Comments (11)
dingmyu
commented on July 30, 2024
Hi,
Just remove all the depth-guided modules, set corner_loss=True and corner_in_3D=False, because only 2D corners are helpful and the 3D corner hurts the overall performance.
Regards,
Mingyu
from d4lcn.
kaixinbear
commented on July 30, 2024
Hi, thanks for reply ~
I wanna to confirm twothings here:
1、I just need to set conf.use_corner = True right ?
conf.use_corner = True
conf.corner_in_3d = False
conf.use_hill_loss = False
This item allows network to predict 8 corners projected cx, cy (image plane) and cz3d(camera plane).But this branch is not used in the inference stage?
2、I have some confusion about data augmentation code when doing randommirror. When the image is flipped, the vertices need to be modified like cx2d
But it seems you didn't do it in the code, which makes me confused.
Sincerely
from d4lcn.
dingmyu
commented on July 30, 2024
Hi,
-
yes, it is an auxiliary loss and not used in the inference.
-
The vertex should be flipped when the image is flipped, check here https://github.com/dingmyu/D4LCN/blob/master/lib/augmentations.py#L260.
Mingyu
from d4lcn.
kaixinbear
commented on July 30, 2024
2. https://github.com/dingmyu/D4LCN/blob/master/lib/augmentations.py#L260.
But it seems it only filps the center of x,have you filpped obj.vertices?
from d4lcn.
dingmyu
commented on July 30, 2024
Hi, thanks for pointing out this. The vertices should be flipped as well. But it may not affect the training accuracy much (like only use half of the data for auxiliary loss).
I will do some experiments to verify this bug and update the code when I have time.
Thanks
Mingyu
from d4lcn.
kaixinbear
commented on July 30, 2024
Thanks, waiting for it ! I will do some experiements either.
from d4lcn.
kaixinbear
commented on July 30, 2024
Hi, dingmyu, I remove all depth ralative code and set config according to your guidance.But the performance is still low.Is my config file setting right?All could you please provide your config?
`conf: {
model: resnet_dilate
solver_type: sgd
lr: 0.004
stage2_lr: 0.001
momentum: 0.9
weight_decay: 0.0005
max_iter: 50000
snapshot_iter: 10000
display: 250
do_test: True
use_corner: True
corner_in_3d: False
use_hill_loss: False
lr_policy: poly
lr_steps: None
lr_target: 4e-08
rng_seed: 2
cuda_seed: 2
image_means: [0.485, 0.456, 0.406]
image_stds: [0.229, 0.224, 0.225]
feat_stride: 16
has_3d: True
test_scale: 512
crop_size: [512, 1760]
mirror_prob: 0.5
distort_prob: -1
dataset_test: kitti_split1
datasets_train: [{'anno_fmt': 'kitti_det',
'im_ext': '.png',
'name': 'kitti_split1',
'scale': 1}]
use_3d_for_2d: True
percent_anc_h: [0.0625, 0.75]
min_gt_h: 32.0
max_gt_h: 384.0
min_gt_vis: 0.65
ilbls: ['Van', 'ignore']
lbls: ['Car', 'Pedestrian', 'Cyclist']
batch_size: 2
fg_image_ratio: 1.0
box_samples: 0.2
fg_fraction: 0.2
bg_thresh_lo: 0
bg_thresh_hi: 0.5
fg_thresh: 0.5
ign_thresh: 0.5
best_thresh: 0.35
nms_topN_pre: 3000
nms_topN_post: 40
nms_thres: 0.4
clip_boxes: False
test_protocol: kitti
test_db: kitti
test_min_h: 0
min_det_scales: [0, 0]
cluster_anchors: 0
even_anchors: 0
expand_anchors: 0
anchors: [[-0.5, -8.5, 15.5, 23.5, 51.969, 0.531,
1.713, 1.025, -0.799],
[-8.5, -8.5, 23.5, 23.5, 52.176, 1.618,
1.6, 3.811, -0.453],
[-16.5, -8.5, 31.5, 23.5, 48.334,
1.644, 1.529, 3.966, 0.673],
[-2.528, -12.555, 17.528, 27.555,
44.781, 0.534, 1.771, 0.971, 0.093],
[-12.555, -12.555, 27.555, 27.555,
