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Running
on
Zero
| from yacs.config import CfgNode as CN | |
| _CN = CN() | |
| _CN.name = 'default' | |
| _CN.suffix ='sintel' | |
| _CN.gamma = 0.75 | |
| _CN.max_flow = 400 | |
| _CN.batch_size = 6 | |
| _CN.sum_freq = 100 | |
| _CN.val_freq = 100000000 | |
| _CN.image_size = [432, 960] | |
| _CN.add_noise = False | |
| _CN.use_smoothl1 = False | |
| _CN.critical_params = [] | |
| _CN.transformer = 'percostformer3' | |
| ### change the path here | |
| _CN.model = "pretrained/sintel.pth" | |
| _CN.percostformer3 = CN() | |
| _CN.percostformer3.pe = 'linear' | |
| _CN.percostformer3.dropout = 0.0 | |
| _CN.percostformer3.droppath = 0.0 | |
| _CN.percostformer3.encoder_latent_dim = 256 # in twins, this is 256 | |
| _CN.percostformer3.query_latent_dim = 64 | |
| _CN.percostformer3.cost_latent_input_dim = 64 | |
| _CN.percostformer3.cost_latent_token_num = 8 | |
| _CN.percostformer3.cost_latent_dim = 128 | |
| _CN.percostformer3.cost_heads_num = 1 | |
| # encoder | |
| _CN.percostformer3.pretrain = True | |
| _CN.percostformer3.use_convertor = False | |
| _CN.percostformer3.del_layers = True | |
| _CN.percostformer3.encoder_depth = 3 | |
| _CN.percostformer3.expand_factor = 4 | |
| _CN.percostformer3.vertical_encoder_attn = "twins" | |
| _CN.percostformer3.attn_dim = 128 | |
| _CN.percostformer3.patch_size = 8 | |
| _CN.percostformer3.patch_embed = 'single' | |
| _CN.percostformer3.cross_attn = "all" | |
| _CN.percostformer3.gma = "GMA" | |
| _CN.percostformer3.vert_c_dim = 64 | |
| _CN.percostformer3.cost_encoder_res = True | |
| _CN.percostformer3.cnet = 'twins' | |
| _CN.percostformer3.fnet = 'twins' | |
| _CN.percostformer3.flow_or_pe = "and" | |
| _CN.percostformer3.use_patch = False # use cost patch rather than local cost as query | |
| _CN.percostformer3.use_rpe = False | |
| _CN.percostformer3.detach_local = False | |
| _CN.percostformer3.no_sc = False | |
| _CN.percostformer3.r_16 =-1 | |
| _CN.percostformer3.quater_refine = False | |
| # pretrain config | |
| _CN.percostformer3.pretrain_mode = False | |
| _CN.percostformer3.pic_size = [368, 496, 368, 496] | |
| _CN.percostformer3.mask_ratio = 0.5 | |
| _CN.percostformer3.query_num = 30 | |
| _CN.percostformer3.no_border = True | |
| _CN.percostformer3.gt_r = 15 | |
| _CN.percostformer3.fix_pe = False | |
| # decoder | |
| _CN.percostformer3.decoder_depth = 12 | |
| _CN.percostformer3.critical_params = ['vert_c_dim', 'encoder_depth', 'vertical_encoder_attn', "use_patch", "flow_or_pe", "use_rpe", "dropout", "detach_local", "expand_factor"] | |
| ### TRAINER | |
| _CN.trainer = CN() | |
| _CN.trainer.scheduler = 'OneCycleLR' | |
| _CN.trainer.optimizer = 'adamw' | |
| _CN.trainer.canonical_lr = 12.5e-5 | |
| _CN.trainer.adamw_decay = 1e-5 | |
| _CN.trainer.clip = 1.0 | |
| _CN.trainer.num_steps = 120000 | |
| _CN.trainer.epsilon = 1e-8 | |
| _CN.trainer.anneal_strategy = 'linear' | |
| def get_cfg(): | |
| return _CN.clone() |