Age | Commit message (Collapse) | Author | |
---|---|---|---|
2020-12-31 | Make HPM capable of processing frames in all batches | Jordan Gong | |
2020-12-31 | Make super class constructor revoke consistent | Jordan Gong | |
2020-12-31 | Bug Fixes in HPM and PartNet | Jordan Gong | |
1. Register list of torch.nn.Module to the network using torch.nn.ModuleList 2. Fix operation error in squeeze list of tensor 3. Replace squeeze with view in HP in case batch size is 1 | |||
2020-12-30 | Correct and refine PartNet | Jordan Gong | |
1. Let FocalConv block capable of processing frames in all batches 2. Correct input dims of TFA and output dims of HP 3. Change torch.unsqueeze and torch.cat to torch.stack | |||
2020-12-30 | Combine FPFE and TFA to PartNet | Jordan Gong | |
2020-12-30 | Combine FPFE and TFA to GaitPart | Jordan Gong | |
2020-12-30 | Add pooling options in HPM | Jordan Gong | |
According to [1], we can use GAP and GMP together, or one of both in ablation study. [1]Y. Fu et al., “Horizontal pyramid matching for person re-identification,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 8295–8302. | |||
2020-12-29 | Return canonical features at condition 1 for later aggregation | Jordan Gong | |
2020-12-29 | Correct batch splitter | Jordan Gong | |
We can disentangle features from different subjects, but cannot do it at different temporal orders | |||
2020-12-29 | Add type hint for new label (numpy.int64) | Jordan Gong | |
2020-12-29 | Encode class names to label and some access improvement | Jordan Gong | |
1. Encode class names using LabelEncoder from sklearn 2. Remove unneeded class variables 3. Protect some variables from being accessed in userspace | |||
2020-12-28 | Wrap the auto-encoder, return 3 losses at t2 | Jordan Gong | |
2020-12-27 | Try some unit tests on CASIA-B dataset | Jordan Gong | |
2020-12-27 | Change default dataset directory | Jordan Gong | |
2020-12-27 | Implement some parts of main model structure | Jordan Gong | |
1. Configuration parsers 2. Model signature generator | |||
2020-12-27 | Fix inconsistency and API deprecation issues in decoder | Jordan Gong | |
1. Add default output channels of decoder 2. Replace deprecated torch.nn.functional.sigmoid with torch.sigmoid | |||
2020-12-27 | Refine auto-encoder | Jordan Gong | |
1. Wrap fully connected layers 2. Introduce hyperparameter tuning in constructor | |||
2020-12-27 | Prepare for FVG dataset | Jordan Gong | |
2020-12-27 | Make naming scheme consistent | Jordan Gong | |
Use `dir` instead of `path` | |||
2020-12-27 | Add dataset selector to config type hint, change ClipLabels typo to ClipViews | Jordan Gong | |
2020-12-27 | Adopt type hinting generics in standard collections (PEP 585) | Jordan Gong | |
2020-12-26 | Implement batch splitter to split sampled data | Jordan Gong | |
Disentanglement cannot be processed on different subjects at the same time, we need to load `pr` subjects one by one. The batch splitter will return a pr-length list of tuples (with 2 dicts containing k-length lists of labels, conditions, view and k-length tensor of clip data, representing condition 1 and condition 2 respectively). | |||
2020-12-26 | Sample k more clips for disentanglement | Jordan Gong | |
2020-12-26 | Add config file and corresponding type hint | Jordan Gong | |
2020-12-26 | Combine transformed height and width to `frame_size` | Jordan Gong | |
2020-12-26 | Bump up version for tqdm | Jordan Gong | |
2020-12-24 | Implement Horizontal Pyramid Matching (HPM) | Jordan Gong | |
2020-12-24 | Optimize imports | Jordan Gong | |
2020-12-24 | Change the usage of layers and reorganize relations of layers | Jordan Gong | |
1. Add batch normalization and activation to layers 2. VGGConv2d and FocalConv2d inherits to BasicConv2d; DCGANConvTranspose2d inherits to BasicConvTranspose2d | |||
2020-12-23 | Make activation inplace | Jordan Gong | |
2020-12-23 | Modify activation functions after conv or trans-conv in auto-encoder | Jordan Gong | |
1. Make activation functions be inplace ops 2. Change Leaky ReLU to ReLU in decoder | |||
2020-12-23 | Refactor and refine auto-encoder | Jordan Gong | |
1. Wrap Conv2d 3x3-padding-1 to VGGConv2d 2. Wrap ConvTranspose2d 4x4-stride-4-padding-1 to DCGANConvTranspose2d 3. Turn off bias in conv since the employment of batch normalization | |||
2020-12-23 | Wrap Conv1d no bias layer | Jordan Gong | |
2020-12-23 | Reshape feature before decode | Jordan Gong | |
2020-12-23 | Remove redundant Leaky ReLU in FocalConv2d | Jordan Gong | |
2020-12-23 | Split modules to different files | Jordan Gong | |
2020-12-22 | Remove unused Matplotlib dependency | Jordan Gong | |
2020-12-22 | Implement prototype layers | Jordan Gong | |
2020-12-22 | Enforce PEP 8 length limit | Jordan Gong | |
2020-12-21 | Change image loading technique | Jordan Gong | |
1. Use Pillow.Image.open instead of torchvision.io.read_image to read image 2. Transforming PIL images instead of tensors which performs better and device option is removed 3. Images are normalized now | |||
2020-12-19 | 1. Delete unused transform function | Jordan Gong | |
2. Reorganize the initialization cache dicts | |||
2020-12-19 | Fix indexing error when no clip to be discard | Jordan Gong | |
2020-12-19 | Add cache switch, allowing load all data into RAM before sampling | Jordan Gong | |
2020-12-18 | Add torch and numpy to requirements.txt | Jordan Gong | |
2020-12-18 | Implement triplet sampler | Jordan Gong | |
2020-12-18 | Implement CASIA-B dataset | Jordan Gong | |
2020-12-16 | Ignore dataset | Jordan Gong | |
2020-12-16 | Init the repo | Jordan Gong | |