PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

Related tags

Deep Learning unsupervised-learning semantic-segmentation unsupervised-clustering self-supervised-learning
Overview

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

Jang Hyun Cho1, Utkarsh Mall2, Kavita Bala2, Bharath Hariharan2

1 The University of Texas at Austin, 2 Cornell University

[paper] [supp] [project page]

This repository is the official implementation of PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering, CVPR 2021.

Contact: Jang Hyun Cho [email protected].

Please feel free to reach out for any questions or discussions!

Setup

Setting up for this project involves installing dependencies and preparing the datasets.

Installing dependencies

To install all the dependencies, please run the following:

conda env create -f env.yml

Preparing Dataset

Please download the trainset and the validset of COCO dataset as well as the annotations. Place the dataset as following:

/your/dataset/directory/
      └── coco/
            ├── images/
            │     ├── train2017/
            │     │       ├── xxxxxxxxx.jpg
            │     │       └── ...
            │     └── val2017/
            │             ├── xxxxxxxxx.jpg
            │             └── ...
            └── annotations/
                  ├── COCO_2017_train.json
                  └── COCO_2017_val.json

Then, create a symbolic link as following:

cd PiCIE
ln -s /your/dataset/directory/ datasets

Similarly, setup a symbolic link for the save directory as following:

ln -s /your/save/directory/ results

Finally, move curated folder to datasets/coco/:

mv curated datasets/coco/

This will setup the dataset that contains the same set of images with IIC.

Running PiCIE

Below are training and testing commands to train PiCIE.

Training

Below line will run the training code with default setting in the background.

nohup ./sh_files/train_picie.sh > logs/picie_train.out &

Below line will run the testing code with default setting in the background.

Testing

nohup ./sh_files/test_picie.sh > logs/picie_test.out &

Pretrained Models (To be updated soon)

We have pretrained PiCIE weights.

Method Dataset Pre-trained weight Train log
PiCIE COCO weight log
PiCIE Cityscapes weight log
MDC COCO weight log
MDC Cityscapes weight log

Visualization (To be updated soon)

We prepared a jupyter notebook for visualization.

Citation

If you find PiCIE useful in your research, please consider citing:

@inproceedings{Cho2021PiCIE,
  title = {PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering},
  author = {Jang Hyun Cho and  Utkarsh Mall and  Kavita Bala and  Bharath Hariharan},
  year = {2021},
  booktitle = {CVPR}
}

Acknowledgements

We thank Facebook AI Research for the open-soource library Faiss. Also, our implementation largely borrows from DeepCluster and DeeperCluster for clustering with Faiss.

TODO's

  • Dependency & dataset setup.
  • Clear up and add complete train & test codes.
  • Baseline MDC code.
  • Weights and logs.
  • Make visualization notebook easier to use + better colors.
Comments
  • how to get the pickle file

    how to get the pickle file "fine_to_coarse_dick.pickle"

    Traceback (most recent call last): File "train_picie.py", line 358, in main(args, logger) File "train_picie.py", line 190, in main testset = EvalCOCO(args.data_root, res=args.res, split='val', mode='test', stuff=args.stuff, thing=args.thing) File "/home/jinglai.whr/code/PiCIE/data/coco_eval_dataset.py", line 40, in init self.fine_to_coarse = self._get_fine_to_coarse() File "/home/jinglai.whr/code/PiCIE/data/coco_eval_dataset.py", line 100, in _get_fine_to_coarse with open(os.path.join(self.root, FINE_TO_COARSE_PATH), "rb") as dict_f: FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/fine_to_coarse_dict.pickle'

    Hi, how to get the pickle file "fine_to_coarse_dick.pickle", thanks!

    opened by HRanWang 11
  • Cityscapes model and evaluation

    Cityscapes model and evaluation

    Dear authors,

    thank you very much for your work. I would be interested in using your model trained on Cityscapes, can you please provide it? Also, I wonder how exactly do you evaluate your Cityscapes model. In the paper, you write

    After filtering out void group, we have 27 categories.

    However, in the standard Cityscapes evaluation, only 19 classes are evaluated. Can you please explain how exactly do you do the evaluation?

    Thank you very much in advance.

    opened by vobecant 7
  • Results on Cityscapes

    Results on Cityscapes

    Hi Cho:

    It's me again:) This time I try to reproduce the result on Cityscapes, which are 65.50 acc and 12.31 mIoU that reported in your paper, by doing the following steps:

    1. Download Cityscapes data 'gtFine_trainvaltest.zip' and 'leftImg8bit_trainvaltest.zip' from the official Cityscapes website.

    2. Make a projection that change the indices in each 'xxx_gtFine_labelIds.png' by the following dictionary that maps indices from [-1, 33] to [-1, 26]: dict = {0: -1, 1: -1, 2: -1, 3: -1, 4: -1, 5: -1, 6: -1, 7: 0, 8: 1, 9: 2, 10: 3, 11: 4, 12: 5, 13: 6, 14: 7, 15: 8, 16: 9, 17: 10, 18: 11, 19: 12, 20: 13, 21: 14, 22: 15, 23: 16, 24: 17, 25: 18, 26: 19, 27: 20, 28: 21, 29: 22, 30: 23, 31: 24, 32: 25, 33: 26, -1: -1}

    3. Keep all the rest hyperparameters the same as what they are in 'train_picie.py' and 'train_picie.sh'.

    4. Use 2975 training samples for training, and 500 validation samples for evaluation.

    However, I cannot get the desired results but only got 7.293 +/- 0.074 and 34.779 +/- 0.350 for mIoU and accuracy, respectively.

    I wonder if I took the right way to reproduce the results on Cityscapes, or some additional modifications to the hyperparameters or code are needed to get the right results?

    Thanks!

    opened by zyy-cn 5
  • Can you provide an average performance?

    Can you provide an average performance?

    When I train the model, the performance is lower than the paper. I think you also experimented with many models to get the best model. I'm doing additional training now, and it would be very helpful if you could provide the results for previously trained models.

    Can you provide the mean and standard deviation of the performance of the previously run models? Can you provide performance for stuff only and thing only for models that do not use auxiliary clustering (+H)?

    Thanks.

    This is my conda environment for RTX 3090 GPU that supports CUDA>=11.0. python=3.7 -> 3.6 torch=1.2.0 -> 1.8.1 torchvision=0.4.0 -> 0.9.1 cudatoolkit=10.0 -> 11.0 faiss-gpu=1.6.3 -> 1.7.1

    I trained 2 models using the train_picie.sh.

