art-programmer / floorplantransformation Goto Github PK

First of all, very amazing work. And I am trying to test on my own test images.

I installed torch and most of the torch packages successfully. But I found the link of lua--opencv and lunatic-python is out-of-date. I tried to complie the source code, but I failed. I want to know which version of lua are you using? And how did you install lua--opencv, by using luarocks or source code? And which source code did you use specificly?

Here is some code I found:
https://github.com/satoren/luaOpenCV
https://github.com/VisionLabs/torch-opencv
https://github.com/bastibe/lunatic-python
https://github.com/OddSource/lunatic-python

Could someone provide an instruction(lua and packages installation) for running annotator on Ubuntu?
Thank you in advance!

Since we need to show the 3D model for atleast 2-3 floorplan but not able to create floorplan.txt file. please suggest proper steps to do that in python.

Is it possible to have a docker file with this repo? It would facilitie figuring out the dependencies, and allow people to try the code with zero configuration. Would be highly appreciated.
Thanks!

I run into a lot of problem with the library versions of the dependencies that going through them has become a bit painful. Can you please make a list of the dependencies with versions, e.g., opencv 2.4 or 3.x, the head for lua---opencv, etc.

As the pytorch pre-trained model gives alot of prediction errors as compared to lua implementation. Can you please provide evaluated checkpoint for pytorch implementation. and also single image prediction function .

Hello! When I run python train.py --restore=0 I have:

keyname=floorplan task=train started
Traceback (most recent call last):
File "train.py", line 171, in
main(args)
File "train.py", line 25, in main
dataset = FloorplanDataset(options, split='train', random=True)
File "/home/user/FloorplanTransformation-master/pytorch/datasets/floorplan_dataset.py", line 236, in init
with open(self.dataFolder + split + '.txt') as f:

IOError: [Errno 2] No such file or directory: '../data/train.txt'

Tell me please, where can I find this file?

The error showed that the “cv” module was missing, then I installed the torch-opencv module and put it in the corresponding path, but the same problem still exists.May I ask if my "cv" module is installed correctly?

Hi,

I have installed cv library using luarocks install cv but even after the installation when I kick off the training process, I get the following error. I was wondering if you can point in the direction if I am installing something wrong.

Hello @art-programmer Thank for you share this amazing code.
I wanna ask if it's possiable to disable Gurobi module? Cause I don't have Gurobi License.
Will it increase the computation time?

Converting 2d floorplan to 3D model, using https://github.com/art-programmer/FloorplanTransformation. In rendering folder, while after running viewer.py we get the 3d model. But the floor and ceiling is not visible the function for floor and ceiling in floorplan.py is and output is also attached

def generateFloor(self, data):
floorGroup = EggGroup('floor')
data.addChild(floorGroup)

vp = EggVertexPool('floor_vertex')
floorGroup.addChild(vp)


exteriorWalls = []
for wall in self.walls:
  if (wall[4] == 6 or wall[5] == 6):
    exteriorWalls.append(copy.deepcopy(wall))
    pass
  continue    


exteriorOpenings = []
for wall in exteriorWalls:
  lineDim = calcLineDim((wall[:2], wall[2:4]))
  for doorIndex, door in enumerate(self.doors):
    if calcLineDim((door[:2], door[2:4])) != lineDim:
      continue
    if door[lineDim] >= wall[lineDim] and door[2 + lineDim] <= wall[2 + lineDim] and abs(door[1 - lineDim] - wall[1 - lineDim]) <= self.wallWidth:
      exteriorOpenings.append(doorIndex)
      pass
    continue
  continue

minDistance = 10000
mainDoorIndex = -1
for icon in self.icons:
  if icon[4] == 'entrance':
    for doorIndex in exteriorOpenings:
      door = self.doors[doorIndex]
      distance = pow(pow((door[0] + door[2]) / 2 - (icon[0] + icon[2]) / 2, 2) + pow((door[1] + door[3]) / 2 - (icon[1] + icon[3]) / 2, 2), 0.5)
      if distance < minDistance:
        minDistance = distance
        mainDoorIndex = doorIndex
        pass
      continue
    break
  continue

