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| """ | |
| This file contains some useful functions for train / val. | |
| """ | |
| import os | |
| import numpy as np | |
| import torch | |
| ################# | |
| ## image utils ## | |
| ################# | |
| def convert_image(input_tensor, axis): | |
| """ Convert single channel images to 3-channel images. """ | |
| image_lst = [input_tensor for _ in range(3)] | |
| outputs = np.concatenate(image_lst, axis) | |
| return outputs | |
| ###################### | |
| ## checkpoint utils ## | |
| ###################### | |
| def get_latest_checkpoint(checkpoint_root, checkpoint_name, | |
| device=torch.device("cuda")): | |
| """ Get the latest checkpoint or by filename. """ | |
| # Load specific checkpoint | |
| if checkpoint_name is not None: | |
| checkpoint = torch.load( | |
| os.path.join(checkpoint_root, checkpoint_name), | |
| map_location=device) | |
| # Load the latest checkpoint | |
| else: | |
| lastest_checkpoint = sorted(os.listdir(os.path.join( | |
| checkpoint_root, "*.tar")))[-1] | |
| checkpoint = torch.load(os.path.join( | |
| checkpoint_root, lastest_checkpoint), map_location=device) | |
| return checkpoint | |
| def remove_old_checkpoints(checkpoint_root, max_ckpt=15): | |
| """ Remove the outdated checkpoints. """ | |
| # Get sorted list of checkpoints | |
| checkpoint_list = sorted( | |
| [_ for _ in os.listdir(os.path.join(checkpoint_root)) | |
| if _.endswith(".tar")]) | |
| # Get the checkpoints to be removed | |
| if len(checkpoint_list) > max_ckpt: | |
| remove_list = checkpoint_list[:-max_ckpt] | |
| for _ in remove_list: | |
| full_name = os.path.join(checkpoint_root, _) | |
| os.remove(full_name) | |
| print("[Debug] Remove outdated checkpoint %s" % (full_name)) | |
| def adapt_checkpoint(state_dict): | |
| new_state_dict = {} | |
| for k, v in state_dict.items(): | |
| if k.startswith('module.'): | |
| new_state_dict[k[7:]] = v | |
| else: | |
| new_state_dict[k] = v | |
| return new_state_dict | |
| ################ | |
| ## HDF5 utils ## | |
| ################ | |
| def parse_h5_data(h5_data): | |
| """ Parse h5 dataset. """ | |
| output_data = {} | |
| for key in h5_data.keys(): | |
| output_data[key] = np.array(h5_data[key]) | |
| return output_data | |