dlyldxwl / fssd.pytorch Goto Github PK

您好，我有两个问题想请教一下。
第一个是train.py第114行，torch.load(resume_net)这里的resume_net没定义吧
第二个问题是test.py运行时报错RuntimeError: Error(s) in loading state_dict for FSSD:size mismatch for conf.0.bias: copying a param with shape torch.Size([126]) from checkpoint, the shape in current model is torch.Size([486])........
这里net.load_state_dict(new_state_dict)加载fssd_voc_79_74.pth导致了size不匹配

im very interested in this project you make,but im a greenhand in object detection,can you tell me how to train?

I notice that for the backbone network vgg_prune, convolutions have BN but no bias, could you tell me where I could find the pre-trained vgg for FSSD_VGG_BN?
Thank you!

运行报错：local variable 'result' referenced before assignment
result之前没定义呢

when i find some wrong i can't solve
`
rw@rw:/新建文件夹/fssd.pytorch-master$ source activate pytorch0.4.0
(pytorch0.4.0) rw@rw:/新建文件夹/fssd.pytorch-master$ python demo.py
Finished loading model
Traceback (most recent call last):
File "demo.py", line 114, in
test_net(net, img, img_name, detector, transform, priors,top_k=200, thresh=0.4)
File "demo.py", line 38, in test_net
out = net(x,test=True)
File "/home/rw/anaconda3/envs/pytorch0.4.0/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in call
result = self.forward(*input, **kwargs)
File "/home/rw/新建文件夹/fssd.pytorch-master/models/FSSD_VGG.py", line 105, in forward
transformed_features.append(v(source_features[k]))
File "/home/rw/anaconda3/envs/pytorch0.4.0/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in call
result = self.forward(*input, **kwargs)
File "/home/rw/新建文件夹/fssd.pytorch-master/models/FSSD_VGG.py", line 28, in forward
x=F.interpolate(input=x,size=(self.up_size, self.up_size), mode='bilinear')
AttributeError: module 'torch.nn.functional' has no attribute 'interpolate'

`
i have put weight into the folder
but something is wrong ?
would you help me??

when i train VOC2007，it work ,but the progress is terrible
Epoch:1 || epochiter: 0/1034|| Totel iter 0 || L: 201.1674 C: 330.5661 S: 531.7335||Batch time: 12.2342 ||LR: 0.0010000 Epoch:1 || epochiter: 10/1034|| Totel iter 10 || L: nan C: nan S: nan||Batch time: 0.8318 ||LR: 0.0010000 Epoch:1 || epochiter: 20/1034|| Totel iter 20 || L: nan C: nan S: nan||Batch time: 0.8154 ||LR: 0.0010000 Epoch:1 || epochiter: 30/1034|| Totel iter 30 || L: nan C: nan S: nan||Batch time: 0.8430 ||LR: 0.0010000 Epoch:1 || epochiter: 40/1034|| Totel iter 40 || L: nan C: nan S: nan||Batch time: 0.8290 ||LR: 0.0010000 Epoch:1 || epochiter: 50/1034|| Totel iter 50 || L: nan C: nan S: nan||Batch time: 0.8428 ||LR: 0.0010000 Epoch:1 || epochiter: 60/1034|| Totel iter 60 || L: nan C: nan S: nan||Batch time: 0.8450 ||LR: 0.0010000 Epoch:1 || epochiter: 70/1034|| Totel iter 70 || L: nan C: nan S: nan||Batch time: 0.8438 ||LR: 0.0010000 Epoch:1 || epochiter: 80/1034|| Totel iter 80 || L: nan C: nan S: nan||Batch time: 0.8202 ||LR: 0.0010000 Epoch:1 || epochiter: 90/1034|| Totel iter 90 || L: nan C: nan S: nan||Batch time: 0.8445 ||LR: 0.0010000 Epoch:1 || epochiter: 100/1034|| Totel iter 100 || L: nan C: nan S: nan||Batch time: 0.8235 ||LR: 0.0010000 Epoch:1 || epochiter: 110/1034|| Totel iter 110 || L: nan C: nan S: nan||Batch time: 0.8498 ||LR: 0.0010000 Epoch:1 || epochiter: 120/1034|| Totel iter 120 || L: nan C: nan S: nan||Batch time: 0.8209 ||LR: 0.0010000 Epoch:1 || epochiter: 130/1034|| Totel iter 130 || L: nan C: nan S: nan||Batch time: 0.8657 ||LR: 0.0010000 Epoch:1 || epochiter: 140/1034|| Totel iter 140 || L: nan C: nan S: nan||Batch time: 0.8319 ||LR: 0.0010000 Epoch:1 || epochiter: 150/1034|| Totel iter 150 || L: nan C: nan S: nan||Batch time: 0.8366 ||LR: 0.0010000 Epoch:1 || epochiter: 160/1034|| Totel iter 160 || L: nan C: nan S: nan||Batch time: 0.8760 ||LR: 0.0010000 Epoch:1 || epochiter: 170/1034|| Totel iter 170 || L: nan C: nan S: nan||Batch time: 0.8323 ||LR: 0.0010000 Epoch:1 || epochiter: 180/1034|| Totel iter 180 || L: nan C: nan S: nan||Batch time: 0.8389 ||LR: 0.0010000 Epoch:1 || epochiter: 190/1034|| Totel iter 190 || L: nan C: nan S: nan||Batch time: 0.8319 ||LR: 0.0010000 Epoch:1 || epochiter: 200/1034|| Totel iter 200 || L: nan C: nan S: nan||Batch time: 0.8414 ||LR: 0.0010000 Epoch:1 || epochiter: 210/1034|| Totel iter 210 || L: nan C: nan S: nan||Batch time: 0.8519 ||LR: 0.0010000
could you help me?

Traceback (most recent call last):
File "train.py", line 268, in
train()
File "train.py", line 233, in train
loss.backward()
File "/home/huhuai/anaconda3/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/huhuai/anaconda3/lib/python3.6/site-packages/torch/autograd/init.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

Hi...Is the pruning for 300X300 only? I didnt find any pruning for 512X512. Thanks

Dear @dlyldxwl ,
Great work! have you ever tried focal loss in FSSD? It seems to be better than ohem in your programme.

Following repos might help:
https://github.com/marvis/pytorch-yolo2/blob/master/FocalLoss.py
https://github.com/yhenon/pytorch-retinanet/blob/master/losses.py

I have tried myself, but it seems to be worse than ohem...

Best regards,

thx

(pytorch0.4.0) rw@rw:~/fssd.pytorch-master$ python demo.py
Traceback (most recent call last):
File "demo.py", line 13, in
from data import VOCroot,COCOroot
File "/home/rw/fssd.pytorch-master/data/init.py", line 3, in
from .coco import COCODetection
File "/home/rw/fssd.pytorch-master/data/coco.py", line 21, in
from utils.pycocotools.coco import COCO
File "/home/rw/fssd.pytorch-master/utils/pycocotools/coco.py", line 55, in
from . import mask as maskUtils
File "/home/rw/fssd.pytorch-master/utils/pycocotools/mask.py", line 4, in
from . import _mask
ImportError: /home/rw/fssd.pytorch-master/utils/pycocotools/_mask.cpython-35m-x86_64-linux-gnu.so: undefined symbol: PyFPE_jbuf

请问大佬，我看了您的csdn，所以慕名前来。但是我想尝试图像结果可视化，不知道如何写，请问大佬能不能提供思路帮助下我，刚上路，感觉还是看着图片可视化来的舒服，望交流回复

I am sorry to bother you.
I apply the way of pruning for the other object detection network. But the FPS reduced after pruning.