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数据集链接失效

您好,您共享的数据集链接目前失效了,可以方便再共享一下吗,谢谢!

关于更改了batch_size 的一些疑问

我使用的是python 3 windows pytorch3.1,,显卡为1080ti 11G

python train.py --arch pspnet-densenet-s1s2-crf2 --img_rows 256 --img_cols 256 --n_epoch 50 --l_rate 1e-3 --batch_size 32 --gpu 0 --step 50 --traindir "dataset/stage1&stage2-train-crf2"

当batch_size =32的时候,提示内存不足·
image

当batch_size 改为4 或者8 ,提示了以下错误

D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
epoch 0 with learning rate: 0.001000
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
D:\Anaconda3\envs\fangxu\lib\site-packages\matplotlib\colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.
  not cbook.is_string_like(colors[0]):
E:\fxworkspace\satellite_seg\utils\loss.py:16: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an arg
ument.
  log_p = F.log_softmax(input)
D:\Anaconda3\envs\fangxu\lib\site-packages\torch\autograd\_functions\tensor.py:465: UserWarning: self and mask not broadcastable, but have the same number of element
s.  Falling back to deprecated pointwise behavior.
  return tensor.masked_select(mask)
Traceback (most recent call last):
  File "train.py", line 193, in <module>
    train(args)
  File "train.py", line 152, in train
    loss.backward()
  File "D:\Anaconda3\envs\fangxu\lib\site-packages\torch\autograd\variable.py", line 167, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, retain_variables)
  File "D:\Anaconda3\envs\fangxu\lib\site-packages\torch\autograd\__init__.py", line 99, in backward
    variables, grad_variables, retain_graph)
  File "D:\Anaconda3\envs\fangxu\lib\site-packages\torch\autograd\function.py", line 91, in apply
    return self._forward_cls.backward(self, *args)
  File "D:\Anaconda3\envs\fangxu\lib\site-packages\torch\autograd\_functions\tensor.py", line 481, in backward
    grad_tensor = grad_tensor.masked_scatter(mask, grad_output)
  File "D:\Anaconda3\envs\fangxu\lib\site-packages\torch\autograd\variable.py", line 427, in masked_scatter
    return self.clone().masked_scatter_(mask, variable)
RuntimeError: invalid argument 1: the number of sizes provided must be greater or equal to the number of dimensions in the tensor at c:\anaconda2\conda-bld\pytorch_1
519501749874\work\torch\lib\thc\generic/THCTensor.c:326

RuntimeError: invalid argument 1: the number of sizes provided must be greater or equal to the number of dimensions in the tensor at c:\anaconda2\conda-bld\pytorch_1

您好,models名称报错了

cb@ceo-pc:~/code/satellite_seg$ sh run_train.sh
Checking for scripts.
It's Alive!
Traceback (most recent call last):
File "/home/cb/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/home/cb/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/cb/anaconda3/lib/python3.6/site-packages/visdom/server.py", line 1308, in
download_scripts_and_run()
File "/home/cb/anaconda3/lib/python3.6/site-packages/visdom/server.py", line 1304, in download_scripts_and_run
main()
File "/home/cb/anaconda3/lib/python3.6/site-packages/visdom/server.py", line 1299, in main
print_func=print_func, user_credential=user_credential)
File "/home/cb/anaconda3/lib/python3.6/site-packages/visdom/server.py", line 1219, in start_server
app.listen(port, max_buffer_size=1024 ** 3)
File "/home/cb/anaconda3/lib/python3.6/site-packages/tornado/web.py", line 1943, in listen
server.listen(port, address)
File "/home/cb/anaconda3/lib/python3.6/site-packages/tornado/tcpserver.py", line 142, in listen
sockets = bind_sockets(port, address=address)
File "/home/cb/anaconda3/lib/python3.6/site-packages/tornado/netutil.py", line 197, in bind_sockets
sock.bind(sockaddr)
OSError: [Errno 98] Address already in use
WARNING:root:Setting up a new session...
Traceback (most recent call last):
File "train.py", line 193, in
train(args)
File "train.py", line 111, in train
model = get_model(args.arch, n_classes)
File "/home/cb/code/satellite_seg/models/init.py", line 26, in get_model
fmodel = get_instance(name)
File "/home/cb/code/satellite_seg/models/init.py", line 23, in get_instance
}[name]
KeyError: 'pspnet-densenet-s1s2-crf2'

您好,有些问题想请教

您好。我做的语义分割,也是在该数据集实践和训练(我用的 keras),存在些问题想请教您哈?

1、在数据增强方面,您当时考虑过哪些数据方式吗?我看有的不会添加噪声,有的会,有时候我也迷茫,到底采用哪些增强方式,一般“旋转”应该需要的,但诸如光照、噪声等增强,不知道该不该加?
2、“多次度训练/预测”,请问多尺度训练、多尺度预测指的是?
3、您尝试过其他 backbone 及分割模型组合,亦或改模型如添加空洞卷积、特征金字塔、Attention 等吗?有觉得哪些方式能在该数据集取得比较不错的结果吗,或是您觉得哪些方式可以考虑尝试(我最近在尝试改模型,不知道该怎么改好,如果随便改觉得有些茫然)?
:看您这里用的是 densenet+pspnet,你当时没考虑用 resnet 吗,还是说您当时实践发现 resnet 做主干不好?
5、看您这里提到使用 CRF 后处理,您这里是在一开始使用的 CRF 处理吗?还是对预测后的图像进行 CRF 处理?
6、您当时实践过程中,交叉熵 loss 和 focal loss,在该数据集上训练区别大吗?:本来我想对少类别的过采样,但没找到代码如何过采样,所以想考虑 focal loss 试试,不造如何。
7、还有预测后拼接痕迹的问题,采用滑动比“预测小图”小的步伐进行预测,但有的地方还是会看到的拼接痕迹,如果训练的本身不好,那拼接痕迹更加严重,请问您知道还有什么办法消除或是减小该痕迹吗?

求数据集

数据集链接已经失效,可以再发一下链接吗,十分感谢~

关于每个类别的准确率的问题

作者你好,我想请教一下。我利用你的code训练,然后在那0.05部分做验证时,五个类别中,有的准确率能给出来,有的则一直是nan,这是为什么?
不过用最后的模型也可以对测试图片中做分割,效果还可以,能否解答下为什么吗?

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