Age | Commit message (Collapse) | Author | |
---|---|---|---|
2021-02-18 | Merge branch 'master' into data_parallel | Jordan Gong | |
2021-02-18 | Implement adjustable input size and change some default configs | Jordan Gong | |
2021-02-18 | Remove 1x1 conv layers when not used | Jordan Gong | |
2021-02-18 | Decode mean appearance feature | Jordan Gong | |
2021-02-18 | Decode mean appearance feature | Jordan Gong | |
2021-02-17 | Merge branch 'master' into data_parallel | Jordan Gong | |
2021-02-17 | Fix type hints and add constrains to height and width | Jordan Gong | |
2021-02-17 | Add new preprocess script | Jordan Gong | |
2021-02-16 | Merge branch 'master' into data_parallel | Jordan Gong | |
2021-02-16 | Split transform and evaluate method | Jordan Gong | |
2021-02-15 | Add DataParallel support on new codebase | Jordan Gong | |
2021-02-15 | Revert "Memory usage improvement" | Jordan Gong | |
This reverts commit be508061 | |||
2021-02-14 | Memory usage improvement | Jordan Gong | |
This update separates input data to two batches, which reduces ~30% memory usage. | |||
2021-02-14 | Prepare for DataParallel | Jordan Gong | |
2021-02-13 | Sum gallery dimension instead of all dimensions | Jordan Gong | |
2021-02-10 | Implement 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. | |||
2021-02-10 | Save scheduler state_dict | Jordan Gong | |
2021-02-09 | Improve performance when disentangling | Jordan 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. | |||
2021-02-09 | Some optimizations | Jordan Gong | |
1. Scheduler will decay the learning rate of auto-encoder only 2. Write learning rate history to tensorboard 3. Reduce image log frequency | |||
2021-02-08 | Code refactoring, modifications and new features | Jordan 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 | |||
2021-01-23 | Remove the third term in canonical consistency loss | Jordan Gong | |
2021-01-23 | Add late start support for non-disentangling parts | Jordan Gong | |
2021-01-23 | Transform all frames together in evaluation | Jordan Gong | |
2021-01-23 | Evaluation bug fixes and code review | Jordan Gong | |
1. Return full cached clip in evaluation 2. Add multi-iter checkpoints support in evaluation 3. Remove duplicated code while transforming | |||
2021-01-22 | Handle unexpected restore iter | Jordan Gong | |
1. Skip finished model before load it 2. Raise error when restore iter is greater than total iter | |||
2021-01-21 | Print average losses after 100 iters | Jordan Gong | |
2021-01-21 | Bug fixes | Jordan Gong | |
1. Turn off autograd while decoding canonical and pose features 2. Change default batch size to (4, 8) | |||
2021-01-20 | Bump up version for scikit-learn | Jordan Gong | |
2021-01-14 | Enable optimizer fine tuning | Jordan Gong | |
2021-01-14 | Remove DataParallel | Jordan Gong | |
2021-01-13 | Tensorboard typo fix | Jordan Gong | |
2021-01-13 | Add server startup script | Jordan Gong | |
2021-01-13 | Update config file and convert int to str when joining | Jordan Gong | |
2021-01-13 | Add multiple checkpoints for different model and set default config value | Jordan Gong | |
2021-01-12 | Move the model to GPU before constructing optimizer | Jordan Gong | |
2021-01-12 | Some changes in hyperparameter config | Jordan Gong | |
1. Separate hyperparameter configs in model, optimizer and scheduler 2. Add more tunable hyperparameters in optimizer and scheduler | |||
2021-01-12 | Some type hint fixes | Jordan Gong | |
2021-01-12 | Print result after evaluation | Jordan Gong | |
2021-01-12 | Typo correct in evaluate function | Jordan Gong | |
2021-01-12 | Bump up version for tqdm | Jordan Gong | |
2021-01-11 | Add evaluation script, code review and fix some bugs | Jordan Gong | |
1. Add new `train_all` method for one shot calling 2. Print time used in 1k iterations 3. Correct label dimension in predict function 4. Transpose distance matrix for convenient indexing 5. Sort dictionary before generate signature 6. Extract visible CUDA setting function | |||
2021-01-11 | Implement evaluator | Jordan Gong | |
2021-01-10 | Make predict function transform samples different conditions in a single shot | Jordan Gong | |
2021-01-09 | Add prototype predict function | Jordan Gong | |
2021-01-09 | Change auto-encoder input in evaluation | Jordan Gong | |
2021-01-09 | Fix NaN when separate sum is zero | Jordan Gong | |
2021-01-07 | Train different models in different conditions | Jordan Gong | |
2021-01-07 | Add typical training script and some bug fixes | Jordan Gong | |
1. Resolve deprecated scheduler stepping issue 2. Make losses in the same scale(replace mean with sum in separate triplet loss, enlarge pose similarity loss 10x) 3. Add ReLU when compute distance in triplet loss 4. Remove classes except Model from `models` package init | |||
2021-01-07 | Change device config and add enable multi-GPU computing | Jordan Gong | |
1. Add `disable_acc` switch for disabling accelerator. When it is off, system will automatically choosing accelerator. 2. Enable multi-GPU training using torch.nn.DataParallel | |||
2021-01-06 | Add CUDA support | Jordan Gong | |