reorganize file.

README.md

@@ -15,31 +15,20 @@ A pytorch implementation for paper *[Unsupervised Domain Adaptation by Backpropa
 ## Note
 
 - `Config()` 为针对特定任务的配置参数。
-- `MNISTmodel()` 完全按照论文中的结构，但是 feature 部分添加了 `Dropout2d()`，实验发现是否添
-  加 `Dropout2d()` 对于最后的性能影响很大。最后实验重现结果高于论文，因为使用了额外的技巧，这里
-  还有值得探究的地方。
+- `MNISTmodel()` 完全按照论文中的结构，但是 feature 部分添加了 `Dropout2d()`，实验发现是否添加 `Dropout2d()` 对于最后的性能影响很大。最后实验重现结果高于论文，因为使用了额外的技巧，这里还有值得探究的地方。
 - `SVHNmodel()` 无法理解论文中提出的结构，为自定义结构。最后实验重现结果完美。
-
-## Result
-
-|                 | MNIST-MNISTM   | SVHN-MNIST | Amazon-Webcam |
-| :-------------: | :------------: | :--------: | :--------: |
-| Source Only     |   0.5225       |  0.5490    |  0.6420    |
-| DANN            |   0.7666       |  0.7385    |  0.7300    |
-| This Repo       |   0.8400       |  0.7339    |  0.6428    |
-
 - MNIST-MNISTM: `python mnist_mnistm.py`
 - SVHN-MNIST: `python svhn_mnist.py`
-- Amazon-Webcam: 没有复现成功
+- Amazon-Webcam: `python office.py` 没有复现成功
 
-## Other implementations
+## Result
 
-- authors(caffe) <https://github.com/ddtm/caffe>
-- TensorFlow, <https://github.com/pumpikano/tf-dann>
-- Theano, <https://github.com/shucunt/domain_adaptation>
-- PyTorch, <https://github.com/fungtion/DANN>
-- numpy, <https://github.com/GRAAL-Research/domain_adversarial_neural_network>
-- lua, <https://github.com/gmarceaucaron/dann>
+|                      | MNIST-MNISTM   | SVHN-MNIST | Amazon-Webcam |Amazon-Webcam10 |
+| :------------------: | :------------: | :--------: | :-----------: |:-------------: |
+| Source Only          |   0.5225       |  0.5490    |  0.6420       | 0.             |
+| DANN(paper)          |   0.7666       |  0.7385    |  0.7300       | 0.             |
+| This Repo Source Only|   -            |  -         |  -            | 0.             |
+| This Repo            |   0.8400       |  0.7339    |  0.6528       | 0.             |
 
 ## Credit
 

core/dann.py

@@ -1,17 +1,19 @@
 """Train dann."""
 
+import numpy as np
+
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-from utils import make_variable, save_model
-import numpy as np
-from core.test import eval, eval_src
+from core.test import eval
+from utils.utils import save_model
 
 import torch.backends.cudnn as cudnn
 cudnn.benchmark = True
 
-def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval):
+
+def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device):
     """Train dann."""
     ####################
     # 1. setup network #
@@ -24,20 +26,23 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
         optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
     else:
         print("training office task")
-        parameter_list = [
-             {"params": model.features.parameters(), "lr": 0.001},
-             {"params": model.fc.parameters(), "lr": 0.001},
-             {"params": model.bottleneck.parameters()},
-             {"params": model.classifier.parameters()},
-             {"params": model.discriminator.parameters()}
-        ]
+        parameter_list = [{
+            "params": model.features.parameters(),
+            "lr": 0.001
+        }, {
+            "params": model.fc.parameters(),
+            "lr": 0.001
+        }, {
+            "params": model.bottleneck.parameters()
+        }, {
+            "params": model.classifier.parameters()
+        }, {
+            "params": model.discriminator.parameters()
+        }]
         optimizer = optim.SGD(parameter_list, lr=0.01, momentum=0.9)
 
     criterion = nn.CrossEntropyLoss()
 
-    for p in model.parameters():
-        p.requires_grad = True
-
     ####################
     # 2. train network #
     ####################
@@ -50,9 +55,9 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
         data_zip = enumerate(zip(src_data_loader, tgt_data_loader))
         for step, ((images_src, class_src), (images_tgt, _)) in data_zip:
 
