# task 0: attr, task 1: pose, task 2:caption task3: parsing task4: smpl, task 5: det # fixed parameter with diverse shape among different tasks should also be set in the task_spec_list, # e.g., text_vectors, pos_embed, etc. # attr: 5: multi_rap2_PA_100k_parse27k_market_HARDHC 6: luperson # caption: 7: image_caption_joint # skeleton action: 0: ntu60+ntu120+gym 1: k400+diving48+gym # smpl: 2: 3dpw , human3.6m , coco , muco , up3d , mpii , gta_human # det: 3: Crowdhuman 4: 5set # pose: 8: coco 9:aic 10: human3.6m 11: posetrack 12: jrdb 13: MHP 14: mpii 15: 3dpw 16: aist++ # parsing: 17:LIP 18:CIHP 19: human3.6m 20: modanet 21: VIP 22: deepfashion 23: PaperDoll common: # prefix share_backbone_group: [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] share_decoder_group: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # use modality groups to control the communication of neck, adapter, and output proj share_rgb_group: [-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # rgb share_dense_labeling_group: [-1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # dense_labeling share_text_group: [0, 0, -1, -1, -1, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # text share_sparse_labeling_group: [ 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] share_video_group: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # share modality is truly the share task group, e.g., parsing datasets share a group share_modality_group: [ 2, 2, 3, 4, 4, 0, 0, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6 ] solver: type: SolverMAEDev model_entry_type: aio_entry_v2mae_shareneck lr_scheduler: type: 'Cosine' kwargs: eta_min: 0. base_lr: 1.e-5 warmup_lr: 1.e-3 warmup_steps: 1500 backbone_multiplier: 1. pos_embed_multiplier: 1. layer_decay: num_layers: 12 layer_decay_rate: 0.75 lpe_lr: True optimizer: type: Adafactor_dev kwargs: beta1: 0.9 clip_beta2: 0.999 clip_threshold: 1. decay_rate: -0.8 scale_parameter: False relative_step: False weight_decay: 0.05 auto_denan: False workers: 2 max_iter: 60000 #61446 # 0.1628001628001628 * |61446 for 149813 // 512 * 210 deterministic: True # seed control cudnn_deterministic: False worker_rank: True random_seed: 233 print_freq: 10 verbose_loss: False vis_batch: False save_interval: 10000 use_ceph: True sync: True collate: det # task_specific_param = ['backbone', 'neck', 'decoder', 'dataset', 'sampler', 'lr_scheduler', 'optimizer'] tasks : # prefix 0: name: NUTRGBD_skeleton #SPECIFIC loss_weight: 4.4 gres_ratio: 2 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: False drop_path_rate: 0.1 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: mmSkeletonDataset # train for 150 epochs kwargs: ann_file: - /mnt/path...to...//skaction_public/ntu60_hrnet.pkl - /mnt/path...to...//skaction_public/ntu120_hrnet.pkl - /mnt/path...to...//skaction_public/gym_hrnet.pkl dataset_name: - 2dntu60 - 2dntu120 - gym kp_dim: 2d #SPECIFIC one_hot: True num_classes: - 60 - 120 - 99 centernorm: False scale_range: [ 0.75,1.25 ] data_pipeline: - type: PreNormalize2D kwargs: { } - type: GenSkeFeat kwargs: dataset: coco feats: [ 'j' ] - type: UniformSampleGivenFrames kwargs: clip_len: 25 given_len: 7 - type: PoseDecode kwargs: { } - type: FormatGCNInput2D kwargs: num_person: 2 window: False rotate: True mode: zero - type: Collect kwargs: keys: [ 'keypoint', 'label' ] meta_keys: [ ] - type: ToTensor kwargs: keys: [ 'keypoint' ] flip: True sampler: batch_size: 120 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: sparse_labeling label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: text patch_adapter: type: sparse_labeling_adapter_skaction kwargs: pretrained: True # should be changed to True later in_chans: 3 num_joints: 17 #SPECIFIC num_frames: 175 embed_dim: 768 patch_size: [ 7, 2 ] stride_level: [ 1, 1 ] use_abs_pos_emb: True learnable_pos: False test_pos_mode: learnable_interpolate type_embed: False joint_with_text_embedding: True joint_names: coco_body_17joints #SPECIFIC proj_norm: 'LN' stride_text_embedding: True is_2d_dataset: True #SPECIFIC modality_share_list: [ 'merge_kernel', 'proj_kernel', 'proj', ] task_sp_list: [ 'text_embedding', 'pos_embed', ] patch_proj: type: sparse_labeling_projector kwargs: task: skeleton loss_cfg: type: MaskDetFocalDiceLoss kwargs: cfg: deep_supervision: True focal_alpha: 0.25 class_weight: 2.0 bbox_weight: 5.0 giou_weight: 2. ign_thr: 0.7 dec_layers: 6 num_classes: 1 predict3d: True xyxy: True in_chans: 3 # predefined in patch adapter, set in solver num_joints: 17 #SPECIFIC num_frames: 175 modality_share_list: [ 'output_proj', 'translate_weight', 'translate_bias', 'post_mul_norm', 'patch_proj', 'class_proj' ] task_sp_list: [ 'text_vectors', # useless 'text_features', ] label_adapter: type: text_adapter kwargs: pretrained: True #close_set: True description_dict_name: - ntu60_name - ntu120_name - gym_cls_name one_way_semantics: False skeleton_action: True # use skeleton action to Double the text embedding (when M=2) skeleton_action_one_hot_label: True task_sp_list: [ 'text_vectors', ] label_proj: type: text_projector kwargs: one_way_semantics: False description_dict_name: - ntu60_name - ntu120_name - gym_cls_name skeleton_action: True skeleton_action_one_hot_label: True pre_proj_type: 'pool' replace_post_mul_norm: False post_mul_norm: True task_sp_list: [ 'text_vectors', 'translate_weight', 'translate_bias', 'post_mul_norm', ] loss_cfg: type: CELoss kwargs: loss_weight: 1.0 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token', ] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor self_attn_mask_type: patch_diag_label_row detach_from_peddet: True loss_cfg: type: CEL_Sigmoid 1: name: k400_skeleton #SPECIFIC loss_weight: 1 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: False drop_path_rate: 0.1 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: mmSkeletonDataset # train for 150 epochs kwargs: ann_file: - /mnt/path...to.../skaction_public/diving48_hrnet.pkl - /mnt/path...to.../skaction_public/ucf101_hrnet.pkl - /mnt/path...to.../skaction_public/k400_hrnet.pkl dataset_name: - diving - ucf - k400 kp_dim: 2d #SPECIFIC one_hot: True num_classes: - 48 - 101 - 400 centernorm: False scale_range: [ 0.75,1.25 ] data_pipeline: - type: PreNormalize2D kwargs: { } - type: GenSkeFeat kwargs: dataset: coco feats: [ 'j' ] - type: UniformSampleGivenFrames kwargs: clip_len: 25 given_len: 7 - type: PoseDecode kwargs: { } - type: FormatGCNInput2D kwargs: num_person: 2 window: False rotate: True mode: zero - type: Collect kwargs: keys: [ 'keypoint', 'label' ] meta_keys: [ ] - type: ToTensor kwargs: keys: [ 'keypoint' ] flip: True sampler: batch_size: 90 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: sparse_labeling label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: text patch_adapter: type: sparse_labeling_adapter_skaction kwargs: pretrained: True # should be changed to True later in_chans: 3 num_joints: 17 #SPECIFIC num_frames: 175 embed_dim: 768 patch_size: [ 7, 2 ] stride_level: [ 1, 1 ] use_abs_pos_emb: True learnable_pos: False test_pos_mode: learnable_interpolate type_embed: False joint_with_text_embedding: True joint_names: coco_body_17joints #SPECIFIC proj_norm: 'LN' stride_text_embedding: True is_2d_dataset: True #SPECIFIC modality_share_list: [ 'merge_kernel', 'proj_kernel', 'proj', ] task_sp_list: [ 'text_embedding', 'pos_embed', ] patch_proj: type: sparse_labeling_projector kwargs: task: skeleton loss_cfg: type: MaskDetFocalDiceLoss kwargs: cfg: deep_supervision: True focal_alpha: 0.25 class_weight: 2.0 bbox_weight: 5.0 giou_weight: 2. ign_thr: 0.7 dec_layers: 6 num_classes: 1 predict3d: True xyxy: True in_chans: 3 # predefined in patch adapter, set in solver num_joints: 17 #SPECIFIC num_frames: 175 modality_share_list: [ 'output_proj', 'translate_weight', 'translate_bias', 'post_mul_norm', 'patch_proj', 'class_proj' ] task_sp_list: [ 'text_vectors', # useless 'text_features', ] label_adapter: type: text_adapter kwargs: pretrained: True description_dict_name: - diving48_cls_name - ucf101_cls_name - k400_cls_name one_way_semantics: False skeleton_action: True # use skeleton action to Double the text embedding (when M=2) skeleton_action_one_hot_label: True task_sp_list: [ 'text_vectors', ] label_proj: type: text_projector kwargs: one_way_semantics: False description_dict_name: - diving48_cls_name - ucf101_cls_name - k400_cls_name skeleton_action: True skeleton_action_one_hot_label: True pre_proj_type: 'pool' replace_post_mul_norm: False post_mul_norm: True task_sp_list: [ 'text_vectors', 'translate_weight', 'translate_bias', 'post_mul_norm', ] loss_cfg: type: CELoss kwargs: loss_weight: 1.0 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: [ 'predictor.mask_token', ] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor self_attn_mask_type: patch_diag_label_row detach_from_peddet: True loss_cfg: type: CEL_Sigmoid 2: name: smpl loss_weight: 0.5 gres_ratio: 3 dataset: type: MeshTSVYamlDataset # train for 150 epochs kwargs: is_composite: True is_train: True cv2_output: False augmentation: scale_factor: 0.25 noise_factor: 0.4 rot_factor: 30 img_res: 224 cfg: data_path: - /mnt/path...to.../Processed_SMPL/3dpw/dataset.pkl # problem - /mnt/path...to.../Processed_SMPL/human3.6m/dataset.pkl #running - /mnt/path...to.../Processed_SMPL/coco_smpl/dataset.pkl # problem - /mnt/path...to.../Processed_SMPL/muco/dataset.pkl #running - /mnt/path...to.../Processed_SMPL/up3d/dataset.pkl # done - /mnt/path...to.../Processed_SMPL/mpii/dataset.pkl #done - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_1396913.pkl - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_200000.pkl - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_400000.pkl - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_600000.pkl - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_800000.pkl - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_1000000.pkl - /mnt/path...to.../Processed_SMPL/gta_human/dataset_pkl/v2_dataset_1200000.pkl root_path: - /mnt/path...to.../Processed_SMPL/3dpw/images - /mnt/path...to.../Processed_SMPL/human3.6m/images - /mnt/path...to.../Processed_SMPL/coco_smpl/images - /mnt/path...to.../Processed_SMPL/muco/images - /mnt/path...to.../Processed_SMPL/up3d/images - /mnt/path...to.../Processed_SMPL/mpii/images - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human - /mnt/path...to.../Processed_SMPL/gta_human_openxlab/gta_human sampler: batch_size: 165 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: sparse_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 use_abs_pos_emb: True learnable_pos: False # useless test_pos_mode: False img_size: [ 224, 224 ] task_sp_list: [ 'pos_embed' ] label_adapter: type: sparse_labeling_adapter kwargs: pretrained: True in_chans: 3 num_joints: 446 # 1 + 14 + 431 num_frames: 1 embed_dim: 768 patch_size: [ 1,1 ] stride_level: [ 1, 1 ] use_abs_pos_emb: True learnable_pos: False test_pos_mode: learnable_interpolate type_embed: False proj_norm: 'LN' task_sp_list: [ 'pos_embed', 'text_embedding', 'proj_kernel', 'proj',] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: sparse_labeling_projector kwargs: task_sp_list: [ 'output_proj', 'text_features', 'loss_fn', 'translate', 'post_mul_norm', 'patch_proj', 'class_proj', 'proj' ] pre_proj_type: 'fix_text_tokens' num_classes: 14 # pred_joints_class: True reference_type: 'smpl' in_chans: 3 # XYZ num_joints: 446 num_frames: 1 hidden_dim: 256 patch_size: [ 1, 1 ] stride_level: [ 1, 1 ] replace_post_mul_norm: False task: smpl # for smpl task, do not predict joints classes, so text_prototype and learn_text is not useful text_prototype: True learn_text: True loss_cfg: type: SMPL_LOSS_FASTMETRO kwargs: # use_pred_joints_class_loss: True cfg: use_smpl_param_regressor: True joints_2d_loss_weight: 100.0 vertices_3d_loss_weight: 100.0 edge_normal_loss_weight: 100.0 joints_3d_loss_weight: 1000.0 vertices_fine_loss_weight: 0.25 vertices_intermediate_loss_weight: 0.50 vertices_coarse_loss_weight: 0.25 edge_gt_loss_weight: 5.0 edge_self_loss_weight: 1.e-4 normal_loss_weight: 0.1 smpl_param_loss_weight: 1000.0 except_smpl_param_loss_weight: 1.e-8 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 smpl_attention_mask_flag: True smpl_mae_pe: True use_adapt_pos2d: True use_adapt_pos1d: True self_attn_mask_type: full adding_per_layer_pe: True detach_from_peddet: True use_adapt_position: 'before' use_smpl_label_attention_mask: True label_pos_mode: 'smpl_xyz' loss_cfg: type: CEL_Sigmoid # useless 3: name: Peddet loss_weight: 15 gres_ratio: 8 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: PedestrainDetectionDataset_v2 # train for 150 epochs kwargs: task_spec: img_folder: - /mnt/path...to.../PedDet2d/CrowdHuman/Images ann_file: - /mnt/path...to.../PedDet2d/CrowdHuman/annotations/train.json return_masks: False augmentation: max_size: 1120 vit: True num_append_fake_boxes: 867 return_box_xyxy: True append_z: True sampler: batch_size: 4 # per card shuffle_strategy: 1 batch_accumulation: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 attn_calcul_method: 'math' vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: sparse_labeling patch_adapter: type: rgb_adapter kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False use_abs_pos_emb: True test_pos_mode: interpolate_with_nomask img_size: 1344 # dynamic input size: TODO: nested round_padding: True # should fix in rgb pad_attn_mask: True task_sp_list: [ 'pos_embed' ] label_adapter: type: sparse_labeling_adapter kwargs: pretrained: True in_chans: 3 # xyz num_joints: 867 # boxes with random gts num_frames: 2 # 2 for x1y1 and x2y2 embed_dim: 768 patch_size: [ 2, 1 ] stride_level: [ 1, 1 ] use_abs_pos_emb: True learnable_pos: False test_pos_mode: learnable_interpolate type_embed: False proj_norm: 'LN' task_sp_list: [ 'pos_embed', 'text_embedding', 'proj_kernel', 'proj', 'merge_kernel', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: sparse_labeling_projector kwargs: task_sp_list: [ 'text_vectors', # useless 'text_features', ] modality_share_list: [ 'text_vectors', # useless 'output_proj', 'translate_weight', 'translate_bias', 'post_mul_norm', 'patch_proj', 'class_proj' ] in_chans: 3 num_joints: 867 # boxes with random gts num_frames: 2 # 2 for x1y1 and x2y2 pre_proj_type: fix_text_tokens num_classes: 1 reference_type: four_points box_mlp: True replace_post_mul_norm: True translate_weight_scale: 4 text_prototype: True loss_cfg: type: MaskDetFocalDiceLoss kwargs: cfg: deep_supervision: True focal_alpha: 0.25 class_weight: 2.0 bbox_weight: 5.0 giou_weight: 2. ign_thr: 0.7 dec_layers: 9 num_classes: 1 predict3d: True xyxy: True decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.anchor', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor patch_pos_mode: interpolate_with_nomask label_pos_mode: simple_interpolate self_attn_mask_type: patch_diag_label_row_nested adding_per_layer_pe: True mask_token_normal_init: True intermediate_output: True peddet_cfgs: share_content_query: 3 num_queries: 867 pre_defined_path: '289_points_3d.npy' query_pe_dim: 3 xattn: False anchor_requires_grad: False loss_cfg: type: CEL_Sigmoid 4: name: Peddet_5set loss_weight: 42.4 gres_ratio: 20 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: PedestrainDetectionDataset_v2 # train for 150 epochs kwargs: task_spec: img_folder: - /mnt/path...to.../peddet_public/CrowdHuman/Images - /mnt/path...to.../peddet_public/ECP/ - /mnt/path...to.../peddet_public/CityPersons/ - /mnt/path...to.../peddet_public/WiderPerson/Images - /mnt/path...to.../peddet_public/coco/train2017/ - /mnt/path...to.../peddet_public/WIDER_Pedestrian/Images/ ann_file: - /mnt/path...to.../peddet_public/CrowdHuman/annotations/train.json - /mnt/path...to.../peddet_public/ECP/ECP_remove_no_person_img.json - /mnt/path...to.../peddet_public/CityPersons/CityPersons_remove_no_person_img.json - /mnt/path...to.../peddet_public/WiderPerson/WiderPerson_remove_no_person_img.json - /mnt/path...to.../peddet_public/cocopersons/coco_person_remove_no_person_img.json - /mnt/path...to.../peddet_public/WIDER_Pedestrian/WIDER_Pedestrian_remove_no_person_img.json return_masks: False augmentation: max_size: 1120 vit: True num_append_fake_boxes: 867 return_box_xyxy: True append_z: True sampler: batch_size: 4 # per card shuffle_strategy: 1 batch_accumulation: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 attn_calcul_method: 'math' vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: sparse_labeling patch_adapter: type: rgb_adapter kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False use_abs_pos_emb: True test_pos_mode: interpolate_with_nomask img_size: 1344 round_padding: True # should fix in rgb pad_attn_mask: True task_sp_list: [ 'pos_embed' ] label_adapter: type: sparse_labeling_adapter kwargs: pretrained: True in_chans: 3 # xyz num_joints: 867 # boxes with random gts num_frames: 2 # 2 for x1y1 and x2y2 embed_dim: 768 patch_size: [ 2, 1 ] stride_level: [ 1, 1 ] use_abs_pos_emb: True learnable_pos: False test_pos_mode: learnable_interpolate type_embed: False proj_norm: 'LN' task_sp_list: [ 'pos_embed', 'text_embedding', 'proj_kernel', 'proj', 'merge_kernel', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: sparse_labeling_projector kwargs: task_sp_list: [ 'text_vectors', # useless 'text_features', ] modality_share_list: [ 'text_vectors', # useless 'output_proj', 'translate_weight', 'translate_bias', 'post_mul_norm', 'patch_proj', 'class_proj' ] in_chans: 3 num_joints: 867 # boxes with random gts num_frames: 2 # 2 for x1y1 and x2y2 pre_proj_type: fix_text_tokens num_classes: 1 reference_type: four_points box_mlp: True replace_post_mul_norm: True translate_weight_scale: 4 text_prototype: True loss_cfg: type: MaskDetFocalDiceLoss kwargs: cfg: deep_supervision: True focal_alpha: 0.25 class_weight: 2.0 bbox_weight: 5.0 giou_weight: 2. ign_thr: 0.7 dec_layers: 9 num_classes: 1 predict3d: True xyxy: True decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: [ 'predictor.mask_token' ] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.anchor', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: # lms_checkpoint_train: fairscale hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor patch_pos_mode: interpolate_with_nomask label_pos_mode: simple_interpolate self_attn_mask_type: patch_diag_label_row_nested adding_per_layer_pe: True mask_token_normal_init: True intermediate_output: True peddet_cfgs: share_content_query: 3 num_queries: 867 pre_defined_path: '289_points_3d.npy' query_pe_dim: 3 xattn: False anchor_requires_grad: False loss_cfg: type: CEL_Sigmoid 5: # prefix name: pedattr_multi_rap2_PA_100k_parse27k_market_HARDHC loss_weight: 5 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiAttrDataset kwargs: text_label_return: True task_spec: dataset: - rap2 - PA_100k - parse27k - market - HARDHC data_path: - /mnt/path...to.../pedattr_public/rap2/dataset.pkl - /mnt/path...to.../pedattr_public/PA-100k/dataset.pkl - /mnt/path...to.../pedattr_public/Parse27k/parse27k/parse27k/dataset.pkl - /mnt/path...to.../pedattr_public/market/dataset.pkl - /mnt/path...to.../pedattr_public/HARDHC/dataset.pkl root_path: - /mnt/path...to.../pedattr_public/rap2/RAP_dataset/ - /mnt/path...to.../pedattr_public/PA-100k/data/ - /mnt/path...to.../pedattr_public/Parse27k/parse27k/parse27k/images - /mnt/path...to.../pedattr_public/market/bounding_box_train - /mnt/path...to.../pedattr_public/HARDHC/croped_image/ augmentation: height: 256 width: 192 sampler: batch_size: 147 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: [256, 192] task_sp_list: [ 'pos_embed' ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_adapter: type: text_adapter kwargs: pretrained: True task_sp_list: ['text_vectors'] one_way_semantics: True description_dict_name: 'multi_rap2_PA_100k_parse27k_market_HARDHC_attr_name' label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: text label_proj: type: text_projector kwargs: task_sp_list: ['text_vectors', 'translate_weight', 'translate_bias', 'post_mul_norm',] one_way_semantics: True post_mul_norm: True replace_post_mul_norm: False translate_weight_scale: 5 description_dict_name: 'multi_rap2_PA_100k_parse27k_market_HARDHC_attr_name' pre_proj_type: '' loss_cfg: type: MaskedOneSideBCELoss kwargs: use_focal_weight: True loss_weight: 1. dataset_weight: [ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ] sample_weight: [0.00172477, 0.05791431, 0.2792891 , 0.00459644, 0.01987675, 0.06484867, 0.02327336, 0.01420398, 0.06937013, 0.03476447, 0.08533858, 0.0091179 , 0.0125145 , 0.02894172, 0.00816949, 0.17255632, 0.00890175, 0.00613153, 0.00838123, 0.07975844, 0.03529381, 0.07885856, 0.06067129, 0.02532455, 0.00429207, 0.06790121, 0.02532014, 0.00639179, 0.02070164, 0.00790041, 0.01142935, 0.00823125, 0.00310547, 0.00732696, 0.08890281, 0.00265994, 0.12081324, 0.16404275, 0.010578 , 0.09486231, 0.040896 , 0.23313939, 0.02223673, 0.28135352, 0.01603462, 0.01012806, 0.00799305, 0.01450835, 0.00697848, 0.00314958, 0.00536399, 0.00762692, 0.03982408, 0.00306577, # rap2 0.01728739, 0.0714522 , 0.23161312, 0.16539257, 0.01964296, 0.0599655 , 0.04277957, 0.01663895, 0.00187475, 0.00670499, 0.0128674 , 0.28255336, 0.06885843, 0.0455939 , 0.00238203, 0.07344605, 0.07651623, 0.06356061, 0.00378038, 0.00534193, 0.36698324, 0.02468052, 0.18279907, 0.14001068, 0.1169667 , 0.14002832, # pa100k 0.00080283, 0.04727897, 0.05596016, 0.00868119, 0.00850474, 0.00013234, 0.02891966, 0.0113279 , 0.00466261, 0.00932522, 0.04154444, 0.00932522, 0.00466261, 0.0113279 , 0.0128277 , 0.05136371, 0.05703648, 0.00839005, 0.00951049, 0.10332735, 0.04794505, 