| import logging |
| import os |
| import time |
| import torch |
| import shutil |
| import numpy as np |
| import nibabel as nib |
| import pandas |
| from typing import List, Tuple, Type, Union |
|
|
| def save_checkpoint(state, is_best, checkpoint): |
| filepath_last = os.path.join(checkpoint, "last.pth.tar") |
| filepath_best = os.path.join(checkpoint, "best.pth.tar") |
| if not os.path.exists(checkpoint): |
| print("Checkpoint Directory does not exist! Masking directory {}".format(checkpoint)) |
| os.mkdir(checkpoint) |
| else: |
| print("Checkpoint Directory exists!") |
| torch.save(state, filepath_last) |
| if is_best: |
| if os.path.isfile(filepath_best): |
| os.remove(filepath_best) |
| shutil.copyfile(filepath_last, filepath_best) |
|
|
|
|
| def setup_logger(logger_name, root, level=logging.INFO, screen=False, tofile=False): |
| """set up logger""" |
| lg = logging.getLogger(logger_name) |
| formatter = logging.Formatter("[%(asctime)s.%(msecs)03d] %(message)s", datefmt="%H:%M:%S") |
| lg.setLevel(level) |
|
|
| log_time = get_timestamp() |
| if tofile: |
| log_file = os.path.join(root, "{}_{}.log".format(logger_name, log_time)) |
| fh = logging.FileHandler(log_file, mode="w") |
| fh.setFormatter(formatter) |
| lg.addHandler(fh) |
| if screen: |
| sh = logging.StreamHandler() |
| sh.setFormatter(formatter) |
| lg.addHandler(sh) |
| return lg, log_time |
|
|
|
|
| def get_timestamp(): |
| timestampTime = time.strftime("%H%M%S") |
| timestampDate = time.strftime("%Y%m%d") |
| return timestampDate + "-" + timestampTime |
|
|
|
|
|
|
| def save_csv(args, logger, patient_list, |
| loss, loss_nsd, |
| ): |
| save_predict_dir = os.path.join(args.save_base_dir, 'csv_file') |
| if not os.path.exists(save_predict_dir): |
| os.makedirs(save_predict_dir) |
|
|
| df_dict = {'patient': patient_list, |
| 'dice': loss, |
| 'nsd': loss_nsd, |
| } |
|
|
| df = pandas.DataFrame(df_dict) |
| df.to_csv(os.path.join(save_predict_dir, 'prompt_' + str(args.num_prompts) |
| + '_' + str(args.save_name) + '.csv'), index=False) |
| logger.info("- CSV saved") |
|
|
|
|
| def save_image(save_array, test_data, image_data, save_prediction_path): |
| nib.save(nib.Nifti1Image(save_array[0, 0, :].permute(test_data.dataset.spatial_index).cpu().numpy(), |
| image_data.affine, image_data.header), save_prediction_path) |
|
|
|
|
| def _bbox_mask(mask_volume: torch.Tensor, diff=1, mode='train', dynamic=False, max_diff=10, return_extend=False) -> torch.Tensor: |
| bbox_coords = [] |
| for volume in mask_volume: |
| i_any = volume.any(dim=2).any(dim=1) |
| j_any = volume.any(dim=2).any(dim=0) |
| k_any = volume.any(dim=1).any(dim=0) |
|
|
| i_min, i_max = torch.where(i_any)[0][[0, -1]] |
| j_min, j_max = torch.where(j_any)[0][[0, -1]] |
| k_min, k_max = torch.where(k_any)[0][[0, -1]] |
|
|
| |
| |
|
|
| if dynamic and mode == 'train': |
| |
| diff_ = np.random.choice(range(0, max_diff), size=6, replace=True) |
|
|
| if max(0, i_min - diff_[0]) < min(i_max + diff_[1], 126): |
| i_min, i_max = max(0, i_min - diff_[0]), min(i_max + diff_[1], 126) |
| if max(0, j_min - diff_[2]) < min(j_max + diff_[3], 126): |
| j_min, j_max = max(0, j_min - diff_[2]), min(j_max + diff_[3], 126) |
| if max(0, k_min - diff_[4]) < min(k_max + diff_[5], 126): |
| k_min, k_max = max(0, k_min - diff_[4]), min(k_max + diff_[5], 126) |
|
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|
| bb = torch.tensor([[i_min, j_min, k_min, i_max + 1, j_max + 1, k_max + 1]]) |
| |
|
|
| |
| bbox_coords.append(bb) |
| |
| bbox_coords = torch.stack(bbox_coords) |
| return bbox_coords |
|
|
|
|
