add syndigits-svhn experiment.

datasets/syndigits.py

@@ -0,0 +1,27 @@
+"""Dataset setting and data loader for syn-digits."""
+
+import os
+import torch
+from torchvision import datasets, transforms
+import torch.utils.data as data
+
+
+def get_syndigits(dataset_root, batch_size, train):
+    """Get synth digits datasets loader."""
+    # image pre-processing
+    pre_process = transforms.Compose([
+        transforms.Resize(32),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+    ])
+
+    # datasets and data loader
+    if train:
+        syndigits_dataset = datasets.ImageFolder(os.path.join(dataset_root, 'TRAIN_separate_dirs'), transform=pre_process)
+    else:
+        syndigits_dataset = datasets.ImageFolder(os.path.join(dataset_root, 'TEST_separate_dirs'), transform=pre_process)
+
+    syndigits_dataloader = torch.utils.data.DataLoader(
+        dataset=syndigits_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
+
+    return syndigits_dataloader

experiments/syndigits_svhn.py

@@ -0,0 +1,82 @@
+import os
+import sys
+import datetime
+from tensorboardX import SummaryWriter
+
+import torch
+sys.path.append('../')
+from models.model import SVHNmodel
+from core.train import train_dann
+from utils.utils import get_data_loader, init_model, init_random_seed
+
+
+class Config(object):
+    # params for path
+    model_name = "syndigits-svhn"
+    model_base = '/home/wogong/models/pytorch-dann'
+    note = 'default'
+    model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
+    os.makedirs(model_root)
+    config = os.path.join(model_root, 'config.txt')
+    finetune_flag = False
+    lr_adjust_flag = 'simple'
+    src_only_flag = False
+
+    # params for datasets and data loader
+    batch_size = 128
+
+    # params for source dataset
+    src_dataset = "syndigits"
+    src_image_root = os.path.join('/home/wogong/datasets', 'syndigits')
+    src_model_trained = True
+    src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
+
+    # params for target dataset
+    tgt_dataset = "svhn"
+    tgt_image_root = os.path.join('/home/wogong/datasets', 'svhn')
+    tgt_model_trained = True
+    dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
+
+    # params for GPU device
+    gpu_id = '0'
+
+    ## for digit
+    num_epochs = 200
+    log_step = 200
+    save_step = 100
+    eval_step = 1
+
+    manual_seed = 42
+    alpha = 0
+
+    # params for SGD optimizer
+    lr = 0.01
+    momentum = 0.9
+    weight_decay = 1e-6
+
+    def __init__(self):
+        public_props = (name for name in dir(self) if not name.startswith('_'))
+        with open(self.config, 'w') as f:
+            for name in public_props:
+                f.write(name + ': ' + str(getattr(self, name)) + '\n')
+
+params = Config()
+logger = SummaryWriter(params.model_root)
+device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
+
+# init random seed
+init_random_seed(params.manual_seed)
+
+# load dataset
+src_data_loader = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=True)
+src_data_loader_eval = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=False)
+tgt_data_loader = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=True)
+tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=False)
+
+# load dann model
+dann = init_model(net=SVHNmodel(), restore=None)
+
+# train dann model
+print("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, device, logger)

models/model.py

@@ -160,12 +160,12 @@ class SVHNmodel(nn.Module):
             nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(5, 5)),  # 28
             nn.BatchNorm2d(64),
             nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)),  # 14
-            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5)),  # 10
+            nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)),  # 13
+            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5)),  # 9
             nn.BatchNorm2d(64),
             nn.Dropout2d(),
             nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)),  # 5
+            nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)),  # 4
             nn.ReLU(inplace=True),
             nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(4, 4)),  # 1
         )

utils/utils.py

@@ -7,6 +7,7 @@ import torch.backends.cudnn as cudnn
 from datasets import get_mnist, get_mnistm, get_svhn
 from datasets.office import get_office
 from datasets.officecaltech import get_officecaltech
+from datasets.syndigits import get_syndigits
 from datasets.synsigns import get_synsigns
 from datasets.gtsrb import get_gtsrb
 
@@ -61,6 +62,8 @@ def get_data_loader(name, dataset_root, batch_size, train=True):
         return get_office(dataset_root, batch_size, 'webcam')
     elif name == "webcam10":
         return get_officecaltech(dataset_root, batch_size, 'webcam')
+    elif name == "syndigits":
+        return get_syndigits(dataset_root, batch_size, train)
     elif name == "synsigns":
         return get_synsigns(dataset_root, batch_size, train)
     elif name == "gtsrb":