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_base_=['../_base_/losses/all_losses.py',
'../_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py',
'../_base_/datasets/diode.py',
'../_base_/datasets/_data_base_.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_1m.py'
]
import numpy as np
model=dict(
decode_head=dict(
type='RAFTDepthNormalDPT5',
iters=8,
n_downsample=2,
detach=False,
)
)
# model settings
find_unused_parameters = True
# data configs, some similar data are merged together
data_array = [
# group 1
[
dict(DIODE='DIODE_dataset'),
#dict(DIODE_indoor='DIODE_dataset')
#dict(DIODE_outdoor='DIODE_dataset')
],
]
data_basic=dict(
canonical_space = dict(
img_size=(540, 960),
focal_length=1000.0,
),
depth_range=(0, 1),
depth_normalize=(0.1, 200),# (0.3, 160),
# crop_size = (512, 960),
clip_depth_range=(0.1, 150),
)
# indoor (544, 928), outdoor: (768, 1088)
test_metrics = ['abs_rel', 'rmse', 'silog', 'delta1', 'delta2', 'delta3', 'normal_median' , 'normal_mean', 'normal_rmse', 'normal_a1', 'normal_a2', 'normal_a3', 'normal_a4', 'normal_a5']
DIODE_dataset=dict(
data = dict(
test=dict(
pipeline=[dict(type='BGR2RGB'),
dict(type='LabelScaleCononical'),
dict(type='ResizeKeepRatio',
resize_size=(616, 1064), #(544, 992), #(768, 1088), #(768, 1120), # (768, 1216), #(768, 1024), # (768, 1216), #(768, 1312), #
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_ratio = 1.0,
sample_size = -1,
),
))
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