Making an even deeper model.

[pysvrt.git] / cnn-svrt.py

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 1511e82..cb94184 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -223,11 +223,60 @@ class DeepNet2(nn.Module):
     def __init__(self):
         super(DeepNet2, self).__init__()
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
-        self.conv2 = nn.Conv2d( 32, 128, kernel_size=5, padding=2)
-        self.conv3 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
-        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
-        self.conv5 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
-        self.fc1 = nn.Linear(2048, 512)
+        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.fc2 = nn.Linear(512, 512)
+        self.fc3 = nn.Linear(512, 2)
+
+    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, 4096)
+
+        x = self.fc1(x)
+        x = fn.relu(x)
+
+        x = self.fc2(x)
+        x = fn.relu(x)
+
+        x = self.fc3(x)
+
+        return x
+
+######################################################################
+
+class DeepNet3(nn.Module):
+    name = 'deepnet3'
+
+    def __init__(self):
+        super(DeepNet2, self).__init__()
+        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.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
+        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(4096, 512)
         self.fc2 = nn.Linear(512, 512)
         self.fc3 = nn.Linear(512, 2)
 
@@ -250,7 +299,13 @@ class DeepNet2(nn.Module):
         x = fn.max_pool2d(x, kernel_size=2)
         x = fn.relu(x)
 
-        x = x.view(-1, 2048)
+        x = self.conv6(x)
+        x = fn.relu(x)
+
+        x = self.conv7(x)
+        x = fn.relu(x)
+
+        x = x.view(-1, 4096)
 
         x = self.fc1(x)
         x = fn.relu(x)
@@ -279,7 +334,7 @@ def nb_errors(model, data_set):
 
 ######################################################################
 
-def train_model(model, train_set, validation_set):
+def train_model(model, model_filename, train_set, validation_set, nb_epochs_done = 0):
     batch_size = args.batch_size
     criterion = nn.CrossEntropyLoss()
 
@@ -290,7 +345,7 @@ def train_model(model, train_set, validation_set):
 
     start_t = time.time()
 
-    for e in range(0, args.nb_epochs):
+    for e in range(nb_epochs_done, args.nb_epochs):
         acc_loss = 0.0
         for b in range(0, train_set.nb_batches):
             input, target = train_set.get_batch(b)
@@ -305,6 +360,8 @@ def train_model(model, train_set, validation_set):
         log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss),
                    ' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']')
 
+        torch.save([ model.state_dict(), e + 1 ], model_filename)
+
         if validation_set is not None:
             nb_validation_errors = nb_errors(model, validation_set)
 
@@ -383,7 +440,7 @@ else:
 
 ########################################
 model_class = None
-for m in [ AfrozeShallowNet, AfrozeDeepNet, DeepNet2 ]:
+for m in [ AfrozeShallowNet, AfrozeDeepNet, DeepNet2, DeepNet3 ]:
     if args.model == m.name:
         model_class = m
         break
@@ -404,7 +461,7 @@ for problem_number in map(int, args.problems.split(',')):
 
     model_filename = model.name + '_pb:' + \
                      str(problem_number) + '_ns:' + \
-                     int_to_suffix(args.nb_train_samples) + '.param'
+                     int_to_suffix(args.nb_train_samples) + '.state'
 
     nb_parameters = 0
     for p in model.parameters(): nb_parameters += p.numel()
@@ -413,17 +470,18 @@ for problem_number in map(int, args.problems.split(',')):
     ##################################################
     # Tries to load the model
 
-    need_to_train = False
     try:
-        model.load_state_dict(torch.load(model_filename))
+        model_state_dict, nb_epochs_done = torch.load(model_filename)
+        model.load_state_dict(model_state_dict)
         log_string('loaded_model ' + model_filename)
     except:
-        need_to_train = True
+        nb_epochs_done = 0
+
 
     ##################################################
     # Train if necessary
 
-    if need_to_train:
+    if nb_epochs_done < args.nb_epochs:
 
         log_string('training_model ' + model_filename)
 
@@ -450,8 +508,7 @@ for problem_number in map(int, args.problems.split(',')):
         else:
             validation_set = None
 
-        train_model(model, train_set, validation_set)
-        torch.save(model.state_dict(), model_filename)
+        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)
@@ -466,7 +523,7 @@ for problem_number in map(int, args.problems.split(',')):
     ##################################################
     # Test if necessary
 
-    if need_to_train or args.test_loaded_models:
+    if nb_epochs_done < args.nb_epochs or args.test_loaded_models:
 
         t = time.time()