https://github.com/kamenbliznashki/normalizing_flows

Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
https://github.com/kamenbliznashki/normalizing_flows

deep-learning density-estmation normalizing-flows probability

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Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows

• Host: GitHub
• URL: https://github.com/kamenbliznashki/normalizing_flows
• Owner: kamenbliznashki
• Created: 2018-12-30T08:50:07.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-07-12T12:40:38.000Z (almost 3 years ago)
• Last Synced: 2024-01-08T01:04:21.904Z (6 months ago)
• Topics: deep-learning, density-estmation, normalizing-flows, probability
• Language: Python
• Homepage:
• Size: 2.84 MB
• Stars: 565
• Watchers: 16
• Forks: 102
• Open Issues: 10
• Metadata Files:
  • Readme: readme.md

Lists

• awesome-normalizing-flows - normalizing_flows
• awesome-stars - kamenbliznashki/normalizing_flows - Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows (Python)
• awesome-energy-based-model - `normalizing_flows`

README

        
# Normalizing flows

Reimplementations of density estimation algorithms from:

* [Block Neural Autoregressive Flow](https://arxiv.org/abs/1904.04676)

* [Glow: Generative Flow with Invertible 1×1 Convolutions](https://arxiv.org/abs/1807.03039)

* [Masked Autoregressive Flow for Density Estimation](https://arxiv.org/abs/1705.07057)

* [Density Estimation using RealNVP](https://arxiv.org/abs/1605.08803)

* [Variational Inference with Normalizing Flows](https://arxiv.org/abs/1505.05770)

## Block Neural Autoregressive Flow

https://arxiv.org/abs/1904.04676

Implementation of BNAF on toy density estimation datasets.

#### Results

Density estimation of 2d toy data and density estimation of 2d test energy potentials (cf. Figure 2 & 3 in paper):

The models were trained for 20,000 steps with the architectures and hyperparameters described in the Section 5 of the paper, with the exception of `rings` dataset (bottom right) which had 5 hidden layers. The models trained significantly faster than the planar flow model in Rezende & Mohamed and were much more stable; interestingly, BNAF stretches space differently and requires a lot more test points to show a smooth potential.

| Density matching on 2d energy potentials | Density estimation on 2d toy data |

| --- | --- |

| ![bnaf_u1](images/bnaf/bnaf_u1_vis_step_20000.png) | ![bnaf_8gaussians](images/bnaf/bnaf_8gaussians_vis_step_20000.png) |

| ![bnaf_u2](images/bnaf/bnaf_u2_vis_step_20000.png) | ![bnaf_checkerboard](images/bnaf/bnaf_checkerboard_vis_step_20000.png) |

| ![bnaf_u3](images/bnaf/bnaf_u3_vis_step_20000.png) | ![bnaf_2spirals](images/bnaf/bnaf_2spirals_vis_step_20000.png) |

| ![bnaf_u4](images/bnaf/bnaf_u4_vis_step_20000.png) | ![bnaf_rings](images/bnaf/bnaf_rings_vis_step_20000.png) |

#### Usage

To train model:

```

python bnaf.py --train

               --dataset      # choice from u1, u2, u3, u4, 8gaussians, checkerboard, 2spirals

               --log_interval # how often to save the model and visualize results

               --n_steps      # number of training steps

               --n_hidden     # number of hidden layers

               --hidden_dim   # dimension of the hidden layer

               --[add'l options]

```

Additional options are: learning rate, learning rate decay and patience, cuda device id, batch_size.

To plot model:

```

python bnaf.py --plot

               --restore_file [path to .pt checkpoint]

```

#### Useful resources

* Official implementation by the authors https://github.com/nicola-decao/BNAF

## Glow: Generative Flow with Invertible 1x1 Convolutions

https://arxiv.org/abs/1807.03039

Implementation of Glow on CelebA and MNIST datasets.

#### Results

I trained two models:

- Model A with 3 levels, 32 depth, 512 width (~74M parameters). Trained on 5 bit images, batch size of 16 per GPU over 100K iterations.

- Model B with 3 levels, 24 depth, 256 width (~22M parameters). Trained on 4 bit images, batch size of 32 per GPU over 100K iterations.

In both cases, gradients were clipped at norm 50, learning rate was 1e-3 with linear warmup from 0 over 2 epochs. Both reached similar results and 4.2 bits/dim.

