Merge pull request #107 from slimgroup/add_cuda

change vers and add cudnn dep
slimgroup · May 10, 2024 · e3ab4a4 · rafaelorozco · May 10, 2024 · rafaelorozco
2 parents c1e544a + fad1d6f
commit e3ab4a4
Show file tree

Hide file tree

Showing 8 changed files with 221 additions and 44 deletions.
diff --git a/CITATION.bib b/CITATION.bib
@@ -0,0 +1,6 @@
+@article{orozco2023invertiblenetworks,
+  title={InvertibleNetworks. jl: A Julia package for scalable normalizing flows},
+  author={Orozco, Rafael and Witte, Philipp and Louboutin, Mathias and Siahkoohi, Ali and Rizzuti, Gabrio and Peters, Bas and Herrmann, Felix J},
+  journal={arXiv preprint arXiv:2312.13480},
+  year={2023}
+}
diff --git a/Project.toml b/Project.toml
@@ -1,11 +1,12 @@
 name = "InvertibleNetworks"
 uuid = "b7115f24-5f92-4794-81e8-23b0ddb121d3"
 authors = ["Philipp Witte <p.witte@ymail.com>", "Ali Siahkoohi <alisk@gatech.edu>", "Mathias Louboutin <mlouboutin3@gatech.edu>", "Gabrio Rizzuti <g.rizzuti@umcutrecht.nl>", "Rafael Orozco <rorozco@gatech.edu>", "Felix J. Herrmann <fherrmann@gatech.edu>"]
-version = "2.2.7"
+version = "2.2.8"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 JOLI = "bb331ad6-a1cf-11e9-23da-9bcb53c69f6f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -15,11 +16,11 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Wavelets = "29a6e085-ba6d-5f35-a997-948ac2efa89a"
 
 [compat]
-CUDA = "1, 2, 3, 4"
+CUDA = "1, 2, 3, 4, 5"
 ChainRulesCore = "0.8, 0.9, 0.10, 1"
-Flux = "0.11, 0.12, 0.13"
+Flux = "0.11, 0.12, 0.13, 0.14"
 JOLI = "0.7, 0.8"
-NNlib = "0.7, 0.8"
+NNlib = "0.7, 0.8, 0.9"
 TimerOutputs = "0.5"
 Wavelets = "0.9, 0.10"
 julia = "1"

diff --git a/README.md b/README.md
@@ -16,45 +16,20 @@ Building blocks for invertible neural networks in the [Julia] programming langua
 
 ## Installation
 
-
 InvertibleNetworks is registered and can be added like any standard Julia package with the command:
 
 ```
 ] add InvertibleNetworks
 ```
 
 
-## Papers
-
-The following publications use [InvertibleNetworks.jl]:
-
-- **["Reliable amortized variational inference with physics-based latent distribution correction"]**
-    - paper: [https://arxiv.org/abs/2207.11640](https://arxiv.org/abs/2207.11640)
-    - [presentation](https://slim.gatech.edu/Publications/Public/Submitted/2022/siahkoohi2022ravi/slides.pdf)
-    - code: [ReliableAVI.jl]
-
-- **["Learning by example: fast reliability-aware seismic imaging with normalizing flows"]**
-    - paper: [https://arxiv.org/abs/2104.06255](https://arxiv.org/abs/2104.06255)
-    - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/KAUST/2021/siahkoohi2021EarthMLfar/siahkoohi2021EarthMLfar.pdf)
-    - code: [ReliabilityAwareImaging.jl]
+## Uncertainty-aware image reconstruction
 
-- **["Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF)—an interpolation example"]**
-    - [paper](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/kumar2021SEGeuq/kumar2021SEGeuq.pdf)
-    - code: [WavefieldRecoveryUQ.jl]
 