0.01736679, 0.05591605, 0.04794505, 0.01736679, 0.05591605, 0.04949779, 0.01482155, 0.05690856, 0.04949779, 0.01482155, 0.05690856, 0.00515225, 0.00014998, 0.11592566, 0.02974014, 0.00336131, 0.08812644, 0.00546986, 0.00292902, 0.11282902, 0.03215746, 0.00087341, 0.08819702, # parse27k 0.01577436, 0.01377169, 0.00681968, 0.02183531, 0.00826654, 0.00613153, 0.0091179 , 0.00096605, 0.00241732, 0.00012792, 0.00481259, 0.00091752, 0.00754752, 0.00346277, 0.00502433, 0.00635209, 0.00219676, 0.00692113, 0.01726093, 0.00282756, 0.04876553, 0.03532027, 0.05422657, 0.01836813, 0.00129247, 0.0237233 , 0.00093958, 0.04455727, 0.01074562, 0.00082048, # market 0.07086552, 0.02805507, 0.0062771 , 0.02825357, 0.0273978 , 0.05809076, 0.00874295, 0.01927683, 0.01020305, 0.04525424, 0.01257185, 0.00412004, 0.03352934, 0.00677998, # HARDHC ] decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor self_attn_mask_type: patch_diag_label_row cls_out_dim: 1 detach_from_peddet: True loss_cfg: type: CEL_Sigmoid 6: # prefix name: attr_luperson loss_weight: 5 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiAttrDataset kwargs: text_label_return: True task_spec: dataset: - lup_0_600w - lup_600_1200w data_path: - /mnt/path...to.../attribute/dataset_0_600w_pjlab.pkl - /mnt/path...to.../attribute/dataset_600_1200w_pjlab.pkl root_path: - /mnt/path...to.../reid/LUPerson-NL/LUPws - /mnt/path...to.../reid/LUPerson-NL/LUPws augmentation: height: 256 width: 192 sampler: batch_size: 300 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb patch_adapter: type: rgb_adapter kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_adapter: type: text_adapter kwargs: pretrained: True task_sp_list: [ 'text_vectors' ] one_way_semantics: True description_dict_name: 'lup_lup_attr_base' label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: text label_proj: type: text_projector kwargs: task_sp_list: [ 'text_vectors', 'translate_weight', 'translate_bias', 'post_mul_norm', ] one_way_semantics: True post_mul_norm: True replace_post_mul_norm: False translate_weight_scale: 5 description_dict_name: 'lup_lup_attr_base' pre_proj_type: '' loss_cfg: type: MaskedOneSideBCELoss kwargs: loss_weight: 1. use_focal_weight: True sample_weight: [ 3.705390e-01, 6.184500e-03, 6.679500e-03, 9.445730e-01, 3.924500e-02, 4.686065e-01, 7.492855e-01, 6.642300e-02, 7.882115e-01, 1.606450e-02, 1.043025e-01, 8.040050e-02, 1.102100e-02, 5.510935e-01, 4.074950e-02, 1.142160e-01, 3.731000e-02, 5.566250e-02, 1.852115e-01, 1.524850e-02, 5.085000e-04, 9.421990e-01, 1.484350e-02, 3.347200e-02, 5.750000e-03, 3.735500e-03, 1.509560e-01, 3.741515e-01, 3.318200e-02, 2.215850e-02, 4.213145e-01, 5.177550e-02, 3.974550e-02, 3.878800e-01, 1.321270e-01, 1.337740e-01, 9.478400e-02, 3.324350e-02, 1.095815e-01, 2.231600e-02, 1.592250e-02, 2.386005e-01, 1.999500e-01, 1.321300e-02, 7.382405e-01, 4.859650e-02, 2.932510e-01, 8.297100e-02, 9.567325e-01, 2.430700e-02, 3.554500e-03, 1.751500e-03, #lup_0_600w 3.705390e-01, 6.184500e-03, 6.679500e-03, 9.445730e-01, 3.924500e-02, 4.686065e-01, 7.492855e-01, 6.642300e-02, 7.882115e-01, 1.606450e-02, 1.043025e-01, 8.040050e-02, 1.102100e-02, 5.510935e-01, 4.074950e-02, 1.142160e-01, 3.731000e-02, 5.566250e-02, 1.852115e-01, 1.524850e-02, 5.085000e-04, 9.421990e-01, 1.484350e-02, 3.347200e-02, 5.750000e-03, 3.735500e-03, 1.509560e-01, 3.741515e-01, 3.318200e-02, 2.215850e-02, 4.213145e-01, 5.177550e-02, 3.974550e-02, 3.878800e-01, 1.321270e-01, 1.337740e-01, 9.478400e-02, 3.324350e-02, 1.095815e-01, 2.231600e-02, 1.592250e-02, 2.386005e-01, 1.999500e-01, 1.321300e-02, 7.382405e-01, 4.859650e-02, 2.932510e-01, 8.297100e-02, 9.567325e-01, 2.430700e-02, 3.554500e-03, 1.751500e-03 # lup_600_1200w ] decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ # 'predictor.text_features', 'predictor.query_embed_patch', 'predictor.query_embed_label', # 'predictor.mask_token', # 'predictor.text_pe', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor self_attn_mask_type: patch_diag_label_row cls_out_dim: 1 detach_from_peddet: True loss_cfg: type: CEL_Sigmoid 7: name: image_caption_joint loss_weight: 90 gres_ratio: 3 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: CocoCaption kwargs: bert_dir: /mnt/path...to.../Hulk/experiments/release/bert-base-uncased max_words: 40 img_size: 384 prompt: '' split_type: train joint_train: True joint_train_anno_root: /mnt/path...to.../textreid/joint_reid_caption_train.json synth_peds_root: /mnt/path...to.../textreid/SYNTH-PEDES/ cuhk_peds_root: /mnt/path...to.../textreid/CUHK-PEDES/imgs/ mals_root: /mnt/path...to.../textreid/MALS luperson_root: /mnt/path...to.../textreid/LUPerson-T/imgs/ sampler: batch_size: 100 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: text patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: [ 384, 384 ] task_sp_list: [ 'pos_embed' ] label_adapter: type: text_adapter kwargs: image_caption: True pretrained: True max_tokens: 40 task_sp_list: [ ] # fix kwargs of the project, which should be the same as that in the adapter, such as # hidden_dim, patch_size, in_chans, stride_level are set in the solver - create_modal patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: text_projector kwargs: description_dict_name: caption_bert image_caption: True one_way_semantics: True post_mul_norm: True loss_cfg: type: LabelSmoothingCrossEntropy kwargs: epsilon: 0.1 loss_weight: 1. task_sp_list: [ 'post_mul_norm', 'text_vectors', 'loss_fn'] decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.mask_token_buffer', 'predictor.mask_token_proj', 'predictor.captiontoken_ln', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 self_attn_mask_type: caption_mask caption_cfgs: { nn.parameter: True, vocal_size: 30522, lndo: True ,bert_feats_for_embedding: True } mask_token_normal_init: True detach_from_peddet: True loss_cfg: type: CEL_Sigmoid 8: name: cocopose_256x192 loss_weight: 28000 gres_ratio: 3 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: COCOPosDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/coco/annotations/person_keypoints_train2017.json img_prefix: /mnt/path...to.../pose_public/coco/train2017/ use_udp: True data_use_ratio: 1 data_cfg: { 'image_size':[192, 256], 'heatmap_size':[48, 64], # originally, 'heatmap_size':[48, 64] 'num_output_channels': 17, 'num_joints': 17, 'dataset_channel': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],], 'inference_channel': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': False, 'det_bqbox_thr': 0.0, 'bbox_file': './COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 176 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: # for supervised training, the results of label adapter is useless type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 17 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed',] # fix kwargs of the project, which should be the same as that in the adapter, such as # hidden_dim, patch_size, in_chans, stride_level are set in the solver - create_modal patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'loss_fn', 'upsample_network', 'text_features',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 cls_loss_branch: True description_dict_name: checked_pose_coco_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 17 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.38647058, 0.33606767, 0.33835369, 0.29253424, 0.29636332, 0.4987484 , 0.49978854, 0.39467358, 0.40091822, 0.36039853, 0.36918446, 0.43343303, 0.4345989 , 0.32999829, 0.33092793, 0.27714171, 0.27754939 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: full # full for all attention # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 9: name: aic loss_weight: 56000 gres_ratio: 7 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: dataset_name: aic ann_file: /mnt/path...to.../pose_public/ai_challenge/annotations/aic_train.json img_prefix: /mnt/path...to.../pose_public/ai_challenge/ai_challenger_keypoint_train_20170902/keypoint_train_images_20170902/ use_udp: True data_use_ratio: 1 data_cfg: { 'image_size': [ 192, 256 ], 'heatmap_size': [ 48, 64 ], # originally, 'heatmap_size':[48, 64] 'num_output_channels': 14, 'num_joints': 14, 'dataset_channel': [ [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ], ], 'inference_channel': [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ], 'flip_pairs': [ [ 0, 3 ], [ 1, 4 ], [ 2, 5 ], [ 6, 9 ], [ 7, 10 ], [ 8, 11 ], ], 'upper_body_ids': [ 0, 1, 2, 3, 4, 5, 12, 13 ], 'lower_body_ids': [ 6, 7, 8, 9, 10, 11 ], 'use_different_joint_weights': False, 'joint_weights': [ 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1. ], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': False, 'det_bqbox_thr': 0.0, 'bbox_file': './COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 189 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 14 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed', ] # fix kwargs of the project, which should be the same as that in the adapter, such as # hidden_dim, patch_size, in_chans, stride_level are set in the solver - create_modal patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'upsample_network', 'loss_fn', 'text_features', ] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_aic_name # this key is only valid when we set text_prototype to be True task: pose upsample_hidden_dim: 256 # dim of hidden features in upsampling network loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 14 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.98064613, 0.977893565, 0.97715356, 0.98064613, 0.977893565, 0.97715356, 0.9594528200000001, 0.85703431, 0.7504981850000001, 0.9594528200000001, 0.85703431, 0.7504981850000001, 0.97149646, 0.98605877 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. label_pos_mode: False self_attn_mask_type: full detach_from_peddet: True adding_per_layer_pe: True use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 10: name: h36m_pose_256x256 loss_weight: 3192 gres_ratio: 2 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: COCOPosDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/h36m/processed/annotation_body2d/h36m_coco_train.json img_prefix: /mnt/path...to.../pose_public/h36m/processed/images/ use_udp: True data_use_ratio: 1 data_cfg: { 'image_size': [ 256, 256 ], 'heatmap_size': [ 64, 64 ], # originally, 'heatmap_size':[48, 64] 'num_output_channels': 17, 'num_joints': 17, 'dataset_channel': [ [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ], ], 'inference_channel': [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bqbox_thr': 0.0, 'bbox_file': './COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 132 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 256 ] task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 17 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 256 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features', ] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_h3m6_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 17 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1. ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: [ 'predictor.mask_token' ] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. label_pos_mode: False self_attn_mask_type: full # full for all attention detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 11: name: posetrack_256x192 loss_weight: 12335 gres_ratio: 2 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/PoseChallenge2018/annotations/posetrack18_train.json img_prefix: /mnt/path...to.../pose_public/PoseChallenge2018/ use_udp: True dataset_name: 'posetrack' data_cfg: { 'image_size':[192, 256], 'heatmap_size':[48, 64], 'num_output_channels': 15, 'num_joints': 15, 'dataset_channel': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],], 'inference_channel': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'flip_pairs': [[3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], ], 'upper_body_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8,], 'lower_body_ids': [9, 10, 11, 12, 13, 14], 'use_different_joint_weights': False, 'joint_weights': [1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, 1.5], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bbox_thr': 0.0, 'bbox_file': 'COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 170 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 15 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed',] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_posetrack_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 15 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.81831569, 0.75692071, 0.74175951, 0.789882655, 0.789882655, 0.659771425, 0.659771425, 0.625614735, 0.625614735, 0.737772405, 0.737772405, 0.665022735, 0.665022735, 0.59563039, 0.5956303 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 # currently 6 layers for debug but we should use 9 layers afterwards pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: full # full for all attention # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 12: name: jrdb_256x192 loss_weight: 8223 gres_ratio: 2 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/JRDB2019/train.json img_prefix: /mnt/path...to.../pose_public/JRDB2022/images/ use_udp: True dataset_name: 'JRDB2022' data_cfg: { 'image_size':[192, 256], 'heatmap_size':[48, 64], 'num_output_channels': 17, 'num_joints': 17, 'dataset_channel': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],], 'inference_channel': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,], 'flip_pairs': [[2, 5], [3, 6], [4, 7], [8, 11], [9, 12], [10, 13], ], 'upper_body_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 14, 15, 16,], 'lower_body_ids': [9, 10, 12, 13], 'use_different_joint_weights': False, 'joint_weights': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bbox_thr': 0.0, 'bbox_file': 'COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 170 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: # for supvervised training, the results of label adapter is useless type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 17 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features', ] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_jrdb_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 17 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.90384634, 0.82524231, 0.89927266, 0.90945538, 0.92796942, 0.89927266, 0.90945538, 