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test.py
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import joblib,copy
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
import torch,sys
from tqdm import tqdm
from collections import OrderedDict
from lib.visualize import save_img,group_images,concat_result
import os
import argparse
from lib.logger import Logger, Print_Logger
from lib.extract_patches import *
from os.path import join
from lib.dataset import TestDataset
from lib.metrics import Evaluate
import models
from lib.common import setpu_seed,dict_round
from config import parse_args
from lib.pre_processing import my_PreProc
setpu_seed(2021)
class Test():
def __init__(self, args):
self.args = args
assert (args.stride_height <= args.test_patch_height and args.stride_width <= args.test_patch_width)
# save path
self.path_experiment = join(args.outf, args.save)
# 设置test的
self.patches_imgs_test, self.test_imgs, self.test_masks, self.test_FOVs, self.new_height, self.new_width = get_data_test_overlap(
test_data_path_list=args.test_data_path_list, # 数据txt文件，里面是各个图片的位置
patch_height=args.test_patch_height,
patch_width=args.test_patch_width,
stride_height=args.stride_height,
stride_width=args.stride_width
)
self.img_height = self.test_imgs.shape[2]
self.img_width = self.test_imgs.shape[3]
test_set = TestDataset(self.patches_imgs_test)
self.test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=3)
self.lc = args.largest_connected
self.binary_images = None
# Inference prediction process
def inference(self, net):
net.eval()
preds = []
with torch.no_grad():
for batch_idx, inputs in tqdm(enumerate(self.test_loader), total=len(self.test_loader)):
inputs = inputs.cuda()
outputs = net(inputs)
outputs = outputs[:,1].data.cpu().numpy()
preds.append(outputs)
predictions = np.concatenate(preds, axis=0)
self.pred_patches = np.expand_dims(predictions,axis=1)
# Evaluate ate and visualize the predicted images
def evaluate(self):
self.pred_imgs = recompone_overlap(
self.pred_patches, self.new_height, self.new_width, self.args.stride_height, self.args.stride_width)
## restore to original dimensions
self.pred_imgs = self.pred_imgs[:, :, 0:self.img_height, 0:self.img_width]
#predictions only inside the FOV
y_scores, y_true, self.binary_images = pred_only_in_FOV(self.pred_imgs, self.test_masks, self.test_FOVs,largest_connected=self.lc)
eval = Evaluate(save_path=self.path_experiment,lc=self.lc)
eval.add_batch(y_true, y_scores, self.binary_images)
log = eval.save_all_result(plot_curve=True,save_name="performance.txt")
# save labels and probs for plot ROC and PR curve when k-fold Cross-validation
np.save('{}/result.npy'.format(self.path_experiment), np.asarray([y_true, y_scores]))
return dict_round(log, 6)
# save segmentation imgs
def save_segmentation_result(self):
img_path_list, _, _ = load_file_path_txt(self.args.test_data_path_list)
img_name_list = [item.split('/')[-1].split('.')[0] for item in img_path_list]
kill_border(self.pred_imgs, self.test_FOVs) # only for visualization
self.save_img_path = join(self.path_experiment,'result_img')
if not os.path.exists(join(self.save_img_path)):
os.makedirs(self.save_img_path)
# self.test_imgs = my_PreProc(self.test_imgs) # Uncomment to save the pre processed image
for i in range(self.test_imgs.shape[0]):
total_img = concat_result(self.test_imgs[i],self.pred_imgs[i],self.test_masks[i], self.lc)
# print(sum(self.pred_imgs[i]))
save_img(total_img,join(self.save_img_path, "Result_"+img_name_list[i]+'.png'))
# Val on the test set at each epoch
def val(self):
self.pred_imgs = recompone_overlap(
self.pred_patches, self.new_height, self.new_width, self.args.stride_height, self.args.stride_width)
## recover to original dimensions
self.pred_imgs = self.pred_imgs[:, :, 0:self.img_height, 0:self.img_width]
#predictions only inside the FOV
y_scores, y_true, self.binary_images = pred_only_in_FOV(self.pred_imgs, self.test_masks, self.test_FOVs) # test 这里的self.image改成了images
eval = Evaluate(save_path=self.path_experiment, lc=self.lc)
eval.add_batch(y_true, y_scores, self.binary_images)
confusion,accuracy,specificity,sensitivity,precision = eval.confusion_matrix()
log = OrderedDict([('F1 Score', eval.f1_score()),
('Sensitivity', sensitivity),
('Specifity', specificity),
('Accuracy', accuracy),
('AUC-ROC', eval.auc_roc()),
('Mean-IOU', eval.jaccard_index())])
return dict_round(log, 6)
def predict_wo_gt(self, net):
net.eval()
preds = []
with torch.no_grad():
for batch_idx, inputs in tqdm(enumerate(self.test_loader), total=len(self.test_loader)):
inputs = inputs.cuda()
outputs = net(inputs)
outputs = outputs[:,1].data.cpu().numpy()
preds.append(outputs)
predictions = np.concatenate(preds, axis=0)
self.pred_patches = np.expand_dims(predictions,axis=1)
self.pred_imgs = recompone_overlap(
self.pred_patches, self.new_height, self.new_width, self.args.stride_height, self.args.stride_width)
## restore to original dimensions
self.pred_imgs = self.pred_imgs[:, :, 0:self.img_height, 0:self.img_width]
#predictions only inside the FOV
y_scores, y_true, self.binary_images = pred_only_in_FOV(self.pred_imgs, self.test_masks, self.test_FOVs,largest_connected=self.lc)
img_path_list, _, _ = load_file_path_txt(self.args.test_data_path_list)
img_name_list = [item.split('/')[-1].split('.')[0] for item in img_path_list]
kill_border(self.pred_imgs, self.test_FOVs) # only for visualization
self.save_img_path = join(self.path_experiment,'result_img')
if not os.path.exists(join(self.save_img_path)):
os.makedirs(self.save_img_path)
# self.test_imgs = my_PreProc(self.test_imgs) # Uncomment to save the pre processed image
for i in range(self.test_imgs.shape[0]):
total_img = concat_result(self.test_imgs[i],self.pred_imgs[i],self.test_masks[i], self.lc)
# print(sum(self.pred_imgs[i]))
save_img(total_img,join(self.save_img_path, "Result_"+img_name_list[i].split('\\')[-1]+'.png'))
if __name__ == '__main__':
# 基础设置
args = parse_args()
save_path = join(args.outf, args.save)
sys.stdout = Print_Logger(os.path.join(save_path, 'test_log.txt'))
device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
cudnn.benchmark = True
# 模型设置
# net = models.UNetFamily.U_Net(1,2).to(device)
# net = models.MF_UNet(2, 1, 10).to(device)
# net = models.LadderNet(inplanes=args.in_channels, num_classes=args.classes, layers=3, filters=16).to(device)
net = models.Dense_Unet(in_chan=1).to(device)
# net = models.LadderNet(inplanes=1, num_classes=2, layers=3, filters=16).to(device)
# Load checkpoint
print('==> Loading checkpoint...')
checkpoint = torch.load(join(save_path, 'best_model.pth'))
net.load_state_dict(checkpoint['net'])
eval = Test(args)
# eval.predict_wo_gt(net)
eval.inference(net)
print(eval.evaluate())
# print(eval.val())
eval.save_segmentation_result()