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Invalid output and model #18

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kkkmax opened this issue Jan 7, 2019 · 4 comments
Open

Invalid output and model #18

kkkmax opened this issue Jan 7, 2019 · 4 comments

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@kkkmax
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kkkmax commented Jan 7, 2019

No description provided.

@YealT
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YealT commented Jan 11, 2019

嗨,我们通过培训获得了模型文件,然后使用模型文件来测试任何照片,但最后我们得到了一个灰色图像,我不知道是什么导致了以下图片:
4

我们使用它的方式如下:

训练模型命令:
$ Python scripts / train.py --mode seg --out result-seg

训练38纪元后的结果图(max_iteration = 30000)
五

6
7

在此输出目录中,您可以看到:
模型文件“model_best”和训练过程生成文件“snapshot_epoch_x”

打开这两个文件,可以看到里面的结构,如下图所示

8

测试模型命令:
$ python scripts / test.py --mode seg --model_path ./result-seg/model_best --model_mode seg -i my.jpg
结果是看到第一个灰色图

尝试再次运行该命令会产生以下错误:
$ python scripts / test.py --mode seg --model_path ./result-seg/snapshot_epoch_35 --model_mode seg -i M_81_0183.jpg

6

我们检查了输入参数并逐一进行比较。

并且读取的模型文件在加载过程中输出每层的W和B值的平方和。可以看出,他们中的大多数都是0。
6

看到这个结果让我很难过。我再次做了以下尝试:

我后来训练了。
Mode = seg +
Mode = seg_tri
训练是正常的,但测试结果和mode = seg
相同的问题:

  1. 给定任何输入的肖像,{seg,seg +}测试结果图像的颜色是灰色的。
    {seg_tri}测试结果图像颜色为白色
    2){seg,seg +,seg_tri}我从模型中读取的路径为:“。/ result / model_best”
  2. {seg,seg +,seg_tri}仅保存模型大小约500k
  3. 我打印了由模型保存的网络的每一层的持久性参数:
    W(weigths)在所有层中几乎都是0
    (偏差)在解码器的最后几层中具有值。
    这看起来非常不合理

我不熟悉这个Chainer,我很难完成整个过程。看到一些指导,我将非常感激和高兴。

@kkkmax Hello, I have encountered the same problem. Can you solve it? Is it convenient to leave a contact?

@YealT
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YealT commented Jan 12, 2019

I'm not sure if I can't solve another method I'm going to try, you leave your other contact so I can reach you, for example wechat ID??

My Wechat is yoni_mmm ,thank you

@billzw
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billzw commented May 17, 2019

No description provided.

same question:

Given a portrait of any input, the color of the {seg, seg+} test result image is gray.
{seg_tri}Test result image color is white

Have you solved this problem?

@kkkmax

@vlordier
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vlordier commented Sep 6, 2019

You need to train the model properly first, using a pre-trained FCN model.
See #22

This is the sort of masks (trimap) you can generate after a just few epochs and 2h of training on a p3.8xlarge AWS GPU instance.

vis_epoch=20_idx=73

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