# Created by BVH, Jul 2025. import os from hydra.core.config_store import ConfigStore from custom.experiment.template import template_any4d_2b ################################################################# job = dict( # this becomes JobConfig project='a4d2', group='cvpr', name='dvs2', prepend_datetime=True, # becomes {now:%m-%d-%H-%M}_{config.job.name} ) wandb = dict( enabled=True, entity='tri', project='a4d2_cvpr', num_validation_logs=5, ) any4d_config = dict( # this is part of Any4DConfig, which inherits from Predict2Video2WorldModelConfig transforms='default', # reference_camera now separate per dataset dataloader='basile', vae='a4d_vae', video_entries=[ # first entry = base / pretrained dict( # key: (channel_start, channel_end, in_proj_init, out_proj_init) # NOTE: there will be T*H*W tokens with this information rgb0=(0, 16, 'load', 'load'), # do not change rgb0_input_mask=(16, 17, 'load', None), # do not change rgb0_output_mask=(17, 18, 'zero', None), # do not change cams0=(18, 50, 'zero/load', 'zero/load'), cams0_input_mask=(50, 51, 'zero/load', None), cams0_output_mask=(51, 52, 'zero', None), ), # later entries = new viewpoints dict( # key: (channel_start, channel_end, in_proj_init, out_proj_init) rgb1=(0, 16, 'copy:rgb0/load', 'copy:rgb0/load'), rgb1_input_mask=(16, 17, 'copy:rgb0_input_mask/load', None), rgb1_output_mask=(17, 18, 'zero', None), cams1=(18, 50, 'zero/load', 'zero/load'), cams1_input_mask=(50, 51, 'zero/load', None), cams1_output_mask=(51, 52, 'zero', None), ), ], num_views=2, video_concat_mode='view', video_proj_mode='per_view', view_timestep_mode='per_view', loss_weights=dict( rgb0=1.0, # rgb1=1.0, ), harmonize_streams=True, harmonize_frames=True, load_modals=['rgb', 'cams'], # 'language', 'action', ], # =['rgb', 'language', 'action', 'cams'], task_probs=dict( cross_modal=0.0, dyn_view_synth=1.0, forecast=0.0, pose_estimation=0.0, inverse_dynamics=0.0, policy=0.0, world_model=0.0, ), use_views='all', # shuffle_cams2views=True, shuffle_cams2views=False, train_directives=dict( num_pred_views=1, ), val_directives=dict( tasks='dyn_view_synth', num_pred_views=1, remove_cond=True, ), data_val_overrides=dict(frame_stride=1), track_metrics=dict( rgb0=['psnr', 'ssim'], # rgb1=['psnr', 'ssim'], ), train_visuals_interval=99, train_visuals_detail=1, # 2, val_visuals_detail=1, # 2, viz_input_blacklist=[], # overridden to show cams viz_mask_border_width=0, # overridden because DVS is temporally invariant # val_num_steps=15, # val_num_steps=25, val_num_steps=35, ) ################################################################# S3_PRETRAINED_PREFIX = r's3://tri-ml-sandbox-16011-us-west-2-datasets/cosmos-predict-2/checkpoints' model = dict( # this makes up parts of Predict2Video2WorldModelConfig and Predict2ModelManagerConfig # dit_path='', # dit_path='s3://tri-ml-sandbox-16011-us-west-2-datasets/sagemaker/cosmos-predict2/a4d2/cvpr/10-19-19-24_s23_rdvs2_mix_b6_scratch/model/iter_000003000_001152000.pt', # dit_path='s3://tri-ml-sandbox-16011-us-west-2-datasets/sagemaker/cosmos-predict2/a4d2/cvpr/10-21-04-09_s24_rdvs2_mix_b8_scratch/model/iter_000004000_002048000.pt', dit_path='s3://tri-ml-sandbox-16011-us-west-2-datasets/sagemaker/cosmos-predict2/a4d2/cvpr/10-24-00-43_s25_rdvs2_mix_b8_scratch/model/iter_000001000_000512000.pt', # text_encoder_path=f'{S3_PRETRAINED_PREFIX}/google-t5/t5-11b', text_encoder_path='', vae_path=f'{S3_PRETRAINED_PREFIX}/nvidia/Cosmos-Predict2-2B-Video2World/tokenizer/tokenizer.pth', fsdp_shard_size=8, ############ run_validation=True, validation_iter=500, # frequency / interval of validation runs # max_val_iter=8, # number of steps per validation run # max_iter=120000, # total number of training steps # max_iter=60000, # total number of training steps max_iter=50000, # total number of training steps # grad_accum_iter=2, grad_accum_iter=1, context_parallel_size=1, device_monitor=0, manual_gc_iter=288, manual_gc_warm_up=-1, # never disable automatic GC