The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track.

Overview

ISC21-Descriptor-Track-1st

The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track.

You can check our solution tech report from: Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection

setup

OS

Ubuntu 18.04

CUDA Version

11.1

environment

Run this for python env

conda env create -f environment.yml

data download

mkdir -p input/{query,reference,train}_images
aws s3 cp s3://drivendata-competition-fb-isc-data/all/query_images/ input/query_images/ --recursive --no-sign-request
aws s3 cp s3://drivendata-competition-fb-isc-data/all/reference_images/ input/reference_images/ --recursive --no-sign-request
aws s3 cp s3://drivendata-competition-fb-isc-data/all/train_images/ input/train_images/ --recursive --no-sign-request
aws s3 cp s3://drivendata-competition-fb-isc-data/all/query_images_phase2/ input/query_images_phase2/ --recursive --no-sign-request

train

Run below lines step by step.

cd exp

CUDA_VISIBLE_DEVICES=0,1,2,3 python v83.py \
  -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 9 \
  --epochs 5 --lr 0.1 --wd 1e-6 --batch-size 128 --ncrops 2 \
  --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.0 \
  --input-size 256 --sample-size 1000000 --memory-size 20000 \
  ../input/training_images/
CUDA_VISIBLE_DEVICES=0,1,2,3 python v83.py \
  -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 90 \
  --epochs 10 --lr 0.1 --wd 1e-6 --batch-size 128 --ncrops 2 \
  --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.0 \
  --input-size 256 --sample-size 1000000 --memory-size 20000 \
  --resume ./v83/train/checkpoint_0004.pth.tar \
  ../input/training_images/

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python v86.py \
  -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 99 \
  --epochs 7 --lr 0.1 --wd 1e-6 --batch-size 128 --ncrops 2 \
  --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.0 \
  --input-size 384 --sample-size 1000000 --memory-size 20000 --weight ./v83/train/checkpoint_0005.pth.tar \
  ../input/training_images/

python v98.py \
  -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 999 \
  --epochs 3 --lr 0.1 --wd 1e-6 --batch-size 64 --ncrops 2 \
  --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.0 --weight ./v86/train/checkpoint_0005.pth.tar \
  --input-size 512 --sample-size 1000000 --memory-size 20000 \
  ../input/training_images/

python v107.py \
  -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 99999 \
  --epochs 10 --lr 0.5 --wd 1e-6 --batch-size 16 --ncrops 2 \
  --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.1 --weight ./v98/train/checkpoint_0001.pth.tar \
  --input-size 512 --sample-size 1000000 --memory-size 1000 \
  ../input/training_images/

The final model weight can be downloaded from here: https://drive.google.com/file/d/1ySea-NJp_J0aWvma_WmVbc3Hnwf5LHUf/view?usp=sharing You can execute inference code without run training with this model weight. To locate the model weight to suitable location, run following commands after downloaded the model weight.

mkdir -p exp/v107/train
mv checkpoint_009.pth.tar exp/v107/train/

inference

Note that faiss doesn't work with A100, so I used 4x GTX 1080 Ti for post-process.

cd exp

python v107.py -a tf_efficientnetv2_m_in21ft1k --batch-size 128 --mode extract --gem-eval-p 1.0 --weight ./v107/train/checkpoint_0009.pth.tar --input-size 512 --target-set qrt ../input/

# this script generates final prediction result files
python ../scripts/postprocess.py

Submission files are outputted here:

  • exp/v107/extract/v107_iso.h5 # descriptor track
  • exp/v107/extract/v107_iso.csv # matching track

descriptor track local evaluation score:

{
  "average_precision": 0.9479039085717805,
  "recall_p90": 0.9192546583850931
}
Comments
  • Bugs?

    Bugs?

    Congratulations! We really appreciate the work. When I run the

    python v107.py \
      -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 99999 \
      --epochs 10 --lr 0.5 --wd 1e-6 --batch-size 16 --ncrops 2 \
      --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.1 --weight ./v98/train/checkpoint_0001.pth.tar \
      --input-size 512 --sample-size 1000000 --memory-size 1000 \
      ../input/training_images/
    

    I come across

    Traceback (most recent call last):                                              
      File "v107.py", line 774, in <module>
        train(args)
      File "v107.py", line 425, in train
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
      File "/home/wangwenhao/anaconda3/envs/ISC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn
        return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
      File "/home/wangwenhao/anaconda3/envs/ISC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes
        while not context.join():
      File "/home/wangwenhao/anaconda3/envs/ISC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 150, in join
        raise ProcessRaisedException(msg, error_index, failed_process.pid)
    torch.multiprocessing.spawn.ProcessRaisedException: 
    
    -- Process 5 terminated with the following error:
    Traceback (most recent call last):
      File "/home/wangwenhao/anaconda3/envs/ISC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 59, in _wrap
        fn(i, *args)
      File "/home/wangwenhao/fbisc-descriptor-1st/exp/v107.py", line 573, in main_worker
        train_one_epoch(train_loader, model, loss_fn, optimizer, scaler, epoch, args)
      File "/home/wangwenhao/fbisc-descriptor-1st/exp/v107.py", line 595, in train_one_epoch
        labels = torch.cat([torch.tile(i, dims=(args.ncrops,)), torch.tensor(j)])
    ValueError: only one element tensors can be converted to Python scalars
    

    Do you know how to fix it? Thanks.

