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semantic_nusc.py
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semantic_nusc.py
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_base_ = [
'../_base_/default_runtime.py'
]
# model type
type = 'Mapper'
plugin = True
# plugin code dir
plugin_dir = 'plugin/'
# img configs
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
img_size = (128, 352)
# category configs
cat2id = {
'ped_crossing': 0,
'divider': 1,
'boundary': 2,
}
num_classes = max(cat2id.values()) + 1
# rasterize configs
roi_size = (60, 30) # bev range, 60m in x-axis, 30m in y-axis
canvas_size = (400, 200) # bev feature size
coords_dim = 2 # polylines coordinates dimension, 2 or 3
thickness = 3 # thickness of rasterized polylines
# meta info for submission pkl
meta = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_external=False,
output_format='raster')
# model config
model = dict(
type='HDMapNet_Semantic',
backbone_cfg=dict(
type='HDMapNetBackbone',
img_res=img_size,
out_channels=64,
canvas_size=canvas_size,
n_views=7,
),
head_cfg=dict(
type='BevEncode',
in_channels=64,
out_channels=num_classes,
),
loss_cfg=dict(
type='SimpleLoss',
pos_weight=2.13,
loss_weight=1.0
),
)
# data processing pipelines
train_pipeline = [
dict(
type='RasterizeMap',
roi_size=roi_size,
coords_dim=coords_dim,
canvas_size=canvas_size,
thickness=thickness,
),
dict(type='LoadMultiViewImagesFromFiles'),
dict(type='ResizeMultiViewImages',
size=img_size, # (H, W)
change_intrinsics=True,
),
dict(type='Normalize3D', **img_norm_cfg),
dict(type='PadMultiViewImages', size_divisor=32, change_intrinsics=True),
dict(type='FormatBundleMap'),
dict(type='Collect3D', keys=['img', 'semantic_mask'], meta_keys=(
'token', 'cam_intrinsics', 'cam_extrinsics'))
]
# configs for evaluation code
# DO NOT CHANGE
eval_config = dict(
type='NuscDataset',
data_root='./datasets/nuScenes',
ann_file='./datasets/nuScenes/nuscenes_map_infos_val.pkl',
meta=meta,
roi_size=roi_size,
cat2id=cat2id,
pipeline=[
dict(
type='RasterizeMap',
roi_size=roi_size,
coords_dim=coords_dim,
canvas_size=canvas_size,
thickness=thickness,
),
dict(type='FormatBundleMap'),
dict(type='Collect3D', keys=['semantic_mask'], meta_keys=['token'])
],
interval=1,
)
# dataset configs
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='NuscDataset',
data_root='./datasets/nuScenes',
ann_file='./datasets/nuScenes/nuscenes_map_infos_train.pkl',
meta=meta,
roi_size=roi_size,
cat2id=cat2id,
pipeline=train_pipeline,
interval=1,
),
val=dict(
type='NuscDataset',
data_root='./datasets/nuScenes',
ann_file='./datasets/nuScenes/nuscenes_map_infos_val.pkl',
meta=meta,
roi_size=roi_size,
cat2id=cat2id,
pipeline=train_pipeline,
eval_config=eval_config,
test_mode=True,
interval=1,
),
test=dict(
type='NuscDataset',
data_root='./datasets/nuScenes',
ann_file='./datasets/nuScenes/nuscenes_map_infos_val.pkl',
meta=meta,
roi_size=roi_size,
cat2id=cat2id,
pipeline=train_pipeline,
eval_config=eval_config,
test_mode=True,
interval=1,
),
)
# optimizer
optimizer = dict(
type='AdamW',
lr=1e-3,
paramwise_cfg=dict(
custom_keys={
# 'backbone': dict(lr_mult=0.1),
}),
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy & schedule
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=200,
warmup_ratio=0.1,
step=[9, 11])
checkpoint_config = dict(interval=3)
total_epochs = 12
# kwargs for dataset evaluation
eval_kwargs = dict()
evaluation = dict(
interval=3,
**eval_kwargs)
runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
find_unused_parameters = True
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])