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2021-03-01Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/model.py
2021-03-01New scheduler and new configJordan Gong
2021-03-01Merge branch 'master' into python3.8Jordan Gong
2021-03-01Change flat distance calculation methodJordan Gong
2021-03-01Merge branch 'master' into python3.8Jordan Gong
2021-03-01Move pairs variable to localJordan Gong
2021-03-01Remove identical sample in Batch All caseJordan Gong
2021-02-28Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # utils/configuration.py # utils/triplet_loss.py
2021-02-28Implement sum of loss default in [1]Jordan Gong
[1]A. Hermans, L. Beyer, and B. Leibe, “In defense of the triplet loss for person re-identification,” arXiv preprint arXiv:1703.07737, 2017.
2021-02-28Log n-ile embedding distance and normJordan Gong
2021-02-28Modify default parametersJordan Gong
1. Change ReLU to Leaky ReLU in decoder 2. Add 8-scale-pyramid in HPM
2021-02-28Bump up version for tqdmJordan Gong
2021-02-27Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/model.py # utils/configuration.py # utils/triplet_loss.py
2021-02-27Implement Batch Hard triplet loss and soft marginJordan Gong
2021-02-26Merge branch 'master' into python3.8Jordan Gong
2021-02-26Update default configJordan Gong
2021-02-26Fix predict functionJordan Gong
2021-02-21Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/part_net.py # models/rgb_part_net.py
2021-02-21Remove FConv blocksJordan Gong
2021-02-20Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/model.py # models/rgb_part_net.py
2021-02-20Separate triplet loss from modelJordan Gong
2021-02-19Merge branch 'master' into python3.8Jordan Gong
2021-02-19Allow evaluate unfinished modelJordan Gong
2021-02-19Merge branch 'master' into python3.8Jordan Gong
2021-02-19Bump up tqdm versionJordan Gong
2021-02-18Merge branch 'master' into python3.8Jordan Gong
2021-02-18Implement adjustable input size and change some default configsJordan Gong
2021-02-18Remove 1x1 conv layers when not usedJordan Gong
2021-02-18Decode mean appearance featureJordan Gong
2021-02-18Decode mean appearance featureJordan Gong
2021-02-17Type hint fixesJordan Gong
2021-02-17Merge branch 'master' into python3.8Jordan Gong
2021-02-17Fix type hints and add constrains to height and widthJordan Gong
2021-02-17Add new preprocess scriptJordan Gong
2021-02-16Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/model.py
2021-02-16Split transform and evaluate methodJordan Gong
2021-02-15Revert "Memory usage improvement"Jordan Gong
This reverts commit be508061
2021-02-15Revert "Memory usage improvement"Jordan Gong
This reverts commit be508061
2021-02-14Merge branch 'master' into python3.8Jordan Gong
2021-02-14Memory usage improvementJordan Gong
This update separates input data to two batches, which reduces ~30% memory usage.
2021-02-14Prepare for DataParallelJordan Gong
2021-02-13Sum gallery dimension instead of all dimensionsJordan Gong
2021-02-10Merge branch 'master' into python3.8Jordan Gong
2021-02-10Implement new sampling technique mentioned in GaitPart[1]Jordan Gong
[1]C. Fan et al., “GaitPart: Temporal Part-Based Model for Gait Recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14225–14233.
2021-02-10Save scheduler state_dictJordan Gong
2021-02-09Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/rgb_part_net.py
2021-02-09Improve performance when disentanglingJordan Gong
This is a HUGE performance optimization, up to 2x faster than before. Mainly because of the replacement of randomized for-loop with randomized tensor.
2021-02-09Some optimizationsJordan Gong
1. Scheduler will decay the learning rate of auto-encoder only 2. Write learning rate history to tensorboard 3. Reduce image log frequency
2021-02-08Merge branch 'master' into python3.8Jordan Gong
# Conflicts: # models/hpm.py # models/layers.py # models/model.py # models/rgb_part_net.py # utils/configuration.py
2021-02-08Code refactoring, modifications and new featuresJordan Gong
1. Decode features outside of auto-encoder 2. Turn off HPM 1x1 conv by default 3. Change canonical feature map size from `feature_channels * 8 x 4 x 2` to `feature_channels * 2 x 16 x 8` 4. Use mean of canonical embeddings instead of mean of static features 5. Calculate static and dynamic loss separately 6. Calculate mean of parts in triplet loss instead of sum of parts 7. Add switch to log disentangled images 8. Change default configuration
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