From daabf7699a1489ee879c2205dc98fa9a2caa1137 Mon Sep 17 00:00:00 2001 From: Varun Kelkar Date: Tue, 2 Jan 2024 01:10:03 -0500 Subject: [PATCH] initial commit --- .gitignore | 2 + README.md | 57 + docs/AmbientFlow_generated_images_mri.png | Bin 0 -> 1143573 bytes docs/schematic.png | Bin 0 -> 70804 bytes masks_mri/cartesian_2fold_128.npy | Bin 0 -> 65664 bytes masks_mri/cartesian_2fold_128.png | Bin 0 -> 169 bytes masks_mri/cartesian_4fold_128.npy | Bin 0 -> 131200 bytes masks_mri/cartesian_4fold_128.png | Bin 0 -> 144 bytes masks_mri/cartesian_8fold_128.npy | Bin 0 -> 131200 bytes masks_mri/cartesian_8fold_128.png | Bin 0 -> 125 bytes recon/recon_bm3d.py | 86 + recon/recon_csgm.py | 108 ++ recon/recon_dip_tv.py | 97 ++ recon/recon_fista.py | 127 ++ recon/recon_inpaint_navier.py | 77 + recon/recon_langevin.py | 158 ++ recon/recon_postnet.py | 96 ++ recon/recon_wiener.py | 91 ++ requirements.txt | 15 + scripts/training/paper_configs.txt | 6 + scripts/training/train_ambient_celeba.sh | 55 + scripts/training/train_ambient_mnist.sh | 20 + scripts/training/train_ambient_mri.sh | 54 + scripts/training/train_vanilla_celeba.sh | 26 + scripts/training/train_vanilla_mnist.sh | 13 + scripts/training/train_vanilla_mri.sh | 24 + src/.gitignore | 1 + src/dataset_tool.py | 296 ++++ src/degradations.py | 251 +++ src/generate.py | 68 + src/generate.sh | 15 + src/metrics/__init__.py | 0 src/metrics/clean_fid.py | 18 + src/metrics/clean_fid.sh | 16 + src/metrics/log_odds.py | 101 ++ src/metrics/log_odds.sh | 28 + src/metrics/mean_and_cov.py | 112 ++ src/metrics/mean_and_cov.sh | 16 + src/metrics/radiomic_features.py | 235 +++ src/model_tools.py | 1347 ++++++++++++++++ src/models.py | 746 +++++++++ src/models2.py | 1776 +++++++++++++++++++++ src/sparsifiers.py | 258 +++ src/train_ambient.py | 560 +++++++ src/train_ambient_mnist.py | 245 +++ src/train_vanilla.py | 161 ++ src/utils.py | 87 + src/wavelet_utils.py | 60 + toy/ambient_model.py | 393 +++++ toy/data.py | 137 ++ toy/plotting.py | 121 ++ toy/vanilla_model.py | 161 ++ 52 files changed, 8321 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 docs/AmbientFlow_generated_images_mri.png create mode 100644 docs/schematic.png create mode 100644 masks_mri/cartesian_2fold_128.npy create mode 100644 masks_mri/cartesian_2fold_128.png create mode 100644 masks_mri/cartesian_4fold_128.npy create mode 100644 masks_mri/cartesian_4fold_128.png create mode 100644 masks_mri/cartesian_8fold_128.npy create mode 100644 masks_mri/cartesian_8fold_128.png create mode 100644 recon/recon_bm3d.py create mode 100644 recon/recon_csgm.py create mode 100644 recon/recon_dip_tv.py create mode 100644 recon/recon_fista.py create mode 100644 recon/recon_inpaint_navier.py create mode 100644 recon/recon_langevin.py create mode 100644 recon/recon_postnet.py create mode 100644 recon/recon_wiener.py create mode 100644 requirements.txt create mode 100644 scripts/training/paper_configs.txt create mode 100755 scripts/training/train_ambient_celeba.sh create mode 100755 scripts/training/train_ambient_mnist.sh create mode 100755 scripts/training/train_ambient_mri.sh create mode 100755 scripts/training/train_vanilla_celeba.sh create mode 100644 scripts/training/train_vanilla_mnist.sh create mode 100755 scripts/training/train_vanilla_mri.sh create mode 100644 src/.gitignore create mode 100644 src/dataset_tool.py create mode 100644 src/degradations.py create mode 100644 src/generate.py create mode 100755 src/generate.sh create mode 100644 src/metrics/__init__.py create mode 100644 src/metrics/clean_fid.py create mode 100644 src/metrics/clean_fid.sh create mode 100644 src/metrics/log_odds.py create mode 100644 src/metrics/log_odds.sh create mode 100644 src/metrics/mean_and_cov.py create mode 100644 src/metrics/mean_and_cov.sh create mode 100644 src/metrics/radiomic_features.py create mode 100644 src/model_tools.py create mode 100644 src/models.py create mode 100644 src/models2.py create mode 100644 src/sparsifiers.py create mode 100644 src/train_ambient.py create mode 100644 src/train_ambient_mnist.py create mode 100644 src/train_vanilla.py create mode 100644 src/utils.py create mode 100644 src/wavelet_utils.py create mode 100644 toy/ambient_model.py create mode 100644 toy/data.py create mode 100644 toy/plotting.py create mode 100644 toy/vanilla_model.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..26fa5a7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +results +**/__pycache__ diff --git a/README.md b/README.md new file mode 100644 index 0000000..3b8325c --- /dev/null +++ b/README.md @@ -0,0 +1,57 @@ +# AmbientFlow: Invertible generative models from incomplete, noisy measurements + +PyTorch implementation + +**Paper**: https://arxiv.org/abs/2309.04856 + +Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark A. Anastasio
+University of Illinois at Urbana-Champaign, Urbana, IL - 61801, USA + +**Contact**: vak2@illinois.edu, maa@illinois.edu + +**Abstract**: Generative models have gained popularity for their potential applications in imaging science, such as image reconstruction, posterior sampling and data sharing. Flow-based generative models are particularly attractive due to their ability to tractably provide exact density estimates along with fast, inexpensive and diverse samples. Training such models, however, requires a large, high quality dataset of objects. In applications such as computed imaging, it is often difficult to acquire such data due to requirements such as long acquisition time or high radiation dose, while acquiring noisy or partially observed measurements of these objects is more feasible. In this work, we propose AmbientFlow, a framework for learning flow-based generative models directly from noisy and incomplete data. Using variational Bayesian methods, a novel framework for establishing flow-based generative models from noisy, incomplete data is proposed. Extensive numerical studies demonstrate the effectiveness of AmbientFlow in learning the object distribution. The utility of AmbientFlow in a downstream inference task of image reconstruction is demonstrated. + +

+AmbientFlow MRI images +AmbientFlow MRI images +

+ + +## System Requirements +- Linux/Unix-based systems recommended. The code hasn't been tested on Windows. +- 64 bit Python 3.8+. The code has been tested with Python 3.8.13 installed via Anaconda + +Other packages required are listed in the `requirements.txt` file. + +## Directory structure + +- The directory `toy` contains the codes corresponding to the toy 2D distribution. + +- The directory `scripts/training` contains the top level shell scripts for training the regular and ambient models. Within this directory, `paper_configs.txt` contains the hyperparameter configurations used for the results in the paper. The number of GPUs and batch sizes listed are according to training on a single NVIDIA Quadro RTX 8000 48 GB GPU. + +- The directory `src` stores all python code defining the flow networks, forward models (degradation models), data processing pipelines, and utilities. The subdirectory `src/metrics` contains codes for evaluating the trained generative models via FID score, log-odds, empirical mean and covariance estimates, and radiomic features (for the MRI study). + +- The directory `masks_mri` contains the undersampling masks relevant to the Fourier undersampling forward operators used for the stylized MRI study. + +- The directory `recon` contains the python codes for running image reconstruction/restoration algorithms including baseline classical algorithms, compressed sensing using generative models (CSGM), and posterior sampling via annealed Langevin dynamics. The details of these approaches can be found in our paper. + +## Data + +The data for the toy study is generated according to `toy/data.py`. The data for the MNIST, CelebA and MRI studies are obtained from the following resources: +- MNIST : Torchvision dataset: https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html +- CelebA-HQ : https://github.com/tkarras/progressive_growing_of_gans +- MRI: T2-weighted brain image volumes from the FastMRI Initiative Database: https://fastmri.med.nyu.edu/ + +## Model weights + +The weights of our trained models can be found at (coming soon). + +## Citations +``` +@article{kelkar2023ambientflow, + title={AmbientFlow: Invertible generative models from incomplete, noisy measurements}, + author={Kelkar, Varun A and Deshpande, Rucha and Banerjee, Arindam and Anastasio, Mark A}, + journal={arXiv preprint arXiv:2309.04856}, + year={2023} +} +``` diff --git a/docs/AmbientFlow_generated_images_mri.png b/docs/AmbientFlow_generated_images_mri.png new file mode 100644 index 0000000000000000000000000000000000000000..f625931f7c27a36d2ec01fada751f8492f461684 GIT binary patch literal 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index 0000000..28c04db --- /dev/null +++ b/recon/recon_bm3d.py @@ -0,0 +1,86 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. + +""" + +import numpy as np +import bm3d +import torch +import dataset_tool +import degradations +import ast +import os +import argparse +import imageio as io +import glob +import numpy.linalg as la +import sys +sys.path.append('../src') + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--num_images", type=int, default=50, help="Index of the image to be reconstructed") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--dataset", action='store_true') + +# data args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) +parser.add_argument("--tune", action='store_true', help="Tune the regularization parameter") + +args = parser.parse_args() + +torch.manual_seed(0) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +# data +fnames = glob.glob(f"/shared/aristotle/SOMS/varun/ambientflow/data/CelebAHQDataset-GaussianNoise-0.2-image-data/*.png") +fnames_gt = glob.glob(f"/shared/aristotle/SOMS/varun/ambientflow/data/CelebAHQDataset-real-image-data/*.png") + +for idx in range(args.num_images): + print(idx) + ymeas = io.imread(fnames[5000+idx]) / 255 + xgt = io.imread(fnames_gt[5000+idx]) / 255 + # ymeas = np.swapaxes(ymeas,0,1).T + # ymeas = ymeas.reshape(1,3,64,64) + # ymeas,_ = noisy_dataset[idx]; ymeas = ymeas.reshape(1, *ymeas.shape) + # ymeas = degradation.rev(ymeas, mode='real', use_device=False) + # print(xgt.min(), xgt.max(), ymeas.min(), ymeas.max()) + + # ymeas = np.squeeze(ymeas.numpy()) + # ymeas = np.swapaxes(ymeas.T, 0,1) + # xgt = np.squeeze(xgt.numpy()) + # xgt = np.swapaxes(xgt.T, 0,1) + + # if args.tune: + # xests = [] + # for i in range(5): + # reg_parameter = args.reg_parameter * ( args.reg_parameter2 / args.reg_parameter )**(i/4) + xest = bm3d.bm3d(ymeas, sigma_psd=args.reg_parameter, stage_arg=bm3d.BM3DStages.HARD_THRESHOLDING) + print("RMSE error : ", la.norm(xest - xgt)/np.sqrt(np.prod(xgt.shape))) + + # ymeas = np.clip(ymeas,-0.5,0.5) + if args.dataset: + os.makedirs(os.path.join(args.results_dir, 'dataset'), exist_ok=True) + + if args.dataset: + io.imsave( os.path.join(args.results_dir, 'dataset', f'xest_{idx}_reg{args.reg_parameter}.png' ), xest) + else: + np.save( os.path.join(args.results_dir, f'ymeas_{idx}_reg{args.reg_parameter}.npy'), ymeas) + np.save( os.path.join(args.results_dir, f'xest_{idx}_reg{args.reg_parameter}.npy' ), xest) + np.save( os.path.join(args.results_dir, f'xgt_{idx}_reg{args.reg_parameter}.npy' ), xgt) + io.imsave( os.path.join(args.results_dir, f'ymeas_{idx}_reg{args.reg_parameter}.png' ), ymeas) + io.imsave( os.path.join(args.results_dir, f'xest_{idx}_reg{args.reg_parameter}.png' ), xest) + io.imsave( os.path.join(args.results_dir, f'xgt_{idx}_reg{args.reg_parameter}.png' ), xgt) + diff --git a/recon/recon_csgm.py b/recon/recon_csgm.py new file mode 100644 index 0000000..33d2547 --- /dev/null +++ b/recon/recon_csgm.py @@ -0,0 +1,108 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +from models2 import * +import degradations +import ast +import os +import argparse +import imageio as io +import sys +sys.path.append('../src') + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--idx", type=int, default=0, help="Index of the image to be reconstructed") +parser.add_argument("--lamda", type=float, default=0, help="MAP regularization parameter") +parser.add_argument("--tv", type=float, default=0, help="TV regularization parameter") +parser.add_argument("--step", type=float, default=1e-03, help="Step size") +parser.add_argument("--num_iter", type=int, default=10000, help="Number of iterations for recon") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--project_on_mask", action='store_true', help="Only applicable for inpainting") + +# model args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--model_path", type=str, default='', help="Path to model pkl file") + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() +print(args, flush=True) + +def total_variation(img): + x = img[:,:,1:,:] - img[:,:,:-1,:] + y = img[:,:,:,1:] - img[:,:,:,:-1] + tvnorm = torch.sum(torch.abs(x)) + torch.sum(torch.abs(y)) + return tvnorm + +device = f'cuda:0' if torch.cuda.is_available() else 'cpu' +torch.manual_seed(0) + +# data +# noisy_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) +clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, input_shape=args.input_shape, **args.data_args) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +xgt ,_ = clean_dataset[args.idx]; xgt = xgt.reshape(1,*xgt.shape) +# ymeas,_ = noisy_dataset[args.idx]; ymeas = ymeas.reshape(1, *ymeas.shape); ymeas = ymeas.to(device) +ymeas = degradation(xgt).to(device) +xgt = xgt.to(device) +print(xgt.min(), xgt.max(), abs(ymeas).min(), abs(ymeas).max()) + +# model +if '/ambient/' in args.model_path: + model = load_model(args.model_path, ambient=True).to(device) +else: model = load_model(args.model_path).to(device) +model.eval() + +# z = torch.zeros([1, np.prod(args.input_shape)], requires_grad=True).to(device) +z = torch.zeros( [1, np.prod(args.input_shape)], requires_grad=True, device=device) +# z = torch.nn.Parameter(z).to(device) +# model.initialize_actnorm(ymeas) +optimizer = torch.optim.Adam([z], lr=args.step) + +for i in range(args.num_iter): + + optimizer.zero_grad() + x,_ = model.reverse(z) + map_reg = args.lamda * torch.sum(z**2) + tv_reg = args.tv * total_variation(x) + loss = 0.5*torch.norm(ymeas - degradation.fwd_noiseless(x, use_device=True))**2 + tv_reg + map_reg + loss.backward() + optimizer.step() + + if (i < 10) or (i < 50 and i % 10 == 0) or (i % 50 == 0) or (i == args.num_iter-1): + if args.project_on_mask: + x[:, degradation.mask.astype(bool)] = xgt[:, degradation.mask.astype(bool)] + recon_error = torch.mean( (x - xgt)**2 ) + print(f"Idx : {i:05}, loss : {loss.cpu().detach().numpy()}, tv reg : {tv_reg}, recon. error : {recon_error}", flush=True) + +ymeas = np.squeeze(ymeas.cpu().detach().numpy()) +ymeas = np.swapaxes(ymeas.T, 0,1) +xest = np.squeeze(x.cpu().detach().numpy()) +xest = np.swapaxes(xest.T, 0,1) +xgt = np.squeeze(xgt.cpu().detach().numpy()) +xgt = np.swapaxes(xgt.T, 0,1) +np.save( os.path.join(args.results_dir, f'ymeas_{args.idx}_lam{args.lamda}_tv{args.tv}_niter{args.num_iter}_step{args.step}.npy'), ymeas) +np.save( os.path.join(args.results_dir, f'xest_{args.idx}_lam{args.lamda}_tv{args.tv}_niter{args.num_iter}_step{args.step}.npy' ), xest) +np.save( os.path.join(args.results_dir, f'xgt_{args.idx}_lam{args.lamda}_tv{args.tv}_niter{args.num_iter}_step{args.step}.npy' ), xgt) +io.imsave( os.path.join(args.results_dir, f'ymeas_{args.idx}_lam{args.lamda}_tv{args.tv}_niter{args.num_iter}_step{args.step}.png' ), ymeas) +io.imsave( os.path.join(args.results_dir, f'xest_{args.idx}_lam{args.lamda}_tv{args.tv}_niter{args.num_iter}_step{args.step}.png' ), (np.clip((xest+0.5)*255, 0, 255))) +io.imsave( os.path.join(args.results_dir, f'xgt_{args.idx}_lam{args.lamda}_tv{args.tv}_niter{args.num_iter}_step{args.step}.png' ), xgt) diff --git a/recon/recon_dip_tv.py b/recon/recon_dip_tv.py new file mode 100644 index 0000000..4a9f92e --- /dev/null +++ b/recon/recon_dip_tv.py @@ -0,0 +1,97 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +import models +import degradations +import ast +import os +import argparse +import imageio as io +import sys +sys.path.append('../src') + + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--idx", type=int, default=0, help="Index of the image to be reconstructed") +parser.add_argument("--reg_parameter", type=float, default=0, help="TV regularization parameter") +parser.add_argument("--step", type=float, default=1e-03, help="Step size") +parser.add_argument("--num_iter", type=int, default=10000, help="Number of iterations for recon") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") + +# model args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--model_type", type=str, default='ConvINN') +parser.add_argument("--model_args", type=ast.literal_eval, default={'num_conv_layers': [4,12], 'num_fc_layers': [4]}) + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() + +def total_variation(img): + x = img[:,:,1:,:] - img[:,:,:-1,:] + y = img[:,:,:,1:] - img[:,:,:,:-1] + tvnorm = torch.sum(torch.abs(x)) + torch.sum(torch.abs(y)) + return tvnorm + +device = f'cuda:0' if torch.cuda.is_available() else 'cpu' +torch.manual_seed(0) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +# model +model = getattr(models, args.model_type)(args.input_shape, **args.model_args, device=device).to(device) + +# data +noisy_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) +clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, **args.data_args) + +xgt ,_ = clean_dataset[args.idx]; xgt = xgt.reshape(1,*xgt.shape); xgt = xgt.to(device) +ymeas,_ = noisy_dataset[args.idx]; ymeas = ymeas.reshape(1, *ymeas.shape); ymeas = ymeas.to(device) +print(xgt.min(), xgt.max(), ymeas.min(), ymeas.max()) + +z = torch.randn([1, *args.input_shape]).to(device) +# model.initialize_actnorm(ymeas) +optimizer = torch.optim.Adam(list(model.trainable_parameters), lr=args.step) + +for i in range(args.num_iter): + + optimizer.zero_grad() + x = model(z) + tv_reg = args.reg_parameter * total_variation(x) + loss = 0.5*torch.norm(ymeas - x)**2 + tv_reg + loss.backward() + optimizer.step() + + if (i < 10) or (i < 50 and i % 10 == 0) or (i % 50 == 0): + recon_error = torch.mean( (x - xgt)**2 ) + print(f"Idx : {i:05}, loss : {loss.cpu().detach().numpy()}, tv reg : {tv_reg}, recon. error : {recon_error}") + +ymeas = np.squeeze(ymeas.cpu().detach().numpy()) +ymeas = np.swapaxes(ymeas.T, 0,1) +xest = np.squeeze(x.cpu().detach().numpy()) +xest = np.swapaxes(xest.T, 0,1) +xgt = np.squeeze(xgt.cpu().detach().numpy()) +xgt = np.swapaxes(xgt.T, 0,1) +np.save( os.path.join(args.results_dir, f'ymeas_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.npy'), ymeas) +np.save( os.path.join(args.results_dir, f'xest_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.npy' ), xest) +np.save( os.path.join(args.results_dir, f'xgt_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.npy' ), xgt) +io.imsave( os.path.join(args.results_dir, f'ymeas_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), ymeas) +io.imsave( os.path.join(args.results_dir, f'xest_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), (np.clip((xest+0.5)*255, 0, 255))) +io.imsave( os.path.join(args.results_dir, f'xgt_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), xgt) diff --git a/recon/recon_fista.py b/recon/recon_fista.py new file mode 100644 index 0000000..fd521bc --- /dev/null +++ b/recon/recon_fista.py @@ -0,0 +1,127 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +import models +import degradations +import ast +import os +import argparse +import imageio as io +import torch.distributions as dists +import sys +sys.path.append('../src') + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--idx", type=int, default=0, help="Index of the image to be reconstructed") +parser.add_argument("--reg_parameter", type=float, default=0, help="TV regularization parameter") +parser.add_argument("--step", type=float, default=1e-03, help="Step size") +parser.add_argument("--num_iter", type=int, default=10000, help="Number of iterations for recon") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--dataset", action='store_true') +parser.add_argument("--tune", action='store_true') +parser.add_argument("--reg_parameter2", type=float, default=1e-01, help="In case tune is true, use this to try multiple reg parameters") + +# data args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() + +def total_variation(img): + x = img[:,:,1:,:] - img[:,:,:-1,:] + y = img[:,:,:,1:] - img[:,:,:,:-1] + tvnorm = torch.sum(torch.abs(x)) + torch.sum(torch.abs(y)) + return tvnorm + +device = f'cuda:0' if torch.cuda.is_available() else 'cpu' +torch.manual_seed(0) + +# forward model +args.data_args['input_shape'] = args.input_shape +fwd = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits, device=device) + +# data +if args.dataset: + clean_dataset = getattr(dataset_tool, args.data_type)(train=True, ambient=False, **args.data_args) +else: + clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, **args.data_args) + +xgt ,_ = clean_dataset[args.idx]; xgt = xgt.reshape(1,*xgt.shape) +ymeas = fwd(xgt).to(device) +xgt = xgt.to(device) +# print(xgt.min(), xgt.max(), ymeas.numpy().min(), ymeas.cpu().numpy().max()) + +def run_iters(): + x = torch.zeros([1, *args.input_shape], requires_grad=True, device=device) + optimizer = torch.optim.Adamax([x], lr=args.step) + + for i in range(args.num_iter): + + optimizer.zero_grad() + tv_reg = args.reg_parameter * total_variation(x) + loss = 0.5*torch.norm(ymeas - fwd.fwd_noiseless(x, use_device=True))**2 + tv_reg + loss.backward() + optimizer.step() + + if (i < 10) or (i < 50 and i % 10 == 0) or (i % 50 == 0): + recon_error = torch.mean( (x - xgt)**2 ) + print(f"Idx : {i:05}, loss : {loss.cpu().detach().numpy()}, tv reg : {tv_reg}, recon. error : {recon_error}") + + ymeas_np = np.squeeze(ymeas.cpu().detach().numpy()) + ymeas_np = np.swapaxes(ymeas_np.T, 0,1) + xest = np.squeeze(x.cpu().detach().numpy()) + xest = np.swapaxes(xest.T, 0,1) + xgt_np = np.squeeze(xgt.cpu().detach().numpy()) + xgt_np = np.swapaxes(xgt_np.T, 0,1) + + if (not args.dataset) or (args.dataset and args.idx < 20): + np.save( os.path.join(args.results_dir, f'ymeas_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.npy'), ymeas_np) + np.save( os.path.join(args.results_dir, f'xest_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.npy' ), xest) + np.save( os.path.join(args.results_dir, f'xgt_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.npy' ), xgt_np) + io.imsave( os.path.join(args.results_dir, f'ymeas_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), ymeas_np) + io.imsave( os.path.join(args.results_dir, f'xest_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), xest) + io.imsave( os.path.join(args.results_dir, f'xgt_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), xgt_np) + + return xgt_np, xest + +if not args.tune: + _, xest = run_iters() + if args.dataset: + os.makedirs(os.path.join(args.results_dir, 'dataset'), exist_ok=True) + io.imsave( os.path.join(args.results_dir, 'dataset', f'xest_{args.idx}_reg{args.reg_parameter}_niter{args.num_iter}_step{args.step}.png' ), xest) +else: + xests = [] + reg_parameter_init = args.reg_parameter + mses = [] + for i in range(5): + args.reg_parameter = reg_parameter_init * ( args.reg_parameter2 / reg_parameter_init )**(i/4) + xgt_np, xest = run_iters() + xests.append(xest) + mse = np.linalg.norm(xgt_np - xest) + mses.append(mse) + + min_idx = np.where(np.array(mses) == min(mses))[0][0] + xest = xests[min_idx] + if args.dataset: + os.makedirs(os.path.join(args.results_dir, 'dataset'), exist_ok=True) + io.imsave( os.path.join(args.results_dir, 'dataset', f'xest_{args.idx}_reg{reg_parameter_init * ( args.reg_parameter2 / reg_parameter_init )**(min_idx/4)}_niter{args.num_iter}_step{args.step}.png' ), xest) + + + + + + diff --git a/recon/recon_inpaint_navier.py b/recon/recon_inpaint_navier.py new file mode 100644 index 0000000..da28ef7 --- /dev/null +++ b/recon/recon_inpaint_navier.py @@ -0,0 +1,77 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import numpy as np +import dataset_tool +import degradations +import ast +import os +import argparse +import imageio as io +import cv2 +import sys +sys.path.append('../src') + + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--idx", type=int, default=0, help="Index of the image to be reconstructed") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--project_on_mask", action='store_true', help="Only applicable for inpainting") + +# data args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() +print(args, flush=True) + +device = f'cuda:0' if torch.cuda.is_available() else 'cpu' +torch.manual_seed(0) + +# data +# noisy_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) +clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, input_shape=args.input_shape, **args.data_args) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +xgt ,_ = clean_dataset[args.idx]; xgt = xgt.reshape(1,*xgt.shape) +xgt = torch.flip(xgt, dims=[-1]) +# ymeas,_ = noisy_dataset[args.idx]; ymeas = ymeas.reshape(1, *ymeas.shape); ymeas = ymeas.to(device) +ymeas = degradation(xgt) +ymeas = np.squeeze(ymeas.cpu().detach().numpy()) +ymeas = np.swapaxes(ymeas.T, 0,1) +ymeas = (ymeas + 0.5) * 255 +xgt = np.squeeze(xgt.cpu().detach().numpy()) +xgt = np.swapaxes(xgt.T, 0,1) +xgt = (xgt + 0.5) * 255 + +print(xgt.min(), xgt.max(), abs(ymeas).min(), abs(ymeas).max()) + +mask = (1 - degradation.mask[0])*255 +mask = mask.astype(np.uint8) +ymeas = np.clip(ymeas,0,255).astype(np.uint8) + +xest = cv2.inpaint(ymeas, mask, 3, cv2.INPAINT_NS) +xest = xest / 255 - 0.5 +xgt = xgt / 255 - 0.5 +ymeas = ymeas / 255 - 0.5 + +np.save( os.path.join(args.results_dir, f'ymeas_{args.idx}.npy'), ymeas) +np.save( os.path.join(args.results_dir, f'xest_{args.idx}.npy' ), xest) +np.save( os.path.join(args.results_dir, f'xgt_{args.idx}.npy' ), xgt) +io.imsave( os.path.join(args.results_dir, f'ymeas_{args.idx}.png' ), ymeas) +io.imsave( os.path.join(args.results_dir, f'xest_{args.idx}.png' ), (np.clip((xest+0.5)*255, 0, 255))) +io.imsave( os.path.join(args.results_dir, f'xgt_{args.idx}.png' ), xgt) diff --git a/recon/recon_langevin.py b/recon/recon_langevin.py new file mode 100644 index 0000000..4ef73c1 --- /dev/null +++ b/recon/recon_langevin.py @@ -0,0 +1,158 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +from models2 import * +import degradations +import ast +import os +import argparse +import imageio as io +import sys +sys.path.append('../src') + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--idx", type=int, default=0, help="Index of the image to be reconstructed") +parser.add_argument("--T", type=int, default=200, help="Number of iterations T for each level of noise in Langevin annealing (except the last level, which may have a different number of iterations)") +parser.add_argument("--L", type=int, default=10, help='number of noise levels for annealing Langevin') +parser.add_argument("--num_iter", type=int, default=2000, help="Number of iterations") +parser.add_argument("--sigma_init", type=float, default=0.4, help="Initial noise level for annealing Langevin") +parser.add_argument("--sigma_final", type=float, default=0.1, help="Final noise level for annealing Langevin") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--anneal", action='store_true', help="Whether or not to anneal Langevin iterations") +parser.add_argument("--batch_size", type=int, default=50, help="Number of samples to draw from the posterior") +parser.add_argument("--step", type=float, default=0.001, help="Basic step size") +parser.add_argument("--momentum", type=float, default=0, help="Momentum between [0,1]") +parser.add_argument("--temperature", type=float, default=0.7, help="Temperature of the latent distribution") +parser.add_argument("--project_on_mask", action='store_true', help="Project on a mask, only applicable for inpainting.") + +# model args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--model_path", type=str, default='', help="Path to model pkl file") + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() +print(args, flush=True) + +device = f'cuda:0' if torch.cuda.is_available() else 'cpu' +torch.manual_seed(0) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +# data +# noisy_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) +clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, **args.data_args) + +xgt ,_ = clean_dataset[args.idx]; xgt = xgt.reshape(1,*xgt.shape) +# ymeas,_ = noisy_dataset[args.idx]; ymeas = ymeas.reshape(1, *ymeas.shape); ymeas = ymeas.to(device) +ymeas = degradation(xgt).to(device) +y_noiseless = degradation.fwd_noiseless(xgt).to(device) +ybatch = torch.cat([ymeas]*args.batch_size) +xgt = xgt.to(device) +print(xgt.min(), xgt.max(), abs(ymeas).min(), abs(ymeas).max()) + +# model +if '/ambient/' in args.model_path: + model = load_model(args.model_path, ambient=True).to(device) +else: model = load_model(args.model_path).to(device) +model.eval() + +# setup langevin params +T = args.T; L = args.L +sigma1 = args.sigma_init +sigmaT = args.sigma_final +factor = np.power(sigmaT / sigma1, 1/(L-1)) +if args.anneal: + sigma_func = lambda i: max( sigmaT, sigma1 * np.power(factor, i//T) ) + # sigma_func = lambda i: sigma1 * np.power(factor, i//T) + lr_func = lambda t : (sigma1 * np.power(factor, t//T))**2 / (sigmaT **2) +else: + lr_func = lambda i : 1 + # sigma_func = lambda i : args.degradation_args['std'] + sigma_func = lambda i : args.sigma_final + +zprior_weight = 0.5 / (args.temperature**2) +mloss_weight = 0.5 * np.prod(ymeas.shape) / (sigma1**2) +z = args.temperature * torch.randn( [args.batch_size, np.prod(args.input_shape)]) +z = torch.tensor(z, requires_grad=True, device=device) + +print("Training ...") +print("Measurement MSE : ", torch.mean(torch.abs(ymeas - y_noiseless)**2), flush=True) +optimizer = torch.optim.SGD([z], lr=args.step, momentum=args.momentum) +for i in range(args.num_iter): + + sigma = sigma_func(i) + step_size = lr_func(i) * args.step + mloss_weight = 0.5 * np.prod(ymeas.shape) / (sigma**2) + + optimizer.zero_grad() + x,_ = model.reverse(z) + z_loss_batch = torch.sum(z**2, dim=1) + y_loss_batch = torch.sum( torch.abs( ybatch - degradation.fwd_noiseless(x, use_device=True))**2, dim=[1,2,3]) + loss_batch = mloss_weight * y_loss_batch + zprior_weight * z_loss_batch + loss = torch.sum(loss_batch) #/ np.prod(ymeas.shape) + loss.backward() + + # gradient step + optimizer.param_groups[0]["lr"] = step_size + optimizer.step() + + # randomization step + grad_noise_weight = np.sqrt(2*step_size/(1-args.momentum)) + randomizing_noise = grad_noise_weight * torch.randn(z.shape).to(device) + with torch.no_grad(): + z += randomizing_noise + + if (i < 10) or (i < 50 and i % 10 == 0) or (i % 50 == 0) or (i == args.num_iter-1): + x,_ = model.reverse(z) + x_mse = torch.mean(x, dim=0, keepdim=True) + if args.project_on_mask: + x[:, degradation.mask.astype(bool)] = xgt[:, degradation.mask.astype(bool)] + x_mse[:, degradation.mask.astype(bool)] = xgt[:, degradation.mask.astype(bool)] + recon_error = torch.mean( (x_mse - xgt)**2 ) + meas_error = torch.mean( torch.mean(y_loss_batch) / np.prod(ymeas.shape) ) + print(f"Idx : {i:05}, loss : {loss.cpu().detach().numpy()}, recon. error : {recon_error}, meas. error : {meas_error}", flush=True) + +with open(os.path.join(args.results_dir, 'recon_error.txt'), 'w') as fid: + print(f"MMSE Recon. error : {recon_error}", file=fid) + +ymeas = np.squeeze(ymeas[0].cpu().detach().numpy()) +xests = x.cpu().detach().numpy() +xmse = np.squeeze(x_mse.cpu().detach().numpy()) +xgt = np.squeeze(xgt.cpu().detach().numpy()) +xvar = np.squeeze( np.std(xests, axis=0)**2 ) +np.save( os.path.join(args.results_dir, f'ymeas_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.npy'), ymeas) +np.save( os.path.join(args.results_dir, f'xests_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.npy' ), xests) +np.save( os.path.join(args.results_dir, f'xmse_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.npy' ), xmse) +np.save( os.path.join(args.results_dir, f'xgt_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.npy' ), xgt) +np.save( os.path.join(args.results_dir, f'xvar_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.npy' ), xvar) + +ymeas = np.swapaxes(ymeas.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'ymeas_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.png' ), ymeas) +for i,xest in enumerate(xests): + xest = np.swapaxes(xest.T, 0,1) + xest = np.squeeze(xest) + io.imsave( os.path.join(args.results_dir, f'xest_{args.idx}_samp{i}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.png'), xest ) +xmse = np.swapaxes(xmse.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'xmse_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.png' ), xmse) +xgt = np.swapaxes(xgt.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'xgt_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.png' ), xgt) +xvar = np.swapaxes(xvar.