├── LICENSE ├── README.md ├── data ├── imagenet_caltech │ ├── caltech_84_list.txt │ ├── caltech_list.txt │ ├── imagenet_84_list.txt │ └── imagenet_list.txt ├── office │ ├── amazon_10_list.txt │ ├── amazon_list.txt │ ├── dslr_10_list.txt │ ├── dslr_list.txt │ ├── webcam_10_list.txt │ └── webcam_list.txt └── office_home │ ├── Art_25_list.txt │ ├── Art_list.txt │ ├── Clipart_25_list.txt │ ├── Clipart_list.txt │ ├── Clipart_list_l.txt │ ├── Clipart_list_u.txt │ ├── Product_25_list.txt │ ├── Product_list.txt │ ├── RealWorld_25_list.txt │ └── RealWorld_list.txt ├── data_list.py ├── framework.jpg ├── lr_schedule.py ├── my_loss.py ├── network.py ├── run.sh └── run_partial.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 tim-learn 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Code for our ECCV (2020) paper [**A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation**](https://arxiv.org/abs/2003.02541). 2 | 3 | ![framework](framework.jpg) 4 | 5 | ### Prerequisites: 6 | - python == 3.6.8 7 | - pytorch ==1.1.0 8 | - torchvision == 0.3.0 9 | - numpy, scipy, PIL, argparse, tqdm 10 | 11 | ### Dataset: 12 | 13 | - Please manually download the datasets [Office](https://drive.google.com/file/d/0B4IapRTv9pJ1WGZVd1VDMmhwdlE/view), [Office-Home](https://drive.google.com/file/d/0B81rNlvomiwed0V1YUxQdC1uOTg/view), [ImageNet-Caltech](http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz) from the official websites, and modify the path of images in each '.txt' under the folder './data/'. 14 | - We adopt the same data protocol as [PADA](https://github.com/thuml/PADA/tree/master/pytorch/data). 15 | 16 | ### Training: 17 | 1. ##### Partial Domain Adaptation (PDA) on the Office-Home dataset [Art(s=0) -> Clipart(t=1)] 18 | ```python 19 | python run_partial.py --s 0 --t 1 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 20 | ``` 21 | 22 | 2. ##### Partial Domain Adaptation (PDA) on the Office dataset [Amazon(s=0) -> DSLR(t=1)] 23 | ```python 24 | python run_partial.py --s 0 --t 1 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 25 | python run_partial.py --s 0 --t 1 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 26 | ``` 27 | 28 | 3. ##### Partial Domain Adaptation (PDA) on the ImageNet-Caltech dataset [ImageNet(s=0) -> Caltech(t=1)] 29 | ```python 30 | python run_partial.py --s 0 --t 1 --dset imagenet_caltech --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 31 | ``` 32 | 33 | 34 | ### Citation 35 | 36 | If you find this code useful for your research, please cite our paper 37 | 38 | > @inproceedings{liang2020baus, 39 | >     title={A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation}, 40 | >     author={Liang, Jian, and Wang, Yunbo, and Hu, Dapeng, and He, Ran and Feng, Jiashi}, 41 | >     booktitle={European Conference on Computer Vision (ECCV)}, 42 | >     pages={xx-xx}, 43 | >     month = {August}, 44 | >     year={2020} 45 | > } 46 | 47 | ### Acknowledgement 48 | 49 | Some parts of this project are built based on the following open-source implementation 50 | - CDAN [https://github.com/thuml/CDAN/tree/master/pytorch](https://github.com/thuml/CDAN/tree/master/pytorch) 51 | - COT [https://github.com/henry8527/COT](https://github.com/henry8527/COT) 52 | 53 | ### Contact 54 | - [liangjian92@gmail.com](mailto:liangjian92@gmail.com) 55 | - [dapeng.hu@u.nus.edu](mailto:dapeng.hu@u.nus.edu) 56 | - [elefjia@nus.edu.sg](mailto:elefjia@nus.edu.sg) 57 | -------------------------------------------------------------------------------- /data/office/dslr_10_list.txt: -------------------------------------------------------------------------------- 1 | /Checkpoint/liangjian/da_dataset/office31/dslr/images/back_pack/frame_0001.jpg 0 2 | /Checkpoint/liangjian/da_dataset/office31/dslr/images/back_pack/frame_0002.jpg 0 3 | /Checkpoint/liangjian/da_dataset/office31/dslr/images/back_pack/frame_0003.jpg 0 4 | /Checkpoint/liangjian/da_dataset/office31/dslr/images/back_pack/frame_0004.jpg 0 5 | /Checkpoint/liangjian/da_dataset/office31/dslr/images/back_pack/frame_0005.jpg 0 6 | /Checkpoint/liangjian/da_dataset/office31/dslr/images/back_pack/frame_0006.jpg 