-- **["Preconditioned training of normalizing flows for variational inference in inverse problems"]**
-    - paper: [https://arxiv.org/abs/2101.03709](https://arxiv.org/abs/2101.03709)
-    - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/AABI/2021/siahkoohi2021AABIpto/siahkoohi2021AABIpto_pres.pdf)
-    - code: [FastApproximateInference.jl]
+Due to its memory scaling InvertibleNetworks.jl has been particularily successful at Bayesian posterior sampling with simulation-based inference. To get started with this application refer to a simple example ([Conditional sampling for MNSIT inpainting](https://github.com/slimgroup/InvertibleNetworks.jl/tree/master/examples/applications/application_conditional_mnist_inpainting.jl)) but feel free to modify this script for your application and please reach out to us if you run into any trouble. 
 
-- **["Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization"]**
-    - paper: [https://arxiv.org/abs/2004.07871](https://arxiv.org/abs/2004.07871)
-    - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/rizzuti2020SEGuqavp/rizzuti2020SEGuqavp_pres.pdf)
-    - code: [https://github.com/slimgroup/Software.SEG2020](https://github.com/slimgroup/Software.SEG2020)
+![mnist_sampling_cond](docs/src/figures/mnist_sampling_cond.png)
 
-- **["Generalized Minkowski sets for the regularization of inverse problems"]**
-    - paper: [http://arxiv.org/abs/1903.03942](http://arxiv.org/abs/1903.03942)
-    - code: [SetIntersectionProjection.jl]
 