    - stuff and things All ACC : (1st) 39.3131 (± 16.0990) (2nd) 44.2331 (± 19.2532) m IOU : (1st) 9.0956 (2nd) 10.9407

    mean All ACC : 41.7731 ± 2.4600 best All ACC : 44.2331 <---> 48.09 (paper), 49.99 (paper, +H) mean mIOU : 10.0182 ± 0.9226 best mIOU : 10.9407 <---> 13.84 (paper), 14.36 (paper, +H)

    - stuff All ACC : (1st) 42.6251 (± 22.3882) (2nd) 48.9712 (± 27.5140) m IOU : (1st) 7.8613 (2nd) 9.9796

    mean All ACC : 45.7982 ± 3.1731 best All ACC : 48.9712 <---> 74.56 (paper, +H) mean mIOU : 8.9205 ± 1.0592 best mIOU : 9.9796 <---> 17.32 (paper, +H)

    - thing All ACC : (1st) 32.7959 (± 17.7133) (2nd) 47.6261 (± 26.1709) m IOU : (1st) 4.7463 (2nd) 7.2779

    mean All ACC : 40.2110 ± 7.4150 best All ACC : 47.6261 <---> 69.39 (paper, +H)
    mean mIOU : 6.0121 ± 1.2658 best mIOU : 7.2779 <---> 23.83 (paper, +H)

    Additionally, there are minor mistakes.

    1. It is not working for evaluation of stuff only. (I modified it by using the thing only part.) error part : link

    2. 'is_first' option is not pre-defined. (first_batch?) error part : link definition : link

    3. test bash code also require 'save_root'.

    opened by Jeonghoon4 4
  • Request of Cityscapes models

    Request of Cityscapes models

    Hi,

    Thank you for your inspiring work. I am also working on self-supervised approaches for semantic segmentation. I hope to have a fair comparison with your work on Cityscapes. However, I could not find enough information to reproduce the best results in the paper and this repo. Could you provide your models trained on Cityscapes? I'd highly appreciate some help.

    opened by LeungTsang 3
  • NameError: name 'get_args' is not defined

    NameError: name 'get_args' is not defined

    Hello @janghyuncho !

    Your research is amazing, great job :)

    I have been working with it, but when I run:

    python prepare_histogram.py --data_root datasets --save_root results --stuff --thing

    I get the following error:

    C: ...\PiCIE\prepare_histogram.py", line 51, in args = get_args() NameError: name 'get_args' is not defined

    Am I missing something or should I change something in the prepare_hsitogram.py? I am guessing that function is not called, but I can't figure out where is located, so I can add it, any ideas?

    Thanks for your time!

    opened by aipkk 3
  • Train/val on MS-COCO with fully (80 things + 91 stuff) categories

    Train/val on MS-COCO with fully (80 things + 91 stuff) categories

    Hi:

    Thanks for sharing this great work! Recently I try to adapt this work to the task of training PiCIE on MS-COCO with full class categories without merging them into superclasses (i.e. 12 things + 15 stuff), by doing the following modifications:

    1. Re-arrage the class-ids of ground-truth label to keep the consecutiveness by mapping indices 0-90 into 0-79, and 91-181 into 80-170, instead of merging them into 0-26 using 'fine_to_coarse_dict.pickle'.
    2. Set both K_train and K_test to 171 in train_picie.sh and keep the rest hyperparameters the same as ./sh_files/train_picie.sh.

    here are my train logs:

    2021-10-09 00:26:42,166:INFO: Namespace(K_stuff=91, K_test=171, K_things=80, K_train=171, X=80, arch='resnet18', augment=True, batch_size_cluster=256, batch_size_test=128, batch_size_train=128, blur=True, comment='', data_root='datasets/coco/', equiv=True, eval_only=False, eval_path=None, fullcoco=False, grey=True, h_flip=True, in_dim=128, jitter=True, kmeans_n_iter=20, lr=0.0001, metric_test='cosine', metric_train='cosine', min_scale=0.5, momentum=0.9, mse=False, no_balance=False, no_merge_coco=True, num_batches=1, num_epoch=10, num_init_batches=20, num_workers=4, optim_type='Adam', pretrain=True, random_crop=True, repeats=1, res=320, res1=320, res2=640, restart=False, restart_path=None, save_eval_path='results/picie/train/2/augmented/res1=320_res2=640/jitter=True_blur=True_grey=True/equiv/h_flip=True_v_flip=False_crop=True/min_scale\=0.5/K_train=171_cosine/K_test=171_cosine', save_model_path='results/picie/train/2/augmented/res1=320_res2=640/jitter=True_blur=True_grey=True/equiv/h_flip=True_v_flip=False_crop=True/min_scale\=0.5/K_train=171_cosine', save_root='results/picie/train/2/augmented/res1=320_res2=640/jitter=True_blur=True_grey=True/equiv/h_flip=True_v_flip=False_crop=True/min_scale\=0.5', seed=1, stuff=True, thing=True, v_flip=False, val_type='train', version=7, weight_decay=0.0005) 2021-10-09 00:26:47,642:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 00:26:53,735:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 00:26:53,736:INFO: Batch label size : [128, 320, 320] 2021-10-09 00:26:53,736:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 00:26:54,537:INFO: 0/17 2021-10-09 00:27:10,761:INFO: ACC - All: 9.8964 2021-10-09 00:27:10,762:INFO: mIOU - All: 1.5880 2021-10-09 00:27:10,762:INFO: ACC - Thing: 17.9023 2021-10-09 00:27:10,762:INFO: mIOU - Thing: 3.0928 2021-10-09 00:27:10,762:INFO: ACC - Stuff: 17.3441 2021-10-09 00:27:10,762:INFO: mIOU - Stuff: 3.0644 2021-10-09 00:27:11,293:INFO: ============================= [Epoch 0] =============================

    2021-10-09 00:27:11,293:INFO: Start computing centroids. 2021-10-09 00:27:34,421:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 00:27:34,421:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 00:27:35,483:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 00:31:38,532:INFO: Initial k-means loss: 0.4483 2021-10-09 00:38:18,528:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.4512 2021-10-09 00:45:39,889:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 00:45:39,890:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 00:45:40,660:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 00:49:32,051:INFO: Initial k-means loss: 0.4343 2021-10-09 00:56:00,442:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.4384 2021-10-09 01:02:49,640:INFO: -Centroids ready. [Loss: 0.45128| 0.43836/ Time: 0:0:35:38]

    2021-10-09 01:03:10,389:INFO: Centroid size : [171, 128] 2021-10-09 01:03:10,389:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 01:03:10,390:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 01:03:13,166:INFO: [Assigning labels] 0 / 194 2021-10-09 01:18:31,839:INFO: Centroid size : [171, 128] 2021-10-09 01:18:31,840:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 01:18:31,840:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 01:18:33,925:INFO: [Assigning labels] 0 / 194 2021-10-09 01:31:45,003:INFO: -Cluster labels ready. [0:0:28:56]