to be safe (weird VRAM issue) ############ state_t=11, # for noise level; = latent # frames for now tokenizer_chunk_duration=41, # for VAE; = num_frames for now ) checkpoint = dict( save_iter=1000, s3_folder='s3://tri-ml-sandbox-16011-us-west-2-datasets/sagemaker/cosmos-predict2/', early_sanity_check=5, ) optimizer = dict( lr=5.0e-5, ) scheduler = dict( # warm_up_steps = [200], warm_up_steps = [1000], cycle_lengths = [model['max_iter']], f_start=[0.01], f_max=[1.0], f_min=[0.05], ) dataset_train = dict( config='custom/config/cvpr/mixed/train_2v.yaml', num_workers=4, batch_size=8, ) dataset_val = dict( config=dict( ### DRIVING ArgoVerseFront = 'custom/config/cvpr/mixed/val_2v/argoverse_front.yaml', ArgoVerseLeft = 'custom/config/cvpr/mixed/val_2v/argoverse_left.yaml', ArgoVerseRight = 'custom/config/cvpr/mixed/val_2v/argoverse_right.yaml', DDADFront = 'custom/config/cvpr/mixed/val_2v/ddad_front.yaml', DDADLeft = 'custom/config/cvpr/mixed/val_2v/ddad_left.yaml', DDADRight = 'custom/config/cvpr/mixed/val_2v/ddad_right.yaml', LyftL5Front = 'custom/config/cvpr/mixed/val_2v/lyftl5_front.yaml', LyftL5Left = 'custom/config/cvpr/mixed/val_2v/lyftl5_left.yaml', LyftL5Right = 'custom/config/cvpr/mixed/val_2v/lyftl5_right.yaml', WaymoFront = 'custom/config/cvpr/mixed/val_2v/waymo_front.yaml', WaymoLeft = 'custom/config/cvpr/mixed/val_2v/waymo_left.yaml', WaymoRight = 'custom/config/cvpr/mixed/val_2v/waymo_right.yaml', ### ROBOTICS DROIDIDLeft = 'custom/config/cvpr/mixed/val_2v/droidcalib_id_left.yaml', DROIDIDRight = 'custom/config/cvpr/mixed/val_2v/droidcalib_id_right.yaml', DROIDOODLeft = 'custom/config/cvpr/mixed/val_2v/droidcalib_ood_left.yaml', DROIDOODRight = 'custom/config/cvpr/mixed/val_2v/droidcalib_ood_right.yaml', LBMv12Left = 'custom/config/cvpr/mixed/val_2v/lbmv12_left.yaml', LBMv12Right = 'custom/config/cvpr/mixed/val_2v/lbmv12_right.yaml', ### 3D TartanAir = 'custom/config/cvpr/mixed/val_2v/tartanair.yaml', DL3DV0 = 'custom/config/cvpr/mixed/val_2v/dl3dv_0.yaml', # DL3DV1 = 'custom/config/cvpr/mixed/val_2v/dl3dv_1.yaml', MVImgNet0 = 'custom/config/cvpr/mixed/val_2v/mvimgnet_0.yaml', # MVImgNet1 = 'custom/config/cvpr/mixed/val_2v/mvimgnet_1.yaml', RE10K0 = 'custom/config/cvpr/mixed/val_2v/re10k_0.yaml', # RE10K1 = 'custom/config/cvpr/mixed/val_2v/re10k_1.yaml', WildRGBD0 = 'custom/config/cvpr/mixed/val_2v/wildrgbd_0.yaml', # WildRGBD1 = 'custom/config/cvpr/mixed/val_2v/wildrgbd_1.yaml', ScanNet0 = 'custom/config/cvpr/mixed/val_2v/scannet_0.yaml', # ScanNet1 = 'custom/config/cvpr/mixed/val_2v/scannet_1.yaml', ### 4D DyCheck = 'custom/config/cvpr/mixed/val_2v/dycheck.yaml', GCDKB4DDirect = 'custom/config/cvpr/mixed/val_2v/gcd_kubric4d_dir.yaml', GCDKB4DGradual = 'custom/config/cvpr/mixed/val_2v/gcd_kubric4d_grad.yaml', GCDPD4DDirect = 'custom/config/cvpr/mixed/val_2v/gcd_pd4d_dir.yaml', GCDPD4DGradual = 'custom/config/cvpr/mixed/val_2v/gcd_pd4d_grad.yaml', Kubric4D = 'custom/config/cvpr/mixed/val_2v/kubric4d.yaml', Kubric5D = 'custom/config/cvpr/mixed/val_2v/kubric5d.yaml', ParDom4D = 'custom/config/cvpr/mixed/val_2v/pd4d.yaml', EgoExo4D0 = 'custom/config/cvpr/mixed/val_2v/egoexo4d_0.yaml', EgoExo4D1 = 'custom/config/cvpr/mixed/val_2v/egoexo4d_1.yaml', EgoExo4D2 = 'custom/config/cvpr/mixed/val_2v/egoexo4d_2.yaml', EgoExo4D3 = 'custom/config/cvpr/mixed/val_2v/egoexo4d_3.yaml', ), num_workers=0, # can crash debugger otherwise batch_size=1, ) metrics = dict( # gt_source='rec', gt_source='gt', modes=['r','rpv','apv','ipv'], rgb=dict(), ) ################################################################# cs = ConfigStore.instance() this_config = template_any4d_2b( job, wandb, any4d_config, model, checkpoint, optimizer, scheduler, metrics, dataset_train, dataset_val) # Use the filename (without extension) as the experiment name experiment_name = 'any4d_' + os.path.splitext(os.path.basename(__file__))[0] cs.store( group='experiment', package='_global_', name=experiment_name, node=this_config, )