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'xvar_{args.idx}_niter{args.num_iter}_step{args.step}_mom{args.momentum}_temp{args.temperature}.png' ), xvar) \ No newline at end of file diff --git a/recon/recon_postnet.py b/recon/recon_postnet.py new file mode 100644 index 0000000..634dced --- /dev/null +++ b/recon/recon_postnet.py @@ -0,0 +1,96 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +from models2 import * +import degradations +import ast +import os +import argparse +import imageio as io +import sys +sys.path.append('../src') + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--idx", type=int, default=0, help="Index of the image to be reconstructed") +parser.add_argument("--temperature", type=float, default=1, help="Temperature of the images sampled from the posterior network") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--batch_size", type=int, default=50, help="Number of samples to draw from the posterior") + +# model args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--model_path", type=str, default='', help="Path to posterior model pkl file") + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() +print(args, flush=True) + +device = f'cuda:0' if torch.cuda.is_available() else 'cpu' +torch.manual_seed(0) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +# data +# noisy_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) +clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, **args.data_args) + +xgt ,_ = clean_dataset[args.idx]; xgt = xgt.reshape(1,*xgt.shape) +# ymeas,_ = noisy_dataset[args.idx]; ymeas = ymeas.reshape(1, *ymeas.shape); ymeas = ymeas.to(device) +ymeas = degradation(xgt).to(device) +xrev = degradation.rev(ymeas) +xgt = xgt.to(device) +print(xgt.min(), xgt.max(), abs(ymeas).min(), abs(ymeas).max()) + +# model +post_model = load_post_model(args.model_path).to(device) +post_model.eval() + +xests = post_model.sample(args.batch_size, xrev, temp=args.temperature) +x_mse = np.mean(xests, axis=0) + + + +ymeas = np.squeeze(ymeas[0].cpu().detach().numpy()) +xmse = np.squeeze(x_mse) +xgt = np.squeeze(xgt.cpu().detach().numpy()) +xvar = np.squeeze( np.std(xests, axis=0)**2 ) + +recon_error = np.sqrt( np.mean( (xmse - xgt)**2 ) ) +print(f"MMSE Recon. error : {recon_error}", flush=True) +with open(os.path.join(args.results_dir, 'recon_error.txt'), 'w') as fid: + print(f"MMSE Recon. error : {recon_error}", file=fid) + +np.save( os.path.join(args.results_dir, f'ymeas_{args.idx}_temp{args.temperature}.npy'), ymeas) +np.save( os.path.join(args.results_dir, f'xests_{args.idx}_temp{args.temperature}.npy' ), xests) +np.save( os.path.join(args.results_dir, f'xmse_{args.idx}_temp{args.temperature}.npy' ), xmse) +np.save( os.path.join(args.results_dir, f'xgt_{args.idx}_temp{args.temperature}.npy' ), xgt) +np.save( os.path.join(args.results_dir, f'xvar_{args.idx}_temp{args.temperature}.npy' ), xvar) + +ymeas = np.swapaxes(ymeas.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'ymeas_{args.idx}_temp{args.temperature}.png' ), ymeas) +for i,xest in enumerate(xests): + xest = np.squeeze(xest) + io.imsave( os.path.join(args.results_dir, f'xest_{args.idx}_samp{i}_temp{args.temperature}.png'), xest ) +xmse = np.swapaxes(xmse.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'xmse_{args.idx}_temp{args.temperature}.png' ), xmse) +xgt = np.swapaxes(xgt.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'xgt_{args.idx}_temp{args.temperature}.png' ), xgt) +xvar = np.swapaxes(xvar.T, 0,1) +io.imsave( os.path.join(args.results_dir, f'xvar_{args.idx}_temp{args.temperature}.png' ), xvar) \ No newline at end of file diff --git a/recon/recon_wiener.py b/recon/recon_wiener.py new file mode 100644 index 0000000..a3385a2 --- /dev/null +++ b/recon/recon_wiener.py @@ -0,0 +1,91 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +from skimage.restoration import wiener +from scipy.ndimage import gaussian_filter +import torch +import dataset_tool +import degradations +import ast +import os +import argparse +import imageio as io +import sys +sys.path.append('../src') + +parser = argparse.ArgumentParser() +# recon args +parser.add_argument("--num_images", type=int, default=50, help="Index of the image to be reconstructed") +parser.add_argument("--reg_parameter", type=float, default=0.2, help="Wiener regularization parameter") +parser.add_argument("--num_bits", type=int, default=0) +parser.add_argument("--results_dir", type=str, default='', help="Results dir") +parser.add_argument("--dataset", action='store_true') + +# data args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +args = parser.parse_args() + +torch.manual_seed(0) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + +# data +noisy_dataset = getattr(dataset_tool, args.data_type)(train=True, ambient=True, degradation=degradation, **args.data_args) +clean_dataset = getattr(dataset_tool, args.data_type)(train=True, ambient=False, **args.data_args) +kernel = np.zeros(args.input_shape[1:]) +kernel[args.input_shape[1]//2, args.input_shape[2]//2] = 1 +kernel = gaussian_filter(kernel, args.degradation_args['kernel_sigma']) +kernel = kernel / kernel.max() +lw = int(4 * args.degradation_args['kernel_sigma'] + 0.5) +kernel = kernel[ + args.input_shape[1]//2 - lw : args.input_shape[1]//2 + lw + 1, + args.input_shape[1]//2 - lw : args.input_shape[1]//2 + lw + 1 ] + +for idx in range(args.num_images): + print(idx) + xgt ,_ = clean_dataset[idx]; xgt = xgt.reshape(1,*xgt.shape) + ymeas,_ = noisy_dataset[idx]; ymeas = ymeas.reshape(1, *ymeas.shape) + ymeas = degradation.rev(ymeas, mode='real') + # print(xgt.min(), xgt.max(), ymeas.min(), ymeas.max()) + + ymeas = np.squeeze(ymeas.numpy()) + ymeas = np.swapaxes(ymeas.T, 0,1) + xgt = np.squeeze(xgt.numpy()) + xgt = np.swapaxes(xgt.T, 0,1) + + xest = np.stack([ + wiener(ymeas[...,0], kernel, balance=args.reg_parameter), + wiener(ymeas[...,1], kernel, balance=args.reg_parameter), + wiener(ymeas[...,2], kernel, balance=args.reg_parameter), + ], axis=-1) + + mse_error = np.linalg.norm(xest - xgt)**2 + print(mse_error) + + # ymeas = np.clip(ymeas,-0.5,0.5) + if args.dataset: + os.makedirs(os.path.join(args.results_dir, 'dataset'), exist_ok=True) + + if args.dataset: + io.imsave( os.path.join(args.results_dir, 'dataset', f'xest_{idx}_reg{args.reg_parameter}.png' ), xest) + else: + np.save( os.path.join(args.results_dir, f'ymeas_{idx}_reg{args.reg_parameter}.npy'), ymeas) + np.save( os.path.join(args.results_dir, f'xest_{idx}_reg{args.reg_parameter}.npy' ), xest) + np.save( os.path.join(args.results_dir, f'xgt_{idx}_reg{args.reg_parameter}.npy' ), xgt) + io.imsave( os.path.join(args.results_dir, f'ymeas_{idx}_reg{args.reg_parameter}.png' ), ymeas) + io.imsave( os.path.join(args.results_dir, f'xest_{idx}_reg{args.reg_parameter}.png' ), xest) + io.imsave( os.path.join(args.results_dir, f'xgt_{idx}_reg{args.reg_parameter}.png' ), xgt) + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..759f500 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,15 @@ +bm3d +FrEIA +imageio +matplotlib +numpy +Pillow +ptwt==0.1.2 +scikit-image +scipy +torch==1.11.0 +torchvision==0.12.0 +wandb +pyradiomics +SimpleITK +clean-fid \ No newline at end of file diff --git a/scripts/training/paper_configs.txt b/scripts/training/paper_configs.txt new file mode 100644 index 0000000..2013920 --- /dev/null +++ b/scripts/training/paper_configs.txt @@ -0,0 +1,6 @@ +Dataset Forward model reg_parameter threshold_weight sparsity_weight + +CelebA Gaussian noise (std. 0.2) 0.7 for 4000 kimgs, then 2.3 0 for 4000 kimgs, then 0.133 0 for 4000 kimgs, then 0.0097 +CelebA Blur (kernel width 1.5) + noise (std. 0.2) 0.7 for 4000 kimgs, then 2.3 0 for 4000 kimgs, then 0.2 0 for 4000 kimgs, then 0.005 +MRI Fully sampled + noise (std. 0.1) 1.5 for 10000 kimgs, then 5 0 for 18000 kimgs, then 0.05 0 for 18000 kimgs, then 0.03 +MRI 4x undersampled + noise (std. 0.1) 1.5 for 2000 kimgs, then 5 0 for 2000 kimgs, then 0.05 0 for 2000 kimgs, then 0.03 \ No newline at end of file diff --git a/scripts/training/train_ambient_celeba.sh b/scripts/training/train_ambient_celeba.sh new file mode 100755 index 0000000..7c96bb6 --- /dev/null +++ b/scripts/training/train_ambient_celeba.sh @@ -0,0 +1,55 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Following hyperparameter configurations are for example only. The optimal parameter configurations are provided in `paper_configs.txt`. + +cd .. +cd .. +cd src + +# export NCCL_IB_DISABLE=1 + +num_gpus=1 +num_bits=0 # 0 +lr=1e-04 +lr_post=2e-05 +batch_size=92 +num_z=2 +reg_parameter=2.3 +importance_weighting=1 +noise_std=0.2 +threshold_weight=0.133 +sparsity_weight=0.0097 +other=${1} # --port 12357 + +post_model_args="{'num_conv_layers':[2, 4, 4], 'num_fc_layers':[4], 'cond_layer_thicknesses':[32, 64, 64, 256]}" + +# Gaussian noise degradation +degradation_type=GaussianNoise +degradation_args="{'mean': 0.0, 'std': 0.2}" + +# Blur plus noise degradation +degradation_type=GaussianBlurNoise +degradation_args="{'kernel_sigma': 1.5, 'mean': 0.0, 'std': 0.2}" + +results_dir=../results/celeba/ambient/${degradation_type}/ +mkdir -p $results_dir + +echo "Training for 4000 kimgs without sparsity constraint ..." + +python -u train_ambient.py --num_gpus $num_gpus --data_type CelebAHQDataset --data_args "{'on_the_fly': False, 'augmentation': 'vf'}" --results_dir $results_dir --input_shape 3 64 64 --main_model_type Glow --main_model_args "{'num_flow':32, 'num_block':4, 'filter_size':512}" --post_model_type CondConvINN2 --post_model_args "$post_model_args" --sparsifier_type GradientTransform --sparsifier_args "{}" --reg_parameter $reg_parameter --sparsity_weight 0 --threshold_weight 0 --num_z $num_z --lr $lr --lr_post $lr_post --batch_size $batch_size --main_actnorm 1 --post_actnorm 1 --num_bits $num_bits --num_kimgs_post 0 --num_kimgs_main 0 --num_kimgs 4000 --degradation_type $degradation_type --degradation_args "$degradation_args" --schedule_sparsity_weight 0 --benchmark_cudnn 1 --importance_weighting $importance_weighting $other + +echo "Resuming training with sparsity constraint ..." + +resume_from=../results/celeba/ambient/noisy/000-*-spparam0.0-thw0.0-*/main-network_004000.pt + +python -u train_ambient.py --num_gpus $num_gpus --data_type CelebAHQDataset --data_args "{'on_the_fly': False, 'augmentation': 'vf'}" --results_dir $results_dir --input_shape 3 64 64 --main_model_type Glow --main_model_args "{'num_flow':32, 'num_block':4, 'filter_size':512}" --post_model_type CondConvINN2 --post_model_args "$post_model_args" --sparsifier_type GradientTransform --sparsifier_args "{}" --reg_parameter $reg_parameter --sparsity_weight $sparsity_weight --threshold_weight $threshold_weight --num_z $num_z --lr $lr --lr_post $lr_post --batch_size $batch_size --main_actnorm 1 --post_actnorm 1 --num_bits $num_bits --num_kimgs_post 0 --num_kimgs_main 0 --num_kimgs 20000 --degradation_type $degradation_type --degradation_args "$degradation_args" --schedule_sparsity_weight 0 --benchmark_cudnn 1 --importance_weighting $importance_weighting $other --resume_from $resume_from + +python -c "import time; print('\a'); time.sleep(0.5); print('\a'); time.sleep(0.5); print('\a')" + diff --git a/scripts/training/train_ambient_mnist.sh b/scripts/training/train_ambient_mnist.sh new file mode 100755 index 0000000..a7ad84f --- /dev/null +++ b/scripts/training/train_ambient_mnist.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Following hyperparameter configurations are for example only. The optimal parameter configurations are provided in `paper_configs.txt`. + +cd .. +cd .. +cd src + +# python train_ambient.py --data_type MNISTDataset --results_dir ../results/MNIST/ambient --lr 1e-05 --reg_parameter 0.1 --num_iters_post 1000 --num_iters_main 10000 + +blur_sigma=1.5 +other=$2 +python train_ambient_mnist.py --data_type MNISTDataset --data_args "{'power_of_two': True}" --results_dir ../results/MNIST/ambient --input_shape 3 32 32 --main_model_type Glow --main_model_args "{'num_flow':3, 'num_block':3, 'filter_size':512}" --post_model_type CondConvINN --post_model_args "{'num_conv_layers':[4, 12], 'num_fc_layers':[4]}" --reg_parameter 0.1 --lr_post 1e-03 --lr_main 1e-03 --batch_size 32 --main_actnorm 1 --num_bits 0 --num_iters_post 1000 --num_iters_main 10000 --num_iters 50000 --degradation_type GaussianBlurNoise --degradation_args "{'kernel_sigma':$blur_sigma, 'mean':0., 'std':0.2}" $other \ No newline at end of file diff --git a/scripts/training/train_ambient_mri.sh b/scripts/training/train_ambient_mri.sh new file mode 100755 index 0000000..2f64627 --- /dev/null +++ b/scripts/training/train_ambient_mri.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Following hyperparameter configurations are for example only. The optimal parameter configurations are provided in `paper_configs.txt`. + +cd .. +cd .. +cd src + +# export NCCL_IB_DISABLE=1 + +num_gpus=1 +num_bits=0 # 0 +lr=1e-04 +lr_post=1e-04 +batch_size=4 +num_z=2 +reg_parameter=5 # weight of likelihood term for second stage of training. +importance_weighting=1 +noise_std=0.1 +subsampling=4 +threshold_weight=0.05 # second stage of training +sparsity_weight=0.03 # second stage of training +cutoff_dim=0 # 0.75 +cutoff_mul=1 +other=${1} # other arguments, eg. --port 12357 + +post_model_args="{'num_flow': 4, 'num_block': 4, 'num_cond_chans': 32, 'filter_size': 32, 'final_cond_dim': 512, 'first_split_ratio': 0.25, 'squeezer': 'HaarSqueeze', 'sparsity_weight': ${sparsity_weight}, 'cutoff_dim': ${cutoff_dim}, 'cutoff_mul': ${cutoff_mul}}" + +# Fully sampled +# degradation_args="{'random': False, 'mask_file':None, 'mean':0., 'std':$noise_std}" + +# Downsampled +degradation_args="{'mask_file':'../masks_mri/cartesian_${subsampling}fold_128.npy', 'mean':0., 'std':$noise_std}" + +results_dir=../results/mri/ambient/${subsampling}/ +mkdir -p $results_dir + +echo "Training for 2000 kimgs without sparsity constraint ..." + +python -u train_ambient.py --num_gpus $num_gpus --data_type FastMRIT2Dataset --data_args "{'on_the_fly': False}" --results_dir $results_dir --input_shape 1 128 128 --main_model_type Glow --main_model_args "{'num_flow':16, 'num_block':4, 'filter_size':512}" --post_model_type CondGlow3 --post_model_args "$post_model_args" --sparsifier_type GradientTransform --sparsifier_args "{}" --reg_parameter 1.5 --sparsity_weight 0 --threshold_weight 0 --cutoff_dim $cutoff_dim --num_z $num_z --lr $lr --lr_post $lr_post --batch_size $batch_size --main_actnorm 1 --post_actnorm 1 --num_bits $num_bits --num_kimgs_post 0 --num_kimgs_main 0 --num_kimgs 2000 --degradation_type FourierSamplerGaussianNoise --degradation_args "$degradation_args" --schedule_sparsity_weight 0 --benchmark_cudnn 1 --importance_weighting $importance_weighting $other + +resume_from=../results/celeba/ambient/${subsampling}/000-*-spparam0.0-thw0.0-*/main-network_002000.pt + +python -u train_ambient.py --num_gpus $num_gpus --data_type FastMRIT2Dataset --data_args "{'on_the_fly': False}" --results_dir $results_dir --input_shape 1 128 128 --main_model_type Glow --main_model_args "{'num_flow':16, 'num_block':4, 'filter_size':512}" --post_model_type CondGlow3 --post_model_args "$post_model_args" --sparsifier_type GradientTransform --sparsifier_args "{}" --reg_parameter $reg_parameter --sparsity_weight $sparsity_weight --threshold_weight $threshold_weight --cutoff_dim $cutoff_dim --num_z $num_z --lr $lr --lr_post $lr_post --batch_size $batch_size --main_actnorm 1 --post_actnorm 1 --num_bits $num_bits --num_kimgs_post 0 --num_kimgs_main 0 --num_kimgs 20000 --degradation_type FourierSamplerGaussianNoise --degradation_args "$degradation_args" --schedule_sparsity_weight 0 --benchmark_cudnn 1 --importance_weighting $importance_weighting $other --resume_from $resume_from + +python -c "import time; print('\a'); time.sleep(0.5); print('\a'); time.sleep(0.5); print('\a')" + diff --git a/scripts/training/train_vanilla_celeba.sh b/scripts/training/train_vanilla_celeba.sh new file mode 100755 index 0000000..82e4ffa --- /dev/null +++ b/scripts/training/train_vanilla_celeba.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Following hyperparameter configurations are for example only. The optimal parameter configurations are provided in `paper_configs.txt`. + +cd .. +cd .. +cd src + +lr=1e-04 +num_bits=8 +gpus=1 +batch_size=128 + +results_dir=../results/celeba/vanilla +echo $results_dir + +# python train_vanilla.py --data_type MNISTDataset --results_dir ../results/MNIST/vanilla --input_shape 1 28 28 --num_conv_layers 12 4 --lr 1e-05 +python train_vanilla.py --gpus $gpus --num_kimgs 20000 --data_type CelebAHQDataset --results_dir $results_dir --input_shape 3 64 64 --model_type Glow --model_args "{'num_flow':32, 'num_block':4, 'filter_size':512}" --lr $lr --batch_size $batch_size --actnorm 1 --num_bits $num_bits + diff --git a/scripts/training/train_vanilla_mnist.sh b/scripts/training/train_vanilla_mnist.sh new file mode 100644 index 0000000..4ce05e1 --- /dev/null +++ b/scripts/training/train_vanilla_mnist.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +lr=$1 # 1e-04 +num_bits=$2 # 8 +# python train_vanilla.py --data_type MNISTDataset --results_dir ../results/MNIST/vanilla --input_shape 1 28 28 --num_conv_layers 12 4 --lr 1e-05 +python train_vanilla.py --data_type MNISTDataset --data_args "{'power_of_two': True}" --results_dir ../results/MNIST/vanilla --input_shape 3 32 32 --model_type Glow --model_args "{'num_flow':3, 'num_block':3, 'filter_size':512}" --lr $lr --batch_size 256 --actnorm 1 --num_bits $num_bits \ No newline at end of file diff --git a/scripts/training/train_vanilla_mri.sh b/scripts/training/train_vanilla_mri.sh new file mode 100755 index 0000000..ae35b8d --- /dev/null +++ b/scripts/training/train_vanilla_mri.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Following hyperparameter configurations are for example only. The optimal parameter configurations are provided in `paper_configs.txt`. + +cd .. +cd .. +cd src + +lr=1e-04 +num_bits=0 # 8 +gpus=1 +batch_size=18 + +results_dir=../results/mri/vanilla +echo $results_dir + +python train_vanilla.py --gpus $gpus --num_kimgs 20000 --data_type FastMRIT2Dataset --results_dir $results_dir --input_shape 1 256 256 --model_type Glow --model_args "{'num_flow':16, 'num_block':6, 'filter_size':512}" --lr $lr --batch_size $batch_size --actnorm 1 --num_bits $num_bits diff --git a/src/.gitignore b/src/.gitignore new file mode 100644 index 0000000..fff9600 --- /dev/null +++ b/src/.gitignore @@ -0,0 +1 @@ +wandb diff --git a/src/dataset_tool.py b/src/dataset_tool.py new file mode 100644 index 0000000..b012cda --- /dev/null +++ b/src/dataset_tool.py @@ -0,0 +1,296 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torchvision +from torchvision import transforms +import numpy as np +import imageio as io +from skimage.transform import resize as skresize +import os +import glob + + +def discretize(t, num_bits): + t = t * (2**num_bits - 1) + t = torch.bucketize(t, boundaries=torch.arange(2**num_bits + 1)) + t = t / (2**num_bits - 1) + return t + + +class MNISTDataset(torchvision.datasets.MNIST): + + def __init__(self, ambient=False, degradation=None, train=True, power_of_two=False, num_bits=0, data_root_path='/home/vak2/Documents/ambientflow/data', **kwargs): + + transform = [ + transforms.ToTensor(), + transforms.Lambda( lambda x : torch.cat([x,x,x],0) ), + ] + if power_of_two: + transform.append(transforms.Pad((32-28)//2)) + if num_bits: + transform.append( + transforms.Lambda( lambda x : discretize(x, num_bits) ) + ) + if ambient: + transform.append(degradation) + transform.append(transforms.Lambda( lambda x : x - 0.5 )) + transform = transforms.Compose(transform) + + super(MNISTDataset, self).__init__(root=data_root_path, train=train, transform=transform) + +class CelebADataset(torchvision.datasets.CelebA): + + def __init__(self, ambient=False, degradation=None, train=True, input_shape=[3,128,128], num_bits=0, data_root_path='/shared/anastasio1/Other/ambientflow/data', **kwargs): + + if train == True: split = 'train' + else: split = 'test' + + def standardize(x): return x - 0.5 + transform = [ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float), + transforms.Resize(input_shape[1:]), + transforms.RandomHorizontalFlip(), + ] + if num_bits: + transform.append( + transforms.Lambda( lambda x : discretize(x, num_bits) ) + ) + if ambient: + transform.append(degradation) + transform.append(transforms.Lambda(standardize)) + transform = transforms.Compose(transform) + + super(CelebADataset, self).__init__(root=data_root_path, split=split, transform=transform) + +class CelebAHQDataset(torchvision.datasets.VisionDataset): + + def __init__(self, ambient=False, degradation=None, train=True, input_shape=[3,128,128], num_bits=0, on_the_fly=True, data_root_path='/shared/anastasio1/Other/ambientflow/data', augmentation='', flip=True, **kwargs): + + if train == True: split = 'train' + else: split = 'test' + + self.augmentation = augmentation + + # def standardize(x): return x - 0.5 + transform = [ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float), + transforms.Resize(input_shape[1:], antialias=True), + ] + if num_bits: + transform.append( + transforms.Lambda( lambda x : discretize(x, num_bits) ) + ) + if ambient: + transform.append(degradation) + if flip: + transform.append(transforms.RandomHorizontalFlip()) + transform.append(transforms.Lambda(lambda x: x - 0.5)) + transform = transforms.Compose(transform) + + super(CelebAHQDataset, self).__init__(root=data_root_path, transform=transform, target_transform=None) + + self.base_folder = 'CelebAMask-HQ' + if split == 'train': + self.image_filenames = sorted(glob.glob( + os.path.join(self.root, self.base_folder, 'CelebA-HQ-img', '*.jpg' ) + ))[:-500] + elif split == 'test': + self.image_filenames = sorted(glob.glob( + os.path.join(self.root, self.base_folder, 'CelebA-HQ-img', '*.jpg' ) + ))[-500:] + + if ambient and (not on_the_fly): + self.noise_seeds = np.fromfile('/home/vak2/system_seeds.dat', np.int32) + self.ambient = ambient + self.on_the_fly = on_the_fly + self.degradation = degradation + + def __getitem__(self, index: int): + + X = io.imread(self.image_filenames[index]) + # X = np.swapaxes(X.T, 1,2) + if self.ambient and (not self.on_the_fly): + self.degradation.rng.manual_seed(int(self.noise_seeds[index])) + + if self.transform is not None: + X = self.transform(X) + + X,label = self.augment(X) + + return X, label # label is used to inform about the kind of augmentation. It will be used to reverse the augmentation when needed. + + def augment(self, X): + if 'vf' in self.augmentation: + label = np.random.randint(2) + if label==1: X = torch.flip(X, dims=[1]) + else: label = 0 + + return X, label + + def deaugment(self, X_batch, labels): + X_batch_out = X_batch.clone() + if 'vf' in self.augmentation: + X_batch_out[...,labels==1,:,:,:] = torch.flip(X_batch[...,labels==1,:,:,:], dims=[len(X_batch.shape)-2,]) + return X_batch_out, labels + + def __len__(self): + return len(self.image_filenames) + + +class FastMRIT2Dataset(torchvision.datasets.VisionDataset): + + def __init__(self, ambient=False, degradation=None, train=True, input_shape=[1,128,128], num_bits=0, volume_norm=True, on_the_fly=True, data_root_path='/shared/radon/SOMS/MRI_data', **kwargs): + + if train == True: split = 'train' + else: split = 'test' + + # def standardize(x): return x - 0.5 + transform = [ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float), + transforms.Resize(input_shape[1:], antialias=True), + ] + if num_bits: + transform.append( + transforms.Lambda( lambda x : discretize(x, num_bits) ) + ) + transform.append(transforms.Lambda(lambda x: x - 0.5)) + if ambient: + transform.append(degradation) + transform = transforms.Compose(transform) + + super(FastMRIT2Dataset, self).__init__(root=data_root_path, transform=transform, target_transform=None) + + self.base_folder = 'multicoil_AXT2_3T_volumes' + fnames = glob.glob( + os.path.join(self.root, self.base_folder, 'vol_*.npy' ) + ) + fnames = sorted(fnames, key=lambda x: int(os.path.basename(x).strip('vol_').strip('.npy'))) + if split == 'train': + self.fnames = fnames[:-20] + elif split == 'test': + self.fnames = fnames[-20:] + + self.volume_norm = volume_norm + self.volume_max = np.load(os.path.join(self.root, self.base_folder+'_max_values.npy')) + + if ambient and (not on_the_fly): + self.noise_seeds = np.fromfile('/home/vak2/system_seeds.dat', np.int32) + self.ambient = ambient + self.on_the_fly = on_the_fly + self.degradation = degradation + + def __getitem__(self, index: int): + + idx = index % self.__len__() + slice_idx = np.random.randint(10) + flip = np.random.randint(2) + fname = self.fnames[idx] + X = np.load(fname, mmap_mode='r')[slice_idx] + X = X / self.volume_max[idx] if self.volume_norm else X / X.max() + if flip: X = X[:,::-1] + X = X[::-1].copy() + + if self.ambient and (not self.on_the_fly): + self.degradation.rng.manual_seed(int(self.noise_seeds[20*idx + 2*slice_idx + flip])) + + if self.transform is not None: + X = self.transform(X) + + return X, 0 + + def get_all_noiseless(self): + data = [] + for i,f in enumerate(self.fnames): + print(i) + X = np.load(f, mmap_mode='r')[:10] + X = X / self.volume_max[i] if self.volume_norm else X / X.max() + + X = np.swapaxes(X, 0, 1) + X = np.swapaxes(X, 1, 2) + if self.transform is not None: + X = self.transform(X) + data.append(X) + + return data + + def augment(self, X): + return X, 0 + + def deaugment(self, X, labels): + return X, labels + + def __len__(self): + return len(self.fnames) + + +class EllipsesDataset(torchvision.datasets.VisionDataset): + + def __init__(self, ambient=False, degradation=None, train=True, input_shape=[1,32,32], num_bits=0, on_the_fly=True, data_root_path='/shared/radon/SOMS/ellipses', **kwargs): + + if train == True: split = 'train' + else: split = 'test' + + # def standardize(x): return x - 0.5 + transform = [ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float), + transforms.Resize(input_shape[1:], antialias=True), + ] + if num_bits: + transform.append( + transforms.Lambda( lambda x : discretize(x, num_bits) ) + ) + transform.append(transforms.Lambda(lambda x: x - 0.5)) + if ambient: + transform.append(degradation) + transform = transforms.Compose(transform) + + super(EllipsesDataset, self).__init__(root=data_root_path, transform=transform, target_transform=None) + + self.data = np.load(os.path.join(data_root_path, 'ellipses.npy'), mmap_mode='r') + if split == 'train': + self.data = self.data[:-500] + if split == 'test': + self.data = self.data[-500:] + + if ambient and (not on_the_fly): + self.noise_seeds = np.fromfile('/home/vak2/system_seeds.dat', np.int32) + self.ambient = ambient + self.on_the_fly = on_the_fly + self.degradation = degradation + + def __getitem__(self, index: int): + + flip_x = np.random.randint(2) + flip_y = np.random.randint(2) + X = self.data[index] + if flip_x: X = X[::-1] + if flip_y: X = X[:,::-1] + X = X.copy() + + if self.ambient and (not self.on_the_fly): + self.degradation.rng.manual_seed(int(self.noise_seeds[4*index + 2*flip_x + flip_y])) + + if self.transform is not None: + X = self.transform(X) + + return X, 0 + + def __len__(self): + return len(self.data) + +if __name__ == '__main__': + + dset = MNISTDataset(train=True, power_of_two=True) + # dset = CelebAHQDataset(train=True, ambient=True, input_shape=[3,64,64], on_the_fly=False) + # dset = FastMRIT2Dataset(train=True, ambient=False, volume_norm=True, on_the_fly=False) + # dset = EllipsesDataset(ambient=False) \ No newline at end of file diff --git a/src/degradations.py b/src/degradations.py new file mode 100644 index 0000000..b4bbb67 --- /dev/null +++ b/src/degradations.py @@ -0,0 +1,251 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch +import utils +import torchvision +import torchvision.transforms + +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' + +class GaussianNoise(object): + def __init__(self, input_shape=[3,32,32], mean=0., std=0.2, num_bits=0, device=None): + self.device = device if device != None else DEVICE + self.std = std + self.mean = mean + self.num_bits = num_bits + self.input_shape = input_shape + self.output_shape = input_shape + self.rng = torch.Generator() + + def __call__(self, tensor): + # if self.num_bits: + # t = tensor * (2**self.num_bits - 1) + # t = torch.bucketize(t, boundaries=torch.arange(2**self.num_bits + 1)) + # t = t / (2**self.num_bits - 1) + # else: t = tensor + t = tensor + # if seed != None: self.rng.manual_seed(seed) + return t + torch.randn(*tensor.shape, generator=self.rng) * self.std + self.mean + + def rev(self, tensor, **kwargs): + return tensor + + def __repr__(self): + return self.__class__.__name__ + f'(mean={self.mean}, std={self.std})' + + def log_prob(self, y, x): + # if self.num_bits: + # t = x + 0.5 + # t *= (2**self.num_bits - 1) + # t = torch.bucketize(t, boundaries=torch.arange(2**self.num_bits + 1).to(device)) + # t = t.float() + # t /= (2**self.num_bits - 1) + # t -= 0.5 + # else: t = x + # return utils.log_normal( y - t - self.mean, std=self.std) + return utils.log_normal( y - x - self.mean, std=self.std) + + +class GaussianBlurNoise(object): + def __init__(self, kernel_sigma=1.5, mean=0., std=0.2, input_shape=[3,28,28], num_bits=0, device=None): + self.device = device if device != None else DEVICE + self.kernel_sigma = kernel_sigma + self.std = std + self.mean = mean + self.num_bits = num_bits + self.input_shape = input_shape + self.output_shape = input_shape + self.rng = torch.Generator() + + W = 2*int(4*kernel_sigma + 0.5) + 1 + self.blur = torchvision.transforms.GaussianBlur(kernel_size=W, sigma=self.kernel_sigma) + + def __call__(self, tensor): + # if self.num_bits: + # t = tensor * (2**self.num_bits - 1) + # t = torch.bucketize(t, boundaries=torch.arange(2**self.num_bits + 1)) + # t = t / (2**self.num_bits - 1) + # else: t = tensor + t = tensor + + # Gaussian blur + t = self.blur(t) + return t + torch.randn(*tensor.shape, generator=self.rng) * self.std + self.mean + + def rev(self, tensor, **kwargs): + return tensor + + def __repr__(self): + return self.__class__.__name__ + f'(kernel_sigma={self.kernel_sigma}, mean={self.mean}, std={self.std}, input_shape={self.input_shape}, num_bits={self.num_bits})' + + def log_prob(self, y, x): + # if self.num_bits: + # t = x + 0.5 + # t *= (2**self.num_bits - 1) + # t = torch.bucketize(t, boundaries=torch.arange(2**self.num_bits + 1).to(device)) + # t = t.float() + # t /= (2**self.num_bits - 1) + # t -= 0.5 + # else: t = x + t = self.blur(x) + return utils.log_normal( y - t - self.mean, std=self.std) + + +class BlockMask(object): + def __init__(self, start_coord=[16,16], height=16, width=16, input_shape=[3,32,32], num_bits=0, device=None): + self.device = device if device != None else DEVICE + self.start_coord = start_coord + self.height = height + self.width = width + self.num_bits = num_bits + self.input_shape = input_shape + self.output_shape = input_shape + self.rng = torch.Generator() + self.mask = np.ones(input_shape) + self.mask[:, + self.start_coord[0]:self.start_coord[0]+self.height, + self.start_coord[1]:self.start_coord[1]+self.width] = 0 + + def __call__(self, tensor, **kwargs): + # if self.num_bits: + # t = tensor * (2**self.num_bits - 1) + # t = torch.bucketize(t, boundaries=torch.arange(2**self.num_bits + 1)) + # t = t / (2**self.num_bits - 1) + # else: t = tensor + t = tensor.clone() + t[:,:, + self.start_coord[0]:self.start_coord[0]+self.height, + self.start_coord[1]:self.start_coord[1]+self.width] = 0 + return t + + def fwd_noiseless(self, tensor, **kwargs): + return self(tensor) + + def rev(self, tensor, **kwargs): + t = tensor.clone() + t[:, + self.start_coord[0]:self.start_coord[0]+self.height, + self.start_coord[1]:self.start_coord[1]+self.width] = 0 + return t + + def __repr__(self): + return self.__class__.__name__ + f'(start_coord={self.start_coord}, height={self.height}, width={self.width}, input_shape={self.input_shape}, num_bits={self.num_bits})' + + def log_prob(self, y, x): + # if self.num_bits: + # t = x + 0.5 + # t *= (2**self.num_bits - 1) + # t = torch.bucketize(t, boundaries=torch.arange(2**self.num_bits + 1).to(device)) + # t = t.float() + # t /= (2**self.num_bits - 1) + # t -= 0.5 + # else: t = x + t = self(x) + return utils.log_normal(y - t, std=1/255) + + +class FourierSamplerGaussianNoise(object): + def __init__(self, random=False, mask_file=None, samp=1, center_fraction=0.1, mean=0, std=0.1, input_shape=[1,128,128], num_bits=0, device=None): + """ + random : Instead of loading from file, generate random masks. (A different mask for each measurement) + mask_file : File path to load the undersampling mask from. Located in `../masks_mri/` + samp : Undersampling ratio. samp and center_fraction are only relevant if random=True. Else load mask from the mask file or default to fully sampled FFT. + center_fraction : Fully sampled FFT fraction near the 0 frequency. Between 0-1. Must be smaller than 1/samp. + mean : Mean of the noise + std : Standard deviation of the noise + input_shape : + num_bits : + device : + """ + self.device = device if device != None else DEVICE + self.samp = samp + self.center_fraction = center_fraction + self.mask_file = mask_file + self.random = random + self.mean = mean + self.std = std + self.num_bits = num_bits + self.input_shape = input_shape + self.output_shape = [2*input_shape[0], input_shape[1], input_shape[2]] + self.rng = torch.Generator() + + if self.mask_file != None: + self.mask = torch.Tensor(np.load(self.mask_file)) + else: + self.mask = torch.Tensor(np.ones(input_shape)) + + self.mask_device = self.mask.to(self.device) + + def __call__(self, tensor, use_device=False): + + t = self.fwd_noiseless(tensor, use_device=use_device) + mask = self.mask_device if use_device else self.mask + noise = torch.randn(*tensor.shape, dtype=torch.cfloat, generator=self.rng) * self.std / np.sqrt(2) + self.mean + if use_device: noise = noise.to(self.device) + t = t + noise + return t * mask + + def fwd_noiseless(self, tensor, use_device=False): + + # masked fft + if self.random: + L = self.mask.shape[-1] + num_low_freqs = int(self.center_fraction*L) + num_high_freqs = L - num_low_freqs + prob = (L / self.samp - num_low_freqs) / (L - num_low_freqs) + probs = prob * torch.ones([num_high_freqs]) + samp_values = torch.bernoulli(probs, generator=self.rng) + self.mask[:,:,:num_high_freqs//2] = samp_values[:num_high_freqs//2] + self.mask[:,:,num_high_freqs//2+num_low_freqs:] = samp_values[num_high_freqs//2:] + self.mask[:,:,num_high_freqs//2:num_high_freqs//2+num_low_freqs] = 1 + # self.mask = self.mask[0].T.unsqueeze(0) + self.mask_device = self.mask.to(self.device) + + mask = self.mask_device if use_device else self.mask + t = torch.fft.fft2(tensor, norm="ortho") + t = torch.fft.fftshift(t, dim=[-2,-1]) * mask + return t + + def rev(self, meas, mode="twochannel", use_device=True): + + t = meas * self.mask_device if use_device else meas * self.mask + t = torch.fft.ifftshift(t, dim=[-2,-1]) + t = torch.fft.ifft2(t, norm="ortho") + if mode == "complex": + return t + if mode == "twochannel": + return torch.concat([ t.real, t.imag ], axis=-3) + elif mode == "real": + return t.real + return t + + def __repr__(self): + return self.