(test_save_path):
shutil.rmtree(test_save_path)
os.makedirs(test_save_path)
logdir = os.path.join(test_save_path, "{}_log.txt".format(strftime("%Y_%m_%d_%H_%M_%S")))
pyutils.Logger(logdir)
print("log in ", logdir)
net = getattr(importlib.import_module(FLAGS.network), 'scribbleVC_' + FLAGS.arch)(linear_layer=FLAGS.linear_layer, bilinear=FLAGS.bilinear) # get Net_sm from network.conformer_CAM
print('network is from', net.__class__)
if FLAGS.train_epochs == "best":
save_mode_path = os.path.join(
snapshot_path, '{}_best_model.pth'.format(FLAGS.model))
elif FLAGS.train_epochs == "final":
save_mode_path = os.path.join(
snapshot_path, '{}_final_model.pth'.format(FLAGS.model))
else:
save_mode_path = os.path.join(
snapshot_path, '{}_{}_model.pth'.format(FLAGS.model, FLAGS.train_epochs))
print("init weight from {}".format(save_mode_path))
net.load_state_dict(torch.load(save_mode_path))
net.eval()
net.cuda()
first_total = 0.0
second_total = 0.0
third_total = 0.0
for case in tqdm(image_list):
print(case)
first_metric, second_metric, third_metric = test_single_volume(
case, net, test_save_path, FLAGS)
print((first_metric[0] + second_metric[0] + third_metric[0]) / 3)
first_total += np.asarray(first_metric)
second_total += np.asarray(second_metric)
third_total += np.asarray(third_metric)
avg_metric = [first_total / len(image_list), second_total /
len(image_list), third_total / len(image_list)]
print("RV:", avg_metric[0], " | Myo:", avg_metric[1], " | Lv:", avg_metric[2])
print((avg_metric[0] + avg_metric[1] + avg_metric[2]) / 3)
return ((avg_metric[0] + avg_metric[1] + avg_metric[2]) / 3)[0]
if __name__ == '__main__':
FLAGS = parser.parse_args()
total = 0.0
if FLAGS.fold == "all":
for i in [1, 2, 3, 4, 5]:
FLAGS.fold = "fold{}".format(i)
print("Inference fold{}".format(i))
mean_dice = Inference(FLAGS)
print("Dice fold{}: {}".format(i, mean_dice))
total += mean_dice
print("mean dice of all 5-fold: ", total/5.0)
else:
print("Inference {}".format(FLAGS.fold))
mean_dice = Inference(FLAGS)
print("Dice {}: {}".format(FLAGS.fold, mean_dice))