 ## Building blocks
 
@@ -112,37 +87,70 @@ AN = ActNorm(k; logdet=true) |> gpu
 Y_, logdet = AN.forward(X)
 ```
 
-## Acknowledgments
+## Reference
 
-This package uses functions from [NNlib.jl](https://github.com/FluxML/NNlib.jl), [Flux.jl](https://github.com/FluxML/Flux.jl) and [Wavelets.jl](https://github.com/JuliaDSP/Wavelets.jl)
+If you use InvertibleNetworks.jl in your research, we would be grateful if you cite us with the following bibtex:
+
+```
+@article{orozco2023invertiblenetworks,
+  title={InvertibleNetworks. jl: A Julia package for scalable normalizing flows},
+  author={Orozco, Rafael and Witte, Philipp and Louboutin, Mathias and Siahkoohi, Ali and Rizzuti, Gabrio and Peters, Bas and Herrmann, Felix J},
+  journal={arXiv preprint arXiv:2312.13480},
+  year={2023}
+}
+```
 
 
-## References
+## Papers
+
+The following publications use [InvertibleNetworks.jl]:
+
+- **["Reliable amortized variational inference with physics-based latent distribution correction"]**
+    - paper: [https://arxiv.org/abs/2207.11640](https://arxiv.org/abs/2207.11640)
+    - [presentation](https://slim.gatech.edu/Publications/Public/Submitted/2022/siahkoohi2022ravi/slides.pdf)
+    - code: [ReliableAVI.jl]
 
- - Yann Dauphin, Angela Fan, Michael Auli and David Grangier, "Language modeling with gated convolutional networks", Proceedings of the 34th International Conference on Machine Learning, 2017. https://arxiv.org/pdf/1612.08083.pdf
+- **["Learning by example: fast reliability-aware seismic imaging with normalizing flows"]**
+    - paper: [https://arxiv.org/abs/2104.06255](https://arxiv.org/abs/2104.06255)
+    - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/KAUST/2021/siahkoohi2021EarthMLfar/siahkoohi2021EarthMLfar.pdf)
+    - code: [ReliabilityAwareImaging.jl]
 
- - Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio, "Density estimation using Real NVP",  International Conference on Learning Representations, 2017, https://arxiv.org/abs/1605.08803
+- **["Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF)—an interpolation example"]**
+    - [paper](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/kumar2021SEGeuq/kumar2021SEGeuq.pdf)
+    - code: [WavefieldRecoveryUQ.jl]
 
- - Diederik P. Kingma and Prafulla Dhariwal, "Glow: Generative Flow with Invertible 1x1 Convolutions", Conference on Neural Information Processing Systems, 2018. https://arxiv.org/abs/1807.03039
+- **["Preconditioned training of normalizing flows for variational inference in inverse problems"]**
+    - paper: [https://arxiv.org/abs/2101.03709](https://arxiv.org/abs/2101.03709)
+    - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/AABI/2021/siahkoohi2021AABIpto/siahkoohi2021AABIpto_pres.pdf)
+    - code: [FastApproximateInference.jl]
 
- - Keegan Lensink, Eldad Haber and Bas Peters, "Fully Hyperbolic Convolutional Neural Networks", arXiv Computer Vision and Pattern Recognition, 2019. https://arxiv.org/abs/1905.10484
+- **["Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization"]**
+    - paper: [https://arxiv.org/abs/2004.07871](https://arxiv.org/abs/2004.07871)
+    - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/rizzuti2020SEGuqavp/rizzuti2020SEGuqavp_pres.pdf)
+    - code: [https://github.com/slimgroup/Software.SEG2020](https://github.com/slimgroup/Software.SEG2020)
 
- - Patrick Putzky and Max Welling, "Invert to learn to invert", Advances in Neural Information Processing Systems, 2019. https://arxiv.org/abs/1911.10914
+- **["Generalized Minkowski sets for the regularization of inverse problems"]**
+    - paper: [http://arxiv.org/abs/1903.03942](http://arxiv.org/abs/1903.03942)
+    - code: [SetIntersectionProjection.jl]
 