-            p = float(step + epoch * len_dataloader) / params.num_epochs / len_dataloader
+            p = float(step + epoch * len_dataloader) / \
+                params.num_epochs / len_dataloader
             alpha = 2. / (1. + np.exp(-10 * p)) - 1
-            alpha = 2*alpha
 
             if params.src_dataset == 'mnist' or params.tgt_dataset == 'mnist':
                 adjust_learning_rate(optimizer, p)
@@ -62,13 +67,13 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
             # prepare domain label
             size_src = len(images_src)
             size_tgt = len(images_tgt)
-            label_src = make_variable(torch.zeros(size_src).long())  # source 0
-            label_tgt = make_variable(torch.ones(size_tgt).long())  # target 1
+            label_src = torch.zeros(size_src).long().to(device)  # source 0
+            label_tgt = torch.ones(size_tgt).long().to(device)  # target 1
 
             # make images variable
-            class_src = make_variable(class_src)
-            images_src = make_variable(images_src)
-            images_tgt = make_variable(images_tgt)
+            class_src = class_src.to(device)
+            images_src = images_src.to(device)
+            images_tgt = images_tgt.to(device)
 
             # zero gradients for optimizer
             optimizer.zero_grad()
@@ -90,32 +95,29 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
 
             # print step info
             if ((step + 1) % params.log_step == 0):
-                print("Epoch [{:4d}/{}] Step [{:2d}/{}]: src_loss_class={:.6f}, src_loss_domain={:.6f}, tgt_loss_domain={:.6f}, loss={:.6f}"
-                      .format(epoch + 1,
-                              params.num_epochs,
-                              step + 1,
-                              len_dataloader,
-                              src_loss_class.data[0],
-                              src_loss_domain.data[0],
-                              tgt_loss_domain.data[0],
-                              loss.data[0]))
-
-        # eval model on test set
+                print(
+                    "Epoch [{:4d}/{}] Step [{:2d}/{}]: src_loss_class={:.6f}, src_loss_domain={:.6f}, tgt_loss_domain={:.6f}, loss={:.6f}"
+                    .format(epoch + 1, params.num_epochs, step + 1, len_dataloader, src_loss_class.data.item(),
+                            src_loss_domain.data.item(), tgt_loss_domain.data.item(), loss.data.item()))
+
+        # eval model
         if ((epoch + 1) % params.eval_step == 0):
             print("eval on target domain")
-            eval(model, tgt_data_loader)
+            eval(model, tgt_data_loader, device, flag='target')
             print("eval on source domain")
-            eval_src(model, src_data_loader)
+            eval(model, src_data_loader, device, flag='source')
 
         # save model parameters
         if ((epoch + 1) % params.save_step == 0):
-            save_model(model, params.model_root, params.src_dataset + '-' + params.tgt_dataset + "-dann-{}.pt".format(epoch + 1))
+            save_model(model, params.model_root,
+                       params.src_dataset + '-' + params.tgt_dataset + "-dann-{}.pt".format(epoch + 1))
 
     # save final model
     save_model(model, params.model_root, params.src_dataset + '-' + params.tgt_dataset + "-dann-final.pt")
 
     return model
 
+
 def adjust_learning_rate(optimizer, p):
     lr_0 = 0.01
     alpha = 10
@@ -124,6 +126,7 @@ def adjust_learning_rate(optimizer, p):
     for param_group in optimizer.param_groups:
         param_group['lr'] = lr
 
+
 def adjust_learning_rate_office(optimizer, p):
     lr_0 = 0.001
     alpha = 10

core/pretrain.py

@@ -3,10 +3,11 @@
 import torch.nn as nn
 import torch.optim as optim
 
-from utils import make_variable, save_model
-from core.test import eval_src
+from utils.utils import save_model
+from core.test import eval
 
-def train_src(model, params, data_loader):
+
+def train_src(model, params, data_loader, device):
     """Train classifier for source domain."""
     ####################
     # 1. setup network #
@@ -26,8 +27,8 @@ def train_src(model, params, data_loader):
     for epoch in range(params.num_epochs_src):
         for step, (images, labels) in enumerate(data_loader):
             # make images and labels variable
-            images = make_variable(images)
-            labels = make_variable(labels.squeeze_())
+            images = images.to(device)
+            labels = labels.squeeze_().to(device)
 