0 7 | 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/Checkpoint/liangjian/da_dataset/office_home/Clipart/Webcam/00012.jpg 64 196 | -------------------------------------------------------------------------------- /data_list.py: -------------------------------------------------------------------------------- 1 | #from __future__ import print_function, division 2 | 3 | import torch 4 | import numpy as np 5 | import random 6 | from PIL import Image 7 | from torch.utils.data import Dataset 8 | import os 9 | import os.path 10 | 11 | def make_dataset(image_list, labels): 12 | if labels: 13 | len_ = len(image_list) 14 | images = [(image_list[i].strip(), labels[i, :]) for i in range(len_)] 15 | else: 16 | if len(image_list[0].split()) > 2: 17 | images = [(val.split()[0], np.array([int(la) for la in val.split()[1:]])) for val in image_list] 18 | else: 19 | images = [(val.split()[0], int(val.split()[1])) for val in image_list] 20 | return images 21 | 22 | 23 | def rgb_loader(path): 24 | with open(path, 'rb') as f: 25 | with Image.open(f) as img: 26 | return img.convert('RGB') 27 | 28 | def l_loader(path): 29 | with open(path, 'rb') as f: 30 | with Image.open(f) as img: 31 | return img.convert('L') 32 | 33 | class ImageList(Dataset): 34 | def __init__(self, image_list, labels=None, transform=None, target_transform=None, mode='RGB'): 35 | imgs = make_dataset(image_list, labels) 36 | if len(imgs) == 0: 37 | raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n" 38 | "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) 39 | 40 | self.imgs = imgs 41 | self.transform = transform 42 | self.target_transform = target_transform 43 | if mode == 'RGB': 44 | self.loader = rgb_loader 45 | elif mode == 'L': 46 | self.loader = l_loader 47 | 48 | def __getitem__(self, index): 49 | path, target = self.imgs[index] 50 | img = self.loader(path) 51 | if self.transform is not None: 52 | img = self.transform(img) 53 | if self.target_transform is not None: 54 | target = self.target_transform(target) 55 | 56 | return img, target 57 | 58 | def __len__(self): 59 | return len(self.imgs) 60 | 61 | class ImageValueList(Dataset): 62 | def __init__(self, image_list, labels=None, transform=None, target_transform=None, 63 | loader=rgb_loader): 64 | imgs = make_dataset(image_list, labels) 65 | if len(imgs) == 0: 66 | raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n" 67 | "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) 68 | 69 | self.imgs = imgs 70 | self.values = [1.0] * len(imgs) 71 | self.transform = transform 72 | self.target_transform = target_transform 73 | self.loader = loader 74 | 75 | def set_values(self, values): 76 | self.values = values 77 | 78 | def __getitem__(self, index): 79 | path, target = self.imgs[index] 80 | img = self.loader(path) 81 | if self.transform is not None: 82 | img = self.transform(img) 83 | if self.target_transform is not None: 84 | target = self.target_transform(target) 85 | 86 | return img, target 87 | 88 | def __len__(self): 89 | return len(self.imgs) -------------------------------------------------------------------------------- /framework.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tim-learn/BA3US/787f688e8f316c90b5fc099aca4939b8c5c240d1/framework.jpg -------------------------------------------------------------------------------- /lr_schedule.py: -------------------------------------------------------------------------------- 1 | def inv_lr_scheduler(optimizer, iter_num, gamma, power, lr=0.001, weight_decay=0.0005): 2 | """Decay learning rate by a factor of 0.1 every lr_decay_epoch epochs.""" 3 | lr = lr * (1 + gamma * iter_num) ** (-power) 4 | i=0 5 | for param_group in optimizer.param_groups: 6 | param_group['lr'] = lr * param_group['lr_mult'] 7 | param_group['weight_decay'] = weight_decay * param_group['decay_mult'] 8 | i+=1 9 | 10 | return optimizer 11 | 12 | 13 | schedule_dict = {"inv":inv_lr_scheduler} 14 | -------------------------------------------------------------------------------- /my_loss.