__class__.__name__ + f'(samp={self.samp}, mask_file={self.mask_file}, mean={self.mean}, std={self.std}, input_shape={self.input_shape}, num_bits={self.num_bits})' + + def log_prob(self, y, x): + + t = torch.fft.fft2(x, norm="ortho") + t = torch.fft.fftshift(t, dim=[-2,-1]) * self.mask_device + return utils.log_normal( y - t - self.mean, std=self.std) + + +if __name__ == '__main__': + + import imageio as io + + fwd = GaussianNoise([3,64,64], std=0.2) + # fwd = FourierSamplerGaussianNoise(samp=4, mask_file='../masks_mri/cartesian_4fold_128.npy') + # fwd = FourierSamplerGaussianNoise(random=True, mask_file=None, samp=2, center_fraction=0.2) + # x = np.load("xgt.npy") + # x = x.reshape(1,1,128,128) + # x = torch.Tensor(x) + # y = fwd(x) + # mask = fwd.mask + diff --git a/src/generate.py b/src/generate.py new file mode 100644 index 0000000..551376c --- /dev/null +++ b/src/generate.py @@ -0,0 +1,68 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch +import argparse +import models2 +import os +import imageio as io + +device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu') + +parser = argparse.ArgumentParser() +parser.add_argument("--gen_dir", type=str, default='') +parser.add_argument("--num_images", type=int, default=10000) +parser.add_argument("--iter", type=str, default=20000) +parser.add_argument("--temperature", type=float, default=0.7) +parser.add_argument("--ambient", type=int, default=0) +parser.add_argument("--save_as", type=str, default='png') +parser.add_argument("--range", type=int, nargs='+', default=[-0.5,0.5]) +parser.add_argument("--batch_size", type=int, default=10) +args = parser.parse_args() +print(args) + +if args.ambient: + args.iter = f'{int(args.iter):06}' + model_path = os.path.join(args.gen_dir, f'main-network_{args.iter}.pt') +else: + model_path = os.path.join(args.gen_dir, f'network_{args.iter}.pt') +model = models2.load_model(model_path, ambient=args.ambient, load_post_model=False).to(device) +fake_data = [] +bs = args.batch_size +for i in range(int(np.ceil(args.num_images / bs))): + fd = model.sample(bs, temp=args.temperature) + fake_data.append(fd) + print(i) +fake_data = np.concatenate(fake_data) +fake_data = fake_data[:args.num_images] +fake_data = (fake_data - args.range[0]) / (args.range[1] - args.range[0]) + +if args.save_as == 'npy': + np.save(os.path.join(args.gen_dir, f'fake-{args.iter:06}-{args.temperature}-{args.num_images}.npy'), fake_data) + +elif args.save_as == 'png': + # process data to save as images + fake_data = np.squeeze(fake_data) + if len(fake_data.shape) == 4: + fake_data = np.swapaxes(fake_data, 1, 2) + fake_data = np.swapaxes(fake_data, 2, 3) + fake_data = np.clip(fake_data, 0, 1) + fake_data *= 255 + fake_data = fake_data.astype(np.uint8) + fakes_folder = os.path.join(args.gen_dir, f'fake-{args.iter}-{args.temperature}-{args.num_images}') + os.makedirs(fakes_folder, exist_ok=True) + for i in range(args.num_images): + io.imsave( + os.path.join(fakes_folder, f'img_{i:05}.png'), + fake_data[i], + ) + # np.save(os.path.join(args.gen_dir, f'fake-{args.iter:06}-{args.temp}-{args.num_images}.npy'), fake_data) + + + \ No newline at end of file diff --git a/src/generate.sh b/src/generate.sh new file mode 100755 index 0000000..189b467 --- /dev/null +++ b/src/generate.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +iter=$1 +temperature=$2 +batch_size=$3 +gen_dir="" + +python generate.py --gen_dir $gen_dir --iter $iter --temperature $temperature --ambient 0 --num_images 5000 --batch_size $batch_size \ No newline at end of file diff --git a/src/metrics/__init__.py b/src/metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/metrics/clean_fid.py b/src/metrics/clean_fid.py new file mode 100644 index 0000000..0251d97 --- /dev/null +++ b/src/metrics/clean_fid.py @@ -0,0 +1,18 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu +""" + +from cleanfid import fid +import argparse +import sys + +parser = argparse.ArgumentParser() +parser.add_argument("--path_to_reals", type=str) +parser.add_argument("--path_to_fakes", type=str) +parser.add_argument("--num_images", type=int, default=5000) +parser.add_argument("--batch_size", type=int, default=200) +args = parser.parse_args() + +score = fid.compute_fid(args.path_to_fakes, args.path_to_reals, batch_size=args.batch_size, num_images=args.num_images) +print(score) \ No newline at end of file diff --git a/src/metrics/clean_fid.sh b/src/metrics/clean_fid.sh new file mode 100644 index 0000000..49e40b0 --- /dev/null +++ b/src/metrics/clean_fid.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +path_to_reals="" +path_to_fakes="" + +num_images=5000 + +results_dir="" + +mkdir -p $results_dir + +python clean_fid.py --path_to_reals $path_to_reals --path_to_fakes $path_to_fakes --num_images $num_images diff --git a/src/metrics/log_odds.py b/src/metrics/log_odds.py new file mode 100644 index 0000000..c8f50c9 --- /dev/null +++ b/src/metrics/log_odds.py @@ -0,0 +1,101 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu +""" + +import sys +sys.path.append("../") +import argparse +import ast +import os +import numpy as np +import torch +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +font = {'size' : 17} + +matplotlib.rc('font', **font) + +import models2 +import dataset_tool +import degradations + +parser = argparse.ArgumentParser() +parser.add_argument("--results_dir", type=str, default='') + +# Model args +parser.add_argument("--network_path", type=str, default='') +parser.add_argument("--num_images", type=int, default=500) +parser.add_argument("--ambient", type=int, default=1) + +# degradation args +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) + +# Data args +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +args = parser.parse_args() + +# data +clean_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=False, input_shape=args.input_shape, **args.data_args) + +# forward model +args.data_args['input_shape'] = args.input_shape +degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=0) + +# models +main_model, post_model = models2.load_model(args.network_path, ambient=bool(args.ambient), load_post_model=True) +main_model = main_model.to('cuda') +post_model = post_model.to('cuda') + +colors = ['b', 'orange', 'g', 'r', 'purple'] +for j in range(3): + logpxgys = [] + logpxs = [] + logpygxs = [] + + for i in range(args.num_images): + print(i) + xgt ,_ = clean_dataset[3*j]; xgt = xgt.reshape(1,*xgt.shape) + ymeas = degradation(xgt).to('cuda') + xgt = xgt.to('cuda') + + post_loss, x_posts = post_model.get_loss(ymeas, degradation, num_z=1, reg_parameter=0, num_bits=0, tiled=True, importance_weighting=1) + logpxgys.append(post_loss.detach().cpu().numpy().squeeze()) + x_posts = x_posts[0] + + nll = main_model.get_loss(x_posts, importance_weighting=True) # importance_weighting=True only gives separate nlls instead of sum/average in this case + logpxs.append(nll.detach().cpu().numpy().squeeze()) + + l = - degradation.log_prob(ymeas, x_posts) / np.log(2) / np.prod(xgt.shape) + logpygxs.append(l.detach().cpu().numpy().squeeze()) + + logpxgys = np.array(logpxgys) + logpxs = np.array(logpxs) + logpygxs = np.array(logpygxs) + + pphi = logpxgys + ptheta = -logpxs - logpygxs + m, b = np.polyfit(pphi, ptheta, 1) + + plt.scatter(pphi, ptheta, c=colors[j], alpha=0.2) + plt.plot( [pphi.min(), pphi.max()], [m*pphi.min()+b, m*pphi.max()+b], color=colors[j], label=f"Slope : {m:.1f}") +plt.legend() +plt.xlabel(r"$\log p_\phi ~(\mathbf{f}~ |~ \mathbf{g})$ (bits/dim)", math_fontfamily='cm') +plt.ylabel(r"$\log p_\theta ~(\mathbf{f}) ~+~ \log~ q_{\mathbf{g} | \mathbf{f}}~ (\mathbf{g}~ | ~\mathbf{f})$ (bits/dim)", math_fontfamily='cm') +plt.savefig(os.path.join(args.results_dir, 'log-odds.png'), bbox_inches='tight');plt.close() +plt.savefig(os.path.join(args.results_dir, 'log-odds.svg'), bbox_inches='tight');plt.close() + + + +# print(f"Mean NLL : {mean_nll}, Median NLL : {medn_nll}, Std. NLL : {stdv_nll}") +# with open( os.path.join( args.results_dir, 'nll.txt' ), 'w') as fid: +# print(f"Mean NLL : {mean_nll}, Median NLL : {medn_nll}, Std. NLL : {stdv_nll}", file=fid) + + + + + diff --git a/src/metrics/log_odds.sh b/src/metrics/log_odds.sh new file mode 100644 index 0000000..b37fbf9 --- /dev/null +++ b/src/metrics/log_odds.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +path_to_reals=/shared/aristotle/SOMS/varun/ambientflow/data/FastMRIT2Dataset-real-image-data +# path_to_reals=/shared/aristotle/SOMS/varun/ambientflow/data/CelebAHQDataset-real-image-data + +num_images=50 +data_type=FastMRIT2Dataset +degradation_type=FourierSamplerGaussianNoise + +# degradation_args="{'mask_file':None, 'mean':0., 'std':0.1}" +degradation_args="{'mask_file':'../../masks_mri/cartesian_4fold_128.npy', 'mean':0., 'std':0.1}" + +# 0x 0.1 noise +# network_path=/shared/anastasio1/SOMS/varun/ambientflow/results/mri/ambient/condglow/001-anastasio6-1gpu-degFourierSamplerGaussianNoise-Glow-CondGlow3-reg5.0-spparam0.03-thw0.05-bit0-lr0.0001-nz2-impw1/main-network_019906.pt + +# 4x 0.1 noise +network_path=/shared/radon/SOMS/varun/ambientflow/results/mri/ambient/condglow/003-anastasio6-1gpu-degFourierSamplerGaussianNoise-Glow-CondGlow3-reg5.0-spparam0.03-thw0.05-bit0-lr0.0001-nz2-impw1/main-network_019301.pt + +# results_dir=../../results/mri/ambient/log-odds/FourierSamplerGaussianNoise-0x-0.1 +results_dir=../../results/mri/ambient/log-odds/FourierSamplerGaussianNoise-4x-0.1 + +mkdir -p $results_dir + +python log_odds.py --data_type $data_type --data_args "{'power_of_two': True}" --input_shape 1 128 128 --degradation_type $degradation_type --degradation_args "$degradation_args" --network_path $network_path --results_dir $results_dir --num_images $num_images diff --git a/src/metrics/mean_and_cov.py b/src/metrics/mean_and_cov.py new file mode 100644 index 0000000..8cce94e --- /dev/null +++ b/src/metrics/mean_and_cov.py @@ -0,0 +1,112 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu +""" + +import argparse +import sys +from sklearn.utils.extmath import randomized_svd +import glob +import imageio as io +import numpy as np +from numpy import pi, sin, cos +import numpy.linalg as la +import os +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +from scipy.signal import fftconvolve + +parser = argparse.ArgumentParser() +parser.add_argument("--path_to_reals", type=str) +parser.add_argument("--path_to_fakes", type=str) +parser.add_argument("--num_images", type=int, default=5000) +parser.add_argument("--svd_comps", type=int, default=1000) +parser.add_argument("--results_dir", type=str, default='') +args = parser.parse_args() + + +fnames_real = glob.glob(os.path.join(args.path_to_reals, '*.png'))[:args.num_images] +fnames_fake = glob.glob(os.path.join(args.path_to_fakes, '*.png'))[:args.num_images] + +img_real = [] +img_fake = [] +for i in range(args.num_images): + print(i) + img_real.append(io.imread(fnames_real[i])) + img_fake.append(io.imread(fnames_fake[i])) + +img_real = np.stack(img_real, axis=0)/255 +img_fake = np.stack(img_fake, axis=0)/255 + +# Mean +mean_real = np.mean(img_real, axis=0) +mean_fake = np.mean(img_fake, axis=0) +io.imsave( os.path.join(args.results_dir, "mean_real.png"), mean_real ) +io.imsave( os.path.join(args.results_dir, "mean_fake.png"), mean_fake ) +print(la.norm(mean_real - mean_fake)**2 / la.norm(mean_real)**2) + +# Autocorrelation +def radial_profile(data, center): + # https://stackoverflow.com/questions/21242011/most-efficient-way-to-calculate-radial-profile + y, x = np.indices((data.shape)) + r = np.sqrt((x - center[0])**2 + (y - center[1])**2) + r = r.astype(int) + + tbin = np.bincount(r.ravel(), data.ravel()) + nr = np.bincount(r.ravel()) + radialprofile = tbin / nr + return radialprofile + +def autocorrelation(imgs): + X = np.stack(np.meshgrid(np.arange(imgs.shape[1]), imgs.shape[1]), axis=-1) + rw = imgs.shape[1]//2 + papoulis_filter = lambda r: (r<=rw)*( 1/pi * abs( sin(pi*r/rw) ) + ( 1 - r/rw )*cos(pi*r/rw) ) + papoulis_filter = papoulis_filter( la.norm(X - rw, axis=-1) ) + # imgs_fil = imgs*papoulis_filter + imgs_fil = imgs + ac = fftconvolve(imgs_fil, imgs_fil[:,::-1,::-1], axes=[1,2], mode='full') + ac = np.mean(ac, axis=0) + rw = ac.shape[0]//2 + # radprof = radial_profile(ac, [rw,rw]) + radprof = ac[rw, rw:] + radprof = radprof / radprof.max() + ac = ac / ac.max() + return ac, radprof + +ac_real, ac_radprof_real = autocorrelation(img_real) +ac_fake, ac_radprof_fake = autocorrelation(img_fake) +plt.plot(ac_radprof_real[:len(ac_radprof_real)//2], lw=8) +plt.plot(ac_radprof_fake[:len(ac_radprof_real)//2], '--', lw=8) +plt.xlabel("Distance from the center") +plt.ylabel("Autocorrelation") +plt.legend(["Real images", "Synthetic images"]) +plt.savefig( os.path.join(args.results_dir, 'autocorrelation_radprof.png'), bbox_inches='tight' ) +plt.savefig( os.path.join(args.results_dir, 'autocorrelation_radprof.svg'), bbox_inches='tight' );plt.close() + +io.imsave( os.path.join(args.results_dir, 'autocorrelation_real.png'), ac_real) +io.imsave( os.path.join(args.results_dir, 'autocorrelation_fake.png'), ac_fake) + +img_real = (img_real - mean_real.reshape(1, *mean_real.shape)).reshape(args.num_images, -1).T +img_fake = (img_fake - mean_fake.reshape(1, *mean_fake.shape)).reshape(args.num_images, -1).T + +U_real,s_real,_ = randomized_svd(img_real, args.svd_comps) +U_fake,s_fake,_ = randomized_svd(img_fake, args.svd_comps) + +c_real = U_real @ (U_real * s_real).T +c_fake = U_fake @ (U_fake * s_fake).T +print( la.norm(c_real - c_fake)**2 / la.norm(c_real)**2 ) +print(la.norm(mean_real - mean_fake)**2 / la.norm(mean_real)**2) + +plt.loglog(np.arange(1,len(s_real)+1), s_real**2) +plt.loglog(np.arange(1,len(s_real)+1), s_fake**2, '--') +plt.xlim([1,len(s_real)]) +plt.ylabel(r"$i$-th Singular value of covariance matrix", math_fontfamily='stixsans') +plt.xlabel(r"$i$", math_fontfamily='stixsans') +plt.savefig( os.path.join(args.results_dir, 'svals.png'), bbox_inches='tight' ) +plt.savefig( os.path.join(args.results_dir, 'svals.svg'), bbox_inches='tight' );plt.close() + + + + + diff --git a/src/metrics/mean_and_cov.sh b/src/metrics/mean_and_cov.sh new file mode 100644 index 0000000..20c4710 --- /dev/null +++ b/src/metrics/mean_and_cov.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +# Copyright (c) 2022-2023 authors +# Author : Varun A. Kelkar +# Email : vak2@illinois.edu + +path_to_reals="" +path_to_fakes="" + +num_images=5000 + +results_dir="" + +mkdir -p $results_dir + +python mean_and_cov.py --path_to_reals $path_to_reals --path_to_fakes $path_to_fakes --results_dir $results_dir --num_images $num_images diff --git a/src/metrics/radiomic_features.py b/src/metrics/radiomic_features.py new file mode 100644 index 0000000..4fef03c --- /dev/null +++ b/src/metrics/radiomic_features.py @@ -0,0 +1,235 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar, Rucha Deshpande +Email : vak2@illinois.edu, r.deshpande@wustl.edu +""" + +# import cv2 +import numpy as np +import imageio as io +import SimpleITK as sitk +import radiomics +from radiomics import featureextractor, logging +import argparse +import os.path as p + +from scipy.ndimage import binary_fill_holes +from skimage.filters import gaussian +from skimage import measure +from skimage.transform import resize +import glob +import pandas as pd +import scipy.linalg as la + +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import PCA +import scipy.stats as st + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +def clean_mask(input_mask): + labels_mask = measure.label(input_mask) + regions = measure.regionprops(labels_mask) + regions.sort(key=lambda x: x.area, reverse=True) + if len(regions) > 1: + for rg in regions[1:]: + labels_mask[rg.coords[:,0], rg.coords[:,1]] = 0 + labels_mask[labels_mask!=0] = 1 + cleaned_mask = labels_mask + return cleaned_mask + +def get_mask(arr): + width = 10 + + arr = np.where(arr > np.percentile(arr,99), np.percentile(arr,99), arr) + arr_in = (arr - np.min(arr)) / (np.max(arr) - np.min(arr))*255 + + blurred = gaussian(arr_in, sigma=1) + + thresh = np.max(blurred[:,:width]) + binary = blurred > thresh + + mask_ret = (binary_fill_holes(binary)*1).astype(np.uint8) + mask_ret = clean_mask(mask_ret) + + area_out = np.count_nonzero(mask_ret)/(mask_ret.shape[0]*mask_ret.shape[1]) + + if area_out < 0.1: + mask_ret = np.ones((arr.shape[0], arr.shape[1]), dtype = np.uint8) + area_out = 1 + + # mask_ret_im = sitk.GetImageFromArray(mask_ret) + + return mask_ret, area_out + +settings = {} +settings['binCount'] = 32 #binCount +settings['resampledPixelSpacing'] = None # [3,3,3] is an example for defining resampling (voxels with size 3x3x3mm) +settings['interpolator'] = sitk.sitkLinear +settings['sigma'] = [1, 2] +settings['distances'] = [1,2,3] +# settings['level'] = 3 # Wavelet level of decompsition, default 1, count staarts at 0 + +extractor = featureextractor.RadiomicsFeatureExtractor(**settings) +extractor.enableAllImageTypes() + +logger = radiomics.logger +logger.setLevel(logging.CRITICAL) + +def extract_features(fnames): + + for i,f in enumerate(fnames): + print(f"{i}/{len(fnames)}", end='\r') + + img = io.imread(f) + + try: + mask, _ = get_mask(img) + sitk_img = sitk.GetImageFromArray(img) + sitk_mask = sitk.GetImageFromArray(mask) + feature_dict = dict(extractor.execute(sitk_img, sitk_mask)) + df = pd.DataFrame(feature_dict.items()).T + df = pd.DataFrame(df.values[1:], columns=df.iloc[0]) + for c in df.columns: + if 'diagnostics' in c: + df.drop(c, axis=1, inplace=True) + if i == 0: + features = df.copy() + else: + features = pd.concat([features, df]) + + except Exception as e: + print(e) + df = features.iloc[-1].copy() + df *= np.nan + features = features.append(df) + + return features + +def compute_pca(data, num_components=2, pca=None): + """ + Data is in the shape n_features x n_points + """ + scalar = StandardScaler() + scalar.fit(data) + data_scaled = scalar.transform(data) + if pca == None: + pca = PCA(n_components=num_components, random_state=1234) + + data_pcs = pca.fit_transform(data_scaled) + return data_pcs, pca + +def compute_contour_value(hist, percent): + mode = hist.max() + levels = np.arange(mode+1) + masses = np.array([np.sum( hist*(hist < l) ) for l in levels]) + masses /= np.sum(hist) + percents = np.arange(101) + interp_levels = np.interp( percents/100, masses, levels ) + return interp_levels[int(percent)] + +def compute_contour_value2(hist, percent): + mode = hist.max() + levels = np.linspace(0, mode, 100) + masses = np.array([np.sum( hist*(hist < l) ) for l in levels]) + masses /= np.sum(hist) + percents = np.arange(101) + interp_levels = np.interp( percents/100, masses, levels ) + return interp_levels[int(percent)] + +def save_features(features_path, features): + print(f"Saving features to {features_path}") + features.to_csv(features_path, index=False) + +def compute_pca_all(features_list): + features_list = [ ff.dropna(axis=0) for ff in features_list ] + features_all = [ff.to_numpy().astype(float) for ff in features_list] + features_size = [ff.shape[0] for ff in features_all] + features_all = np.concatenate(features_all, axis=0) + features_pcs,_ = compute_pca(features_all, num_components=2) + split_locs = np.cumsum(features_size)[:-1] + features_pcs_list = np.split(features_pcs, split_locs) + return features_pcs_list + +def plot_histograms(features_pcs_list, num_bins=50): + F = np.concatenate(features_pcs_list, axis=0) + xbins = np.linspace(F[:,0].min(), F[:,0].max(), num_bins) + ybins = np.linspace(F[:,1].min(), F[:,1].max(), num_bins) + + hists = [] + for i, f in enumerate(features_pcs_list): + hist,_,_ = np.histogram2d(f[:,0], f[:,1], bins=[xbins, ybins]) + plt.imshow(hist, cmap='hot') + plt.savefig(f'temp_{i}.png', bbox_inches='tight') + plt.close() + +def compute_frd(real_pcs, fake_pcs): + real_mean = np.mean(real_pcs, axis=0) + fake_mean = np.mean(fake_pcs, axis=0) + real_cov = (real_pcs - real_mean.reshape(1,-1)).T @ (real_pcs - real_mean.reshape(1,-1)) + fake_cov = (fake_pcs - fake_mean.reshape(1,-1)).T @ (fake_pcs - fake_mean.reshape(1,-1)) + fd2 = la.norm( real_mean - fake_mean )**2 + \ + np.trace( real_cov + fake_cov - 2*la.sqrtm( real_cov @ fake_cov ) ) + return fd2 + +def plot_histograms2(features_pcs_list, num_bins=100): + F = np.concatenate(features_pcs_list, axis=0) + xbins = np.linspace(F[:,0].min(), F[:,0].max(), num_bins) + ybins = np.linspace(F[:,1].min(), F[:,1].max(), num_bins) + + xx, yy = np.meshgrid(xbins, ybins) + positions = np.vstack([xx.ravel(), yy.ravel()]) + + for i, f in enumerate(features_pcs_list): + # hist,_,_ = np.histogram2d(f[:,0], f[:,1], bins=[xbins, ybins]) + kernel = st.gaussian_kde(f.T, bw_method=0.1) + dens = kernel(positions).T.reshape(xx.shape) + if i == 0: + dens0 = dens.copy() + c0 = compute_contour_value2(dens0, 20) + c = compute_contour_value2(dens, 20) + + plt.imshow(dens, cmap='GnBu') + if i != 0: + plt.contour(dens0, levels=[c0], linestyles='dashed') + plt.contour(dens, levels=[c]) + plt.xticks([]) + plt.yticks([]) + plt.savefig(f'temp_{i}.png', bbox_inches='tight') + # plt.savefig(p.join(results_dir, f'pca_plot_{names[i]}.png'), bbox_inches='tight') + # plt.savefig(p.join(results_dir, f'pca_plot_{names[i]}.svg'), bbox_inches='tight') + plt.close() + +if __name__ == '__main__': + + num_images = 2000 + results_dir = '/home/vak2/Documents/ambientflow/results/mri/radiomic_features' + names = ['degraded-4x-0.1', 'plstv-4x-0.1', 'ambient-4x-0.1'] + + path_to_obj = '/shared/aristotle/SOMS/varun/ambientflow/data/FastMRIT2Dataset-real-image-data/*.png' + path_to_van = '/shared/aristotle/SOMS/varun/ambientflow/results/mri/vanilla/008-Glow-flow16-block5-lr0.0001-bits0/fake-010152-0.7-10000/*.png' + # path_to_deg = '/shared/aristotle/SOMS/varun/ambientflow/data/FastMRIT2Dataset-FourierSamplerGaussianNoise-0x-0.1-image-data/*.png' + path_to_deg = '/shared/aristotle/SOMS/varun/ambientflow/data/FastMRIT2Dataset-FourierSamplerGaussianNoise-4x-0.1-image-data/*.png' + # path_to_pls = '/shared/curie/SOMS/varun/ambientflow/results/mri/recon/fista-FastMRIT2Dataset-FourierSamplerGaussianNoise-0x-0.1/dataset/*.png' + path_to_pls = '/shared/curie/SOMS/varun/ambientflow/results/mri/recon/fista-FastMRIT2Dataset-FourierSamplerGaussianNoise-4x-0.1/dataset/*.png' + # path_to_amb = '/shared/anastasio1/SOMS/varun/ambientflow/results/mri/ambient/condglow/001-anastasio6-1gpu-degFourierSamplerGaussianNoise-Glow-CondGlow3-reg5.0-spparam0.03-thw0.05-bit0-lr0.0001-nz2-impw1/fake-019906-0.85-10000/*.png' + path_to_amb = '/shared/radon/SOMS/varun/ambientflow/results/mri/ambient/condglow/003-anastasio6-1gpu-degFourierSamplerGaussianNoise-Glow-CondGlow3-reg5.0-spparam0.03-thw0.05-bit0-lr0.0001-nz2-impw1/fake-019301-0.85-10000/*.png' + + fnames_obj = sorted(glob.glob(path_to_obj))[:num_images] + fnames_van = sorted(glob.glob(path_to_van))[:num_images] + fnames_deg = sorted(glob.glob(path_to_deg))[::-1][:num_images] + fnames_pls = sorted(glob.glob(path_to_pls))[::-1][:num_images] + fnames_amb = sorted(glob.glob(path_to_amb))[:num_images] + + features_list = [] + for i, fnames in enumerate([fnames_deg, fnames_pls, fnames_amb]): + + print(f"Computing features for {names[i]}") + features = extract_features(fnames) + features_list.append(features) + save_features(p.join(results_dir, f'features_{names[i]}.csv'), features) + + + + diff --git a/src/model_tools.py b/src/model_tools.py new file mode 100644 index 0000000..a8a3e82 --- /dev/null +++ b/src/model_tools.py @@ -0,0 +1,1347 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +from torch import nn +from torch.nn import functional as F +from math import log, pi, exp +import numpy as np +from scipy import linalg as la +from FrEIA.modules.coupling_layers import _BaseCouplingBlock +from FrEIA.modules import InvertibleModule +from typing import Callable, Union +import sparsifiers + +logabs = lambda x: torch.log(torch.abs(x)) + +def forward_squeeze(input): + # input dim: NxCxHxW + b_size, n_channel, height, width = input.shape + squeezed = input.view(b_size, n_channel, height // 2, 2, width // 2, 2) + squeezed = squeezed.permute(0, 1, 3, 5, 2, 4) + out = squeezed.contiguous().view(b_size, n_channel * 4, height // 2, width // 2) + return out + + +class Squeeze(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + + def forward(self, input): + # input dim: NxCxHxW + b_size, n_channel, height, width = input.shape + squeezed = input.view(b_size, n_channel, height // 2, 2, width // 2, 2) + squeezed = squeezed.permute(0, 1, 3, 5, 2, 4) + out = squeezed.contiguous().view(b_size, n_channel * 4, height // 2, width // 2) + return out + + def reverse(self, input): + b_size, n_channel, height, width = input.shape + + unsqueezed = input.view(b_size, n_channel // 4, 2, 2, height, width) + unsqueezed = unsqueezed.permute(0, 1, 4, 2, 5, 3) + out = unsqueezed.contiguous().view( + b_size, n_channel // 4, height * 2, width * 2 + ) + return out + + +class IRevNetSqueeze(nn.Module): + def __init__(self, in_channel, block_size=2): + super().__init__() + + self.channels = in_channel + self.block_size = block_size + self.block_size_sq = self.block_size**2 + + self.downsample_kernel = torch.zeros(4, 1, 2, 2) + + self.downsample_kernel[0, 0, 0, 0] = 1 + self.downsample_kernel[1, 0, 0, 1] = 1 + self.downsample_kernel[2, 0, 1, 0] = 1 + self.downsample_kernel[3, 0, 1, 1] = 1 + + self.downsample_kernel = torch.cat([self.downsample_kernel] * self.channels, 0) + self.downsample_kernel = nn.Parameter(self.downsample_kernel) + self.downsample_kernel.requires_grad = False + + def forward(self, input): + return F.conv2d(input, self.downsample_kernel, stride=self.block_size, groups=self.channels) + + def reverse(self, output): + return F.conv_transpose2d(output, self.downsample_kernel, + stride=self.block_size, groups=self.channels) + + +class HaarSqueeze(nn.Module): + def __init__(self, in_channel, order_by_wavelet=True): + + super().__init__() + self.in_channel = in_channel + self.order_by_wavelet = order_by_wavelet + + self.haar_weights = torch.ones(4, 1, 2, 2) / 2 + + self.haar_weights[1, 0, 0, 1] = -1/2 + self.haar_weights[1, 0, 1, 1] = -1/2 + + self.haar_weights[2, 0, 1, 0] = -1/2 + self.haar_weights[2, 0, 1, 1] = -1/2 + + self.haar_weights[3, 0, 1, 0] = -1/2 + self.haar_weights[3, 0, 0, 1] = -1/2 + + self.haar_weights = torch.cat([self.haar_weights] * self.in_channel, 0) + + if order_by_wavelet: + permutation = [] + for i in range(4): + permutation += [i + 4 * j for j in range(self.in_channel)] + + self.perm = torch.LongTensor(permutation) + self.perm_inv = torch.LongTensor(permutation) + + # clever trick to invert a permutation + for i, p in enumerate(self.perm): + self.perm_inv[p] = i + + self.haar_weights = nn.Parameter(self.haar_weights) + self.haar_weights.requires_grad = False + + def forward(self, input): + out = F.conv2d(input, self.haar_weights, bias=None, stride=2, groups=self.in_channel) + if self.order_by_wavelet: + return out[:, self.perm] + else: return out + + def reverse(self, output): + if self.order_by_wavelet: + x_perm = output[:, self.perm_inv] + else: + x_perm = output + return F.conv_transpose2d(output, self.haar_weights, bias=None, stride=2, groups=self.in_channel) + + +class EVSCouplingBlock(_BaseCouplingBlock): + ''' Coupling block that supports evidence-softmax based coupling to promote sparsity. + ''' + + def __init__(self, dims_in, dims_c=[], + subnet_constructor: Callable = None, + epsilon: float = 1.e-06, + avg_scale: float = 1., + clamp: float = 2., + clamp_activation: Union[str, Callable] = "ATAN", + split_len: Union[float, int] = 0.5): + ''' + Additional args in docstring of base class. + + Args: + subnet_constructor: function or class, with signature + constructor(dims_in, dims_out). The result should be a torch + nn.Module, that takes dims_in input channels, and dims_out output + channels. See tutorial for examples. Two of these subnetworks will be + initialized in the block. + epsilon: + avg_scale: + clamp: Soft clamping for the multiplicative component. The + amplification or attenuation of each input dimension can be at most + exp(±clamp). + clamp_activation: Function to perform the clamping. String values + "ATAN", "TANH", and "SIGMOID" are recognized, or a function of + object can be passed. TANH behaves like the original realNVP paper. + A custom function should take tensors and map -inf to -1 and +inf to +1. + ''' + + super().__init__(dims_in, dims_c, clamp, clamp_activation, + split_len=split_len) + self.epsilon = epsilon + self.avg_scale = avg_scale + + self.subnet1 = subnet_constructor(self.split_len1 + self.condition_length, self.split_len2 * 2) + self.subnet2 = subnet_constructor(self.split_len2 + self.condition_length, self.split_len1 * 2) + + def _coupling1(self, x1, u2, rev=False): + + # notation (same for _coupling2): + # x: inputs (i.e. 'x-side' when rev is False, 'z-side' when rev is True) + # y: outputs (same scheme) + # *_c: variables with condition appended + # *1, *2: left half, right half + # a: all affine coefficients + # s, t: multiplicative and additive coefficients + # j: log det Jacobian + + a2 = self.subnet2(u2) + s2, t2 = a2[:, :self.split_len1], a2[:, self.split_len1:] + s2 = self.clamp * self.f_clamp(s2) + j1 = torch.sum(s2, dim=tuple(range(1, self.ndims + 1))) + + if rev: + y1 = (x1 - t2) * torch.exp(-s2) + return y1, -j1 + else: + y1 = torch.exp(s2) * x1 + t2 + return y1, j1 + + def _coupling2(self, x2, u1, rev=False): + a1 = self.subnet1(u1) + s1, t1 = a1[:, :self.split_len2], a1[:, self.split_len2:] + s1 = self.clamp * self.f_clamp(s1) + # s1_idn = torch.abs(s1.detach()) - self.clamp # no gradients + s1_idn = s1 - self.clamp + s1_avg = self.avg_scale * torch.mean(s1_idn, dim=tuple(range(1, self.ndims + 1)), keepdim=True) + nzcond = (s1_idn <= s1_avg) + self.epsilon + # nzcond = torch.sigmoid( -100*(s1_idn - s1_avg) ) + # ev_s1 = 1./( nzcond + self.epsilon ) * torch.exp(s1) + # ev_s1_inv = ( nzcond + self.epsilon ) * torch.exp(-s1) + ev_s1 = 1./( nzcond ) * torch.exp(s1) + ev_s1_inv = ( nzcond ) * torch.exp(-s1) + + t1 = nzcond * t1 + # j2 = torch.sum(s1 * nzcond, dim=tuple(range(1, self.ndims + 1))) + j2 = torch.sum(s1 - torch.log(nzcond), dim=tuple(range(1, self.ndims + 1))) + + if rev: + y2 = (x2 - t1) * ev_s1_inv + return y2, -j2 + else: + y2 = ev_s1 * x2 + t1 + return y2, j2 + + +class SparsifierHardThreshold(InvertibleModule): + '''Uses Haar wavelets to split each channel into 4 channels, with half the + width and height dimensions.''' + + def __init__(self, dims_in, dims_c = None, + sparsifier = sparsifiers.SparsifyingTransform([1,64,64]), + threshold: float = 1., + epsilon: float = 1.e-06): + '''See docstring of base class (FrEIA.modules.InvertibleModule) for more. + + Args: + sparsifier: sparsifying transform (such as orthogonal wavelet transform) + ASSUMES THAT THE SPARSIFYING TRANSFORM IS ISOMETRIC (LOGDET IS 0). + threshold: Threshold for hard thresholding + + ''' + super().__init__(dims_in, dims_c) + + self.in_channels = dims_in[0][0] + self.sparsifier = sparsifier + self.threshold = threshold + self.epsilon = epsilon + self.final_scale = nn.Parameter(torch.ones((1,)), requires_grad=False) + + self.jac_fwd = 0. + self.jac_rev = 0. + + def forward(self, x, c=None, jac=True, rev=False): + '''See docstring of base class (FrEIA.modules.InvertibleModule).''' + + clip_thresh = 0.51 + inp = x[0] #* self.final_scale # N x C x H x W + coeffs = self.sparsifier(inp + 0.5) # N x C*H*W + + #number total entries except for batch dimension: + self.ndims = len(coeffs.shape) - 1 + + coeff_abs = torch.abs(coeffs) + coeff_avg = self.threshold * torch.mean(coeff_abs, dim=tuple(range(1, self.ndims + 1)), keepdim=True) + nzcond = (coeff_abs >= coeff_avg) + self.epsilon + + # ASSUMES THAT THE SPARSIFIER IS ISOMETRIC + # jac = -torch.cat([torch.log(self.final_scale)]*inp.shape[0]) + jac = 0 + # jac += -torch.sum(np.log(1/28) + torch.log((abs(inp) <= clip_thresh) + 1e-06), dim=(1,2,3)) + jac += -torch.sum(torch.log(nzcond), dim=tuple(range(1, self.ndims + 1))) + + if not rev: + raise ValueError("This function is only good for reverse computation, it can be highly unstable in the reverse computation.") + # out = coeffs / nzcond + # out = self.sparsifier.inv(out) + # return (out,), jac + else: + out = coeffs * nzcond + out = self.sparsifier.inv(out) - 0.5 + return (out,), -jac + + def output_dims(self, input_dims): + '''See docstring of base class (FrEIA.modules.InvertibleModule).''' + + if len(input_dims) != 1: + raise ValueError("HaarDownsampling must have exactly 1 input") + if len(input_dims[0]) != 3: + raise ValueError("HaarDownsampling can only transform 2D images" + "of the shape CxWxH (channels, width, height)") + + c, w, h = input_dims[0] + + return ((c, w, h),) + +class Inv1x1Conv(InvertibleModule): + '''Given an invertible matrix M, a 1x1 convolution is performed using M as + the convolution kernel. Effectively, a matrix muplitplication along the + channel dimension is performed in each pixel.''' + + def __init__(self, dims_in, dims_c=None): + '''Additional args in docstring of base class FrEIA.modules.InvertibleModule. + ''' + super().