FloorplanTransformation
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art-programmer
bad performance of pytorch model compared to lua
Hi,
I compared the model performance of both pytorch and lua. Pytorch model is trained 30 epochs while lua model is provided by the author. The same training data is used.
However, when I test the following image, 33 walls were detected by lua model while only 11 were detected by pytorch model.
The picture is
lua output: (in which 33 walls are detected)
222 121 222 144 door 1 1
128 161 128 210 door 1 1
232 77 232 102 door 1 1
155 96 155 143 door 1 1
186 68 186 83 door 1 1
41 67 41 115 door 1 1
45 48 97 48 door 1 1
242 48 259 48 door 1 1
46 227 96 227 door 1 1
159 48 178 48 door 1 1
17 145 40 145 door 1 1
59 145 110 145 door 1 1
159 147 181 147 door 1 1
141 227 190 227 door 1 1
188 50 201 61 washing_basin 1 1
212 50 229 66 washing_basin 2 1
203 173 220 226 cooking_counter 1 1
234 51 273 73 bathtub 1 1
200 121 220 145 entrance 1 1
164 52 175 71 toilet 1 1
2 71.5 52 91.5 closet 1 1
73 85.5 123 105.5 bedroom 1 1
145 60 195 80 restroom 1 1
188.14285714286 82.247619047771 238.14285714286 102.24761904777 washing_room 1 1
166.52380952371 113.07619047614 216.52380952371 133.07619047614 corridor 1 1
227.5 65.5 277.5 85.5 bathroom 1 1
2 120 52 140 closet 1 1
46 175.5 96 195.5 bedroom 1 1
148.54046242775 177.54046242775 198.54046242775 197.54046242775 kitchen 1 1
203 117.666666667 222 117.666666667 wall 1 1
273.5 47.8666666667 273.5 104 wall 1 1
232 104 273.5 104 wall 1 1
232 104 232 117.666666667 wall 1 1
222 117.666666667 232 117.666666667 wall 1 1
185.5 47.8666666667 185.5 93 wall 1 1
155.333333333 93 185.5 93 wall 1 1
222 156.5 222 227.333333333 wall 1 1
128 227.333333333 222 227.333333333 wall 1 1
197.5 156.5 222 156.5 wall 1 1
197.5 146.733333333 197.5 156.5 wall 1 1
14 227.333333333 128 227.333333333 wall 1 1
14 144.6 14 227.333333333 wall 1 1
13.6666666667 47.6666666667 13.6666666667 116 wall 1 1
13.6666666667 47.6666666667 41.3333333333 47.6666666667 wall 1 1
232 47.8666666667 273.5 47.8666666667 wall 1 1
222 117.666666667 222 146.733333333 wall 1 1
185.5 47.8666666667 232 47.8666666667 wall 1 1
155.333333333 47.8666666667 185.5 47.8666666667 wall 1 1
232 47.8666666667 232 104 wall 1 1
41.3333333333 47.8666666667 155.333333333 47.8666666667 wall 1 1
41.3333333333 47.8666666667 41.3333333333 116 wall 1 1
128 144.6 155.333333333 144.6 wall 1 1
128 144.6 128 227.333333333 wall 1 1
41.3333333333 144.6 128 144.6 wall 1 1
155.333333333 47.8666666667 155.333333333 93 wall 1 1
197.5 146.733333333 222 146.733333333 wall 1 1
155.333333333 146.733333333 197.5 146.733333333 wall 1 1
222 146.733333333 222 156.5 wall 1 1
41.3333333333 116 41.3333333333 144.6 wall 1 1
13.6666666667 116 41.3333333333 116 wall 1 1
155.333333333 93 155.333333333 146.733333333 wall 1 1
13.6666666667 144.6 41.3333333333 144.6 wall 1 1
13.6666666667 116 13.6666666667 144.6 wall 1 1
pytorch output: (in which only 11 walls are detected)
256 256
11
12.449275362318842 208.3768115942029 116.59958071278825 208.3768115942029 3 0
37.315602836879435 107.09478021978023 37.315602836879435 135.16484142914493 0 6
203.3 135.16484142914493 203.3 208.3768115942029 0 2
116.59958071278825 135.16484142914493 203.3 135.16484142914493 0 2
116.59958071278825 135.16484142914493 116.59958071278825 208.3768115942029 2 3
37.315602836879435 135.16484142914493 116.59958071278825 135.16484142914493 0 3
116.59958071278825 208.3768115942029 203.3 208.3768115942029 2 0
12.449275362318842 107.09478021978023 37.315602836879435 107.09478021978023 0 6
12.449275362318842 135.16484142914493 37.315602836879435 135.16484142914493 6 3
12.449275362318842 107.09478021978023 12.449275362318842 135.16484142914493 6 0
12.449275362318842 135.16484142914493 12.449275362318842 208.3768115942029 3 0
142 135.0 166 135.0 door 1 1
15 135.0 36 135.0 door 1 1
53 135.0 102 135.0 door 1 1
116.0 147 116.0 193 door 1 1
12.0 161 12.0 179 door 1 1
42 208.0 88 208.0 door 1 1
130 208.0 173 208.0 door 1 1
213 46 249 67 bathtub 1 1
186 159 201 207 cooking_counter 1 1
149 46 162 64 toilet 1 1
185 109 201 134 entrance 1 1
173 45 186 58 washing_basin 1 1
191 46 209 61 washing_basin 1 1
While the corners are all predicted correctly, the walls are predicted poorly. Since the performance of pytorch model is not checked, is it a bad model or something wrong with IP ? or should I train more epoch ?
