cfernandezlab / cfl Goto Github PK

Any recommendation how to launch this with mac?

I suppose the make.sh script is a CUDA supporting one only ?

Thanks for your great work!
First, I meet some diffculty when I saw formula(5). I can't understand this formula, your aim is project each point in kernel into the sphere surface, but can you explain why your rotation matrix like this? And the code of rotation matrix is bot same with Rodrigues' Rotation Formula, I found when theta = pi/2, the result is different.
Second, have you trid DCN-V1 or V2?

Hello,

Let me start by thanking you for the wonderful work and for making the code publicly available! Interested by your work, I have been trying to set up the pipeline. As mentioned in the readme, I installed all the dependencies in python and compiled the deform_conv_layer. But when I try to run the test_deform_conv.py, it fails with the following error:

tensorflow.python.framework.errors_impl.NotFoundError: ~/CFLE2E/CFL/Models/deform_conv_layer/deform_conv.so: undefined symbol: _ZN10tensorflow8internal21CheckOpMessageBuilder9NewStringB5cxx11Ev

From my previous experience, I had recognized this as being the result of a mismatched gcc that was used to compile tensorflow and the target custom layer. I installed tensorflow from the conda repositories. So I don't know which exact gcc was used to compile the binaries. I am using Arch, which comes with gcc 8.3. I am also using Cuda 10. I tried compiling the custom layer with both 4.9 (as mentioned) as well as gcc 5.5. However, both of them results in the same error. So it would be a great help if you could point me to a solution.

Hello, thanks for the code!

a small doubt, I followed the 2Dto3D python notebook and understood how to get the wireframe. can you please tell me what to change to get the room layouts in 3D like the teaser gif you have in this repo?

Thanks

Thanks for your great work!
First, I meet some diffculty when I saw formula(5). I can't understand this formula, your aim is project each point in kernel into the sphere surface, but can you explain why your rotation matrix like this? And the code of rotation matrix is bot same with Rodrigues' Rotation Formula, I found when theta = pi/2, the result is different.
Second, have you trid DCN-V1 or V2?

Hi！
Thanks for your great work! I have a question about CE, hoping to get your help.
In your code, I can't find how to calculate CE and PE. The output of network is an image, but I wonder how to get the corner id from Corner Map, especially for complete segmentation, can you spare your code or describe your method. Thank you!
Best wishes for you.

Hi!
When I use EquiConv to train, an error occur: error in deformable_im2col: out of memory, and err = 2, cudaSuccess = 0, have you ever encountered this kind of error. Online solutions are ：change to a small model 、small dataset. If you are free
（1）Can you tell me the specific machine configuration during your training？ GPU……
（2） In your paper, the input of the network is image of 256128. So do you encountered similar errors when using larger images？If not, why image size is designed to be 256128?
（3）I know that EquiConvs are built on top of deformable convolutions, the only difference is the way to get offset. My question is whether we need to change the deformed convolution other than the way to find the offset？ I got the Deformablle_Convolution_pytorch from https://github.com/open-mmlab/mmdetection, then I only to change the way to find offset in x.py? And no need to change .cu and .cpp files that they define this op(DCN).
（4）Can you share train.py with me? I want to use your code to test whether equiconv can run on my computer. It ’s okay if it ’s inconvenient.
Thank you very much for your help during this time.

Hello! Thanks for your great work.
When I read your code, I have some confusion.
I have seen the formula(3) and (5) in 4.1 EquiConvsDetails, and you define a matrix K = np.array([ [focal, 0, c_x], [0, focal, c_y], [0,0,1] ]) and rays = np.matmul(inv_K, rays.reshape(3, r_h*r_w)). I guess your aim is to put the coordinates on the sphere? But I don't understand the geometric principle behind. Can you introduce it to me? Thank you very much!

I am so sorry to trouble you again. I am not understand the code below.
According to the paper,I know rays is pij = [i j d]，rays[0] is corresponding to i, rays[1] is corresponding to j, … but I can't know the role of K and c_x, c_y. As you say in the paper, i and j are integer in the range [- (r-1)/2, (r-1)/2]. So why do you need to multiply inv_K and rays.
Another, the value of c_x and c_y is always 0?
c_x = 0 c_y = 0 K = np.array([[d,0,c_x],[0,d,c_y],[0,0,1]]) inv_K = np.linalg.inv(K) rays = np.matmul(inv_K, rays.reshape(3,r_h*r_w))

From the results of your paper, I find that the network constructed by Equirectangular Convolutions (EquiConvs) has no great improvement in indicators compared with the network constructed by traditional convolution (StdConvs). How can you explain the benefits of EquiConvs in processing ERP projection images?

In addition, the computation time of the network constructed by EquiConvs is 10 times that of the network constructed by StdConvs. So what are the advantages of EquiConvs compared to StdConvs?

Hello, Thanks for your great work!
In your code, I can't find train.py. My question is how to use optimizer. Because EquiConv is an op that we defined, so how to update it like we can directly use Adam to update tf.nn.conv2D

Is there any training code based TF?