| import numpy as np | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def str2ind(categoryname, classlist): | |
| return [ | |
| i for i in range(len(classlist)) if categoryname == classlist[i].decode("utf-8") | |
| ][0] | |
| def strlist2indlist(strlist, classlist): | |
| return [str2ind(s, classlist) for s in strlist] | |
| def strlist2multihot(strlist, classlist): | |
| return np.sum(np.eye(len(classlist))[strlist2indlist(strlist, classlist)], axis=0) | |
| def idx2multihot(id_list, num_class): | |
| return np.sum(np.eye(num_class)[id_list], axis=0) | |
| def write_results_to_eval_file(args, dmap, itr1, itr2): | |
| file_folder = "./ckpt/" + args.dataset_name + "/eval/" | |
| file_name = args.dataset_name + "-results.log" | |
| fid = open(file_folder + file_name, "a+") | |
| string_to_write = str(itr1) | |
| string_to_write += " " + str(itr2) | |
| for item in dmap: | |
| string_to_write += " " + "%.2f" % item | |
| fid.write(string_to_write + "\n") | |
| fid.close() | |
| def write_results_to_file(args, dmap, cmap, itr): | |
| file_folder = "./ckpt/" + args.dataset_name + "/" + str(args.model_id) + "/" | |
| file_name = args.dataset_name + "-results.log" | |
| fid = open(file_folder + file_name, "a+") | |
| string_to_write = str(itr) | |
| for item in dmap: | |
| string_to_write += " " + "%.2f" % item | |
| string_to_write += " " + "%.2f" % cmap | |
| fid.write(string_to_write + "\n") | |
| fid.close() | |
| def write_settings_to_file(args): | |
| file_folder = "./ckpt/" + args.dataset_name + "/" + str(args.model_id) + "/" | |
| file_name = args.dataset_name + "-results.log" | |
| fid = open(file_folder + file_name, "a+") | |
| string_to_write = "#" * 80 + "\n" | |
| for arg in vars(args): | |
| string_to_write += str(arg) + ": " + str(getattr(args, arg)) + "\n" | |
| string_to_write += "*" * 80 + "\n" | |
| fid.write(string_to_write) | |
| fid.close() | |