18a33f7962
feat: add MGN model
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support MGN architecture and training config
2020-05-15 11:39:54 +08:00
bf18479541
fix: revise syncBN bug
2020-05-14 14:52:37 +08:00
0872a32621
feat: add syncBN support
2020-05-14 13:15:09 +08:00
0356ef8c5c
feat: add SyncBN and GroupNorm suppor
2020-05-14 11:36:28 +08:00
5ae3d4fecf
feat: add aqe support in test phase
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query expansion will combine the retrived topk nearest neighbors with the original query feature,
it will enhance mAP by a large margin.feat:
2020-05-13 16:27:22 +08:00
320010f2ae
feat: support re-rank in test phase
2020-05-13 11:47:52 +08:00
01d940bfdd
update vehicle dataset
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Summary: Pull Request resolved : #49
Reviewed By: xingyu liao
2020-05-12 21:44:05 +08:00
9addfb0ae2
feat: support visualizing label list
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add features to support label list visualization, which can be used
for label correction or check the hardest sample
2020-05-12 21:35:33 +08:00
640c9bfc97
Add small, medium and large vehicle test datasets
2020-05-11 01:34:19 -05:00
e059b751a6
Add modified vehicle datasets with small, medium and large test datastes
2020-05-11 01:30:56 -05:00
9b6fda3830
style: remove title in visualization
2020-05-11 14:12:29 +08:00
13bb03eb07
feat: add rank result visualization tools
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Update visualization tools which can save rank list with AP metrics from high to low, vice versa.
In order to compute AP fast in visualizer, modify rank_cylib to get all_AP instead of mAP.
In this way, we can use Cython to compute results.
2020-05-10 23:17:10 +08:00
651e6ba9c4
feat: support multiprocess predictor
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add asyncpredictor to support multiprocessing feature extraction with dataloader
2020-05-09 18:23:36 +08:00
4be4cacb73
fix: add a simple way to reset data prefetcher when resume training
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use data prefetcher build-in reset function to reload it rather than
redefining a new data prefetcher, otherwise it will introduce other
problems in eval-only mode.
2020-05-09 11:58:27 +08:00
9fae467adf
feat(engine/defaults): add DefaultPredictor to get image reid features
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Add a new predictor interface, and modify demo code to predict image features.
2020-05-08 19:24:27 +08:00
8ab0bc2455
style(backbone): make parameters loading logging more elegant
2020-05-08 12:22:06 +08:00
0b15ac4e03
feat(hooks&optim): update stochastic weight averging hooks
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Update swa method which will do after regular training if you
set this option enabled.
2020-05-08 12:20:04 +08:00
afac8aad5d
Fix(engine): fix preciseBN dataloader bugs
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preciseBN needs to pass data prefetcher, but now a DataLoader is passed
2020-05-06 14:26:34 +08:00
948af64fd1
feat: add swa algorithm
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Add swa and related config options,
if it is enabled, model will do swa after regular training
2020-05-06 10:17:44 +08:00
6d96529d4c
fix(data): fix resume training bug
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fix dataset pid dictionary loading bug when resume training,
data prefetcher will pre-load a batch of data, this will lead to
misalignment of old pid dict and updated pid dict.
We can address this problem by redefine a prefetcher in resume_or_load
2020-05-05 23:20:42 +08:00
a2dcd7b4ab
feat(layers/norm): add ghost batchnorm
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add a get_norm fucntion to easily change normalization between batchnorm, ghost bn and group bn
2020-05-01 09:02:46 +08:00
329764bb60
refactor(heads): move num_classes out from heads
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set parameter num_classes in meta_arch to easily modify different heads fc layer
2020-04-29 21:29:48 +08:00
d27729c5bb
refactor(preciseBN): add preciseBN datasets show
2020-04-29 21:05:53 +08:00
ec19bcc1d3
style(configs): put all config files together
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put all config files into one place for easily control,
and add tools for put train_net.py which almost the same in
different projects
2020-04-29 16:18:54 +08:00
e38a799b63
fix(engine/defaults): fix precise bn bug
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fix problem in precise bn, which will not use precise bn datasets, and throw some errors
2020-04-29 16:16:54 +08:00
2f3f6e3267
Add files via upload
2020-04-27 16:27:44 +08:00
5daf322ac6
fix(data/samplers): fix bug in triplet sampler
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use drop indices to avoid two groups of same id,
but did not consider drop indices = 0, then will lead to
indentites[:0], this is a no teminal loop
2020-04-27 15:25:29 +08:00
325d9abb76
feat($solver): change scheduler call methods
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using name of lr scheduler in config to call
2020-04-27 15:12:01 +08:00
9e3f2c1e7a
fix($data/transforms): change augmix augmentation pool
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change augmentation_all from augmentation_reid in augmix
because we found AutoAugmentation using ImageNet Policy will not harm performance
2020-04-27 15:06:27 +08:00
4d3e5fd378
refactor(evaluation): add feature l2 norm in evaluation
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change the l2 norm function from inference function in Module to reid evaluation.
