Add code of GaitBase (#115)
* add resnet9 backbone and regular da ops * add gait3d config * fix invalid path CASIA-B* in windows * add gaitbase config for all datasets * rm unused OpenGait transform
This commit is contained in:
Junhao Liang
GitHub
@@ -0,0 +1,108 @@
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data_cfg:
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dataset_name: CASIA-B
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dataset_root: your_path
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dataset_partition: ./datasets/CASIA-B/CASIA-B.json
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num_workers: 1
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: CASIA-B
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: true
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restore_hint: 0
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save_name: GaitBase_DA
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#eval_func: GREW_submission
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sampler:
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batch_shuffle: false
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batch_size: 16
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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: 720 # 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: BaseSilCuttingTransform
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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: Baseline
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backbone_cfg:
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type: ResNet9
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block: BasicBlock
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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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- 2
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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: 16
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SeparateBNNecks:
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class_num: 74
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in_channels: 256
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parts_num: 16
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bin_num:
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- 16
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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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- 20000
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- 40000
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- 50000
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scheduler: MultiStepLR
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trainer_cfg:
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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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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: 60000
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save_name: GaitBase_DA
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sync_BN: true
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total_iter: 60000
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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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- 16 # 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_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: BaseSilCuttingTransform
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- type: RandomRotate
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prob: 0.3
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- type: RandomErasing
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prob: 0.3
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@@ -0,0 +1,110 @@
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data_cfg:
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dataset_name: Gait3D
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dataset_root: your_path
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dataset_partition: ./datasets/Gait3D/Gait3D.json
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num_workers: 1
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: Gait3D
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: true
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restore_hint: 60000
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save_name: GaitBase_DA
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eval_func: evaluate_Gait3D
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sampler:
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batch_shuffle: false
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batch_size: 16
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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: 720 # 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: BaseSilTransform
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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: Baseline
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backbone_cfg:
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type: ResNet9
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block: BasicBlock
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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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- 2
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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: 16
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SeparateBNNecks:
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class_num: 3000
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in_channels: 256
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parts_num: 16
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bin_num:
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- 16
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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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- 20000
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- 40000
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- 50000
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scheduler: MultiStepLR
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trainer_cfg:
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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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with_test: true
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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: 20000
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save_name: GaitBase_DA
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sync_BN: true
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total_iter: 60000
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sampler:
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batch_shuffle: true
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batch_size:
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- 32 # 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_num_max: 50 # max frames number for unfixed training
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frames_num_min: 10 # min frames number for unfixed traing
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sample_type: unfixed_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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||||||
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- type: Compose
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trf_cfg:
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||||||
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- type: RandomPerspective
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prob: 0.2
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- type: BaseSilTransform
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- type: RandomHorizontalFlip
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prob: 0.2
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- type: RandomRotate
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prob: 0.2
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@@ -0,0 +1,108 @@
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data_cfg:
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dataset_name: GREW
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dataset_root: your_path
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dataset_partition: ./datasets/GREW/GREW.json
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num_workers: 1
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: GREW
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: true
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restore_hint: 180000
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save_name: GaitBase_DA
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eval_func: GREW_submission
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sampler:
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batch_shuffle: false
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batch_size: 16
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||||||
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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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||||||
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frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
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||||||
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metric: euc # cos
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||||||
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transform:
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||||||
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- type: BaseSilCuttingTransform
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||||||
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|
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loss_cfg:
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||||||
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- loss_term_weight: 1.0
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||||||
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margin: 0.2
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||||||
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type: TripletLoss
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||||||
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log_prefix: triplet
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||||||
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- loss_term_weight: 1.0
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||||||
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scale: 16
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||||||
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type: CrossEntropyLoss
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||||||
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log_prefix: softmax
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||||||
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log_accuracy: true
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||||||
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model_cfg:
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||||||
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model: Baseline
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||||||
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backbone_cfg:
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||||||
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type: ResNet9