             # zero gradients for optimizer
             optimizer.zero_grad()
@@ -42,16 +43,12 @@ def train_src(model, params, data_loader):
 
             # print step info
             if ((step + 1) % params.log_step_src == 0):
-                print("Epoch [{}/{}] Step [{}/{}]: loss={}"
-                      .format(epoch + 1,
-                              params.num_epochs_src,
-                              step + 1,
-                              len(data_loader),
-                              loss.data[0]))
+                print("Epoch [{}/{}] Step [{}/{}]: loss={}".format(epoch + 1, params.num_epochs_src, step + 1,
+                                                                   len(data_loader), loss.data[0]))
 
         # eval model on test set
         if ((epoch + 1) % params.eval_step_src == 0):
-            eval_src(model, data_loader)
+            eval(model, data_loader, flag='source')
             model.train()
 
         # save model parameters

core/test.py

@@ -1,9 +1,8 @@
 import torch.utils.data
 import torch.nn as nn
 
-from utils import make_variable
 
-def test_from_save(model, saved_model, data_loader):
+def test_from_save(model, saved_model, data_loader, device):
     """Evaluate classifier for source domain."""
     # set eval state for Dropout and BN layers
     classifier = model.load_state_dict(torch.load(saved_model))
@@ -18,13 +17,13 @@ def test_from_save(model, saved_model, data_loader):
 
     # evaluate network
     for (images, labels) in data_loader:
-        images = make_variable(images, volatile=True)
-        labels = make_variable(labels) #labels = labels.squeeze(1)
+        images = images.to(device)
+        labels = labels.to(device)  #labels = labels.squeeze(1)
         preds = classifier(images)
 
         criterion(preds, labels)
 
-        loss += criterion(preds, labels).data[0]
+        loss += criterion(preds, labels).data.item()
 
         pred_cls = preds.data.max(1)[1]
         acc += pred_cls.eq(labels.data).cpu().sum()
@@ -34,43 +33,8 @@ def test_from_save(model, saved_model, data_loader):
 
     print("Avg Loss = {}, Avg Accuracy = {:.2%}".format(loss, acc))
 
-def eval(model, data_loader):
-    """Evaluate model for dataset."""
-    # set eval state for Dropout and BN layers
-    model.eval()
-
-    # init loss and accuracy
-    loss = 0.0
-    acc = 0.0
-    acc_domain = 0.0
-
-    # set loss function
-    criterion = nn.CrossEntropyLoss()
-
-    # evaluate network
-    for (images, labels) in data_loader:
-        images = make_variable(images, volatile=True)
-        labels = make_variable(labels) #labels = labels.squeeze(1)
-        size_tgt = len(labels)
-        labels_domain = make_variable(torch.ones(size_tgt).long())
-
-        preds, domain = model(images, alpha=0)
-
-        loss += criterion(preds, labels).data[0]
-
-        pred_cls = preds.data.max(1)[1]
-        pred_domain = domain.data.max(1)[1]
-
-        acc += pred_cls.eq(labels.data).cpu().sum()
-        acc_domain += pred_domain.eq(labels_domain.data).cpu().sum()
-
-    loss /= len(data_loader)
-    acc /= len(data_loader.dataset)
-    acc_domain /= len(data_loader.dataset)
-
-    print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_domain))
 
-def eval_src(model, data_loader):
+def eval(model, data_loader, device, flag):
     """Evaluate model for dataset."""
     # set eval state for Dropout and BN layers
     model.eval()
@@ -85,54 +49,25 @@ def eval_src(model, data_loader):
 
     # evaluate network
     for (images, labels) in data_loader:
-        images = make_variable(images, volatile=True)
-        labels = make_variable(labels) #labels = labels.squeeze(1)
-        size_tgt = len(labels)
-        labels_domain = make_variable(torch.zeros(size_tgt).long())
+        images = images.to(device)
+        labels = labels.to(device)  #labels = labels.squeeze(1)
+        size = len(labels)
+        if flag == 'target':
+            labels_domain = torch.ones(size).long().to(device)
+        else:
+            labels_domain = torch.zeros(size).long().to(device)
 
         preds, domain = model(images, alpha=0)
 