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn as nn 4 | from torch.autograd import Variable 5 | import math 6 | import torch.nn.functional as F 7 | import pdb 8 | 9 | def Entropy(input_): 10 | bs = input_.size(0) 11 | entropy = -input_ * torch.log(input_ + 1e-7) 12 | entropy = torch.sum(entropy, dim=1) 13 | return entropy 14 | 15 | def grl_hook(coeff): 16 | def fun1(grad): 17 | return -coeff*grad.clone() 18 | return fun1 19 | 20 | def DANN(features, ad_net, entropy=None, coeff=None, cls_weight=None, len_share=0): 21 | ad_out = ad_net(features) 22 | train_bs = (ad_out.size(0) - len_share) // 2 23 | dc_target = torch.from_numpy(np.array([[1]] * train_bs + [[0]] * (train_bs + len_share))).float().cuda() 24 | if entropy is not None: 25 | entropy.register_hook(grl_hook(coeff)) 26 | entropy = 1.0 + torch.exp(-entropy) 27 | else: 28 | entropy = torch.ones(ad_out.size(0)).cuda() 29 | 30 | source_mask = torch.ones_like(entropy) 31 | source_mask[train_bs : 2 * train_bs] = 0 32 | source_weight = entropy * source_mask 33 | source_weight = source_weight * cls_weight 34 | 35 | target_mask = torch.ones_like(entropy) 36 | target_mask[0 : train_bs] = 0 37 | target_mask[2 * train_bs::] = 0 38 | target_weight = entropy * target_mask 39 | target_weight = target_weight * cls_weight 40 | 41 | weight = (1.0 + len_share / train_bs) * source_weight / (torch.sum(source_weight).detach().item()) + \ 42 | target_weight / torch.sum(target_weight).detach().item() 43 | 44 | weight = weight.view(-1, 1) 45 | return torch.sum(weight * nn.BCELoss(reduction='none')(ad_out, dc_target)) / (1e-8 + torch.sum(weight).detach().item()) 46 | 47 | def marginloss(yHat, y, classes=65, alpha=1, weight=None): 48 | batch_size = len(y) 49 | classes = classes 50 | yHat = F.softmax(yHat, dim=1) 51 | Yg = torch.gather(yHat, 1, torch.unsqueeze(y, 1))#.detach() 52 | Yg_ = (1 - Yg) + 1e-7 # avoiding numerical issues (first) 53 | Px = yHat / Yg_.view(len(yHat), 1) 54 | Px_log = torch.log(Px + 1e-10) # avoiding numerical issues (second) 55 | y_zerohot = torch.ones(batch_size, classes).scatter_( 56 | 1, y.view(batch_size, 1).data.cpu(), 0) 57 | 58 | output = Px * Px_log * y_zerohot.cuda() 59 | loss = torch.sum(output, dim=1)/ np.log(classes - 1) 60 | Yg_ = Yg_ ** alpha 61 | if weight is not None: 62 | weight *= (Yg_.view(len(yHat), )/ Yg_.sum()) 63 | else: 64 | weight = (Yg_.view(len(yHat), )/ Yg_.sum()) 65 | 66 | weight = weight.detach() 67 | loss = torch.sum(weight * loss) / torch.sum(weight) 68 | 69 | return loss -------------------------------------------------------------------------------- /network.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn as nn 4 | import torchvision 5 | from torchvision import models 6 | from torch.autograd import Variable 7 | import math 8 | import pdb 9 | import torch.utils.model_zoo as model_zoo 10 | 11 | def calc_coeff(iter_num, high=1.0, low=0.0, alpha=10.0, max_iter=10000.0): 12 | return np.float(2.0 * (high - low) / (1.0 + np.exp(-alpha*iter_num / max_iter)) - (high - low) + low) 13 | 14 | def init_weights(m): 15 | classname = m.__class__.__name__ 16 | if classname.find('Conv2d') != -1 or classname.find('ConvTranspose2d') != -1: 17 | nn.init.kaiming_uniform_(m.weight) 18 | nn.init.zeros_(m.bias) 19 | elif classname.find('BatchNorm') != -1: 20 | nn.init.normal_(m.weight, 1.0, 0.02) 21 | nn.init.zeros_(m.bias) 22 | elif classname.find('Linear') != -1: 23 | nn.init.xavier_normal_(m.weight) 24 | nn.init.zeros_(m.bias) 25 | 26 | resnet_dict = {"ResNet18":models.resnet18, "ResNet34":models.resnet34, "ResNet50":models.resnet50, "ResNet101":models.resnet101, "ResNet152":models.resnet152} 27 | 28 | def grl_hook(coeff): 29 | def fun1(grad): 30 | return -coeff*grad.clone() 31 | return fun1 32 | 33 | class ResNetFc(nn.Module): 34 | def __init__(self, resnet_name, use_bottleneck=True, bottleneck_dim=256, new_cls=False, class_num=1000): 35 | super(ResNetFc, self).