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||||||
|
block: BasicBlock
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||||||
|
channels: # Layers configuration for automatically model construction
|
||||||
|
- 64
|
||||||
|
- 128
|
||||||
|
- 256
|
||||||
|
- 512
|
||||||
|
layers:
|
||||||
|
- 1
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||||||
|
- 1
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||||||
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- 1
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||||||
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- 1
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||||||
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strides:
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||||||
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- 1
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- 2
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||||||
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- 2
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- 1
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||||||
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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: 16
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SeparateBNNecks:
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class_num: 20000
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in_channels: 256
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parts_num: 16
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bin_num:
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- 16
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optimizer_cfg:
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lr: 0.1
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momentum: 0.9
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||||||
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solver: SGD
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||||||
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weight_decay: 0.0005
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||||||
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|
||||||
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scheduler_cfg:
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||||||
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gamma: 0.1
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milestones: # Learning Rate Reduction at each milestones
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- 80000
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- 120000
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- 150000
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scheduler: MultiStepLR
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trainer_cfg:
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enable_float16: true # half_percesion float for memory reduction and speedup
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||||||
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fix_BN: false
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||||||
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with_test: false
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||||||
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log_iter: 100
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||||||
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restore_ckpt_strict: true
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||||||
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restore_hint: 0
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save_iter: 60000
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save_name: GaitBase_DA
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sync_BN: true
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||||||
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total_iter: 180000
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||||||
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sampler:
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||||||
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batch_shuffle: true
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||||||
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batch_size:
|
||||||
|
- 32 # TripletSampler, batch_size[0] indicates Number of Identity
|
||||||
|
- 4 # batch_size[1] indicates Samples sequqnce for each Identity
|
||||||
|
frames_num_fixed: 30 # fixed frames number for training
|
||||||
|
frames_num_max: 40 # max frames number for unfixed training
|
||||||
|
frames_num_min: 20 # min frames number for unfixed traing
|
||||||
|
sample_type: unfixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
|
||||||
|
type: TripletSampler
|
||||||
|
transform:
|
||||||
|
- type: Compose
|
||||||
|
trf_cfg:
|
||||||
|
- type: RandomPerspective
|
||||||
|
prob: 0.2
|
||||||
|
- type: BaseSilCuttingTransform
|
||||||
|
- type: RandomHorizontalFlip
|
||||||
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prob: 0.2
|
||||||
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- type: RandomRotate
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||||||
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prob: 0.2
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@@ -0,0 +1,103 @@
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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: ./datasets/OUMVLP/OUMVLP.json
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num_workers: 1
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: OUMVLP
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|
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: true
|
||||||
|
restore_hint: 120000
|
||||||
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save_name: GaitBase
|
||||||
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#eval_func: GREW_submission
|
