X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pysvrt.git;a=blobdiff_plain;f=cnn-svrt.py;h=63b11ee4c33831bd0ec8236f7892554bcab0b47a;hp=d44de2aed08f7097f6c255587d035ede105a14bd;hb=4c7ff07760d015a2efad8b7eb0bd44dd9acc9106;hpb=a0de65c26758ec247ff419b9b53725733ed0e76c

diff --git a/cnn-svrt.py b/cnn-svrt.py
index d44de2a..63b11ee 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -19,11 +19,12 @@
 #  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
 import math
+import distutils.util
 
 from colorama import Fore, Back, Style
 
@@ -40,7 +41,7 @@ from torchvision import datasets, transforms, utils
 
 # SVRT
 
-import vignette_set
+import svrtset
 
 ######################################################################
 
@@ -65,17 +66,21 @@ parser.add_argument('--log_file',
                     type = str, default = 'default.log')
 
 parser.add_argument('--compress_vignettes',
-                    action='store_true', default = True,
+                    type = distutils.util.strtobool, default = 'True',
                     help = 'Use lossless compression to reduce the memory footprint')
 
 parser.add_argument('--deep_model',
-                    action='store_true', default = True,
+                    type = distutils.util.strtobool, default = 'True',
                     help = 'Use Afroze\'s Alexnet-like deep model')
 
 parser.add_argument('--test_loaded_models',
-                    action='store_true', default = False,
+                    type = distutils.util.strtobool, default = 'False',
                     help = 'Should we compute the test errors of loaded models')
 
+parser.add_argument('--problems',
+                    type = str, default = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23',
+                    help = 'What problems to process')
+
 args = parser.parse_args()
 
 ######################################################################
@@ -143,22 +148,6 @@ class AfrozeShallowNet(nn.Module):
 
 # Afroze's DeepNet
 
-#                       map size   nb. maps
-#                     ----------------------
-#    input                128x128    1
-# -- conv(21x21 x 32 stride=4) -> 28x28    32
-# -- max(2x2)                  -> 14x14      6
-# -- conv(7x7 x 96)            -> 8x8      16
-# -- max(2x2)                  -> 4x4      16
-# -- conv(5x5 x 96)            -> 26x36      16
-# -- conv(3x3 x 128)           -> 36x36      16
-# -- conv(3x3 x 128)           -> 36x36      16
-
-# -- conv(5x5 x 120) -> 1x1        120
-# -- reshape           -> 120        1
-# -- full(3x84)      -> 84         1
-# -- full(84x2)        -> 2          1
-
 class AfrozeDeepNet(nn.Module):
     def __init__(self):
         super(AfrozeDeepNet, self).__init__()
@@ -262,6 +251,22 @@ def int_to_suffix(n):
     else:
         return str(n)
 
+class vignette_logger():
+    def __init__(self, delay_min = 60):
+        self.start_t = time.time()
+        self.last_t = self.start_t
+        self.delay_min = delay_min
+
+    def __call__(self, n, m):
+        t = time.time()
+        if t > self.last_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.last_t = t
+
 ######################################################################
 
 if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_size > 0:
@@ -269,11 +274,13 @@ if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_
     raise
 
 if args.compress_vignettes:
-    VignetteSet = vignette_set.CompressedVignetteSet
+    log_string('using_compressed_vignettes')
+    VignetteSet = svrtset.CompressedVignetteSet
 else:
-    VignetteSet = vignette_set.VignetteSet
+    log_string('using_uncompressed_vignettes')
+    VignetteSet = svrtset.VignetteSet
 
-for problem_number in range(1, 24):
+for problem_number in map(int, args.problems.split(',')):
 
     log_string('############### problem ' + str(problem_number) + ' ###############')
 
@@ -284,8 +291,8 @@ for problem_number in range(1, 24):
 
     if torch.cuda.is_available(): model.cuda()
 
-    model_filename = model.name + '_' + \
-                     str(problem_number) + '_' + \
+    model_filename = model.name + '_pb:' + \
+                     str(problem_number) + '_ns:' + \
                      int_to_suffix(args.nb_train_samples) + '.param'
 
     nb_parameters = 0
@@ -313,7 +320,8 @@ 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))