__init__() 36 | model_resnet = resnet_dict[resnet_name](pretrained=True) 37 | self.conv1 = model_resnet.conv1 38 | self.bn1 = model_resnet.bn1 39 | self.relu = model_resnet.relu 40 | self.maxpool = model_resnet.maxpool 41 | self.layer1 = model_resnet.layer1 42 | self.layer2 = model_resnet.layer2 43 | self.layer3 = model_resnet.layer3 44 | self.layer4 = model_resnet.layer4 45 | self.avgpool = model_resnet.avgpool 46 | self.feature_layers = nn.Sequential(self.conv1, self.bn1, self.relu, self.maxpool, \ 47 | self.layer1, self.layer2, self.layer3, self.layer4, self.avgpool) 48 | 49 | self.use_bottleneck = use_bottleneck 50 | self.new_cls = new_cls 51 | if new_cls: 52 | if self.use_bottleneck: 53 | self.bottleneck = nn.Linear(model_resnet.fc.in_features, bottleneck_dim) 54 | self.fc = nn.Linear(bottleneck_dim, class_num) 55 | self.bottleneck.apply(init_weights) 56 | self.fc.apply(init_weights) 57 | self.__in_features = bottleneck_dim 58 | else: 59 | self.fc = nn.Linear(model_resnet.fc.in_features, class_num) 60 | self.fc.apply(init_weights) 61 | self.__in_features = model_resnet.fc.in_features 62 | else: 63 | self.fc = model_resnet.fc 64 | self.__in_features = model_resnet.fc.in_features 65 | 66 | def forward(self, x): 67 | x = self.feature_layers(x) 68 | x = x.view(x.size(0), -1) 69 | if self.use_bottleneck and self.new_cls: 70 | x = self.bottleneck(x) 71 | y = self.fc(x) 72 | return x, y 73 | 74 | def output_num(self): 75 | return self.__in_features 76 | 77 | def get_parameters(self): 78 | if self.new_cls: 79 | if self.use_bottleneck: 80 | parameter_list = [{"params":self.feature_layers.parameters(), "lr_mult":1, 'decay_mult':2}, \ 81 | {"params":self.bottleneck.parameters(), "lr_mult":10, 'decay_mult':2}, \ 82 | {"params":self.fc.parameters(), "lr_mult":10, 'decay_mult':2}] 83 | else: 84 | parameter_list = [{"params":self.feature_layers.parameters(), "lr_mult":1, 'decay_mult':2}, \ 85 | {"params":self.fc.parameters(), "lr_mult":10, 'decay_mult':2}] 86 | else: 87 | parameter_list = [{"params":self.parameters(), "lr_mult":1, 'decay_mult':2}] 88 | return parameter_list 89 | 90 | vgg_dict = {"VGG11":models.vgg11, "VGG13":models.vgg13, "VGG16":models.vgg16, "VGG19":models.vgg19, "VGG11BN":models.vgg11_bn, "VGG13BN":models.vgg13_bn, "VGG16BN":models.vgg16_bn, "VGG19BN":models.vgg19_bn} 91 | class VGGFc(nn.Module): 92 | def __init__(self, vgg_name, use_bottleneck=True, bottleneck_dim=256, new_cls=False, class_num=1000): 93 | super(VGGFc, self).__init__() 94 | model_vgg = vgg_dict[vgg_name](pretrained=True) 95 | self.features = model_vgg.features 96 | self.classifier = nn.Sequential() 97 | for i in range(6): 98 | self.classifier.add_module("classifier"+str(i), model_vgg.classifier[i]) 99 | self.feature_layers = nn.Sequential(self.features, self.classifier) 100 | 101 | self.use_bottleneck = use_bottleneck 102 | self.new_cls = new_cls 103 | if new_cls: 104 | if self.use_bottleneck: 105 | self.bottleneck = nn.Linear(4096, bottleneck_dim) 106 | self.fc = nn.Linear(bottleneck_dim, class_num) 107 | self.bottleneck.apply(init_weights) 108 | self.fc.apply(init_weights) 109 | self.__in_features = bottleneck_dim 110 | else: 111 | self.fc = nn.Linear(4096, class_num) 112 | self.fc.apply(init_weights) 113 | self.__in_features = 4096 114 | else: 115 | self.fc = model_vgg.classifier[6] 116 | self.__in_features = 4096 117 | 118 | def forward(self, x): 119 | x = self.features(x) 120 | x = x.view(x.size(0), -1) 121 | x = self.classifier(x) 122 | if self.use_bottleneck and self.new_cls: 123 | x = self.bottleneck(x) 124 | y = self.fc(x) 125 | return x, y 126 | 127 | def output_num(self): 128 | return self.__in_features 129 | 130 | def get_parameters(self): 131 | if self.new_cls: 132 | if self.use_bottleneck: 133 | parameter_list = [{"params":self.features.parameters(), "lr_mult":1, 'decay_mult':2}, \ 134 | {"params":self.classifier.parameters(), "lr_mult":1, 'decay_mult':2}, \ 135 | {"params":self.bottleneck.parameters(), "lr_mult":10, 'decay_mult':2}, \ 136 | {"params":self.fc.parameters(), "lr_mult":10, 'decay_mult':2}] 137 | else: 138 | parameter_list = [{"params":self.feature_layers.parameters(), "lr_mult":1, 'decay_mult':2}, \ 139 | {"params":self.classifier.parameters(), "lr_mult":1, 'decay_mult':2}, \ 140 | {"params":self.fc.parameters(), "lr_mult":10, 'decay_mult':2}] 141 | else: 142 | parameter_list = [{"params":self.parameters(), "lr_mult":1, 'decay_mult':2}] 143 | return parameter_list 144 | 145 | 146 | class AdversarialNetwork(nn.Module): 147 | def __init__(self, in_feature, hidden_size, max_iter = 10000): 148 | super(AdversarialNetwork, self).