||||||
|
sampler:
|
||||||
|
batch_shuffle: false
|
||||||
|
batch_size: 16
|
||||||
|
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order;
|
||||||
|
Other options: fixed_unordered
|
||||||
|
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
|
||||||
|
metric: euc # cos
|
||||||
|
transform:
|
||||||
|
- type: BaseSilCuttingTransform
|
||||||
|
|
||||||
|
loss_cfg:
|
||||||
|
- loss_term_weight: 1.0
|
||||||
|
margin: 0.2
|
||||||
|
type: TripletLoss
|
||||||
|
log_prefix: triplet
|
||||||
|
- loss_term_weight: 1.0
|
||||||
|
scale: 16
|
||||||
|
type: CrossEntropyLoss
|
||||||
|
log_prefix: softmax
|
||||||
|
log_accuracy: true
|
||||||
|
|
||||||
|
model_cfg:
|
||||||
|
model: Baseline
|
||||||
|
backbone_cfg:
|
||||||
|
type: ResNet9
|
||||||
|
block: BasicBlock
|
||||||
|
channels: # Layers configuration for automatically model construction
|
||||||
|
- 64
|
||||||
|
- 128
|
||||||
|
- 256
|
||||||
|
- 512
|
||||||
|
layers:
|
||||||
|
- 1
|
||||||
|
- 1
|
||||||
|
- 1
|
||||||
|
- 1
|
||||||
|
strides:
|
||||||
|
- 1
|
||||||
|
- 2
|
||||||
|
- 2
|
||||||
|
- 1
|
||||||
|
maxpool: false
|
||||||
|
SeparateFCs:
|
||||||
|
in_channels: 512
|
||||||
|
out_channels: 256
|
||||||
|
parts_num: 16
|
||||||
|
SeparateBNNecks:
|
||||||
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class_num: 5153
|
||||||
|
in_channels: 256
|
||||||
|
parts_num: 16
|
||||||
|
bin_num:
|
||||||
|
- 16
|
||||||
|
|
||||||
|
optimizer_cfg:
|
||||||
|
lr: 0.1
|
||||||
|
momentum: 0.9
|
||||||
|
solver: SGD
|
||||||
|
weight_decay: 0.0005
|
||||||
|
|
||||||
|
scheduler_cfg:
|
||||||
|
gamma: 0.1
|
||||||
|
milestones: # Learning Rate Reduction at each milestones
|
||||||
|
- 60000
|
||||||
|
- 80000
|
||||||
|
- 100000
|
||||||
|
scheduler: MultiStepLR
|
||||||
|
trainer_cfg:
|
||||||
|
enable_float16: true # half_percesion float for memory reduction and speedup
|
||||||
|
fix_BN: false
|
||||||
|
with_test: false
|
||||||
|
log_iter: 100
|
||||||
|
restore_ckpt_strict: true
|
||||||
|
restore_hint: 0
|
||||||
|
save_iter: 60000
|
||||||
|
save_name: GaitBase
|
||||||
|
sync_BN: true
|
||||||
|
total_iter: 120000
|
||||||
|
sampler:
|
||||||
|
batch_shuffle: true
|
||||||
|
batch_size:
|
||||||
|
- 32 # TripletSampler, batch_size[0] indicates Number of Identity
|
||||||
|
- 8 # batch_size[1] indicates Samples sequqnce for each Identity
|
||||||
|
frames_num_fixed: 30 # fixed frames number for training
|
||||||
|
frames_num_max: 40 # max frames number for unfixed training
|
||||||
|
frames_num_min: 20 # min frames number for unfixed traing
|
||||||
|
sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
|
||||||
|
type: TripletSampler
|
||||||
|
transform:
|
||||||
|
- type: BaseSilCuttingTransform
|
||||||
@@ -8,7 +8,7 @@ Gait is one of the most promising biometrics to identify individuals at a long d
|
|||||||
|
|
||||||
## CASIA-B*
|
## CASIA-B*
|
||||||
Since the silhouettes of CASIA-B were obtained by the outdated background subtraction, there exists much noise caused by the background and clothes of subjects. Hence, we re-annotate the
|
Since the silhouettes of CASIA-B were obtained by the outdated background subtraction, there exists much noise caused by the background and clothes of subjects. Hence, we re-annotate the
|
||||||
silhouettes of CASIA-B and denote it as CASIA-B*. You can visit [this link](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) to apply for CASIA-B*. More details about CASIA-B* can be found in [this link](../../datasets/CASIA-B*/README.md).
|
silhouettes of CASIA-B and denote it as CASIA-B*. You can visit [this link](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) to apply for CASIA-B*. More details about CASIA-B* can be found in [this link](../../datasets/CASIA-B/README.md).
|
||||||
|
|
||||||
## Performance
|
## Performance
|
||||||
| Model | NM | BG | CL | TTG-200 (cross-domain) | Configuration |
|
| Model | NM | BG | CL | TTG-200 (cross-domain) | Configuration |
|
||||||
|
|||||||
@@ -3,7 +3,7 @@ data_cfg:
|
|||||||
dataset_name: CASIA-B*
|
dataset_name: CASIA-B*
|
||||||
dataset_root: your_path
|
dataset_root: your_path
|
||||||
data_in_use: [true, false, false, false]
|
data_in_use: [true, false, false, false]
|
||||||
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
|
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
|
||||||
num_workers: 1
|
num_workers: 1
|
||||||
remove_no_gallery: false
|
remove_no_gallery: false
|
||||||
test_dataset_name: CASIA-B
|
test_dataset_name: CASIA-B
|
||||||
|
|||||||
@@ -3,7 +3,7 @@ data_cfg:
|
|||||||
dataset_name: CASIA-B*
|
dataset_name: CASIA-B*
|
||||||
dataset_root: your_path
|
dataset_root: your_path
|
||||||
data_in_use: [false, false, true, true]
|
data_in_use: [false, false, true, true]
|
||||||
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
|
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
|
||||||
num_workers: 1
|
num_workers: 1
|
||||||
remove_no_gallery: false
|
remove_no_gallery: false
|
||||||
test_dataset_name: CASIA-B
|
test_dataset_name: CASIA-B
|
||||||
|
|||||||
@@ -2,7 +2,7 @@ data_cfg:
|
|||||||
dataset_name: CASIA-B*
|
dataset_name: CASIA-B*
|
||||||
dataset_root: your_path
|
dataset_root: your_path
|
||||||
data_in_use: [false, true, true, true]
|
data_in_use: [false, true, true, true]
|
||||||
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
|
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
|
||||||
num_workers: 1
|
num_workers: 1
|
||||||
remove_no_gallery: false # Remove probe if no gallery for it
|
remove_no_gallery: false # Remove probe if no gallery for it
|
||||||
test_dataset_name: CASIA-B
|
test_dataset_name: CASIA-B
|
||||||
|
|||||||
@@ -2,7 +2,7 @@ data_cfg:
|
|||||||
dataset_name: CASIA-B*
|
dataset_name: CASIA-B*
|
||||||
dataset_root: your_path
|
dataset_root: your_path
|
||||||
data_in_use: [false, true, true, true]
|
data_in_use: [false, true, true, true]
|
||||||
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
