Spaces:
Sleeping
Sleeping
File size: 2,971 Bytes
1f418ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
_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]))) |