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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='vitb', | |
img_size=[392, 518], | |
# some important params | |
# midas_model_type='DPT_BEiT_L_384', | |
pretrained_resource='local::./work_dir/DepthAnything_vitb.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', | |
min_depth=min_depth, | |
max_depth=max_depth, | |
target='coarse', | |
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]))) |