|
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
|
||||||
num_workers: 1
|
num_workers: 1
|
||||||
remove_no_gallery: false # Remove probe if no gallery for it
|
remove_no_gallery: false # Remove probe if no gallery for it
|
||||||
test_dataset_name: CASIA-B
|
test_dataset_name: CASIA-B
|
||||||
|
|||||||
@@ -1,130 +0,0 @@
|
|||||||
{
|
|
||||||
"TRAIN_SET": [
|
|
||||||
"001",
|
|
||||||
"002",
|
|
||||||
"003",
|
|
||||||
"004",
|
|
||||||
"005",
|
|
||||||
"006",
|
|
||||||
"007",
|
|
||||||
"008",
|
|
||||||
"009",
|
|
||||||
"010",
|
|
||||||
"011",
|
|
||||||
"012",
|
|
||||||
"013",
|
|
||||||
"014",
|
|
||||||
"015",
|
|
||||||
"016",
|
|
||||||
"017",
|
|
||||||
"018",
|
|
||||||
"019",
|
|
||||||
"020",
|
|
||||||
"021",
|
|
||||||
"022",
|
|
||||||
"023",
|
|
||||||
"024",
|
|
||||||
"025",
|
|
||||||
"026",
|
|
||||||
"027",
|
|
||||||
"028",
|
|
||||||
"029",
|
|
||||||
"030",
|
|
||||||
"031",
|
|
||||||
"032",
|
|
||||||
"033",
|
|
||||||
"034",
|
|
||||||
"035",
|
|
||||||
"036",
|
|
||||||
"037",
|
|
||||||
"038",
|
|
||||||
"039",
|
|
||||||
"040",
|
|
||||||
"041",
|
|
||||||
"042",
|
|
||||||
"043",
|
|
||||||
"044",
|
|
||||||
"045",
|
|
||||||
"046",
|
|
||||||
"047",
|
|
||||||
"048",
|
|
||||||
"049",
|
|
||||||
"050",
|
|
||||||
"051",
|
|
||||||
"052",
|
|
||||||
"053",
|
|
||||||
"054",
|
|
||||||
"055",
|
|
||||||
"056",
|
|
||||||
"057",
|
|
||||||
"058",
|
|
||||||
"059",
|
|
||||||
"060",
|
|
||||||
"061",
|
|
||||||
"062",
|
|
||||||
"063",
|
|
||||||
"064",
|
|
||||||
"065",
|
|
||||||
"066",
|
|
||||||
"067",
|
|
||||||
"068",
|
|
||||||
"069",
|
|
||||||
"070",
|
|
||||||
"071",
|
|
||||||
"072",
|
|
||||||
"073",
|
|
||||||
"074"
|
|
||||||
],
|
|
||||||
"TEST_SET": [
|
|
||||||
"075",
|
|
||||||
"076",
|
|
||||||
"077",
|
|
||||||
"078",
|
|
||||||
"079",
|
|
||||||
"080",
|
|
||||||
"081",
|
|
||||||
"082",
|
|
||||||
"083",
|
|
||||||
"084",
|
|
||||||
"085",
|
|
||||||
"086",
|
|
||||||
"087",
|
|
||||||
"088",
|
|
||||||
"089",
|
|
||||||
"090",
|
|
||||||
"091",
|
|
||||||
"092",
|
|
||||||
"093",
|
|
||||||
"094",
|
|
||||||
"095",
|
|
||||||
"096",
|
|
||||||
"097",
|
|
||||||
"098",
|
|
||||||
"099",
|
|
||||||
"100",
|
|
||||||
"101",
|
|
||||||
"102",
|
|
||||||
"103",
|
|
||||||
"104",
|
|
||||||
"105",
|
|
||||||
"106",
|
|
||||||
"107",
|
|
||||||
"108",
|
|
||||||
"109",
|
|
||||||
"110",
|
|
||||||
"111",
|
|
||||||
"112",
|
|
||||||
"113",
|
|
||||||
"114",
|
|
||||||
"115",
|
|
||||||
"116",
|
|
||||||
"117",
|
|
||||||
"118",
|
|
||||||
"119",
|
|
||||||
"120",
|
|
||||||
"121",
|
|
||||||
"122",
|
|
||||||
"123",
|
|
||||||
"124"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
@@ -1,21 +0,0 @@
|
|||||||
# CASIA-B\*
|
|
||||||
## Introduction
|
|
||||||
CASIA-B\* is a re-segmented version of CASIA-B processed by Liang et al. The extra import of CASIA-B* owes to the background subtraction algorithm that CASIA-B uses for generating the silhouette data tends to produce much noise and is outdated for real-world applications nowadays. We use the up-to-date pretreatment strategy to re-segment the raw videos, i.e., the deep pedestrian track and segmentation algorithms. As a result, CASIA-B\* consists of the cropped RGB images, binary silhouettes, the height-width ratio of the obtained bounding boxes and the aligned silhouettes. Please refer to [GaitEdge](../../configs/gaitedge/README.md) for more details. If you need this sub-set, please apply with the instruction mentioned in [http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp]. In the Email Subject, please mark the specific dataset you need, i.e., Dataset B*.
|
|
||||||
|
|
||||||
## Data structure
|
|
||||||
```
|
|
||||||
casiab-128-end2end/
|
|
||||||
001 (subject)
|
|
||||||
bg-01 (type)
|
|
||||||
000 (view)
|
|
||||||
000-aligned-sils.pkl (aligned sils, nx64x44)
|
|
||||||
000-ratios.pkl (aspect ratio of bounding boxes, n)
|
|
||||||
000-rgbs.pkl (cropped RGB images, nx3x128x128)
|
|
||||||
000-sils.pkl (binary silhouettes, nx128x128)
|
|
||||||
......
|
|
||||||
......
|
|
||||||
......
|
|
||||||
```
|
|
||||||
|
|
||||||
## How to use
|
|
||||||
By default, it loads all file directory information like other datasets before training starts. If you need to use some of these data separately, such as `aligned-sils`, then you can use the `data_in_use` parameter in `data_cfg` lexicographically, *i.e.* `data_in_use: [true, false, false, false]`.
|
|
||||||
@@ -25,3 +25,25 @@ Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip
|
|||||||
......
|
......
|
||||||
......
|
......
|
||||||
```
|
```
|
||||||
|
|
||||||
|
# CASIA-B\*
|
||||||
|
## Introduction
|
||||||
|
CASIA-B\* is a re-segmented version of CASIA-B processed by Liang et al. The extra import of CASIA-B* owes to the background subtraction algorithm that CASIA-B uses for generating the silhouette data tends to produce much noise and is outdated for real-world applications nowadays. We use the up-to-date pretreatment strategy to re-segment the raw videos, i.e., the deep pedestrian track and segmentation algorithms. As a result, CASIA-B\* consists of the cropped RGB images, binary silhouettes, the height-width ratio of the obtained bounding boxes and the aligned silhouettes. Please refer to [GaitEdge](../../configs/gaitedge/README.md) for more details. If you need this sub-set, please apply with the instruction mentioned in [http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp]. In the Email Subject, please mark the specific dataset you need, i.e., Dataset B*.
|
||||||
|
|
||||||
|
## Data structure
|
||||||
|
```
|
||||||
|
casiab-128-end2end/
|
||||||
|
001 (subject)
|
||||||
|
bg-01 (type)
|
||||||
|
000 (view)
|
||||||
|
000-aligned-sils.pkl (aligned sils, nx64x44)
|
||||||
|
000-ratios.pkl (aspect ratio of bounding boxes, n)
|
||||||
|
000-rgbs.pkl (cropped RGB images, nx3x128x128)
|
||||||
|
000-sils.pkl (binary silhouettes, nx128x128)
|
||||||
|
......