self.startCameraPos = [0.5, -0.5, self.wallHeight * 0.5]
self.startTarget = [0.5, 0.5, self.wallHeight * 0.5]
if mainDoorIndex >= 0:
  mainDoor = self.doors[mainDoorIndex]
  lineDim = calcLineDim((mainDoor[:2], mainDoor[2:4]))
  fixedValue = (mainDoor[1 - lineDim] + mainDoor[3 - lineDim]) / 2
  imageSize = [self.width / self.maxDim, self.height / self.maxDim]
  side = int(fixedValue < imageSize[1 - lineDim] * 0.5) * 2 - 1
  self.startCameraPos[lineDim] = (mainDoor[lineDim] + mainDoor[2 + lineDim]) / 2
  self.startTarget[lineDim] = (mainDoor[lineDim] + mainDoor[2 + lineDim]) / 2
  self.startCameraPos[1 - lineDim] = fixedValue - 0.5 * side
  self.startTarget[1 - lineDim] = fixedValue + 0.5 * side
  
  self.startCameraPos[0] = 1 - self.startCameraPos[0]
  self.startTarget[0] = 1 - self.startTarget[0]
  pass

newDoors = []
self.windows = []
for doorIndex, door in enumerate(self.doors):
  if doorIndex == mainDoorIndex or doorIndex not in exteriorOpenings:
    newDoors.append(door)
  else:
    self.windows.append(door)
    pass
  continue
self.doors = newDoors


exteriorWallLoops = []
visitedMask = {}
gap = 5.0 / self.maxDim
for wallIndex, wall in enumerate(exteriorWalls):
  if wallIndex in visitedMask:
    continue
  visitedMask[wallIndex] = True
  exteriorWallLoop = []
  exteriorWallLoop.append(wall)
  for loopWall in exteriorWallLoop:
    for neighborWallIndex, neighborWall in enumerate(exteriorWalls):
      if neighborWallIndex in visitedMask:
        continue
      #if calcDistance(neighborWall[:2], loopWall[:2]) < gap or calcDistance(neighborWall[2:4], loopWall[:2]) < gap or calcDistance(neighborWall[:2], loopWall[2:4]) < gap or calcDistance(neighborWall[2:4], loopWall[2:4]) < gap:
      if calcDistance(neighborWall[:2], loopWall[2:4]) < gap:
        exteriorWallLoop.append(neighborWall)
        visitedMask[neighborWallIndex] = True
        break
      elif calcDistance(neighborWall[2:4], loopWall[2:4]) < gap:
        neighborWall[0], neighborWall[2] = neighborWall[2], neighborWall[0]
        neighborWall[1], neighborWall[3] = neighborWall[3], neighborWall[1]
        exteriorWallLoop.append(neighborWall)
        visitedMask[neighborWallIndex] = True
        break
      continue
    continue
  exteriorWallLoops.append(exteriorWallLoop)
  continue


for exteriorWallLoop in exteriorWallLoops:
  poly = EggPolygon()
  floorGroup.addChild(poly)
  
  poly.setTexture(self.floorMat.getEggTexture())
  poly.setMaterial(self.floorMat.getEggMaterial())
  
  
  for wallIndex, wall in enumerate(exteriorWallLoop):
    if wallIndex == 0:
      v = EggVertex()
      v.setPos(Point3D(1 - wall[0], wall[1], 0))
      v.setUv(Point2D(wall[0] * self.maxDim / self.width, 1 - wall[1] * self.maxDim / self.height))
      poly.addVertex(vp.addVertex(v))
    else:
      v = EggVertex()
      v.setPos(Point3D(1 - (wall[0] + exteriorWallLoop[wallIndex - 1][2]) / 2, (wall[1] + exteriorWallLoop[wallIndex - 1][3]) / 2, 0))
      v.setUv(Point2D((wall[0] + exteriorWallLoop[wallIndex - 1][2]) / 2 * self.maxDim / self.width, 1 - (wall[1] + exteriorWallLoop[wallIndex - 1][3]) / 2 * self.maxDim / self.height))
      poly.addVertex(vp.addVertex(v))
      pass
    if wallIndex == len(exteriorWallLoop) - 1:
      v = EggVertex()
      v.setPos(Point3D(1 - wall[2], wall[3], 0))
      v.setUv(Point2D(wall[2] * self.maxDim / self.width, 1 - wall[3] * self.maxDim / self.height))
      poly.addVertex(vp.addVertex(v))
      pass
    continue
  continue


ceilingGroup = EggGroup('ceiling')
data.addChild(ceilingGroup)

vp = EggVertexPool('ceiling_vertex')
ceilingGroup.addChild(vp)