- - Jakob Kruse, Gianluca Detommaso, Robert Scheichl and Ullrich Köthe, "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference", arXiv Statistics and Machine Learning, 2020. https://arxiv.org/abs/1905.10687
 
 ## Authors
 
+ - Rafael Orozco, Georgia Institute of Technology [rorozco@gatech.edu]
+
  - Philipp Witte, Georgia Institute of Technology (now Microsoft)
 
  - Gabrio Rizzuti, Utrecht University
 
  - Mathias Louboutin, Georgia Institute of Technology
 
- - Rafael Orozco, Georgia Institute of Technology 
-
  - Ali Siahkoohi, Georgia Institute of Technology
 
+
+ ## Acknowledgments
+
+This package uses functions from [NNlib.jl](https://github.com/FluxML/NNlib.jl), [Flux.jl](https://github.com/FluxML/Flux.jl) and [Wavelets.jl](https://github.com/JuliaDSP/Wavelets.jl)
+
 [Flux]:https://fluxml.ai
 [Julia]:https://julialang.org
 [Zygote]:https://github.com/FluxML/Zygote.jl

diff --git a/docs/src/figures/mnist_sampling_cond.png b/docs/src/figures/mnist_sampling_cond.png
diff --git a/examples/VI/amortized_posterior_inference.jl → ...rtized_posterior_inference_1d_gaussian.jl b/examples/VI/amortized_posterior_inference.jl → ...rtized_posterior_inference_1d_gaussian.jl
diff --git a/examples/applications/application_conditional_mnist_inpainting.jl b/examples/applications/application_conditional_mnist_inpainting.jl
@@ -0,0 +1,162 @@
+using Pkg
+Pkg.activate(".")
+
+# Take around 6 minutes on CPU
+using InvertibleNetworks
+using Flux
+using LinearAlgebra
+using MLDatasets
+using Statistics
+using PyPlot
+using ProgressMeter: Progress, next!
+using Images
+using MLUtils
+
+function posterior_sampler(G, y, size_x; device=gpu, num_samples=1, batch_size=16)
+    # make samples from posterior for train sample 
+    X_dummy = randn(Float32, size_x[1:end-1]...,batch_size) |> device
+    Y_repeat = repeat(y |>cpu, 1, 1, 1, batch_size) |> device
+    _, Zy_fixed, _ = G.forward(X_dummy, Y_repeat); #needs to set the proper sizes here
+
+    X_post = zeros(Float32, size_x[1:end-1]...,num_samples)
+    for i in 1:div(num_samples, batch_size)
+      Zx_noise_i = randn(Float32, size_x[1:end-1]...,batch_size)|> device
+      X_post[:,:,:, (i-1)*batch_size+1 : i*batch_size] = G.inverse(
+          Zx_noise_i,
+          Zy_fixed
+        ) |> cpu;
+  end
+  X_post
+end
+
+# Training hyperparameters
+device = cpu #GPU does not accelerate at this small size. quicker on cpu
+lr     = 2f-3
+epochs = 30
+batch_size = 128
+
+# Load in training data
+n_total = 2048
+validation_perc = 0.9
+X, _ = MNIST(split=:train)[1:(n_total)];
+
+# Resize spatial size to a power of two to make Real-NVP multiscale easier. 
+nx = 16; ny = 16;
+N = nx*ny
+Xs = zeros(Float32, nx, ny, 1, n_total);
+for i in 1:n_total
+	Xs[:,:,:,i] = imresize(X[:,:,i]', (nx, ny));
+end
+
+# Make Forward operator A
+mask_size = 3 #number of pixels to zero out
+mask_start = div((nx-mask_size),2)
+A  = ones(Float32,nx,ny)
+A[mask_start:(end-mask_start),mask_start:(end-mask_start)] .= 0f0
+
+# Make observations y 
+Ys = A .* Xs;
+
+# To modify for your aplpication load in your own Ys and Xs here.
+# julia> size(Ys)
+# (16, 16, 1, 2048) (nx,ny,n_chan,n_batch)
+
+# julia> size(Xs)
+# (16, 16, 1, 2048) (nx,ny,n_chan,n_batch)
+
+# Use MLutils to split into training and validation/test set
+XY_train, XY_val = splitobs((Xs, Ys); at=validation_perc, shuffle=true);
+train_loader = DataLoader(XY_train, batchsize=batch_size, shuffle=true, partial=false);
+
+# Number of training batches 
+n_train  = numobs(XY_train)
+n_val    = numobs(XY_val)
+batches  = cld(n_train, batch_size)
+progress = Progress(epochs*batches);
+
+# Architecture parametrs
+chan_x = 1    # not RGB so chan=1
+chan_y = 1    # not RGB so chan=1
+L = 2         # Number of multiscale levels
+K = 10        # Number of Real-NVP layers per multiscale level
