1.Compiling DCNv2:
python ./models/modules/DCNv2/setup.py build develop2.Model training:
Modify the data set path and training parameters in configs/TMNet_multiple_frames.yaml, then run
sh train.sh3. Fast test:
Modify the test configurations in Python file test_multi_frame_psnr.py. Note that we have uploaded the weights of our models in file check_points.
result_folder = './results'
N_ot = 1 # control the slice number you want to interpolate. Select [1, 3, 5] for [x2, x4, x6] respectively.
use_time = True # multi-slice interpolation
save_imgs = False # wheather save test images
# pre-trained model path
model_path = './check_points/x2.pth'
# model_path = './check_points/x4.pth'
# model_path = './check_points/x6.pth'
# test file path
dataset_folder = '/data/LITS_volume_img/*'
# dataset_folder = '/data/vessel_volume_img/*'
# dataset_folder = '/data/kidney_volume_img/*'
# dataset_folder = '/data/colon_volume_img/*'Then run:
sh test.shWe get the testing results as:
23-09-23 11:40:04.600 - INFO: Data: temp - /data/LITS_volume_img/*
23-09-23 11:40:04.600 - INFO: Padding mode: replicate
23-09-23 11:40:04.600 - INFO: Model path: ./check_points/x2.pth
23-09-23 11:40:04.600 - INFO: Model parameters: 1.507629 M
23-09-23 11:40:04.600 - INFO: Save images: False
23-09-23 11:40:04.600 - INFO: Flip Test: False
23-09-23 11:40:13.454 - INFO: 2 - 2.png PSNR: 39.060180 dB SSIM: 0.972012 dB
23-09-23 11:40:13.828 - INFO: 4 - 4.png PSNR: 39.320951 dB SSIM: 0.973429 dB
23-09-23 11:40:14.202 - INFO: 6 - 6.png PSNR: 39.100264 dB SSIM: 0.972124 dB
23-09-23 11:40:14.576 - INFO: 8 - 8.png PSNR: 39.151693 dB SSIM: 0.972499 dB
23-09-23 11:40:14.949 - INFO: 10 - 10.png PSNR: 39.002313 dB SSIM: 0.971758 dB
23-09-23 11:40:15.322 - INFO: 12 - 12.png PSNR: 39.097148 dB SSIM: 0.973091 dB
23-09-23 11:40:15.695 - INFO: 14 - 14.png PSNR: 39.057419 dB SSIM: 0.973872 dB
... ...