    2021-10-09 01:31:45,007:INFO: Start training ... 2021-10-09 01:32:02,046:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 01:32:02,047:INFO: Batch label size : [128, 80, 80] 2021-10-09 01:32:02,047:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 01:32:02,929:INFO: 0 / 388 2021-10-09 01:45:10,229:INFO: 200 / 388 2021-10-09 01:57:20,011:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 01:57:25,053:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 01:57:25,053:INFO: Batch label size : [128, 320, 320] 2021-10-09 01:57:25,053:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 01:57:25,869:INFO: 0/17 2021-10-09 01:57:42,822:INFO: ACC - All: 24.2850 2021-10-09 01:57:42,823:INFO: mIOU - All: 7.1373 2021-10-09 01:57:42,823:INFO: ACC - Thing: 38.0029 2021-10-09 01:57:42,823:INFO: mIOU - Thing: 12.7254 2021-10-09 01:57:42,824:INFO: ACC - Stuff: 38.6157 2021-10-09 01:57:42,824:INFO: mIOU - Stuff: 13.0482 2021-10-09 01:57:42,832:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 01:57:47,818:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 01:57:47,819:INFO: Batch label size : [128, 320, 320] 2021-10-09 01:57:47,819:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 01:57:48,595:INFO: 0/17 2021-10-09 01:58:05,249:INFO: ACC - All: 27.1829 2021-10-09 01:58:05,250:INFO: mIOU - All: 7.6102 2021-10-09 01:58:05,250:INFO: ACC - Thing: 41.4974 2021-10-09 01:58:05,250:INFO: mIOU - Thing: 12.1366 2021-10-09 01:58:05,250:INFO: ACC - Stuff: 38.8955 2021-10-09 01:58:05,250:INFO: mIOU - Stuff: 14.1767 2021-10-09 01:58:05,259:INFO: ============== Epoch [0] ============== 2021-10-09 01:58:05,259:INFO: Time: [0:1:30:54] 2021-10-09 01:58:05,259:INFO: K-Means loss : 0.45128 | 0.43836 2021-10-09 01:58:05,259:INFO: Training Total Loss : 4.83569 2021-10-09 01:58:05,259:INFO: Training CE Loss (Total | Within | Across) : 4.83569 | 4.81816 | 4.85321 2021-10-09 01:58:05,259:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 01:58:05,259:INFO: [View 1] ACC: 24.2850 | mIoU: 7.1373 2021-10-09 01:58:05,259:INFO: [View 2] ACC: 27.1829 | mIoU: 7.6102 2021-10-09 01:58:05,259:INFO: ========================================

    2021-10-09 01:58:06,719:INFO: ============================= [Epoch 1] =============================

    2021-10-09 01:58:06,719:INFO: Start computing centroids. 2021-10-09 01:58:26,708:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 01:58:26,709:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 01:58:27,838:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 02:02:34,372:INFO: Initial k-means loss: 0.2232 2021-10-09 02:09:18,356:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.2253 2021-10-09 02:16:39,888:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 02:16:39,889:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 02:16:40,660:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 02:20:36,539:INFO: Initial k-means loss: 0.2123 2021-10-09 02:27:04,407:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.2143 2021-10-09 02:33:55,241:INFO: -Centroids ready. [Loss: 0.22525| 0.21440/ Time: 0:0:35:49]

    2021-10-09 02:34:15,001:INFO: Centroid size : [171, 128] 2021-10-09 02:34:15,002:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 02:34:15,002:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 02:34:18,144:INFO: [Assigning labels] 0 / 194 2021-10-09 02:49:25,416:INFO: Centroid size : [171, 128] 2021-10-09 02:49:25,417:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 02:49:25,417:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 02:49:27,891:INFO: [Assigning labels] 0 / 194 2021-10-09 03:02:35,559:INFO: -Cluster labels ready. [0:0:28:40]

    2021-10-09 03:02:35,563:INFO: Start training ... 2021-10-09 03:02:53,373:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 03:02:53,374:INFO: Batch label size : [128, 80, 80] 2021-10-09 03:02:53,374:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 03:02:54,179:INFO: 0 / 388 2021-10-09 03:15:55,349:INFO: 200 / 388 2021-10-09 03:27:54,270:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 03:27:59,453:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 03:27:59,453:INFO: Batch label size : [128, 320, 320] 2021-10-09 03:27:59,453:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 03:28:00,256:INFO: 0/17 2021-10-09 03:28:17,828:INFO: ACC - All: 22.7207 2021-10-09 03:28:17,829:INFO: mIOU - All: 7.3259 2021-10-09 03:28:17,829:INFO: ACC - Thing: 32.4342 2021-10-09 03:28:17,829:INFO: mIOU - Thing: 13.8477 2021-10-09 03:28:17,829:INFO: ACC - Stuff: 35.6936 2021-10-09 03:28:17,829:INFO: mIOU - Stuff: 11.8657 2021-10-09 03:28:17,840:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 03:28:22,962:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 03:28:22,962:INFO: Batch label size : [128, 320, 320] 2021-10-09 03:28:22,962:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 03:28:23,765:INFO: 0/17 2021-10-09 03:28:40,975:INFO: ACC - All: 23.1472 2021-10-09 03:28:40,976:INFO: mIOU - All: 7.2321 2021-10-09 03:28:40,977:INFO: ACC - Thing: 38.1891 2021-10-09 03:28:40,977:INFO: mIOU - Thing: 14.3081 2021-10-09 03:28:40,977:INFO: ACC - Stuff: 35.3766 2021-10-09 03:28:40,977:INFO: mIOU - Stuff: 10.9369 2021-10-09 03:28:40,987:INFO: ============== Epoch [1] ============== 2021-10-09 03:28:40,987:INFO: Time: [0:1:30:34] 2021-10-09 03:28:40,987:INFO: K-Means loss : 0.22525 | 0.21440 2021-10-09 03:28:40,987:INFO: Training Total Loss : 4.39373 2021-10-09 03:28:40,987:INFO: Training CE Loss (Total | Within | Across) : 4.39373 | 4.36556 | 4.42191 2021-10-09 03:28:40,987:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 03:28:40,987:INFO: [View 1] ACC: 22.7207 | mIoU: 7.3259 2021-10-09 03:28:40,987:INFO: [View 2] ACC: 23.1472 | mIoU: 7.2321 2021-10-09 03:28:40,987:INFO: ========================================

    2021-10-09 03:28:42,444:INFO: ============================= [Epoch 2] =============================

    2021-10-09 03:28:42,444:INFO: Start computing centroids. 2021-10-09 03:29:03,685:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 03:29:03,686:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 03:29:04,706:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 03:33:12,056:INFO: Initial k-means loss: 0.1106 2021-10-09 03:39:53,673:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.1097 2021-10-09 03:47:17,752:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 03:47:17,753:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 03:47:18,500:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 03:51:12,067:INFO: Initial k-means loss: 0.1047 2021-10-09 03:57:38,881:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.1043 2021-10-09 04:04:33,968:INFO: -Centroids ready. [Loss: 0.11023| 0.10459/ Time: 0:0:35:51]

