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nuscenes_gs25600.py
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nuscenes_gs25600.py
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_base_ = [
'../_base_/misc.py',
'../_base_/model.py',
'../_base_/surroundocc.py'
]
# =========== data config ==============
input_shape = (1600, 864)
data_aug_conf = {
"resize_lim": (1.0, 1.0),
"final_dim": input_shape[::-1],
"bot_pct_lim": (0.0, 0.0),
"rot_lim": (0.0, 0.0),
"H": 900,
"W": 1600,
"rand_flip": True,
}
val_dataset_config = dict(
data_aug_conf=data_aug_conf
)
train_dataset_config = dict(
data_aug_conf=data_aug_conf
)
# =========== misc config ==============
optimizer = dict(
optimizer = dict(
type="AdamW", lr=4e-4, weight_decay=0.01,
),
paramwise_cfg=dict(
custom_keys={
'img_backbone': dict(lr_mult=0.1)}
)
)
grad_max_norm = 35
# ========= model config ===============
loss = dict(
type='MultiLoss',
loss_cfgs=[
dict(
type='OccupancyLoss',
weight=1.0,
empty_label=17,
num_classes=18,
use_focal_loss=False,
use_dice_loss=False,
balance_cls_weight=True,
multi_loss_weights=dict(
loss_voxel_ce_weight=10.0,
loss_voxel_lovasz_weight=1.0),
use_sem_geo_scal_loss=False,
use_lovasz_loss=True,
lovasz_ignore=17,
manual_class_weight=[
1.01552756, 1.06897009, 1.30013094, 1.07253735, 0.94637502, 1.10087012,
1.26960524, 1.06258364, 1.189019, 1.06217292, 1.00595144, 0.85706115,
1.03923299, 0.90867526, 0.8936431, 0.85486129, 0.8527829, 0.5 ],
ignore_empty=False,
lovasz_use_softmax=False),
dict(
type="PixelDistributionLoss",
weight=1.0,
use_sigmoid=False),
# dict(
# type="BinaryCrossEntropyLoss",
# weight=10.0,
# empty_label=17,
# class_weights=[1.0, 1.0]),
# dict(
# type='DensityLoss',
# weight=0.01,
# thresh=0.0)
])
loss_input_convertion = dict(
pred_occ="pred_occ",
sampled_xyz="sampled_xyz",
sampled_label="sampled_label",
occ_mask="occ_mask",
bin_logits="bin_logits",
density="density",
pixel_logits="pixel_logits",
pixel_gt="pixel_gt"
)
# ========= model config ===============
embed_dims = 128
num_decoder = 4
pc_range = [-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]
scale_range = [0.01, 1.8]
xyz_coordinate = 'cartesian'
phi_activation = 'sigmoid'
include_opa = True
load_from = 'ckpts/r101_dcn_fcos3d_pretrain.pth'
semantics = True
semantic_dim = 17
model = dict(
freeze_lifter=True,
img_backbone_out_indices=[0, 1, 2, 3],
img_backbone=dict(
_delete_=True,
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN2d', requires_grad=False),
norm_eval=True,
style='caffe',
with_cp = True,
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), # original DCNv2 will print log when perform load_state_dict
stage_with_dcn=(False, False, True, True)),
img_neck=dict(
start_level=1),
lifter=dict(
type='GaussianLifterV2',
num_anchor=19200,
embed_dims=embed_dims,
anchor_grad=False,
feat_grad=False,
semantics=semantics,
semantic_dim=semantic_dim,
include_opa=include_opa,
num_samples=128,
anchors_per_pixel=1,
random_sampling=False,
projection_in=None,
initializer=dict(
type="ResNetSecondFPN",
img_backbone_out_indices=[0, 1, 2, 3],
img_backbone_config=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN2d', requires_grad=False),
norm_eval=True,
style='caffe',
with_cp=True,
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), # original DCNv2 will print log when perform load_state_dict
stage_with_dcn=(False, False, True, True)),
neck_confifg=dict(
type='SECONDFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=[embed_dims] * 4,
upsample_strides=[0.5, 1, 2, 4])),
initializer_img_downsample=None,
pretrained_path="out/prob/init/init.pth",
deterministic=False,
random_samples=6400),
encoder=dict(
type='GaussianOccEncoder',
anchor_encoder=dict(
type='SparseGaussian3DEncoder',
embed_dims=embed_dims,
include_opa=include_opa,
semantics=semantics,
semantic_dim=semantic_dim
),
norm_layer=dict(type="LN", normalized_shape=embed_dims),
ffn=dict(
_delete_=True,
type="AsymmetricFFN",
in_channels=embed_dims,
embed_dims=embed_dims,
feedforward_channels=embed_dims * 4,
ffn_drop=0.1,
add_identity=False,
),
deformable_model=dict(
embed_dims=embed_dims,
residual_mode="none",
kps_generator=dict(
embed_dims=embed_dims,
phi_activation=phi_activation,
xyz_coordinate=xyz_coordinate,
num_learnable_pts=6,
pc_range=pc_range,
scale_range=scale_range,
learnable_fixed_scale=6.0,
),
),
refine_layer=dict(
type='SparseGaussian3DRefinementModuleV2',
embed_dims=embed_dims,
pc_range=pc_range,
scale_range=scale_range,
unit_xyz=[4.0, 4.0, 1.0],
semantics=semantics,
semantic_dim=semantic_dim,
include_opa=include_opa,
xyz_coordinate=xyz_coordinate,
semantics_activation='identity',
),
spconv_layer=dict(
_delete_=True,
type="SparseConv3D",
in_channels=embed_dims,
embed_channels=embed_dims,
pc_range=pc_range,
grid_size=[1.0, 1.0, 1.0],
phi_activation=phi_activation,
xyz_coordinate=xyz_coordinate,
use_out_proj=True,
use_multi_layer=True,
),
num_decoder=num_decoder,
operation_order=[
"identity",
"deformable",
"add",
"norm",
"identity",
"ffn",
"add",
"norm",
"identity",
"spconv",
"add",
"norm",
"identity",
"ffn",
"add",
"norm",
"refine",
] * num_decoder,
),
head=dict(
type='GaussianHead',
apply_loss_type='random_1',
num_classes=semantic_dim + 1,
empty_args=dict(
_delete_=True,
mean=[0, 0, -1.0],
scale=[100, 100, 8.0],
),
with_empty=False,
use_localaggprob=True,
use_localaggprob_fast=False,
combine_geosem=True,
cuda_kwargs=dict(
_delete_=True,
scale_multiplier=4,
H=200, W=200, D=16,
pc_min=[-50.0, -50.0, -5.0],
grid_size=0.5),
)
)