Hello @shuuchen Did you used the same code of pytorch as provided in this repository as it is or you modified it also? I am trying to run the pytorch code but I am having trouble in setting the path to the input files. Looking forward to your reply.
@riti1302 My version. https://github.com/shuuchen/FloorplanTransformation/tree/master/pytorch
@shuuchen Thanks for sharing your version. I am getting the same error as I was getting in @art-programmer code. Do you know how to solve this?
keyname=floorplan task=train started
Traceback (most recent call last):
File "train.py", line 175, in
@riti1302
IOError: [Errno 2] No such file or directory: '../data/train_1.txt'
It says you don' t have this file. Use '../data/train.txt' instead.
@shuuchen There is no train.txt file in data directory. Have you used the same data directory? And also can you tell me where have you put your input data?
@shuuchen Thanks. It solved my problem. Actually, I am new to pytorch and I am having trouble writing the code for prediction. It will help a lot if you send me your code for prediction.
@shuuchen If I enter a input floorplan image then a txt file (similar as one used during training) should be given out as an output.
@shuuchen I did that.
I want to know how you got the output (shown below) that you posted in this issue.
@shuuchen Hi, I also face issue training this network. I went into the Pytorch folder and follow the instruction (1: install requirment.txt, 2: python train.py -restore = 0) then it returned No such file or directory: '../data/train.txt' error. How can I solve this? Thanks
@sanwong15
you don't have the '../data/train.txt'. just download the file, put it in the right path, and try again.
Hi. Thanks for your reply. Where can I download it? How about the test.txt file? I am not sure if I have misread the documents. Thank you for helping me out with this
San
On Sat, Jun 22, 2019, 11:35 PM Shuchen Du [email protected] wrote:
@sanwong15 https://github.com/sanwong15
you don't have the '../data/train.txt'. just download the file, put it in the right path, and try again.
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@riti1302 you said the walls are predicted poorly, so what model did you use ? I want details about that
@sanwong15 my version https://github.com/shuuchen/FloorplanTransformation/tree/master/pytorch
@shuuchen As you said in this issue that you tested the image. Can you tell me what command you used to test the image?
@riti1302 Ok. That is and old issue. I have already fixed that. Just use the following command
python train.py --task=test
@riti1302 test_batch is for test, while test is for validation refer to ../data and you will find the files
@riti1302 it is in test folder. I suggest you reading the source code as it is easy to understand. this discussion page is too long, I recommend open a new issue in my personal page https://github.com/shuuchen/FloorplanTransformation/tree/master/pytorch
@shuuchen I have tried training the model with 100 epochs, batch size=5 and number of images = 600. The performance of the model is very poor. Have you tried improving the accuracy? If yes, then can you tell me what changes should I make to improve the accuracy?
what train data did you use? you can use the data on my github, the result is good.
2019年6月25日(火) 14:26 Ritika Kumari [email protected]:
@shuuchen https://github.com/shuuchen I have tried training the model with 100 epochs, batch size=5 and number of images = 600. The performance of the model is very poor. Have you tried improving the accuracy? If yes, then can you tell me what changes should I make to improve the accuracy?
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@shuuchen I used your data. Can you give me some of your training details like number of epochs, learning rate and batch size?
@shuuchen Yes, a little. I am saving the checkpoint every 5 epochs. Nothing else.
@shuuchen Also the the default epochs is 1000. Have you trained the model on 1000 epochs?
I think 500 epoch is enough. Have you trained that much? I updated my repository. Please reload that
Ritika Kumari [email protected]于2019年6月26日 周三下午3:41写道：
@shuuchen https://github.com/shuuchen Also the the default epochs is 1000. Have you trained the model on 1000 epochs?
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@shuuchen I trained it at 100 epochs with your dataset and it is showing infeasible.
@shuuchen I figured out my mistake. I was saving the checkpoint after every 5 epochs and the base was set to 0. When I was executing the code for validation it was still set to 0. For validation I have to change the base to 95 so that it will load the latest checkpoint. Now it's giving good accuracy. Thanks a lot for your help.
@shuuchen What is the meaning of last two columns of the text file? Also can you tell me how to detect different type of doors?
The last two columns. For example: 1 1 after kitchen
@riti1302 Well, the last two columns are not used yet. If you dive into the code, you can realize that.
@shuuchen Okay, i got it. I am trying to train the model on my dataset but it is giving error. I posted that error above.
@riti1302 it seems you use bool object as dataloader just check the variables according to the error messege
@shuuchen @riti1302 hello guys, can i use the task=test_batch or task=test without drn? i have a model_floorplan.t7 that i plan to use it instead of the drn dataset because i don't have an account(no access). If you can help me with that, it will be appreciated.
@riti1302 it seems you use bool object as dataloader just check the variables according to the error messege
@sanwong15 my version https://github.com/shuuchen/FloorplanTransformation/tree/master/pytorch
hello @shuuchen i have traind the model for 200 epochs,but the result of the predict is very poor,can you give me a trained model to have a test? thanks,[email protected]
when I run “python train.py --task=test”，output:
It's not what you run it
How can I solve this problem?@shuuchen
@shuuchen I figured out my mistake. I was saving the checkpoint after every 5 epochs and the base was set to 0. When I was executing the code for validation it was still set to 0. For validation I have to change the base to 95 so that it will load the latest checkpoint. Now it's giving good accuracy. Thanks a lot for your help.
Hello, could you explain what the change was? I do not understand what base you were referring to in your comment.
