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| _base_=['../_base_/losses/all_losses.py', | |
| '../_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py', | |
| '../_base_/datasets/nyu.py', | |
| '../_base_/datasets/kitti.py' | |
| ] | |
| import numpy as np | |
| model=dict( | |
| decode_head=dict( | |
| type='RAFTDepthNormalDPT5', | |
| iters=8, | |
| n_downsample=2, | |
| detach=False, | |
| ), | |
| ) | |
| # loss method | |
| losses=dict( | |
| decoder_losses=[ | |
| dict(type='VNLoss', sample_ratio=0.2, loss_weight=0.1), | |
| dict(type='GRUSequenceLoss', loss_weight=1.0, loss_gamma=0.9, stereo_sup=0), | |
| dict(type='DeNoConsistencyLoss', loss_weight=0.001, loss_fn='CEL', scale=2) | |
| ], | |
| ) | |
| data_array = [ | |
| [ | |
| dict(KITTI='KITTI_dataset'), | |
| ], | |
| ] | |
| # configs of the canonical space | |
| data_basic=dict( | |
| canonical_space = dict( | |
| # img_size=(540, 960), | |
| focal_length=1000.0, | |
| ), | |
| depth_range=(0, 1), | |
| depth_normalize=(0.1, 200), | |
| # crop_size=(544, 1216), | |
| # crop_size = (544, 992), | |
| crop_size = (616, 1064), # %28 = 0 | |
| ) | |
| # online evaluation | |
| # evaluation = dict(online_eval=True, interval=1000, metrics=['abs_rel', 'delta1', 'rmse'], multi_dataset_eval=True) | |
| #log_interval = 100 | |
| interval = 4000 | |
| log_interval = 100 | |
| evaluation = dict( | |
| online_eval=False, | |
| interval=interval, | |
| metrics=['abs_rel', 'delta1', 'rmse', 'normal_mean', 'normal_rmse', 'normal_a1'], | |
| multi_dataset_eval=True, | |
| exclude=['DIML_indoor', 'GL3D', 'Tourism', 'MegaDepth'], | |
| ) | |
| # save checkpoint during training, with '*_AMP' is employing the automatic mix precision training | |
| checkpoint_config = dict(by_epoch=False, interval=interval) | |
| runner = dict(type='IterBasedRunner_AMP', max_iters=20010) | |
| # optimizer | |
| optimizer = dict( | |
| type='AdamW', | |
| encoder=dict(lr=5e-7, betas=(0.9, 0.999), weight_decay=0, eps=1e-10), | |
| decoder=dict(lr=1e-5, betas=(0.9, 0.999), weight_decay=0, eps=1e-10), | |
| strict_match = True | |
| ) | |
| # schedule | |
| lr_config = dict(policy='poly', | |
| warmup='linear', | |
| warmup_iters=20, | |
| warmup_ratio=1e-6, | |
| power=0.9, min_lr=1e-8, by_epoch=False) | |
| acc_batch = 1 | |
| batchsize_per_gpu = 2 | |
| thread_per_gpu = 2 | |
| KITTI_dataset=dict( | |
| data = dict( | |
| train=dict( | |
| pipeline=[dict(type='BGR2RGB'), | |
| dict(type='LabelScaleCononical'), | |
| dict(type='RandomResize', | |
| prob=0.5, | |
| ratio_range=(0.85, 1.15), | |
| is_lidar=True), | |
| dict(type='RandomCrop', | |
| crop_size=(0,0), # crop_size will be overwriteen by data_basic configs | |
| crop_type='rand', | |
| ignore_label=-1, | |
| padding=[0, 0, 0]), | |
| dict(type='RandomEdgeMask', | |
| mask_maxsize=50, | |
| prob=0.2, | |
| rgb_invalid=[0,0,0], | |
| label_invalid=-1,), | |
| dict(type='RandomHorizontalFlip', | |
| prob=0.4), | |
| dict(type='PhotoMetricDistortion', | |
| to_gray_prob=0.1, | |
| distortion_prob=0.1,), | |
| dict(type='Weather', | |
| prob=0.05), | |
| dict(type='RandomBlur', | |
| prob=0.05), | |
| dict(type='RGBCompresion', prob=0.1, compression=(0, 40)), | |
| dict(type='ToTensor'), | |
| dict(type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), | |
| ], | |
| #sample_size = 10, | |
| ), | |
| val=dict( | |
| pipeline=[dict(type='BGR2RGB'), | |
| dict(type='LabelScaleCononical'), | |
| dict(type='RandomCrop', | |
| crop_size=(0,0), # crop_size will be overwriteen by data_basic configs | |
| crop_type='center', | |
| ignore_label=-1, | |
| padding=[0, 0, 0]), | |
| dict(type='ToTensor'), | |
| dict(type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), | |
| ], | |
| sample_size = 1200, | |
| ), | |
| )) | |