haohaonju / centerfusion Goto Github PK

According to https://github.com/HaohaoNJU/CenterFusion/blob/main/tools/CenterFusion/src/convert_onnx.py#L141-L142

博主您好，感谢您的分享，运行了您的示例代码，效果是正常的，但是当使用本地数据训练的pth->onnx，效果就很不正常，框非常大，debug了一下，发现第二阶段的fusion-onnx是没有问题的，问题有可能出现在图片预处理阶段或者是centernet-onnx文件，看了create_data.py中`def prefetch_test(opt):
"""
imgs = data['images'][1.0].numpy() : 1 x 2 x 3 x H_in x W_in, two input image, uint8 dtype
img = data['image'].numpy().squeeze() : torch.Size([1, 900, 1600, 3]), one input image , fp32 dtype
meta = data['meta'] :
{1.0: {'calib': tensor([[[1.2528e+03, 0.0000e+00, 8.2659e+02, 0.0000e+00],
[0.0000e+00, 1.2528e+03, 4.6998e+02, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00]]]),
'c': tensor([[800., 450.]]),
's': tensor([1600.], dtype=torch.float64),
'height': tensor([900]),
'width': tensor([1600]),
'out_height': tensor([112]),
'out_width': tensor([200]),
'inp_height': tensor([448]),
'inp_width': tensor([800]),
'trans_input': tensor([[[ 0.5000, -0.0000, 0.0000], [ 0.0000, 0.5000, -1.0000]]], dtype=torch.float64),
'trans_output': tensor([[[ 0.1250, -0.0000, 0.0000], [ 0.0000, 0.1250, -0.2500]]], dtype=torch.float64)}}

            pc_2d = data['pc_2d']    : 1 x 3 x 1000, point clout dense tensor
            pc_N = data['pc_N']    : 53, valid point num
            pc_dep = data['pc_dep'] : torch.Size([1, 3, 112, 200]),    radar points    in    sparse image shape
            pc_3d = data['pc_3d'] : 1 x 18 x 1000, point cloud dense tensor , why 18 channels ? 
            """

`
我们的图片尺寸是[1280720],这块我们后续预处理为[1280768]，想请教下, 看看您这边有没有什么排查错误的思路。

作者您好：
我在nvidia Xavier上跑，各模块耗时如下，
[06/12/2023-15:49:10] [I] Average PreProcess Time: 0.39536 ms
[06/12/2023-15:49:10] [I] Average DetectionInfer Time: 98.6102 ms
[06/12/2023-15:49:10] [I] Average FrustumAssoc Time: 41.1462 ms
[06/12/2023-15:49:10] [I] Average merge Time: 0.177472 ms
[06/12/2023-15:49:10] [I] Average FusInfer Time: 9.79856 ms
[06/12/2023-15:49:10] [I] Average PostProcess Time: 1.84218 ms
平均单帧用时大概在150-170ms，但是您的computation speed如下，
engine_fp16 | 0.09ms | 7.44ms | 7.64ms | 0.05ms | 1.00ms | 0.62ms | 16.84ms。
我想请问一下您是在什么设备上跑的，有什么好的思路或者方法来排查耗时吗？

Hello @HaohaoNJU. Thank you for providing this repo. I wanted to ask you that while running requirements.txt file, there are many packages that are not getting installed as it says (This for an example)

ERROR: Could not find a version that satisfies the requirement image-geometry==1.16.0 (from -r requirements.txt (line 54)) (from versions: none)
ERROR: No matching distribution found for image-geometry==1.16.0 (from -r requirements.txt (line 54))

In fact there are almost every package that shows this kind of error. Can you guide me on how I can build this environment. My current virtual env is running on python 3.8.

尽管你给出了比较详尽的注释，但是我实在是太菜了导致对您的代码文件不是很理解，如果可以的话可以加VX咨询一下您吗，我的VX是lengyue0115 万分感谢