| 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() |
|
|