-        loss += criterion(preds, labels).data[0]
+        loss += criterion(preds, labels).data.item()
 
         pred_cls = preds.data.max(1)[1]
         pred_domain = domain.data.max(1)[1]
-
-        acc += pred_cls.eq(labels.data).cpu().sum()
-        acc_domain += pred_domain.eq(labels_domain.data).cpu().sum()
+        acc += pred_cls.eq(labels.data).sum().item()
+        acc_domain += pred_domain.eq(labels_domain.data).sum().item()
 
     loss /= len(data_loader)
     acc /= len(data_loader.dataset)
     acc_domain /= len(data_loader.dataset)
 
     print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_domain))
-
-def eval_src_(model, data_loader):
-    """Evaluate classifier for source domain."""
-    # set eval state for Dropout and BN layers
-    model.eval()
-
-    # init loss and accuracy
-    loss = 0.0
-    acc = 0.0
-
-    # set loss function
-    criterion = nn.NLLLoss()
-
-    # evaluate network
-    for (images, labels) in data_loader:
-        images = make_variable(images, volatile=True)
-        labels = make_variable(labels) #labels = labels.squeeze(1)
-        preds = model(images)
-
-        criterion(preds, labels)
-
-        loss += criterion(preds, labels).data[0]
-
-        pred_cls = preds.data.max(1)[1]
-        acc += pred_cls.eq(labels.data).cpu().sum()
-
-
-    loss /= len(data_loader)
-    acc /= len(data_loader.dataset)
-
-    print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}".format(loss, acc))

datasets/office.py

@@ -1,30 +1,25 @@
 """Dataset setting and data loader for Office."""
 
+import os
 import torch
 from torchvision import datasets, transforms
 import torch.utils.data as data
-import os
 
 
 def get_office(dataset_root, batch_size, category):
     """Get Office datasets loader."""
     # image pre-processing
-    pre_process = transforms.Compose([transforms.Resize(227),
+    pre_process = transforms.Compose([
+        transforms.Resize(227),
         transforms.ToTensor(),
-                                     transforms.Normalize(
-                                         mean=(0.485, 0.456, 0.406),
-                                         std=(0.229, 0.224, 0.225)
-                                     )])
+        transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+    ])
 
     # datasets and data_loader
     office_dataset = datasets.ImageFolder(
-        os.path.join(dataset_root, 'office', category, 'images'),
-        transform=pre_process)
+        os.path.join(dataset_root, 'office', category, 'images'), transform=pre_process)
 
     office_dataloader = torch.utils.data.DataLoader(
-        dataset=office_dataset,
-        batch_size=batch_size,
-        shuffle=True,
-        num_workers=4)
+        dataset=office_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
 
     return office_dataloader

mnist_mnistm.py → experiments/mnist_mnistm.py

@@ -1,8 +1,10 @@
 import os
+import sys
 
+sys.path.append('../')
 from models.model import MNISTmodel
 from core.dann import train_dann
-from utils import get_data_loader, init_model, init_random_seed
+from utils.utils import get_data_loader, init_model, init_random_seed
 
 
 class Config(object):

office.py → experiments/office.py

@@ -1,15 +1,19 @@
 import os
+import sys
+
+import torch
+
+sys.path.append('../')
 from core.dann import train_dann
 from core.test import eval
 from models.model import AlexModel
-
-from utils import get_data_loader, init_model, init_random_seed
+from utils.utils import get_data_loader, init_model, init_random_seed
 
 
 class Config(object):
     # params for path
     dataset_root = os.path.expanduser(os.path.join('~', 'Datasets'))
-    model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN'))
+    model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-dann'))
 
     # params for datasets and data loader
     batch_size = 32
@@ -28,12 +32,13 @@ class Config(object):
     num_epochs_src = 100
     log_step_src = 5
     save_step_src = 50
-    eval_step_src = 20
+    eval_step_src = 10
 
     # params for training dann
+    gpu_id = '0'
 
     ## for office
-    num_epochs = 2000
+    num_epochs = 1000
     log_step = 10  # iters
     save_step = 500
     eval_step = 5  # epochs
@@ -44,11 +49,15 @@ class Config(object):
     # params for optimizing models
     lr = 2e-4
 
+
 params = Config()
 
