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_base_=['../_base_/losses/all_losses.py',
'../_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py',
'../_base_/datasets/kitti.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(KITTI='KITTI_dataset'),
],
]
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 = (1120, 2016),
clip_depth_range=(0.1, 80),
vit_size=(616,1064),
)
clip_depth = True
test_metrics = ['abs_rel', 'rmse', 'silog', 'delta1', 'delta2', 'delta3', 'rmse_log',
'log10']
KITTI_dataset=dict(
data = dict(
test=dict(
pipeline=[dict(type='BGR2RGB'),
dict(type='LabelScaleCononical'),
dict(type='ResizeKeepRatio',
resize_size=(616, 1064), #(416, 1248), #(480, 1216), #(512, 1088), #(512, 1312), #(480, 1248), # #
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,
),
))
# DDAD_dataset=dict(
# data = dict(
# test=dict(
# anno_path='DDAD/annotations/test_annotations.json',
# pipeline=[dict(type='BGR2RGB'),
# dict(type='KeepResizeCanoSize',
# resize_size=(640, 1088), #(1216, 1952), #(512, 960), #
# 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 = 80,
# ),
# )) |