for exteriorWallLoop in exteriorWallLoops:
  poly = EggPolygon()
  ceilingGroup.addChild(poly)
  
  poly.setTexture(self.ceilingMat.getEggTexture())
  poly.setMaterial(self.ceilingMat.getEggMaterial())

  for wallIndex, wall in enumerate(exteriorWallLoop):
    if wallIndex == 0:
      v = EggVertex()
      v.setPos(Point3D(1 - wall[0], wall[1], self.wallHeight))
      v.setUv(Point2D(wall[0], 1 - wall[1]))
      poly.addVertex(vp.addVertex(v))
    else:
      v = EggVertex()
      v.setPos(Point3D(1 - (wall[0] + exteriorWallLoop[wallIndex - 1][2]) / 2, (wall[1] + exteriorWallLoop[wallIndex - 1][3]) / 2, self.wallHeight))
      v.setUv(Point2D((wall[0] + exteriorWallLoop[wallIndex - 1][2]) / 2, 1 - (wall[1] + exteriorWallLoop[wallIndex - 1][3]) / 2))
      poly.addVertex(vp.addVertex(v))
      pass
    if wallIndex == len(exteriorWallLoop) - 1:
      v = EggVertex()
      v.setPos(Point3D(1 - wall[2], wall[3], self.wallHeight))
      v.setUv(Point2D(wall[2], 1 - wall[3]))
      poly.addVertex(vp.addVertex(v))
      pass
    continue
  continue

return 

=> Setting up data loader
=> Loading trainer
=> Training epoch # 1
/root/torch/install/bin/luajit: /root/torch/install/share/lua/5.1/threads/threads.lua:183: [thread 1 callback] /root/torch/install/share/lua/5.1/cv/imgproc/init.lua:1837: 'struct TensorPlusInt' has no member named 'val'
stack traceback:
/root/torch/install/share/lua/5.1/cv/imgproc/init.lua:1837: in function 'connectedComponents'
../util/lua/floorplan_utils.lua:311: in function 'findConnectedComponents'
../util/lua/floorplan_utils.lua:5819: in function 'getSegmentation'
./models/heatmap-segmentation-dataloader.lua:71: in function <./models/heatmap-segmentation-dataloader.lua:64>
[C]: in function 'xpcall'
/root/torch/install/share/lua/5.1/threads/threads.lua:234: in function 'callback'
/root/torch/install/share/lua/5.1/threads/queue.lua:65: in function </root/torch/install/share/lua/5.1/threads/queue.lua:41>
[C]: in function 'pcall'
/root/torch/install/share/lua/5.1/threads/queue.lua:40: in function 'dojob'
[string " local Queue = require 'threads.queue'..."]:15: in main chunk
stack traceback:
[C]: in function 'error'
/root/torch/install/share/lua/5.1/threads/threads.lua:183: in function 'dojob'
./models/heatmap-segmentation-dataloader.lua:127: in function '(for generator)'
./models/heatmap-segmentation-train.lua:65: in function 'train'
main.lua:69: in main chunk
[C]: in function 'dofile'
/root/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x562049049570

Hi, I've been making a prediction using the following command "python train.py --task=test"

below is a partial output:

Optimization information Optimal -234.7307451974677
wall 17 [0, 2] [[200, 9], [199, 51]] dict_keys([]) dict_keys([55, 56, 57, 58, 59, 60])
wall 23 [1, 3] [[115, 85], [115, 107]] dict_keys([55, 56, 57, 60, 142, 143, 144, 146, 150, 151, 152, 153]) dict_keys([11, 12, 14, 70, 71, 73, 147, 148, 149])
wall 24 [0, 8] [[242, 107], [243, 143]] dict_keys([]) dict_keys([19, 20, 27, 28, 30, 31, 76, 77, 79, 80, 90, 167, 168])
.........

I would like to understand the output above, I need a file like the representation prediction represented below:

81	162	81	188	door	1	1	
81	67	81	100	door	1	1	
15	228.666666667	77	228.666666667	door	1	1	
19	41.5	33	41.5	door	1	1	
87	41	116	41	door	1	1	
97	228.666666667	148	228.666666667	door	1	1	
47	387	115	387	door	1	1	
49	104.5	75	104.5	door	1	1	
52	41	71	41	door	1	1	

Can you help me, please?
Thanks,
Giovanni.

Do you mind if I use the vector outputs of your system as a dataset for my own paper? Naturally I will reference yours.

