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Train

All the training details (hyper-parameters, trained layers, backbones, etc.) strictly follow the original MatConvNet version of NetVLAD and SARE. Note: the results of all three methods (SFRS, NetVLAD, SARE) can be reproduced by training on Pitts30k-train and directly testing on the other datasets.

The default scripts adopt 4 GPUs (require ~11G per GPU) for training, where each GPU loads one tuple (anchor, positive(s), negatives).

  • In case you want to fasten training, enlarge GPUS for more GPUs, or enlarge the --tuple-size for more tuples on one GPU;
  • In case your GPU does not have enough memory (e.g. <11G), reduce --pos-num (only for SFRS) or --neg-num for fewer positives or negatives in one tuple.

PyTorch launcher: single-node multi-gpu distributed training

NetVLAD:

./scripts/train_baseline_dist.sh triplet

SARE:

./scripts/train_baseline_dist.sh sare_ind
# or
./scripts/train_baseline_dist.sh sare_joint

SFRS (state-of-the-art):

./scripts/train_sfrs_dist.sh

Slurm launcher: single/multi-node multi-gpu distributed training

Change GPUS and GPUS_PER_NODE accordingly in the scripts for your need.

NetVLAD:

./scripts/train_baseline_slurm.sh <PARTITION NAME> triplet

SARE:

./scripts/train_baseline_slurm.sh <PARTITION NAME> sare_ind
# or
./scripts/train_baseline_slurm.sh <PARTITION NAME> sare_joint

SFRS (state-of-the-art):

./scripts/train_sfrs_slurm.sh <PARTITION NAME>

Test

Trained models can be found in MODEL_ZOO.md.

During testing, the python scripts will automatically compute the PCA weights from Pitts30k-train or directly load from local files. Generally, model_best.pth.tar which is selected by validation in the training performs the best.

The default scripts adopt 8 GPUs (require ~11G per GPU) for testing.

  • In case you want to fasten testing, enlarge GPUS for more GPUs, or enlarge the --test-batch-size for larger batch size on one GPU, or add --sync-gather for faster gathering from multiple threads;
  • In case your GPU does not have enough memory (e.g. <11G), reduce --test-batch-size for smaller batch size on one GPU.

PyTorch launcher: single-node multi-gpu distributed testing

Pitts250k-test:

./scripts/test_dist.sh <PATH TO MODEL> pitts 250k

Pitts30k-test:

./scripts/test_dist.sh <PATH TO MODEL> pitts 30k

Tokyo 24/7:

./scripts/test_dist.sh <PATH TO MODEL> tokyo

Slurm launcher: single/multi-node multi-gpu distributed testing

Pitts250k-test:

./scripts/test_slurm.sh <PARTITION NAME> <PATH TO MODEL> pitts 250k

Pitts30k-test:

./scripts/test_slurm.sh <PARTITION NAME> <PATH TO MODEL> pitts 30k

Tokyo 24/7:

./scripts/test_slurm.sh <PARTITION NAME> <PATH TO MODEL> tokyo