58% office task commit.

core/dann.py

@@ -18,12 +18,20 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
     ####################
 
     # setup criterion and optimizer
-    # parameter_list = [
-    #     #     {"params": model.feature.parameters(), "lr": 1e-5},
-    #     #     {"params": model.classifier.parameters(), "lr": 1e-4},
-    #     #     {"params": model.discriminator.parameters(), "lr": 1e-4}
-    #     # ]
+
+    if params.src_dataset == 'mnist' or params.tgt_dataset == 'mnist':
+        print("training mnist task")
         optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+    else:
+        print("training office task")
+        parameter_list = [
+             {"params": model.features.parameters(), "lr": 1e-5},
+             {"params": model.fc.parameters(), "lr": 1e-5},
+             {"params": model.bottleneck.parameters(), "lr": 1e-4},
+             {"params": model.classifier.parameters(), "lr": 1e-4},
+             {"params": model.discriminator.parameters(), "lr": 1e-4}
+        ]
+        optimizer = optim.SGD(parameter_list)
 
     criterion = nn.CrossEntropyLoss()
 
@@ -44,6 +52,8 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
 
             p = float(step + epoch * len_dataloader) / params.num_epochs / len_dataloader
             alpha = 2. / (1. + np.exp(-10 * p)) - 1
+            if params.src_dataset == 'mnist' or params.tgt_dataset == 'mnist':
+                print("training mnist task")
                 adjust_learning_rate(optimizer, p)
 
             # prepare domain label
@@ -90,7 +100,7 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
         # eval model on test set
         if ((epoch + 1) % params.eval_step == 0):
             print("eval on target domain")
-            eval(model, tgt_data_loader_eval)
+            eval(model, tgt_data_loader)
             print("eval on source domain")
             eval_src(model, src_data_loader)
 

models/model.py

@@ -154,34 +154,28 @@ class AlexModel(nn.Module):
 
         self.features = model_alexnet.features
 
-        # self.classifier = nn.Sequential()
-        # for i in range(5):
-        #     self.classifier.add_module(
-        #         "classifier" + str(i), model_alexnet.classifier[i])
-        # self.__in_features = model_alexnet.classifier[4].in_features
-        # self.classifier.add_module('classifier5', nn.Dropout())
-        # self.classifier.add_module('classifier6', nn.Linear(self.__in_features, 256))
-        # self.classifier.add_module('classifier7', nn.BatchNorm2d(256))
-        # self.classifier.add_module('classifier8', nn.ReLU())
-        # self.classifier.add_module('classifier9', nn.Dropout(0.5))
-        # self.classifier.add_module('classifier10', nn.Linear(256, params.class_num_src))
-        self.classifier = nn.Sequential(
-            nn.Dropout(0.5),
-            nn.Linear(256 * 6 * 6, 4096),
-            nn.ReLU(inplace=True),
-            nn.Dropout(0.5),
+        self.fc = nn.Sequential()
+        for i in range(6):
+            self.fc.add_module("classifier" + str(i), model_alexnet.classifier[i])
+        self.__in_features = model_alexnet.classifier[6].in_features # 4096
+
+        self.bottleneck = nn.Sequential(
             nn.Linear(4096, 256),
             nn.ReLU(inplace=True),
-            nn.Linear(256, 31),
+            nn.Dropout(),
+        )
+
+        self.classifier = nn.Sequential(
+            nn.Linear(256, 31)
         )
 
         self.discriminator = nn.Sequential(
-            nn.Linear(256 * 6 * 6, 1024),
+            nn.Linear(256, 1024),
             nn.ReLU(),
-            nn.Dropout(0.5),
+            nn.Dropout(),
             nn.Linear(1024, 1024),
             nn.ReLU(),
-            nn.Dropout(0.5),
+            nn.Dropout(),
             nn.Linear(1024, 2),
         )
 
@@ -189,11 +183,12 @@ class AlexModel(nn.Module):
         input_data = input_data.expand(input_data.data.shape[0], 3, 227, 227)
         feature = self.features(input_data)
         feature = feature.view(-1, 256*6*6)
+        fc = self.fc(feature)
+        bottleneck = self.bottleneck(fc)
 
-        reverse_feature = ReverseLayerF.apply(feature, alpha)
+        reverse_bottleneck = ReverseLayerF.apply(bottleneck, alpha)
 
-        class_output = self.classifier(feature)
-
-        domain_output = self.discriminator(reverse_feature)
+        class_output = self.classifier(bottleneck)
+        domain_output = self.discriminator(reverse_bottleneck)
 
         return class_output, domain_output

main_office.py → office.py

@@ -12,7 +12,7 @@ class Config(object):
     model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN'))
 
     # params for datasets and data loader
-    batch_size = 128
+    batch_size = 32
 
     # params for source dataset
     src_dataset = "amazon31"
@@ -33,10 +33,10 @@ class Config(object):
     # params for training dann
 
     ## for office
-    num_epochs = 1000
-    log_step = 10  # iters
+    num_epochs = 4000
+    log_step = 25  # iters
     save_step = 500
-    eval_step = 5  # epochs
+    eval_step = 50 # epochs
 
     manual_seed = 8888
     alpha = 0
@@ -50,17 +50,17 @@ params = Config()
 init_random_seed(params.manual_seed)
 
 # load dataset
-src_data_loader = get_data_loader(params.src_dataset)
-tgt_data_loader = get_data_loader(params.tgt_dataset)
+src_data_loader = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size)
+tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size)
 
 # load dann model
-dann = init_model(net=AlexModel(), restore=params.dann_restore)
+dann = init_model(net=AlexModel(), restore=None)
 
 # train dann model
 print("Start training dann model.")
 
 if not (dann.restored and params.dann_restore):
-    dann = train_dann(dann, src_data_loader, tgt_data_loader, tgt_data_loader)
+    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader)
 
 # eval dann model
 print("Evaluating dann for source domain")