__init__(dims_in, dims_c) + + # TODO: it should be possible to give conditioning instead of M, so that the condition + # provides M and b on each forward pass. + + in_channel = dims_in[0][0] + weight = np.random.randn(in_channel, in_channel) + q, _ = la.qr(weight) + w_p, w_l, w_u = la.lu(q.astype(np.float32)) + w_s = np.diag(w_u) + w_u = np.triu(w_u, 1) + u_mask = np.triu(np.ones_like(w_u), 1) + l_mask = u_mask.T + + w_p = torch.from_numpy(w_p) + w_l = torch.from_numpy(w_l) + w_s = torch.from_numpy(w_s) + w_u = torch.from_numpy(w_u) + + self.register_buffer("w_p", w_p) + self.register_buffer("u_mask", torch.from_numpy(u_mask)) + self.register_buffer("l_mask", torch.from_numpy(l_mask)) + self.register_buffer("s_sign", torch.sign(w_s)) + self.register_buffer("l_eye", torch.eye(l_mask.shape[0])) + self.w_l = nn.Parameter(w_l) + self.w_s = nn.Parameter(logabs(w_s)) + self.w_u = nn.Parameter(w_u) + + def calc_weight(self): + weight = ( + self.w_p + @ (self.w_l * self.l_mask + self.l_eye) + @ ((self.w_u * self.u_mask) + torch.diag(self.s_sign * torch.exp(self.w_s))) + ) + + return weight.unsqueeze(2).unsqueeze(3) + + def forward(self, x, rev=False, jac=True): + + n_pixels = x[0][0, 0].numel() + weight = self.calc_weight() + + j = n_pixels * torch.sum(self.w_s) + + if not rev: + out = F.conv2d(x[0], weight.squeeze().inverse().unsqueeze(2).unsqueeze(3)) + return (out,), -j + # return (F.conv2d(x[0], weight),), j + else: + return (F.conv2d(x[0], weight),), j + # out = F.conv2d(x[0], weight.squeeze().inverse().unsqueeze(2).unsqueeze(3)) + # return (out,), -j + + def output_dims(self, input_dims): + '''See base class for docstring''' + if len(input_dims) != 1: + raise ValueError(f"{self.__class__.__name__} can only use 1 input") + if len(input_dims[0]) != 3: + raise ValueError(f"{self.__class__.__name__} requires 3D input (channels, height, width)") + return input_dims + + + +class ActNormCoarse(InvertibleModule): + '''A technique to achieve a stable initlization. + + First introduced in Kingma et al 2018: https://arxiv.org/abs/1807.03039 + The module is similar to a traditional batch normalization layer, but the + data mean and standard deviation is only computed for the first batch of + data. To ensure invertibility, the mean and standard devation are kept + fixed from that point on. + Using ActNorm layers interspersed throughout an INN ensures that + intermediate outputs of the INN have standard deviation 1 and mean 0, so + that the training is stable at the start, avoiding exploding or zeroed + outputs. + Just as with standard batch normalization layers, ActNorm contains + additional channel-wise scaling and bias parameters. + ''' + + def __init__(self, dims_in, dims_c=None, init_data: Union[torch.Tensor, None] = None, inverse: bool = False, scaling: float = 1.): + ''' + Args: + init_data: If ``None``, use the first batch of data passed through this + module to initialize the mean and standard deviation. + If ``torch.Tensor``, use this as data to initialize instead of the + first real batch. + ''' + + super().__init__(dims_in, dims_c) + self.dims_in = dims_in[0] + self.inverse = inverse + self.scaling = scaling + self.scale = nn.Parameter(torch.zeros(1)) + self.bias = nn.Parameter(torch.zeros(1)) + + if init_data: + self._initialize_with_data(init_data) + else: + # self.init_on_next_batch = True + self.init_on_next_batch = False + + def on_load_state_dict(*args): + # when this module is loading state dict, we SHOULDN'T init with data, + # because that will reset the trained parameters. Registering a hook + # that disable this initialisation. + self.init_on_next_batch = False + self._register_load_state_dict_pre_hook(on_load_state_dict) + + def _initialize_with_data(self, data): + # Initialize to mean 0 and std 1 with sample batch + # 'data' expected to be of shape (batch, channels[, ...]) + # If actnorm is computed during a reverse computation, flip the direction of actnorm + sign = (-1)**int(self.inverse) + + assert all([data.shape[i+1] == self.dims_in[i] for i in range(len(self.dims_in))]),\ + "Can't initialize ActNorm layer, provided data don't match input dimensions." + self.scale.data.view(-1)[:] \ + = sign * torch.log(self.scaling / data.std()) + + data = data * self.scale.exp() + self.bias.data.view(-1)[:] \ + = sign * -data.mean() + self.init_on_next_batch = False + + def forward(self, x, rev=False, jac=True): + if self.init_on_next_batch: + self._initialize_with_data(x[0]) + + jac = (self.scale.sum() * np.prod(self.dims_in[1:])).repeat(x[0].shape[0]) + if rev: + jac = -jac + + if not rev: + return [x[0] * self.scale.exp() + self.bias], jac + else: + return [(x[0] - self.bias) / self.scale.exp()], jac + + def output_dims(self, input_dims): + assert len(input_dims) == 1, "Can only use 1 input" + return input_dims + + +class ActNorm2(InvertibleModule): + '''A technique to achieve a stable initlization. + + First introduced in Kingma et al 2018: https://arxiv.org/abs/1807.03039 + The module is similar to a traditional batch normalization layer, but the + data mean and standard deviation is only computed for the first batch of + data. To ensure invertibility, the mean and standard devation are kept + fixed from that point on. + Using ActNorm layers interspersed throughout an INN ensures that + intermediate outputs of the INN have standard deviation 1 and mean 0, so + that the training is stable at the start, avoiding exploding or zeroed + outputs. + Just as with standard batch normalization layers, ActNorm contains + additional channel-wise scaling and bias parameters. + ''' + + def __init__(self, dims_in, dims_c=None, init_data: Union[torch.Tensor, None] = None, inverse: bool = False, scaling: float = 1.): + ''' + Args: + init_data: If ``None``, use the first batch of data passed through this + module to initialize the mean and standard deviation. + If ``torch.Tensor``, use this as data to initialize instead of the + first real batch. + ''' + + super().__init__(dims_in, dims_c) + self.dims_in = dims_in[0] + self.inverse = inverse + self.scaling = scaling + self.scale = nn.Parameter(torch.zeros(1)) + self.bias = nn.Parameter(torch.zeros(1)) + + if init_data: + self._initialize_with_data(init_data) + else: + # self.init_on_next_batch = True + self.init_on_next_batch = False + + def on_load_state_dict(*args): + # when this module is loading state dict, we SHOULDN'T init with data, + # because that will reset the trained parameters. Registering a hook + # that disable this initialisation. + self.init_on_next_batch = False + self._register_load_state_dict_pre_hook(on_load_state_dict) + + def _initialize_with_data(self, data): + # Initialize to mean 0 and std 1 with sample batch + # 'data' expected to be of shape (batch, channels[, ...]) + # If actnorm is computed during a reverse computation, flip the direction of actnorm + sign = (-1)**int(self.inverse) + + assert all([data.shape[i+1] == self.dims_in[i] for i in range(len(self.dims_in))]),\ + "Can't initialize ActNorm layer, provided data don't match input dimensions." + self.scale.data.view(-1)[:] \ + = sign * torch.log(self.scaling / data.transpose(0,1).contiguous().view(self.dims_in[0], -1).std(dim=-1)) + + data = data * self.scale.exp() + self.bias.data.view(-1)[:] \ + = sign * -data.transpose(0,1).contiguous().view(self.dims_in[0], -1).mean(dim=-1) + self.init_on_next_batch = False + + def forward(self, x, rev=False, jac=True): + if self.init_on_next_batch: + self._initialize_with_data(x[0]) + + jac = (self.scale.sum() * np.prod(self.dims_in[1:])).repeat(x[0].shape[0]) + if rev: + jac = -jac + + if not rev: + return [x[0] * self.scale.exp() + self.bias], jac + else: + return [(x[0] - self.bias) / self.scale.exp()], jac + + def output_dims(self, input_dims): + assert len(input_dims) == 1, "Can only use 1 input" + return input_dims + + + +class ActNorm(nn.Module): + def __init__(self, in_channel, logdet=True): + super().__init__() + + self.loc = nn.Parameter(torch.zeros(1, in_channel, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, in_channel, 1, 1)) + + self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8)) + self.logdet = logdet + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input): + _, _, height, width = input.shape + + if self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + log_abs = logabs(self.scale) + + logdet = height * width * torch.sum(log_abs) + + if self.logdet: + return self.scale * (input + self.loc), logdet + else: + return self.scale * (input + self.loc) + + def reverse(self, output, jac=False): + if jac: + _, _, height, width = output.shape + log_abs = logabs(self.scale) + logdet = height * width * torch.sum(log_abs) + return output / self.scale - self.loc, -logdet + + return output / self.scale - self.loc + + +class InvConv2d(nn.Module): + def __init__(self, in_channel): + super().__init__() + + weight = torch.randn(in_channel, in_channel) + q, _ = torch.linalg.qr(weight) + weight = q.unsqueeze(2).unsqueeze(3) + self.weight = nn.Parameter(weight) + + def forward(self, input): + _, _, height, width = input.shape + + out = F.conv2d(input, self.weight) + logdet = ( + height * width * torch.slogdet(self.weight.squeeze().double())[1].float() + ) + + return out, logdet + + def reverse(self, output, jac=False): + out = F.conv2d( + output, self.weight.squeeze().inverse().unsqueeze(2).unsqueeze(3) + ) + if jac: + _, _, height, width = output.shape + logdet = ( + height * width * torch.slogdet(self.weight.squeeze().double())[1].float() + ) + return out, -logdet + + return out + + +class Inj2xConv2d(nn.Module): + def __init__(self, in_channel, gamma=0.): + super().__init__() + + self.gamma = gamma + w1 = torch.randn(in_channel//2, in_channel//2) + w2 = torch.randn(in_channel//2, in_channel//2) + q1,_ = torch.linalg.qr(w1) + q2,_ = torch.linalg.qr(w2) + q = torch.concat([q1, q2], dim=1) + weight = q.unsqueeze(2).unsqueeze(3) + self.weight = nn.Parameter(weight) + + def forward(self, input): + _, _, height, width = input.shape + + self.w_gram = torch.matmul( self.weight.squeeze(), self.weight.squeeze().T ) + self.w_gram += self.gamma **2 * torch.eye(*self.w_gram.shape).to(self.w_gram.device) + out = F.conv2d(input, self.weight) + logdet = ( + 0.5 * height * width * torch.slogdet(self.w_gram.double())[1].float() + ) + + return out, logdet + + def reverse(self, output, jac=False): + + self.w_gram = torch.matmul( self.weight.squeeze(), self.weight.squeeze().T ) + self.w_gram += self.gamma **2 * torch.eye(*self.w_gram.shape).to(self.w_gram.device) + weight_inv = torch.matmul(self.weight.squeeze().T, self.w_gram.inverse()) # Moore-Penrose pseudoinverse + out = F.conv2d( + output, weight_inv.unsqueeze(2).unsqueeze(3) + ) + + if jac: + _, _, height, width = output.shape + logdet = ( + 0.5 * height * width * torch.slogdet(self.w_gram.double())[1].float() + ) + return out, -logdet + + return out + + +class InvConv2dLU(nn.Module): + def __init__(self, in_channel): + super().__init__() + + weight = np.random.randn(in_channel, in_channel) + q, _ = la.qr(weight) + w_p, w_l, w_u = la.lu(q.astype(np.float32)) + w_s = np.diag(w_u) + w_u = np.triu(w_u, 1) + u_mask = np.triu(np.ones_like(w_u), 1) + l_mask = u_mask.T + + w_p = torch.from_numpy(w_p) + w_l = torch.from_numpy(w_l) + w_s = torch.from_numpy(w_s) + w_u = torch.from_numpy(w_u) + + self.register_buffer("w_p", w_p) + self.register_buffer("u_mask", torch.from_numpy(u_mask)) + self.register_buffer("l_mask", torch.from_numpy(l_mask)) + self.register_buffer("s_sign", torch.sign(w_s)) + self.register_buffer("l_eye", torch.eye(l_mask.shape[0])) + self.w_l = nn.Parameter(w_l) + self.w_s = nn.Parameter(logabs(w_s)) + self.w_u = nn.Parameter(w_u) + + def forward(self, input): + _, _, height, width = input.shape + + weight = self.calc_weight() + + out = F.conv2d(input, weight) + logdet = height * width * torch.sum(self.w_s) + + return out, logdet + + def calc_weight(self): + weight = ( + self.w_p + @ (self.w_l * self.l_mask + self.l_eye) + @ ((self.w_u * self.u_mask) + torch.diag(self.s_sign * torch.exp(self.w_s))) + ) + + return weight.unsqueeze(2).unsqueeze(3) + + def reverse(self, output, jac=False): + weight = self.calc_weight() + + out = F.conv2d(output, weight.squeeze().inverse().unsqueeze(2).unsqueeze(3)) + + if jac: + _, _, height, width = output.shape + logdet = height * width * torch.sum(self.w_s) + return out, -logdet + return out + + +class ZeroConv2d(nn.Module): + def __init__(self, in_channel, out_channel, padding=1): + super().__init__() + + self.conv = nn.Conv2d(in_channel, out_channel, 3, padding=0) + self.conv.weight.data.zero_() + self.conv.bias.data.zero_() + self.scale = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) + + def forward(self, input): + out = F.pad(input, [1, 1, 1, 1], value=1) + out = self.conv(out) + out = out * torch.exp(self.scale * 3) + + return out + +class ZeroLinear(nn.Module): + def __init__(self, in_channel, out_channel, padding=1): + super().__init__() + + self.conv = nn.Conv2d(in_channel, out_channel, 3, padding=0) + self.linear = nn.Linear(in_channel, out_channel) + self.linear.weight.data.zero_() + self.linear.bias.data.zero_() + self.scale = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) + + def forward(self, input): + out = self.linear(input) + out = out * torch.exp(self.scale * 3) + + return out + +class AffineCoupling(nn.Module): + def __init__(self, in_channel, filter_size=512, affine=True, condition=False, num_cond_layers=0, fully_connected=False): + super().__init__() + + self.affine = affine + self.condition = condition + self.num_cond_layers = int(num_cond_layers * int(self.condition)) + + if not fully_connected: + self.net = nn.Sequential( + nn.Conv2d(in_channel // 2 + self.num_cond_layers, filter_size, 3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(filter_size, filter_size, 1), + nn.ReLU(inplace=True), + ZeroConv2d(filter_size, in_channel if self.affine else in_channel // 2), + ) + else: + self.net = nn.Sequential( + nn.Linear(in_channel // 2 + self.num_cond_layers, filter_size), + nn.ReLU(inplace=True), + nn.Linear(filter_size, filter_size), + nn.ReLU(inplace=True), + nn.ZeroLinear(filter_size, in_channel if self.affine else in_channel // 2), + ) + + self.net[0].weight.data.normal_(0, 0.05) + self.net[0].bias.data.zero_() + + self.net[2].weight.data.normal_(0, 0.05) + self.net[2].bias.data.zero_() + + def forward(self, input, cond=None): + in_a, in_b = input.chunk(2, 1) + + if self.affine: + if self.condition: + log_s, t = self.net( + torch.concat([in_a, cond], dim=1) + ).chunk(2, 1) + else: + log_s, t = self.net(in_a).chunk(2, 1) + # s = torch.exp(log_s) + s = torch.sigmoid(log_s + 2) + # out_a = s * in_a + t + out_b = (in_b + t) * s + + logdet = torch.sum(torch.log(s).view(input.shape[0], -1), 1) + + else: + if self.condition: + net_out = self.net( + torch.concat([in_a, cond], dim=1) + ) + else: + net_out = self.net(in_a) + out_b = in_b + net_out + logdet = 0 + + return torch.cat([in_a, out_b], 1), logdet + + def reverse(self, output, cond=None, jac=False): + out_a, out_b = output.chunk(2, 1) + + if self.affine: + if self.condition: + log_s, t = self.net( + torch.concat([out_a, cond], dim=1) + ).chunk(2, 1) + else: + log_s, t = self.net(out_a).chunk(2, 1) + # s = torch.exp(log_s) + s = torch.sigmoid(log_s + 2) + # in_a = (out_a - t) / s + in_b = out_b / s - t + + logdet = torch.sum(torch.log(s).view(output.shape[0], -1), 1) + + else: + if self.condition: + net_out = self.net( + torch.concat([out_a, cond], dim=1) + ) + else: + net_out = self.net(out_a) + in_b = out_b - net_out + + logdet = 0 + + if jac: + return torch.cat([out_a, in_b], 1), -logdet + return torch.cat([out_a, in_b], 1) + +class Flow(nn.Module): + def __init__(self, in_channel, affine=True, filter_size=512, conv_lu=True): + super().__init__() + + self.actnorm = ActNorm(in_channel) + + if conv_lu: + self.invconv = InvConv2dLU(in_channel) + + else: + self.invconv = InvConv2d(in_channel) + + self.coupling = AffineCoupling(in_channel, affine=affine, filter_size=filter_size) + + def forward(self, input): + out, logdet = self.actnorm(input) + out, det1 = self.invconv(out) + out, det2 = self.coupling(out) + + logdet = logdet + det1 + logdet = logdet + det2 + + return out, logdet + + def reverse(self, output, jac=False): + if jac: + input, logdet = self.coupling.reverse(output, jac=jac) + input, det1 = self.invconv.reverse(input, jac=jac) + input, det2 = self.actnorm.reverse(input, jac=jac) + logdet += (det1 + det2) + return input, logdet + + input = self.coupling.reverse(output) + input = self.invconv.reverse(input) + input = self.actnorm.reverse(input) + return input + + +class CondFlow(nn.Module): + def __init__(self, in_channel, affine=True, filter_size=512, num_cond_layers=32, conv_lu=True): + super().__init__() + + self.actnorm = ActNorm(in_channel) + + if conv_lu: + self.invconv = InvConv2dLU(in_channel) + + else: + self.invconv = InvConv2d(in_channel) + + self.coupling = AffineCoupling(in_channel, affine=affine, filter_size=filter_size, condition=True, num_cond_layers=num_cond_layers) + + def forward(self, input, cond): + out, logdet = self.actnorm(input) + out, det1 = self.invconv(out) + out, det2 = self.coupling(out, cond) + + logdet = logdet + det1 + det2 + + return out, logdet + + def reverse(self, output, cond, jac=False): + if jac: + input, logdet = self.coupling.reverse(output, cond, jac=jac) + input, det1 = self.invconv.reverse(input, jac=jac) + input, det2 = self.actnorm.reverse(input, jac=jac) + logdet += (det1 + det2) + return input, logdet + + input = self.coupling.reverse(output, cond) + input = self.invconv.reverse(input) + input = self.actnorm.reverse(input) + return input + + +class InjFlow(nn.Module): + def __init__(self, in_channel, affine=True, filter_size=512, gamma=0.): + super().__init__() + + self.actnorm = ActNorm(in_channel) + + self.injconv = Inj2xConv2d(in_channel, gamma=gamma) + + self.coupling = AffineCoupling(in_channel//2, affine=affine, filter_size=filter_size) + + def forward(self, input): + out, logdet = self.actnorm(input) + out, det1 = self.injconv(out) + out, det2 = self.coupling(out) + + logdet = logdet + det1 + det2 + + return out, logdet + + def reverse(self, output, jac=False): + if jac: + input, logdet = self.coupling.reverse(output, jac=jac) + input, det1 = self.injconv.reverse(input, jac=jac) + input, det2 = self.actnorm.reverse(input, jac=jac) + logdet += (det1 + det2) + return input, logdet + + input = self.coupling.reverse(output) + input = self.injconv.reverse(input) + input = self.actnorm.reverse(input) + return input + +def gaussian_log_p(x, mean, log_sd): + return -0.5 * log(2 * pi) - log_sd - 0.5 * (x - mean) ** 2 / torch.exp(2 * log_sd) + + +def gaussian_sample(eps, mean, log_sd): + return mean + torch.exp(log_sd) * eps + + +class InvBlock(nn.Module): + def __init__(self, in_channel, n_flow, split=True, affine=True, filter_size=512, conv_lu=True, extract_z=True, squeeze=True, squeezer=Squeeze): + super().__init__() + + squeeze_dim = in_channel * 4 if squeeze else in_channel + + self.flows = nn.ModuleList() + for i in range(n_flow): + self.flows.append(Flow(squeeze_dim, affine=affine, conv_lu=conv_lu, filter_size=filter_size)) + + self.split = split + self.extract_z = extract_z + self.squeeze = squeeze + if self.squeeze: self.squeezer = squeezer(in_channel=in_channel) + + if split: + self.prior = ZeroConv2d(squeeze_dim // 2, squeeze_dim) + + elif (not split) and extract_z: + self.prior = ZeroConv2d(squeeze_dim, squeeze_dim * 2) + + def forward(self, input): + b_size, n_channel, height, width = input.shape + if self.squeeze: + # squeezed = input.view(b_size, n_channel, height // 2, 2, width // 2, 2) + # squeezed = squeezed.permute(0, 1, 3, 5, 2, 4) + # out = squeezed.contiguous().view(b_size, n_channel * 4, height // 2, width // 2) + out = self.squeezer(input) + else: out = input + + logdet = 0 + + for flow in self.flows: + out, det = flow(out) + logdet = logdet + det + + if self.extract_z: + if self.split: + out, z_new = out.chunk(2, 1) + mean, log_sd = self.prior(out).chunk(2, 1) + log_p = gaussian_log_p(z_new, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + + else: + zero = torch.zeros_like(out) + mean, log_sd = self.prior(zero).chunk(2, 1) + log_p = gaussian_log_p(out, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + z_new = out + + return out, logdet, log_p, z_new + + return out, logdet, 0, None + + def reverse(self, output, eps=None, reconstruct=False, jac=False): + b_size, n_channel, height, width = output.shape + input = output + + if reconstruct: + if self.split: + input = torch.cat([output, eps], 1) + else: + input = eps + + if jac: + mean = torch.zeros_like(eps) + log_sd = torch.zeros_like(eps) + log_p = gaussian_log_p(eps, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + + else: + if self.extract_z: + if self.split: + mean, log_sd = self.prior(input).chunk(2, 1) + z = gaussian_sample(eps, mean, log_sd) + input = torch.cat([output, z], 1) + else: + zero = torch.zeros_like(input) + # zero = F.pad(zero, [1, 1, 1, 1], value=1) + mean, log_sd = self.prior(zero).chunk(2, 1) + z = gaussian_sample(eps, mean, log_sd) + input = z + if jac: + log_p = gaussian_log_p(z, mean, log_sd) + log_p = -log_p.view(b_size, -1).sum(1) + else: + input = output + log_p = 0 + + logdet = 0 + for flow in self.flows[::-1]: + if jac: + input, det = flow.reverse(input, jac=True) + logdet += det + else: + input = flow.reverse(input) + + b_size, n_channel, height, width = input.shape + if self.squeeze: + # unsqueezed = input.view(b_size, n_channel // 4, 2, 2, height, width) + # unsqueezed = unsqueezed.permute(0, 1, 4, 2, 5, 3) + # out = unsqueezed.contiguous().view( + # b_size, n_channel // 4, height * 2, width * 2 + # ) + out = self.squeezer.reverse(input) + else: out = input + + if jac: + return out, logdet, log_p + return out + + +class InjBlock(nn.Module): + def __init__(self, in_channel, n_flow=1, gamma=1e-08, affine=True, filter_size=512, conv_lu=True, extract_z=True, squeeze=True, squeezer=Squeeze): + super().__init__() + + squeeze_dim = in_channel * 4 if squeeze else in_channel + in_dim = squeeze_dim + + self.flows = nn.ModuleList() + for i in range(n_flow): + self.flows.append(InjFlow(in_dim, affine=affine, filter_size=filter_size, gamma=gamma)) + in_dim = in_dim // 2 + + self.extract_z = extract_z + self.squeeze = squeeze + if self.squeeze: self.squeezer = squeezer(in_channel=in_channel) + + if extract_z: + self.prior = ZeroConv2d(in_dim, in_dim * 2) + + def forward(self, input): + b_size, n_channel, height, width = input.shape + if self.squeeze: + # squeezed = input.view(b_size, n_channel, height // 2, 2, width // 2, 2) + # squeezed = squeezed.permute(0, 1, 3, 5, 2, 4) + # out = squeezed.contiguous().view(b_size, n_channel * 4, height // 2, width // 2) + out = self.squeezer(input) + else: out = input + + logdet = 0 + + for flow in self.flows: + out, det = flow(out) + logdet = logdet + det + + if self.extract_z: + zero = torch.zeros_like(out) + mean, log_sd = self.prior(zero).chunk(2, 1) + log_p = gaussian_log_p(out, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + z_new = out + + return out, logdet, log_p, z_new + + return out, logdet, 0, None + + def reverse(self, output, eps=None, reconstruct=False, jac=False): + input = output + b_size = input.shape[0] + + if reconstruct: + input = eps + if jac: + mean = torch.zeros_like(eps) + log_sd = torch.zeros_like(eps) + log_p = gaussian_log_p(eps, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + + else: + if self.extract_z: + zero = torch.zeros_like(input) + # zero = F.pad(zero, [1, 1, 1, 1], value=1) + mean, log_sd = self.prior(zero).chunk(2, 1) + z = gaussian_sample(eps, mean, log_sd) + input = z + if jac: + log_p = gaussian_log_p(z, mean, log_sd) + log_p = -log_p.view(b_size, -1).sum(1) + else: + input = output + log_p = 0 + + logdet = 0 + for flow in self.flows[::-1]: + if jac: + input, det = flow.reverse(input, jac=True) + logdet += det + else: + input = flow.reverse(input) + + _, n_channel, height, width = input.shape + if self.squeeze: + # unsqueezed = input.view(b_size, n_channel // 4, 2, 2, height, width) + # unsqueezed = unsqueezed.permute(0, 1, 4, 2, 5, 3) + # out = unsqueezed.contiguous().view( + # b_size, n_channel // 4, height * 2, width * 2 + # ) + out = self.squeezer.reverse(input) + else: out = input + + if jac: + return out, logdet, log_p + return out + + +class InvCondBlock(nn.Module): + def __init__(self, in_channel, n_flow, split=True, affine=True, filter_size=512, num_cond_layers=32, conv_lu=True, extract_z=True, squeeze=True, squeezer=Squeeze): + super().__init__() + + squeeze_dim = in_channel * 4 if squeeze else in_channel + self.num_cond_layers = num_cond_layers * 4 if squeeze else num_cond_layers + self.extract_z = extract_z + self.squeeze = squeeze + if self.squeeze: + self.squeezer = squeezer(in_channel=in_channel) + self.cond_squeezer = squeezer(in_channel=num_cond_layers) + + self.flows = nn.ModuleList() + for i in range(n_flow): + self.flows.append(CondFlow(squeeze_dim, affine=affine, conv_lu=conv_lu, filter_size=filter_size, num_cond_layers=self.num_cond_layers)) + + self.split = split + + if split: + self.prior = ZeroConv2d(squeeze_dim // 2, squeeze_dim) + + elif (not split) and extract_z: + self.prior = ZeroConv2d(squeeze_dim, squeeze_dim * 2) + + def forward(self, input, cond): + b_size, n_channel, height, width = input.shape + if self.squeeze: + # squeezed = input.view(b_size, n_channel, height // 2, 2, width // 2, 2) + # squeezed = squeezed.permute(0, 1, 3, 5, 2, 4) + # out = squeezed.contiguous().view(b_size, n_channel * 4, height // 2, width // 2) + out = self.squeezer(input) + cond = self.cond_squeezer(cond) + else: out = input + + logdet = 0 + + for flow in self.flows: + out, det = flow(out, cond) + logdet = logdet + det + + if self.extract_z: + if self.split: + out, z_new = out.chunk(2, 1) + mean, log_sd = self.prior(out).chunk(2, 1) + log_p = gaussian_log_p(z_new, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + + else: + zero = torch.zeros_like(out) + mean, log_sd = self.prior(zero).chunk(2, 1) + log_p = gaussian_log_p(out, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + z_new = out + + return out, logdet, log_p, z_new + + return out, logdet, 0, None + + def reverse(self, output, cond, eps=None, reconstruct=False, jac=False): + input = output + b_size = input.shape[0] + if self.squeeze: + cond = self.cond_squeezer(cond) + + if reconstruct: + if self.split: + input = torch.cat([output, eps], 1) + else: + input = eps + + if jac: + mean = torch.zeros_like(eps) + log_sd = torch.zeros_like(eps) + log_p = gaussian_log_p(eps, mean, log_sd) + log_p = log_p.view(b_size, -1).sum(1) + + else: + if self.extract_z: + if self.split: + mean, log_sd = self.prior(input).chunk(2, 1) + z = gaussian_sample(eps, mean, log_sd) + input = torch.cat([output, z], 1) + + else: + zero = torch.zeros_like(input) + # zero = F.pad(zero, [1, 1, 1, 1], value=1) + mean, log_sd = self.prior(zero).chunk(2, 1) + z = gaussian_sample(eps, mean, log_sd) + input = z + if jac: + log_p = gaussian_log_p(z, mean, log_sd) + log_p = -log_p.view(b_size, -1).sum(1) + else: + input = output + log_p = 0 + + logdet = 0 + for flow in self.flows[::-1]: + if jac: + input, det = flow.reverse(input, cond, jac=True) + logdet += det + else: + input = flow.reverse(input, cond) + + _, n_channel, height, width = input.shape + if self.squeeze: + # unsqueezed = input.view(b_size, n_channel // 4, 2, 2, height, width) + # unsqueezed = unsqueezed.permute(0, 1, 4, 2, 5, 3) + # out = unsqueezed.contiguous().view( + # b_size, n_channel // 4, height * 2, width * 2 + # ) + out = self.squeezer.reverse(input) + else: out = input + + if jac: + return out, logdet, log_p + return out + +class ConvBlock(nn.Module): + """ + A Convolutional Block that consists of two convolution layers each followed by + instance normalization, LeakyReLU activation and dropout. + """ + + def __init__(self, in_chans: int, out_chans: int, drop_prob: float): + """ + Args: + in_chans: Number of channels in the input. + out_chans: Number of channels in the output. + drop_prob: Dropout probability. + """ + super().__init__() + + self.in_chans = in_chans + self.out_chans = out_chans + self.drop_prob = drop_prob + + self.layers = nn.Sequential( + nn.Conv2d(in_chans, out_chans, kernel_size=3, padding=1, bias=False), + nn.InstanceNorm2d(out_chans), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Dropout2d(drop_prob), + nn.Conv2d(out_chans, out_chans, kernel_size=3, padding=1, bias=False), + nn.InstanceNorm2d(out_chans), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Dropout2d(drop_prob), + ) + + def forward(self, image: torch.Tensor) -> torch.Tensor: + """ + Args: + image: Input 4D tensor of shape `(N, in_chans, H, W)`. + + Returns: + Output tensor of shape `(N, out_chans, H, W)`. + """ + return self.layers(image) + + +class TransposeConvBlock(nn.Module): + """ + A Transpose Convolutional Block that consists of one convolution transpose + layers followed by instance normalization and LeakyReLU activation. + """ + + def __init__(self, in_chans: int, out_chans: int): + """ + Args: + in_chans: Number of channels in the input. + out_chans: Number of channels in the output. + """ + super().__init__() + + self.in_chans = in_chans + self.out_chans = out_chans + + self.layers = nn.Sequential( + nn.ConvTranspose2d( + in_chans, out_chans, kernel_size=2, stride=2, bias=False + ), + nn.InstanceNorm2d(out_chans), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + ) + + def forward(self, image: torch.Tensor) -> torch.Tensor: + """ + Args: + image: Input 4D tensor of shape `(N, in_chans, H, W)`. + + Returns: + Output tensor of shape `(N, out_chans, H*2, W*2)`. + """ + return self.layers(image) + diff --git a/src/models.py b/src/models.py new file mode 100644 index 0000000..9e3f153 --- /dev/null +++ b/src/models.py @@ -0,0 +1,746 @@ +""" +Copyright (c) authors. +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +Parts copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from time import time + +import torch +import torch.nn as nn +import torch.optim +import numpy as np +import utils +from model_tools import * +import torch.distributions as dists +# from tqdm import tqdm + +import FrEIA.framework as Ff +import FrEIA.modules as Fm + +torch.manual_seed(1234) +np.random.seed(42) + +# from FrEIA.framework import InputNode, OutputNode, Node, ReversibleGraphNet +# from FrEIA.modules import GLOWCouplingBlock, PermuteRandom + +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' + +def subnet_fc(c_in, c_out): + return nn.Sequential(nn.Linear(c_in, 512), nn.ReLU(), + nn.Linear(512, c_out)) + +def subnet_conv(c_in, c_out): + return nn.Sequential(nn.Conv2d(c_in, 256, 3, padding=1), nn.ReLU(), + nn.Conv2d(256, c_out, 3, padding=1)) + +def subnet_conv_1x1(c_in, c_out): + return nn.Sequential(nn.Conv2d(c_in, 256, 1), nn.ReLU(), + nn.Conv2d(256, c_out, 1)) + +class ConvINN(object): + + def __init__(self, input_shape=[3,28,28], num_conv_layers=[4,12], num_fc_layers=[12], device=None): + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + # print(input_shape, num_conv_layers, num_fc_layers) + nodes = [Ff.InputNode(*input_shape, name='input')] + ndim_x = np.prod(input_shape) + + # Higher resolution convolutional part + for k in range(num_conv_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_conv, 'clamp':1.2}, + name=F'conv_high_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_high_res_{k}')) + + nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + + # Lower resolution convolutional part + for k in range(num_conv_layers[1]): + if k%2 == 0: + subnet = subnet_conv_1x1 + else: + subnet = subnet_conv + + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.2}, + name=F'conv_low_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_low_res_{k}')) + + # Make the outputs into a vector, then split off 1/4 of the outputs for the + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + split_node = Ff.Node(nodes[-1], + Fm.Split, + {'section_sizes':(ndim_x // 4, 3 * ndim_x // 4), 'dim':0}, + name='split') + nodes.append(split_node) + + # Fully connected part + for k in range(num_fc_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_fc, 'clamp':2.0}, + name=F'fully_connected_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_{k}')) + + # Concatenate the fully connected part and the skip connection to get a single output + nodes.append(Ff.Node([nodes[-1].out0, split_node.out1], + Fm.Concat1d, {'dim':0}, name='concat')) + nodes.append(Ff.OutputNode(nodes[-1], name='output')) + + conv_inn = Ff.GraphINN(nodes) + + self.model = conv_inn.to(self.device) + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + self.identifier = "ConvINN-" + '-'.join(['{}']*(len(num_fc_layers) + len(num_conv_layers))).format(*num_conv_layers, *num_fc_layers) + + def sample(self, num_samples, temp=1.): + z = np.random.randn(num_samples, np.prod(self.input_shape)) * temp + x,_ = self.model(torch.Tensor(z).to(self.device), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch): + + zbatch, jac = self.model([data_batch.to(self.device)], jac=True) + return - torch.mean(utils.log_normal(zbatch) + jac) + + def save(self, *args, **kwargs): + torch.save(self.model.state_dict(), *args, **kwargs) + + +class CondNet(nn.Module): + '''conditioning network''' + def __init__(self, cond_shape): + super().