Added the command line arguments test_loaded_models.

[pysvrt.git] / cnn-svrt.py

diff --git a/cnn-svrt.py b/cnn-svrt.py
index aa3690d..5913345 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -73,6 +73,10 @@ 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 error of models we load')
+
 args = parser.parse_args()
 
 ######################################################################
@@ -180,6 +184,8 @@ for arg in vars(args):
 
 for problem_number in range(1, 24):
 
+    log_string('**** problem ' + str(problem_number) + ' ****')
+
     model = AfrozeShallowNet()
 
     if torch.cuda.is_available():
@@ -210,20 +216,12 @@ for problem_number in range(1, 24):
             train_set = CompressedVignetteSet(problem_number,
                                               args.nb_train_batches, args.batch_size,
                                               cuda=torch.cuda.is_available())
-            test_set = CompressedVignetteSet(problem_number,
-                                             args.nb_test_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())
-            test_set = VignetteSet(problem_number,
-                                   args.nb_test_batches, args.batch_size,
-                                   cuda=torch.cuda.is_available())
 
-        log_string('data_generation {:0.2f} samples / s'.format(
-            (train_set.nb_samples + test_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)
@@ -238,6 +236,21 @@ for problem_number in range(1, 24):
             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('data_generation {:0.2f} samples / s'.format(test_set.nb_samples / (time.time() - t)))
+
         nb_test_errors = nb_errors(model, test_set)
 
         log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(