Added Afroze's DeepNet.

diff --git a/cnn-svrt.py b/cnn-svrt.py
index c75b336..c7e0585 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -73,6 +73,10 @@ parser.add_argument('--compress_vignettes',
                     action='store_true', default = False,
                     help = 'Use lossless compression to reduce the memory footprint')
 
+parser.add_argument('--deep_model',
+                    action='store_true', default = False,
+                    help = 'Use Afroze\'s Alexnet-like deep model')
+
 parser.add_argument('--test_loaded_models',
                     action='store_true', default = False,
                     help = 'Should we compute the test errors of loaded models')
@@ -86,7 +90,9 @@ pred_log_t = None
 
 print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
 
-def log_string(s):
+# Log and prints the string, with a time stamp. Does not log the
+# remark
+def log_string(s, remark = ''):
     global pred_log_t
 
     t = time.time()
@@ -98,10 +104,10 @@ def log_string(s):
 
     pred_log_t = t
 
-    s = Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s
+    s = Fore.BLUE + '[' + time.ctime() + '] ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s
     log_file.write(s + '\n')
     log_file.flush()
-    print(s)
+    print(s + Fore.CYAN + remark + Style.RESET_ALL)
 
 ######################################################################
 
@@ -140,6 +146,70 @@ 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__()
+        self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
+        self.conv2 = nn.Conv2d( 32,  96, kernel_size=5, padding=2)
+        self.conv3 = nn.Conv2d( 96, 128, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+        self.conv5 = nn.Conv2d(128,  96, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(1536, 256)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 2)
+        self.name = 'deepnet'
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = fn.max_pool2d(x, kernel_size=2)
+        x = fn.relu(x)
+
+        x = self.conv2(x)
+        x = fn.max_pool2d(x, kernel_size=2)
+        x = fn.relu(x)
+
+        x = self.conv3(x)
+        x = fn.relu(x)
+
+        x = self.conv4(x)
+        x = fn.relu(x)
+
+        x = self.conv5(x)
+        x = fn.max_pool2d(x, kernel_size=2)
+        x = fn.relu(x)
+
+        x = x.view(-1, 1536)
+
+        x = self.fc1(x)
+        x = fn.relu(x)
+
+        x = self.fc2(x)
+        x = fn.relu(x)
+
+        x = self.fc3(x)
+
+        return x
+
+######################################################################
+
 def train_model(model, train_set):
     batch_size = args.batch_size
     criterion = nn.CrossEntropyLoss()
@@ -161,9 +231,9 @@ def train_model(model, train_set):
             model.zero_grad()
             loss.backward()
             optimizer.step()
-        log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss))
         dt = (time.time() - start_t) / (e + 1)
-        print(Fore.CYAN + 'ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + Style.RESET_ALL)
+        log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss),
+                   ' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']')
 
     return model
 
@@ -193,7 +263,10 @@ for problem_number in range(1, 24):
 
     log_string('**** problem ' + str(problem_number) + ' ****')
 
-    model = AfrozeShallowNet()
+    if args.deep_model:
+        model = AfrozeDeepNet()
+    else:
+        model = AfrozeShallowNet()
 
     if torch.cuda.is_available():
         model.cuda()