__init__() + + class Flatten(nn.Module): + def __init__(self, *args): + super().__init__() + def forward(self, x): + return x.view(x.shape[0], -1) + + self.resolution_levels = nn.ModuleList([ + nn.Sequential(nn.Conv2d(3, 16, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(16, 16, 3, padding=1)), + + nn.Sequential(nn.LeakyReLU(), + nn.Conv2d(16, 32, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(32, 32, 3, padding=1, stride=2)), + + nn.Sequential(nn.LeakyReLU(), + nn.AvgPool2d(2), + Flatten(), + nn.Linear(32 * cond_shape[1]//4 * cond_shape[2]//4, 256)) + ]) + + def forward(self, c): + outputs = [c] + for m in self.resolution_levels: + outputs.append(m(outputs[-1])) + return outputs[1:] + +class CondConvINN(nn.Module): + + def __init__(self, input_shape=[3,28,28], cond_shape=[3,28,28], num_conv_layers=[4,12], num_fc_layers=[12], device=None): + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + # print(input_shape, num_conv_layers, num_fc_layers) + nodes = [Ff.InputNode(*input_shape, name='cond_input')] + ndim_x = np.prod(input_shape) + + # Define the conditions + # num_conds = int(np.ceil(np.log2(input_shape[-1]))) - 3 + # cond_channels = [np.min( base_cond_channels * 2**i , 256 ) for i in range(num_conds)] + # conditions = [ + # Ff.ConditionNode( np.min( base_cond_channels * 2**i, 256), + # input_shape[1] // 2**i, input_shape[2] // 2**i) for i in range(num_conds) + # ] + + conditions = [Ff.ConditionNode(16, input_shape[1], input_shape[2]), + Ff.ConditionNode(32, input_shape[1]//2, input_shape[2]//2), + Ff.ConditionNode(256) + ] + + # Higher resolution convolutional part + for k in range(num_conv_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_conv, 'clamp':1.2}, + conditions = conditions[0], + name = F'cond_conv_high_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'cond_permute_high_res_{k}')) + + nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + + # Lower resolution convolutional part + for k in range(num_conv_layers[1]): + if k%2 == 0: + subnet = subnet_conv_1x1 + else: + subnet = subnet_conv + + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.2}, + conditions = conditions[1], + name = F'cond_conv_low_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'cond_permute_low_res_{k}')) + + # Make the outputs into a vector, then split off 1/4 of the outputs for the + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + split_node = Ff.Node(nodes[-1], + Fm.Split, + {'section_sizes':(ndim_x // 4, 3 * ndim_x // 4), 'dim':0}, + name='split') + nodes.append(split_node) + + # Fully connected part + for k in range(num_fc_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_fc, 'clamp':2.0}, + conditions = conditions[2], + name = F'cond_fully_connected_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'cond_permute_{k}')) + + # Concatenate the fully connected part and the skip connection to get a single output + nodes.append(Ff.Node([nodes[-1].out0, split_node.out1], + Fm.Concat1d, {'dim':0}, name='cond_concat')) + nodes.append(Ff.OutputNode(nodes[-1], name='cond_output')) + + conv_inn = Ff.GraphINN(nodes + conditions) + + self.model = conv_inn.to(self.device) + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet(cond_shape=self.cond_shape).to(self.device) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = "CondConvINN-" + '-'.join(['{}']*(len(num_fc_layers) + len(num_conv_layers))).format(*num_conv_layers, *num_fc_layers) + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def forward(self, input, cond): + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + def sample(self, num_samples, cond, temp=1.): + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + cond = torch.cat([cond]*num_samples) + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False): + + batch_size = len(data_batch) + n_pixel = np.prod(self.input_shape) + num_bins = 2 ** num_bits + + if not tiled: + x_posts = [] + jac_rev = torch.zeros(size=(batch_size,)).to(self.device) + data_fidelity_prob = torch.zeros(size=(batch_size,)).to(self.device) + for i in range(num_z): + # z = torch.randn(batch_size, np.prod(self.input_shape)).to(self.device) + z = self.latent_dist.sample([batch_size, np.prod(self.input_shape)]) + x_post,jac_r = self.model(z, self.cond_net(data_batch), rev=True) + x_posts.append(x_post) + jac_rev += jac_r/ num_z + data_fidelity_prob += degradation_op.log_prob(data_batch, x_post) / num_z + x_posts = torch.stack(x_posts, dim=0) + else: + # z = torch.randn(batch_size*num_z, np.prod(self.input_shape)).to(self.device) + z = self.latent_dist.sample([batch_size*num_z, np.prod(self.input_shape)]) + data_batch_tiled = torch.cat([data_batch]*num_z, 0) + x_posts, jac_rev = self.model(z, self.cond_net(data_batch_tiled), rev=True) + data_fidelity_prob = degradation_op.log_prob(data_batch, x_posts) + x_posts = torch.reshape(x_posts, [num_z, batch_size, *self.input_shape]) + + loss = reg_parameter*data_fidelity_prob + jac_rev - np.log(num_bins) * n_pixel + loss = - torch.mean(loss / np.log(2) / n_pixel ) + return loss, x_posts + + def save(self, *args, **kwargs): + torch.save(self.model.state_dict(), *args, **kwargs) + + +class Glow(nn.Module): + def __init__( + self, input_shape, num_flow, num_block, + filter_size = 512, + affine = True, + conv_lu = True, + device = None, + ): + super().__init__() + + self.device = device if device != None else DEVICE + self.blocks = nn.ModuleList() + self.input_shape = input_shape + self.num_flow = num_flow + self.num_block = num_block + n_channel = input_shape[0] + for i in range(num_block - 1): + self.blocks.append(InvBlock(n_channel, num_flow, affine=affine, filter_size=filter_size, conv_lu=conv_lu)) + n_channel *= 2 + self.blocks.append(InvBlock(n_channel, num_flow, split=False, filter_size=filter_size, affine=affine)) + + self.z_shapes, self.cum_idxs = self.calc_z_shapes() + + self.identifier = f"Glow-flow{num_flow}-block{num_block}" + self.trainable_parameters = self.parameters() + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def calc_z_shapes(self): + z_shapes = [] + input_size = self.input_shape[1] + n_channel = self.input_shape[0] + + for i in range(self.num_block - 1): + input_size //= 2 + n_channel *= 2 + + z_shapes.append((n_channel, input_size, input_size)) + + input_size //= 2 + z_shapes.append((n_channel * 4, input_size, input_size)) + + z_inds = [np.prod(zz) for zz in z_shapes] + z_inds_c = [np.sum(z_inds[:i+1]) for i in range(len(z_shapes))] + return z_shapes, z_inds_c + + def initialize_actnorm(self, data_batch): + with torch.no_grad(): + _,_,_ = self(data_batch.to(self.device)) + + def list2array_z(self, z): + bs = z[0].shape[0] + assert np.prod( [ zz.shape[0] == bs for zz in z ] ), "Wrong shapes in z" + + if isinstance(z[0], np.ndarray): + return np.concatenate([zz.reshape(bs, -1) for zz in z], axis=1) + elif isinstance(z[0], torch.Tensor): + return torch.cat( [torch.reshape(zz, [bs, -1]) for zz in z], dim=1) + + def array2list_z(self, z): + bs = z.shape[0] + z_shapes = [(0,)] + self.z_shapes + z_idxs = [0] + self.cum_idxs + z_list = [] + for i in range(1,len(z_shapes)): + z_list.append( z[:,z_idxs[i-1]:z_idxs[i]].reshape(bs, *z_shapes[i]) ) + return z_list + + def forward(self, input): + log_p_sum = 0 + logdet = 0 + out = input + z_outs = [] + + for block in self.blocks: + out, det, log_p, z_new = block(out) + z_outs.append(z_new) + logdet = logdet + det + + if log_p is not None: + log_p_sum = log_p_sum + log_p + + z_outs = self.list2array_z(z_outs) + return z_outs, log_p_sum, logdet + + def reverse(self, z, reconstruct=False): + z_list = self.array2list_z(z) + for i, block in enumerate(self.blocks[::-1]): + if i == 0: + input = block.reverse(z_list[-1], z_list[-1], reconstruct=reconstruct) + + else: + input = block.reverse(input, z_list[-(i + 1)], reconstruct=reconstruct) + + return input, None + + def sample(self, num_samples, temp=1.): + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + x,_ = self.reverse(z) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, num_bits=0): + + npixel = np.prod(self.input_shape) + if num_bits == 0: + _,log_p,logdet = self(data_batch.to(self.device)) + loss = log_p + logdet + else: + num_bins = 2 ** num_bits + dbatch = data_batch + torch.rand_like(data_batch) / num_bins + _,log_p,logdet = self(dbatch.to(self.device)) + loss = log_p + logdet - np.log(num_bins) * npixel + return - torch.mean( loss / np.log(2) / npixel ) + + def save(self, *args, **kwargs): + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict) + + +class CondNet2(nn.Module): + '''conditioning network''' + def __init__(self, cond_shape, cond_layer_thicknesses=[64, 128, 128, 512]): + super().__init__() + + ct = cond_layer_thicknesses + + class Flatten(nn.Module): + def __init__(self, *args): + super().__init__() + def forward(self, x): + return x.view(x.shape[0], -1) + + self.resolution_levels = nn.ModuleList([ + nn.Sequential(nn.Conv2d(3, ct[0], 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ct[0], ct[0], 3, padding=1)), + + nn.Sequential(nn.LeakyReLU(), + nn.Conv2d(ct[0], ct[1], 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ct[1], ct[1], 3, padding=1, stride=2)), + + nn.Sequential(nn.LeakyReLU(), + nn.Conv2d(ct[1], ct[2], 3, padding=1, stride=2)), + + nn.Sequential(nn.LeakyReLU(), + nn.AvgPool2d(4), + Flatten(), + nn.Linear(ct[2] * cond_shape[1] // 16 * cond_shape[2] // 16, ct[3]))]) + + def forward(self, c): + outputs = [c] + for m in self.resolution_levels: + outputs.append(m(outputs[-1])) + return outputs[1:] + + +class CondConvINN2(nn.Module): + '''cINN, including the ocnditioning network''' + def __init__(self, + input_shape = [3,64,64], + cond_shape = [3,64,64], + num_conv_layers = [2,4,4], + num_fc_layers = [4], + cond_layer_thicknesses = [64, 128, 128, 512], + device = None): + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + self.num_conv_layers = num_conv_layers + self.num_fc_layers = num_fc_layers + self.cond_layer_thicknesses = cond_layer_thicknesses + # nodes = [Ff.InputNode(*input_shape, name='cond_input')] + # ndim_x = np.prod(input_shape) + + self.model = self.build_inn().to(device) + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet2(cond_shape=self.cond_shape, cond_layer_thicknesses=cond_layer_thicknesses).to(self.device) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = "CondConvINN2-" + '-'.join(['{}']*(len(num_fc_layers) + len(num_conv_layers))).format(*num_conv_layers, *num_fc_layers) + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def build_inn(self): + + input_shape = self.input_shape + ct = self.cond_layer_thicknesses + + def sub_conv(ch_hidden, kernel): + pad = kernel // 2 + return lambda ch_in, ch_out: nn.Sequential( + nn.Conv2d(ch_in, ch_hidden, kernel, padding=pad), + nn.ReLU(), + nn.Conv2d(ch_hidden, ch_out, kernel, padding=pad)) + + def sub_fc(ch_hidden): + return lambda ch_in, ch_out: nn.Sequential( + nn.Linear(ch_in, ch_hidden), + nn.ReLU(), + nn.Linear(ch_hidden, ch_out)) + + nodes = [Ff.InputNode(*input_shape)] + # outputs of the cond. net at different resolution levels + conditions = [Ff.ConditionNode(ct[0], input_shape[1], input_shape[2]), + Ff.ConditionNode(ct[1], input_shape[1] // 2, input_shape[2] // 2), + Ff.ConditionNode(ct[2], input_shape[1] // 4, input_shape[1] // 4), + Ff.ConditionNode(ct[3])] + + split_nodes = [] + + subnet = sub_conv(32, 3) + for k in range(self.num_conv_layers[0]): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.0}, + conditions=conditions[0])) + + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + + for k in range(self.num_conv_layers[1]): + subnet = sub_conv(64, 3 if k%2 else 1) + + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.0}, + conditions=conditions[1])) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + + #split off 75% of the channels + section_sizes = [ input_shape[0], 3*input_shape[0] ] + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + + for k in range(self.num_conv_layers[2]): + subnet = sub_conv(128, 3 if k%2 else 1) + + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':0.6}, + conditions=conditions[2])) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + + #split off 50% ch + section_sizes = [2*input_shape[0], 2*input_shape[0]] + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + + # fully_connected part + subnet = sub_fc(512) + for k in range(self.num_fc_layers[0]): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':0.6}, + conditions=conditions[3])) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + + # concat everything + nodes.append(Ff.Node([s.out0 for s in split_nodes] + [nodes[-1].out0], + Fm.Concat1d, {'dim':0})) + nodes.append(Ff.OutputNode(nodes[-1])) + + return Ff.ReversibleGraphNet(nodes + split_nodes + conditions, verbose=False) + + def forward(self, input, cond): + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + def sample(self, num_samples, cond, temp=1.): + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + cond = torch.cat([cond]*num_samples) + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False): + + batch_size = len(data_batch) + n_pixel = np.prod(self.input_shape) + num_bins = 2 ** num_bits + + if not tiled: + x_posts = [] + jac_rev = torch.zeros(size=(batch_size,)).to(self.device) + data_fidelity_prob = torch.zeros(size=(batch_size,)).to(self.device) + zs = self.latent_dist.sample([batch_size*num_z, np.prod(self.input_shape)]) + for i in range(num_z): + # z = torch.randn(batch_size, np.prod(self.input_shape)).to(self.device) + z = zs[i*batch_size:(i+1)*batch_size] + x_post,jac_r = self.model(z, self.cond_net(degradation_op.rev(data_batch)), rev=True) + x_posts.append(x_post) + jac_rev += jac_r/ num_z + data_fidelity_prob_single = degradation_op.log_prob(data_batch, x_post) + data_fidelity_prob += data_fidelity_prob_single / num_z + x_posts = torch.stack(x_posts, dim=0) + else: + # z = torch.randn(batch_size*num_z, np.prod(self.input_shape)).to(self.device) + z = self.latent_dist.sample([batch_size*num_z, np.prod(self.input_shape)]) + data_batch_tiled = torch.cat([data_batch]*num_z, 0) + x_posts, jac_rev = self.model(z, self.cond_net(degradation_op.rev(data_batch_tiled)), rev=True) + data_fidelity_prob = degradation_op.log_prob(data_batch_tiled, x_posts) + x_posts = torch.reshape(x_posts, [num_z, batch_size, *self.input_shape]) + jac_rev = jac_rev.reshape(num_z, batch_size) + jac_rev = torch.mean(jac_rev, axis=0) + data_fidelity_prob = data_fidelity_prob.reshape(num_z, batch_size) + data_fidelity_prob = torch.mean(data_fidelity_prob, axis=0) + + loss = reg_parameter*data_fidelity_prob + jac_rev - np.log(num_bins) * n_pixel + loss = - torch.mean(loss / np.log(2) / n_pixel ) + return loss, x_posts + + def save(self, *args, **kwargs): + torch.save(self.model.state_dict(), *args, **kwargs) + + +class Unet(nn.Module): + """ + PyTorch implementation of a U-Net model. + + O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks + for biomedical image segmentation. In International Conference on Medical + image computing and computer-assisted intervention, pages 234-241. + Springer, 2015. + """ + + def __init__( + self, + input_shape: list, + in_chans: int, + out_chans: int, + chans: int = 32, + num_pool_layers: int = 4, + drop_prob: float = 0.0, + *args, + **kwargs, + ): + """ + Args: + in_chans: Number of channels in the input to the U-Net model. + out_chans: Number of channels in the output to the U-Net model. + chans: Number of output channels of the first convolution layer. + num_pool_layers: Number of down-sampling and up-sampling layers. + drop_prob: Dropout probability. + """ + super().__init__() + + self.in_chans = in_chans + self.out_chans = out_chans + self.chans = chans + self.num_pool_layers = num_pool_layers + self.drop_prob = drop_prob + + self.down_sample_layers = nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)]) + ch = chans + for _ in range(num_pool_layers - 1): + self.down_sample_layers.append(ConvBlock(ch, ch * 2, drop_prob)) + ch *= 2 + self.conv = ConvBlock(ch, ch * 2, drop_prob) + + self.up_conv = nn.ModuleList() + self.up_transpose_conv = nn.ModuleList() + for _ in range(num_pool_layers - 1): + self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch)) + self.up_conv.append(ConvBlock(ch * 2, ch, drop_prob)) + ch //= 2 + + self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch)) + self.up_conv.append( + nn.Sequential( + ConvBlock(ch * 2, ch, drop_prob), + nn.Conv2d(ch, self.out_chans, kernel_size=1, stride=1), + ) + ) + + self.trainable_parameters = self.parameters() + + def forward(self, image: torch.Tensor) -> torch.Tensor: + """ + Args: + image: Input 4D tensor of shape `(N, in_chans, H, W)`. + + Returns: + Output tensor of shape `(N, out_chans, H, W)`. + """ + stack = [] + output = image + + # apply down-sampling layers + for layer in self.down_sample_layers: + output = layer(output) + stack.append(output) + output = F.avg_pool2d(output, kernel_size=2, stride=2, padding=0) + + output = self.conv(output) + + # apply up-sampling layers + for transpose_conv, conv in zip(self.up_transpose_conv, self.up_conv): + downsample_layer = stack.pop() + output = transpose_conv(output) + + # reflect pad on the right/botton if needed to handle odd input dimensions + padding = [0, 0, 0, 0] + if output.shape[-1] != downsample_layer.shape[-1]: + padding[1] = 1 # padding right + if output.shape[-2] != downsample_layer.shape[-2]: + padding[3] = 1 # padding bottom + if torch.sum(torch.tensor(padding)) != 0: + output = F.pad(output, padding, "reflect") + + output = torch.cat([output, downsample_layer], dim=1) + output = conv(output) + + return output + + +if __name__ == '__main__': + + import degradations + import scipy.misc as misc + + degradation = degradations.GaussianBlurNoise(input_shape=[3,64,64]) + cond_net = CondConvINN2() + test_image = misc.face() + test_image = misc.imresize(test_image, [64,64,3]) + test_image = np.swapaxes(test_image.T, 1,2) + test_image = np.stack([test_image, test_image[::-1], test_image[:,::-1]]) + loss = cond_net.get_loss(test_image, degradation, num_z=4, reg_parameter=1) + loss_tiled = cond_net.get_loss(test_image, degradation, num_z=4, reg_parameter=1, tiled=True) + print(loss, loss_tiled) + diff --git a/src/models2.py b/src/models2.py new file mode 100644 index 0000000..8fb08a3 --- /dev/null +++ b/src/models2.py @@ -0,0 +1,1776 @@ +""" +Copyright (c) authors. +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +Parts copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from time import time + +import torch +import torch.nn as nn +import torch.optim +import numpy as np +import utils +import model_tools +from model_tools import * +import torch.distributions as dists +import ast +import os +# from tqdm import tqdm + +import FrEIA.framework as Ff +import FrEIA.modules as Fm + +torch.manual_seed(1234) +np.random.seed(42) + +# from FrEIA.framework import InputNode, OutputNode, Node, ReversibleGraphNet +# from FrEIA.modules import GLOWCouplingBlock, PermuteRandom + +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' + +def load_model(path_to_pkl, ambient=False, load_post_model=False): + if ambient: + main = 'main_' + else: main = '' + folder_path = os.path.split(path_to_pkl)[0] + configpath = os.path.join(folder_path, 'config.txt') + with open(configpath, 'r') as fid: + configs = fid.read() + configs = ast.literal_eval(configs) + main_model = globals()[configs[main+'model_type']]( + configs['input_shape'], + **configs[main+'model_args'] + ) + main_model.load(path_to_pkl) + if load_post_model: + # raise NotImplementedError() + post_model = globals()[configs['post_model_type']]( + configs['input_shape'], + configs['cond_shape'], + **configs['post_model_args'] + ) + suffix = path_to_pkl.split('_')[-1] + path_to_post = os.path.join(folder_path, f'post-network_{suffix}') + post_model.load(path_to_post) + return main_model, post_model + return main_model + +def load_post_model(path_to_pkl): + folder_path = os.path.split(path_to_pkl)[0] + configpath = os.path.join(folder_path, 'config.txt') + with open(configpath, 'r') as fid: + configs = fid.read() + configs = ast.literal_eval(configs) + # raise NotImplementedError() + post_model = globals()[configs['post_model_type']]( + configs['input_shape'], + configs['cond_shape'], + **configs['post_model_args'] + ) + post_model.load(path_to_pkl) + return post_model + + +def subnet_fc(c_in, c_out): + return nn.Sequential(nn.Linear(c_in, 512), nn.ReLU(), + nn.Linear(512, c_out)) + +def subnet_conv(c_in, c_out, interm=256): + return nn.Sequential(nn.Conv2d(c_in, interm, 3, padding=1), nn.ReLU(), + nn.Conv2d(interm, c_out, 3, padding=1)) + +def subnet_conv_1x1(c_in, c_out): + return nn.Sequential(nn.Conv2d(c_in, 256, 1), nn.ReLU(), + nn.Conv2d(256, c_out, 1)) + + +# Loss function for the conditional INNs +def get_conditional_loss_freia(cond_model, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False, cutoff_dim=0, cutoff_mul=1): + + batch_size = len(data_batch) + n_pixel = np.prod(cond_model.input_shape) + num_bins = 2 ** num_bits + + with torch.no_grad(): + zs = cond_model.latent_dist.sample([batch_size*num_z, np.prod(cond_model.input_shape)]) + # cutoff_dim = int(np.prod(cond_model.input_shape) * cutoff_dim) + # zs[:,:cutoff_dim] *= cutoff_mul + + if tiled: + data_batch_tiled = torch.cat([data_batch]*num_z, 0) + cond = cond_model.cond_net(degradation_op.rev(data_batch_tiled)) + x_posts, jac_rev = cond_model.model(zs, cond, rev=True) + data_fidelity_prob = degradation_op.log_prob(data_batch_tiled, x_posts) + x_posts = x_posts.view(num_z, batch_size, *cond_model.input_shape) + jac_rev = jac_rev.view(num_z, batch_size) + data_fidelity_prob = data_fidelity_prob.view(num_z, batch_size) + + if importance_weighting: # return an array of losses for diffeernt z and diffeent data samples + losses = reg_parameter*data_fidelity_prob + jac_rev - np.log(num_bins) * n_pixel + losses = - losses / np.log(2) / n_pixel + return losses, x_posts + else: + jac_rev = torch.mean(jac_rev, axis=0) + data_fidelity_prob = torch.mean(data_fidelity_prob, axis=0) + else: + if importance_weighting: raise("Importance sampling is only impemented with tiling for loss") + x_posts = [] + cond = cond_model.cond_net(degradation_op.rev(data_batch)) + jac_rev = torch.zeros(size=(batch_size,)).to(cond_model.device) + data_fidelity_prob = torch.zeros(size=(batch_size,)).to(cond_model.device) + for i in range(num_z): + # z = torch.randn(batch_size, np.prod(cond_model.input_shape)).to(cond_model.device) + z = zs[i*batch_size:(i+1)*batch_size] + x_post,jac_r = cond_model.model(z, cond, rev=True) + x_posts.append(x_post) + jac_rev += jac_r/ num_z + data_fidelity_prob_single = degradation_op.log_prob(data_batch, x_post) + data_fidelity_prob += data_fidelity_prob_single / num_z + x_posts = torch.stack(x_posts, dim=0) + + loss = reg_parameter*data_fidelity_prob + jac_rev - np.log(num_bins) * n_pixel + loss = - torch.mean(loss / np.log(2) / (n_pixel-cutoff_dim) ) + return loss, x_posts + +def get_conditional_loss(cond_model, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False): + + batch_size = len(data_batch) + n_pixel = np.prod(cond_model.input_shape) + num_bins = 2 ** num_bits + + if tiled: + # z = torch.randn(batch_size*num_z, np.prod(cond_model.input_shape)).to(cond_model.device) + z = cond_model.latent_dist.sample([batch_size*num_z, cond_model.latent_shape]) + data_batch_tiled = torch.cat([data_batch]*num_z, 0) + cond = degradation_op.rev(data_batch_tiled) + x_posts, jac_rev, log_p_rev = cond_model.reverse(z, cond) + jac_rev += log_p_rev + data_fidelity_prob = degradation_op.log_prob(data_batch_tiled, x_posts) + x_posts = torch.reshape(x_posts, [num_z, batch_size, *cond_model.input_shape]) + jac_rev = jac_rev.reshape(num_z, batch_size) + data_fidelity_prob = data_fidelity_prob.reshape(num_z, batch_size) + + if importance_weighting: # return an array of losses for diffeernt z and diffeent data samples + losses = reg_parameter*data_fidelity_prob + jac_rev - np.log(num_bins) * n_pixel + losses = - losses / np.log(2) / n_pixel + return losses, x_posts + else: + jac_rev = torch.mean(jac_rev, axis=0) + data_fidelity_prob = torch.mean(data_fidelity_prob, axis=0) + else: + if importance_weighting: raise("Importance sampling requires tiling for loss") + x_posts = [] + jac_rev = torch.zeros(size=(batch_size,)).to(cond_model.device) + data_fidelity_prob = torch.zeros(size=(batch_size,)).to(cond_model.device) + zs = cond_model.latent_dist.sample([batch_size*num_z, cond_model.latent_shape]) + for i in range(num_z): + # z = torch.randn(batch_size, np.prod(cond_model.input_shape)).to(cond_model.device) + z = zs[i*batch_size:(i+1)*batch_size] + x_post, jac_r, log_p_r = cond_model.reverse(z, degradation_op.rev(data_batch)) + jac_r += log_p_r + x_posts.append(x_post) + jac_rev += jac_r/ num_z + data_fidelity_prob_single = degradation_op.log_prob(data_batch, x_post) + data_fidelity_prob += data_fidelity_prob_single / num_z + x_posts = torch.stack(x_posts, dim=0) + + loss = reg_parameter*data_fidelity_prob + jac_rev - np.log(num_bins) * n_pixel + loss = - torch.mean(loss / np.log(2) / n_pixel ) + return loss, x_posts + + +class ConvINN(object): + + def __init__(self, input_shape=[3,28,28], num_conv_layers=[4,12], num_fc_layers=[12], device=None): + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + # print(input_shape, num_conv_layers, num_fc_layers) + nodes = [Ff.InputNode(*input_shape, name='input')] + ndim_x = np.prod(input_shape) + + # Higher resolution convolutional part + for k in range(num_conv_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_conv, 'clamp':1.2}, + name=F'conv_high_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_high_res_{k}')) + + nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + + # Lower resolution convolutional part + for k in range(num_conv_layers[1]): + if k%2 == 0: + subnet = subnet_conv_1x1 + else: + subnet = subnet_conv + + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.2}, + name=F'conv_low_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_low_res_{k}')) + + # Make the outputs into a vector, then split off 1/4 of the outputs for the + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + split_node = Ff.Node(nodes[-1], + Fm.Split, + {'section_sizes':(ndim_x // 4, 3 * ndim_x // 4), 'dim':0}, + name='split') + nodes.append(split_node) + + # Fully connected part + for k in range(num_fc_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_fc, 'clamp':2.0}, + name=F'fully_connected_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_{k}')) + + # Concatenate the fully connected part and the skip connection to get a single output + nodes.append(Ff.Node([nodes[-1].out0, split_node.out1], + Fm.Concat1d, {'dim':0}, name='concat')) + nodes.append(Ff.OutputNode(nodes[-1], name='output')) + + conv_inn = Ff.GraphINN(nodes) + + self.model = conv_inn.to(self.device) + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + self.identifier = "ConvINN-" + '-'.join(['{}']*(len(num_fc_layers) + len(num_conv_layers))).format(*num_conv_layers, *num_fc_layers) + + def sample(self, num_samples, temp=1.): + z = np.random.randn(num_samples, np.prod(self.input_shape)) * temp + x,_ = self.model(torch.Tensor(z).to(self.device), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch): + + zbatch, jac = self.model([data_batch.to(self.device)], jac=True) + return - torch.mean(utils.log_normal(zbatch) + jac) + + def save(self, *args, **kwargs): + torch.save(self.model.state_dict(), *args, **kwargs) + + +class CondNet(nn.Module): + '''conditioning network''' + def __init__(self, cond_shape): + super().__init__() + + class Flatten(nn.Module): + def __init__(self, *args): + super().__init__() + def forward(self, x): + return x.view(x.shape[0], -1) + + self.resolution_levels = nn.ModuleList([ + nn.Sequential(nn.Conv2d(3, 16, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(16, 16, 3, padding=1)), + + nn.Sequential(nn.LeakyReLU(), + nn.Conv2d(16, 32, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(32, 32, 3, padding=1, stride=2)), + + nn.Sequential(nn.LeakyReLU(), + nn.AvgPool2d(2), + Flatten(), + nn.Linear(32 * cond_shape[1]//4 * cond_shape[2]//4, 256)) + ]) + + def forward(self, c): + outputs = [c] + for m in self.resolution_levels: + outputs.append(m(outputs[-1])) + return outputs[1:] + +class CondConvINN(nn.Module): + + def __init__(self, input_shape=[3,28,28], cond_shape=[3,28,28], num_conv_layers=[4,12], num_fc_layers=[12], device=None): + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + # print(input_shape, num_conv_layers, num_fc_layers) + nodes = [Ff.InputNode(*input_shape, name='cond_input')] + ndim_x = np.prod(input_shape) + + # Define the conditions + # num_conds = int(np.ceil(np.log2(input_shape[-1]))) - 3 + # cond_channels = [np.min( base_cond_channels * 2**i , 256 ) for i in range(num_conds)] + # conditions = [ + # Ff.ConditionNode( np.min( base_cond_channels * 2**i, 256), + # input_shape[1] // 2**i, input_shape[2] // 2**i) for i in range(num_conds) + # ] + + conditions = [Ff.ConditionNode(16, input_shape[1], input_shape[2]), + Ff.ConditionNode(32, input_shape[1]//2, input_shape[2]//2), + Ff.ConditionNode(256) + ] + + # Higher resolution convolutional part + for k in range(num_conv_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_conv, 'clamp':1.2}, + conditions = conditions[0], + name = F'cond_conv_high_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'cond_permute_high_res_{k}')) + + nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + + # Lower resolution convolutional part + for k in range(num_conv_layers[1]): + if k%2 == 0: + subnet = subnet_conv_1x1 + else: + subnet = subnet_conv + + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.2}, + conditions = conditions[1], + name = F'cond_conv_low_res_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'cond_permute_low_res_{k}')) + + # Make the outputs into a vector, then split off 1/4 of the outputs for the + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + split_node = Ff.Node(nodes[-1], + Fm.Split, + {'section_sizes':(ndim_x // 4, 3 * ndim_x // 4), 'dim':0}, + name='split') + nodes.append(split_node) + + # Fully connected part + for k in range(num_fc_layers[0]): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_fc, 'clamp':2.0}, + conditions = conditions[2], + name = F'cond_fully_connected_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'cond_permute_{k}')) + + # Concatenate the fully connected part and the skip connection to get a single output + nodes.append(Ff.Node([nodes[-1].out0, split_node.out1], + Fm.Concat1d, {'dim':0}, name='cond_concat')) + nodes.append(Ff.OutputNode(nodes[-1], name='cond_output')) + + conv_inn = Ff.GraphINN(nodes + conditions) + + self.model = conv_inn.to(self.device) + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet(cond_shape=self.cond_shape).to(self.device) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = "CondConvINN-" + '-'.join(['{}']*(len(num_fc_layers) + len(num_conv_layers))).format(*num_conv_layers, *num_fc_layers) + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def forward(self, input, cond): + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + def sample(self, num_samples, cond, temp=1.): + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + cond = torch.cat([cond]*num_samples) + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False): + + return get_conditional_loss_freia(self, data_batch, degradation_op, num_z=num_z, reg_parameter=reg_parameter, num_bits=num_bits, tiled=tiled, importance_weighting=importance_weighting) + + def save(self, *args, **kwargs): + torch.save(self.model.state_dict(), *args, **kwargs) + + +class Glow(nn.Module): + def __init__( + self, input_shape, num_flow, num_block, + filter_size = 512, + affine = True, + conv_lu = True, + device = None, + ): + super().