fix code

datasets/mnistm_weight.py

@@ -37,8 +37,11 @@ def __getitem__(self, item):
 
     def __len__(self):
         return self.n_data
+    def get_labels(self): 
+        return torch.tensor([int(label) for label in self.img_labels])
 
-def get_mnistm_weight(dataset_root, batch_size, train, weights = None):
+
+def get_mnistm_weight(dataset_root, batch_size, train, weights):
     """Get MNISTM datasets loader."""
     # image pre-processing, each image from MNIST-m has shape 32x32
     pre_process = transforms.Compose([transforms.Resize(28),
@@ -64,13 +67,20 @@ def get_mnistm_weight(dataset_root, batch_size, train, weights = None):
             data_list=train_list,
             transform=pre_process)
     num_sample = len(mnistm_dataset)
-    if sampler is not None: 
-        sampler.num_samples = num_sample
-    mnistm_dataloader = torch.utils.data.DataLoader(
-        dataset=mnistm_dataset,
-        batch_size=batch_size,
-        shuffle=True,
-        sampler=sampler,
-        num_workers=8)
+    if len(weights) == 10: 
+        sample_weight = torch.tensor([weights[label] for label in mnistm_dataset.get_labels()])
+        mnistm_dataloader = torch.utils.data.DataLoader(
+            dataset=mnistm_dataset,
+            batch_size=batch_size,
+            sampler=torch.utils.data.sampler.WeightedRandomSampler(
+                sample_weight,len(sample_weight)),
+            shuffle=True,
+            num_workers=8)
+    else: 
+        mnistm_dataloader = torch.utils.data.DataLoader(
+            dataset=mnistm_dataset,
+            batch_size=batch_size,
+            shuffle=True,
+            num_workers=8)
 
     return mnistm_dataloader, num_sample

datasets/usps.py

@@ -11,9 +11,10 @@ def get_usps(dataset_root, batch_size, train):
     pre_process = transforms.Compose([transforms.Resize(28),
                                       transforms.ToTensor(),
                                       transforms.Normalize(
-                                          mean=[0.5],
-                                          std=[0.5]
-                                      )])
+                                          mean=[0.2473], # Mean for USPS train data
+                                          std=[0.2665] # std for USPS train data
+                                      )
+                                      ])
 
     # datasets and data loader
     usps_dataset = datasets.USPS(root=os.path.join(dataset_root),

datasets/usps_weight.py

@@ -23,8 +23,8 @@ def get_usps_weight(dataset_root, batch_size, train, weights):
     pre_process = transforms.Compose([transforms.Resize(28),
                                       transforms.ToTensor(),
                                       transforms.Normalize(
-                                          mean=[0.5],
-                                          std=[0.5]
+                                      mean=[0.2473], # Mean for USPS train data
+                                      std=[0.2665] # std for USPS train data
                                       )])
 
     # datasets and data loader

mean.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,32 +22,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "output_type": "error",
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '/nobackup/yguo/datasets/gtsrb_train.p'",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-d3dc473fda6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_gtsrb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/nobackup/yguo/datasets'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/nobackup/yguo/pytorch-dann/datasets/gtsrb.py\u001b[0m in \u001b[0;36mget_gtsrb\u001b[0;34m(dataset_root, batch_size, train)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;31m# datasets and data_loader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0mgtsrb_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGTSRB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_root\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gtsrb_train.p'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_process\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0mdataset_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgtsrb_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/nobackup/yguo/pytorch-dann/datasets/gtsrb.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath, transform)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mGTSRB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'images'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/nobackup/yguo/datasets/gtsrb_train.p'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "loader = get_gtsrb('/nobackup/yguo/datasets', 10000, True)"
+    "loader = get_usps('/nobackup/yguo/datasets', 10, True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
@@ -69,16 +55,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "tensor([0.4376, 0.4438, 0.4729])"
+      "text/plain": "tensor([0.2473])"
      },
      "metadata": {},
-     "execution_count": 11
+     "execution_count": 6
     }
    ],
    "source": [
@@ -87,16 +73,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "tensor([0.1201, 0.1231, 0.1052])"
+      "text/plain": "tensor([0.2665])"
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 7
     }
    ],
    "source": [

