X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=142b81f00b271d9604dc3c80c65ba229fc0401f5;hb=d150b39b0cf1ee7cbfcecc9d2b3bbc01411662ff;hp=0770bf3825f26d4397522b49f4f661be9ab2535e;hpb=e6757e3b043bf0ade8ad71335f72c7159865f218;p=pysvrt.git
diff --git a/cnn-svrt.py b/cnn-svrt.py
index 0770bf3..142b81f 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 .
+# along with svrt. If not, see .
import time
import argparse
@@ -41,7 +41,7 @@ from torchvision import datasets, transforms, utils
# SVRT
-import vignette_set
+import svrtset
######################################################################
@@ -247,6 +247,20 @@ 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)) + ']'
+ )
+
######################################################################
if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_size > 0:
@@ -255,10 +269,10 @@ 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 +314,8 @@ 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))