Also regarding that, what is the scale of the numbers indicating the positions of the wall endpoints?

I want to use these buildings to simulate WiFi signals, so it would be useful to know the pixel scale in meters if you have that.

Hi.

There seems to be an error when running the renderer.py file. This is the output

Traceback (most recent call last): File "renderer.py", line 181, in <module> renderer.loadModels(['/home/simon/Desktop/FloorplanTransformation/data/floorplan_representation/00/0b/0513fae730eaf65b98f9580d024d/0001', ]) File "renderer.py", line 78, in loadModels floorplan.read() File "/home/simon/Desktop/FloorplanTransformation/rendering/floorplan.py", line 130, in read item[pointIndex * 2 + 0] /= self.maxDim AttributeError: Floorplan instance has no attribute 'maxDim'

maxDim seems to be causing the problem although it's defined on line 101 at floorplan.py

self.maxDim = max(self.width, self.height).

Any ideas on what might be causing this? @art-programmer

hi dude, could u explain how to define the dump1 and dump2 in the dataset? went through your code, it's hard to understand everything without comments

cheers

Hi chenliu ,how to make prediction on a floorplan image with PyTorch version？？？

...e/ubuntu/torch/install/share/lua/5.1/threads/threads.lua:183: [thread 1 callback] ../util/lua/floorplan_utils.lua:5766: attempt to index local 'representation' (a nil value)
stack traceback:
../util/lua/floorplan_utils.lua:5766: in function 'getSegmentation'
./models/heatmap-segmentation-dataloader.lua:71: in function <./models/heatmap-segmentation-dataloader.lua:64>
[C]: in function 'xpcall'
...e/ubuntu/torch/install/share/lua/5.1/threads/threads.lua:234: in function 'callback'
/home/ubuntu/torch/install/share/lua/5.1/threads/queue.lua:65: in function </home/ubuntu/torch/install/share/lua/5.1/threads/queue.lua:41>
[C]: in function 'pcall'
/home/ubuntu/torch/install/share/lua/5.1/threads/queue.lua:40: in function 'dojob'
[string " local Queue = require 'threads.queue'..."]:15: in main chunk
stack traceback:
[C]: in function 'error'
...e/ubuntu/torch/install/share/lua/5.1/threads/threads.lua:183: in function 'dojob'
./models/heatmap-segmentation-dataloader.lua:127: in function '(for generator)'
./models/heatmap-segmentation-train.lua:65: in function 'train'
main.lua:69: in main chunk
[C]: in function 'dofile'
...untu/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405d50

Hi , it will work for the Geforce 720M with CUDA 10.00.. or where I can train and build the model ??

Hi Chen Liu,

I tried to run the model but I have got the error message as below. I don't have permission to access this URL. Could you help me?

Hi! Is there a way to run a single prediction on the PyTorch implementation similar to the one with Lua?

I've attempted to benchmark the model using evaluate.lua script, but when I try to run the evaluate command, I get the error down below. I have installed all the libraries/packages as according to the links in the readme except opencv from VisionLabs (https://github.com/VisionLabs/torch-opencv) and lunatic python from OddSource (https://github.com/OddSource/lunatic-python). The lunatic python linked in the readme does not compile. Does anyone know what is going wrong?

/home/joacho/torch/install/bin/luajit: /home/joacho/torch/install/share/lua/5.1/trepl/init.lua:389: /home/joacho/torch/install/share/lua/5.1/trepl/init.lua:389: module 'python' not found:No LuaRocks module found for python
no field package.preload['python']
no file '../util/lua/python.lua'
no file '/home/joacho/.luarocks/share/lua/5.1/python.lua'
no file '/home/joacho/.luarocks/share/lua/5.1/python/init.lua'
no file '/home/joacho/torch/install/share/lua/5.1/python.lua'
no file '/home/joacho/torch/install/share/lua/5.1/python/init.lua'
no file './python.lua'
no file '/home/joacho/torch/install/share/luajit-2.1.0-beta1/python.lua'
no file '/usr/local/share/lua/5.1/python.lua'
no file '/usr/local/share/lua/5.1/python/init.lua'
no file '/home/joacho/.luarocks/lib/lua/5.1/python.so'
no file '/home/joacho/torch/install/lib/lua/5.1/python.so'
no file '/home/joacho/torch/install/lib/python.so'
no file './python.so'
no file '/usr/local/lib/lua/5.1/python.so'
no file '/usr/local/lib/lua/5.1/loadall.so'
stack traceback:
[C]: in function 'error'
/home/joacho/torch/install/share/lua/5.1/trepl/init.lua:389: in function 'require'
evaluate.lua:6: in main chunk
[C]: in function 'dofile'
...acho/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405d50

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 ?

