emerld2011/CDTrans

[ICLR2022] CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

Introduction

This is the official code of CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation.

Results

Table 1 [UDA results on Office-31]

Methods	Avg.	A->D	A->W	D->A	D->W	W->A	W->D
Baseline(DeiT-S)	86.7	87.6	86.9	74.9	97.7	73.5	99.6
		model		model		model
CDTrans(DeiT-S)	90.4	94.6	93.5	78.4	98.2	78	99.6
		model	model	model	model	model	model
Baseline(DeiT-B)	88.8	90.8	90.4	76.8	98.2	76.4	100
		model		model		model
CDTrans(DeiT-B)	92.6	97	96.7	81.1	99	81.9	100
		model	model	model	model	model	model

Table 2 [UDA results on Office-Home]

Methods	Avg.	Ar->Cl	Ar->Pr	Ar->Re	Cl->Ar	Cl->Pr	Cl->Re	Pr->Ar	Pr->Cl	Pr->Re	Re->Ar	Re->Cl	Re->Pr
Baseline(DeiT-S)	69.8	55.6	73	79.4	70.6	72.9	76.3	67.5	51	81	74.5	53.2	82.7
		model			model			model			model
CDTrans(DeiT-S)	74.7	60.6	79.5	82.4	75.6	81.0	82.3	72.5	56.7	84.4	77.0	59.1	85.5
		model	model	model	model	model	model	model	model	model	model	model	model
Baseline(DeiT-B)	74.8	61.8	79.5	84.3	75.4	78.8	81.2	72.8	55.7	84.4	78.3	59.3	86
		model			model			model			model
CDTrans(DeiT-B)	80.5	68.8	85	86.9	81.5	87.1	87.3	79.6	63.3	88.2	82	66	90.6
		model	model	model	model	model	model	model	model	model	model	model	model

Table 3 [UDA results on VisDA-2017]

Methods	Per-class	plane	bcycl	bus	car	horse	knife	mcycl	person	plant	sktbrd	train	truck
Baseline(DeiT-B)	67.3 (model)	98.1	48.1	84.6	65.2	76.3	59.4	94.5	11.8	89.5	52.2	94.5	34.1
CDTrans(DeiT-B)	88.4 (model)	97.7	86.39	86.87	83.33	97.76	97.16	95.93	84.08	97.93	83.47	94.59	55.3

Table 4 [UDA results on DomainNet]

Base-S	clp	info	pnt	qdr	rel	skt	Avg.	CDTrans-S	clp	info	pnt	qdr	rel	skt	Avg.
clp	-	21.2	44.2	15.3	59.9	46.0	37.3	clp	-	25.3	52.5	23.2	68.3	53.2	44.5
	model								model	model	model	model	model	model
info	36.8	-	39.4	5.4	52.1	32.6	33.3	info	47.6	-	48.3	9.9	62.8	41.1	41.9
	model								model	model	model	model	model	model
pnt	47.1	21.7	-	5.7	60.2	39.9	34.9	pnt	55.4	24.5	-	11.7	67.4	48.0	41.4
	model								model	model	model	model	model	model
qdr	25.0	3.3	10.4	-	18.8	14.0	14.3	qdr	36.6	5.3	19.3	-	33.8	22.7	23.5
	model								model	model	model	model	model	model
rel	54.8	23.9	52.6	7.4	-	40.1	35.8	rel	61.5	28.1	56.8	12.8	-	47.2	41.3
	model								model	model	model	model	model	model
skt	55.6	18.6	42.7	14.9	55.7	-	37.5	skt	64.3	26.1	53.2	23.9	66.2	-	46.7
	model								model	model	model	model	model	model
Avg.	43.9	17.7	37.9	9.7	49.3	34.5	32.2	Avg.	53.08	21.86	46.02	16.3	59.7	42.44	39.9

