######################################################################
log_file = open(args.log_file, 'a')
+log_file.write('\n')
+log_file.write('@@@@@@@@@@@@@@@@@@@ ' + time.ctime() + ' @@@@@@@@@@@@@@@@@@@\n')
+log_file.write('\n')
+
pred_log_t = None
last_tag_t = time.time()
img = input[i].clone()
img.sub_(img.min())
img.div_(img.max())
- torchvision.utils.save_image(img,
- mistake_filename_pattern.format(b + i, target[i]))
-
+ k = b * data_set.batch_size + i
+ filename = mistake_filename_pattern.format(k, target[i])
+ torchvision.utils.save_image(img, filename)
+ print(Fore.RED + 'Wrote ' + filename + Style.RESET_ALL)
return ne
######################################################################
print('The number of samples must be a multiple of the batch size.')
raise
-log_string('############### start ###############')
-
if args.compress_vignettes:
log_string('using_compressed_vignettes')
VignetteSet = svrtset.CompressedVignetteSet
cuda = torch.cuda.is_available())
nb_test_errors = nb_errors(model, test_set,
- mistake_filename_pattern = 'mistake_{:d}_{:06d}.png')
+ mistake_filename_pattern = 'mistake_{:06d}_{:d}.png')
log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
problem_number,