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Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision (CVPR 2020 Oral)

Home Page: https://arxiv.org/pdf/2004.07703.pdf

License: MIT License

Python 87.82% Dockerfile 0.60% CSS 3.84% HTML 7.74%

domain-adaptation pytorch semantic-segmentation

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intrada's Issues

Two-phase Pseudo Label Densification for Self-training based Domain Adaptation

Sorry to bother you. Your team will release the code of the above paper?

About Pretrained and evaluation models

Hey, the pretrained and evaluation models links are broken.
And I want to ask how can I get the result about "Without adaptation" on you paper page#5(table a).

IntraDA trained from scratch or from InterDA params?

Hey, how do you initialize the model in the intra-da stage, train from scratch, or resume from where the inter-da stopped?
It seems that resume from inter-da step is a more reasonable way.
By the way, how does the quality of masks of easy samples influence the intra-da step? Will not-so-good masks lead to worse results?

The initial parameters of the model for intra-domain adaptation

Hello, thank you for sharing your codes.

could you tell me the initial parameters of the model in step 3, please?
Are they loaded from DeepLab_resnet_pretrained_imagenet.pth or retrained by ADVENT?
Thank you in advance.

Can't get the 47% mIoU

I use the published advent model to generate color masks, then train the intrada model. But I got 45.56% mIoU instead. How to get the 47% mIoU.