__init__() + + self.device = device if device != None else DEVICE + self.blocks = nn.ModuleList() + self.input_shape = input_shape + self.num_flow = num_flow + self.num_block = num_block + n_channel = input_shape[0] + for i in range(num_block - 1): + self.blocks.append(InvBlock(n_channel, num_flow, affine=affine, filter_size=filter_size, conv_lu=conv_lu)) + n_channel *= 2 + self.blocks.append(InvBlock(n_channel, num_flow, split=False, filter_size=filter_size, affine=affine)) + + self.z_shapes, self.cum_idxs = self.calc_z_shapes() + + self.identifier = f"Glow-flow{num_flow}-block{num_block}" + self.trainable_parameters = self.parameters() + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def calc_z_shapes(self): + z_shapes = [] + input_size = self.input_shape[1] + n_channel = self.input_shape[0] + + for i in range(self.num_block - 1): + input_size //= 2 + n_channel *= 2 + + z_shapes.append((n_channel, input_size, input_size)) + + input_size //= 2 + z_shapes.append((n_channel * 4, input_size, input_size)) + + z_inds = [np.prod(zz) for zz in z_shapes] + z_inds_c = [np.sum(z_inds[:i+1]) for i in range(len(z_shapes))] + return z_shapes, z_inds_c + + def initialize_actnorm(self, data_batch): + with torch.no_grad(): + _,_,_ = self(data_batch.to(self.device)) + + def list2array_z(self, z): + bs = z[0].shape[0] + assert np.prod( [ zz.shape[0] == bs for zz in z ] ), "Wrong shapes in z" + + if isinstance(z[0], np.ndarray): + return np.concatenate([zz.reshape(bs, -1) for zz in z], axis=1) + elif isinstance(z[0], torch.Tensor): + return torch.cat( [torch.reshape(zz, [bs, -1]) for zz in z], dim=1) + + def array2list_z(self, z): + bs = z.shape[0] + z_shapes = [(0,)] + self.z_shapes + z_idxs = [0] + self.cum_idxs + z_list = [] + for i in range(1,len(z_shapes)): + z_list.append( z[:,z_idxs[i-1]:z_idxs[i]].reshape(bs, *z_shapes[i]) ) + return z_list + + def forward(self, input): + log_p_sum = 0 + logdet = 0 + out = input + z_outs = [] + + for block in self.blocks: + out, det, log_p, z_new = block(out) + z_outs.append(z_new) + logdet = logdet + det + + if log_p is not None: + log_p_sum = log_p_sum + log_p + + z_outs = self.list2array_z(z_outs) + return z_outs, log_p_sum, logdet + + def reverse(self, z, reconstruct=False): + z_list = self.array2list_z(z) + for i, block in enumerate(self.blocks[::-1]): + if i == 0: + input = block.reverse(z_list[-1], z_list[-1], reconstruct=reconstruct) + + else: + input = block.reverse(input, z_list[-(i + 1)], reconstruct=reconstruct) + + return input, None + + def sample(self, num_samples, temp=1.): + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + x,_ = self.reverse(z) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, num_bits=0, importance_weighting=False): + + data_shape = list(data_batch.shape) + if len(data_shape) == 5: + data_batch = data_batch.reshape( -1, *data_shape[2:] ) + npixel = np.prod(self.input_shape) + + if num_bits == 0: + _,log_p,logdet = self(data_batch.to(self.device)) + loss = log_p + logdet + else: + num_bins = 2 ** num_bits + dbatch = data_batch + torch.rand_like(data_batch) / num_bins + _,log_p,logdet = self(dbatch.to(self.device)) + loss = log_p + logdet - np.log(num_bins) * npixel + + if not importance_weighting: + return - torch.mean( loss / np.log(2) / npixel ) + else: # return per-data-point array of losses (1D, will need to be reshaped) + return - loss.reshape(data_shape[:-3]) / np.log(2) / npixel + + def save(self, *args, **kwargs): + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict) + + +class CondNet2(nn.Module): + '''conditioning network''' + def __init__(self, cond_shape, cond_layer_thicknesses=[64, 128, 128, 512], avg_pool=4): + super().__init__() + + ct = cond_layer_thicknesses + self.cond_shape = cond_shape + + class Flatten(nn.Module): + def __init__(self, *args): + super().__init__() + def forward(self, x): + return x.view(x.shape[0], -1) + + in_channels = 3 if self.cond_shape[0] == 3 else 4 + self.resolution_levels = nn.ModuleList([ + nn.Sequential(nn.Conv2d(in_channels, ct[0], 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ct[0], ct[0], 3, padding=1)), + + nn.Sequential(nn.LeakyReLU(), + nn.Conv2d(ct[0], ct[1], 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ct[1], ct[1], 3, padding=1, stride=2)), + + nn.Sequential(nn.LeakyReLU(), + nn.Conv2d(ct[1], ct[2], 3, padding=1, stride=2)), + + nn.Sequential(nn.LeakyReLU(), + nn.AvgPool2d(avg_pool), + Flatten(), + nn.Linear(ct[2] * cond_shape[1] // 16 * cond_shape[2] // 16, ct[3]))]) + + def forward(self, c): + if self.cond_shape[0] != 3: + outputs = [forward_squeeze(c)] + else: outputs = [c] + for m in self.resolution_levels: + outputs.append(m(outputs[-1])) + return outputs[1:] + +class CondConvINN2(nn.Module): + '''cINN, including the ocnditioning network''' + def __init__(self, + input_shape = [3,64,64], + cond_shape = [3,64,64], + num_conv_layers = [2,4,4], + num_fc_layers = [4], + cond_layer_thicknesses = [64, 128, 128, 512], + cond_avg_pool = 4, + device = None, + **kwargs): + """ + `input_shape` : Shape of the *output* image of the conditional INN + `cond_shape` : Shape of the conditioning input + `num_conv_layers` : List containing the number of convolutional layers in each block. The len of the list is the number of convolutional blocks. + `num_fc_layers` : List containing the number of fully connected layers in each block. The len of the list is the number of fully connected blocks. + `cond_layer_thicknesses` : Number of channels in conditioning layer input. The conditioning input will be preprocessed to have these many channels. + `cond_avg_pool` : Average pooling ratio to use in the conditioning subnetwork. + `device` : CPU/GPU device to load the model on. Defaults to DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' + """ + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + self.num_conv_layers = num_conv_layers + self.num_fc_layers = num_fc_layers + self.cond_layer_thicknesses = cond_layer_thicknesses + # nodes = [Ff.InputNode(*input_shape, name='cond_input')] + # ndim_x = np.prod(input_shape) + + self.model = self.build_inn() + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet2(cond_shape=self.cond_shape, cond_layer_thicknesses=cond_layer_thicknesses, avg_pool=cond_avg_pool) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = "CondConvINN2-" + '-'.join(['{}']*(len(num_fc_layers) + len(num_conv_layers))).format(*num_conv_layers, *num_fc_layers) + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def build_inn(self): + + input_shape = self.input_shape + cond_shape = self.cond_shape if input_shape[0] >= 2 else [self.cond_shape[0]*4, self.cond_shape[1]//2, self.cond_shape[2]//2] + ct = self.cond_layer_thicknesses + + def sub_conv(ch_hidden, kernel): + pad = kernel // 2 + return lambda ch_in, ch_out: nn.Sequential( + nn.Conv2d(ch_in, ch_hidden, kernel, padding=pad), + nn.ReLU(), + nn.Conv2d(ch_hidden, ch_out, kernel, padding=pad)) + + def sub_fc(ch_hidden): + return lambda ch_in, ch_out: nn.Sequential( + nn.Linear(ch_in, ch_hidden), + nn.ReLU(), + nn.Linear(ch_hidden, ch_out)) + + nodes = [Ff.InputNode(*input_shape)] + # outputs of the cond. net at different resolution levels + conditions = [Ff.ConditionNode(ct[0], cond_shape[1], cond_shape[2]), + Ff.ConditionNode(ct[1], cond_shape[1] // 2, cond_shape[2] // 2), + Ff.ConditionNode(ct[2], cond_shape[1] // 4, cond_shape[1] // 4), + Ff.ConditionNode(ct[3])] + + split_nodes = [] + + nodes.append(Ff.Node(nodes[-1], model_tools.ActNormCoarse, {'inverse': True, 'scaling': 1./6})) + + subnet = sub_conv(32, 3) + if self.input_shape[0] < 2: + nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + + for k in range(self.num_conv_layers[0]): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.0}, + conditions=conditions[0])) + + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + + for k in range(self.num_conv_layers[1]): + subnet = sub_conv(64, 3 if k%2 else 1) + + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':1.0}, + conditions=conditions[1])) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + + #split off 75% of the channels + section_sizes = [ cond_shape[0], 3*cond_shape[0] ] + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + + for k in range(self.num_conv_layers[2]): + subnet = sub_conv(128, 3 if k%2 else 1) + + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':0.6}, + conditions=conditions[2])) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + + #split off 50% ch + section_sizes = [2*cond_shape[0], 2*cond_shape[0]] + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + + # fully_connected part + subnet = sub_fc(512) + for k in range(self.num_fc_layers[0]): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':0.6}, + conditions=conditions[3])) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + + # concat everything + nodes.append(Ff.Node([s.out0 for s in split_nodes] + [nodes[-1].out0], + Fm.Concat1d, {'dim':0})) + nodes.append(Ff.OutputNode(nodes[-1])) + + return Ff.ReversibleGraphNet(nodes + split_nodes + conditions, verbose=False) + + def initialize_actnorm(self, data_batch, degradation_op): + for m in self.model.module_list: + if isinstance(m, model_tools.ActNormCoarse): + m.init_on_next_batch = True + with torch.no_grad(): + data_batch_rev = degradation_op.rev(data_batch) + x = self.sample(data_batch.shape[0], data_batch_rev) + # After 1 pass through self.model, init_on_next_batch on all the actnorms automatically is set to False again. + + def forward(self, input, cond): + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + def sample(self, num_samples, cond, temp=1.): + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + if cond.shape[0] == 1: + cond = torch.cat([cond]*num_samples) + else: assert cond.shape[0] == num_samples, "batch size of condition must be equal to 1 or num_samples" + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False, cutoff_dim=0., cutoff_mul=0.): + + return get_conditional_loss_freia(self, data_batch, degradation_op, num_z=num_z, reg_parameter=reg_parameter, num_bits=num_bits, tiled=tiled, importance_weighting=importance_weighting) + + def save(self, *args, **kwargs): + # torch.save(self.model.state_dict(), *args, **kwargs) + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict, strict=False) + + +class CondNet3(nn.Module): + '''conditioning network''' + def __init__(self, cond_shape, + num_cond_chans = 4, + num_block = 5, + num_noncond_blocks = 0, + ): + super().__init__() + + self.cond_shape = cond_shape + self.num_cond_chans = num_cond_chans + self.num_noncond_blocks = num_noncond_blocks + + class Flatten(nn.Module): + def __init__(self, *args): + super().__init__() + def forward(self, x): + return x.view(x.shape[0], -1) + + # Note that at the beginning the input is Haar Squeezed based on the num_noncond_blocks to match the sizes + self.blocks = nn.ModuleList() + C = self.cond_shape[0] * (4 ** num_noncond_blocks) + ncc = num_cond_chans + self.blocks.append( + nn.Sequential( + nn.Conv2d(C, ncc, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ncc, ncc, 3, padding=1), + ) + ) + for i in range(num_block - num_noncond_blocks - 1): + self.blocks.append( + nn.Sequential( + nn.LeakyReLU(), + nn.Conv2d(ncc, ncc, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ncc, ncc*2, 3, padding=1, stride=2), + ) + ) + ncc *= 2 + + def forward(self, c): + for i in range(self.num_noncond_blocks): + c = Squeeze()(c) + outputs = [c] + for m in self.blocks: + outputs.append(m(outputs[-1])) + return outputs[1:] + + +class CondLDFlow(nn.Module): + "Conditional low dimenional injective flow achitecture" + def __init__(self, + input_shape = [3,64,64], + cond_shape = [3,64,64], + num_flow = 2, + num_block = 4, + num_cond_chans = 16, + compression_ratio = 4, + num_noncond_blocks = 1, + filter_size = 512, + gamma = 1e-08, + squeezer = 'Squeeze', + device = None): + """ + input_shape : shape of image output of the posterior network + cond_shape : shape of the input to the conditioning network + num_flow : number of flows in each invertible block + num_cond_chans : number of channels in the first conditioning layer + compression_ratio : ratio of latent space size and image size + num_noncond_blocks : num of blocks to go without conditioning + filter_size : filter size in affine coupling + tikhonov_gamma : reg parameter for tikhonov inversion + """ + + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + self.num_flow = num_flow + self.num_block = num_block + self.num_cond_chans = num_cond_chans + self.gamma = gamma + self.compression_ratio = compression_ratio # must be a power of two + self.num_inj_flows = num_inj_flows = int(np.log2(compression_ratio)) + self.squeezer = getattr(model_tools, squeezer) + + C = input_shape[0] + self.blocks = nn.ModuleList([]) + nif = 0 + for i in range(num_noncond_blocks): + if nif < num_inj_flows: + self.blocks.append( + InvBlock(C, num_flow, split=False, filter_size=filter_size, extract_z=False, squeeze=True, squeezer=self.squeezer).to(self.device) + ) + self.blocks.append( + InjBlock(4*C, 1, gamma=gamma, filter_size=filter_size, extract_z=False, squeeze=False).to(self.device) + ) + nif += 1 + else: + self.blocks.append( + InvBlock(C, num_flow, split=True, filter_size=filter_size, extract_z=True, squeeze=True, squeezer=self.squeezer).to(self.device) + ) + C *= 2 + + ncc = num_cond_chans + for i in range(num_block - num_noncond_blocks - 1): + if nif < num_inj_flows: + self.blocks.append( + InvCondBlock(C, num_flow, split=False, filter_size=filter_size, num_cond_layers=ncc, extract_z=False, squeeze=True, squeezer=self.squeezer).to(self.device) + ) + self.blocks.append( + InjBlock(4*C, 1, gamma=gamma, filter_size=filter_size, extract_z=False, squeeze=False).to(self.device) + ) + nif += 1 + else: + self.blocks.append( + InvCondBlock(C, num_flow, split=True, filter_size=filter_size, num_cond_layers=ncc, extract_z=True, squeeze=True, squeezer=self.squeezer).to(self.device) + ) + C *= 2 + ncc *= 2 + + self.blocks.append( + InvCondBlock(C, num_flow, split=False, filter_size=filter_size, num_cond_layers=ncc, extract_z=True, squeeze=True, squeezer=self.squeezer).to(self.device) + ) + + self.cond_net = CondNet3( + cond_shape = self.cond_shape, + num_cond_chans = self.num_cond_chans, + num_block = self.num_block, + num_noncond_blocks = num_noncond_blocks, + ).to(self.device) + + self.trainable_parameters = self.parameters() + + self.z_shapes, self.cum_idxs = self.calc_z_shapes() + self.latent_shape = np.sum([np.prod(zsh) for zsh in self.z_shapes]) + self.identifier = f"CondLDFlow-nf{num_flow}-nb{num_block}-nc{num_cond_chans}-cr{compression_ratio}-nn{num_noncond_blocks}" + + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def calc_z_shapes(self): + z_shapes = [] + input_size = self.input_shape[1] + n_channel = self.input_shape[0] + + for i in range(self.num_block - 1): + input_size //= 2 + n_channel *= 2 + + z_shapes.append((n_channel, input_size, input_size)) + + input_size //= 2 + z_shapes.append((n_channel * 4, input_size, input_size)) + z_shapes = z_shapes[self.num_inj_flows:] + + z_inds = [np.prod(zz) for zz in z_shapes] + z_inds_c = [np.sum(z_inds[:i+1]) for i in range(len(z_shapes))] + + return z_shapes, z_inds_c + + def initialize_actnorm(self, data_batch, cond_batch): + with torch.no_grad(): + _,_,_ = self(data_batch.to(self.device), cond_batch.to(self.device)) + + def list2array_z(self, z): + bs = z[0].shape[0] + assert np.prod( [ zz.shape[0] == bs for zz in z ] ), "Wrong shapes in z" + + if isinstance(z[0], np.ndarray): + return np.concatenate([zz.reshape(bs, -1) for zz in z], axis=1) + elif isinstance(z[0], torch.Tensor): + return torch.cat( [torch.reshape(zz, [bs, -1]) for zz in z], dim=1) + + def array2list_z(self, z): + bs = z.shape[0] + z_shapes = [(0,)] + self.z_shapes + z_idxs = [0] + self.cum_idxs + z_list = [] + for i in range(1,len(z_shapes)): + z_list.append( z[:,z_idxs[i-1]:z_idxs[i]].reshape(bs, *z_shapes[i]) ) + return z_list + + def forward(self, input, cond): + + log_p_sum = 0 + logdet = 0 + out = input + z_outs = [] + conditions = self.cond_net(cond) + conds = iter(conditions) + + for i,block in enumerate(self.blocks): + if isinstance(block, InvCondBlock): + cond = next(conds) + out, det, log_p, z_new = block(out, cond) + else: + out, det, log_p, z_new = block(out) + + logdet += det + log_p_sum += log_p + if z_new != None: + z_outs.append(z_new) + + z_outs = self.list2array_z(z_outs) + return z_outs, log_p_sum, logdet + + def reverse(self, z, cond): + + log_p_sum = 0 + logdet = 0 + z_list = self.array2list_z(z) + conditions = self.cond_net(cond) + out = z_list[-1] + conds = iter(conditions[::-1]) + zs = iter(z_list[::-1]) + + for i, block in enumerate(self.blocks[::-1]): + try: z = next(zs) + except StopIteration: z = out + + if isinstance(block, InvCondBlock): + cond = next(conds) + out, det, log_p = block.reverse(out, cond, z, jac=True) + else: + out, det, log_p = block.reverse(out, z, jac=True) + + logdet += det + log_p_sum += log_p + + return out, log_p_sum, logdet + + def sample(self, num_samples, cond, temp=1.): + if len(cond.shape) == 3: cond = cond.unsqueeze(0) + if cond.shape[0] == 1: cond = torch.cat([cond]*num_samples, dim=0) + assert cond.shape[0] == num_samples, "Num. conditionals should be 1 or equal to num_samples" + + z = self.latent_dist.sample([num_samples, self.latent_shape]) * temp + x,_,_ = self.reverse(z, cond) + return x.cpu().detach().numpy() + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False): + + return get_conditional_loss(self, data_batch, degradation_op, num_z=num_z, reg_parameter=reg_parameter, num_bits=num_bits, tiled=tiled, importance_weighting=importance_weighting) + + def save(self, *args, **kwargs): + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict) + +class CondNet4(nn.Module): + '''conditioning network''' + def __init__(self, cond_shape, + num_cond_chans = 4, + num_block = 5, + linear_layer_dim = 512, + squeezer = Squeeze(), + squeeze_first = True, + ): + super().__init__() + + self.cond_shape = cond_shape + self.num_cond_chans = num_cond_chans + self.squeezer = squeezer + self.squeeze_first = squeeze_first + + class Flatten(nn.Module): + def __init__(self, *args): + super().__init__() + def forward(self, x): + return x.view(x.shape[0], -1) + + # Note that at the beginning the input is Squeezed to match the sizes + self.blocks = nn.ModuleList() + if squeeze_first: + C = 4*self.cond_shape[0] + H = self.cond_shape[1] // 2 + else: + C = self.cond_shape[0] + H = self.cond_shape[1] + ncc = num_cond_chans + self.blocks.append( + nn.Sequential( + nn.Conv2d(C, ncc, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ncc, ncc, 3, padding=1), + ) + ) + for i in range(num_block - 2): + self.blocks.append( + nn.Sequential( + nn.LeakyReLU(), + nn.Conv2d(ncc, ncc, 3, padding=1), + nn.LeakyReLU(), + nn.Conv2d(ncc, ncc*2, 3, padding=1, stride=2), + ) + ) + ncc *= 2 + + self.blocks.append( + nn.Sequential( + nn.LeakyReLU(), + Flatten(), + nn.Linear( + ncc * (H // 2**(num_block - 2)) * (H // 2**(num_block - 2)), + linear_layer_dim + ) + ) + ) + + def forward(self, c): + if self.squeeze_first: + c = self.squeezer(c) + outputs = [c] + for m in self.blocks: + outputs.append(m(outputs[-1])) + return outputs[1:] + + +class CondGlow(nn.Module): + "Conditional flow architecture" + def __init__(self, + input_shape = [3,64,64], + cond_shape = [3,64,64], + num_flow = 2, + num_block = 4, + num_cond_chans = 16, + filter_size = 32, + final_cond_dim = 512, + first_split_ratio = 0.25, + squeezer = 'Squeeze', + device = None): + """ + input_shape : shape of image output of the posterior network + cond_shape : shape of the input to the conditioning network + num_flow : number of flows in each invertible block + num_cond_chans : number of channels in the first conditioning layer + compression_ratio : ratio of latent space size and image size + num_noncond_blocks : num of blocks to go without conditioning + filter_size : filter size in affine coupling + """ + + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + self.num_flow = num_flow + self.num_block = num_block + self.num_cond_chans = num_cond_chans + self.filter_size = filter_size + self.final_cond_dim = final_cond_dim + self.first_split_ratio = first_split_ratio + self.squeezer = getattr(model_tools, squeezer)(cond_shape[0]) + + self.model = self.build_inn() + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet4(cond_shape=self.cond_shape, num_cond_chans=self.num_cond_chans, num_block=self.num_block, linear_layer_dim=self.final_cond_dim, squeezer=self.squeezer) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = f"CondGlow-nf{num_flow}-nb{num_block}-nc{num_cond_chans}-fil{filter_size}-spl{first_split_ratio}" + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def build_inn(self): + + C = self.input_shape[0] + filter_size = self.filter_size + + def subnet_conv(ch_hidden, kernel): + pad = kernel // 2 + return lambda ch_in, ch_out: nn.Sequential( + nn.Conv2d(ch_in, ch_hidden, kernel, padding=pad), + nn.ReLU(), + nn.Conv2d(ch_hidden, ch_out, kernel, padding=pad)) + + def subnet_fc(ch_hidden): + return lambda ch_in, ch_out: nn.Sequential( + nn.Linear(ch_in, ch_hidden), + nn.ReLU(), + nn.Linear(ch_hidden, ch_out)) + + def add_block(nodes, num_flow, subnet, condition, clamp=1.0): + for k in range(num_flow): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':clamp}, + conditions=condition)) + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + return nodes + + def split(nodes, split_nodes, section_sizes): + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + return nodes, split_nodes + + nodes = [Ff.InputNode(*self.input_shape)] + conditions = [] + ncc = self.num_cond_chans + chans = C + cs = self.cond_shape[1] // 2 + for i in range(self.num_block - 1): + conditions.append(Ff.ConditionNode(ncc, cs, cs)) + ncc *= 2; cs //= 2 + conditions.append(Ff.ConditionNode(self.final_cond_dim)) + + split_nodes = [] + + # First block with Wavelet Downsampling + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + subnet = subnet_conv(filter_size, 3) + nodes = add_block(nodes, self.num_flow, subnet, conditions[0]) + section_sizes = [ int(4*chans*self.first_split_ratio), int(4*chans*(1-self.first_split_ratio)) ] + print(section_sizes) + nodes, split_nodes = split(nodes, split_nodes, section_sizes=section_sizes) + chans = int(4*chans*self.first_split_ratio) + filter_size *= 2 + + for bl in range(1,self.num_block-1): + # nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + subnet = subnet_conv(filter_size, 3) + nodes = add_block(nodes, self.num_flow, subnet, conditions[bl], clamp=0.6) + section_sizes = [ 2*chans, 2*chans ] + nodes, split_nodes = split(nodes, split_nodes, section_sizes=section_sizes) + chans *= 2 + filter_size *= 2 + + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + subnet = subnet_fc(filter_size) + nodes = add_block(nodes, self.num_flow, subnet, conditions[-1], clamp=0.6) + + # concat the split nodes + nodes.append(Ff.Node([s.out0 for s in split_nodes] + [nodes[-1].out0], + Fm.Concat1d, {'dim':0})) + nodes.append(Ff.OutputNode(nodes[-1])) + + return Ff.ReversibleGraphNet(nodes + split_nodes + conditions, verbose=False) + + def forward(self, input, cond): + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + def sample(self, num_samples, cond, temp=1.): + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + cond = torch.cat([cond]*num_samples) + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False): + + return get_conditional_loss_freia(self, data_batch, degradation_op, num_z=num_z, reg_parameter=reg_parameter, num_bits=num_bits, tiled=tiled, importance_weighting=importance_weighting) + + def save(self, *args, **kwargs): + # torch.save(self.model.state_dict(), *args, **kwargs) + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict, strict=False) + + +class CondGlow2(nn.Module): + "Conditional flow architecture" + def __init__(self, + input_shape = [3,64,64], + cond_shape = [3,64,64], + num_flow = 2, + num_block = 4, + num_cond_chans = 16, + filter_size = 32, + final_cond_dim = 512, + first_split_ratio = 0.25, + squeezer = 'Squeeze', + sparsity_weight = 0.1, + sparsifier = None, + cutoff_dim = 0, + cutoff_mul = 1, + permutation = 'random', + device = None): + """ + input_shape : shape of image output of the posterior network + cond_shape : shape of the input to the conditioning network + num_flow : number of flows in each invertible block + num_cond_chans : number of channels in the first conditioning layer + compression_ratio : ratio of latent space size and image size + num_noncond_blocks : num of blocks to go without conditioning + filter_size : filter size in affine coupling + """ + + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + self.num_flow = num_flow + self.num_block = num_block + self.num_cond_chans = num_cond_chans + self.filter_size = filter_size + self.final_cond_dim = final_cond_dim + self.first_split_ratio = first_split_ratio + self.squeezer = getattr(model_tools, squeezer)(cond_shape[0]) + self.sparsity_weight = sparsity_weight + self.sparsifier = sparsifier + self.cutoff_dim = cutoff_dim + self.cutoff_mul = cutoff_mul + self.permutation = permutation + + self.model = self.build_inn() + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet4(cond_shape=self.cond_shape, num_cond_chans=self.num_cond_chans, num_block=self.num_block, linear_layer_dim=self.final_cond_dim, squeezer=self.squeezer, squeeze_first=False) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = f"CondGlow-nf{num_flow}-nb{num_block}-nc{num_cond_chans}-fil{filter_size}-spl{first_split_ratio}" + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def build_inn(self): + + C = self.input_shape[0] + filter_size = self.filter_size + + def subnet_conv(ch_hidden, kernel): + pad = kernel // 2 + return lambda ch_in, ch_out: nn.Sequential( + nn.Conv2d(ch_in, ch_hidden, kernel, padding=pad), + nn.ReLU(), + nn.Conv2d(ch_hidden, ch_out, kernel, padding=pad)) + + def subnet_fc(ch_hidden): + return lambda ch_in, ch_out: nn.Sequential( + nn.Linear(ch_in, ch_hidden), + nn.ReLU(), + nn.Linear(ch_hidden, ch_out)) + + def add_block(nodes, num_flow, subnet, condition, clamp=1.0, permutation='invconv'): + for k in range(num_flow): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':clamp}, + conditions=condition)) + # nodes.append(Ff.Node(nodes[-1], model_tools.ActNormCoarse, {'inverse': True, 'scaling': 1.})) + # nodes.append(Ff.Node(nodes[-1], model_tools.ActNorm2, {'inverse': True, 'scaling': 1.})) + if permutation=='random': + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + elif permutation=='invconv': + nodes.append(Ff.Node(nodes[-1], model_tools.Inv1x1Conv, {})) + return nodes + + def split(nodes, split_nodes, section_sizes): + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + return nodes, split_nodes + + nodes = [Ff.InputNode(*self.input_shape)] + conditions = [] + ncc = self.num_cond_chans + chans = C + cs = self.cond_shape[1] + for i in range(self.num_block - 1): + conditions.append(Ff.ConditionNode(ncc, cs, cs)) + ncc *= 2; cs //= 2 + conditions.append(Ff.ConditionNode(self.final_cond_dim)) + + split_nodes = [] + + + nodes.append(Ff.Node(nodes[-1], model_tools.ActNormCoarse, {'inverse': True, 'scaling': 1./6})) + + subnet = subnet_conv(filter_size, 3) + nodes = add_block(nodes, self.num_flow, subnet, conditions[0], permutation=self.permutation) + # nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + section_sizes = [ int(4*chans*self.first_split_ratio), int(4*chans*(1-self.first_split_ratio)) ] + nodes, split_nodes = split(nodes, split_nodes, section_sizes=section_sizes) + chans = int(4*chans*self.first_split_ratio) + filter_size *= 2 + + for bl in range(1,self.num_block-1): + subnet = subnet_conv(filter_size, 3) + nodes = add_block(nodes, self.num_flow, subnet, conditions[bl], clamp=0.6, permutation=self.permutation) + # nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + section_sizes = [ 2*chans, 2*chans ] + nodes, split_nodes = split(nodes, split_nodes, section_sizes=section_sizes) + chans *= 2 + filter_size *= 2 + + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + subnet = subnet_fc(filter_size) + nodes = add_block(nodes, self.num_flow, subnet, conditions[-1], clamp=0.6, permutation='random') + + # concat the split nodes + nodes.append(Ff.Node([s.out0 for s in split_nodes] + [nodes[-1].out0], + Fm.Concat1d, {'dim':0})) + nodes.append(Ff.OutputNode(nodes[-1])) + + return Ff.ReversibleGraphNet(nodes + split_nodes + conditions, verbose=False) + + def change_sparsity_weight(self, new_weight): + self.model.module_list[0].threshold = new_weight + + # def initialize_actnorm(self, data_batch, degradation_op): + # with torch.no_grad(): + # data_batch_rev = degradation_op.rev(data_batch) + # x = self.sample(data_batch.shape[0], data_batch_rev) + # std = np.std(x) + # self.model.module_list[0].final_scale *= 1/std/6 + # self.model.module_list[0].final_scale.to(self.device) + + def initialize_actnorm(self, data_batch, degradation_op): + for m in self.model.module_list: + if isinstance(m, model_tools.ActNormCoarse): + m.init_on_next_batch = True + with torch.no_grad(): + data_batch_rev = degradation_op.rev(data_batch) + x = self.sample(data_batch.shape[0], data_batch_rev) + # After 1 pass through self.model, init_on_next_batch on all the actnorms automatically is set to False again. + + def forward(self, input, cond, rev=False): + + if not rev: + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + else: + x_posts, jac_rev = self.model(input, self.cond_net(cond), rev=True) + return x_posts, jac_rev + + def sample(self, num_samples, cond, temp=1., cutoff_dim=None, cutoff_mul=None): + if cutoff_dim == None: cutoff_dim = self.cutoff_dim + if cutoff_mul == None: cutoff_mul = self.cutoff_mul + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + cutoff_dim = int(np.prod(self.input_shape) * cutoff_dim) + z[:, :cutoff_dim] *= cutoff_mul + if cond.shape[0] == 1: + cond = torch.cat([cond]*num_samples) + else: assert cond.shape[0] == num_samples, "batch size of condition must be equal to 1 or num_samples" + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False, cutoff_dim=None, cutoff_mul=None): + if cutoff_dim == None: cutoff_dim = self.cutoff_dim + if cutoff_mul == None: cutoff_mul = self.cutoff_mul + return get_conditional_loss_freia(self, data_batch, degradation_op, num_z=num_z, reg_parameter=reg_parameter, num_bits=num_bits, tiled=tiled, importance_weighting=importance_weighting, cutoff_dim=cutoff_dim, cutoff_mul=cutoff_mul) + + def save(self, *args, **kwargs): + # torch.save(self.model.state_dict(), *args, **kwargs) + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict, strict=False) + + +class CondGlow3(nn.Module): + "Conditional flow architecture" + def __init__(self, + input_shape = [3,64,64], + cond_shape = [3,64,64], + num_flow = 2, + num_block = 4, + num_cond_chans = 16, + filter_size = 32, + final_cond_dim = 512, + first_split_ratio = 0.25, + squeezer = 'Squeeze', + sparsity_weight = 0.1, + sparsifier = None, + cutoff_dim = 0, + cutoff_mul = 1, + device = None): + """ + input_shape : shape of image output of the posterior network + cond_shape : shape of the input to the conditioning network + num_flow : number of flows in each invertible block + num_cond_chans : number of channels in the first conditioning layer + compression_ratio : ratio of latent space size and image size + num_noncond_blocks : num of blocks to go without conditioning + filter_size : filter size in affine coupling + """ + + + super().__init__() + + self.device = device if device != None else DEVICE + self.input_shape = input_shape + self.cond_shape = cond_shape + self.num_flow = num_flow + self.num_block = num_block + self.num_cond_chans = num_cond_chans + self.filter_size = filter_size + self.final_cond_dim = final_cond_dim + self.first_split_ratio = first_split_ratio + self.squeezer = getattr(model_tools, squeezer)(cond_shape[0]) + self.sparsity_weight = sparsity_weight + self.sparsifier = sparsifier + self.cutoff_dim = cutoff_dim + self.cutoff_mul = cutoff_mul + + self.model = self.build_inn() + self.trainable_parameters = [p for p in self.model.parameters() if p.requires_grad] + for p in self.trainable_parameters: p.data = 0.02 * torch.randn_like(p) + self.cond_net = CondNet4(cond_shape=self.cond_shape, num_cond_chans=self.num_cond_chans, num_block=self.num_block, linear_layer_dim=self.final_cond_dim, squeezer=self.squeezer) + self.trainable_parameters += list(self.cond_net.parameters()) + self.identifier = f"CondGlow-nf{num_flow}-nb{num_block}-nc{num_cond_chans}-fil{filter_size}-spl{first_split_ratio}" + + # define the latent variable distribution + self.latent_dist = dists.normal.Normal(loc=torch.tensor(0, dtype=torch.float32).to(self.device), scale=torch.tensor(1, dtype=torch.float32).to(self.device)) + + def build_inn(self): + + C = self.input_shape[0] + filter_size = self.filter_size + + def subnet_conv(ch_hidden, kernel): + pad = kernel // 2 + return lambda ch_in, ch_out: nn.Sequential( + nn.Conv2d(ch_in, ch_hidden, kernel, padding=pad), + nn.ReLU(), + nn.Conv2d(ch_hidden, ch_out, kernel, padding=pad)) + + def subnet_fc(ch_hidden): + return lambda ch_in, ch_out: nn.Sequential( + nn.Linear(ch_in, ch_hidden), + nn.ReLU(), + nn.Linear(ch_hidden, ch_out)) + + def add_block(nodes, num_flow, subnet, condition, clamp=1.0, permutation='invconv'): + for k in range(num_flow): + nodes.append(Ff.Node(nodes[-1], Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet, 'clamp':clamp}, + conditions=condition)) + # nodes.append(Ff.Node(nodes[-1], model_tools.ActNormCoarse, {'inverse': True, 'scaling': 1.})) + # nodes.append(Ff.Node(nodes[-1], model_tools.ActNorm2, {'inverse': True, 'scaling': 1.