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petrv2_vovnet_gridmask_p4_800x320.yml
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petrv2_vovnet_gridmask_p4_800x320.yml
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batch_size: 1
epochs: 24
train_dataset:
type: NuscenesMVDataset
dataset_root: data/nuscenes/
ann_file: data/nuscenes/petr_nuscenes_annotation_train.pkl
mode: train
use_valid_flag: True
class_names: [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
transforms:
- type: LoadMultiViewImageFromFiles
to_float32: True
- type: LoadMultiViewImageFromMultiSweepsFiles
sweeps_num: 1
to_float32: True
pad_empty_sweeps: True
sweep_range: [3, 27]
test_mode: False
- type: LoadAnnotations3D
with_bbox_3d: True
with_label_3d: True
- type: SampleRangeFilter
point_cloud_range: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
- type: SampleNameFilter
classes: [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
- type: ResizeCropFlipImage
sample_aug_cfg:
resize_lim: [0.47, 0.625]
final_dim: [320, 800]
bot_pct_lim: [0.0, 0.0]
rot_lim: [0.0, 0.0]
H: 900
W: 1600
rand_flip: True
training: True
- type: GlobalRotScaleTransImage
rot_range: [-0.3925, 0.3925]
translation_std: [0, 0, 0]
scale_ratio_range: [0.95, 1.05]
reverse_angle: True
training: True
- type: NormalizeMultiviewImage
mean: [103.530, 116.280, 123.675]
std: [57.375, 57.120, 58.395]
- type: PadMultiViewImage
size_divisor: 32
- type: SampleFilerByKey
keys: ['gt_bboxes_3d', 'gt_labels_3d', 'img']
meta_keys: ['filename', 'ori_shape', 'img_shape', 'lidar2img',
'intrinsics', 'extrinsics', 'pad_shape',
'scale_factor', 'flip', 'box_type_3d', 'img_norm_cfg', 'sample_idx',
'timestamp']
val_dataset:
type: NuscenesMVDataset
dataset_root: data/nuscenes/
ann_file: data/nuscenes/petr_nuscenes_annotation_val.pkl
mode: val
class_names: ['car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone']
transforms:
- type: LoadMultiViewImageFromFiles
to_float32: True
- type: LoadMultiViewImageFromMultiSweepsFiles
sweeps_num: 1
to_float32: True
pad_empty_sweeps: True
sweep_range: [3, 27]
- type: ResizeCropFlipImage
sample_aug_cfg:
resize_lim: [0.47, 0.625]
final_dim: [320, 800]
bot_pct_lim: [0.0, 0.0]
rot_lim: [0.0, 0.0]
H: 900
W: 1600
rand_flip: True
training: False
- type: NormalizeMultiviewImage
mean: [103.530, 116.280, 123.675]
std: [57.375, 57.120, 58.395]
- type: PadMultiViewImage
size_divisor: 32
- type: SampleFilerByKey
keys: ['img']
meta_keys: ['filename', 'ori_shape', 'img_shape', 'lidar2img',
'intrinsics', 'extrinsics', 'pad_shape',
'scale_factor', 'flip', 'box_type_3d', 'img_norm_cfg', 'sample_idx',
'timestamp']
optimizer:
type: AdamW
weight_decay: 0.01
grad_clip:
type: ClipGradByGlobalNorm
clip_norm: 35
# auto_skip_clip: True
lr_scheduler:
type: LinearWarmup
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.0002
T_max: 84408 # 3517 * 24
eta_min: 0.0000002
warmup_steps: 500
start_lr: 0.00006666666
end_lr: 0.0002
model:
type: Petr3D
use_recompute: True
use_grid_mask: True
backbone:
type: VoVNetCP ###use checkpoint to save memory
spec_name: V-99-eSE
norm_eval: True
frozen_stages: -1
input_ch: 3
out_features: ('stage4','stage5',)
neck:
type: CPFPN ###remove unused parameters
in_channels: [768, 1024]
out_channels: 256
num_outs: 2
pts_bbox_head:
type: PETRHead
num_classes: 10
in_channels: 256
num_query: 900
LID: true
with_multiview: true
with_position: true
with_fpe: true
with_time: true
with_multi: true
position_range: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
normedlinear: False
transformer:
type: PETRTransformer
decoder_embed_dims: 256
decoder:
type: PETRTransformerDecoder
return_intermediate: True
num_layers: 6
transformerlayers:
type: PETRTransformerDecoderLayer
attns:
- type: MultiHeadAttention
embed_dims: 256
num_heads: 8
attn_drop: 0.1
drop_prob: 0.1
- type: PETRMultiheadAttention
embed_dims: 256
num_heads: 8
attn_drop: 0.1
drop_prob: 0.1
batch_first: True
feedforward_channels: 2048
ffn_dropout: 0.1
operation_order: ['self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm']
positional_encoding:
type: SinePositionalEncoding3D
num_feats: 128
normalize: True
bbox_coder:
type: NMSFreeCoder
post_center_range: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
point_cloud_range: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
max_num: 300
voxel_size: [0.2, 0.2, 8]
num_classes: 10
loss_cls:
type: WeightedFocalLoss
gamma: 2.0
alpha: 0.25
loss_weight: 2.0
reduction: sum
loss_bbox:
type: WeightedL1Loss
loss_weight: 0.25
reduction: sum