add BiggerGait
This commit is contained in:
bugjudger
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data_cfg:
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dataset_name: CCPG
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# TODO
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dataset_root: your_path # use datasets/pretreatment_rgb.py for data preprocessing!
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dataset_partition: ./datasets/CCPG/CCPG.json
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data_in_use: [True, False] # images / real_ratios
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num_workers: 8
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: CCPG
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: False # Rename some module name for clarity, so it is fasle.
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restore_hint: 30000 # BiggerGait__SmallDINOv2_Gaitbase_84Frame30_448224_6432HPP32_NoAlign_Sep12B_WiMask-30000.pt in HuggingFace
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save_name: BiggerGait__Dinov2
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eval_func: evaluate_CCPG
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sampler:
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batch_shuffle: false
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batch_size: 8 # GPUs number
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sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
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frames_all_limit: 250 # limit the number of sampled frames to prevent out of memory
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metric: euc # cos
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transform:
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- type: BaseRgbTransform
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loss_cfg:
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- loss_term_weight: 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_weight: 1.0
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scale: 16
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type: CrossEntropyLoss
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log_prefix: softmax
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log_accuracy: true
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model_cfg:
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model: BiggerGait__DINOv2
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pretrained_lvm: ./pretrained_LVMs/dinov2-small # DINOv2-S HuggingFace Link: "git clone https://huggingface.co/facebook/dinov2-small" + "git lfs pull"
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pretrained_mask_branch: ./pretrained_LVMs/MaskBranch_vits14.pt # Using BigGait Code for pretraining at first, it is very lightly. pretrained_mask_branch: None or MaskBranch Download Link: https://drive.google.com/drive/folders/1zrWPUsrbCpwxoLgfom3d2irgxkBqtXqc?usp=sharing
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image_size: 224 # 448x224
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sils_size: 32 # 64x32
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source_dim: 384
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num_unknown: 16
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total_layer_num: 12
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group_layer_num: 1
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head_num: 12
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Mask_Branch:
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source_dim: 384
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target_dim: 2
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p: 0.
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softmax: True
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backbone_cfg:
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type: ResNet9
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block: BasicBlock
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in_channel: 16
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channels: # Layers configuration for automatically model construction
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- 64
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- 128
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- 256
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- 512
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layers:
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- 1
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- 1
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- 1
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- 1
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strides:
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- 1
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- 2
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- 1
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- 1
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maxpool: false
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SeparateFCs:
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in_channels: 512
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out_channels: 256
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parts_num: 32
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SeparateBNNecks:
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class_num: 100
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in_channels: 256
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parts_num: 32
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bin_num:
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- 32
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optimizer_cfg:
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lr: 0.1
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momentum: 0.9
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solver: SGD
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weight_decay: 0.0005
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scheduler_cfg:
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gamma: 0.1
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milestones: # Learning Rate Reduction at each milestones
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- 15000
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- 25000
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scheduler: MultiStepLR
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trainer_cfg:
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find_unused_parameters: True
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enable_float16: true # half_percesion float for memory reduction and speedup
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fix_BN: false
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log_iter: 100
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with_test: true
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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: BiggerGait__Dinov2
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sync_BN: true
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total_iter: 30000
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sampler:
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batch_shuffle: true
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batch_size:
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- 8 # TripletSampler, batch_size[0] indicates Number of Identity
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- 4 # batch_size[1] indicates Samples sequqnce for each Identity
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frames_num_fixed: 30 # fixed frames number for training
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frames_skip_num: 4
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frames_num_max: 40 # max frames number for unfixed training
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frames_num_min: 20 # min frames number for unfixed traing
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sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
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type: TripletSampler
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transform:
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- type: Compose
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trf_cfg:
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- type: RandomHorizontalFlip
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- type: BaseRgbTransform
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@@ -0,0 +1,127 @@
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data_cfg:
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dataset_name: CCPG
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# TODO
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dataset_root: your_path # use datasets/pretreatment_rgb.py for data preprocessing!
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dataset_partition: ./datasets/CCPG/CCPG.json
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data_in_use: [True, False] # images / real_ratios
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num_workers: 8
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: CCPG
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: False # Rename some module name for clarity, so it is fasle.
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restore_hint: 30000 # BiggerGait__SmallDINOv2_Gaitbase_84Frame30_448224_6432HPP32_NoAlign_Sep12B_WiMask_2B_6G-30000.pt in HuggingFace
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save_name: BiggerGait__Dinov2_Group
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eval_func: evaluate_CCPG
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sampler:
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batch_shuffle: false
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batch_size: 8 # GPUs number
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sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
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frames_all_limit: 250 # limit the number of sampled frames to prevent out of memory
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metric: euc # cos
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transform:
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- type: BaseRgbTransform
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loss_cfg:
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- loss_term_weight: 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_weight: 1.0
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scale: 16
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type: CrossEntropyLoss
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log_prefix: softmax
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log_accuracy: true
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model_cfg:
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model: BiggerGait__DINOv2
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pretrained_lvm: ./pretrained_LVMs/dinov2-small # DINOv2-S HuggingFace Link: "git clone https://huggingface.co/facebook/dinov2-small" + "git lfs pull"
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pretrained_mask_branch: ./pretrained_LVMs/MaskBranch_vits14.pt # Using BigGait Code for pretraining at first, it is very lightly. pretrained_mask_branch: None or MaskBranch Download Link: https://drive.google.com/drive/folders/1zrWPUsrbCpwxoLgfom3d2irgxkBqtXqc?usp=sharing
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image_size: 224 # 448x224
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sils_size: 32 # 64x32
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source_dim: 384
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num_unknown: 16
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total_layer_num: 12
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group_layer_num: 2
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head_num: 2
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Mask_Branch:
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source_dim: 384
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target_dim: 2
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p: 0.
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softmax: True
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backbone_cfg:
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type: ResNet9
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block: BasicBlock
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in_channel: 16
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channels: # Layers configuration for automatically model construction
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- 64
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- 128
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- 256
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- 512
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layers:
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- 1
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- 1
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- 1
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- 1
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strides:
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- 1
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- 2
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- 1
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- 1
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maxpool: false
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SeparateFCs:
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in_channels: 512
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out_channels: 256
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parts_num: 32
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SeparateBNNecks:
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class_num: 100
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in_channels: 256
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parts_num: 32
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bin_num:
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- 32
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optimizer_cfg:
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lr: 0.1
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momentum: 0.9
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solver: SGD
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weight_decay: 0.0005
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scheduler_cfg:
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gamma: 0.1
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milestones: # Learning Rate Reduction at each milestones
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- 15000
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- 25000
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scheduler: MultiStepLR
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trainer_cfg:
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find_unused_parameters: True
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enable_float16: true # half_percesion float for memory reduction and speedup
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fix_BN: false
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log_iter: 100
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with_test: true
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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: BiggerGait__Dinov2_Group
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sync_BN: true
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total_iter: 30000
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sampler:
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batch_shuffle: true
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batch_size:
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- 8 # TripletSampler, batch_size[0] indicates Number of Identity
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- 4 # batch_size[1] indicates Samples sequqnce for each Identity
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frames_num_fixed: 30 # fixed frames number for training
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frames_skip_num: 4
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frames_num_max: 40 # max frames number for unfixed training
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frames_num_min: 20 # min frames number for unfixed traing
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sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
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type: TripletSampler
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transform:
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- type: Compose
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trf_cfg:
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- type: RandomHorizontalFlip
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- type: BaseRgbTransform
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