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final_exp_res18_s8.py
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final_exp_res18_s8.py
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from configs.culane.test_common_s8 import *
"""
config file of the small version of GANet for tusimple
"""
# global settings
dataset_type = 'CulaneDataset'
data_root = "/mnt/lustre/wangjinsheng/project/lane-detection/GANet/datasets/culane"
test_mode = False
fpn_layer_num = 3 # check
fpn_down_scale = [8,16,32] # check
mask_down_scale = 8 # check
hm_down_scale = 8 # check
line_width = 3
radius = 2 # gaussian circle radius
root_radius = 4
vaniehsd_radius = 8
joint_nums = 1 # check
joint_weights = [1, 0.4, 0.2] # check
sample_per_lane = [41, 21, 11] # check
dcn_point_num = [7, 5, 3] # check
sample_gt_points = [41, 21, 11] # check
loss_weights = dict(center=0.0,
point=1.0,
error=1.0,
offset=0.5,
aux=0.2
) # check
use_smooth = False # check
deconv_before = False # check
dcn_only_cls = True # check
point_scale = False # check
deconv_layer = [True, False, False] # check
nms_thr = 2
num_lane_classes = 1
batch_size = 32 # check
img_norm_cfg = dict(
mean=[75.3, 76.6, 77.6], std=[50.5, 53.8, 54.3], to_rgb=False)
ori_scale = (1640, 590) # for culane
crop_bbox = [0, 270, 1640, 590]
img_scale = (800, 320)
train_cfg = dict(out_scale=mask_down_scale)
test_cfg = dict(out_scale=mask_down_scale)
assigner_cfg = dict(
init=dict(
assigner=dict(type='LaneAssigner')),
refine=dict(
assigner=dict(type='LaneAssigner'))
)
# model settings
model = dict(
type='GANet',
pretrained='torchvision://resnet18',
train_cfg=train_cfg,
test_cfg=test_cfg,
num_classes=num_lane_classes,
sample_gt_points=sample_gt_points,
use_smooth=use_smooth,
point_scale=point_scale,
backbone=dict(
type='ResNet',
depth=18,
strides=(1, 2, 2, 2),
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='DeformFPN',
in_channels=[128, 256, 512],
out_channels=64,
dcn_point_num=dcn_point_num,
deconv_layer=deconv_layer,
deconv_before=deconv_before,
trans_idx=-1,
dcn_only_cls=dcn_only_cls,
trans_cfg=dict(
in_dim=512,
attn_in_dims=[512, 64],
attn_out_dims=[64, 64],
strides=[1, 1],
ratios=[4, 4],
pos_shape=(1, 10, 25),
),
),
head=dict(
type='GANetHeadFast',
heads=dict(hm=num_lane_classes),
in_channels=64,
branch_in_channels=64,
num_classes=num_lane_classes,
hm_idx=0,
joint_nums=joint_nums,
),
loss=dict(type='LaneLossAggress'),
loss_weights=loss_weights
)
train_compose = dict(bboxes=False, keypoints=True, masks=False)
# data pipeline settings
train_al_pipeline = [
dict(type='Compose', params=train_compose),
dict(
type='Crop',
x_min=crop_bbox[0],
x_max=crop_bbox[2],
y_min=crop_bbox[1],
y_max=crop_bbox[3],
p=1),
dict(type='HorizontalFlip', p=0.5),
dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),
dict(
type='OneOf',
transforms=[
dict(
type='RGBShift',
r_shift_limit=10,
g_shift_limit=10,
b_shift_limit=10,
p=1.0),
dict(
type='HueSaturationValue',
hue_shift_limit=(-10, 10),
sat_shift_limit=(-15, 15),
val_shift_limit=(-10, 10),
p=1.0),
],
p=0.7),
dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.2),
dict(type='RandomBrightness', limit=0.2, p=0.6),
dict(
type='ShiftScaleRotate',