0.92796942, 0.9912784, 0.84353379, 0.97898463, 0.9912784, 0.84353379, 0.97898463, 0.97418356, 0.94284516, 0.93372039, ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: [ 'predictor.mask_token' ] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 # currently 6 layers for debug but we should use 9 layers afterwards pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. label_pos_mode: False self_attn_mask_type: full detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 13: name: MHP_256x192 loss_weight: 3192 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/pose_MHPv2/train.json img_prefix: /mnt/path...to.../pose_public/pose_MHPv2/train/images use_udp: True dataset_name: 'mhp' data_cfg: { 'image_size':[192, 256], 'heatmap_size':[48, 64], 'num_output_channels': 16, 'num_joints': 16, 'dataset_channel': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,],], 'inference_channel': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,], 'flip_pairs': [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13], ], 'upper_body_ids': [7, 8, 9, 10, 11, 12, 13, 14, 15], 'lower_body_ids': [0, 1, 2, 3, 4, 5, 6], 'use_different_joint_weights': False, 'joint_weights': [1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bbox_thr': 0.0, 'bbox_file': 'COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 132 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality # task_sp_list: ['mask_map'] label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 16 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed',] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_mhp_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 16 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.463188095, 0.6055728499999999, 0.732992125, 0.732992125, 0.6055728499999999, 0.463188095, 0.74209784, 0.92598716, 0.9642093, 0.98767263, 0.67156195, 0.6861140800000001, 0.85427203, 0.85427203, 0.6861140800000001, 0.67156195 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: full # full for all attention # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 14: name: mpi_inf_3dhp_256x192 loss_weight: 8223 gres_ratio: 2 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/mpi_inf_3dhp/train.json img_prefix: /mnt/path...to.../pose_public/mpi_inf_3dhp/processed/images/ use_udp: True dataset_name: '3DHP' data_cfg: { 'image_size':[192, 256], 'heatmap_size':[48, 64], 'num_output_channels': 136, 'num_joints': 17, 'dataset_channel': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],], 'inference_channel': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,], 'flip_pairs': [[2, 5], [3, 6], [4, 7], [8, 11], [9, 12], [10, 13], ], 'upper_body_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 14, 15, 16,], 'lower_body_ids': [9, 10, 12, 13], 'use_different_joint_weights': False, 'joint_weights': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bbox_thr': 0.0, 'bbox_file': 'COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 170 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality # task_sp_list: ['mask_map'] label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: # for supvervised training, the results of label adapter is useless type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 17 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed',] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_mpi_inf_3dhp_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 17 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.97905498, 0.98151887, 0.98018951, 0.97778281, 0.97704955, 0.98018951, 0.97778281, 0.97704955, 0.98309006, 0.98060388, 0.97209657, 0.98309006, 0.98060388, 0.97209657, 0.98405158, 0.98242514, 0.98066688 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: full # full for all attention # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 15: name: 3dpw_256x192 loss_weight: 2055 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/3DPW/dataset_merged.json img_prefix: /mnt/path...to.../pose_public/3DPW/imageFiles use_udp: True dataset_name: '3DPW' data_cfg: { 'image_size':[192, 256], 'heatmap_size':[48, 64], 'num_output_channels': 18, 'num_joints': 18, 'dataset_channel': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],], 'inference_channel': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], 'flip_pairs': [[2, 5], [3, 6], [4, 7], [8, 11], [9, 12], [10, 13], [14, 15], [16, 17]], 'upper_body_ids': [0, 1, 2 ,3, 4, 5, 6, 714, 15, 16, 17], 'lower_body_ids': [8, 9, 10, 11, 12, 13], 'use_different_joint_weights': False, 'joint_weights': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bbox_thr': 0.0, 'bbox_file': 'COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 170 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: # for supvervised training, the results of label adapter is useless type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 18 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed',] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_3dpw_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 18 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.81362905, 0.92006165, 0.90966899, 0.83948673, 0.78390512, 0.90966899, 0.83948673, 0.78390512, 0.916771645, 0.895912625, 0.86267757, 0.916771645, 0.895912625, 0.86267757, 0.683630395, 0.683630395, 0.6390913949999999, 0.6390913949999999 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: full # full for all attention # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 16: name: aist++_256x192 loss_weight: 2055 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: False # when torch.compile is True, this should be False learnable_pos: True drop_path_rate: 0.2 img_size: 1344 num_encoded_tokens: 192 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. dataset: type: MultiPoseDatasetDev kwargs: ann_file: /mnt/path...to.../pose_public/aistplusplus/merged_train_1m_filter.json img_prefix: /mnt/path...to.../pose_public/aistplusplus/images/ use_udp: True dataset_name: 'AIST' data_cfg: { 'image_size': [ 192, 256 ], 'heatmap_size': [ 48, 64 ], 'num_output_channels': 136, 'num_joints': 17, 'dataset_channel': [ [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ], ], 'inference_channel': [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ], 'flip_pairs': [ [ 1, 2 ], [ 3, 4 ], [ 5, 6 ], [ 7, 8 ], [ 9, 10 ], [ 11, 12 ], [ 13, 14 ], [ 15, 16 ] ], 'upper_body_ids': [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ], 'lower_body_ids': [ 13, 14, 15, 16 ], 'use_different_joint_weights': False, 'joint_weights': [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], 'soft_nms': False, 'nms_thr': 1.0, 'oks_thr': 0.9, 'vis_thr': 0.2, 'use_gt_bbox': True, 'det_bbox_thr': 0.0, 'bbox_file': 'COCO_val2017_detections_AP_H_56_person.json' } sampler: batch_size: 170 # per card shuffle_strategy: 1 patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: rgb # patch modality label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 # project to 256 dim for decoder modality: dense_labeling # label modality patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 # 3 for rgb learnable_pos: False # fixed position embedding, redundant parameter test_pos_mode: False # PE parameters are