+n_hidden = 32 # Number of hidden channels in convolutional residual blocks
+
+# Create network
+G = NetworkConditionalGlow(chan_x, chan_y, n_hidden,  L, K; split_scales=true ) |> device;
+
+# Optimizer
+opt = ADAM(lr)
+
+# Training logs 
+loss_train = []; loss_val = [];
+
+for e=1:epochs # epoch loop
+    for (X, Y) in train_loader #batch loop
+        ZX, ZY, logdet_i = G.forward(X|> device, Y|> device);
+        G.backward(ZX / batch_size, ZX, ZY)
+
+        for p in get_params(G) 
+        	Flux.update!(opt, p.data, p.grad)
+        end; clear_grad!(G) # clear gradients unless you need to accumulate
+
+        #Progress meter
+        append!(loss_train, norm(ZX)^2 / (N*batch_size) - logdet_i / N)  # normalize by image size and batch size
+    	next!(progress; showvalues=[(:objective, loss_train[end]),(:l2norm, norm(ZX)^2 / (N*batch_size))])
+    end
+    # Evaluate network on validation set 
+    X = getobs(XY_val[1]) |> device;
+    Y = getobs(XY_val[2]) |> device;
+
+    ZX, ZY,logdet_i = G.forward(X, Y); 
+    append!(loss_val, norm(ZX)^2 / (N*n_val) - logdet_i / N)  # normalize by image size and batch size
+end
+
+# Training logs
+final_obj_train = round(loss_train[end];digits=3)
+final_obj_val = round(loss_val[end];digits=3)
+
+fig = figure()
+title("Objective value: train=$(final_obj_train) validation=$(final_obj_val)")
+plot(loss_train;label="Train"); 
+plot(batches:batches:batches*(epochs), loss_val;label="Validation"); 
+xlabel("Parameter update"); ylabel("Negative log likelihood objective") ;
+legend()
+savefig("log.png",dpi=300)
+
+# Make Figure of README
+num_plot = 2
+fig = figure(figsize=(11, 5));
+for (i,ind) in enumerate([1,3])
+	x = XY_val[1][:,:,:,ind:ind]
+	y = XY_val[2][:,:,:,ind:ind]
+	X_post = posterior_sampler(G, y, size(x); device=device, num_samples=64) |> cpu
+
+	X_post_mean = mean(X_post; dims=ndims(X_post)) 
+	X_post_var  = var(X_post;  dims=ndims(X_post)) 
+
+	ssim_val = round(assess_ssim(X_post_mean[:,:,1,1], x[:,:,1,1]) ,digits=2)
+
+	subplot(num_plot,7,1+7*(i-1)); imshow(x[:,:,1,1],  vmin=0, vmax=1, cmap="gray")
+	axis("off"); title(L"$x$");
+
+	subplot(num_plot,7,2+7*(i-1)); imshow(y[:,:,1,1] |> cpu,  cmap="gray")
+	axis("off"); title(L"$y$"); 
+
+	subplot(num_plot,7,3+7*(i-1)); imshow(X_post_mean[:,:,1,1] ,  vmin=0, vmax=1, cmap="gray")
+	axis("off"); title("SSIM="*string(ssim_val)*" \n"*"Conditional Mean") ; 
+
+	subplot(num_plot,7,4+7*(i-1)); imshow(X_post_var[:,:,1,1] ,  cmap="magma")
+	axis("off"); title(L"$UQ$") ; 
+
+	subplot(num_plot,7,5+7*(i-1)); imshow(X_post[:,:,1,1] |> cpu,   vmin=0, vmax=1,cmap="gray")
+	axis("off"); title("Posterior Sample") ; 
+
+	subplot(num_plot,7,6+7*(i-1)); imshow(X_post[:,:,1,2] |> cpu,  vmin=0, vmax=1,cmap="gray")
+	axis("off"); title("Posterior Sample") ; 
+
+	subplot(num_plot,7,7+7*(i-1)); imshow(X_post[:,:,1,3] |> cpu, vmin=0, vmax=1, cmap="gray")
+	axis("off"); title("Posterior Sample") ; 
+end
+tight_layout()
+savefig("mnist_sampling_cond.png",dpi=300,bbox_inches="tight")
+
diff --git a/...enerative_sampling/generative_sampling.jl → examples/applications/generative_sampling.jl b/...enerative_sampling/generative_sampling.jl → examples/applications/generative_sampling.jl
diff --git a/src/utils/dimensionality_operations.jl b/src/utils/dimensionality_operations.jl
@@ -477,7 +477,7 @@ end
 
 # Split and reshape 1D vector Y in latent space back to states Zi
 # where Zi is the split tensor at each multiscale level.
-function split_states(Y::AbstractVector{T}, Z_dims; L_net=2) where {T, N}
+function split_states(Y::AbstractVector{T}, Z_dims; L_net=2) where {T}
     L = length(Z_dims) + 1
     inds = cumsum([1, [prod(Z_dims[j]) for j=1:L-1]...])
     Z_save = [reshape(Y[inds[j]:inds[j+1]-1], xy_dims(Z_dims[j], Val(j==L), Val(length(Z_dims[j])))) for j=1:L-1]