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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,
),
))
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