Bigger DeepNet2.

[pysvrt.git] / cnn-svrt.py

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 0091aba..4531fb1 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -105,6 +105,10 @@ args = parser.parse_args()
 ######################################################################
 
 log_file = open(args.log_file, 'a')
+log_file.write('\n')
+log_file.write('@@@@@@@@@@@@@@@@@@@ ' + time.ctime() + ' @@@@@@@@@@@@@@@@@@@\n')
+log_file.write('\n')
+
 pred_log_t = None
 last_tag_t = time.time()
 
@@ -244,12 +248,13 @@ class DeepNet2(nn.Module):
 
     def __init__(self):
         super(DeepNet2, self).__init__()
+        nb_channels = 512
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
-        self.conv2 = nn.Conv2d( 32, 256, kernel_size=5, padding=2)
-        self.conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.conv4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.fc1 = nn.Linear(4096, 512)
+        self.conv2 = nn.Conv2d( 32, nb_channels, kernel_size=5, padding=2)
+        self.conv3 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
+        self.conv5 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(16 * nb_channels, 512)
         self.fc2 = nn.Linear(512, 512)
         self.fc3 = nn.Linear(512, 2)
 
@@ -355,7 +360,8 @@ def nb_errors(model, data_set, mistake_filename_pattern = None):
                     img = input[i].clone()
                     img.sub_(img.min())
                     img.div_(img.max())
-                    filename = mistake_filename_pattern.format(b + i, target[i])
+                    k = b * data_set.batch_size + i
+                    filename = mistake_filename_pattern.format(k, target[i])
                     torchvision.utils.save_image(img, filename)
                     print(Fore.RED + 'Wrote ' + filename + Style.RESET_ALL)
     return ne
@@ -457,8 +463,6 @@ if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_
     print('The number of samples must be a multiple of the batch size.')
     raise
 
-log_string('############### start ###############')
-
 if args.compress_vignettes:
     log_string('using_compressed_vignettes')
     VignetteSet = svrtset.CompressedVignetteSet