shift_limit=0.1,
scale_limit=(-0.2, 0.2),
rotate_limit=10,
border_mode=0,
p=0.6),
dict(
type='RandomResizedCrop',
height=img_scale[1],
width=img_scale[0],
scale=(0.8, 1.2),
ratio=(1.7, 2.7),
p=0.6),
dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),
]
val_al_pipeline = [
dict(type='Compose', params=train_compose),
dict(
type='Crop',
x_min=crop_bbox[0],
x_max=crop_bbox[2],
y_min=crop_bbox[1],
y_max=crop_bbox[3],
p=1),
dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),
]
train_pipeline = [
dict(type='albumentation', pipelines=train_al_pipeline),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(
type='CollectLanePoints',
fpn_layer_num=fpn_layer_num,
down_scale=mask_down_scale,
hm_down_scale=hm_down_scale,
max_mask_sample=5,
line_width=line_width,
radius=radius,
root_radius=root_radius,
vanished_radius=vaniehsd_radius,
joint_nums=joint_nums,
joint_weights=joint_weights,
sample_per_lane=sample_per_lane,
fpn_down_scale=fpn_down_scale,
keys=['img', 'gt_cpts_hm', 'gt_kpts_hm', 'int_offset', 'pts_offset',
'gt_masks', *[f'lane_points_l{i}' for i in range(fpn_layer_num)],
'offset_mask', 'offset_mask_weight', 'gt_vp_hm'],
meta_keys=[
'filename', 'sub_img_name',
'ori_shape', 'img_shape', 'down_scale', 'hm_down_scale',
'img_norm_cfg', 'gt_points'
]),
]
val_pipeline = [
dict(type='albumentation', pipelines=val_al_pipeline),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(
type='CollectLanePoints',
fpn_layer_num=fpn_layer_num,
down_scale=mask_down_scale,
hm_down_scale=hm_down_scale,
max_mask_sample=5,
line_width=line_width,
radius=radius,
root_radius=root_radius,
vanished_radius=vaniehsd_radius,
joint_nums=joint_nums,
joint_weights=joint_weights,
sample_per_lane=sample_per_lane,
fpn_down_scale=fpn_down_scale,
keys=['img', 'gt_cpts_hm', 'gt_kpts_hm', 'int_offset', 'pts_offset',
'gt_masks', *[f'lane_points_l{i}' for i in range(fpn_layer_num)],
'offset_mask', 'offset_mask_weight', 'gt_vp_hm'],
meta_keys=[
'filename', 'sub_img_name',
'ori_shape', 'img_shape', 'down_scale', 'hm_down_scale',
'img_norm_cfg', 'gt_points'
]),
]
data = dict(
samples_per_gpu=batch_size,
workers_per_gpu=8,
train=dict(
type=dataset_type,
data_root=data_root,
data_list=data_root + '/list/train.txt',
pipeline=train_pipeline,
test_mode=False,
),
val=dict(
type=dataset_type,
data_root=data_root,
data_list=data_root + '/list/test.txt',
pipeline=val_pipeline,
test_mode=False,
),
test=dict(
type=dataset_type,
data_root=data_root,
data_list=data_root + '/list/test.txt',
test_suffix='.jpg',
pipeline=val_pipeline,
test_mode=True,
))
# optimizer
# optimizer = dict(type='Adam', lr=2.5e-4, betas=(0.9, 0.999), eps=1e-8)
optimizer = dict(type='Adam', lr=1e-4, betas=(0.9, 0.999), eps=1e-8)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='constant',
warmup_iters=100,
warmup_ratio=1.0 / 3,
step=[50, 100])
# runtime settings
checkpoint_config = dict(interval=20)
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook'),
])
total_epochs = 200
device_ids = "0,1"
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/tusimple/small'
load_from = None
resume_from = None
workflow = [('train', 500), ('val', 1)]
from configs.culane.common import *