From: Francois Fleuret Date: Sat, 17 Jun 2017 18:55:53 +0000 (+0200) Subject: Cleaning up. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=d21f7d8eecb12aa4cc60360db6aa33324327e987;p=pysvrt.git Cleaning up. --- diff --git a/cnn-svrt.py b/cnn-svrt.py index 5dc91c8..153bdc9 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -40,7 +40,7 @@ from torchvision import datasets, transforms, utils # SVRT -from vignette_set import VignetteSet, CompressedVignetteSet +import vignette_set ###################################################################### @@ -268,17 +268,21 @@ if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_ print('The number of samples must be a multiple of the batch size.') raise +if args.compress_vignettes: + VignetteSet = vignette_set.CompressedVignetteSet +else: + VignetteSet = vignette_set.VignetteSet + for problem_number in range(1, 24): - log_string('**** problem ' + str(problem_number) + ' ****') + log_string('############### problem ' + str(problem_number) + ' ###############') if args.deep_model: model = AfrozeDeepNet() else: model = AfrozeShallowNet() - if torch.cuda.is_available(): - model.cuda() + if torch.cuda.is_available(): model.cuda() model_filename = model.name + '_' + \ str(problem_number) + '_' + \ @@ -288,6 +292,9 @@ for problem_number in range(1, 24): for p in model.parameters(): nb_parameters += p.numel() log_string('nb_parameters {:d}'.format(nb_parameters)) + ################################################## + # Tries to load the model + need_to_train = False try: model.load_state_dict(torch.load(model_filename)) @@ -295,20 +302,18 @@ for problem_number in range(1, 24): except: need_to_train = True + ################################################## + # Train if necessary + if need_to_train: log_string('training_model ' + model_filename) t = time.time() - if args.compress_vignettes: - train_set = CompressedVignetteSet(problem_number, - args.nb_train_samples, args.batch_size, - cuda = torch.cuda.is_available()) - else: - train_set = VignetteSet(problem_number, - args.nb_train_samples, args.batch_size, - cuda = torch.cuda.is_available()) + train_set = VignetteSet(problem_number, + args.nb_train_samples, args.batch_size, + cuda = torch.cuda.is_available()) log_string('data_generation {:0.2f} samples / s'.format( train_set.nb_samples / (time.time() - t)) @@ -327,18 +332,16 @@ for problem_number in range(1, 24): train_set.nb_samples) ) + ################################################## + # Test if necessary + if need_to_train or args.test_loaded_models: t = time.time() - if args.compress_vignettes: - test_set = CompressedVignetteSet(problem_number, - args.nb_test_samples, args.batch_size, - cuda = torch.cuda.is_available()) - else: - test_set = VignetteSet(problem_number, - args.nb_test_samples, args.batch_size, - cuda = torch.cuda.is_available()) + test_set = VignetteSet(problem_number, + args.nb_test_samples, args.batch_size, + cuda = torch.cuda.is_available()) log_string('data_generation {:0.2f} samples / s'.format( test_set.nb_samples / (time.time() - t))