mnist_mnistm_weight.py

@@ -5,15 +5,16 @@
 sys.path.append('../')
 from models.model import MNISTmodel, MNISTmodel_plain
 from core.train_weight import train_dann
-from utils.utils import get_data_loader, get_data_loader_weight, init_model, init_random_seed
+from utils.utils import get_data_loader, get_data_loader_weight, init_model, init_random_seed, get_dataset_root
 from torch.utils.tensorboard import SummaryWriter
 
 
+
 class Config(object):
     # params for path
     model_name = "mnist-mnistm-weight"
-    dataset_root = os.path.expanduser('/nobackup/yguo/dataset')
-    model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN', model_name))
+    dataset_root = get_dataset_root()
+    model_root = os.path.expanduser(os.path.join('runs', model_name))
     finetune_flag = False
 
     # params for datasets and data loader
@@ -63,7 +64,7 @@ class Config(object):
 
 params = Config()
 
-logger = SummaryWriter(params.model_root, flush_secs = 10)
+logger = SummaryWriter(params.model_root)
 
 # init random seed
 init_random_seed(params.manual_seed)
@@ -86,7 +87,7 @@ class Config(object):
 
 
 # load dann model
-dann = init_model(net=MNISTmodel_plain(), restore=None)
+dann = init_model(net=MNISTmodel(), restore=None)
 
 # train dann model
 print("Training dann model")

mnist_mnistm_winsorize.py

@@ -0,0 +1,96 @@
+import os
+import sys
+
+import torch
+sys.path.append('../')
+from models.model import MNISTmodel, MNISTmodel_plain
+from core.train_weight import train_dann
+from utils.utils import get_data_loader, get_data_loader_weight, init_model, init_random_seed, get_dataset_root
+from torch.utils.tensorboard import SummaryWriter
+
+
+
+class Config(object):
+    # params for path
+    model_name = "mnist-mnistm-weight"
+    dataset_root = get_dataset_root()
+    model_root = os.path.expanduser(os.path.join('runs', model_name))
+    finetune_flag = False
+
+    # params for datasets and data loader
+    batch_size = 64
+
+    # params for source dataset
+    src_dataset = "mnist"
+    src_model_trained = True
+    src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
+    class_num_src = 31
+
+    # params for target dataset
+    tgt_dataset = "mnistm"
+    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 = 10
+    save_step_src = 50
+    eval_step_src = 20
+
+    # params for training dann
+    gpu_id = '0'
+
+    ## for digit
+    num_epochs = 100
+    log_step = 20
+    save_step = 50
+    eval_step = 1
+
+    ## for office
+    # num_epochs = 1000
+    # log_step = 10  # iters
+    # save_step = 500
+    # eval_step = 5  # epochs
+    lr_adjust_flag = 'simple'
+    src_only_flag = False
+
+    manual_seed = 8888
+    alpha = 0
+
+    # params for optimizing models
+    lr = 5e-4
+    momentum = 0
+    weight_decay = 0
+
+params = Config()
+
+logger = SummaryWriter(params.model_root)
+
+# 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")
+
+
+WEIGHTS = torch.ones(10)
+# load dataset
+src_data_loader, num_src_train = get_data_loader_weight(params.src_dataset, params.dataset_root, params.batch_size, train=True)
+src_data_loader_eval = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=False)
+tgt_data_loader, num_tgt_train = get_data_loader_weight(
+    params.tgt_dataset, params.dataset_root, params.batch_size, train=True, sampler=torch.utils.data.sampler.WeightedRandomSampler(
+    WEIGHTS, 1))
+tgt_data_loader_eval, _ = get_data_loader_weight(
+    params.tgt_dataset, params.dataset_root, params.batch_size, train=False, sampler=torch.utils.data.sampler.WeightedRandomSampler(
+    WEIGHTS, 1))
+# Cannot use the same sampler for both training and testing dataset 
+
+
+# load dann model
+dann = init_model(net=MNISTmodel_plain(), 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, num_src_train, num_tgt_train, device, logger)

