data_cfg: dataset_name: CCPG dataset_root: path_for_denoisinggait_ccpg dataset_partition: ./datasets/CCPG/CCPG.json num_workers: 8 data_in_use: [True,True] # 0. denoising feature from diffusion (96x48); 1. silhouette (96x48) remove_no_gallery: false # Remove probe if no gallery for it test_dataset_name: CCPG evaluator_cfg: enable_float16: true restore_ckpt_strict: true restore_hint: 60000 save_name: GaitBasefusion_p0.5_threshold0.5_test_daFalse_8416 sampler: batch_shuffle: false batch_size: 4 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 eval_func: evaluate_CCPG metric: euc # cos transform: - type: NoOperation - type: BaseSilTransform 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: DenoisingGait backbone_cfg: type: ResNet9 block: BasicBlock channels: # Layers configuration for automatically model construction - 64 - 128 - 256 - 512 in_channel: 8 layers: - 1 - 1 - 1 - 1 strides: - 1 - 2 - 2 - 1 maxpool: false SeparateFCs: in_channels: 512 out_channels: 256 parts_num: 24 SeparateBNNecks: class_num: 100 in_channels: 256 parts_num: 24 bin_num: - 24 diffusion_ckpt: ./pretrained_LVMs/segmind/tiny-sd r: 3 p: 0.5 threshold: 0.5 optimizer_cfg: lr: 0.1 #/2 momentum: 0.9 solver: SGD weight_decay: 0.0005 # *2 scheduler_cfg: gamma: 0.1 milestones: # Learning Rate Reduction at each milestones - 20000 - 40000 - 50000 scheduler: MultiStepLR trainer_cfg: enable_float16: true # half_percesion float for memory reduction and speedup fix_BN: false log_iter: 100 with_test: true restore_ckpt_strict: true restore_hint: 0 save_iter: 5000 save_name: GaitBasefusion_p0.5_threshold0.5_test_daFalse_8416 sync_BN: true total_iter: 60000 sampler: batch_shuffle: true batch_size: - 8 # TripletSampler, batch_size[0] indicates Number of Identity - 4 # batch_size[1] indicates Samples sequqnce for each Identity frames_num_fixed: 16 # fixed frames number for training frames_skip_num: 30 frames_num_max: 24 # max frames number for unfixed training frames_num_min: 16 # min # frames_skip_num: 8 sample_type: fixed_allordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered type: TripletSampler transform: - type: NoOperation - type: BaseSilTransform