    2021-10-09 04:04:55,258:INFO: Centroid size : [171, 128] 2021-10-09 04:04:55,259:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 04:04:55,259:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 04:04:57,977:INFO: [Assigning labels] 0 / 194 2021-10-09 04:20:03,796:INFO: Centroid size : [171, 128] 2021-10-09 04:20:03,797:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 04:20:03,797:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 04:20:06,492:INFO: [Assigning labels] 0 / 194 2021-10-09 04:33:18,896:INFO: -Cluster labels ready. [0:0:28:45]

    2021-10-09 04:33:18,900:INFO: Start training ... 2021-10-09 04:33:36,001:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 04:33:36,002:INFO: Batch label size : [128, 80, 80] 2021-10-09 04:33:36,002:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 04:33:36,807:INFO: 0 / 388 2021-10-09 04:46:30,782:INFO: 200 / 388 2021-10-09 04:58:34,859:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 04:58:40,046:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 04:58:40,046:INFO: Batch label size : [128, 320, 320] 2021-10-09 04:58:40,047:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 04:58:40,854:INFO: 0/17 2021-10-09 04:58:58,391:INFO: ACC - All: 24.5717 2021-10-09 04:58:58,392:INFO: mIOU - All: 6.6422 2021-10-09 04:58:58,393:INFO: ACC - Thing: 38.2366 2021-10-09 04:58:58,393:INFO: mIOU - Thing: 12.8897 2021-10-09 04:58:58,393:INFO: ACC - Stuff: 41.9975 2021-10-09 04:58:58,393:INFO: mIOU - Stuff: 11.9853 2021-10-09 04:58:58,402:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 04:59:03,479:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 04:59:03,479:INFO: Batch label size : [128, 320, 320] 2021-10-09 04:59:03,479:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 04:59:04,301:INFO: 0/17 2021-10-09 04:59:21,282:INFO: ACC - All: 25.1037 2021-10-09 04:59:21,282:INFO: mIOU - All: 7.0031 2021-10-09 04:59:21,283:INFO: ACC - Thing: 48.4587 2021-10-09 04:59:21,283:INFO: mIOU - Thing: 13.5206 2021-10-09 04:59:21,283:INFO: ACC - Stuff: 35.8929 2021-10-09 04:59:21,283:INFO: mIOU - Stuff: 11.7855 2021-10-09 04:59:21,292:INFO: ============== Epoch [2] ============== 2021-10-09 04:59:21,293:INFO: Time: [0:1:30:39] 2021-10-09 04:59:21,293:INFO: K-Means loss : 0.11023 | 0.10459 2021-10-09 04:59:21,293:INFO: Training Total Loss : 4.30574 2021-10-09 04:59:21,293:INFO: Training CE Loss (Total | Within | Across) : 4.30574 | 4.28338 | 4.32810 2021-10-09 04:59:21,293:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 04:59:21,293:INFO: [View 1] ACC: 24.5717 | mIoU: 6.6422 2021-10-09 04:59:21,293:INFO: [View 2] ACC: 25.1037 | mIoU: 7.0031 2021-10-09 04:59:21,293:INFO: ========================================

    2021-10-09 04:59:22,772:INFO: ============================= [Epoch 3] =============================

    2021-10-09 04:59:22,772:INFO: Start computing centroids. 2021-10-09 04:59:43,369:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 04:59:43,370:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 04:59:44,473:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 05:03:51,557:INFO: Initial k-means loss: 0.0755 2021-10-09 05:10:37,660:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0761 2021-10-09 05:18:03,777:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 05:18:03,778:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 05:18:04,573:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 05:22:02,173:INFO: Initial k-means loss: 0.0722 2021-10-09 05:28:35,818:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0727 2021-10-09 05:35:33,506:INFO: -Centroids ready. [Loss: 0.07620| 0.07283/ Time: 0:0:36:11]

    2021-10-09 05:35:54,508:INFO: Centroid size : [171, 128] 2021-10-09 05:35:54,509:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 05:35:54,509:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 05:35:57,352:INFO: [Assigning labels] 0 / 194 2021-10-09 05:51:12,051:INFO: Centroid size : [171, 128] 2021-10-09 05:51:12,052:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 05:51:12,052:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 05:51:14,680:INFO: [Assigning labels] 0 / 194 2021-10-09 06:04:25,581:INFO: -Cluster labels ready. [0:0:28:52]

    2021-10-09 06:04:25,585:INFO: Start training ... 2021-10-09 06:04:42,483:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 06:04:42,484:INFO: Batch label size : [128, 80, 80] 2021-10-09 06:04:42,484:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 06:04:43,391:INFO: 0 / 388 2021-10-09 06:17:43,126:INFO: 200 / 388 2021-10-09 06:29:43,005:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 06:29:48,256:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 06:29:48,256:INFO: Batch label size : [128, 320, 320] 2021-10-09 06:29:48,256:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 06:29:49,093:INFO: 0/17 2021-10-09 06:30:06,524:INFO: ACC - All: 24.1964 2021-10-09 06:30:06,525:INFO: mIOU - All: 6.4970 2021-10-09 06:30:06,525:INFO: ACC - Thing: 45.5011 2021-10-09 06:30:06,526:INFO: mIOU - Thing: 12.9584 2021-10-09 06:30:06,526:INFO: ACC - Stuff: 30.5258 2021-10-09 06:30:06,526:INFO: mIOU - Stuff: 9.8375 2021-10-09 06:30:06,534:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 06:30:11,667:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 06:30:11,667:INFO: Batch label size : [128, 320, 320] 2021-10-09 06:30:11,667:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 06:30:12,478:INFO: 0/17 2021-10-09 06:30:29,577:INFO: ACC - All: 24.7228 2021-10-09 06:30:29,578:INFO: mIOU - All: 6.6396 2021-10-09 06:30:29,578:INFO: ACC - Thing: 40.2358 2021-10-09 06:30:29,578:INFO: mIOU - Thing: 13.2917 2021-10-09 06:30:29,579:INFO: ACC - Stuff: 40.0240 2021-10-09 06:30:29,579:INFO: mIOU - Stuff: 10.6481 2021-10-09 06:30:29,589:INFO: ============== Epoch [3] ============== 2021-10-09 06:30:29,589:INFO: Time: [0:1:31:7] 2021-10-09 06:30:29,589:INFO: K-Means loss : 0.07620 | 0.07283 2021-10-09 06:30:29,589:INFO: Training Total Loss : 4.26950 2021-10-09 06:30:29,589:INFO: Training CE Loss (Total | Within | Across) : 4.26950 | 4.25079 | 4.28820 2021-10-09 06:30:29,589:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 06:30:29,589:INFO: [View 1] ACC: 24.1964 | mIoU: 6.4970 2021-10-09 06:30:29,589:INFO: [View 2] ACC: 24.7228 | mIoU: 6.6396 2021-10-09 06:30:29,589:INFO: ========================================

    2021-10-09 06:30:31,229:INFO: ============================= [Epoch 4] =============================

