X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=90b4c6d6693f51a1ef35b54ed2eb8130865277c7;hb=4d3bc68c677cc9554df9c47dd214dfc4cb9c6577;hp=7bef242de186a1bbcbad4a84444c5c3311a9445e;hpb=08ef6b7c332153cd72b7a225e27ee7af8882f313;p=pysvrt.git

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 7bef242..90b4c6d 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -73,16 +73,32 @@ 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()
 
 ######################################################################
 
 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)
@@ -133,6 +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):
@@ -144,6 +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
 
@@ -171,6 +191,8 @@ 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():
@@ -184,29 +206,29 @@ for problem_number in range(1, 24):
     for p in model.parameters(): nb_parameters += p.numel()
     log_string('nb_parameters {:d}'.format(nb_parameters))
 
+    need_to_train = False
     try:
-
         model.load_state_dict(torch.load(model_filename))
         log_string('loaded_model ' + model_filename)
-
     except:
+        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())
-            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 / (time.time() - t)))
 
         train_model(model, train_set)
         torch.save(model.state_dict(), model_filename)
@@ -221,6 +243,21 @@ 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(