interpolated from mae to 'img_size'/16, then use repeat(batchsize, 1, 1) img_size: [ 256, 192 ] task_sp_list: [ 'pos_embed' ] label_adapter: # for supvervised training, the results of label adapter is useless type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 17 # class num learnable_pos: False test_pos_mode: False img_size: [ 256, 192 ] dim_class_embed: 64 # embedding shape for class embedding. TODO: chance to text features emb_padding_idx: 255 # task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'upsample_network', 'text_features', ] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should cls_loss_branch: True description_dict_name: checked_pose_aist_name # this key is only valid when we set text_prototype to be True upsample_hidden_dim: 256 task: pose loss_cfg: type: POS_FocalDiceLoss_bce_cls_emb kwargs: target_type: GaussianHeatMap cfg: num_classes: 17 deep_supervision: True ignore_blank: False class_weight: 0.001 dice_weight: 0.0 mask_weight: 1.0 redundant_queries: 1 dec_layers: 9 sample_weight: [ 0.97905498, 0.98151887, 0.98018951, 0.97778281, 0.97704955, 0.98018951, 0.97778281, 0.97704955, 0.98309006, 0.98060388, 0.97209657, 0.98309006, 0.98060388, 0.97209657, 0.98405158, 0.98242514, 0.98066688 ] eos_coef: 0.1 decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: [ 'predictor.mask_token' ] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed', 'predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 # useless in Hulk nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False # indicate to use pre_norm or post_norm in (self-attn, FFN) arch: fan_in # fan_in type to init the weights enforce_input_project: False # placeholder, useless in Hulk mask_on: False # placeholder, useless in Hulk intermediate_output: True num_feature_levels: 1 # placeholder, useless in Hulk cross_pos_embed: anchor # indicate to use adaptive pose2d. should always be "anchor" in Hulk cls_out_dim: 1 # placeholder, useless in Hulk patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. label_pos_mode: False self_attn_mask_type: full # full for all attention # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: CEL_Sigmoid 17: name: LIP_parsing loss_weight: 1.8 gres_ratio: 4 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: LIPParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/LIP # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 20 label_list: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ] sampler: batch_size: 27 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 20 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_lip_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True upsample_before_product: True upsample_hidden_dim: 256 task: parsing loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: cfg: #for maskedsetloss v2 ignore_index: 20 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.3 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] 18: name: CIHP_parsing loss_weight: 3.6 gres_ratio: 4 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: CIHPParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/CIHP # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 20 label_list: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ] sampler: batch_size: 26 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 20 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_cihp_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True task: parsing upsample_before_product: True upsample_hidden_dim: 256 #dim of hidden features in upsampling network loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: cfg: #for maskedsetloss v2 ignore_index: 20 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.1 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] 19: name: human3.6m_parsing loss_weight: 2.25 gres_ratio: 7 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: Human3M6ParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/human3.6 # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 25 label_list: [0, 1, 2, 3, 6, 7, 8, 17, 18, 19, 25, 26, 27, 32, 33, 34, 38, 39, 43, 44, 46, 49, 50, 56, 58] sampler: batch_size: 31 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 25 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_human_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True task: parsing upsample_before_product: True upsample_hidden_dim: 256 # dim of hidden features in upsampling network loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: cfg: #for maskedsetloss v2 ignore_index: 25 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.1 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [1.0, 0.97325, 0.96685, 0.9903500000000001, 0.97325, 0.96685, 0.9903500000000001, 0.9929, 0.9459, 0.89645, 0.9929, 0.9459, 0.89645, 0.981, 0.9997, 0.99265, 0.9997, 0.99265, 0.9995, 0.9999, 0.9999, 0.9758, 0.9256500000000001, 0.9758, 0.9256500000000001] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 25 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] 20: name: modanet_parsing loss_weight: 0.021 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: ModaNetParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/ModaNet/ # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 14 label_list: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ] sampler: batch_size: 27 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 14 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', # 'upsample_network', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_modanet_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True upsample_before_product: True upsample_hidden_dim: 256 # dim of hidden features in upsampling network task: parsing loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: cfg: #for maskedsetloss v2 ignore_index: 14 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.1 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [ 1.0, 0.3933582160972342, 0.2633553450090918, 0.13557278208440998, 0.7506555651258494, 0.45334481768590296, 0.2760455545985262, 0.16753756340319648, 0.4404249210450761, 0.6636233132357163, 0.13457747152837593, 0.25979519571250836, 0.10422049956933678, 0.0956263757297349 ] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ # 'predictor.text_features', 'predictor.query_embed_patch', 'predictor.query_embed_label', # 'predictor.text_pe', # 'predictor.mask_token', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] 21: name: VIP_parsing loss_weight: 0.021 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: VIPParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/VIP # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 20 label_list: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ] sampler: batch_size: 27 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 20 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', # 'upsample_network', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_vip_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True upsample_before_product: True upsample_hidden_dim: 256 # dim of hidden features in upsampling network task: parsing loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: cfg: #for maskedsetloss v2 ignore_index: 