|
||||||
|
......
|
||||||
|
......
|
||||||
|
```
|
||||||
|
|
||||||
|
## How to use
|
||||||
|
By default, it loads all file directory information like other datasets before training starts. If you need to use some of these data separately, such as `aligned-sils`, then you can use the `data_in_use` parameter in `data_cfg` lexicographically, *i.e.* `data_in_use: [true, false, false, false]`.
|
||||||
|
|||||||
+138
-2
@@ -1,6 +1,9 @@
|
|||||||
from data import transform as base_transform
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import random
|
||||||
|
import torchvision.transforms as T
|
||||||
|
import cv2
|
||||||
|
import math
|
||||||
|
from data import transform as base_transform
|
||||||
from utils import is_list, is_dict, get_valid_args
|
from utils import is_list, is_dict, get_valid_args
|
||||||
|
|
||||||
|
|
||||||
@@ -49,6 +52,139 @@ class BaseRgbTransform():
|
|||||||
return (x - self.mean) / self.std
|
return (x - self.mean) / self.std
|
||||||
|
|
||||||
|
|
||||||
|
# **************** Data Agumentation ****************
|
||||||
|
|
||||||
|
|
||||||
|
class RandomHorizontalFlip(object):
|
||||||
|
def __init__(self, prob=0.5):
|
||||||
|
self.prob = prob
|
||||||
|
|
||||||
|
def __call__(self, seq):
|
||||||
|
if random.uniform(0, 1) >= self.prob:
|
||||||
|
return seq
|
||||||
|
else:
|
||||||
|
return seq[:, :, ::-1]
|
||||||
|
|
||||||
|
|
||||||
|
class RandomErasing(object):
|
||||||
|
def __init__(self, prob=0.5, sl=0.05, sh=0.2, r1=0.3, per_frame=False):
|
||||||
|
self.prob = prob
|
||||||
|
self.sl = sl
|
||||||
|
self.sh = sh
|
||||||
|
self.r1 = r1
|
||||||
|
self.per_frame = per_frame
|
||||||
|
|
||||||
|
def __call__(self, seq):
|
||||||
|
if not self.per_frame:
|
||||||
|
if random.uniform(0, 1) >= self.prob:
|
||||||
|
return seq
|
||||||
|
else:
|
||||||
|
for _ in range(100):
|
||||||
|
seq_size = seq.shape
|
||||||
|
area = seq_size[1] * seq_size[2]
|
||||||
|
|
||||||
|
target_area = random.uniform(self.sl, self.sh) * area
|
||||||
|
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
|
||||||
|
|
||||||
|
h = int(round(math.sqrt(target_area * aspect_ratio)))
|
||||||
|
w = int(round(math.sqrt(target_area / aspect_ratio)))
|
||||||
|
|
||||||
|
if w < seq_size[2] and h < seq_size[1]:
|
||||||
|
x1 = random.randint(0, seq_size[1] - h)
|
||||||
|
y1 = random.randint(0, seq_size[2] - w)
|
||||||
|
seq[:, x1:x1+h, y1:y1+w] = 0.
|
||||||
|
return seq
|
||||||
|
return seq
|
||||||
|
else:
|
||||||
|
self.per_frame = False
|
||||||
|
frame_num = seq.shape[0]
|
||||||
|
ret = [self.__call__(seq[k][np.newaxis, ...])
|
||||||
|
for k in range(frame_num)]
|
||||||
|
self.per_frame = True
|
||||||
|
return np.concatenate(ret, 0)
|
||||||
|
|
||||||
|
|
||||||
|
class RandomRotate(object):
|
||||||
|
def __init__(self, prob=0.5, degree=10):
|
||||||
|
self.prob = prob
|
||||||
|
self.degree = degree
|
||||||
|
|
||||||
|
def __call__(self, seq):
|
||||||
|
if random.uniform(0, 1) >= self.prob:
|
||||||
|
return seq
|
||||||
|
else:
|
||||||
|
_, dh, dw = seq.shape
|
||||||
|
# rotation
|
||||||
|
degree = random.uniform(-self.degree, self.degree)
|
||||||
|
M1 = cv2.getRotationMatrix2D((dh // 2, dw // 2), degree, 1)
|
||||||
|
# affine
|
||||||
|
seq = [cv2.warpAffine(_[0, ...], M1, (dw, dh))
|
||||||
|
for _ in np.split(seq, seq.shape[0], axis=0)]
|
||||||
|
seq = np.concatenate([np.array(_)[np.newaxis, ...]