mnist_usps.py

@@ -5,15 +5,15 @@
 sys.path.append('../')
 from models.model import MNISTmodel, MNISTmodel_plain
 from core.train import train_dann
-from utils.utils import get_data_loader, init_model, init_random_seed
+from utils.utils import get_data_loader, init_model, init_random_seed, get_dataset_root
 from torch.utils.tensorboard import SummaryWriter
 
 
 class Config(object):
     # params for path
     model_name = "mnist-usps"
-    dataset_root = os.path.expanduser('/nobackup/yguo/dataset')
-    model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN', model_name))
+    dataset_root = get_dataset_root()
+    model_root = os.path.expanduser(os.path.join('runs', model_name))
     finetune_flag = False
 
     # params for datasets and data loader

svhn_mnist.py

@@ -7,16 +7,13 @@
 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
+from utils.utils import get_data_loader, init_model, init_random_seed, get_dataset_root
 
 
 class Config(object):
     # params for path
     model_name = "svhn-mnist"
-    model_base = '/nobackup/yguo/pytorch-dann'
-    model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN', model_name))
-    note = 'paper-structure'
-    model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
+    model_root = os.path.expanduser(os.path.join('runs', model_name))
     os.makedirs(model_root)
     config = os.path.join(model_root, 'config.txt')
     finetune_flag = False
@@ -28,13 +25,13 @@ class Config(object):
 
     # params for source dataset
     src_dataset = "svhn"
-    src_image_root = os.path.join('/nobackup/yguo/dataset', 'svhn')
+    src_image_root = get_dataset_root()
     src_model_trained = True
     src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
 
     # params for target dataset
     tgt_dataset = "mnist"
-    tgt_image_root = os.path.join('/nobackup/yguo/dataset', 'mnist')
+    tgt_image_root = get_dataset_root()
     tgt_model_trained = True
     dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
 

svhn_mnist_weight.py

@@ -7,15 +7,13 @@
 sys.path.append('../')
 from models.model import SVHNmodel
 from core.train_weight import train_dann
-from utils.utils import get_data_loader, get_data_loader_weight, init_model, init_random_seed
+from utils.utils import get_data_loader, get_data_loader_weight, init_model, init_random_seed, get_dataset_root
 import numpy as np
 
 class Config(object):
     # params for path
     model_name = "svhn-mnist-weight"
-    model_base = '/nobackup/yguo/pytorch-dann'
     model_root = os.path.expanduser(os.path.join('runs', model_name))
-    model_root = os.path.join(model_base, model_name, '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
     os.makedirs(model_root)
     config = os.path.join(model_root, 'config.txt')
     finetune_flag = False
@@ -27,13 +25,13 @@ class Config(object):
 
     # params for source dataset
     src_dataset = "svhn"
-    src_image_root = os.path.join('/nobackup/yguo/dataset', 'svhn')
+    src_image_root = get_dataset_root()
     src_model_trained = True
     src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
 
     # params for target dataset
     tgt_dataset = "mnist"
-    tgt_image_root = os.path.join('/nobackup/yguo/dataset', 'mnist')
+    tgt_image_root = get_dataset_root()
     tgt_model_trained = True
     dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
 

utils/utils.py

@@ -85,7 +85,9 @@ def get_data_loader(name, dataset_root, batch_size, train=True):
 
 
 def get_data_loader_weight(name, dataset_root, batch_size, train=True, weights = torch.tensor([])):
-    """Get data loader by name."""
+    """Get data loader by name. If len(weights) is 0 (default), no weighted 
+    sampling is performed.
+    """
     if name == "mnist":
         return get_mnist_weight(dataset_root, batch_size, train, weights = weights)
     elif name == "mnistm":