Update.

[pysvrt.git] / cnn-svrt.py

diff --git a/cnn-svrt.py b/cnn-svrt.py
index d44de2a..0770bf3 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -24,6 +24,7 @@
 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__()
@@ -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