__init__() 149 | self.ad_layer1 = nn.Linear(in_feature, hidden_size) 150 | self.ad_layer2 = nn.Linear(hidden_size, hidden_size) 151 | self.ad_layer3 = nn.Linear(hidden_size, 1) 152 | self.relu1 = nn.ReLU() 153 | self.relu2 = nn.ReLU() 154 | self.dropout1 = nn.Dropout(0.5) 155 | self.dropout2 = nn.Dropout(0.5) 156 | self.sigmoid = nn.Sigmoid() 157 | self.apply(init_weights) 158 | self.iter_num = 0 159 | self.alpha = 10 160 | self.low = 0.0 161 | self.high = 1.0 162 | self.max_iter = max_iter 163 | 164 | def forward(self, x): 165 | if self.training: 166 | self.iter_num += 1 167 | coeff = calc_coeff(self.iter_num, self.high, self.low, self.alpha, self.max_iter) 168 | x = x * 1.0 169 | x.register_hook(grl_hook(coeff)) 170 | x = self.ad_layer1(x) 171 | x = self.relu1(x) 172 | x = self.dropout1(x) 173 | x = self.ad_layer2(x) 174 | x = self.relu2(x) 175 | x = self.dropout2(x) 176 | y = self.ad_layer3(x) 177 | y = self.sigmoid(y) 178 | return y 179 | 180 | def output_num(self): 181 | return 1 182 | 183 | def get_parameters(self): 184 | return [{"params":self.parameters(), "lr_mult":10, 'decay_mult':2}] -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | python run_partial.py --s 0 --t 1 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 2 | python run_partial.py --s 0 --t 2 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 3 | python run_partial.py --s 0 --t 3 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 4 | python run_partial.py --s 1 --t 0 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 5 | python run_partial.py --s 1 --t 2 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 6 | python run_partial.py --s 1 --t 3 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 7 | python run_partial.py --s 2 --t 0 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 8 | python run_partial.py --s 2 --t 1 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 9 | python run_partial.py --s 2 --t 3 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 10 | python run_partial.py --s 3 --t 0 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 11 | python run_partial.py --s 3 --t 1 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 12 | python run_partial.py --s 3 --t 2 --dset office_home --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 13 | 14 | python run_partial.py --s 0 --t 1 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 15 | python run_partial.py --s 0 --t 2 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 16 | python run_partial.py --s 1 --t 0 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 17 | python run_partial.py --s 1 --t 2 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 18 | python run_partial.py --s 2 --t 0 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 19 | python run_partial.py --s 2 --t 1 --dset office --net ResNet50 --cot_weight 5. --output run1 --gpu_id 0 20 | 21 | python run_partial.py --s 0 --t 1 --dset imagenet_caltech --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 22 | python run_partial.py --s 1 --t 0 --dset imagenet_caltech --net ResNet50 --cot_weight 1. --output run1 --gpu_id 0 23 | 24 | python run_partial.py --s 0 --t 1 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 25 | python run_partial.py --s 0 --t 2 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 26 | python run_partial.py --s 1 --t 0 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 27 | python run_partial.py --s 1 --t 2 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 28 | python run_partial.py --s 2 --t 0 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 29 | python run_partial.py --s 2 --t 1 --dset office --net VGG16 --cot_weight 5. --output run1 --gpu_id 0 30 | -------------------------------------------------------------------------------- /run_partial.