44.704, 1.599, 1.569, 3.814, -0.187],
[-22.583, -12.555, 37.583, 27.555,
43.492, 1.621, 1.536, 3.91, 0.719],
[-5.069, -17.638, 20.069, 32.638,
34.666, 0.561, 1.752, 0.967, -0.384],
[-17.638, -17.638, 32.638, 32.638,
35.35, 1.567, 1.591, 3.81, -0.511],
[-30.207, -17.638, 45.207, 32.638,
37.128, 1.602, 1.529, 3.904, 0.452],
[-8.255, -24.01, 23.255, 39.01, 28.771,
0.613, 1.76, 0.98, 0.067],
[-24.01, -24.01, 39.01, 39.01, 28.331,
1.543, 1.592, 3.66, -0.811],
[-39.764, -24.01, 54.764, 39.01,
30.541, 1.626, 1.524, 3.908, 0.312],
[-12.248, -31.996, 27.248, 46.996,
23.011, 0.606, 1.758, 0.996, 0.208],
[-31.996, -31.996, 46.996, 46.996,
22.948, 1.51, 1.599, 3.419, -1.076],
[-51.744, -31.996, 66.744, 46.996,
25.0, 1.628, 1.527, 3.917, 0.334],
[-17.253, -42.006, 32.253, 57.006,
18.479, 0.601, 1.747, 1.007, 0.347],
[-42.006, -42.006, 57.006, 57.006,
18.815, 1.487, 1.599, 3.337, -0.862],
[-66.759, -42.006, 81.759, 57.006,
20.576, 1.623, 1.532, 3.942, 0.323],
[-23.527, -54.553, 38.527, 69.553,
15.035, 0.625, 1.744, 0.917, 0.41],
[-54.553, -54.553, 69.553, 69.553,
15.346, 1.29, 1.659, 3.083, -0.275],
[-85.58, -54.553, 100.58, 69.553,
16.326, 1.613, 1.527, 3.934, 0.268],
[-31.39, -70.281, 46.39, 85.281,
12.265, 0.631, 1.747, 0.954, 0.317],
[-70.281, -70.281, 85.281, 85.281,
11.878, 1.044, 1.67, 2.415, -0.211],
[-109.171, -70.281, 124.171, 85.281,
13.58, 1.621, 1.539, 3.961, 0.189],
[-41.247, -89.994, 56.247, 104.994,
9.932, 0.61, 1.771, 0.934, 0.486],
[-89.994, -89.994, 104.994, 104.994,
8.949, 0.811, 1.766, 1.662, 0.08],
[-138.741, -89.994, 153.741, 104.994,
11.043, 1.61, 1.533, 3.899, 0.04],
[-53.602, -114.704, 68.602, 129.704,
8.389, 0.604, 1.793, 0.95, 0.806],
[-114.704, -114.704, 129.704, 129.704,
8.071, 1.01, 1.751, 2.19, -0.076],
[-175.806, -114.704, 190.806, 129.704,
9.184, 1.606, 1.526, 3.869, -0.066],
[-69.089, -145.677, 84.089, 160.677,
6.923, 0.627, 1.791, 0.96, 0.784],
[-145.677, -145.677, 160.677, 160.677,
6.784, 1.384, 1.615, 2.862, -1.035],
[-222.266, -145.677, 237.266, 160.677,
7.863, 1.617, 1.55, 3.948, -0.071],
[-88.5, -184.5, 103.5, 199.5, 5.189,
0.66, 1.755, 0.841, 0.173],
[-184.5, -184.5, 199.5, 199.5, 4.388,
0.743, 1.728, 1.381, 0.642],
[-280.5, -184.5, 295.5, 199.5, 5.583,
1.583, 1.547, 3.862, -0.072]]
bbox_means: [[-0.0, 0.002, 0.064, -0.093, 0.011,
-0.067, 0.192, 0.059, -0.021, 0.069,
-0.004, 0.19, -0.109, -0.109, -0.109,
-0.109, 0.045, 0.045, 0.19, -0.453,
-0.446, 0.329, 0.325, -0.446, -0.453,
0.345, 0.355, 0.06, 0.261, 0.261,
0.317, 0.317, 0.116, 0.116, 0.06,
-0.62, -0.496, -0.496, -0.595, -0.595,
-0.72, -0.72, -0.62, 0.066, 0.066,
1.654, 1.654, 0.066, 0.066, 1.654,
1.654, -0.608, 0.86]]
bbox_stds: [[0.14, 0.126, 0.255, 0.256, 0.164,
0.149, 3.626, 0.387, 0.104, 0.507,
1.855, 0.426, 0.481, 0.481, 0.413,
0.413, 0.49, 0.49, 0.426, 0.484,
0.478, 0.394, 0.392, 0.477, 0.483,
0.407, 0.417, 4.125, 3.825, 3.825,
3.855, 3.855, 4.088, 4.088, 4.125,
5.829, 6.051, 6.051, 5.421, 5.421,
5.222, 5.222, 5.829, 0.302, 0.302,
1.668, 1.668, 0.302, 0.302, 1.668,
1.668, 5.556, 0.895]]
anchor_scales: [32.0, 40.11, 50.276, 63.019, 78.991,
99.012, 124.106, 155.561, 194.989,
244.409, 306.354, 384.0]
anchor_ratios: [0.5, 1.0, 1.5]
hard_negatives: True
focal_loss: 1
cls_2d_lambda: 1
iou_2d_lambda: 1
bbox_2d_lambda: 0
bbox_3d_lambda: 1
bbox_3d_proj_lambda: 0.0
hill_climbing: True
}
[INFO]: 2020-07-17 13:26:13,307 iter: 250, acc (bg: 0.94, fg: 0.16, iou: 0.60), loss (bbox_3d: 2.1292, cls: 0.7291, iou: 0.5210, loss_vertices: 1.3247), misc (ry: 1.43, z: 2.71), dt: 0.76, eta: 10.5h