    opened by WangWenhao0716 14
  • data augment is wrong

    data augment is wrong

    train_dataset = ISCDataset(
        train_paths,
        NCropsTransform(
            transforms.Compose(aug_moderate),
            transforms.Compose(aug_hard),
            args.ncrops,
        ),
    )
    

    error log: apply_transform() takes from 2 to 3 positional arguments but 5 were given

    opened by AItechnology 5
  • Cannot load state dict for model

    Cannot load state dict for model

    Thanks for your amazing work. But I encounter a problem, when I use checkpoint_0009.pth.tar checkpoint,

    • When I don't remove model = nn.DataParallel(model), I encouter error:
            size mismatch for module.backbone.bn1.weight: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is 
    torch.Size([64]).
            size mismatch for module.backbone.bn1.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
            size mismatch for module.backbone.bn1.running_mean: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
            size mismatch for module.backbone.bn1.running_var: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
            size mismatch for module.fc.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([256, 2048])
    
    • Then I remove line model = nn.DataParallel(model), the model seems to load checkpoint successfully, but I feed same input to model, the output feature vector if different for different time I run. I guess the model is not loaded successfully when load state dict, so model will use the weight initialized randomly.
    • Then I change strict=True in model.load_state_dict(state_dict=state_dict, strict=False), I encounter error RuntimeError: Error(s) in loading state_dict for ISCNet: Missing key(s) in state_dict:, I found that the key of state_dict in model and checkpoint totally diffrent even name pattern. Key of model state dict and checkpoint state dict I attached below. checkpoint.txt model.txt How can I solve the this problem?
    opened by NguyenThanhAI 2
  • Unable to reproduce Stage 1 results

    Unable to reproduce Stage 1 results

    Hi, I attempted to reproduce the Stage 1 training using your provided code, but was unable to obtain the reported muAP of 0.5831. I instead obtained this result at epoch 9 (indexed from 0):

    Average Precision: 0.49554
    Recall at P90    : 0.32701
    Threshold at P90 : -0.375733
    Recall at rank 1:  0.62448
    Recall at rank 10: 0.65961
    

    I also saw that you continued training from epoch 5, but these are the results I obtained at epoch 5:

    Average Precision: 0.47977
    Recall at P90    : 0.32501
    Threshold at P90 : -0.376619
    Recall at rank 1:  0.61409
    Recall at rank 10: 0.64903
    

    Both sets of results were obtained on the private ground truth set of Phase 1, using image size 512. Is it possible to provide some insight as to what is happening here? Thank you.

    opened by avrilwongaw 1
  • about the train output feature

    about the train output feature

    sorry to bother you again. I want train the model with a small backbone such as resnet50. Because I only have three GPU and I run with command:

    CUDA_VISIBLE_DEVICES=0,1,2 python v83.py  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 9 \
      --epochs 5 --lr 0.1 --wd 1e-6 --batch-size 96 --ncrops 2 \
      --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.0 \
      --input-size 256 --sample-size 1000000 --memory-size 20000 \
    /root/zhx3/data/fb_train_data/train
    

    I find a strange problem. I test checkpoint_000{0..4}.pth.tar model. only the checkpoint_0002.pth.tar ouput different when the input is different. I mean other model will output same embedding no matter what different you input. thanks in advance. the loss log output such as:

    epoch 5:   0%|          | 0/15873 [00:00<?, ?it/s]=> loading checkpoint './v83/train/checkpoint_0004.pth.tar'
    => loaded checkpoint './v83/train/checkpoint_0004.pth.tar' (epoch 5)
    epoch 6:   0%|          | 0/15873 [00:00<?, ?it/s]epoch=5, loss=1.0154363534772417
    epoch 7:   0%|          | 0/15873 [00:00<?, ?it/s]epoch=6, loss=1.012835873522891
    
    opened by Usernamezhx 1
  • about the memory size

    about the memory size

    python v107.py \
      -a tf_efficientnetv2_m_in21ft1k --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 99999 \
      --epochs 10 --lr 0.5 --wd 1e-6 \
      --gem-p 1.0 --pos-margin 0.0 --neg-margin 1.1 --weight ./v98/train/checkpoint_0001.pth.tar \
      --input-size 512 --sample-size 1000000 --memory-size 1000 \
      ../input/training_images/
    

    why not set the --memory-size large such as 20000 ? thanks in advance

    opened by Usernamezhx 1
  • will v107 overfit for phase2?

    will v107 overfit for phase2?

    Congratulations and thanks for your sharing.

    i find v107 only use the about 5k query-ref pair (i.e. gt in phase1) as positive. How to know whether it overfits for phase2 ?

    opened by liangzimei 1
  • access denied for dataset on aws

    access denied for dataset on aws

    Thanks for you work! I have problems downloading the dataset from the given aws buckets

    $ aws s3 cp s3://drivendata-competition-fb-isc-data/all/query_images/ input/query_images/ --recursive --no-sign-request
    fatal error: An error occurred (AccessDenied) when calling the ListObjectsV2 operation: Access Denied
    

    Do I need special permissions to download the data?

    opened by sebastianlutter 0
  • Final optimizer state for the model

    Final optimizer state for the model

    Hello @lyakaap

    Thanks a lot for this work. I am trying to take this and finetune over a certain task. Is it possible you can provide the state of final optimizer after 4th stage of training. We want to try an experiment where it will be very useful.

    Thank you.

    opened by shubhamjain0594 11
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lyakaap
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lyakaap
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