X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=c75b3364afad3e632f6b1957880c3757ceaf0929;hb=60871ae01df4bb671a75f7bc68072d759fe2f365;hp=084606aa67b18191a969043c214e075d22825fe0;hpb=c71899cfec905c50302be54725a97d7fbff08f54;p=pysvrt.git diff --git a/cnn-svrt.py b/cnn-svrt.py index 084606a..c75b336 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -27,6 +27,8 @@ import math from colorama import Fore, Back, Style +# Pytorch + import torch from torch import optim @@ -36,6 +38,8 @@ from torch import nn from torch.nn import functional as fn from torchvision import datasets, transforms, utils +# SVRT + from vignette_set import VignetteSet, CompressedVignetteSet ###################################################################### @@ -62,23 +66,39 @@ parser.add_argument('--batch_size', help = 'Mini-batch size') parser.add_argument('--log_file', - type = str, default = 'cnn-svrt.log', + type = str, default = 'default.log', help = 'Log file name') parser.add_argument('--compress_vignettes', action='store_true', default = False, help = 'Use lossless compression to reduce the memory footprint') +parser.add_argument('--test_loaded_models', + action='store_true', default = False, + help = 'Should we compute the test errors of loaded models') + args = parser.parse_args() ###################################################################### log_file = open(args.log_file, 'w') +pred_log_t = None print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL) def log_string(s): - s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + s + global pred_log_t + + t = time.time() + + if pred_log_t is None: + elapsed = 'start' + else: + elapsed = '+{:.02f}s'.format(t - pred_log_t) + + pred_log_t = t + + s = Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s log_file.write(s + '\n') log_file.flush() print(s) @@ -107,6 +127,7 @@ class AfrozeShallowNet(nn.Module): self.conv3 = nn.Conv2d(16, 120, kernel_size=18) self.fc1 = nn.Linear(120, 84) self.fc2 = nn.Linear(84, 2) + self.name = 'shallownet' def forward(self, x): x = fn.relu(fn.max_pool2d(self.conv1(x), kernel_size=2)) @@ -117,6 +138,8 @@ class AfrozeShallowNet(nn.Module): x = self.fc2(x) return x +###################################################################### + def train_model(model, train_set): batch_size = args.batch_size criterion = nn.CrossEntropyLoss() @@ -126,6 +149,8 @@ def train_model(model, train_set): optimizer = optim.SGD(model.parameters(), lr = 1e-2) + start_t = time.time() + for e in range(0, args.nb_epochs): acc_loss = 0.0 for b in range(0, train_set.nb_batches): @@ -137,6 +162,8 @@ def train_model(model, train_set): loss.backward() optimizer.step() log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss)) + dt = (time.time() - start_t) / (e + 1) + print(Fore.CYAN + 'ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + Style.RESET_ALL) return model @@ -160,51 +187,84 @@ 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) - test_set = CompressedVignetteSet(problem_number, args.nb_test_batches, args.batch_size) - else: - train_set = VignetteSet(problem_number, args.nb_train_batches, args.batch_size) - test_set = VignetteSet(problem_number, args.nb_test_batches, args.batch_size) + + log_string('**** problem ' + str(problem_number) + ' ****') model = AfrozeShallowNet() if torch.cuda.is_available(): model.cuda() + model_filename = model.name + '_' + \ + str(problem_number) + '_' + \ + str(args.nb_train_batches) + '.param' + 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_' + str(problem_number) + '.param' - + need_to_train = False try: model.load_state_dict(torch.load(model_filename)) log_string('loaded_model ' + model_filename) except: - log_string('training_model') + need_to_train = True + + 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_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()) + + 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) 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) + ) + + if need_to_train or args.test_loaded_models: + + t = time.time() + + if args.compress_vignettes: + test_set = CompressedVignetteSet(problem_number, + args.nb_test_batches, 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()) - 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('data_generation {:0.2f} samples / s'.format(test_set.nb_samples / (time.time() - t))) - 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) + ) ######################################################################