##### Samples at varying temperatures

Temperatures ranging 0, 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 1 (rows, top to bottom):

| Model A | Model B |

| --- | --- |

| ![model_a_range](images/glow/model_3_32_512_generated_samples_at_z_std_range.png) | ![model_b_range](images/glow/model_3_24_256_generated_samples_at_z_std_range.png) |

##### Samples at temperature 0.7:

| Model A | Model B |

| --- | --- |

| ![model_a_range](images/glow/model_3_32_512_generated_samples_at_z_std_0.7_seed_2.png) | ![model_b_range](images/glow/model_3_24_256_generated_samples_at_z_std_0.7.png) |

##### Model A attribute manipulation on in-distribution sample:

Embedding vectors were calculated for the first 30K training images and positive / negative attributes were averaged then subtracting. The resulting `dz` was ranged and applied on a test set image (middle image represents the unchanged / actual data point).

| Attribute | `dz` range [-2, -1, 0, 1, 2] |

| --- | --- |

| Brown hair | ![attr_8](images/glow/manipulated_sample_attr_8.png) |

| Male | ![attr_20](images/glow/manipulated_sample_attr_20.png) |

| Mouth slightly opened | ![attr_21](images/glow/manipulated_sample_attr_21.png) |

| Young | ![attr_39](images/glow/manipulated_sample_attr_39.png) |

##### Model A attribute manipulation on 'out-of-distribution' sample (i.e. me):

| Attribute | `dz` range |

| --- | --- |

| Brown hair | ![me_8](images/glow/manipulated_img_me3_attr_8.png) |

| Mouth slightly opened | ![me_21](images/glow/manipulated_img_me1_attr_21.png) |

#### Usage

To train a model using pytorch distributed package:

```

python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE \

       glow.py --train \

               --distributed \

               --dataset=celeba \

               --data_dir=[path to data source] \

               --n_levels=3 \

               --depth=32 \

               --width=512 \

               --batch_size=16 [this is per GPU]

```

For larger models or image sizes add `--checkpoint_grads` to checkpoint gradients using pytorch's library. I trained a 3 layer / 32 depth / 512 width model with batch size of 16 without gradient checkpointing and a 4 layer / 48 depth / 512 width model with batch size of 16 which had ~190M params so required gradient checkpointing (and was painfully slow on 8 GPUs).

To evaluate model:

```

python glow.py --evaluate \

               --restore_file=[path to .pt checkpoint] \

               --dataset=celeba \

               --data_dir=[path to data source] \

               --[options of the saved model: n_levels, depth, width, batch_size]

```

To generate samples from a trained model:

```

python glow.py --generate \

               --restore_file=[path to .pt checkpoint] \

               --dataset=celeba \

               --data_dir=[path to data source] \

               --[options of the saved model: n_levels, depth, width, batch_size] \

               --z_std=[temperature parameter; if blank, generates range]

```

To visualize manipulations on specific image given a trained model:

```

python glow.py --visualize \

               --restore_file=[path to .pt checkpoint] \

               --dataset=celeba \

               --data_dir=[path to data source] \

               --[options of the saved model: n_levels, depth, width, batch_size] \

               --z_std=[temperature parameter; if blank, uses default] \

               --vis_attrs=[list of indices of attribute to be manipulated, if blank, manipulates every attribute] \

               --vis_alphas=[list of values by which `dz` should be multiplied, defaults [-2,2]] \

               --vis_img=[path to image to manipulate (note: size needs to match dataset); if blank uses example from test dataset]

```

#### Datasets

To download CelebA follow the instructions [here](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). A nice script that simplifies downloading and extracting can be found here: https://github.com/nperraud/download-celebA-HQ/

#### References

* Official implementation in Tensorflow: https://github.com/openai/glow

## Masked Autoregressive Flow

https://arxiv.org/abs/1705.07057

Reimplementation of MADE, MAF, Mixture of Gaussians MADE, Mixture of

Gausssians MAF, and RealNVP modules on UCI datasets and MNIST.

#### Results

Average test log likelihood for un/conditional density estimation (cf.

Table 1 & 2 in paper for results and parameters; models here were trained for 50 epochs):

| Model | POWER | GAS | HEPMASS | MINIBOONE | BSDS300 | MNIST (uncond) | MNIST (cond) |

| --- | --- | --- | --- | --- | --- | --- | --- |

| MADE | -3.10 +/- 0.02 | 2.53 +/- 0.02 | -21.13 +/- 0.01 | -15.36 +/- 15.06 | 146.42 +/- 0.14 | -1393.67 +/- 1.90 | -1340.98 +/- 1.71 |

| MADE MOG | 0.37 +/- 0.01 | 8.08 +/- 0.02 | -15.70 +/- 0.02 | -11.64 +/- 0.44 | 153.56 +/- 0.28 | -1023.13 +/- 1.69 | -1013.75 +/- 1.61 |

| RealNVP (5) | -0.49 +/- 0.01 | 7.01 +/- 0.06 | -19.96 +/- 0.02 | -16.88 +/- 0.21 | 148.34 +/- 0.26 | -1279.76 +/- 9.91 | -1276.33 +/- 12.21 |

| MAF (5) | 0.03 +/- 0.01 | 6.23 +/- 0.01 | -17.97 +/- 0.01 | -11.57 +/- 0.21 | 153.53 +/- 0.27 | -1272.70 +/- 1.87 | -1268.24 +/- 2.73 |

| MAF MOG (5) | 0.09 +/- 0.01 | 7.96 +/- 0.02 | -17.29 +/- 0.02 | -11.27 +/- 0.41 | 153.35 +/- 0.26 | -1080.46 +/- 1.53 | -1070.33 +/- 1.53 |

