From: Francois Fleuret Date: Sat, 17 Jun 2017 17:22:35 +0000 (+0200) Subject: Replaced the nb of batches arguments with nb of samples. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=6efb16f367d497093b06bbad686f0dd7e5fa9ae3;p=pysvrt.git Replaced the nb of batches arguments with nb of samples. --- diff --git a/cnn-svrt.py b/cnn-svrt.py index c7e0585..cc3d35f 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -49,12 +49,12 @@ parser = argparse.ArgumentParser( formatter_class = argparse.ArgumentDefaultsHelpFormatter ) -parser.add_argument('--nb_train_batches', - type = int, default = 1000, +parser.add_argument('--nb_train_samples', + type = int, default = 100000, help = 'How many samples for train') -parser.add_argument('--nb_test_batches', - type = int, default = 100, +parser.add_argument('--nb_test_samples', + type = int, default = 10000, help = 'How many samples for test') parser.add_argument('--nb_epochs', @@ -259,6 +259,20 @@ for arg in vars(args): ###################################################################### +def int_to_suffix(n): + if n > 1000000 and n%1000000 == 0: + return str(n//1000000) + 'M' + elif n > 1000 and n%1000 == 0: + return str(n//1000) + 'K' + else: + return str(n) + +###################################################################### + +if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_size > 0: + print('The number of samples must be a multiple of the batch size.') + raise + for problem_number in range(1, 24): log_string('**** problem ' + str(problem_number) + ' ****') @@ -273,7 +287,7 @@ for problem_number in range(1, 24): model_filename = model.name + '_' + \ str(problem_number) + '_' + \ - str(args.nb_train_batches) + '.param' + int_to_suffix(args.nb_train_samples) + '.param' nb_parameters = 0 for p in model.parameters(): nb_parameters += p.numel() @@ -294,14 +308,16 @@ 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()) + args.nb_train_samples, 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()) + 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))) + log_string('data_generation {:0.2f} samples / s'.format( + train_set.nb_samples / (time.time() - t)) + ) train_model(model, train_set) torch.save(model.state_dict(), model_filename) @@ -322,14 +338,16 @@ for problem_number in range(1, 24): if args.compress_vignettes: test_set = CompressedVignetteSet(problem_number, - args.nb_test_batches, args.batch_size, - cuda=torch.cuda.is_available()) + args.nb_test_samples, args.batch_size, + cuda = torch.cuda.is_available()) else: test_set = VignetteSet(problem_number, - args.nb_test_batches, args.batch_size, - cuda=torch.cuda.is_available()) + 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))) + log_string('data_generation {:0.2f} samples / s'.format( + test_set.nb_samples / (time.time() - t)) + ) nb_test_errors = nb_errors(model, test_set) diff --git a/vignette_set.py b/vignette_set.py index 0b6de7e..5062f3e 100755 --- a/vignette_set.py +++ b/vignette_set.py @@ -41,11 +41,16 @@ def generate_one_batch(s): class VignetteSet: - def __init__(self, problem_number, nb_batches, batch_size, cuda = False): + def __init__(self, problem_number, nb_samples, batch_size, cuda = False): + + if nb_samples%batch_size > 0: + print('nb_samples must be a mutiple of batch_size') + raise + self.cuda = cuda self.batch_size = batch_size self.problem_number = problem_number - self.nb_batches = nb_batches + self.nb_batches = nb_samples // batch_size self.nb_samples = self.nb_batches * self.batch_size seeds = torch.LongTensor(self.nb_batches).random_() @@ -83,11 +88,16 @@ class VignetteSet: ###################################################################### class CompressedVignetteSet: - def __init__(self, problem_number, nb_batches, batch_size, cuda = False): + def __init__(self, problem_number, nb_samples, batch_size, cuda = False): + + if nb_samples%batch_size > 0: + print('nb_samples must be a mutiple of batch_size') + raise + self.cuda = cuda self.batch_size = batch_size self.problem_number = problem_number - self.nb_batches = nb_batches + self.nb_batches = nb_samples // batch_size self.nb_samples = self.nb_batches * self.batch_size self.targets = [] self.input_storages = []