From: Francois Fleuret Date: Fri, 16 Jun 2017 06:29:39 +0000 (+0200) Subject: Now does nothing if the model already exists. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=61e13c9a3cba66d1b6dafaa14efb71e979b8af08;p=pysvrt.git Now does nothing if the model already exists. --- diff --git a/cnn-svrt.py b/cnn-svrt.py index 8b8ec12..6645ac1 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -167,17 +167,13 @@ def nb_errors(model, data_set): for arg in vars(args): log_string('argument ' + str(arg) + ' ' + str(getattr(args, arg))) +###################################################################### + for problem_number in range(1, 24): - if args.compress_vignettes: - train_set = CompressedVignetteSet(problem_number, args.nb_train_batches, args.batch_size, - cuda=torch.cuda.is_available()) - test_set = CompressedVignetteSet(problem_number, args.nb_test_batches, args.batch_size, - cuda=torch.cuda.is_available()) - else: - train_set = VignetteSet(problem_number, args.nb_train_batches, args.batch_size, - cuda=torch.cuda.is_available()) - test_set = VignetteSet(problem_number, args.nb_test_batches, args.batch_size, - cuda=torch.cuda.is_available()) + + model_filename = model.name + '_' + \ + str(problem_number) + '_' + \ + str(args.nb_train_batches) + '.param' model = AfrozeShallowNet() @@ -185,37 +181,53 @@ for problem_number in range(1, 24): model.cuda() nb_parameters = 0 - for p in model.parameters(): - nb_parameters += p.numel() + for p in model.parameters(): nb_parameters += p.numel() log_string('nb_parameters {:d}'.format(nb_parameters)) - model_filename = model.name + '_' + str(problem_number) + '_' + str(train_set.nb_batches) + '.param' - try: + model.load_state_dict(torch.load(model_filename)) log_string('loaded_model ' + model_filename) + except: - log_string('training_model') + + log_string('training_model ' + model_filename) + + if args.compress_vignettes: + train_set = CompressedVignetteSet(problem_number, + args.nb_train_batches, args.batch_size, + cuda=torch.cuda.is_available()) + test_set = CompressedVignetteSet(problem_number, + args.nb_test_batches, args.batch_size, + cuda=torch.cuda.is_available()) + else: + train_set = VignetteSet(problem_number, + args.nb_train_batches, args.batch_size, + cuda=torch.cuda.is_available()) + test_set = VignetteSet(problem_number, + args.nb_test_batches, args.batch_size, + cuda=torch.cuda.is_available()) + train_model(model, train_set) torch.save(model.state_dict(), model_filename) log_string('saved_model ' + model_filename) - nb_train_errors = nb_errors(model, train_set) + nb_train_errors = nb_errors(model, train_set) - log_string('train_error {:d} {:.02f}% {:d} {:d}'.format( - problem_number, - 100 * nb_train_errors / train_set.nb_samples, - nb_train_errors, - train_set.nb_samples) - ) + log_string('train_error {:d} {:.02f}% {:d} {:d}'.format( + problem_number, + 100 * nb_train_errors / train_set.nb_samples, + nb_train_errors, + train_set.nb_samples) + ) - nb_test_errors = nb_errors(model, test_set) + nb_test_errors = nb_errors(model, test_set) - log_string('test_error {:d} {:.02f}% {:d} {:d}'.format( - problem_number, - 100 * nb_test_errors / test_set.nb_samples, - nb_test_errors, - test_set.nb_samples) - ) + log_string('test_error {:d} {:.02f}% {:d} {:d}'.format( + problem_number, + 100 * nb_test_errors / test_set.nb_samples, + nb_test_errors, + test_set.nb_samples) + ) ######################################################################