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"""3D-MOOD model config."""
from __future__ import annotations
from ml_collections import ConfigDict, FieldReference
from vis4d.config import class_config
from vis4d.config.typing import ExperimentParameters
from vis4d.op.fpp.fpn import FPN
from opendet3d.model.detect3d.grounding_dino_3d import GroundingDINO3D
from opendet3d.op.base.swin import SwinTransformer
from opendet3d.op.detect3d.grounding_dino_3d import (
GroundingDINO3DCoder,
GroundingDINO3DHead,
RoI2Det3D,
UniDepthHead,
)
from opendet3d.op.fpp.channel_mapper import ChannelMapper
from opendet3d.zoo.gdino.base.model import GDINO_MODEL_WEIGHTS
def get_gdino3d_hyperparams_cfg() -> ExperimentParameters:
"""Get the hyperparameters for 3D-MOOD."""
params = ExperimentParameters()
# Training
params.samples_per_gpu = 2
params.workers_per_gpu = 4
params.accumulate_grad_batches = 1
params.lr = 0.0004 # bs=128, lr=0.0004
params.weight_decay = 0.0001
# Learning rate schedule
params.num_epochs = 120
params.step_1 = 80
params.step_2 = 110
params.check_val_every_n_epoch = 1
# Grounding DINO 3D Coder
params.center_scale = 10.0
params.depth_scale = 2.0
params.dim_scale = 2.0
params.orientation = "rotation_6d"
# Grounding DINO 3D Loss
params.loss_center_weight = 1.0
params.loss_depth_weight = 1.0
params.loss_dim_weight = 1.0
params.loss_rot_weight = 1.0
# Aux Depth Loss
params.si_log_weight = 10.0
# RoI2Det3D
params.nms = False
params.class_agnostic_nms = False
params.max_per_img = 100
params.score_threshold = 0.0
params.iou_threshold = 0.5
# Depth Head
params.depth_output_scales = 1
return params
def get_gdino3d_head_cfg(params: ExperimentParameters) -> ConfigDict:
"""Get the G-DINO 3D head config."""
box_coder = class_config(
GroundingDINO3DCoder,
center_scale=params.center_scale,
depth_scale=params.depth_scale,
dim_scale=params.dim_scale,
orientation=params.orientation,
)
bbox3d_head = class_config(
GroundingDINO3DHead,
box_coder=box_coder,
depth_output_scales=params.depth_output_scales,
)
roi2det3d = class_config(
RoI2Det3D,
nms=params.nms,
max_per_img=params.max_per_img,
class_agnostic_nms=params.class_agnostic_nms,
score_threshold=params.score_threshold,
iou_threshold=params.iou_threshold,
box_coder=box_coder,
)
return bbox3d_head, roi2det3d, box_coder
def get_gdino3d_cfg(
params: ExperimentParameters,
basemodel: ConfigDict,
neck: ConfigDict,
depth_fpn: ConfigDict,
num_feature_levels: int = 4,
chunked_size: int = -1,
cat_mapping: dict[str, int] | None = None,
pretrained: str | None = None,
use_checkpoint: bool | FieldReference = False,
) -> ConfigDict:
"""Get the Grounding DINO with Swin-B model config."""
# UniDepth Head
depth_head = class_config(
UniDepthHead,
depth_scale=params.depth_scale,
input_dims=[256, 256, 256, 256],
output_scales=params.depth_output_scales,
)
bbox3d_head, roi2det3d, box_coder = get_gdino3d_head_cfg(params=params)
if pretrained is not None:
weights = GDINO_MODEL_WEIGHTS[pretrained]
else:
weights = None
model = class_config(
GroundingDINO3D,
basemodel=basemodel,
neck=neck,
num_feature_levels=num_feature_levels,
bbox3d_head=bbox3d_head,
roi2det3d=roi2det3d,
fpn=depth_fpn,
depth_head=depth_head,
use_checkpoint=use_checkpoint,
weights=weights,
chunked_size=chunked_size,
cat_mapping=cat_mapping,
)
return model, box_coder
def get_gdino3d_swin_tiny_cfg(
params: ExperimentParameters,
chunked_size: int = -1,
cat_mapping: dict[str, int] | None = None,
pretrained: str | None = None,
use_checkpoint: bool | FieldReference = False,
) -> ConfigDict:
"""Get the config of Swin-Tiny."""
basemodel = class_config(
SwinTransformer,
convert_weights=True,
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
drop_path_rate=0.2,
out_indices=(0, 1, 2, 3),
with_cp=use_checkpoint,
pretrained="https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth",
)
neck = class_config(
ChannelMapper,
in_channels=[192, 384, 768],
out_channels=256,
num_outs=4,
kernel_size=1,
norm="GroupNorm",
num_groups=32,
activation=None,
bias=True,
)
depth_fpn = class_config(
FPN,
in_channels_list=[96, 192, 384, 768],
out_channels=256,
extra_blocks=None,
start_index=0,
)
return get_gdino3d_cfg(
params,
basemodel=basemodel,
neck=neck,
depth_fpn=depth_fpn,
chunked_size=chunked_size,
cat_mapping=cat_mapping,
pretrained=pretrained,
use_checkpoint=use_checkpoint,
)
def get_gdino3d_swin_base_cfg(
params: ExperimentParameters,
chunked_size: int = -1,
cat_mapping: dict[str, int] | None = None,
pretrained: str | None = None,
use_checkpoint: bool | FieldReference = False,
) -> ConfigDict:
"""Get the config of Swin-Base."""
basemodel = class_config(
SwinTransformer,
convert_weights=True,
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
out_indices=(0, 1, 2, 3),
with_cp=use_checkpoint,
pretrained="https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth",
)
neck = class_config(
ChannelMapper,
in_channels=[256, 512, 1024],
out_channels=256,
num_outs=4,
kernel_size=1,
norm="GroupNorm",
num_groups=32,
activation=None,
bias=True,
)
depth_fpn = class_config(
FPN,
in_channels_list=[128, 256, 512, 1024],
out_channels=256,
extra_blocks=None,
start_index=0,
)
return get_gdino3d_cfg(
params,
basemodel=basemodel,
neck=neck,
depth_fpn=depth_fpn,
chunked_size=chunked_size,
cat_mapping=cat_mapping,
pretrained=pretrained,
use_checkpoint=use_checkpoint,
)
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