No color_masks

(uda) brcao@dawn:~/Repos/fork/IntraDA/intrada$ python train.py --cfg ./intrada.yml
Called with args:
Namespace(cfg='./intrada.yml', random_train=False, tensorboard=False, viz_every_iter=None, exp_suffix=None)
Using config:
{'DATA_DIRECTORY_SOURCE': '../ADVENT/data/Cityscapes',
 'DATA_DIRECTORY_TARGET': '/home/brcao/Repos/fork/IntraDA/ADVENT/data/Cityscapes',
 'DATA_LIST_SOURCE': '../entropy_rank/easy_split.txt',
 'DATA_LIST_TARGET': '../entropy_rank/hard_split.txt',
 'EXP_NAME': 'CityscapesEasy2CityscapesHard_DeepLabv2_AdvEnt',
 'EXP_ROOT': PosixPath('/home/brcao/Repos/fork/IntraDA/ADVENT/experiments'),
 'EXP_ROOT_LOGS': '/home/brcao/Repos/fork/IntraDA/ADVENT/experiments/logs',
 'EXP_ROOT_SNAPSHOT': '/home/brcao/Repos/fork/IntraDA/ADVENT/experiments/snapshots',
 'GPU_ID': 0,
 'NUM_CLASSES': 19,
 'NUM_WORKERS': 1,
 'SOURCE': 'CityscapesEasy',
 'TARGET': 'CityscapesHard',
 'TEST': {'BATCH_SIZE_TARGET': 1,
          'IMG_MEAN': array([104.00699, 116.66877, 122.67892], dtype=float32),
          'INFO_TARGET': '/home/brcao/Repos/fork/IntraDA/ADVENT/advent/dataset/cityscapes_list/info.json',
          'INPUT_SIZE_TARGET': [1024, 512],
          'MODE': 'best',
          'MODEL': ['DeepLabv2'],
          'MODEL_WEIGHT': [1.0],
          'MULTI_LEVEL': [True],
          'OUTPUT_SIZE_TARGET': [2048, 1024],
          'RESTORE_FROM': [''],
          'SET_TARGET': 'val',
          'SNAPSHOT_DIR': [''],
          'SNAPSHOT_MAXITER': 120000,
          'SNAPSHOT_STEP': 2000,
          'WAIT_MODEL': True},
 'TRAIN': {'BATCH_SIZE_SOURCE': 1,
           'BATCH_SIZE_TARGET': 1,
           'DA_METHOD': 'AdvEnt',
           'EARLY_STOP': 120000,
           'IGNORE_LABEL': 255,
           'IMG_MEAN': array([104.00699, 116.66877, 122.67892], dtype=float32),
           'INFO_SOURCE': '',
           'INFO_TARGET': '/home/brcao/Repos/fork/IntraDA/ADVENT/advent/dataset/cityscapes_list/info.json',
           'INPUT_SIZE_SOURCE': [1024, 512],
           'INPUT_SIZE_TARGET': [1024, 512],
           'LAMBDA_ADV_AUX': 0.0002,
           'LAMBDA_ADV_MAIN': 0.001,
           'LAMBDA_ENT_AUX': 0.0002,
           'LAMBDA_ENT_MAIN': 0.001,
           'LAMBDA_SEG_AUX': 0.1,
           'LAMBDA_SEG_MAIN': 1.0,
           'LEARNING_RATE': 0.00025,
           'LEARNING_RATE_D': 0.0001,
           'MAX_ITERS': 250000,
           'MODEL': 'DeepLabv2',
           'MOMENTUM': 0.9,
           'MULTI_LEVEL': True,
           'POWER': 0.9,
           'RANDOM_SEED': 1234,
           'RESTORE_FROM': '../ADVENT/pretrained_models/DeepLab_resnet_pretrained_imagenet.pth',
           'SAVE_PRED_EVERY': 2000,
           'SET_SOURCE': 'all',
           'SET_TARGET': 'train',
           'SNAPSHOT_DIR': '/home/brcao/Repos/fork/IntraDA/ADVENT/experiments/snapshots/CityscapesEasy2CityscapesHard_DeepLabv2_AdvEnt',
           'TENSORBOARD_LOGDIR': '',
           'TENSORBOARD_VIZRATE': 100,
           'WEIGHT_DECAY': 0.0005}}
Model loaded
  0%|                                                                    | 0/120000 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/brcao/Repos/fork/IntraDA/intrada/train.py", line 149, in <module>
    main()
  File "/home/brcao/Repos/fork/IntraDA/intrada/train.py", line 145, in main
    train_domain_adaptation(model, easy_loader, hard_loader, cfg)
  File "/home/brcao/Repos/fork/IntraDA/intrada/train_UDA.py", line 360, in train_domain_adaptation
    train_advent(model, trainloader, targetloader, cfg)
  File "/home/brcao/Repos/fork/IntraDA/intrada/train_UDA.py", line 115, in train_advent
    _, batch = trainloader_iter.__next__()
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
    data = self._next_data()
           ^^^^^^^^^^^^^^^^^
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
    return self._process_data(data)
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
    data.reraise()
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/_utils.py", line 644, in reraise
    raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 51, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
            ~~~~~~~~~~~~^^^^^
  File "/home/brcao/Repos/fork/IntraDA/intrada/cityscapes.py", line 42, in __getitem__
    label = self.get_labels(label_file)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/brcao/Repos/fork/IntraDA/intrada/../ADVENT/advent/dataset/base_dataset.py", line 44, in get_labels
    return _load_img(file, self.labels_size, Image.NEAREST, rgb=False)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/brcao/Repos/fork/IntraDA/intrada/../ADVENT/advent/dataset/base_dataset.py", line 48, in _load_img
    img = Image.open(file)
          ^^^^^^^^^^^^^^^^
  File "/home/brcao/Apps/miniconda3/envs/uda/lib/python3.11/site-packages/PIL/Image.py", line 3218, in open
    fp = builtins.open(filename, "rb")
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '../entropy/color_masks/weimar_000053_000019_leftImg8bit.png'

pretrained model for resnet50 backbone

Hey, I want to try your model with resnet50 as the backbone, where should I download the pretrained model? Thanks!

is there a batch size of 1 per gpu?

Your research has inspired me very much. So I'm trying to reproduce this experiment, is there a batch size of 1 per gpu?

low performance

I follow your step
python train.py --cfg ./configs/advent.yml
python entropy.py --best_iter BEST_ID --normalize False --lambda1 0.67
python train.py --cfg ./intrada.yml

But i can not reproduce your performance.
For the first stage, i got 41.98 mIoU
For the second stage, i got 44.57 mIoU.

Could you help me?

About trained model of ADVENT

Hi, could you provide the trained model in STEP-1 (ADVENT) ?
We reproduce the first step using your code keeping the default setting, and we got the mIoU=40.9.
I believe many researchers have struggled in the same problem.
If you can provide the trained model of the first phase (ADVENT), we will be grateful.
Thanks.

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