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os, random, pdb, math, sys 3 | import numpy as np 4 | import torch 5 | from torch.utils.data import DataLoader 6 | from torchvision import transforms 7 | import network, my_loss 8 | import lr_schedule, data_list 9 | 10 | def image_train(resize_size=256, crop_size=224): 11 | return transforms.Compose([ 12 | transforms.Resize((resize_size, resize_size)), 13 | transforms.RandomCrop(crop_size), 14 | transforms.RandomHorizontalFlip(), 15 | transforms.ToTensor(), 16 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 17 | ]) 18 | 19 | def image_test(resize_size=256, crop_size=224): 20 | return transforms.Compose([ 21 | transforms.Resize((resize_size, resize_size)), 22 | transforms.CenterCrop(crop_size), 23 | transforms.ToTensor(), 24 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 25 | ]) 26 | 27 | def image_classification(loader, model): 28 | start_test = True 29 | with torch.no_grad(): 30 | iter_test = iter(loader["test"]) 31 | for i in range(len(loader['test'])): 32 | data = iter_test.next() 33 | inputs = data[0] 34 | labels = data[1] 35 | inputs = inputs.cuda() 36 | _, outputs = model(inputs) 37 | if start_test: 38 | all_output = outputs.float().cpu() 39 | all_label = labels.float() 40 | start_test = False 41 | else: 42 | all_output = torch.cat((all_output, outputs.float().cpu()), 0) 43 | all_label = torch.cat((all_label, labels.float()), 0) 44 | _, predict = torch.max(all_output, 1) 45 | accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0]) 46 | mean_ent = torch.mean(my_loss.Entropy(torch.nn.Softmax(dim=1)(all_output))).cpu().data.item() 47 | 48 | hist_tar = torch.nn.Softmax(dim=1)(all_output).sum(dim=0) 49 | hist_tar = hist_tar / hist_tar.sum() 50 | return accuracy, hist_tar, mean_ent 51 | 52 | def train(args): 53 | ## prepare data 54 | train_bs, test_bs = args.batch_size, args.batch_size * 2 55 | 56 | dsets = {} 57 | dsets["source"] = data_list.ImageList(open(args.s_dset_path).readlines(), transform=image_train()) 58 | dsets["target"] = data_list.ImageList(open(args.t_dset_path).readlines(), transform=image_train()) 59 | dsets["test"] = data_list.ImageList(open(args.t_dset_path).readlines(), transform=image_test()) 60 | 61 | dset_loaders = {} 62 | dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=True) 63 | dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=True) 64 | dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, shuffle=False, num_workers=args.worker) 65 | 66 | if "ResNet" in args.net: 67 | params = {"resnet_name":args.net, "use_bottleneck":True, "bottleneck_dim":256, "new_cls":True, 'class_num': args.class_num} 68 | base_network = network.ResNetFc(**params) 69 | 70 | if "VGG" in args.net: 71 | params = {"vgg_name":args.net, "use_bottleneck":True, "bottleneck_dim":256, "new_cls":True, 'class_num': args.class_num} 72 | base_network = network.VGGFc(**params) 73 | 74 | base_network = base_network.cuda() 75 | 76 | ad_net = network.AdversarialNetwork(base_network.output_num(), 1024, args.max_iterations).cuda() 77 | parameter_list = base_network.get_parameters() + ad_net.get_parameters() 78 | # ad_net = torch.nn.DataParallel(ad_net).cuda() 79 | # base_network = torch.nn.DataParallel(base_network).cuda() 80 | 81 | ## set optimizer 82 | optimizer_config = {"type":torch.optim.SGD, "optim_params": 83 | {'lr':args.lr, "momentum":0.9, "weight_decay":5e-4, "nesterov":True}, 84 | "lr_type":"inv", "lr_param":{"lr":args.lr, "gamma":0.001, "power":0.75} 85 | } 86 | optimizer = optimizer_config["type"](parameter_list,**(optimizer_config["optim_params"])) 87 | 88 | param_lr = [] 89 | for param_group in optimizer.param_groups: 90 | param_lr.append(param_group["lr"]) 91 | schedule_param = optimizer_config["lr_param"] 92 | lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]] 93 | 94 | class_weight = None 95 | best_ent = 1000 96 | total_epochs = args.max_iterations // args.test_interval 97 | 98 | for i in range(args.max_iterations + 1): 99 | base_network.train(True) 100 | ad_net.train(True) 101 | optimizer = lr_scheduler(optimizer, i, **schedule_param) 102 | 103 | if (i % args.test_interval == 0 and i > 0) or (i == args.max_iterations): 104 | # obtain