Thank you very much for publishing such an excellent work! I would like to obtain the original R2V JPG format image, thank you very much!

The URL link of Google Drive needs to apply for access permission to download the training model

Hello guys,

I had requested your pre-trained model from the google drive access link. However, I got no responses.

Is it possible to get pre-trained model which was used to trained this pipeline?
Thanks

I used the annotator you provided to annotate my own house plan and placed it under the data folder. After running the command I got an error: I couldn't find the original picture in your model. How to change the data path

  |`-> (2): nn.Sequential {
  |      [input -> (1) -> (2) -> (3) -> output]
  |      (1): nn.Narrow
  |      (2): nn.Transpose
  |      (3): nn.View(-1, 13)
  |    }
   `-> (3): nn.Sequential {
         [input -> (1) -> (2) -> (3) -> output]
         (1): nn.Narrow
         (2): nn.Transpose
         (3): nn.View(-1, 17)
       }
   ... -> output

}
}
=> Setting up data loader
=> Loading trainer
=> Training epoch # 1
/home/ubuntu/torch/install/bin/luajit: /home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:67:
In 9 module of nn.Sequential:
In 1 module of nn.Sequential:
In 1 module of nn.ConcatTable:
In 5 module of nn.Sequential:
In 1 module of nn.Sequential:
In 1 module of nn.ConcatTable:
In 5 module of nn.Sequential:
In 1 module of nn.Sequential:
In 1 module of nn.ConcatTable:
In 5 module of nn.Sequential:
In 1 module of nn.Sequential:
In 1 module of nn.ConcatTable:
In 7 module of nn.Sequential:
...h/install/share/lua/5.1/cudnn/SpatialFullConvolution.lua:31: attempt to perform arithmetic on field 'groups' (a nil value)
stack traceback:
...h/install/share/lua/5.1/cudnn/SpatialFullConvolution.lua:31: in function 'resetWeightDescriptors'
...h/install/share/lua/5.1/cudnn/SpatialFullConvolution.lua:105: in function <...h/install/share/lua/5.1/cudnn/SpatialFullConvolution.lua:103>
[C]: in function 'xpcall'
/home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function </home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:41>
[C]: in function 'xpcall'
/home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/home/ubuntu/torch/install/share/lua/5.1/nn/ConcatTable.lua:11: in function </home/ubuntu/torch/install/share/lua/5.1/nn/ConcatTable.lua:9>
[C]: in function 'xpcall'
/home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
...
/home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function </home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:41>
[C]: in function 'xpcall'
/home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
./models/heatmap-segmentation-train.lua:71: in function 'train'
main.lua:69: in main chunk
[C]: in function 'dofile'
...untu/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405d50

WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above.
stack traceback:
[C]: in function 'error'
/home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:67: in function 'rethrowErrors'
/home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
./models/heatmap-segmentation-train.lua:71: in function 'train'
main.lua:69: in main chunk
[C]: in function 'dofile'
...untu/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405d50
(python2) ubuntu@ip-172-31-31-13:~/FloorplanTransformation/code$

Hi ,
i want to use the torch version with IP.py instead of QP.py because the pytorch version gives many prediction errors even with 1000 epoch.
If someone could help me with that.
thanks

Hey, the DRN weights in pytorch/models/drn.py for the webroot are not available anymore. According to the issue in the original DRN repo it is now 'http://dl.yf.io/drn/'

Are the default hyperparameters (command line arguments) optimal for the model or is there some other hyperparameter set I should use to produce the papers results? The batch size is only 8 because of GPU limitations. Can the batch size be the problem?

Hello.

I'm an engineer and now making an application using machine vision.
I was looking for how to recognize floor plan images and finally found out this project for my work and I'm really interested in using it.
But my question is what open source license does this project have?
Could I use this source code in our Product or only for academic use?

Thanks in advance.

Hi great work guys.
I have a question that how can I get the final Pytorch Testing output results like this?

I checked my test results I could only find separate results.

Also I checked IP.py maybe I missed something but I couldn't find where to dump the final combined results.
Thank you so much.