Hello，what can i do with this project?

[fengziyue]

is this project runnable? can i train the representation network and generation network with the dataset provided by deepmind? or with my own dataset? or i just can inference the model with pre-trained model? @musyoku @ktns

[musyoku]

Hi. The goal of this project is to get the same result as this video. https://youtu.be/G-kWNQJ4idw?t=32

[musyoku]

You can

• create your own dataset
• train a model
• generate images

We don't support dataset provided by deepmind.

[musyoku]

This project is currently under development.

[fengziyue]

Hello，thanks for your reply! This is a wonderful project! I am also very interested in GQN, but I am not able to implement it. So what can we do with this project now?

I saw you ticked in front of "implement GQN" But did not tick the "implement training loop" So is this project only able to inference but can't train? but how can we inference without training? do you have a pre-trained model? @musyoku

[musyoku]

It is currently possible to train the model and inference. I am doing hyperparameter search. (It takes a week to train on GTX 1080)

[fengziyue]

Hi @musyoku ! How many iterations can you finish in two weeks with 1080? I just can finish 1 iteration per day with GTX TITAN X(maxwell) and I saw in your code the iteration range is 2*10**6 . Maybe I will never finish the train. My dataset is generated by your “create_dataset.py”, it contains 2 millions samples. And it is around 500GB and stored in HDD(7200rpm) driver. Will it be much quicker with SSD?

[musyoku]

I think that SSD is faster than HDD because my training code reads the file for each iteration and generates minibatch. I finished 7 iterations (387,800 steps / 2,000,000) in 5 days on a single 1080. Importantly, I have never finished training, so there may be a bug in the code and not get the same results reported by DeepMind.

[fengziyue]

Hello @musyoku will you support maze scenario?

[musyoku]

I will not but I want to support if I have time

[fengziyue]

Hello @musyoku can you tell me how to compute PIG(predicted information gain) for the maze scenario? It's in the third chapter of GQN's supplementary materials. I can understand the IG but I can't understand the PIG. How could we compute it without the known target observation X? could you give me your email address? I think we can talk in email but not in the Github issues~ Thank you very much~

[musyoku]

The PIG is approximated at every point by averaging over 50 samples per heading directions.

PIG is computed by averaging over 50 x that are sampled from the generator network.

x_n ~ g(x|z,y)(z|y) PIG = 1/50 * {IG(x_1, y) + IG(x_2, y) + ... + IG(x_50, y)}

I'm very sorry but I would like to discuss in issues. And my English skill is not good enough to discuss :confused: (I'm using Google translation)

[fengziyue]

@musyoku Do you mean we use the generator network to generate an image x_n then input the (x_n, v, r) to the inference network and output the z_mean_q and z_var_q. Input the (v, r) to the prior network and output the z_mean_p and z_var_p.

The IG(x_n, y) is the KL_divergence(z_mean_p, z_var_p, z_mean_q, z_var_q)?

[musyoku]

My understanding is

• Input (v_q, r) to the generator and output x_n. (v_q is a random query viewpoint covering the maze)
• Input x_n to the inference network and output (z_mean_q, z_var_q, z_mean_p, z_var_p). (Eq. S18-S23)
• Compute IG (KL divergence) between (z_mean_q, z_var_q) and (z_mean_p, z_var_p). (Eq. S27)

[fengziyue]

@musyoku OK Thank you very much! I have another question, I saw your create_dataset.py in the room scenario generates a Cornell box with every wall a solid color( each wall has just one color). I want to add a texture to the floor, how could I implement it? (I have my own picture and want to post it to the floor as a texture map) Thank you again

[musyoku]

I am implementing a texture shader. I will add it to this repo.

[fengziyue]

ok! thank you~

[fengziyue]

Hello, @musyoku which license do you choose? Apache, GPL, or MIT?

[musyoku]

MIT