the class-level weight and evalute the current model 105 | base_network.train(False) 106 | temp_acc, class_weight, mean_ent = image_classification(dset_loaders, base_network) 107 | class_weight = class_weight.cuda().detach() 108 | 109 | temp = [round(i, 4) for i in class_weight.cpu().numpy().tolist()] 110 | log_str = str(temp) 111 | args.out_file.write(log_str+"\n") 112 | args.out_file.flush() 113 | 114 | print(class_weight) 115 | if mean_ent < best_ent: 116 | best_ent, best_acc = mean_ent, temp_acc 117 | best_model = base_network.state_dict() 118 | log_str = "iter: {:05d}, precision: {:.5f}, mean_entropy: {:.5f}".format(i, temp_acc, mean_ent) 119 | args.out_file.write(log_str+"\n") 120 | args.out_file.flush() 121 | print(log_str) 122 | 123 | if i % args.test_interval == 0: 124 | if args.mu > 0: 125 | epoch = i // args.test_interval 126 | len_share = int(max(0, (train_bs // args.mu) * (1 - epoch / total_epochs))) 127 | elif args.mu == 0: 128 | len_share = 0 # no augmentation 129 | else: 130 | len_share = int(train_bs // abs(args.mu)) # fixed augmentation 131 | log_str = "\n{}, iter: {:05d}, source/ target/ middle: {:02d} / {:02d} / {:02d}\n".format(args.name, i, train_bs, train_bs, len_share) 132 | args.out_file.write(log_str) 133 | args.out_file.flush() 134 | print(log_str) 135 | 136 | dset_loaders["middle"] = None 137 | if not len_share == 0: 138 | dset_loaders["middle"] = DataLoader(dsets["source"], batch_size=len_share, shuffle=True, num_workers=args.worker, 139 | drop_last=True) 140 | iter_middle = iter(dset_loaders["middle"]) 141 | 142 | # train one iter 143 | if i % len(dset_loaders["source"]) == 0: 144 | iter_source = iter(dset_loaders["source"]) 145 | if i % len(dset_loaders["target"]) == 0: 146 | iter_target = iter(dset_loaders["target"]) 147 | if dset_loaders["middle"] is not None and i % len(dset_loaders["middle"]) == 0: 148 | iter_middle = iter(dset_loaders["middle"]) 149 | 150 | inputs_source, labels_source = iter_source.next() 151 | inputs_target, _ = iter_target.next() 152 | inputs_source, inputs_target, labels_source = inputs_source.cuda(), inputs_target.cuda(), labels_source.cuda() 153 | 154 | if class_weight is not None and args.weight_cls and class_weight[labels_source].sum() == 0: 155 | continue 156 | 157 | features_source, outputs_source = base_network(inputs_source) 158 | features_target, outputs_target = base_network(inputs_target) 159 | 160 | if dset_loaders["middle"] is not None: 161 | inputs_middle, labels_middle = iter_middle.next() 162 | features_middle, outputs_middle = base_network(inputs_middle.cuda()) 163 | features = torch.cat((features_source, features_target, features_middle), dim=0) 164 | outputs = torch.cat((outputs_source, outputs_target, outputs_middle), dim=0) 165 | else: 166 | features = torch.cat((features_source, features_target), dim=0) 167 | outputs = torch.cat((outputs_source, outputs_target), dim=0) 168 | 169 | cls_weight = torch.ones(outputs.size(0)).cuda() 170 | if class_weight is not None and args.weight_aug: 171 | cls_weight[0:train_bs] = class_weight[labels_source] 172 | if dset_loaders["middle"] is not None: 173 | cls_weight[2*train_bs::] = class_weight[labels_middle] 174 | 175 | # compute source cross-entropy loss 176 | if class_weight is not None and args.weight_cls: 177 | src_ = torch.nn.CrossEntropyLoss(reduction='none')(outputs_source, labels_source) 178 | weight = class_weight[labels_source].detach() 179 | src_loss = torch.sum(weight * src_) / (1e-8 + torch.sum(weight).item()) 180 | else: 181 | src_loss = torch.nn.CrossEntropyLoss()(outputs_source, labels_source) 182 | 183 | softmax_out = torch.nn.Softmax(dim=1)(outputs) 184 | entropy = my_loss.Entropy(softmax_out) 185 | transfer_loss = my_loss.DANN(features, ad_net, entropy, network.calc_coeff(i, 1, 0, 10, args.max_iterations), cls_weight, len_share) 186 | 187 | softmax_tar_out = torch.nn.Softmax(dim=1)(outputs_target) 188 | tar_loss = torch.mean(my_loss.Entropy(softmax_tar_out)) 189 | 190 | total_loss = src_loss + transfer_loss + args.ent_weight * tar_loss 191 | if args.cot_weight > 0: 192 | if class_weight is not