X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=da039614e58c59a617e02d22b963068bb367e4ff;hb=34aeb8100a6c19dae72779f9e46a0acbb5a069c7;hp=153bdc9d23a18a7abe67cfbe3f72246a5ee2fa83;hpb=d21f7d8eecb12aa4cc60360db6aa33324327e987;p=pysvrt.git

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 153bdc9..da03961 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 pysvrt.  If not, see <http://www.gnu.org/licenses/>.
 
 import time
 import argparse
 import math
+import distutils.util
 
 from colorama import Fore, Back, Style
 
@@ -65,15 +66,15 @@ 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')
 
 args = parser.parse_args()
@@ -143,22 +144,6 @@ 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__()
@@ -255,9 +240,9 @@ for arg in vars(args):
 ######################################################################
 
 def int_to_suffix(n):
-    if n > 1000000 and n%1000000 == 0:
+    if n >= 1000000 and n%1000000 == 0:
         return str(n//1000000) + 'M'
-    elif n > 1000 and n%1000 == 0:
+    elif n >= 1000 and n%1000 == 0:
         return str(n//1000) + 'K'
     else:
         return str(n)
@@ -269,8 +254,10 @@ if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_
     raise
 
 if args.compress_vignettes:
+    log_string('using_compressed_vignettes')
     VignetteSet = vignette_set.CompressedVignetteSet
 else:
+    log_string('using_uncompressed_vignettes')
     VignetteSet = vignette_set.VignetteSet
 
 for problem_number in range(1, 24):
@@ -284,8 +271,8 @@ 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