Deal with missing colorama.

[pysvrt.git] / cnn-svrt.py

diff --git a/cnn-svrt.py b/cnn-svrt.py
index fab2772..0c63b85 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -29,7 +29,10 @@ import distutils.util
 import re
 import signal
 
-from colorama import Fore, Back, Style
+try:
+    from colorama import Fore, Back, Style
+except ImportError:
+    Fore, Back, Style = '', '', ''
 
 # Pytorch
 
@@ -250,10 +253,10 @@ class DeepNet2(nn.Module):
         super(DeepNet2, self).__init__()
         self.nb_channels = 512
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
-        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.conv2 = nn.Conv2d( 32, self.nb_channels, kernel_size=5, padding=2)
+        self.conv3 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+        self.conv5 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
         self.fc1 = nn.Linear(16 * self.nb_channels, 512)
         self.fc2 = nn.Linear(512, 512)
         self.fc3 = nn.Linear(512, 2)
@@ -540,7 +543,10 @@ for problem_number in map(int, args.problems.split(',')):
         else:
             validation_set = None
 
-        train_model(model, model_filename, train_set, validation_set, nb_epochs_done = nb_epochs_done)
+        train_model(model, model_filename,
+                    train_set, validation_set,
+                    nb_epochs_done = nb_epochs_done)
+
         log_string('saved_model ' + model_filename)
 
         nb_train_errors = nb_errors(model, train_set)