20 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.1 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [1.0, 0.3266013319616655, 0.9908495316476258, 0.029184038117927337, 0.052466294872489036, 0.991336834695977, 0.10801884238453625, 0.30001624343494504, 0.3465807569440684, 0.9136932156586712, 0.9863555146461639, 0.015810276679841896, 0.11895608858086523, 0.9925821647084303, 0.9789106069630192, 0.9789106069630192, 0.4952081866912123, 0.4952081866912123, 0.7048026422654177, 0.7048026422654177, ] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] 22: name: deepfashion_parsing loss_weight: 0.042 gres_ratio: 2 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: DeepFashionParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/deepfashion2/ # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 14 label_list: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ] sampler: batch_size: 27 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 14 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', # 'upsample_network', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_deepfashion_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True upsample_before_product: True upsample_hidden_dim: 256 # dim of hidden features in upsampling network task: parsing loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: # target_type: GaussianHeatMap cfg: #for maskedsetloss v2 ignore_index: 14 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.1 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [ 1.0, 0.367704898390819, 0.18624095519402378, 0.002807862013638187, 0.06970686754080256, 0.08321481967691353, 0.010231244888284599, 0.18925719286730117, 0.28635504086767627, 0.15953761441126063, 0.0887055183084064, 0.04064888180411646, 0.09255004922874958, 0.03362141268278453, ] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ 'predictor.query_embed_patch', 'predictor.query_embed_label', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ] 23: name: PaperDoll_parsing loss_weight: 0.021 gres_ratio: 1 # int, > 0, = Task_GPUs / (world_Size/sum(all_gres_ratios)) dataset: type: PaperDollParsingDataset # train for 150 epochs kwargs: data_path: /mnt/path...to.../parsing_public/PaperDoll # #sh1424:s3://parsing_public/human3.6 #/mnt/lustre/share/wangyizhou/human3.6 #sh1984:s3://seg_public/human3.6 cfg: stride_level: 1 is_flip: True crop_size: [ 480, 480 ] is_multi_scale: True scale_factor: 11 center_crop_test: False base_size: 480 eval_crop_size: [ 480, 480 ] ignore2endclass: True is_photometricdistortion: True brightness: 32 contrast_range: [ 0.5, 1.5 ] saturation_range: [ 0.5, 1.5 ] hue_delta: 18 is_rotate: True ignore_value: 255 # duplicated with decoder.kwargs.ignore_value num_classes: 20 label_list: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ] sampler: batch_size: 27 # per card shuffle_strategy: 1 backbone: type: vit_base_patch16_mask kwargs: task_sp_list: [ 'rel_pos_h', 'rel_pos_w' ] # wrong list would somehow cause .cuda() stuck without error pretrained: True lms_checkpoint_train: fairscale window: False test_pos_mode: learnable_interpolate learnable_pos: True drop_path_rate: 0.2 vis_patch_token_ratio: 1 vis_label_token_ratio: 0. patch_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: rgb label_neck: type: MAEdecoder_proj_neck kwargs: mask_dim: 256 modality: dense_labeling patch_adapter: type: rgb_adapter # change to adapter_rgb kwargs: pretrained: True stride_level: 1 in_chans: 3 learnable_pos: False test_pos_mode: False img_size: 480 task_sp_list: [ 'pos_embed' ] label_adapter: type: dense_labeling_adapter kwargs: pretrained: True stride_level: 1 in_chans: 20 learnable_pos: False test_pos_mode: False img_size: 480 dim_class_embed: 64 emb_padding_idx: 255 task_sp_list: [ 'pos_embed', 'class_embed', ] patch_proj: type: rgb_projector kwargs: loss_cfg: type: MaskedMSELoss kwargs: stride: 1 norm_pix_loss: True pix_loss: True pix_loss_weight: 1. norm_pix_loss_weight: 1. label_proj: type: dense_labeling_projector kwargs: # kept one task_sp_list: [ 'post_mul_norm', 'post_mul_norm_cls', 'loss_fn', 'text_features' ] modality_share_list: ['upsample_network',] emb_padding_idx: 255 # should be the same with that in the input adapter post_mul_norm: True replace_post_mul_norm: False # replace the post_mul_norm(LN) with a linear layer translate_weight_scale: 1 # scale the translate weight to 6 times of the original value(1), NOTE that we should description_dict_name: checked_par_paperdoll_name # this key is only valid when we set text_prototype to be True cls_loss_branch: True upsample_before_product: True upsample_hidden_dim: 256 # dim of hidden features in upsampling network task: parsing loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight #POS_FocalDiceLoss_bce_cls_emb kwargs: cfg: #for maskedsetloss v2 ignore_index: 20 loss_weight: 1. loss_per_class: True dice_weight: 50.0 mask_weight: 50.0 class_weight: 0.1 deep_supervision: True dec_layers: 9 cls_weight_sample: True sample_weight: [ 1.0, 0.12651171233101552, 0.9445288709780197, 0.022596273603759997, 0.1542096228225839, 0.7740073338443981, 0.3171279444960444, 0.38393872629003634, 0.19776277195374156, 0.5762416654276241, 0.932492136102867, 0.0684559727964192, 0.2131960924782717, 0.9246929266441772, 0.9079233711740138, 0.9079233711740138, 0.5743937220129259, 0.5743937220129259, 0.7146935638660443, 0.7146935638660443, ] #follow v1 parsing decoder: type: UniHCPv2_Head kwargs: predictor: 'hulk' task: recons modality_share_list: ['predictor.mask_token'] task_sp_list: [ # 'predictor.text_features', 'predictor.query_embed_patch', 'predictor.query_embed_label', # 'predictor.text_pe', # 'predictor.mask_token', 'predictor.class_embed','predictor.fc_bias', # useless in Hulk ] # wrong list would somehow cause .cuda() stuck without error loss_weight: 1.0 transformer_predictor_cfg: hidden_dim: 256 num_queries: 20 nheads: 8 dim_feedforward: 2048 dec_layers: 9 pre_norm: False arch: fan_in enforce_input_project: False mask_on: False intermediate_output: True num_feature_levels: 1 cross_pos_embed: anchor cls_out_dim: 1 patch_pos_mode: False # Mode to generate pos_embed for patch tokens in decoder. # given the fixed self.query_embed_patch (which has a same shape of that in adapter), # repeat(batchsize, 1,1) label_pos_mode: False self_attn_mask_type: patch_diag_label_row_textlabelfull # type of mask for self-attention, # shape [patch_tokens(rgb), label_tokens(sparse_labeling), fixed text tokens] detach_from_peddet: True # Not use the peddet_cfgs to modify the model structure adding_per_layer_pe: True # whether to add per-layer pe to the input of each decoder layer use_adapt_pos2d: True loss_cfg: type: FocalDiceLoss_bce_cls_emb_sample_weight kwargs: cfg: deep_supervision: True no_object_weight: 0.1 class_weight: 0.25 dice_weight: 5.0 mask_weight: 5.0 redundant_queries: 1 num_points: 12544 dec_layers: 6 oversample_ratio: 3.0 importance_sample_ratio: 0.75 sample_weight: [ 1.0, 0.25279349, 0.97595474, 0.06368458, 0.08419378, 0.91287129, 0.18341584, 0.50346535, 0.12729844, 0.6937058, 0.96898868, 0.07022631, 0.07464639, 0.99359972, 0.88490099, 0.88490099, 0.27644979000000003, 0.27644979000000003, 0.33016266, 0.33016266 ]