|
||||||
|
for _ in seq], 0)
|
||||||
|
return seq
|
||||||
|
|
||||||
|
|
||||||
|
class RandomPerspective(object):
|
||||||
|
def __init__(self, prob=0.5):
|
||||||
|
self.prob = prob
|
||||||
|
|
||||||
|
def __call__(self, seq):
|
||||||
|
if random.uniform(0, 1) >= self.prob:
|
||||||
|
return seq
|
||||||
|
else:
|
||||||
|
_, h, w = seq.shape
|
||||||
|
cutting = int(w // 44) * 10
|
||||||
|
x_left = list(range(0, cutting))
|
||||||
|
x_right = list(range(w - cutting, w))
|
||||||
|
TL = (random.choice(x_left), 0)
|
||||||
|
TR = (random.choice(x_right), 0)
|
||||||
|
BL = (random.choice(x_left), h)
|
||||||
|
BR = (random.choice(x_right), h)
|
||||||
|
srcPoints = np.float32([TL, TR, BR, BL])
|
||||||
|
canvasPoints = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
|
||||||
|
perspectiveMatrix = cv2.getPerspectiveTransform(
|
||||||
|
np.array(srcPoints), np.array(canvasPoints))
|
||||||
|
seq = [cv2.warpPerspective(_[0, ...], perspectiveMatrix, (w, h))
|
||||||
|
for _ in np.split(seq, seq.shape[0], axis=0)]
|
||||||
|
seq = np.concatenate([np.array(_)[np.newaxis, ...]
|
||||||
|
for _ in seq], 0)
|
||||||
|
return seq
|
||||||
|
|
||||||
|
|
||||||
|
class RandomAffine(object):
|
||||||
|
def __init__(self, prob=0.5, degree=10):
|
||||||
|
self.prob = prob
|
||||||
|
self.degree = degree
|
||||||
|
|
||||||
|
def __call__(self, seq):
|
||||||
|
if random.uniform(0, 1) >= self.prob:
|
||||||
|
return seq
|
||||||
|
else:
|
||||||
|
_, dh, dw = seq.shape
|
||||||
|
# rotation
|
||||||
|
max_shift = int(dh // 64 * 10)
|
||||||
|
shift_range = list(range(0, max_shift))
|
||||||
|
pts1 = np.float32([[random.choice(shift_range), random.choice(shift_range)], [
|
||||||
|
dh-random.choice(shift_range), random.choice(shift_range)], [random.choice(shift_range), dw-random.choice(shift_range)]])
|
||||||
|
pts2 = np.float32([[random.choice(shift_range), random.choice(shift_range)], [
|
||||||
|
dh-random.choice(shift_range), random.choice(shift_range)], [random.choice(shift_range), dw-random.choice(shift_range)]])
|
||||||
|
M1 = cv2.getAffineTransform(pts1, pts2)
|
||||||
|
# affine
|
||||||
|
seq = [cv2.warpAffine(_[0, ...], M1, (dw, dh))
|
||||||
|
for _ in np.split(seq, seq.shape[0], axis=0)]
|
||||||
|
seq = np.concatenate([np.array(_)[np.newaxis, ...]
