X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=153bdc9d23a18a7abe67cfbe3f72246a5ee2fa83;hb=d21f7d8eecb12aa4cc60360db6aa33324327e987;hp=cc3d35f6a4e92a776021114e5c8000f077b420bd;hpb=6efb16f367d497093b06bbad686f0dd7e5fa9ae3;p=pysvrt.git

diff --git a/cnn-svrt.py b/cnn-svrt.py
index cc3d35f..153bdc9 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -40,41 +40,36 @@ from torchvision import datasets, transforms, utils
 
 # SVRT
 
-from vignette_set import VignetteSet, CompressedVignetteSet
+import vignette_set
 
 ######################################################################
 
 parser = argparse.ArgumentParser(
-    description = 'Simple convnet test on the SVRT.',
+    description = "Convolutional networks for the SVRT. Written by Francois Fleuret, (C) Idiap research institute.",
     formatter_class = argparse.ArgumentDefaultsHelpFormatter
 )
 
 parser.add_argument('--nb_train_samples',
-                    type = int, default = 100000,
-                    help = 'How many samples for train')
+                    type = int, default = 100000)
 
 parser.add_argument('--nb_test_samples',
-                    type = int, default = 10000,
-                    help = 'How many samples for test')
+                    type = int, default = 10000)
 
 parser.add_argument('--nb_epochs',
-                    type = int, default = 50,
-                    help = 'How many training epochs')
+                    type = int, default = 50)
 
 parser.add_argument('--batch_size',
-                    type = int, default = 100,
-                    help = 'Mini-batch size')
+                    type = int, default = 100)
 
 parser.add_argument('--log_file',
-                    type = str, default = 'default.log',
-                    help = 'Log file name')
+                    type = str, default = 'default.log')
 
 parser.add_argument('--compress_vignettes',
-                    action='store_true', default = False,
+                    action='store_true', default = True,
                     help = 'Use lossless compression to reduce the memory footprint')
 
 parser.add_argument('--deep_model',
-                    action='store_true', default = False,
+                    action='store_true', default = True,
                     help = 'Use Afroze\'s Alexnet-like deep model')
 
 parser.add_argument('--test_loaded_models',
@@ -104,10 +99,10 @@ def log_string(s, remark = ''):
 
     pred_log_t = t
 
-    s = Fore.BLUE + '[' + time.ctime() + '] ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s
-    log_file.write(s + '\n')
+    log_file.write('[' + time.ctime() + '] ' + elapsed + ' ' + s + '\n')
     log_file.flush()
-    print(s + Fore.CYAN + remark + Style.RESET_ALL)
+
+    print(Fore.BLUE + '[' + time.ctime() + '] ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s + Fore.CYAN + remark + Style.RESET_ALL)
 
 ######################################################################
 
@@ -273,17 +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) + '_' + \
@@ -293,6 +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))
@@ -300,20 +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))
@@ -332,18 +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))