    2021-10-09 06:30:31,229:INFO: Start computing centroids. 2021-10-09 06:30:52,607:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 06:30:52,608:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 06:30:53,638:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 06:34:58,272:INFO: Initial k-means loss: 0.0618 2021-10-09 06:41:42,564:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0622 2021-10-09 06:49:07,210:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 06:49:07,211:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 06:49:07,956:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 06:53:02,606:INFO: Initial k-means loss: 0.0572 2021-10-09 06:59:36,551:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0579 2021-10-09 07:06:33,523:INFO: -Centroids ready. [Loss: 0.06225| 0.05798/ Time: 0:0:36:2]

    2021-10-09 07:06:53,853:INFO: Centroid size : [171, 128] 2021-10-09 07:06:53,854:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 07:06:53,854:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 07:06:56,900:INFO: [Assigning labels] 0 / 194 2021-10-09 07:22:10,938:INFO: Centroid size : [171, 128] 2021-10-09 07:22:10,940:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 07:22:10,940:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 07:22:13,475:INFO: [Assigning labels] 0 / 194 2021-10-09 07:35:22,684:INFO: -Cluster labels ready. [0:0:28:49]

    2021-10-09 07:35:22,688:INFO: Start training ... 2021-10-09 07:35:39,805:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 07:35:39,806:INFO: Batch label size : [128, 80, 80] 2021-10-09 07:35:39,806:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 07:35:40,611:INFO: 0 / 388 2021-10-09 07:48:35,877:INFO: 200 / 388 2021-10-09 08:00:38,165:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 08:00:43,357:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 08:00:43,358:INFO: Batch label size : [128, 320, 320] 2021-10-09 08:00:43,358:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 08:00:44,174:INFO: 0/17 2021-10-09 08:01:01,751:INFO: ACC - All: 24.4110 2021-10-09 08:01:01,752:INFO: mIOU - All: 5.6121 2021-10-09 08:01:01,752:INFO: ACC - Thing: 34.1646 2021-10-09 08:01:01,752:INFO: mIOU - Thing: 11.0015 2021-10-09 08:01:01,753:INFO: ACC - Stuff: 36.5986 2021-10-09 08:01:01,753:INFO: mIOU - Stuff: 8.6366 2021-10-09 08:01:01,761:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 08:01:07,019:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 08:01:07,019:INFO: Batch label size : [128, 320, 320] 2021-10-09 08:01:07,019:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 08:01:07,878:INFO: 0/17 2021-10-09 08:01:24,860:INFO: ACC - All: 24.7779 2021-10-09 08:01:24,860:INFO: mIOU - All: 5.7914 2021-10-09 08:01:24,861:INFO: ACC - Thing: 36.6781 2021-10-09 08:01:24,861:INFO: mIOU - Thing: 9.9772 2021-10-09 08:01:24,861:INFO: ACC - Stuff: 39.3449 2021-10-09 08:01:24,861:INFO: mIOU - Stuff: 9.9257 2021-10-09 08:01:24,871:INFO: ============== Epoch [4] ============== 2021-10-09 08:01:24,871:INFO: Time: [0:1:30:53] 2021-10-09 08:01:24,872:INFO: K-Means loss : 0.06225 | 0.05798 2021-10-09 08:01:24,872:INFO: Training Total Loss : 4.25584 2021-10-09 08:01:24,872:INFO: Training CE Loss (Total | Within | Across) : 4.25584 | 4.23975 | 4.27193 2021-10-09 08:01:24,872:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 08:01:24,872:INFO: [View 1] ACC: 24.4110 | mIoU: 5.6121 2021-10-09 08:01:24,872:INFO: [View 2] ACC: 24.7779 | mIoU: 5.7914 2021-10-09 08:01:24,872:INFO: ========================================

    2021-10-09 08:01:26,333:INFO: ============================= [Epoch 5] =============================

    2021-10-09 08:01:26,334:INFO: Start computing centroids. 2021-10-09 08:01:46,291:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 08:01:46,292:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 08:01:47,424:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 08:05:53,015:INFO: Initial k-means loss: 0.0522 2021-10-09 08:12:38,567:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0524 2021-10-09 08:20:05,849:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 08:20:05,850:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 08:20:06,614:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 08:23:59,591:INFO: Initial k-means loss: 0.0489 2021-10-09 08:30:36,273:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0488 2021-10-09 08:37:34,446:INFO: -Centroids ready. [Loss: 0.05220| 0.04862/ Time: 0:0:36:8]

    2021-10-09 08:37:54,037:INFO: Centroid size : [171, 128] 2021-10-09 08:37:54,038:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 08:37:54,038:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 08:37:57,633:INFO: [Assigning labels] 0 / 194 2021-10-09 08:53:07,770:INFO: Centroid size : [171, 128] 2021-10-09 08:53:07,772:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 08:53:07,772:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 08:53:10,278:INFO: [Assigning labels] 0 / 194 2021-10-09 09:06:18,445:INFO: -Cluster labels ready. [0:0:28:44]

    2021-10-09 09:06:18,448:INFO: Start training ... 2021-10-09 09:06:36,385:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 09:06:36,386:INFO: Batch label size : [128, 80, 80] 2021-10-09 09:06:36,386:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 09:06:37,191:INFO: 0 / 388 2021-10-09 09:19:34,159:INFO: 200 / 388 2021-10-09 09:31:35,026:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 09:31:40,187:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 09:31:40,187:INFO: Batch label size : [128, 320, 320] 2021-10-09 09:31:40,187:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 09:31:41,007:INFO: 0/17 2021-10-09 09:31:58,495:INFO: ACC - All: 26.0197 2021-10-09 09:31:58,496:INFO: mIOU - All: 5.7386 2021-10-09 09:31:58,497:INFO: ACC - Thing: 38.9953 2021-10-09 09:31:58,497:INFO: mIOU - Thing: 10.7847 2021-10-09 09:31:58,497:INFO: ACC - Stuff: 40.8572 2021-10-09 09:31:58,497:INFO: mIOU - Stuff: 9.4083 2021-10-09 09:31:58,507:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 09:32:03,575:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 09:32:03,575:INFO: Batch label size : [128, 320, 320] 2021-10-09 09:32:03,575:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 09:32:04,408:INFO: 0/17 2021-10-09 09:32:21,675:INFO: ACC - All: 26.4511 2021-10-09 09:32:21,675:INFO: mIOU - All: 5.9525 2021-10-09 09:32:21,676:INFO: ACC - Thing: 33.9755 2021-10-09 09:32:21,676:INFO: mIOU - Thing: 10.1442 2021-10-09 09:32:21,676:INFO: ACC - Stuff: 43.5633 2021-10-09 09:32:21,676:INFO: mIOU - Stuff: 10.4873 2021-10-09 09:32:21,685:INFO: ============== Epoch [5] ============== 2021-10-09 09:32:21,685:INFO: Time: [0:1:30:55] 2021-10-09 09:32:21,685:INFO: K-Means loss : 0.05220 | 0.04862 2021-10-09 09:32:21,685:INFO: Training Total Loss : 4.24061 2021-10-09 09:32:21,685:INFO: Training CE Loss (Total | Within | Across) : 4.24061 | 4.22617 | 4.25505 2021-10-09 09:32:21,686:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 09:32:21,686:INFO: [View 1] ACC: 26.0197 | mIoU: 5.7386 2021-10-09 09:32:21,686:INFO: [View 2] ACC: 26.4511 | mIoU: 5.9525 2021-10-09 09:32:21,686:INFO: ========================================