 # init random seed
 init_random_seed(params.manual_seed)
 
+# init device
+device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
+
 # load 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)
@@ -60,7 +69,7 @@ dann = init_model(net=AlexModel(), restore=None)
 print("Start training dann model.")
 
 if not (dann.restored and params.dann_restore):
-    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader)
+    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader, device)
 
 # eval dann model
 print("Evaluating dann for source domain")

experiments/office31_10.py

@@ -0,0 +1,81 @@
+import os
+import sys
+
+sys.path.append('../')
+from core.dann import train_dann
+from core.test import eval
+from models.model import AlexModel
+
+from utils.utils import get_data_loader, init_model, init_random_seed
+
+
+class Config(object):
+    # params for path
+    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
+    batch_size = 32
+
+    # params for source dataset
+    src_dataset = "amazon31"
+    src_model_trained = True
+    src_classifier_restore = os.path.join(
+        model_root, src_dataset + '-source-classifier-final.pt')
+
+    # params for target dataset
+    tgt_dataset = "webcam10"
+    tgt_model_trained = True
+    dann_restore = os.path.join(
+        model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
+
+    # params for pretrain
+    num_epochs_src = 100
+    log_step_src = 5
+    save_step_src = 50
+    eval_step_src = 20
+
+    # params for training dann
+
+    # for office
+    num_epochs = 1000
+    log_step = 10  # iters
+    save_step = 500
+    eval_step = 5  # epochs
+
+    manual_seed = 8888
+    alpha = 0
+
+    # params for optimizing models
+    lr = 2e-4
+
+
+params = Config()
+
+# init random seed
+init_random_seed(params.manual_seed)
+
+# load 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=None)
+
+# train dann model
+print("Start training dann model.")
+
+if not (dann.restored and params.dann_restore):
+    dann = train_dann(dann, params, src_data_loader,
+                      tgt_data_loader, tgt_data_loader)
+
+# eval dann model
+print("Evaluating dann for source domain")
+eval(dann, src_data_loader)
+print("Evaluating dann for target domain")
+eval(dann, tgt_data_loader)
+
+print('done')

svhn_mnist.py → experiments/svhn_mnist.py

@@ -1,8 +1,10 @@
 import os
+import sys
 
+sys.path.append('../')
 from models.model import SVHNmodel
 from core.dann import train_dann
-from utils import get_data_loader, init_model, init_random_seed
+from utils.utils import get_data_loader, init_model, init_random_seed
 
 
 class Config(object):

models/model.py

@@ -179,10 +179,10 @@ class AlexModel(nn.Module):
 
         self.discriminator = nn.Sequential(
             nn.Linear(2048, 1024),
-            nn.ReLU(),
+            nn.ReLU(inplace=True),
             nn.Dropout(),
             nn.Linear(1024, 1024),
-            nn.ReLU(),
+            nn.ReLU(inplace=True),
             nn.Dropout(),
             nn.Linear(1024, 2),
         )

utils.py → utils/utils.py

@@ -1,5 +1,3 @@
-"""Utilities for ADDA."""
-
 import os
 import random
 
@@ -12,13 +10,6 @@ from datasets.office import get_office
 from datasets.officecaltech import get_officecaltech
 
 
-def make_variable(tensor, volatile=False):
-    """Convert Tensor to Variable."""
-    if torch.cuda.is_available():
-        tensor = tensor.cuda()
-    return Variable(tensor, volatile=volatile)
-
-
 def make_cuda(tensor):
     """Use CUDA if it's available."""
     if torch.cuda.is_available():
@@ -71,6 +62,7 @@ def get_data_loader(name, dataset_root, batch_size, train=True):
     elif name == "webcam10":
         return get_officecaltech(dataset_root, batch_size, 'webcam')
 
+
 def init_model(net, restore):
     """Init models with cuda and weights."""
     # init weights of model
@@ -91,10 +83,10 @@ def init_model(net, restore):
 
     return net
 
+
 def save_model(net, model_root, filename):
     """Save trained model."""
     if not os.path.exists(model_root):
         os.makedirs(model_root)
-    torch.save(net.state_dict(),
-               os.path.join(model_root, filename))
+    torch.save(net.state_dict(), os.path.join(model_root, filename))
     print("save pretrained model to: {}".format(os.path.join(model_root, filename)))