None and args.weight_cls: 193 | cot_loss = my_loss.marginloss(outputs_source, labels_source, args.class_num, alpha=args.alpha, weight=class_weight[labels_source].detach()) 194 | else: 195 | cot_loss = my_loss.marginloss(outputs_source, labels_source, args.class_num, alpha=args.alpha) 196 | total_loss += cot_loss * args.cot_weight 197 | 198 | optimizer.zero_grad() 199 | total_loss.backward() 200 | optimizer.step() 201 | 202 | torch.save(best_model, os.path.join(args.output_dir, "best_model.pt")) 203 | 204 | log_str = 'Acc: ' + str(np.round(best_acc*100, 2)) + "\n" + 'Mean_ent: ' + str(np.round(best_ent, 3)) + '\n' 205 | args.out_file.write(log_str) 206 | args.out_file.flush() 207 | print(log_str) 208 | 209 | return best_acc 210 | 211 | if __name__ == "__main__": 212 | 213 | parser = argparse.ArgumentParser(description='BA3US for Partial Domain Adaptation') 214 | parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help="device id to run") 215 | parser.add_argument('--s', type=int, default=0, help="source") 216 | parser.add_argument('--t', type=int, default=1, help="target") 217 | parser.add_argument('--output', type=str, default='run') 218 | parser.add_argument('--seed', type=int, default=2020, help="random seed") 219 | parser.add_argument('--max_iterations', type=int, default=5000, help="max iterations") 220 | parser.add_argument('--batch_size', type=int, default=36, help="batch_size") 221 | parser.add_argument('--worker', type=int, default=4, help="number of workers") 222 | parser.add_argument('--net', type=str, default='ResNet50', choices=["ResNet50", "VGG16"]) 223 | 224 | parser.add_argument('--dset', type=str, default='office_home', choices=["office", "office_home", "imagenet_caltech"]) 225 | parser.add_argument('--test_interval', type=int, default=500, help="interval of two continuous test phase") 226 | parser.add_argument('--lr', type=float, default=0.001, help="learning rate") 227 | parser.add_argument('--mu', type=int, default=4, help="init augmentation size = batch_size//mu") 228 | parser.add_argument('--ent_weight', type=float, default=0.1) 229 | parser.add_argument('--cot_weight', type=float, default=1.0, choices=[0, 1, 5, 10]) 230 | parser.add_argument('--weight_aug', type=bool, default=True) 231 | parser.add_argument('--weight_cls', type=bool, default=True) 232 | parser.add_argument('--alpha', type=float, default=1) 233 | 234 | args = parser.parse_args() 235 | os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id 236 | 237 | if args.dset == 'office_home': 238 | names = ['Art', 'Clipart', 'Product', 'RealWorld'] 239 | k = 25 240 | args.class_num = 65 241 | args.max_iterations = 5000 242 | args.test_interval = 500 243 | args.lr=1e-3 244 | 245 | if args.dset == 'office': 246 | names = ['amazon', 'dslr', 'webcam'] 247 | k = 10 248 | args.class_num = 31 249 | args.max_iterations = 2000 250 | args.test_interval = 200 251 | args.lr=1e-4 252 | 253 | if args.dset == 'imagenet_caltech': 254 | names = ['imagenet', 'caltech'] 255 | k = 84 256 | args.class_num = 1000 257 | if args.s == 1: 258 | args.class_num = 256 259 | 260 | args.max_iterations = 40000 261 | args.test_interval = 4000 262 | args.lr=1e-3 263 | 264 | torch.manual_seed(args.seed) 265 | torch.cuda.manual_seed(args.seed) 266 | np.random.seed(args.seed) 267 | random.seed(args.seed) 268 | 269 | data_folder = './data/' 270 | args.s_dset_path = data_folder + args.dset + '/' + names[args.s] + '_list.txt' 271 | args.t_dset_path = data_folder + args.dset + '/' + names[args.t] + '_' + str(k) + '_list.txt' 272 | 273 | args.name = names[args.s][0].upper() + names[args.t][0].upper() 274 | args.output_dir = os.path.join('ckp/partial', args.net, args.dset, args.name, args.output) 275 | 276 | if not os.path.exists(args.output_dir): 277 | os.system('mkdir -p ' + args.output_dir) 278 | args.out_file = open(os.path.join(args.output_dir, "log.txt"), "w") 279 | if not os.path.exists(args.output_dir): 280 | os.mkdir(args.output_dir) 281 | 282 | args.out_file.write(str(args)+'\n') 283 | args.out_file.flush() 284 | 285 | train(args) 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