[INFO]: 2020-07-17 13:29:22,157 iter: 500, acc (bg: 1.00, fg: 0.48, iou: 0.66), loss (bbox_3d: 1.5033, cls: 0.3121, iou: 0.4334, loss_vertices: 1.3114), misc (ry: 1.24, z: 2.05), dt: 0.76, eta: 10.4h
[INFO]: 2020-07-18 00:00:06,440 iter: 49750, acc (bg: 1.00, fg: 0.98, iou: 0.93), loss (bbox_3d: 0.0498, cls: 0.0299, iou: 0.0695, loss_vertices: 1.2398), misc (ry: 0.11, z: 0.28), dt: 0.77, eta: 3.2m
[INFO]: 2020-07-18 00:03:09,989 iter: 50000, acc (bg: 1.00, fg: 0.98, iou: 0.93), loss (bbox_3d: 0.0505, cls: 0.0298, iou: 0.0702, loss_vertices: 0.9594), misc (ry: 0.12, z: 0.28), dt: 0.77, eta: 0.8s`
[INFO]: 2020-07-18 00:11:54,094 test_iter 50000 2d car --> easy: 0.8244, mod: 0.7791, hard: 0.6236
[INFO]: 2020-07-18 00:11:54,095 test_iter 50000 gr car --> easy: 0.1602, mod: 0.1113, hard: 0.0902
[INFO]: 2020-07-18 00:11:54,096 test_iter 50000 3d car --> easy: 0.1053, mod: 0.0671, hard: 0.0560
[INFO]: 2020-07-18 00:11:54,097 test_iter 50000 2d pedestrian --> easy: 0.4780, mod: 0.3982, hard: 0.3330
[INFO]: 2020-07-18 00:11:54,098 test_iter 50000 gr pedestrian --> easy: 0.0172, mod: 0.0124, hard: 0.0124
[INFO]: 2020-07-18 00:11:54,099 test_iter 50000 3d pedestrian --> easy: 0.0148, mod: 0.0108, hard: 0.0077
[INFO]: 2020-07-18 00:11:54,100 test_iter 50000 2d cyclist --> easy: 0.2836, mod: 0.1646, hard: 0.1651
[INFO]: 2020-07-18 00:11:54,100 test_iter 50000 gr cyclist --> easy: 0.0046, mod: 0.0025, hard: 0.0025
[INFO]: 2020-07-18 00:11:54,101 test_iter 50000 3d cyclist --> easy: 0.0046, mod: 0.0025, hard: 0.0025
Sincerely.
from d4lcn.
dingmyu
commented on July 30, 2024
Hi,
My config file can be found here.
Your config looks normal and correct. What is your training batch size? Does the model converge in 50000 iterations?
Regards,
Mingyu
from d4lcn.
kaixinbear
commented on July 30, 2024
Hi~
My training batch_size is 2, and the loss decrease in 50000 iterations except loss_vertices.The vertices loss doesn't converge. It's a bit strange for me. Do you still have the log file for this ? I think it's hard to find the bug from the config file now.
from d4lcn.
dingmyu
commented on July 30, 2024
Hi,
Sorry I did not keep the log files. I think your batch size of 2 is too small so that 50000 iters are not enough for the training. I usually use 20000 iters for batch size 16, or 40000 iters for batch size 8
If you only have a single GPU, you may try a larger amount of iterations (such as 100000).
Mingyu
from d4lcn.
kaixinbear
commented on July 30, 2024
Thanks,I will try it later.
from d4lcn.
Related Issues (20)
- Optimizer.step() & scheduler.step() in wrong order when restoring HOT 1
- Depth maps estimation
- Reproducing AP|R11 results
- Source of Randomness HOT 1
- data distribute
- Confuion in building project
- sh train.sh undefined symbol: __cudaRegisterFatBinaryEnd HOT 2
- Where is the function ‘projection_ray_trace’？
- Meaning of the Results HOT 2
- result HOT 2
- how to calculate mean and std HOT 1
- How can i train the results like you in paper?
- Replicate results from the paper HOT 5
- sh train.sh TypeError : cannot creat 'generator' instances
- ModuleNotFoundError: No module named 'lib.imdb_util'
- Depth Images for Testing set HOT 1
- Cannot replicate results from the paper
- Training my own dataset
- Question about "gpu_nms"
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from d4lcn.