1.0.0 official release (#18)
* fix bug in fix_BN * gaitgl OUMVLP support. * update ./doc/3.advance_usage.md Cross-Dataset Evalution & Data Agumentation * update config * update docs.3 * update docs.3 * add loss doc and gather input decorator * refine the create model doc * support rearrange directory of unzipped OUMVLP * fix some bugs in loss_aggregator.py * refine docs and little fix * add oumvlp pretreatment description * pretreatment dataset fix oumvlp description * add gaitgl oumvlp result * assert gaitgl input size * add pipeline * update the readme. * update pipeline and readme * Corrigendum. * add logo and remove path * update new logo * Update README.md * modify logo size Co-authored-by: 12131100 <12131100@mail.sustech.edu.cn> Co-authored-by: noahshen98 <77523610+noahshen98@users.noreply.github.com> Co-authored-by: Noah <595311942@qq.com>
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# Note : *** the batch_size should be equal to the gpus number at the test phase!!! ***
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data_cfg:
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dataset_name: OUMVLP
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dataset_root: your_path
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dataset_partition: ./misc/partitions/OUMVLP.json
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num_workers: 1
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remove_no_gallery: false
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test_dataset_name: OUMVLP
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evaluator_cfg:
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enable_distributed: true
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enable_float16: false
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restore_ckpt_strict: true
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restore_hint: 210000
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save_name: GaitGL
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sampler:
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batch_size: 2
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sample_type: all_ordered
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type: InferenceSampler
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loss_cfg:
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- loss_term_weights: 1.0
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margin: 0.2
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type: TripletLoss
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log_prefix: triplet
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- loss_term_weights: 1.0
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scale: 1
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type: CrossEntropyLoss
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log_accuracy: true
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label_smooth: true
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log_prefix: softmax
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model_cfg:
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model: GaitGL
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channels: [32, 64, 128, 256]
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class_num: 5153
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optimizer_cfg:
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lr: 1.0e-4
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solver: Adam
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weight_decay: 0
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scheduler_cfg:
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gamma: 0.1
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milestones:
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- 150000
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- 200000
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scheduler: MultiStepLR
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trainer_cfg:
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enable_distributed: true
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enable_float16: true
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with_test: false
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log_iter: 100
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restore_ckpt_strict: true
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restore_hint: 0
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save_iter: 10000
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save_name: GaitGL
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sync_BN: true
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total_iter: 210000
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sampler:
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batch_shuffle: true
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batch_size:
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- 32
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- 8
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frames_num_fixed: 30
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frames_skip_num: 0
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sample_type: fixed_ordered
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type: TripletSampler
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