From: Francois Fleuret Date: Thu, 15 Jun 2017 10:23:11 +0000 (+0200) Subject: Clean up + argument parsing + logging into a file. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=1b19eb14d0bd617deacfb7d15aa2e7babad3a5a7;p=pysvrt.git Clean up + argument parsing + logging into a file. --- diff --git a/cnn-svrt.py b/cnn-svrt.py index c1fe3ac..755d1c7 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -22,6 +22,7 @@ # along with selector. If not, see . import time +import argparse import torch @@ -36,6 +37,37 @@ import svrt ###################################################################### +parser = argparse.ArgumentParser( + description = 'Simple convnet test on the SVRT.', + formatter_class = argparse.ArgumentDefaultsHelpFormatter +) + +parser.add_argument('--nb_train_samples', + type = int, default = 100000, + help = 'How many samples for train') + +parser.add_argument('--nb_test_samples', + type = int, default = 10000, + help = 'How many samples for test') + +parser.add_argument('--nb_epochs', + type = int, default = 25, + help = 'How many training epochs') + +args = parser.parse_args() + +###################################################################### + +log_file = open('cnn-svrt.log', 'w') + +def log_string(s): + s = time.ctime() + ' ' + str(problem_number) + ' | ' + s + log_file.write(s + '\n') + log_file.flush() + print(s) + +###################################################################### + def generate_set(p, n): target = torch.LongTensor(n).bernoulli_(0.5) input = svrt.generate_vignettes(p, target) @@ -69,50 +101,41 @@ def train_model(train_input, train_target): model.cuda() criterion.cuda() - nb_epochs = 25 optimizer, bs = optim.SGD(model.parameters(), lr = 1e-1), 100 - for k in range(0, nb_epochs): - for b in range(0, nb_train_samples, bs): + for k in range(0, args.nb_epochs): + acc_loss = 0.0 + for b in range(0, train_input.size(0), bs): output = model.forward(train_input.narrow(0, b, bs)) loss = criterion(output, train_target.narrow(0, b, bs)) + acc_loss = acc_loss + loss.data[0] model.zero_grad() loss.backward() optimizer.step() + log_string('TRAIN_LOSS {:d} {:f}'.format(k, acc_loss)) return model ###################################################################### -def print_test_error(model, test_input, test_target): - bs = 100 - nb_test_errors = 0 +def nb_errors(model, data_input, data_target, bs = 100): + ne = 0 - for b in range(0, nb_test_samples, bs): - output = model.forward(test_input.narrow(0, b, bs)) + for b in range(0, data_input.size(0), bs): + output = model.forward(data_input.narrow(0, b, bs)) wta_prediction = output.data.max(1)[1].view(-1) for i in range(0, bs): - if wta_prediction[i] != test_target.narrow(0, b, bs).data[i]: - nb_test_errors = nb_test_errors + 1 + if wta_prediction[i] != data_target.narrow(0, b, bs).data[i]: + ne = ne + 1 - print('TEST_ERROR {:.02f}% ({:d}/{:d})'.format( - 100 * nb_test_errors / nb_test_samples, - nb_test_errors, - nb_test_samples) - ) + return ne ###################################################################### -nb_train_samples = 100000 -nb_test_samples = 10000 - -for p in range(1, 24): - print('-- PROBLEM #{:d} --'.format(p)) - - t1 = time.time() - train_input, train_target = generate_set(p, nb_train_samples) - test_input, test_target = generate_set(p, nb_test_samples) +for problem_number in range(1, 24): + train_input, train_target = generate_set(problem_number, args.nb_train_samples) + test_input, test_target = generate_set(problem_number, args.nb_test_samples) if torch.cuda.is_available(): train_input, train_target = train_input.cuda(), train_target.cuda() @@ -122,17 +145,14 @@ for p in range(1, 24): train_input.data.sub_(mu).div_(std) test_input.data.sub_(mu).div_(std) - t2 = time.time() - print('[data generation {:.02f}s]'.format(t2 - t1)) model = train_model(train_input, train_target) - t3 = time.time() - print('[train {:.02f}s]'.format(t3 - t2)) - print_test_error(model, test_input, test_target) - - t4 = time.time() + nb_test_errors = nb_errors(model, test_input, test_target) - print('[test {:.02f}s]'.format(t4 - t3)) - print() + log_string('TEST_ERROR {:.02f}% ({:d}/{:d})'.format( + 100 * nb_test_errors / test_input.size(0), + nb_test_errors, + test_input.size(0)) + ) ######################################################################