minor update, remove some params

.gitignore

@@ -3,6 +3,7 @@ __pycache__/
 *.py[cod]
 *$py.class
 _gsdata_
+*.ipynb
 
 # C extensions
 *.so
@@ -105,3 +106,4 @@ ENV/
 .idea
 .DS_Store
 main_legacy.py
+test.ipynb

core/dann.py

@@ -101,4 +101,4 @@ def train_dann(model, src_data_loader, tgt_data_loader, tgt_data_loader_eval):
     # save final model
     save_model(model, params.src_dataset + '-' + params.tgt_dataset + "-dann-final.pt")
 
-    return model
+    return model

datasets/mnist.py

@@ -12,8 +12,9 @@ def get_mnist(train):
     # image pre-processing
     pre_process = transforms.Compose([transforms.ToTensor(),
                                       transforms.Normalize(
-                                          mean=params.dataset_mean,
-                                          std=params.dataset_std)])
+                                          mean=(0.5, 0.5, 0.5),
+                                          std=(0.5, 0.5, 0.5)
+                                      )])
 
     # datasets and data loader
     mnist_dataset = datasets.MNIST(root=os.path.join(params.dataset_root,'mnist'),

datasets/mnistm.py

@@ -41,11 +41,12 @@ class GetLoader(data.Dataset):
 def get_mnistm(train):
     """Get MNISTM datasets loader."""
     # image pre-processing
-    pre_process = transforms.Compose([transforms.Resize(params.digit_image_size),
+    pre_process = transforms.Compose([transforms.Resize(28),
                                      transforms.ToTensor(),
                                      transforms.Normalize(
-                                          mean=params.dataset_mean,
-                                          std=params.dataset_std)])
+                                          mean=(0.5, 0.5, 0.5),
+                                          std=(0.5, 0.5, 0.5)
+                                     )])
 
     # datasets and data_loader
     if train:

datasets/office.py

@@ -13,8 +13,9 @@ def get_office(train, category):
     pre_process = transforms.Compose([transforms.Resize(params.office_image_size),
                                      transforms.ToTensor(),
                                      transforms.Normalize(
-                                          mean=params.imagenet_dataset_mean,
-                                          std=params.imagenet_dataset_mean)])
+                                         mean=(0.485, 0.456, 0.406),
+                                         std=(0.229, 0.224, 0.225)
+                                     )])
 
     # datasets and data_loader
     office_dataset = datasets.ImageFolder(
@@ -25,6 +26,6 @@ def get_office(train, category):
         dataset=office_dataset,
         batch_size=params.batch_size,
         shuffle=True,
-        num_workers=8)
+        num_workers=4)
 
     return office_dataloader

datasets/officecaltech.py

@@ -13,8 +13,9 @@ def get_officecaltech(train, category):
     pre_process = transforms.Compose([transforms.Resize(params.office_image_size),
                                      transforms.ToTensor(),
                                      transforms.Normalize(
-                                          mean=params.imagenet_dataset_mean,
-                                          std=params.imagenet_dataset_mean)])
+                                          mean=(0.485, 0.456, 0.406),
+                                          std=(0.229, 0.224, 0.225)
+                                     )])
 
     # datasets and data_loader
     officecaltech_dataset = datasets.ImageFolder(
@@ -25,6 +26,6 @@ def get_officecaltech(train, category):
         dataset=officecaltech_dataset,
         batch_size=params.batch_size,
         shuffle=True,
-        num_workers=8)
+        num_workers=4)
 
     return officecaltech_dataloader

datasets/svhn.py

@@ -15,8 +15,9 @@ def get_svhn(train):
                                       transforms.Resize(params.digit_image_size),
                                       transforms.ToTensor(),
                                       transforms.Normalize(
-                                          mean=params.dataset_mean,
-                                          std=params.dataset_std)])
+                                          mean=(0.5, 0.5, 0.5),
+                                          std=(0.5, 0.5, 0.5)
+                                      )])
 
     # datasets and data loader
     if train:

models/model.py

@@ -3,7 +3,6 @@
 import torch.nn as nn
 from .functions import ReverseLayerF
 from torchvision import models
-import params
 
 
 class Classifier(nn.Module):
@@ -43,6 +42,45 @@ class Classifier(nn.Module):
 
         return class_output
 
+class MNISTmodel(nn.Module):
+    """ MNIST architecture"""
+
+    def __init__(self):
+        super(MNISTmodel, self).__init__()
+        self.restored = False
+
+        self.feature = nn.Sequential(
+            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=(5, 5)), # 1 28 28, 32 24 24
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2)), # 32 12 12
+            nn.Conv2d(in_channels=32, out_channels=48, kernel_size=(5, 5)), # 48 8 8
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2)), # 48 4 4
+        )
+
+        self.classifier = nn.Sequential(
+            nn.Linear(48*4*4, 100),
+            nn.ReLU(inplace=True),
+            nn.Linear(100, 100),
+            nn.ReLU(inplace=True),
+            nn.Linear(100, 10),
+        )
+
+        self.discriminator = nn.Sequential(
+            nn.Linear(48*4*4, 100),
+            nn.ReLU(inplace=True),
+            nn.Linear(100, 2),
+        )
+
+    def forward(self, input_data, alpha):
+        input_data = input_data.expand(input_data.data.shape[0], 1, 28, 28)
+        feature = self.feature(input_data)
+        feature = feature.view(-1, 48 * 4 * 4)
+        reverse_feature = ReverseLayerF.apply(feature, alpha)
+        class_output = self.classifier(feature)
+        domain_output = self.discriminator(reverse_feature)
+
+        return class_output, domain_output
 
 class SVHNmodel(nn.Module):
     """ SVHN architecture"""
@@ -90,7 +128,6 @@ class SVHNmodel(nn.Module):
 
         return class_output, domain_output
 
-
 class AlexModel(nn.Module):
     """ AlexNet pretrained on imagenet for Office dataset"""
 
@@ -119,7 +156,7 @@ class AlexModel(nn.Module):
             nn.Dropout(0.5),
             nn.Linear(4096, 256),
             nn.ReLU(inplace=True),
-            nn.Linear(256, params.class_num_src),
+            nn.Linear(256, 31),
         )
 
         self.discriminator = nn.Sequential(
@@ -143,4 +180,4 @@ class AlexModel(nn.Module):
 
         domain_output = self.discriminator(reverse_feature)
 
-        return class_output, domain_output
+        return class_output, domain_output

params.py

@@ -7,16 +7,9 @@ dataset_root = os.path.expanduser(os.path.join('~', 'Datasets'))
 model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN'))
 
 # params for datasets and data loader
-dataset_mean_value = 0.5
-dataset_std_value = 0.5
-dataset_mean = (dataset_mean_value, dataset_mean_value, dataset_mean_value)
-dataset_std = (dataset_std_value, dataset_std_value, dataset_std_value)
-
-imagenet_dataset_mean = (0.485, 0.456, 0.406)
-imagenet_dataset_std = (0.229, 0.224, 0.225)
 
 batch_size = 64
-digit_image_size = 28
+
 office_image_size = 227
 
 # params for source dataset