    2021-10-09 09:32:23,127:INFO: ============================= [Epoch 6] =============================

    2021-10-09 09:32:23,127:INFO: Start computing centroids. 2021-10-09 09:32:43,925:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 09:32:43,926:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 09:32:45,041:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 09:36:46,013:INFO: Initial k-means loss: 0.0465 2021-10-09 09:43:33,686:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0468 2021-10-09 09:50:58,547:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 09:50:58,548:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 09:50:59,286:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 09:54:55,883:INFO: Initial k-means loss: 0.0424 2021-10-09 10:01:33,208:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0429 2021-10-09 10:08:31,983:INFO: -Centroids ready. [Loss: 0.04700| 0.04308/ Time: 0:0:36:8]

    2021-10-09 10:08:52,443:INFO: Centroid size : [171, 128] 2021-10-09 10:08:52,444:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 10:08:52,444:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 10:08:55,489:INFO: [Assigning labels] 0 / 194 2021-10-09 10:23:56,728:INFO: Centroid size : [171, 128] 2021-10-09 10:23:56,729:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 10:23:56,729:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 10:23:59,243:INFO: [Assigning labels] 0 / 194 2021-10-09 10:37:08,941:INFO: -Cluster labels ready. [0:0:28:37]

    2021-10-09 10:37:08,944:INFO: Start training ... 2021-10-09 10:37:26,120:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 10:37:26,121:INFO: Batch label size : [128, 80, 80] 2021-10-09 10:37:26,121:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 10:37:26,926:INFO: 0 / 388 2021-10-09 10:50:22,936:INFO: 200 / 388 2021-10-09 11:02:26,917:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 11:02:32,012:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 11:02:32,012:INFO: Batch label size : [128, 320, 320] 2021-10-09 11:02:32,012:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 11:02:32,855:INFO: 0/17 2021-10-09 11:02:50,435:INFO: ACC - All: 28.0251 2021-10-09 11:02:50,435:INFO: mIOU - All: 5.6963 2021-10-09 11:02:50,436:INFO: ACC - Thing: 46.5006 2021-10-09 11:02:50,436:INFO: mIOU - Thing: 9.2724 2021-10-09 11:02:50,436:INFO: ACC - Stuff: 38.6916 2021-10-09 11:02:50,436:INFO: mIOU - Stuff: 10.6976 2021-10-09 11:02:50,445:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 11:02:55,470:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 11:02:55,470:INFO: Batch label size : [128, 320, 320] 2021-10-09 11:02:55,470:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 11:02:56,288:INFO: 0/17 2021-10-09 11:03:13,246:INFO: ACC - All: 27.5272 2021-10-09 11:03:13,247:INFO: mIOU - All: 5.5867 2021-10-09 11:03:13,247:INFO: ACC - Thing: 46.3594 2021-10-09 11:03:13,248:INFO: mIOU - Thing: 9.7279 2021-10-09 11:03:13,248:INFO: ACC - Stuff: 35.9791 2021-10-09 11:03:13,248:INFO: mIOU - Stuff: 9.3963 2021-10-09 11:03:13,257:INFO: ============== Epoch [6] ============== 2021-10-09 11:03:13,257:INFO: Time: [0:1:30:50] 2021-10-09 11:03:13,257:INFO: K-Means loss : 0.04700 | 0.04308 2021-10-09 11:03:13,257:INFO: Training Total Loss : 4.24061 2021-10-09 11:03:13,257:INFO: Training CE Loss (Total | Within | Across) : 4.24061 | 4.22644 | 4.25478 2021-10-09 11:03:13,257:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 11:03:13,257:INFO: [View 1] ACC: 28.0251 | mIoU: 5.6963 2021-10-09 11:03:13,257:INFO: [View 2] ACC: 27.5272 | mIoU: 5.5867 2021-10-09 11:03:13,257:INFO: ========================================

    2021-10-09 11:03:14,672:INFO: ============================= [Epoch 7] =============================

    2021-10-09 11:03:14,672:INFO: Start computing centroids. 2021-10-09 11:03:34,989:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 11:03:34,990:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 11:03:36,100:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 11:07:44,139:INFO: Initial k-means loss: 0.0410 2021-10-09 11:14:27,744:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0422 2021-10-09 11:22:00,056:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 11:22:00,057:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 11:22:00,802:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 11:25:56,619:INFO: Initial k-means loss: 0.0381 2021-10-09 11:32:32,358:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0392 2021-10-09 11:39:27,901:INFO: -Centroids ready. [Loss: 0.04175| 0.03870/ Time: 0:0:36:13]

    2021-10-09 11:39:47,830:INFO: Centroid size : [171, 128] 2021-10-09 11:39:47,830:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 11:39:47,831:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 11:39:51,295:INFO: [Assigning labels] 0 / 194 2021-10-09 11:55:01,698:INFO: Centroid size : [171, 128] 2021-10-09 11:55:01,700:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 11:55:01,700:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 11:55:04,478:INFO: [Assigning labels] 0 / 194 2021-10-09 12:08:17,422:INFO: -Cluster labels ready. [0:0:28:50]

