gait-recognition.git - Unnamed repository; edit this file 'description' to name the repository.

Age	Commit message (Collapse)	Author
2021-02-27	Implement Batch Hard triplet loss and soft margin	Jordan Gong

2021-02-26	Fix predict function	Jordan Gong

2021-02-21	Remove FConv blocks	Jordan Gong

2021-02-20	Separate triplet loss from model	Jordan Gong

2021-02-18	Implement adjustable input size and change some default configs	Jordan Gong

2021-02-18	Decode mean appearance feature	Jordan Gong

2021-02-18	Decode mean appearance feature	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

2021-02-09	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.
2021-02-08	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	Transform all frames together in evaluation	Jordan Gong

2021-01-21	Print average losses after 100 iters	Jordan Gong

2021-01-09	Add prototype predict function	Jordan Gong

2021-01-09	Change auto-encoder input in evaluation	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
2021-01-06	Add TensorBoard support	Jordan Gong

2021-01-05	Implement Batch All Triplet Loss	Jordan Gong

2021-01-05	Change and improve weight initialization	Jordan Gong
	1. Change initial weights for Conv layers 2. Find a way to init last fc in init_weights
2021-01-03	Separate last fc matrix from weight init function	Jordan Gong
	Recursive apply will override other parameters too
2021-01-03	Implement weight initialization	Jordan Gong

2021-01-03	Update hyperparameter configuration, implement prototype fit function	Jordan Gong

2021-01-03	Add separate fully connected layers	Jordan Gong

2021-01-02	Separate training and evaluating	Jordan Gong

2021-01-02	Correct feature dims after disentanglement and HPM backbone removal	Jordan Gong
	1. Features used in HPM is decoded canonical embedding without transpose convolution 2. Decode pose embedding to image for Part Net 3. Backbone seems to be redundant, we can use feature map given by auto-decoder
2021-01-02	Change type of pose similarity loss to tensor	Jordan Gong

2020-12-31	Implement some parts of RGB-GaitPart wrapper	Jordan Gong
	1. Triplet loss function and weight init function haven't been implement yet 2. Tuplize features returned by auto-encoder for later unpack 3. Correct comment error in auto-encoder 4. Swap batch_size dim and time dim in HPM and PartNet in case of redundant transpose 5. Find backbone problems in HPM and disable it temporarily 6. Make feature structure by HPM consistent to that by PartNet 7. Fix average pooling dimension issue and incorrect view change in HP