|
||||||
|
for _ in seq], 0)
|
||||||
|
return seq
|
||||||
|
|
||||||
|
# ******************************************
|
||||||
|
|
||||||
|
def Compose(trf_cfg):
|
||||||
|
assert is_list(trf_cfg)
|
||||||
|
transform = T.Compose([get_transform(cfg) for cfg in trf_cfg])
|
||||||
|
return transform
|
||||||
|
|
||||||
|
|
||||||
def get_transform(trf_cfg=None):
|
def get_transform(trf_cfg=None):
|
||||||
if is_dict(trf_cfg):
|
if is_dict(trf_cfg):
|
||||||
transform = getattr(base_transform, trf_cfg['type'])
|
transform = getattr(base_transform, trf_cfg['type'])
|
||||||
|
|||||||
@@ -231,7 +231,7 @@ def evaluate_segmentation(data, dataset):
|
|||||||
return {"scalar/test_accuracy/mIOU": miou}
|
return {"scalar/test_accuracy/mIOU": miou}
|
||||||
|
|
||||||
|
|
||||||
def evaluate_Gait3D(data, conf, metric='euc'):
|
def evaluate_Gait3D(data, dataset, metric='euc'):
|
||||||
msg_mgr = get_msg_mgr()
|
msg_mgr = get_msg_mgr()
|
||||||
|
|
||||||
features, labels, cams, time_seqs = data['embeddings'], data['labels'], data['types'], data['views']
|
features, labels, cams, time_seqs = data['embeddings'], data['labels'], data['types'], data['views']
|
||||||
|
|||||||
@@ -0,0 +1,58 @@
|
|||||||
|
from torch.nn import functional as F
|
||||||
|
import torch.nn as nn
|
||||||
|
from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet
|
||||||
|
from ..modules import BasicConv2d
|
||||||
|
|
||||||
|
|
||||||
|
block_map = {'BasicBlock': BasicBlock,
|
||||||
|
'Bottleneck': Bottleneck}
|
||||||
|
|
||||||
|
|
||||||
|
class ResNet9(ResNet):
|
||||||
|
def __init__(self, block, channels=[32, 64, 128, 256], in_channel=1, layers=[1, 2, 2, 1], strides=[1, 2, 2, 1], maxpool=True):
|
||||||
|
if block in block_map.keys():
|
||||||
|
block = block_map[block]
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"Error type for -block-Cfg-, supported: 'BasicBlock' or 'Bottleneck'.")
|
||||||
|
self.maxpool_flag = maxpool
|
||||||
|
super(ResNet9, self).__init__(block, layers)
|
||||||
|
|
||||||
|
# Not used #
|
||||||
|
self.fc = None
|
||||||
|
############
|
||||||
|
self.inplanes = channels[0]
|
||||||
|
self.bn1 = nn.BatchNorm2d(self.inplanes)
|
||||||
|
|
||||||
|
self.conv1 = BasicConv2d(in_channel, self.inplanes, 3, 1, 1)
|
||||||
|
|
||||||
|
self.layer1 = self._make_layer(
|
||||||
|
block, channels[0], layers[0], stride=strides[0], dilate=False)
|
||||||
|
|
||||||
|
self.layer2 = self._make_layer(
|
||||||
|
block, channels[1], layers[1], stride=strides[1], dilate=False)
|
||||||
|
self.layer3 = self._make_layer(
|
||||||
|
block, channels[2], layers[2], stride=strides[2], dilate=False)
|
||||||
|
self.layer4 = self._make_layer(
|
||||||
|
block, channels[3], layers[3], stride=strides[3], dilate=False)
|
||||||
|
|
||||||
|
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
|
||||||
|
if blocks >= 1:
|
||||||
|
layer = super()._make_layer(block, planes, blocks, stride=stride, dilate=dilate)
|
||||||
|
else:
|
||||||
|
def layer(x): return x
|
||||||
|
return layer
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.conv1(x)
|
||||||
|
x = self.bn1(x)
|
||||||
|
x = self.relu(x)
|
||||||
|
if self.maxpool_flag:
|
||||||
|
x = self.maxpool(x)
|
||||||
|
|
||||||
|
x = self.layer1(x)
|
||||||
|
x = self.layer2(x)
|
||||||
|
x = self.layer3(x)
|
||||||
|
x = self.layer4(x)
|
||||||
|
return x
|
||||||
|
|
||||||
@@ -427,6 +427,7 @@ class BaseModel(MetaModel, nn.Module):
|
|||||||
model.train()
|
model.train()
|
||||||
if model.cfgs['trainer_cfg']['fix_BN']:
|
if model.cfgs['trainer_cfg']['fix_BN']:
|
||||||
model.fix_BN()
|
model.fix_BN()
|
||||||
|
if result_dict:
|
||||||
model.msg_mgr.write_to_tensorboard(result_dict)
|
model.msg_mgr.write_to_tensorboard(result_dict)
|
||||||
model.msg_mgr.reset_time()
|
model.msg_mgr.reset_time()
|
||||||
if model.iteration >= model.engine_cfg['total_iter']:
|
if model.iteration >= model.engine_cfg['total_iter']:
|
||||||
|
|||||||
Reference in New Issue
Block a user