    2021-10-09 12:08:17,425:INFO: Start training ... 2021-10-09 12:08:34,990:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 12:08:34,991:INFO: Batch label size : [128, 80, 80] 2021-10-09 12:08:34,991:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 12:08:35,797:INFO: 0 / 388 2021-10-09 12:21:32,956:INFO: 200 / 388 2021-10-09 12:33:28,059:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 12:33:33,148:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 12:33:33,149:INFO: Batch label size : [128, 320, 320] 2021-10-09 12:33:33,149:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 12:33:33,965:INFO: 0/17 2021-10-09 12:33:51,319:INFO: ACC - All: 29.1677 2021-10-09 12:33:51,320:INFO: mIOU - All: 5.5887 2021-10-09 12:33:51,320:INFO: ACC - Thing: 48.8678 2021-10-09 12:33:51,321:INFO: mIOU - Thing: 10.6066 2021-10-09 12:33:51,321:INFO: ACC - Stuff: 36.8531 2021-10-09 12:33:51,321:INFO: mIOU - Stuff: 8.3272 2021-10-09 12:33:51,330:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 12:33:56,272:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 12:33:56,272:INFO: Batch label size : [128, 320, 320] 2021-10-09 12:33:56,272:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 12:33:57,102:INFO: 0/17 2021-10-09 12:34:14,082:INFO: ACC - All: 29.2309 2021-10-09 12:34:14,083:INFO: mIOU - All: 5.6099 2021-10-09 12:34:14,083:INFO: ACC - Thing: 43.6392 2021-10-09 12:34:14,083:INFO: mIOU - Thing: 10.0625 2021-10-09 12:34:14,083:INFO: ACC - Stuff: 43.5332 2021-10-09 12:34:14,083:INFO: mIOU - Stuff: 9.2222 2021-10-09 12:34:14,092:INFO: ============== Epoch [7] ============== 2021-10-09 12:34:14,092:INFO: Time: [0:1:31:0] 2021-10-09 12:34:14,092:INFO: K-Means loss : 0.04175 | 0.03870 2021-10-09 12:34:14,092:INFO: Training Total Loss : 4.24304 2021-10-09 12:34:14,092:INFO: Training CE Loss (Total | Within | Across) : 4.24304 | 4.22831 | 4.25776 2021-10-09 12:34:14,093:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 12:34:14,093:INFO: [View 1] ACC: 29.1677 | mIoU: 5.5887 2021-10-09 12:34:14,093:INFO: [View 2] ACC: 29.2309 | mIoU: 5.6099 2021-10-09 12:34:14,093:INFO: ========================================

    2021-10-09 12:34:15,525:INFO: ============================= [Epoch 8] =============================

    2021-10-09 12:34:15,525:INFO: Start computing centroids. 2021-10-09 12:34:35,588:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 12:34:35,588:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 12:34:36,683:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 12:38:39,487:INFO: Initial k-means loss: 0.0380 2021-10-09 12:45:18,945:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0379 2021-10-09 12:52:40,855:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 12:52:40,856:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 12:52:41,605:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 12:56:31,773:INFO: Initial k-means loss: 0.0354 2021-10-09 13:02:55,899:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0355 2021-10-09 13:09:40,064:INFO: -Centroids ready. [Loss: 0.03780| 0.03533/ Time: 0:0:35:25]

    2021-10-09 13:10:00,539:INFO: Centroid size : [171, 128] 2021-10-09 13:10:00,540:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 13:10:00,540:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 13:10:03,624:INFO: [Assigning labels] 0 / 194 2021-10-09 13:25:03,904:INFO: Centroid size : [171, 128] 2021-10-09 13:25:03,906:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 13:25:03,906:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 13:25:06,618:INFO: [Assigning labels] 0 / 194 2021-10-09 13:38:03,685:INFO: -Cluster labels ready. [0:0:28:23]

    2021-10-09 13:38:03,689:INFO: Start training ... 2021-10-09 13:38:20,982:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 13:38:20,983:INFO: Batch label size : [128, 80, 80] 2021-10-09 13:38:20,983:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 13:38:21,790:INFO: 0 / 388 2021-10-09 13:51:06,870:INFO: 200 / 388 2021-10-09 14:03:00,153:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 14:03:05,228:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 14:03:05,229:INFO: Batch label size : [128, 320, 320] 2021-10-09 14:03:05,229:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 14:03:06,059:INFO: 0/17 2021-10-09 14:03:23,198:INFO: ACC - All: 30.1595 2021-10-09 14:03:23,199:INFO: mIOU - All: 5.5250 2021-10-09 14:03:23,199:INFO: ACC - Thing: 50.6786 2021-10-09 14:03:23,199:INFO: mIOU - Thing: 10.2774 2021-10-09 14:03:23,200:INFO: ACC - Stuff: 47.0020 2021-10-09 14:03:23,200:INFO: mIOU - Stuff: 9.5605 2021-10-09 14:03:23,209:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 14:03:28,038:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 14:03:28,039:INFO: Batch label size : [128, 320, 320] 2021-10-09 14:03:28,039:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 14:03:28,890:INFO: 0/17 2021-10-09 14:03:45,625:INFO: ACC - All: 29.3897 2021-10-09 14:03:45,626:INFO: mIOU - All: 5.4567 2021-10-09 14:03:45,627:INFO: ACC - Thing: 43.9095 2021-10-09 14:03:45,627:INFO: mIOU - Thing: 10.5183 2021-10-09 14:03:45,627:INFO: ACC - Stuff: 45.5076 2021-10-09 14:03:45,627:INFO: mIOU - Stuff: 9.3557 2021-10-09 14:03:45,636:INFO: ============== Epoch [8] ============== 2021-10-09 14:03:45,636:INFO: Time: [0:1:29:30] 2021-10-09 14:03:45,636:INFO: K-Means loss : 0.03780 | 0.03533 2021-10-09 14:03:45,636:INFO: Training Total Loss : 4.24918 2021-10-09 14:03:45,636:INFO: Training CE Loss (Total | Within | Across) : 4.24918 | 4.23417 | 4.26419 2021-10-09 14:03:45,636:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 14:03:45,636:INFO: [View 1] ACC: 30.1595 | mIoU: 5.5250 2021-10-09 14:03:45,636:INFO: [View 2] ACC: 29.3897 | mIoU: 5.4567 2021-10-09 14:03:45,636:INFO: ========================================

    2021-10-09 14:03:47,060:INFO: ============================= [Epoch 9] =============================

    2021-10-09 14:03:47,060:INFO: Start computing centroids. 2021-10-09 14:04:07,283:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 14:04:07,284:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 14:04:08,379:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 14:08:09,021:INFO: Initial k-means loss: 0.0350 2021-10-09 14:14:49,329:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0352 2021-10-09 14:22:08,428:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 14:22:08,429:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 14:22:09,189:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 14:26:04,157:INFO: Initial k-means loss: 0.0321 2021-10-09 14:32:28,147:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0322 2021-10-09 14:39:12,409:INFO: -Centroids ready. [Loss: 0.03553| 0.03246/ Time: 0:0:35:25]

    2021-10-09 14:39:32,498:INFO: Centroid size : [171, 128] 2021-10-09 14:39:32,499:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 14:39:32,499:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 14:39:35,481:INFO: [Assigning labels] 0 / 194 2021-10-09 14:54:35,884:INFO: Centroid size : [171, 128] 2021-10-09 14:54:35,885:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 14:54:35,885:INFO: Batch feature size : [256, 128, 80, 80]

    2021-10-09 14:54:38,596:INFO: [Assigning labels] 0 / 194 2021-10-09 15:07:35,135:INFO: -Cluster labels ready. [0:0:28:23]