Toy density model (cf. Figure 1 in paper):

| Target density | Learned density with MADE 
 and random numbers driving MADE | Learned density with MAF 5 layers 
 and random numbers driving MAF |

| --- | --- | --- |

| ![fig1a](images/maf/figure_1a.png) | ![fig1b](images/maf/figure_1b.png) | ![fig1c](images/maf/figure_1c.png) |

Class-conditional generated images from MNIST using MAF (5) model; generated data arrange by decreasing log probability (cf. Figure 3 in paper):

![mafmnist](images/maf/generated_samples_maf5.png)

#### Usage

To train model:

```

python maf.py -- train \

              -- model=['made' | 'mademog' | 'maf' | 'mafmog' | 'realnvp'] \

              -- dataset=['POWER' | 'GAS' | 'HEPMASS' | 'MINIBOONE' | 'BSDS300' | MNIST'] \

              -- n_blocks=[for maf/mafmog and realnvp specify # of MADE-blocks / coupling layers] \

              -- n_components=[if mixture of Gaussians, specify # of components] \

              -- conditional [if MNIST, can train class-conditional log likelihood] \

              -- [add'l options see py file]

```

To evaluate model:

```

python maf.py -- evaluate \

              -- restore_file=[path to .pt checkpoint]

              -- [options of the saved model: n_blocks, n_hidden, hidden_size, n_components, conditional]

```

To generate data from a trained model (for MNIST dataset):

```

python maf.py -- generate \

              -- restore_file=[path to .pt checkpoint]

              -- dataset='MNIST'

              -- [options of the saved model: n_blocks, n_hidden, hidden_size, n_components, conditional]

```

#### Datasets

Datasets and preprocessing code are forked from the MAF authors' implementation [here](https://github.com/gpapamak/maf#how-to-get-the-datasets). The unzipped datasets should be symlinked into the `./data` folder or the data_dir argument should be specified to point to the actual data.

#### References

* The original Theano implementation by the authors https://github.com/gpapamak/maf/

* https://github.com/ikostrikov/pytorch-flows

## Variational inference with normalizing flows

Implementation of [Variational Inference with Normalizing Flows](https://arxiv.org/abs/1505.05770)

#### Results

Density estimation of 2-d test energy potentials (cf. Table 1 & Figure 3 in paper).

| Target density | Flow K = 2 | Flow K = 32 | Training parameters |

| --- | --- | --- | --- |

| ![uz1](images/nf/nf_uz1_target_potential_density.png) | ![uz1k2](images/nf/nf_uz1_flow_k2_density.png) | ![uz1k32](images/nf/nf_uz1_flow_k32_density.png) | weight init Normal(0,1), base dist. scale 2 |

| ![uz2](images/nf/nf_uz2_target_potential_density.png) | ![uz2k2](images/nf/nf_uz2_flow_k2_density.png) | ![uz2k32](images/nf/nf_uz2_flow_k32_density.png) | weight init Normal(0,1), base dist. scale 1 |

| ![uz3](images/nf/nf_uz3_target_potential_density.png) | ![uz3k2](images/nf/nf_uz3_flow_k2_density.png) | ![uz3k32](images/nf/nf_uz3_flow_k32_density.png) | weight init Normal(0,1), base dist. scale 1, weight decay 1e-3 |

| ![uz4](images/nf/nf_uz4_target_potential_density.png) | ![uz4k2](images/nf/nf_uz4_flow_k2_density.png) | ![uz4k32](images/nf/nf_uz4_flow_k32_density.png) | weight init Normal(0,1), base dist. scale 4, weight decay 1e-3 |

#### Usage

To train model:

```

python planar_flow.py -- train \

                      -- target_potential=[choice from u_z1 | u_z2 | u_z3 | u_z4] \

                      -- flow_length=[# of layers in flow] \

                      -- [add'l options]

```

Additional options are: base distribution (q0) scale, weight initialization

scale, weight decay, learnable first affine layer (I did not find adding an affine layer beneficial).

To evaluate model:

```

python planar_flow.py -- evaluate \

                      -- restore_file=[path to .pt checkpoint]

```

#### Useful resources

* https://github.com/casperkaae/parmesan/issues/22

## Dependencies

* python 3.6

* pytorch 1.0

* numpy

* matplotlib

* tensorboardX

###### Some of the datasets further require:

* pandas

* sklearn

* h5py