Spaces:
Runtime error
Runtime error
File size: 3,563 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
_base_ = [
'../_base_/datasets/u4k.py',
'../_base_/datasets/general_dataset.py',
'../_base_/run_time.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=None,
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='PatchFusion',
config=dict(
image_raw_shape=(2160, 3840),
patch_split_num=(4, 4),
patch_process_shape=(392, 518),
min_depth=min_depth,
max_depth=max_depth,
load_branch=True,
pretrain_model=['./work_dir/depthanything_vitb_u4k/coarse_pretrain/checkpoint_24.pth', './work_dir/depthanything_vitb_u4k/fine_pretrain/checkpoint_24.pth'], # coarse, fine
coarse_branch=zoe_depth_config,
fine_branch=zoe_depth_config,
guided_fusion=dict(
type='GuidedFusionPatchFusion',
patch_process_shape=(392, 518),
in_channels=[32, 128, 128, 128, 128, 128],
num_patches=[392*518, 224*296, 112*148, 56*74, 28*37, 14*19],
n_channels=5,
g2l=True,),
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=16, val_interval=2, save_checkpoint_interval=16, 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=0.0001, weight_decay=0.001),
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=10,
final_div_factor=10000,
pct_start=0.25,
three_phase=False,)
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])))
general_dataloader=dict(
dataset=dict(
network_process_size=(392, 518),
resize_mode='depth-anything'))
|