    2021-10-09 15:07:35,139:INFO: Start training ... 2021-10-09 15:07:51,720:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 15:07:51,721:INFO: Batch label size : [128, 80, 80] 2021-10-09 15:07:51,721:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 15:07:52,526:INFO: 0 / 388 2021-10-09 15:20:38,585:INFO: 200 / 388 2021-10-09 15:32:32,352:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 15:32:37,493:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 15:32:37,494:INFO: Batch label size : [128, 320, 320] 2021-10-09 15:32:37,494:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 15:32:38,323:INFO: 0/17 2021-10-09 15:32:55,815:INFO: ACC - All: 29.9956 2021-10-09 15:32:55,816:INFO: mIOU - All: 5.1303 2021-10-09 15:32:55,816:INFO: ACC - Thing: 48.4731 2021-10-09 15:32:55,817:INFO: mIOU - Thing: 9.0006 2021-10-09 15:32:55,817:INFO: ACC - Stuff: 41.6060 2021-10-09 15:32:55,817:INFO: mIOU - Stuff: 8.6583 2021-10-09 15:32:55,826:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 15:33:00,872:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 15:33:00,872:INFO: Batch label size : [128, 320, 320] 2021-10-09 15:33:00,872:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 15:33:01,720:INFO: 0/17 2021-10-09 15:33:18,516:INFO: ACC - All: 30.1750 2021-10-09 15:33:18,517:INFO: mIOU - All: 5.0784 2021-10-09 15:33:18,517:INFO: ACC - Thing: 50.6382 2021-10-09 15:33:18,518:INFO: mIOU - Thing: 11.0137 2021-10-09 15:33:18,518:INFO: ACC - Stuff: 38.0510 2021-10-09 15:33:18,518:INFO: mIOU - Stuff: 7.3415 2021-10-09 15:33:18,527:INFO: ============== Epoch [9] ============== 2021-10-09 15:33:18,527:INFO: Time: [0:1:29:31] 2021-10-09 15:33:18,527:INFO: K-Means loss : 0.03553 | 0.03246 2021-10-09 15:33:18,527:INFO: Training Total Loss : 4.25482 2021-10-09 15:33:18,527:INFO: Training CE Loss (Total | Within | Across) : 4.25482 | 4.23967 | 4.26996 2021-10-09 15:33:18,527:INFO: Training MSE Loss (Total) : 0.00000 2021-10-09 15:33:18,527:INFO: [View 1] ACC: 29.9956 | mIoU: 5.1303 2021-10-09 15:33:18,527:INFO: [View 2] ACC: 30.1750 | mIoU: 5.0784 2021-10-09 15:33:18,527:INFO: ========================================

    2021-10-09 15:33:19,426:INFO: Start computing centroids. 2021-10-09 15:33:25,274:INFO: Batch input size : [256, 3, 320, 320] 2021-10-09 15:33:25,275:INFO: Batch feature : [256, 128, 80, 80] 2021-10-09 15:33:26,203:INFO: [Saving features]: 0 / 194 | [K-Means Loss]: 0.0000 2021-10-09 15:36:31,440:INFO: Initial k-means loss: 0.0308 2021-10-09 15:42:45,329:INFO: [Saving features]: 100 / 194 | [K-Means Loss]: 0.0308 2021-10-09 15:49:19,710:INFO: -Centroids ready. [Loss: 0.03062/ Time: 0:0:16:0]

    2021-10-09 15:49:19,713:INFO: ====== METRIC TEST : cosine ======

    2021-10-09 15:49:24,732:INFO: Batch input size : [128, 3, 320, 320] 2021-10-09 15:49:24,732:INFO: Batch label size : [128, 320, 320] 2021-10-09 15:49:24,732:INFO: Batch feature size : [128, 128, 80, 80]

    2021-10-09 15:49:25,579:INFO: 0/17 2021-10-09 15:49:41,669:INFO: ACC - All: 34.5429 2021-10-09 15:49:41,669:INFO: mIOU - All: 2.7377 2021-10-09 15:49:41,670:INFO: ACC - Thing: 40.3941 2021-10-09 15:49:41,670:INFO: mIOU - Thing: 4.2091 2021-10-09 15:49:41,670:INFO: ACC - Stuff: 66.9168 2021-10-09 15:49:41,670:INFO: mIOU - Stuff: 4.6067 2021-10-09 15:49:41,679:INFO: Average overall pixel accuracy [NEW] : 34.543 +/- 0.000. 2021-10-09 15:49:41,679:INFO: Average mIoU [NEW] : 2.738 +/- 0.000. 2021-10-09 15:49:41,679:INFO: Experiment done. [0:15:22:59]

    I got only 2.7377 mIoU after training. Was this result as expected? Or do you have any suggestions for this full-class setting? Thanks.

    opened by zyy-cn 3
  • Question for the dataset.py

    Question for the dataset.py

    Hello, thanks for sharing your excellent work!

    I have a question about your code, especially coco_train_dataset.py.

    https://github.com/janghyuncho/PiCIE/blob/62d274897990f9c980cf1000464628a25d22e45b/data/coco_train_dataset.py#L97-L106 When applying the transform_tensor function to the image, it applies to the variable image not image1.

    Is there any issue for this?

    Thanks!

    opened by halbielee 3
  • How to do the same geometric transformation before and after FPN(input)?

    How to do the same geometric transformation before and after FPN(input)?

    Hi, thanks for your code. I have an issue, in the training processing, we need to do the same transformation for the two input: input1 = eqv_transform_if_needed(args, dataloader, indice, input1.cuda(non_blocking=True)) featmap1 = model(input1)

        input2 = input2.cuda(non_blocking=True)
    featmap2 = eqv_transform_if_needed(args, dataloader, indice, model(input2))

    however, when the input2 image is passed from the FPN model, it generates different dimensional output compared to the input1, how to make sure that these two transformations have the same effects?

    opened by JudgeLJX 2
  • The PiCIE pre-trained weights are not usable

    The PiCIE pre-trained weights are not usable

    When I try to untar the archive using tar -xvf picie_coco.tar, I get this error: tar: This does not look like a tar archive tar: Skipping to next header tar: Exiting with failure status due to previous errors

    I have also tried to extract with archive manager, without a terminal

    opened by vali19th 2
  • VOC2012 dataset with PICIE

    VOC2012 dataset with PICIE

    I tried to run PICIE with the VOC2012 dataset because of its small size, but the results were not good as I expected. Have you ever tried to run with VOC2012 before? Is there a kind of settings I should focus on? Thank you, it's a good work.

    === The best result ==== ACC - All: 30.4907 mIOU - All: 17.7580

    opened by khangt1k25 2
  • CE Weights After Labelling

    CE Weights After Labelling

    Hello, thanks for your work and making it public. In the compute_labels function, after centroid assignments are found, assignment counts are accumulated to get the CE weights due to possible imbalance. However, classes are weighted directly proportional to assignment counts but not inversely proportional. I feel the weights should be inversely related to the pixel counts if the aim is solving the imbalance. Are there any reasons to make it directly proportional?

    opened by gorkaydemir 0
Owner
Jang Hyun Cho
PhD student at UT Austin
Jang Hyun Cho
GitHub
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