X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=26e7de843bccfd7db4d761369b1ac66e35798319;hb=e754d1075d8d0a5949e71f426ab07ce73be6099e;hp=0770bf3825f26d4397522b49f4f661be9ab2535e;hpb=e6757e3b043bf0ade8ad71335f72c7159865f218;p=pysvrt.git

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 0770bf3..26e7de8 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -19,7 +19,7 @@
 #  General Public License for more details.
 #
 #  You should have received a copy of the GNU General Public License
-#  along with selector.  If not, see <http://www.gnu.org/licenses/>.
+#  along with svrt.  If not, see <http://www.gnu.org/licenses/>.
 
 import time
 import argparse
@@ -41,7 +41,7 @@ from torchvision import datasets, transforms, utils
 
 # SVRT
 
-import vignette_set
+import svrtset
 
 ######################################################################
 
@@ -247,6 +247,21 @@ def int_to_suffix(n):
     else:
         return str(n)
 
+class vignette_logger():
+    def __init__(self, delay_min = 60):
+        self.start_t = time.time()
+        self.delay_min = delay_min
+
+    def __call__(self, n, m):
+        t = time.time()
+        if t > self.start_t + self.delay_min:
+            dt = (t - self.start_t) / m
+            log_string('sample_generation {:d} / {:d}'.format(
+                m,
+                n), ' [ETA ' + time.ctime(time.time() + dt * (n - m)) + ']'
+            )
+            self.start_t = t
+
 ######################################################################
 
 if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_size > 0:
@@ -255,10 +270,10 @@ if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_
 
 if args.compress_vignettes:
     log_string('using_compressed_vignettes')
-    VignetteSet = vignette_set.CompressedVignetteSet
+    VignetteSet = svrtset.CompressedVignetteSet
 else:
     log_string('using_uncompressed_vignettes')
-    VignetteSet = vignette_set.VignetteSet
+    VignetteSet = svrtset.VignetteSet
 
 for problem_number in range(1, 24):
 
@@ -300,7 +315,8 @@ for problem_number in range(1, 24):
 
         train_set = VignetteSet(problem_number,
                                 args.nb_train_samples, args.batch_size,
-                                cuda = torch.cuda.is_available())
+                                cuda = torch.cuda.is_available(),
+                                logger = vignette_logger())
 
         log_string('data_generation {:0.2f} samples / s'.format(
             train_set.nb_samples / (time.time() - t))