Base-B	clp	info	pnt	qdr	rel	skt	Avg.	CDTrans-B	clp	info	pnt	qdr	rel	skt	Avg.
clp	-	24.2	48.9	15.5	63.9	50.7	40.6	clp	-	29.4	57.2	26.0	72.6	58.1	48.7
	model								model	model	model	model	model	model
info	43.5	-	44.9	6.5	58.8	37.6	38.3	info	57.0	-	54.4	12.8	69.5	48.4	48.4
	model								model	model	model	model	model	model
pnt	52.8	23.3	-	6.6	64.6	44.5	38.4	pnt	62.9	27.4	-	15.8	72.1	53.9	46.4
	model								model	model	model	model	model	model
qdr	31.8	6.1	15.6	-	23.4	18.9	19.2	qdr	44.6	8.9	29.0	-	42.6	28.5	30.7
	model								model	model	model	model	model	model
rel	58.9	26.3	56.7	9.1	-	45.0	39.2	rel	66.2	31.0	61.5	16.2	-	52.9	45.6
	model								model	model	model	model	model	model
skt	60.0	21.1	48.4	16.6	61.7	-	41.6	skt	69.0	29.6	59.0	27.2	72.5	-	51.5
	model								model	model	model	model	model	model
Avg.	49.4	20.2	42.9	10.9	54.5	39.3	36.2	Avg.	59.9	25.3	52.2	19.6	65.9	48.4	45.2

Requirements

Installation

pip install -r requirements.txt
(Python version is the 3.7 and the GPU is the V100 with cuda 10.1, cudatoolkit 10.1)

Prepare Datasets

Download the UDA datasets Office-31, Office-Home, VisDA-2017, DomainNet

Then unzip them and rename them under the directory like follow: (Note that each dataset folder needs to make sure that it contains the txt file that contain the path and lable of the picture, which is already in data/the_dataset of this project. The 'Real World' domain directory name of the Office-Home should be renamed to 'Real_World' for dataset loading. Otherwise you may encounter "FileNotFoundError: [Errno 2] No such file or directory" )

data
├── OfficeHomeDataset
│   │── class_name
│   │   └── images
│   └── *.txt
├── domainnet
│   │── class_name
│   │   └── images
│   └── *.txt
├── office31
│   │── class_name
│   │   └── images
│   └── *.txt
├── visda
│   │── train
│   │   │── class_name
│   │   │   └── images
│   │   └── *.txt 
│   └── validation
│       │── class_name
│       │   └── images
│       └── *.txt 

Prepare DeiT-trained Models

For fair comparison in the pre-training data set, we use the DeiT parameter init our model based on ViT. You need to download the ImageNet pretrained transformer model : DeiT-Small, DeiT-Base and move them to the ./data/pretrainModel directory.

Training

We utilize 1 GPU for pre-training and 2 GPUs for UDA, each with 16G of memory.

Scripts.

Command input paradigm

bash scripts/[pretrain/uda]/[office31/officehome/visda/domainnet]/run_*.sh [deit_base/deit_small]

For example

DeiT-Base scripts

# Office-31     Source: Amazon   ->  Target: Dslr, Webcam
bash scripts/pretrain/office31/run_office_amazon.sh deit_base
bash scripts/uda/office31/run_office_amazon.sh deit_base

#Office-Home    Source: Art      ->  Target: Clipart, Product, Real_World
bash scripts/pretrain/officehome/run_officehome_Ar.sh deit_base
bash scripts/uda/officehome/run_officehome_Ar.sh deit_base

# VisDA-2017    Source: train    ->  Target: validation
bash scripts/pretrain/visda/run_visda.sh deit_base
bash scripts/uda/visda/run_visda.sh deit_base

# DomainNet     Source: Clipart  ->  Target: painting, quickdraw, real, sketch, infograph
bash scripts/pretrain/domainnet/run_domainnet_clp.sh deit_base
bash scripts/uda/domainnet/run_domainnet_clp.sh deit_base

DeiT-Small scripts Replace deit_base with deit_small to run DeiT-Small results. An example of training on office-31 is as follows:

# Office-31     Source: Amazon   ->  Target: Dslr, Webcam
bash scripts/pretrain/office31/run_office_amazon.sh deit_small
bash scripts/uda/office31/run_office_amazon.sh deit_small

Evaluation

# For example VisDA-2017
python test.py --config_file 'configs/uda.yml' MODEL.DEVICE_ID "('0')" TEST.WEIGHT "('../logs/uda/vit_base/visda/transformer_best_model.pth')" DATASETS.NAMES 'VisDA' DATASETS.NAMES2 'VisDA' OUTPUT_DIR '../logs/uda/vit_base/visda/' DATASETS.ROOT_TRAIN_DIR './data/visda/train/train_image_list.txt' DATASETS.ROOT_TRAIN_DIR2 './data/visda/train/train_image_list.txt' DATASETS.ROOT_TEST_DIR './data/visda/validation/valid_image_list.txt'  

Acknowledgement

Codebase from TransReID