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| _base_ = [ | |
| '../_base_/datasets/u4k.py', | |
| ] | |
| min_depth=1e-3 | |
| max_depth=80 | |
| zoe_depth_config=dict( | |
| type='DA-ZoeDepth', | |
| min_depth=min_depth, | |
| max_depth=max_depth, | |
| depth_anything=True, | |
| midas_model_type='vitl', | |
| img_size=[392, 518], | |
| # some important params | |
| # midas_model_type='DPT_BEiT_L_384', | |
| pretrained_resource='local::./work_dir/DepthAnything_vitl.pt', | |
| use_pretrained_midas=True, | |
| train_midas=True, | |
| freeze_midas_bn=True, | |
| do_resize=False, # do not resize image in midas | |
| # default settings | |
| attractor_alpha=1000, | |
| attractor_gamma=2, | |
| attractor_kind='mean', | |
| attractor_type='inv', | |
| aug=True, | |
| bin_centers_type='softplus', | |
| bin_embedding_dim=128, | |
| clip_grad=0.1, | |
| dataset='nyu', | |
| distributed=True, | |
| force_keep_ar=True, | |
| gpu='NULL', | |
| inverse_midas=False, | |
| log_images_every=0.1, | |
| max_temp=50.0, | |
| max_translation=100, | |
| memory_efficient=True, | |
| min_temp=0.0212, | |
| model='zoedepth', | |
| n_attractors=[16, 8, 4, 1], | |
| n_bins=64, | |
| name='ZoeDepth', | |
| notes='', | |
| output_distribution='logbinomial', | |
| prefetch=False, | |
| print_losses=False, | |
| project='ZoeDepth', | |
| random_crop=False, | |
| random_translate=False, | |
| root='.', | |
| save_dir='', | |
| shared_dict='NULL', | |
| tags='', | |
| translate_prob=0.2, | |
| uid='NULL', | |
| use_amp=False, | |
| use_shared_dict=False, | |
| validate_every=0.25, | |
| version_name='v1', | |
| workers=16, | |
| ) | |
| model=dict( | |
| type='BaselinePretrain', | |
| patch_process_shape=(392, 518), | |
| min_depth=min_depth, | |
| max_depth=max_depth, | |
| target='fine', | |
| coarse_branch=zoe_depth_config, | |
| fine_branch=zoe_depth_config, | |
| sigloss=dict(type='SILogLoss')) | |
| collect_input_args=['image_lr', 'crops_image_hr', 'depth_gt', 'crop_depths', 'bboxs', 'image_hr'] | |
| project='patchfusion' | |
| train_cfg=dict(max_epochs=24, val_interval=2, save_checkpoint_interval=24, log_interval=100, train_log_img_interval=500, val_log_img_interval=50, val_type='epoch_base', eval_start=0) | |
| optim_wrapper=dict( | |
| # optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.01), | |
| optimizer=dict(type='AdamW', lr=0.0002/50, weight_decay=0.01), | |
| clip_grad=dict(type='norm', max_norm=0.1, norm_type=2), # norm clip | |
| paramwise_cfg=dict( | |
| bypass_duplicate=True, | |
| custom_keys={ | |
| })) | |
| param_scheduler=dict( | |
| cycle_momentum=True, | |
| base_momentum=0.85, | |
| max_momentum=0.95, | |
| div_factor=1, | |
| final_div_factor=10000, | |
| pct_start=0.5, | |
| three_phase=False,) | |
| env_cfg=dict( | |
| cudnn_benchmark=True, | |
| mp_cfg=dict(mp_start_method='forkserver'), | |
| dist_cfg=dict(backend='nccl')) | |
| convert_syncbn=True | |
| find_unused_parameters=True | |
| train_dataloader=dict( | |
| dataset=dict( | |
| resize_mode='depth-anything', | |
| transform_cfg=dict( | |
| network_process_size=[392, 518]))) | |
| val_dataloader=dict( | |
| dataset=dict( | |
| resize_mode='depth-anything', | |
| transform_cfg=dict( | |
| network_process_size=[392, 518]))) |