because sometimes we need to use the original features generated by model rather than normalized ones.
2020-04-27 14:51:39 +08:00
9910bb9158
fix($modeling/heads): fix targets missing bug
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fix bug in heads about return outputs without targets.
2020-04-27 14:49:58 +08:00
2efbc6d371
fix($modeling/heads/bnneck_head): fix heads outputs bug
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fix bug of heads outputs, which will lead to no targets return.
2020-04-27 11:48:21 +08:00
a6bd0371e2
feat($data): add autoaugment
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add auto augmentation support with ImageNet policy and CIFAR10 policy.
modify codes in transforms and config for adapting to this augmentation.
2020-04-27 11:41:12 +08:00
8abd3bab03
feat($layers): add new act func
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add mish, gelu supported
2020-04-24 12:17:00 +08:00
3984f0c91d
refactor($modeling/meta): refactor heads output
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without intermediate variables generated by reid heads, make it more flexible
2020-04-24 12:16:18 +08:00
e3ae03cc58
feat($modeling/backbones): add new backbones
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add osnet, resnext and resnest backbone supported
2020-04-24 12:14:56 +08:00
b098b194ba
refactor($modeling/meta_arch): remove bdb_network
2020-04-21 11:44:29 +08:00
6c9af664dc
refactor($modeling/meta_arch): remove useless parts
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remove useless meta_archs and backbones
2020-04-21 11:42:14 +08:00
bb50b6c5a7
docs($projects): update agw readme
2020-04-21 11:35:54 +08:00
95a3c62ad2
refactor(fastreid)
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refactor architecture
2020-04-20 10:59:29 +08:00
9684500a57
chagne arch
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1. change dataset show to trainset show and testset show seperately
2. add cls layer to easily plug in circle loss and arcface
2020-04-19 12:54:01 +08:00
be9faa5605
update focal loss
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update dataset info display
update seperate lr
update adaptive label smooth regularization
2020-04-17 13:46:10 +08:00
9cf222e093
refactor bn_no_bias
2020-04-08 21:04:09 +08:00
4d2fa28dbb
update freeze layer
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update preciseBN
update circle loss with metric learning and cross entropy loss form
update loss call methods
2020-04-06 23:34:27 +08:00
6a8961ce48
1. upload circle loss and arcface
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2. finish freeze training
3. update augmix data augmentation
2020-04-05 23:54:26 +08:00
c6e0176c53
Upload demo.py and example
2020-04-03 15:07:27 +08:00
eacee874aa
fix merge
2020-03-25 11:06:39 +08:00
91dc9bc71f
Merge branch 'master' of github.com:L1aoXingyu/fast-reid
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Conflicts:
fastreid/config/defaults.py
fastreid/layers/gem_pool.py
fastreid/modeling/backbones/resnet.py
fastreid/modeling/heads/__init__.py
fastreid/modeling/heads/build.py
fastreid/modeling/losses/build.py
fastreid/modeling/meta_arch/__init__.py
fastreid/modeling/meta_arch/abd_network.py
fastreid/modeling/meta_arch/baseline.py
fastreid/modeling/meta_arch/bdb_network.py
fastreid/modeling/meta_arch/mf_network.py
projects/StrongBaseline/configs/Base-Strongbaseline.yml
projects/StrongBaseline/configs/baseline_dukemtmc.yml
projects/StrongBaseline/train_net.py
2020-03-25 11:05:28 +08:00
23bedfce12
update version0.2 code
2020-03-25 10:58:26 +08:00
b1058118ca
update BDB-net code
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update MF-net code
2020-03-19 12:23:41 +08:00
liaoxingyu
Jinkai Zheng
zjk15068083791