})) + if permutation=='random': + nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k})) + elif permutation=='invconv': + nodes.append(Ff.Node(nodes[-1], model_tools.Inv1x1Conv, {})) + return nodes + + def split(nodes, split_nodes, section_sizes): + nodes.append(Ff.Node(nodes[-1], Fm.Split, + {'section_sizes':section_sizes, 'dim':0})) + split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {})) + return nodes, split_nodes + + nodes = [Ff.InputNode(*self.input_shape)] + conditions = [] + ncc = self.num_cond_chans + chans = C + cs = self.cond_shape[1] // 2 + for i in range(self.num_block - 1): + conditions.append(Ff.ConditionNode(ncc, cs, cs)) + ncc *= 2; cs //= 2 + conditions.append(Ff.ConditionNode(self.final_cond_dim)) + + split_nodes = [] + + # Sparsifier + # if self.sparsifier != None: + # nodes.append(Ff.Node(nodes[-1], model_tools.SparsifierHardThreshold, + # { + # 'sparsifier' : self.sparsifier, + # 'threshold' : self.sparsity_weight, + # 'epsilon' : 1.e-03, + # })) + nodes.append(Ff.Node(nodes[-1], model_tools.ActNormCoarse, {'inverse': True, 'scaling': 1./6})) + + if self.input_shape[0] == 3: + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + else: + nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + subnet = subnet_conv(filter_size, 3) + nodes = add_block(nodes, self.num_flow, subnet, conditions[0], permutation='random') + section_sizes = [ int(4*chans*self.first_split_ratio), int(4*chans*(1-self.first_split_ratio)) ] + + nodes, split_nodes = split(nodes, split_nodes, section_sizes=section_sizes) + chans = int(4*chans*self.first_split_ratio) + filter_size *= 2 + + for bl in range(1,self.num_block-1): + # nodes.append(Ff.Node(nodes[-1], Fm.IRevNetDownsampling, {})) + nodes.append(Ff.Node(nodes[-1], Fm.HaarDownsampling, {'order_by_wavelet': True, 'rebalance':0.5})) + subnet = subnet_conv(filter_size, 3) + nodes = add_block(nodes, self.num_flow, subnet, conditions[bl], clamp=0.6, permutation='random') + section_sizes = [ 2*chans, 2*chans ] + nodes, split_nodes = split(nodes, split_nodes, section_sizes=section_sizes) + chans *= 2 + filter_size *= 2 + + # fully connected part + nodes.append(Ff.Node(nodes[-1], Fm.Flatten, {}, name='flatten')) + subnet = subnet_fc(filter_size) + nodes = add_block(nodes, self.num_flow, subnet, conditions[-1], clamp=0.6, permutation='random') + + # concat the split nodes + nodes.append(Ff.Node([s.out0 for s in split_nodes] + [nodes[-1].out0], + Fm.Concat1d, {'dim':0})) + nodes.append(Ff.OutputNode(nodes[-1])) + + return Ff.ReversibleGraphNet(nodes + split_nodes + conditions, verbose=False) + + def change_sparsity_weight(self, new_weight): + self.model.module_list[0].threshold = new_weight + + # def initialize_actnorm(self, data_batch, degradation_op): + # with torch.no_grad(): + # data_batch_rev = degradation_op.rev(data_batch) + # x = self.sample(data_batch.shape[0], data_batch_rev) + # std = np.std(x) + # self.model.module_list[0].final_scale *= 1/std/6 + # self.model.module_list[0].final_scale.to(self.device) + + def initialize_actnorm(self, data_batch, degradation_op): + for m in self.model.module_list: + if isinstance(m, model_tools.ActNormCoarse): + m.init_on_next_batch = True + with torch.no_grad(): + data_batch_rev = degradation_op.rev(data_batch) + x = self.sample(data_batch.shape[0], data_batch_rev) + # After 1 pass through self.model, init_on_next_batch on all the actnorms automatically is set to False again. + + def forward(self, input, cond, rev=False): + + if not rev: + z, jac = self.model([input], self.cond_net(cond), jac=True) + return z, jac + + else: + x_posts, jac_rev = self.model(input, self.cond_net(cond), rev=True) + return x_posts, jac_rev + + def sample(self, num_samples, cond, temp=1., cutoff_dim=None, cutoff_mul=None): + if cutoff_dim == None: cutoff_dim = self.cutoff_dim + if cutoff_mul == None: cutoff_mul = self.cutoff_mul + # z = torch.randn(num_samples, np.prod(self.input_shape)).to(self.device) * temp + z = self.latent_dist.sample([num_samples, np.prod(self.input_shape)]) * temp + cutoff_dim = int(np.prod(self.input_shape) * cutoff_dim) + z[:, :cutoff_dim] *= cutoff_mul + if cond.shape[0] == 1: + cond = torch.cat([cond]*num_samples) + else: assert cond.shape[0] == num_samples, "batch size of condition must be equal to 1 or num_samples" + x,_ = self.model(z, self.cond_net(cond), rev=True) + x = x.cpu().detach().numpy() + return x + + def get_loss(self, data_batch, degradation_op, num_z=1, reg_parameter=1, num_bits=0, tiled=False, importance_weighting=False, cutoff_dim=None, cutoff_mul=None): + if cutoff_dim == None: cutoff_dim = self.cutoff_dim + if cutoff_mul == None: cutoff_mul = self.cutoff_mul + return get_conditional_loss_freia(self, data_batch, degradation_op, num_z=num_z, reg_parameter=reg_parameter, num_bits=num_bits, tiled=tiled, importance_weighting=importance_weighting, cutoff_dim=cutoff_dim, cutoff_mul=cutoff_mul) + + def save(self, *args, **kwargs): + # torch.save(self.model.state_dict(), *args, **kwargs) + torch.save(self.state_dict(), *args, **kwargs) + + def load(self, path_to_pkl): + state_dict = torch.load(path_to_pkl) + self.load_state_dict(state_dict, strict=False) + + +class Unet(nn.Module): + """ + PyTorch implementation of a U-Net model. + + O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks + for biomedical image segmentation. In International Conference on Medical + image computing and computer-assisted intervention, pages 234–241. + Springer, 2015. + """ + + def __init__( + self, + input_shape: list, + in_chans: int, + out_chans: int, + chans: int = 32, + num_pool_layers: int = 4, + drop_prob: float = 0.0, + *args, + **kwargs, + ): + """ + Args: + in_chans: Number of channels in the input to the U-Net model. + out_chans: Number of channels in the output to the U-Net model. + chans: Number of output channels of the first convolution layer. + num_pool_layers: Number of down-sampling and up-sampling layers. + drop_prob: Dropout probability. + """ + super().__init__() + + self.in_chans = in_chans + self.out_chans = out_chans + self.chans = chans + self.num_pool_layers = num_pool_layers + self.drop_prob = drop_prob + + self.down_sample_layers = nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)]) + ch = chans + for _ in range(num_pool_layers - 1): + self.down_sample_layers.append(ConvBlock(ch, ch * 2, drop_prob)) + ch *= 2 + self.conv = ConvBlock(ch, ch * 2, drop_prob) + + self.up_conv = nn.ModuleList() + self.up_transpose_conv = nn.ModuleList() + for _ in range(num_pool_layers - 1): + self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch)) + self.up_conv.append(ConvBlock(ch * 2, ch, drop_prob)) + ch //= 2 + + self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch)) + self.up_conv.append( + nn.Sequential( + ConvBlock(ch * 2, ch, drop_prob), + nn.Conv2d(ch, self.out_chans, kernel_size=1, stride=1), + ) + ) + + self.trainable_parameters = self.parameters() + + def forward(self, image: torch.Tensor) -> torch.Tensor: + """ + Args: + image: Input 4D tensor of shape `(N, in_chans, H, W)`. + + Returns: + Output tensor of shape `(N, out_chans, H, W)`. + """ + stack = [] + output = image + + # apply down-sampling layers + for layer in self.down_sample_layers: + output = layer(output) + stack.append(output) + output = F.avg_pool2d(output, kernel_size=2, stride=2, padding=0) + + output = self.conv(output) + + # apply up-sampling layers + for transpose_conv, conv in zip(self.up_transpose_conv, self.up_conv): + downsample_layer = stack.pop() + output = transpose_conv(output) + + # reflect pad on the right/botton if needed to handle odd input dimensions + padding = [0, 0, 0, 0] + if output.shape[-1] != downsample_layer.shape[-1]: + padding[1] = 1 # padding right + if output.shape[-2] != downsample_layer.shape[-2]: + padding[3] = 1 # padding bottom + if torch.sum(torch.tensor(padding)) != 0: + output = F.pad(output, padding, "reflect") + + output = torch.cat([output, downsample_layer], dim=1) + output = conv(output) + + return output + + +if __name__ == '__main__': + + import degradations + import scipy.misc as misc + from skimage.transform import resize + + input_shape = [3,64,64] + degradation = degradations.GaussianBlurNoise(input_shape=input_shape, kernel_sigma=1.5) + # degradation = degradations.FourierSamplerGaussianNoise(input_shape=input_shape, random=False, mask_file='../masks_mri/cartesian_4fold_64.npy') + + # sparsifier = sparsifiers.SparsifyingTransform(input_shape) + sparsifier = sparsifiers.WaveletTransform([1,64,64], level=2) + + # post_model_args = {'num_flow': 2, 'num_block': 4, 'num_cond_chans': 16, 'compression_ratio': 1, 'num_noncond_blocks': 1, 'gamma': 1e-08, 'filter_size': 64} + # post_model_args = {'num_flow': 2, 'num_block': 4, 'num_cond_chans': 16, 'filter_size': 32, 'final_cond_dim': 512, 'first_split_ratio': 0.25, 'squeezer': 'IRevNetSqueeze'} + post_model_args = {'num_flow': 4, 'num_block': 4, 'num_cond_chans': 32, 'filter_size': 32, 'final_cond_dim': 512, 'first_split_ratio': 0.25, 'squeezer': 'HaarSqueeze'} + # post_model_args = {'num_flow': 4, 'num_block': 4, 'num_cond_chans': 32, 'filter_size': 32, 'final_cond_dim': 512, 'first_split_ratio': 0.25, 'squeezer': 'HaarSqueeze'} + post_model = CondGlow2(input_shape, cond_shape=degradation.output_shape, **post_model_args) + post_model = post_model.to(post_model.device) + + xgt = np.load('xgt.npy') + xgt = resize(xgt, (64,64)) + xgt = torch.Tensor(xgt.reshape(1,1,64,64)) + xgt = torch.cat([xgt]*4, axis=0) + xgt = torch.cat([xgt]*3, axis=1) + cond = degradation(xgt).to('cuda') + x = post_model.sample(4, degradation.rev(cond[0:1])) + print(x.shape) + loss, x_posts = post_model.get_loss(cond, num_z=2, tiled=True, degradation_op=degradation) + + # squeeze = HaarSqueeze(1) + # coeffs = squeeze(x_posts[1,0:1].detach().cpu()).numpy() + # coeffs = np.squeeze(coeffs) + # import matplotlib + # matplotlib.use('Agg') + # import matplotlib.pyplot as plt + # temp = '/home/vak2/Documents/temp.png' + # plt.hist(coeffs[3].flatten(), bins=np.linspace(-7,7,100)); + # plt.hist(coeffs[2].flatten(), bins=np.linspace(-7,7,100)); + # plt.hist(coeffs[1].flatten(), bins=np.linspace(-7,7,100)); + # plt.hist(coeffs[0].flatten(), bins=np.linspace(-7,7,100)); + # plt.savefig(temp);plt.close() diff --git a/src/sparsifiers.py b/src/sparsifiers.py new file mode 100644 index 0000000..a23f9f4 --- /dev/null +++ b/src/sparsifiers.py @@ -0,0 +1,258 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.nn as nn +import numpy as np +import pywt +import ptwt +import wavelet_utils as wtutils + +class SparsifyingTransform(object): + + def __init__(self, input_shape, *args, **kwargs): + self.input_shape = input_shape + self.fixed_subspace = np.zeros((np.prod(self.input_shape)), dtype=bool) + + def __call__(self, input): + return input.reshape(input.shape[0], -1) + + def inv(self, output): + return output.reshape(output.shape[0], *self.input_shape) + + def split_leading_coeffs(self, input, k): + + assert k >= np.sum(self.fixed_subspace), "Number of significant coeffs k is smaller than the mandatory subspace dimension" + + if isinstance(input, np.ndarray): + print("Operating on NumPy array") + module = np + else: module = torch + + # get locations of leading k coeffs in each sample + coeffs = self(input) # N x n + if module == torch: + coeffs_mod = coeffs.detach().clone() + else: + coeffs_mod = coeffs.copy() + coeffs_mod[:,self.fixed_subspace] = 0 + sorting_coeff_idxs = module.argsort(module.abs(coeffs_mod)) + sorting_coeff_idxs = module.flip(sorting_coeff_idxs, [-1]) + leading_coeff_locs = module.argsort(sorting_coeff_idxs) < (k - np.sum(self.fixed_subspace)) + leading_coeff_locs[:,self.fixed_subspace] = True + if module == torch: + leading_coeff_locs = leading_coeff_locs.detach() + + leading_coeffs = coeffs * leading_coeff_locs + trailing_coeffs = coeffs * (~leading_coeff_locs) + + return leading_coeffs, trailing_coeffs + + def split_leading_components(self, input, k): + leading_coeffs, trailing_coeffs = self.split_leading_coeffs(input, k) + return self.inv(leading_coeffs), self.inv(trailing_coeffs) + + +class WaveletTransform(SparsifyingTransform): + + def __init__(self, input_shape, wavelet='haar', level=2, fixed_subspace_ratio=1/16, mode='zero', **kwargs): + + super().__init__(input_shape) + self.wavelet = pywt.Wavelet(wavelet) + self.level = level + self.mode = mode + self.fixed_subspace = np.zeros((np.prod(self.input_shape)), dtype=bool) + self.fixed_subspace[:int( len(self.fixed_subspace) * fixed_subspace_ratio )] = True + + def __call__(self, input): + if isinstance(input, np.ndarray): + coeffs = pywt.wavedec2(input, self.wavelet, level=self.level, mode=self.mode) + elif isinstance(input, torch.Tensor): + coeffs = ptwt.wavedec2(input, self.wavelet, level=self.level, mode=self.mode) + + c, shapes = wtutils.coeffs_to_array(coeffs) + self.shapes = shapes + return c + + def inv(self, output, shapes=None): + if shapes == None: shapes = self.shapes + coeffs = wtutils.array_to_coeffs(output, shapes) + + if isinstance(output, np.ndarray): + img = pywt.waverec2(coeffs, self.wavelet) + elif isinstance(output, torch.Tensor): + img = ptwt.waverec2(coeffs, self.wavelet) + return img + +class GradientTransform(SparsifyingTransform): + def __init__(self, input_shape): + super().__init__(input_shape) + C,H,W = input_shape + self.fixed_subspace = np.zeros((2*C,H,W), dtype=bool) + self.fixed_subspace[:C,0,:] = True + self.fixed_subspace[C:,:,0] = True + self.fixed_subspace = self.fixed_subspace.flatten() + + def __call__(self, input): + if isinstance(input, np.ndarray): module = np + elif isinstance(input, torch.Tensor): module = torch + input_x = module.roll(input, 1, 2) + input_x[:,:,0] = 0 + input_y = module.roll(input, 1, 3) + input_y[:,:,:,0] = 0 + input_dx = input - input_x + input_dy = input - input_y + + if isinstance(input, np.ndarray): + coeffs = np.concatenate([input_dx, input_dy], 1) + else: + coeffs = torch.cat([input_dx, input_dy], dim=1) + # return coeffs.reshape(coeffs.shape[0], -1) + return coeffs + + def inv(self, output): + if isinstance(output, np.ndarray): module = np + elif isinstance(output, torch.Tensor): module = torch + C,H,W = self.input_shape + # o = output.reshape(-1,2*C,H,W) + o = output + return 0.5 * ( module.cumsum(o[:,:C], 2) + module.cumsum(o[:,C:], 3) ) + + +class SparsifyingRegularizer(nn.Module): + """ This is a module which takes as input an image and a sparsifying transform, and outputs a regularizer + """ + def __init__(self, input_shape, sparsifying_transform, num_leading_coeffs, + penalty_norm = 1, + weight_norm = 1, + ): + """ + input_shape : Shape of the input image + sparsifying_transform : Sparsifying transform used. + num_leading_coeffs : Number of leading coeffs, should be a divisor of prod(input_shape) + conv_type_switch_thresh : Threshold for the number of channels for switching from regular convolution to group convolution + penalty_norm + weight_norm + """ + super().__init__() + + self.input_shape = input_shape + self.sparsifying_transform = sparsifying_transform + self.num_leading_coeffs = num_leading_coeffs + self.penalty_norm = penalty_norm + self.weight_norm = weight_norm + + final_num_channels = np.prod(input_shape) + C,H,W = input_shape + ch_in = C + layers = [] + while ch_in*16 <= C*H*W: + groups = int(ch_in > C*16)*ch_in + int(ch_in <= C*16) + layers.append( + nn.Conv2d(ch_in, ch_in*16, 3, stride=2, padding=1, groups=groups), + ) + layers.append(nn.AvgPool2d(2)) + layers.append(nn.ReLU()) + ch_in *= 16 + + groups = int(ch_in > C*16)*ch_in + int(ch_in <= C*16) + if ch_in < C*H*W: + layers.append( + nn.Conv2d(ch_in, ch_in*4, 3, stride=2, padding=1, groups=groups), + ) + else: layers = layers[:-1] + + self.weighting_net = nn.Sequential(*layers) + self.trainable_parameters = list(self.weighting_net.parameters()) + + def forward(self, input): + _, trailing_coeffs = self.sparsifying_transform.split_leading_coeffs( + input, self.num_leading_coeffs + ) + coeff_weights = self.weighting_net(input) + assert(coeff_weights.shape[1] == np.prod(self.input_shape)), "Improper number of channels for the coeff weights" + assert(coeff_weights.shape[2] == coeff_weights.shape[3] == 1), "Improper dimensions of coeff weights" + coeff_weights = coeff_weights.squeeze(3).squeeze(2) + weight_norm = torch.norm(coeff_weights, p=self.weight_norm, dim=1, keepdim=True) + coeff_weights = coeff_weights / weight_norm * (coeff_weights.shape[1])**(1/self.weight_norm) + penalty = torch.norm( + # trailing_coeffs / coeff_weights, + trailing_coeffs, + p = self.penalty_norm, + dim = 1 + ) + return penalty / (trailing_coeffs.shape[1])**(1/self.penalty_norm) + + +def sparsifying_regularizer2(x, sp_trans : SparsifyingTransform, threshold_weight=0.1): + + # coeffs = coeffs + 0.5 + # t = int(threshold_weight*np.prod(x.shape[1:])) + # _, coeffs = sp_trans.split_leading_coeffs(x, t) + C = x.shape[1] + n_pixel = np.prod(x.shape[1:]) + coeffs = sp_trans(x) + coeff_abs = torch.abs(coeffs) # N*num_z x C*H*W + # coeff_avg = threshold_weight * torch.mean(coeff_abs, dim=1, keepdim=True) + zero_cond = (coeff_abs < threshold_weight) + # zero_cond = torch.prod(zero_cond, dim=1, keepdim=True) + zero_cond = zero_cond[:,0:C] * zero_cond[:,C:2*C] + zero_cond = torch.cat([zero_cond]*2, dim=1) + + penalty = torch.norm(coeffs * zero_cond, p=1) + # penalty = torch.norm(coeffs, p=1) + return penalty / np.log(2) / n_pixel + +def sparsifying_regularizer3(x, sp_trans : SparsifyingTransform, threshold_weight=0.1): + + # coeffs = coeffs + 0.5 + # t = int(threshold_weight*np.prod(x.shape[1:])) + # _, coeffs = sp_trans.split_leading_coeffs(x, t) + C = x.shape[1] + n_pixel = np.prod(x.shape[1:]) + coeffs = sp_trans(x) + coeff_abs = torch.abs(coeffs) # N*num_z x C*H*W + coeff_sorted,_ = coeff_abs.flatten().sort() + threshw = coeff_sorted[-int(len(coeff_sorted)*threshold_weight)-1] + zero_cond = (coeff_abs < threshw) + # zero_cond = torch.prod(zero_cond, dim=1, keepdim=True) + zero_cond = zero_cond[:,0:C] * zero_cond[:,C:2*C] + zero_cond = torch.cat([zero_cond]*2, dim=1) + + penalty = torch.norm(coeffs * zero_cond, p=1) + # penalty = torch.norm(coeffs, p=1) + return penalty / np.log(2) / n_pixel + + +if __name__ == '__main__': + + import imageio as io + from skimage.transform import resize + + # img = np.load('xgt_s.npy') + # img = np.swapaxes(img,0,2) + # img = np.stack([img, img], axis=0) + # img = img[:,0:1] + # img = resize(img, [2,1,128,128]) + + # input_shape = [1,128,128] + + # # T = SparsifyingTransform([3,64,64]) + # T = WaveletTransform(input_shape, wavelet='haar') + # nlc = np.prod(input_shape) // 4 + # sparse_reg = SparsifyingRegularizer(input_shape, T, nlc) + + # imgt = torch.tensor(img, requires_grad=True, dtype=torch.float32) + # penalty = sparse_reg(imgt) + + # img = np.load('xgt_ell.npy') + input_shape = [1,128,128] + # img = img.reshape(1,1,32,32) + T = GradientTransform(input_shape) + # T = WaveletTransform(input_shape) + # coeffs = T(img) \ No newline at end of file diff --git a/src/train_ambient.py b/src/train_ambient.py new file mode 100644 index 0000000..fd76cc5 --- /dev/null +++ b/src/train_ambient.py @@ -0,0 +1,560 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +import models2 +import degradations +import utils +import ast +import argparse +import sys +import os +import time +import datetime +# import torch.multiprocessing as mp +import torch.distributed as dist +import torch.nn as nn +# import wandb +import socket +import sparsifiers +# from torch.profiler import profile, record_function, ProfilerActivity + +class AttrDataParallel(nn.parallel.DistributedDataParallel): + def __getattr__(self, name): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.module, name) + +# def __setatptr__(self, name, value): +# try: +# return super().__setattr__(name, value) +# except AttributeError: +# return setattr(self.module, name, value) + +parser = argparse.ArgumentParser() +# Multiprocessing arguments +parser.add_argument('--rank', default=0, type=int, help='Current rank') +parser.add_argument('-n', '--num_nodes', default=1, type=int, metavar='N') +parser.add_argument('-g', '--num_gpus', default=1, type=int, help='number of gpus per node') +parser.add_argument('-nr', '--nr', default=0, type=int, help='ranking within the nodes') +parser.add_argument('--port', type=str, default='12355') + +# training arguments +parser.add_argument("--num_kimgs", type=float, default=10000) +parser.add_argument("--num_kimgs_post", type=float, default=10000) +parser.add_argument("--num_kimgs_main", type=float, default=10000) +parser.add_argument("--batch_size", type=int, default=8) +parser.add_argument("--lr_post", type=float, default=1e-06) +# parser.add_argument("--lr_main", type=float, default=1e-06) +parser.add_argument("--lr", type=float, default=1e-06) +# parser.add_argument("--lr_pre", type=float, default=1e-05) +parser.add_argument("--reg_parameter", type=float, default=1e-03) +parser.add_argument("--num_z", type=int, default=10) +parser.add_argument("--results_dir", type=str, default='') +parser.add_argument("--resume_from", type=str, default='') +parser.add_argument("--tiled_for_loss", type=lambda b:bool(int(b)), default=0, help="Whether to tile the data batch while computing loss or loop over various zs") +parser.add_argument("--importance_weighting", type=int, default=0, help="Whether or not to do importance weighting for the gradient update") +parser.add_argument("--schedule_sparsity_weight", type=lambda b:bool(int(b)), help="0 or 1") +parser.add_argument("--benchmark_cudnn", type=lambda b:bool(int(b)), help="0 or 1", default=0) + +# model arguments +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--main_model_type", type=str, default='ConvINN') +parser.add_argument("--main_model_args", type=ast.literal_eval, default={'num_conv_layers': [4,12], 'num_fc_layers': [4]}) +parser.add_argument("--main_actnorm", type=lambda b:bool(int(b)), help="0 or 1") +parser.add_argument("--post_model_type", type=str, default='ConvINN') +parser.add_argument("--post_model_args", type=ast.literal_eval, default={'num_conv_layers': [4,12], 'num_fc_layers': [4]}) +parser.add_argument("--post_actnorm", type=lambda b:bool(int(b)), help="0 or 1") + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) +parser.add_argument("--num_bits", type=int, default=0) + +# sparsity regularization related args +parser.add_argument("--cutoff_dim", type=float, default=0) +parser.add_argument("--sparsifier_type", type=str, default='WaveletTransform') +parser.add_argument("--sparsifier_args", type=ast.literal_eval, default={'level': 2, 'wavelet': 'haar'}) +parser.add_argument("--sparsity_weight", type=float, default=0.0, help="") +parser.add_argument("--threshold_weight", type=float, default=0.0, help="") + +def train(gpu, args): + + # args.lr_post = args.lr_main = args.lr + args.lr_main = args.lr + # args.lr_post = args.lr * 100 + + rank = args.nr * args.num_gpus + gpu + dist.init_process_group( + backend='nccl', + init_method='env://', + world_size=args.world_size, + rank=rank + ) + # print("Im here") + # Configure wandb + if rank == 0: + server_name = socket.gethostname().split('.')[0] + # wandb.init(mode="disabled", settings=wandb.Settings(start_method="fork"), + # project=f'{server_name}-{args.num_gpus}gpu-{args.data_type}-deg{args.degradation_type}-{args.main_model_type}-{args.post_model_type}-reg{args.reg_parameter}-bit{args.num_bits}-lr{args.lr}-nz{args.num_z}-impw{args.importance_weighting}', entity='kvarun95') + # wandb.config = { + # "num_gpus" : args.num_gpus, + # "main_lr" : args.lr_main, + # "post_lr" : args.lr_post, + # "kimgs" : int(args.num_kimgs), + # "reg" : args.reg_parameter, + # "bits" : args.num_bits, + # "num_z" : args.num_z, + # "batch_size" : args.eff_batch_size, + # "deg" : args.degradation_type, + # "deg_args" : args.degradation_args, + # "input_shape" : args.input_shape, + # } + # print("Im here 2") + + device = f'cuda:{gpu}' if torch.cuda.is_available() else 'cpu' + torch.manual_seed(0) + # print("Im here 3") + + def print0(*args, **kwargs): + if rank == 0: print(*args, **kwargs) + + args.num_iters = int( args.num_kimgs * 1000 / args.eff_batch_size ) + args.num_iters_post = int( args.num_kimgs_post * 1000 / args.eff_batch_size ) if args.num_kimgs_post else 1 + args.num_iters_main = int( args.num_kimgs_main * 1000 / args.eff_batch_size ) if args.num_kimgs_main else 1 + + # forward model (degradation) + args.data_args['input_shape'] = args.input_shape + degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits, device=device) + # print("Im here 4") + + # sparsifier + sp_trans = getattr(sparsifiers, args.sparsifier_type)(args.input_shape, **args.sparsifier_args) + args.post_model_args['sparsifier'] = sp_trans + args.post_model_args['sparsity_weight'] = args.sparsity_weight + + # main and posterior models + main_model = getattr(models2, args.main_model_type)(args.input_shape, **args.main_model_args, device=device) + # print("Im here 5") + post_model = getattr(models2, args.post_model_type)(args.input_shape, cond_shape=degradation.output_shape, **args.post_model_args, device=device) + # print("Im here 6") + + # Resume training from previous iteration + if args.resume_from != '': + print0(f"Resuming training from {args.resume_from} ...") + main_model.load(args.resume_from) + post_resume_from = os.path.split(args.resume_from) + post_resume_from = os.path.join(post_resume_from[0], 'post-'+post_resume_from[1][5:]) + post_model.load(post_resume_from) + main_model.to(device) + post_model.to(device) + # print("Im here 7") + + # optimizer + main_optimizer = torch.optim.Adam(main_model.trainable_parameters, lr=args.lr_main) + post_optimizer = torch.optim.Adam(list(post_model.trainable_parameters), lr=args.lr_post) + # tot_optimizer = torch.optim.Adam(main_model.trainable_parameters + post_model.trainable_parameters, lr=args.lr) + # print("Im here 9") + + # wrap models with DDP + main_model = AttrDataParallel(main_model, device_ids=[gpu]) + post_model = AttrDataParallel(post_model, device_ids=[gpu]) + # print("Im hre 10") + + # data loaders using the distributed sampler + train_dataset = getattr(dataset_tool, args.data_type)(train=True, ambient=True, degradation=degradation, **args.data_args) + test_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) + test_dataset_clean = getattr(dataset_tool, args.data_type)(train=False, **args.data_args) + # print("Im here 11") + + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, num_replicas=args.world_size, rank=rank) + # print("im here 12") + train_dataloader = torch.utils.data.DataLoader(train_dataset, + batch_size = args.batch_size, + shuffle = False, + num_workers = 4*args.world_size, + pin_memory = True, + sampler = train_sampler) + # test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) + # print("Im here 13") + + if rank == 0: + save_folder = utils.setup_saver(args.results_dir, f'{server_name}-{args.num_gpus}gpu-deg{args.degradation_type}-{args.main_model_type}-{args.post_model_type}-reg{args.reg_parameter}-spparam{args.sparsity_weight}-thw{args.threshold_weight}-bit{args.num_bits}-lr{args.lr}-nz{args.num_z}-impw{args.importance_weighting}') + # print("Im here 14") + print0(args.__dict__, file=open(f'{save_folder}/config.txt', 'w')) + sys.stdout = utils.Logger(save_folder+'/log.txt') + print0(args) + print(save_folder) + + noisies = [degradation.rev(train_dataset[i][0].to(device), mode='real').cpu().detach().numpy() for i in range(16)] + _ = utils.save_images(np.stack([test_dataset_clean[i][0].detach().numpy() for i in range(16)]), f'{save_folder}/reals.png', imrange=[-0.5,0.5]) + _ = utils.save_images(noisies, f'{save_folder}/noisies.png', imrange=[-0.5-degradation.std*np.sqrt(2/np.pi), 0.5]) + + # define importance weighting functions + # def imp_weighting1(loss): + # loss -= np.log(args.num_z) + # loss = torch.logsumexp(loss, 0) + # loss = torch.mean(loss) + # loss_weight = 1 + # return loss, loss_weight + + def imp_weighting1(loss): + importance_weights = torch.softmax(loss, dim=0).detach() + loss = loss * importance_weights + loss = torch.mean(torch.sum(loss, dim=0)) + loss_weight = 1 + return loss, loss_weight + + def imp_weighting2(loss): + loss -= np.log(args.num_z*args.batch_size) + loss = torch.logsumexp(loss, [0,1]) + total_loss = torch.zeros_like(loss) + dist.barrier() + + # losses_to_scatter = [loss.clone().detach() for _ in range(args.world_size)] + # gathered_losses = list(torch.empty([4], dtype=loss.dtype).to(device).chunk(4)) + # dist.all_to_all(gathered_losses, losses_to_scatter) + # dist.barrier() + # total_l = torch.logsumexp(torch.Tensor(gathered_losses), 0).detach() + # log_weight = loss.detach() - total_loss.detach() + # loss_weight = torch.exp(log_weight) + # return loss, loss_weight + + if rank == 0: + gathered_losses = [torch.zeros_like(loss) for _ in range(args.world_size)] + dist.gather(loss, gathered_losses, dst=0) + total_l = torch.logsumexp(torch.Tensor(gathered_losses), 0).detach() + dist.barrier() + # scattered_losses = [total_l.clone() for _ in range(args.world_size)] + # dist.scatter(total_loss, scattered_losses, src=0) + # dist.broadcast(total_l, src=0) + dist.reduce_scatter(total_loss, [total_l]) + dist.barrier() + else: + dist.gather(loss, dst=0) + dist.barrier() + # dist.broadcast(total_l, src=0) + dist.barrier() + + log_weight = loss.detach() - total_loss.detach() + loss_weight = torch.exp(log_weight) + return loss, loss_weight + + # Use in the case of single GPU + def imp_weighting2_single(loss): + importance_weights = torch.softmax(loss.flatten(), dim=0).detach().reshape(loss.shape) + loss *= importance_weights + loss = torch.mean(loss) + loss_weight = 1 + return loss, loss_weight + + # Switch on CUDNN benchmarking + if args.benchmark_cudnn: + torch.backends.cudnn.benchmark = True + + ################################################################ + print0("Pretraining posterior model") + if rank == 0: + os.makedirs(f'{save_folder}/posterior-pretraining', exist_ok=True) + y_tests = [test_dataset[i][0] for i in range(4)] + y_tests = [yt.reshape(1,*yt.shape).to(device, non_blocking=True) for yt in y_tests] + y_tests = [degradation.rev(yt) for yt in y_tests] + start_time = time.time() + # print("im here 15") + + print_freq = int(np.ceil(2500 / args.eff_batch_size)) + save_freq = int(np.ceil(50000 / args.eff_batch_size)) + idx = 0 + dloader = iter(train_dataloader) + while args.num_iters_post and (args.resume_from == ''): + try: y = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + y = next(dloader)[0] + # print("Im here 16") + + x = degradation.rev(y.to(device), use_device=True).detach() + # print("Im here 17") + if idx == 0 and args.post_actnorm: + # x_in = degradation.rev(y.to(device), mode='real', use_device=True).detach() + # post_model.initialize_actnorm(x_in, x) + post_model.initialize_actnorm(y.to(device), degradation) + idx += 1 + continue + + # y = torch.cat([y]*3, dim=1) + y = y.to(device, non_blocking=True) + post_optimizer.zero_grad() + # print("Im here 17") + loss,_ = post_model.get_loss(y, degradation, num_z=args.num_z, reg_parameter=1., num_bits=args.num_bits, tiled=args.tiled_for_loss, cutoff_dim=args.cutoff_dim) + # print("Im here 18") + + # importance weighting + # if args.importance_weighting == 1: + # loss, loss_weight = imp_weighting1(loss) + # elif args.importance_weighting == 2: + # loss, loss_weight = imp_weighting2(loss) + # else: loss_weight = 1 + + loss.backward() + warmup_lr = args.lr_post * min(1, idx * args.eff_batch_size / (10000 * 10)) + post_optimizer.param_groups[0]["lr"] = warmup_lr + post_optimizer.step() + if rank == 0: + if (idx < print_freq and idx % max(1, print_freq // 5) == 0) or (idx % print_freq == 0): + kimg = idx*args.eff_batch_size/1000 + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + print0(f"kImg. : {kimg:.2f}, time : {timesec} Curr. loss : {loss}") + # wandb.log({"post_loss": loss}, step=int(kimg)) + if (idx < save_freq and idx % max(1, save_freq // 5) == 0) or (idx % save_freq == 0): + kimg = idx*args.eff_batch_size // 1000 + xsamps = [] + for yt in y_tests: + # # yt = torch.cat([yt]*3, dim=0) + # yt = yt.reshape(1,*yt.shape).to(device, non_blocking=True) + # yt = degradation.rev(yt) + # # yt = torch.cat([yt]*4, dim=0) + x_samp = post_model.sample(4, yt, temp=1) + # x_samp = np.swapaxes(x_samp, 1,2) + # x_samp = np.swapaxes(x_samp, 2,3) + xsamps.append(x_samp) + xsamps = np.concatenate(xsamps, axis=0) + _ = utils.save_images(xsamps, f'{save_folder}/posterior-pretraining/fakes_{kimg:06}.png', imrange=[-0.5,0.5]) + # wandb.log({"post_example": wandb.Image(f'{save_folder}/posterior-pretraining/fakes_{kimg:06}.png')}, step=kimg) + post_model.save(f'{save_folder}/posterior-pretraining/network_{kimg:06}.pt') + + idx += 1 + if idx >= args.num_iters_post: + break + + print0("Pretraining main model") + torch.cuda.empty_cache() + if rank == 0: + os.makedirs(f'{save_folder}/main-pretraining', exist_ok=True) + _ = utils.save_images(np.stack([test_dataset_clean[i][0].detach().numpy() for i in range(16)]), f'{save_folder}/main-pretraining/reals.png', imrange=[-0.5,0.5]) + _ = utils.save_images(noisies, f'{save_folder}/main-pretraining/noisies.png', imrange=[-0.5, 0.5]) + idx = 0 + dloader = iter(train_dataloader) + while args.num_iters_main and (args.resume_from == ''): + try: x = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + x = next(dloader)[0] + + # x = torch.cat([x]*3, dim=1) + x = degradation.rev(x.to(device), mode='real', use_device=True).detach() + if idx == 0 and args.main_actnorm: + main_model.initialize_actnorm(x) + # main_model = main_model.to(device) + idx += 1 + continue + + main_optimizer.zero_grad() + loss = main_model.get_loss(x, num_bits=args.num_bits) + + # if args.importance_weighting == 1: + # loss, loss_weight = imp_weighting1(loss) + # elif args.importance_weighting == 2: + # loss, loss_weight = imp_weighting2(loss) + # else: loss_weight = 1 + + loss.backward() + warmup_lr = args.lr_main * min(1, idx * args.eff_batch_size / (10000 * 10)) + main_optimizer.param_groups[0]["lr"] = warmup_lr + main_optimizer.step() + + if idx % int(np.ceil(args.num_iters_main / 50)) == 0 and rank == 0: + kimg = idx*args.eff_batch_size / 1000 + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + print0(f"kImg. : {kimg:.2f}, time : {timesec} Curr. loss : {loss}") + # wandb.log({"main_loss": loss}, step=int(kimg)) + if idx % int(np.ceil(args.num_iters_main / 10)) == 0 and rank == 0: + kimg = idx*args.eff_batch_size // 1000 + for temp in [0.5, 0.7, 1.0]: + x_samp = np.concatenate([main_model.sample(4, temp=temp) for _ in range(4)]) + # x_samp = np.squeeze(x_samp) + # x_samp = np.swapaxes(x_samp, 1,2) + # x_samp = np.swapaxes(x_samp, 2,3) + # x_samp = np.squeeze(x_samp) + _ = utils.save_images(x_samp, f'{save_folder}/main-pretraining/fakes_{kimg:06}_temp{temp}.png', imrange=[-0.5,0.5]) + # wandb.log({f"main_example_{temp}": wandb.Image(f'{save_folder}/main-pretraining/fakes_{kimg:06}_temp{temp}.png')}, step=kimg) + main_model.save(f'{save_folder}/main-pretraining/network_{kimg:06}.pt') + + idx += 1 + if idx >= args.num_iters_main: + break + + print0("Jointly training main and posterior models") + torch.cuda.empty_cache() + scaler = torch.cuda.amp.GradScaler() + # if rank == 0: + # _ = utils.save_images(np.stack([test_dataset_clean[i][0].detach().numpy() for i in range(16)]), f'{save_folder}/reals.png', imrange=[-0.5,0.5]) + # _ = utils.save_images(noisies, f'{save_folder}/noisies.png', imrange=[-0.5, 0.5]) + idx = 0 + dloader = iter(train_dataloader) + print_freq = int(np.ceil(10000 / args.eff_batch_size)) + save_freq = int(np.ceil(100000 / args.eff_batch_size)) + while args.num_iters: + # t = time.time() + try: y,labels = next(dloader) + except StopIteration: + dloader = iter(train_dataloader) + y,labels = next(dloader) + + # torch.cuda.synchronize() + # print("Dataloader get next :", time.time() - t) + # t = time.time() + + # sparsity regularization scheduling + if args.schedule_sparsity_weight: + kimg = idx*args.eff_batch_size // 1000 + # sparsity_weight = (kimg < 1000)*0 + (kimg >= 1000)*args.sparsity_weight*(1-np.exp(-abs(kimg-1000)/1000)) + sparsity_weight = args.sparsity_weight*(1-np.exp(-kimg/100)) + # post_model.change_sparsity_weight(sparsity_weight) + else: sparsity_weight = args.sparsity_weight + # torch.cuda.synchronize() + # print("Sparsity schedule :", time.time() - t) + # t = time.time() + + # y = torch.cat([y]*3, dim=1) + y = y.to(device, non_blocking=True) + # torch.cuda.synchronize() + # print("Data to device", time.time() - t) + # t = time.time() + # tot_optimizer.zero_grad() + main_optimizer.zero_grad(set_to_none=True) + post_optimizer.zero_grad(set_to_none=True) + # torch.cuda.synchronize() + # print("Zero grad :", time.time() - t) + # t = time.time() + with torch.cuda.amp.autocast(): + post_loss, x_posts = post_model.get_loss(y, degradation, num_z=args.num_z, reg_parameter=args.reg_parameter, num_bits=args.num_bits, tiled=args.tiled_for_loss, importance_weighting=args.importance_weighting) + # torch.cuda.synchronize() + # print("Post loss :", time.time() - t) + # t = time.time() + x_posts,labels = train_dataset.deaugment(x_posts, labels) + main_loss = main_model.get_loss(x_posts, num_bits=args.num_bits, importance_weighting=args.importance_weighting) + # torch.cuda.synchronize() + # print("Main loss :", time.time() - t) + # t = time.time() + loss = main_loss + post_loss + + # Importance weighting + if args.importance_weighting == 1: + loss, loss_weight = imp_weighting1(loss) + elif args.importance_weighting == 2 and args.num_gpus == 1: + loss, loss_weight = imp_weighting2_single(loss) + elif args.importance_weighting == 2 and args.num_gpus != 1: + loss, loss_weight = imp_weighting2(loss) + else: loss_weight = 1 + # torch.cuda.synchronize() + # print("Imp weights : ", time.time() - t) + # t = time.time() + + + # Sparsifying penalty + # sparsifying_penalty = utils.total_variation(x_posts) + # sparsifying_penalty = 0 + # coeffs = sp_trans(x_posts.view(-1, *args.input_shape)+0.5) + if args.sparsity_weight: + sparsifying_penalty = sparsifiers.sparsifying_regularizer2(x_posts.view(-1, *args.input_shape)+0.5, sp_trans, threshold_weight=args.threshold_weight) + else: sparsifying_penalty = 0 + # torch.cuda.synchronize() + # print("sparsifying_penalty :", time.time() - t) + # t = time.time() + loss_full = loss * loss_weight + sparsity_weight* sparsifying_penalty + + scaler.scale(loss_full).backward() + # torch.cuda.synchronize() + # print("Backward : ", time.time() - t) + # t = time.time() + # tot_optimizer.step() + warmup_lr = args.lr_main * min(1, (idx + args.num_iters_main) * args.eff_batch_size / (10000 * 10)) + main_optimizer.param_groups[0]["lr"] = warmup_lr + warmup_lr = args.lr_post * min(1, (idx + args.num_iters_post) * args.eff_batch_size / (10000 * 10)) + post_optimizer.param_groups[0]["lr"] = warmup_lr + # main_optimizer.step() + scaler.step(main_optimizer) + # post_optimizer.step() + scaler.step(post_optimizer) + # torch.cuda.synchronize() + # print("Optimizer step :", time.time() - t) + # t = time.time() + scaler.update() + + # Logging + if rank == 0: + if (idx < print_freq and idx % (print_freq // 10) == 0) or (idx % print_freq == 0): + kimg = idx*args.eff_batch_size/1000 + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + if args.importance_weighting == 1: + post_loss_summ = torch.mean(torch.logsumexp(post_loss, 0)) + main_loss_summ = torch.mean(torch.logsumexp(main_loss, 0)) + elif args.importance_weighting == 2: + post_loss_summ = torch.logsumexp(post_loss, [0,1]) + main_loss_summ = torch.logsumexp(main_loss, [0,1]) + else: post_loss_summ = post_loss; main_loss_summ = main_loss + print0(f"kImg. : {kimg:.2f}, time : {timesec}, Post. loss : {post_loss_summ:.2f}, Main loss : {main_loss_summ:.2f}, Curr. loss : {loss}") + # wandb.log({"post_loss" : post_loss_summ, "main_loss": main_loss_summ, "tot_loss": loss}, step=int(kimg)) + if (idx < save_freq and idx % (save_freq // 5) == 0) or (idx % save_freq == 0): + kimg = idx*args.eff_batch_size // 1000 + for temp in [0.5, 0.7, 0.85, 1.0]: + x_samp = np.concatenate([main_model.sample(4, temp=temp) for _ in range(4)]) + _ = utils.save_images(x_samp, f'{save_folder}/fakes_{kimg:06}_temp{temp}.png', imrange=[-0.5,0.5]) + # wandb.log({f"main_example_{temp}": wandb.Image(f'{save_folder}/fakes_{kimg:06}_temp{temp}.png')}, step=kimg) + xsamps = [] + for yt in y_tests: + # yt = yt.reshape(1,*yt.shape).to(device, non_blocking=True) + # yt = degradation.rev(yt) + # yt = torch.cat([yt]*4, dim=0) + x_samp = post_model.sample(4, yt, temp=1) + xsamps.append(x_samp) + xsamps = np.concatenate(xsamps, axis=0) + _ = utils.save_images(xsamps, f'{save_folder}/fakes_{kimg:06}_postsamp.png', imrange=[-0.5,0.5]) + # wandb.log({"post_example": wandb.Image(f'{save_folder}/fakes_{kimg:06}_postsamp.png')}, step=kimg) + main_model.save(f'{save_folder}/main-network_{kimg:06}.pt') + post_model.save(f'{save_folder}/post-network_{kimg:06}.pt') + + idx += 1 + if idx >= args.num_iters: + break + # torch.cuda.synchronize() + # print("Rest :", time.time() - t) + # t = time.time() + + +if __name__ == '__main__': + + args = parser.parse_args() + + args.world_size = args.num_gpus * args.num_nodes + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = args.port + + # Effective batch size + args.eff_batch_size = args.batch_size * args.world_size + + # importance weighting + if args.importance_weighting: args.tiled_for_loss = 1 + + # Spawn multiprocesses (one on each gpu) + print(args) + if args.world_size > 1: + # mp.spawn(train, nprocs=args.num_gpus, args=(args,)) + train(args.rank, args) + else: + train(0, args) \ No newline at end of file diff --git a/src/train_ambient_mnist.py b/src/train_ambient_mnist.py new file mode 100644 index 0000000..15c03ee --- /dev/null +++ b/src/train_ambient_mnist.py @@ -0,0 +1,245 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +import models +import degradations +import utils +import ast +import argparse +import sys +import os +import time +import datetime + +parser = argparse.ArgumentParser() +# Multiprocessing arguments +parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N') +parser.add_argument('-g', '--gpus', default=1, type=int, help='number of gpus per node') +parser.add_argument('-nr', '--nr', default=0, type=int, help='ranking within the nodes') + +# training arguments +parser.add_argument("--num_kimgs", type=int, default=10000) +parser.add_argument("--num_kimgs_post", type=int, default=10000) +parser.add_argument("--num_kimgs_main", type=int, default=10000) +parser.add_argument("--batch_size", type=int, default=8) +parser.add_argument("--lr_post", type=float, default=1e-06) +parser.add_argument("--lr_main", type=float, default=1e-06) +# parser.add_argument("--lr_pre", type=float, default=1e-05) +parser.add_argument("--reg_parameter", type=float, default=1e-03) +parser.add_argument("--num_z", type=int, default=10) +parser.add_argument("--results_dir", type=str, default='') +parser.add_argument("--resume_from", type=str, default='') + +# model arguments +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--main_model_type", type=str, default='ConvINN') +parser.add_argument("--main_model_args", type=ast.literal_eval, default={'num_conv_layers': [4,12], 'num_fc_layers': [4]}) +parser.add_argument("--main_actnorm", type=lambda b:bool(int(b)), help="0 or 1") +parser.add_argument("--post_model_type", type=str, default='ConvINN') +parser.add_argument("--post_model_args", type=ast.literal_eval, default={'num_conv_layers': [4,12], 'num_fc_layers': [4]}) +parser.add_argument("--post_actnorm", type=lambda b:bool(int(b)), help="0 or 1") + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--degradation_type", type=str, default='GaussianNoise') +parser.add_argument("--degradation_args", type=ast.literal_eval, default={'mean':0., 'std':0.3}) +parser.add_argument("--num_bits", type=int, default=0) + + +def train(gpu, args): + + device = f'cuda:{gpu}' if torch.cuda.is_available() else 'cpu' + torch.manual_seed(0) + + def print0(*args, **kwargs): + if gpu == 0: print(*args, **kwargs) + + args.num_iters = args.num_kimgs * 1000 // args.batch_size + args.num_iters_post = args.num_kimgs_post * 1000 // args.batch_size + args.num_iters_main = args.num_kimgs_main * 1000 // args.batch_size + + args.data_args['input_shape'] = args.input_shape + degradation = getattr(degradations, args.degradation_type)(**args.degradation_args, input_shape=args.input_shape, num_bits=args.num_bits) + train_dataset = getattr(dataset_tool, args.data_type)(train=True, ambient=True, degradation=degradation, **args.data_args) + test_dataset = getattr(dataset_tool, args.data_type)(train=False, ambient=True, degradation=degradation, **args.data_args) + test_dataset_clean = getattr(dataset_tool, args.data_type)(train=False, **args.data_args) + train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4) + test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4) + + main_model = getattr(models, args.main_model_type)(args.input_shape, **args.main_model_args) + post_model = getattr(models, args.post_model_type)(args.input_shape, cond_shape=degradation.output_shape, **args.post_model_args) + main_optimizer = torch.optim.Adam(main_model.trainable_parameters, lr=args.lr_main) + post_optimizer = torch.optim.Adam(post_model.trainable_parameters, lr=args.lr_post) + # tot_optimizer = torch.optim.Adam(main_model.trainable_parameters + post_model.trainable_parameters, lr=args.lr) + + save_folder = utils.setup_saver(args.results_dir, main_model.identifier + f'-lr{args.lr_main}' + f'-{post_model.identifier}' + f'-lr{args.lr_post}' + f'-bits{args.num_bits}-{args.degradation_type}') + print0(args.__dict__, file=open(f'{save_folder}/config.txt', 'w')) + sys.stdout = utils.Logger(save_folder+'/log.txt') + print0(args, flush=True) + + if gpu == 0: + noisies = [train_dataset[i][0].detach().numpy() for i in range(16)] + utils.save_images(np.stack([test_dataset_clean[i][0].detach().numpy() for i in range(16)]), f'{save_folder}/reals.png', imrange=[-0.5,0.5]) + utils.save_images(noisies, f'{save_folder}/noisies.png', imrange=[-0.5, 0.5]) + + print("Pretraining posterior model") + + if gpu == 0: + os.makedirs(f'{save_folder}/posterior-pretraining', exist_ok=True) + y_tests = [test_dataset[i][0] for i in range(4)] + start_time = time.time() + idx = 0 + dloader = iter(train_dataloader) + while True: + try: y = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + y = next(dloader)[0] + + # y = torch.cat([y]*3, dim=1) + y = y.to(device) + post_optimizer.zero_grad() + loss,_ = post_model.get_loss(y, degradation, num_z=args.num_z, reg_parameter=args.reg_parameter, num_bits=args.num_bits) + loss.backward() + warmup_lr = args.lr_post * min(1, idx * args.batch_size / (10000 * 10)) + post_optimizer.param_groups[0]["lr"] = warmup_lr + post_optimizer.step() + + if idx % 50 == 0: + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + print0(f"kImg. : {idx*args.batch_size/1000:.2f}, time : {timesec} Curr. loss : {loss}") + if idx % 500 == 0: + xsamps = [] + if gpu == 0: + for yt in y_tests: + # yt = torch.cat([yt]*3, dim=0) + yt = yt.reshape(1,*yt.shape).to(device) + x_samp = post_model.sample(4, yt, temp=1) + # x_samp = np.swapaxes(x_samp, 1,2) + # x_samp = np.swapaxes(x_samp, 2,3) + xsamps.append(x_samp) + xsamps = np.concatenate(xsamps, axis=0) + utils.save_images(xsamps, f'{save_folder}/posterior-pretraining/fakes_{(idx*args.batch_size//1000):06}.png', imrange=[-0.5,0.5]) + post_model.save(f'{save_folder}/posterior-pretraining/network_{(idx*args.batch_size//1000):06}.pt') + + idx += 1 + if idx >= args.num_iters_post: + break + + print("Pretraining main model") + if gpu == 0: + os.makedirs(f'{save_folder}/main-pretraining', exist_ok=True) + utils.save_images(np.stack([test_dataset_clean[i][0].detach().numpy() for i in range(16)]), f'{save_folder}/main-pretraining/reals.png', imrange=[-0.5,0.5]) + utils.save_images(noisies, f'{save_folder}/main-pretraining/noisies.png', imrange=[-0.5, 0.5]) + idx = 0 + dloader = iter(train_dataloader) + while True: + try: x = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + x = next(dloader)[0] + + # x = torch.cat([x]*3, dim=1) + x.to(device) + if idx == 0 and args.main_actnorm: + main_model.initialize_actnorm(x) + main_model = main_model.to(device) + idx += 1 + continue + + main_optimizer.zero_grad() + loss = main_model.get_loss(x, num_bits=args.num_bits) + loss.backward() + warmup_lr = args.lr_main * min(1, idx * args.batch_size / (10000 * 10)) + main_optimizer.param_groups[0]["lr"] = warmup_lr + main_optimizer.step() + + if idx % (50 // args.batch_size) == 0: + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + print0(f"kImg. : {idx*args.batch_size/1000:.2f}, time : {timesec} Curr. loss : {loss}") + if idx % (500 // args.batch_size) == 0: + for temp in [0.5, 0.7, 1.0]: + x_samp = main_model.sample(16, temp=temp) + # x_samp = np.squeeze(x_samp) + # x_samp = np.swapaxes(x_samp, 1,2) + # x_samp = np.swapaxes(x_samp, 2,3) + # x_samp = np.squeeze(x_samp) + utils.save_images(x_samp, f'{save_folder}/main-pretraining/fakes_{(idx*args.batch_size//1000):06}_temp{temp}.png', imrange=[-0.5,0.5]) + main_model.save(f'{save_folder}/main-pretraining/network_{(idx*args.batch_size//1000):06}.pt') + + idx += 1 + if idx >= args.num_iters_main: + break + + + print("Jointly training main and posterior models") + if gpu == 0: + utils.save_images(np.stack([test_dataset_clean[i][0].detach().numpy() for i in range(16)]), f'{save_folder}/reals.png', imrange=[-0.5,0.5]) + utils.save_images(noisies, f'{save_folder}/noisies.png', imrange=[-0.5, 0.5]) + idx = 0 + dloader = iter(train_dataloader) + while True: + try: y = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + y = next(dloader)[0] + + # y = torch.cat([y]*3, dim=1) + y = y.to(device) + # tot_optimizer.zero_grad() + main_optimizer.zero_grad() + post_optimizer.zero_grad() + loss, x_posts = post_model.get_loss(y, degradation, num_z=args.num_z, reg_parameter=args.reg_parameter) + for x_post in x_posts: + loss += main_model.get_loss(x_post, num_bits=args.num_bits) / args.num_z + loss.backward() + # tot_optimizer.step() + warmup_lr = args.lr_main * min(1, (idx + args.num_iters_main) * args.batch_size / (10000 * 10)) + main_optimizer.param_groups[0]["lr"] = warmup_lr + warmup_lr = args.lr_post * min(1, (idx + args.num_iters_post) * args.batch_size / (10000 * 10)) + post_optimizer.param_groups[0]["lr"] = warmup_lr + main_optimizer.step() + post_optimizer.step() + + if idx % (5000 // args.batch_size) == 0: + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + print0(f"kImg. : {idx*args.batch_size/1000:.2f}, time : {timesec} Curr. loss : {loss}") + if idx % (50000 // args.batch_size) == 0: + for temp in [0.5, 0.7, 1.0]: + x_samp = main_model.sample(16, temp=temp) + utils.save_images(x_samp, f'{save_folder}/fakes_{(idx*args.batch_size//1000):06}_temp{temp}.png', imrange=[-0.5,0.5]) + xsamps = [] + if gpu == 0: + for yt in y_tests: + yt = yt.reshape(1,*yt.shape).to(device) + x_samp = post_model.sample(4, yt, temp=1) + xsamps.append(x_samp) + xsamps = np.concatenate(xsamps, axis=0) + utils.save_images(xsamps, f'{save_folder}/fakes_{(idx*args.batch_size//1000):06}_postsamp.png', imrange=[-0.5,0.5]) + main_model.save(f'{save_folder}/main-network_{(idx*args.batch_size//1000):06}.pt') + post_model.save(f'{save_folder}/post-network_{(idx*args.batch_size//1000):06}.pt') + + idx += 1 + if idx >= args.num_iters: + break + + +if __name__ == '__main__': + + args = parser.parse_args() + print(args) + + train(0, args) \ No newline at end of file diff --git a/src/train_vanilla.py b/src/train_vanilla.py new file mode 100644 index 0000000..e642172 --- /dev/null +++ b/src/train_vanilla.py @@ -0,0 +1,161 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.optim +import numpy as np +import dataset_tool +import models +import utils +import ast +import os +import argparse +import sys +import torch.distributed as dist +import torch.multiprocessing as mp +import time +import datetime + +import torch.nn.parallel as parallel + +parser = argparse.ArgumentParser() + +# multiprocessing arguments +parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N') +parser.add_argument('-g', '--gpus', default=1, type=int, help='number of gpus per node') +parser.add_argument('-nr', '--nr', default=0, type=int, help='ranking within the nodes') + +# model arguments +parser.add_argument("--input_shape", type=int, nargs='+', default=[3, 28, 28]) +parser.add_argument("--model_type", type=str, default='ConvINN') +parser.add_argument("--model_args", type=ast.literal_eval, default={'num_conv_layers': [4,12], 'num_fc_layers': [4]}) +parser.add_argument("--actnorm", type=lambda b:bool(int(b)), help="0 or 1") + +# training arguments +parser.add_argument("--num_kimgs", type=int, default=10000) +parser.add_argument("--batch_size", type=int, default=64) +parser.add_argument("--lr", type=float, default=1e-05) +parser.add_argument("--results_dir", type=str, default='') +parser.add_argument("--resume_from", type=str, default='') + +# data args +parser.add_argument("--data_type", type=str, default='MNISTDataset') +parser.add_argument("--data_args", type=ast.literal_eval, default={'power_of_two': False}) +parser.add_argument("--num_bits", type=int, default=0) + +def train(gpu, args): + + def print0(*args, **kwargs): + if gpu == 0: print(*args, **kwargs) + + args.num_iters = args.num_kimgs * 1000 // args.batch_size + + # initialize process + # rank = args.nr * args.gpus + gpu + # dist.init_process_group( + # backend='nccl', + # init_method='env://', + # world_size=args.world_size, + # rank=rank + # ) + + device = f'cuda:{gpu}' if torch.cuda.is_available() else 'cpu' + torch.manual_seed(0) + + args.data_args['input_shape'] = args.input_shape + train_dataset = getattr(dataset_tool, args.data_type)(train=True, **args.data_args) + test_dataset = getattr(dataset_tool, args.data_type)(train=False, **args.data_args) + train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4) + test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4) + + model = getattr(models, args.model_type)(args.input_shape, **args.model_args, device=device).to(device) + optimizer = torch.optim.Adamax(model.trainable_parameters, lr=args.lr) + + # Configure saving + save_folder = utils.setup_saver(args.results_dir, model.identifier+f'-lr{args.lr}-bits{args.num_bits}') + print0(args.__dict__, file=open(f'{save_folder}/config.txt', 'w')) + sys.stdout = utils.Logger(save_folder+'/log.txt') + print0(args, flush=True) + + if gpu == 0: + utils.save_images(np.stack([train_dataset[i][0].detach().numpy() for i in range(16)], axis=0), f'{save_folder}/reals.png', imrange=[-0.5,0.5]) + + if args.resume_from != '': + print0(f"Resuming training from {args.resume_from} ...") + model.load(args.resume_from) + model.to(device) + + print0("Training vanilla model") + idx = 0 + dloader = iter(train_dataloader) + start_time = time.time() + while True: + try: x = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + x = next(dloader)[0] + + # x = torch.cat([x]*3, dim=1) + x.to(device, non_blocking=True) + if args.resume_from == '': + if idx == 0 and args.actnorm: + model.initialize_actnorm(x) + model = model.to(device) + # model = parallel.DistributedDataParallel(model, device_ids=[gpu]) + idx += 1 + continue + + optimizer.zero_grad() + loss = model.get_loss(x, num_bits=args.num_bits) + loss.backward() + # warmup_lr = args.lr * min(1, idx * args.batch_size / (10000 * 10)) + warmup_lr = args.lr * min(1, idx * args.batch_size / (50000 * 10)) + optimizer.param_groups[0]["lr"] = warmup_lr + optimizer.step() + print_freq = int(np.ceil(5000 / args.batch_size)) + save_freq = int(np.ceil(100000 / args.batch_size)) + + if (idx < print_freq and idx % (print_freq // 5) == 0) or (idx % print_freq == 0): + timesec = time.time() - start_time + timesec = str(datetime.timedelta(seconds=int(timesec))) + print0(f"kImg. : {idx*args.batch_size/1000:.2f}, time : {timesec} Curr. loss : {loss}") + if (idx < save_freq and idx % (save_freq // 5) == 0) or (idx % save_freq == 0): + for temp in [0.5, 0.7, 1.0]: + x_samp = model.sample(16, temp=temp) + # x_samp = np.squeeze(x_samp) + # x_samp = np.swapaxes(x_samp, 1,2) + # x_samp = np.swapaxes(x_samp, 2,3) + # x_samp = np.squeeze(x_samp) + if gpu == 0: + utils.save_images(x_samp, f'{save_folder}/fakes_{(idx*args.batch_size//1000):06}_temp{temp}.png', imrange=[-0.5,0.5]) + model.save(f'{save_folder}/network_{(idx*args.batch_size/1000):06}.pt') + opt_state = { + 'idx': idx, + 'opt': optimizer.state_dict(), + 'lr' : optimizer.param_groups[0]["lr"] + } + torch.save(opt_state, f'{save_folder}/optimizer_{(idx*args.batch_size//1000):06}.pt') + + idx += 1 + if idx >= args.num_iters: + break + + +if __name__ == '__main__': + + args = parser.parse_args() + print(args) + + train(0, args) + + # args.world_size = args.gpus * args.nodes + # os.environ['MASTER_ADDR'] = 'localhost' + # os.environ['MASTER_PORT'] = '12355' + # mp.spawn(train, nprocs=args.gpus, args=(args,)) + + diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 0000000..7419f7a --- /dev/null +++ b/src/utils.py @@ -0,0 +1,87 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import numpy as np +import os +import glob +import imageio as io +import json +import sys +import ast + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +class Logger(object): + def __init__(self, logfile): + self.terminal = sys.stdout + self.log = open(logfile, "w") + + def write(self, message): + self.terminal.write(message) + self.log.write(message) + self.log.flush() + + def flush(self): + #this flush method is needed for python 3 compatibility. + #this handles the flush command by doing nothing. + #you might want to specify some extra behavior here. + self.log.flush() + return self.terminal.flush() + +def scatterplot_saver(data, path): + plt.scatter(data[:,0], data[:,1], alpha=0.3, edgecolor='none') + plt.xlim([-5,5]) + plt.ylim([-5,5]) + plt.savefig(path) + plt.close() + +def log_normal(var, std=1.): + K = np.prod(var.shape[1:]) + var = torch.reshape(var, [var.shape[0], -1]) + return -K/2 * np.log(2*np.pi) - K * np.log(std) - 0.5 * torch.norm(var, dim=-1)**2 / std**2 + +def setup_saver(results_dir, identifier): + folder_no = len(glob.glob(f'{results_dir}/*')) + folder = f'{results_dir}/{folder_no:03}-{identifier}' + os.makedirs(folder, exist_ok=True) + return folder + +def save_images(images, path, imrange=[0,1]): + + images = np.squeeze(images) + if len(images.shape) == 4: + images = np.swapaxes(images, 1, 2) + images = np.swapaxes(images, 2, 3) + if images.shape[-1] == 2: + images = images[...,0] + 1.j*images[...,1] + # images = images.detach().numpy() + N = int(np.sqrt(len(images))) + imrows = [ np.concatenate(images[N*i:N*i+N], axis=1) for i in range(N) ] + im = np.concatenate(imrows) + im = np.clip(im, *imrange) + im = (im - imrange[0]) / (imrange[1] - imrange[0]) * 255 + im = abs(im) + im = im.astype(np.uint8) + io.imsave(path, im) + return im + +def get_tv_loss(data_batch): + data_shape = data_batch.shape + if len(data_shape) == 5: + data_batch = data_batch.reshape( -1, *data_shape[2:] ) + +def total_variation(img): + if len(img.shape) == 5: + img = img.reshape( -1, *img.shape[2:] ) + x = img[:,:,1:,:] - img[:,:,:-1,:] + y = img[:,:,:,1:] - img[:,:,:,:-1] + tvnorm = torch.sum(torch.abs(x)) + torch.sum(torch.abs(y)) + return tvnorm \ No newline at end of file diff --git a/src/wavelet_utils.py b/src/wavelet_utils.py new file mode 100644 index 0000000..fbadaf5 --- /dev/null +++ b/src/wavelet_utils.py @@ -0,0 +1,60 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import numpy as np + +def unroll_shape(c): + if isinstance(c, tuple): + return tuple([unroll_shape(cc) for cc in c]) + elif isinstance(c, list): + return [unroll_shape(cc) for cc in c] + else: + return c.shape + +def coeffs_to_array(coeffs): + + shapes = unroll_shape(coeffs) + + def c2a(cofs): + array_list = [] + if isinstance(cofs, list) or isinstance(cofs, tuple): + for c in cofs: + array_list.append(c2a(c)) + if isinstance(array_list[0], np.ndarray): + array = np.concatenate(array_list, axis=-1) + elif isinstance(array_list[0], torch.Tensor): + array = torch.cat(array_list, -1) + return array + elif isinstance(cofs, torch.Tensor) or isinstance(cofs, np.ndarray): + array = cofs.reshape(cofs.shape[0], -1) + return array + + coeff_array = c2a(coeffs) + return coeff_array, shapes + +def array_to_coeffs(coeff_array, shapes): + + def a2c(array, sh): + if (isinstance(sh, tuple) or isinstance(sh, list)) and isinstance(sh[0], int): + ar = array[...,:np.prod(sh[1:])] + ar = ar.reshape(sh) + leftover = array[...,np.prod(sh[1:]):] + return ar, leftover + elif (isinstance(sh, tuple) or isinstance(sh, list)) and isinstance(sh[0], tuple): + leftover = array + array_tuple = [] + for s in sh: + ar, leftover = a2c(leftover, s) + array_tuple.append(ar) + array_tuple = tuple(array_tuple) + return array_tuple, leftover + + coeffs, _ = a2c(coeff_array, shapes) + return list(coeffs) + diff --git a/toy/ambient_model.py b/toy/ambient_model.py new file mode 100644 index 0000000..f940ce8 --- /dev/null +++ b/toy/ambient_model.py @@ -0,0 +1,393 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from time import time + +import torch +import torch.nn as nn +import torch.optim +import numpy as np +# from tqdm import tqdm + +import FrEIA.framework as Ff +import FrEIA.modules as Fm + +torch.manual_seed(1234) +np.random.seed(42) + +# from FrEIA.framework import InputNode, OutputNode, Node, ReversibleGraphNet +# from FrEIA.modules import GLOWCouplingBlock, PermuteRandom + +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +def subnet_fc(c_in, c_out): + return nn.Sequential(nn.Linear(c_in, 512), nn.ReLU(), + nn.Linear(512, c_out)) + +def get_main_model(num_layers=16, block_type='AllInOneBlock'): + + # Define the main flow + mainflow = Ff.SequenceINN(2) + for k in range(num_layers): + mainflow.append(Fm.AllInOneBlock, subnet_constructor=subnet_fc, permute_soft=True) + + mainflow.block_type = block_type + + return mainflow + +def get_main_model2(num_layers=8, block_type='GLOWCouplingBlock'): + + nodes = [Ff.InputNode(2, name='input')] + + for k in range(num_layers): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_fc, 'clamp':2.0}, + name=F'coupling_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_{k}')) + + nodes.append(Ff.OutputNode(nodes[-1], name='output')) + # TODO: Define the posterior flow + # This will be a conditional INN, use the conditional INN paper arch for this. + model = Ff.ReversibleGraphNet(nodes, verbose=False) + model.block_type = block_type + return model + +def get_posterior_model(num_layers=8, block_type='AllInOneBlock'): + + cond = Ff.ConditionNode(2) + nodes = [Ff.InputNode(2, name='Input')] + + for k in range(num_layers): + nodes.append(Ff.Node(nodes[-1], getattr(Fm, block_type), + {'subnet_constructor': subnet_fc, + 'permute_soft' : True}, + conditions=cond)) + + model = Ff.ReversibleGraphNet(nodes + [cond, Ff.OutputNode(nodes[-1])], verbose=False) + model.block_type = block_type + + return model + +def log_normal(var, std=1.): + K = np.prod(var.shape[1:]) + var = torch.reshape(var, [var.shape[0], -1]) + return -K/2 * np.log(2*np.pi) - K * np.log(std) - 0.5 * torch.norm(var, dim=-1)**2 / std**2 + +def get_model_loss(model, importance_weighting=0): + + def loss_fn(data_batch): + zbatch, jac = model([data_batch], jac=True) + return - torch.mean(log_normal(zbatch) + jac) + + def loss_fn_impw(data_batch): + zbatch, jac = model([data_batch], jac=True) + return - log_normal(zbatch) + jac + + if importance_weighting: + return loss_fn_impw + else: return loss_fn + +def get_posterior_loss(model, latent_bs=512, noise_std=0.1, num_z_per_data=1, reg_parameter=1., importance_weighting=0, **kwargs): + + if kwargs['fwd_model'] == 'simple_slant_squish': + R = np.array([ + [1/np.sqrt(2), -1/np.sqrt(2)], + [1/np.sqrt(2), 1/np.sqrt(2)] + ]) + A = torch.Tensor(R.T @ np.diag([np.sqrt(3), 1/np.sqrt(3)]) @ R).to(device) + Ainv = torch.linalg.inv(A) + fwd = lambda x : (A @ x.T).T + fwdinv = lambda y : ( Ainv @ y.T).T + elif kwargs['fwd_model'] == None: + fwd = lambda x : x + fwdinv = lambda y : y + + def loss_fn(data_batch): + + x_posts = [] + jac_rev = torch.zeros(size=(batch_size,)).to(device) + data_fidelity_prob = torch.zeros(size=(batch_size,)).to(device) + for i in range(num_z_per_data): + z = np.random.randn(latent_bs, 2) + x_post,jac_r = model(torch.Tensor(z).to(device), [fwdinv(data_batch)], rev=True) + x_posts.append(x_post) + jac_rev += jac_r/ num_z_per_data + data_fidelity_prob += log_normal( data_batch - fwd(x_post) , std=noise_std) / num_z_per_data + + return - torch.mean(reg_parameter*data_fidelity_prob + jac_rev), x_posts + + def loss_fn_impw(data_batch): + x_posts = [] + jac_rev = [] + data_fidelity_prob = [] + for i in range(num_z_per_data): + z = np.random.randn(latent_bs, 2) + x_post,jac_r = model(torch.Tensor(z).to(device), [fwdinv(data_batch)], rev=True) + x_posts.append(x_post) + # jac_rev += jac_r/ num_z_per_data + jac_rev.append(jac_r) + data_fidelity_prob.append( log_normal( data_batch - fwd(x_post) , std=noise_std) ) + jac_rev = torch.stack(jac_rev, dim=0) + data_fidelity_prob = torch.stack(data_fidelity_prob, dim=0) + + return -(reg_parameter*data_fidelity_prob + jac_rev), x_posts + + if importance_weighting: + return loss_fn_impw + else: + return loss_fn + + +def sample_from_cond(model, **kwargs): + + if kwargs['fwd_model'] == 'simple_slant_squish': + R = np.array([ + [1/np.sqrt(2), -1/np.sqrt(2)], + [1/np.sqrt(2), 1/np.sqrt(2)] + ]) + A = torch.Tensor(R.T @ np.diag([np.sqrt(3), 1/np.sqrt(3)]) @ R).to(device) + Ainv = torch.linalg.inv(A) + fwd = lambda x : (A @ x.T).T + fwdinv = lambda y : ( Ainv @ y.T).T + elif kwargs['fwd_model'] == None: + fwd = lambda x : x + fwdinv = lambda y : y + + def sampler(num_samples, cond): + z = np.random.randn(num_samples, 2) + cond = torch.stack([cond]*num_samples, dim=0).to(device) + assert len(cond) == num_samples, "Wrong number of conditional or num_samples" + x,_ = model(torch.Tensor(z).to(device), [fwdinv(cond)], rev=True) + x = x.cpu().detach().numpy() + return x + + return sampler + + +def sample_from(model): + + def sampler(num_samples, temp=1): + z = np.random.randn(num_samples, 2) * temp + x,_ = model(torch.Tensor(z).to(device), rev=True) + x = x.cpu().detach().numpy() + return x + + return sampler + +def scatterplot_saver(*args, path): + for dat in args: + plt.scatter(dat[:,0], dat[:,1], alpha=0.3, edgecolor='none') + plt.xlim([-5,5]) + plt.ylim([-5,5]) + plt.savefig(path) + plt.close() + +if __name__ == '__main__': + import data + import glob + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + import os + import dill + + N = 50000000 + batch_size = 2000 + num_iters = 100000 + num_iters_pre = 5000 + num_layers = 24 + num_layers_post = 16 + noise_std = 0.4 + reg_parameter = 0.05 + reg_parameter2 = 0.05 + num_z_per_data = 30 + importance_weighting = 0 + # fwd_model = 'simple_slant_squish' + fwd_model = None + + if fwd_model == 'simple_slant_squish': + R = np.array([ + [1/np.sqrt(2), -1/np.sqrt(2)], + [1/np.sqrt(2), 1/np.sqrt(2)] + ]) + A = torch.Tensor(R.T @ np.diag([np.sqrt(3), 1/np.sqrt(3)]) @ R).to(device) + Ainv = torch.linalg.inv(A) + fwd = lambda x : (A @ x.T).T + fwdinv = lambda y : ( Ainv @ y.T).T + elif fwd_model == None: + fwd = lambda x : x + fwdinv = lambda y : y + + dataset, _ = data.generate('all', N, noise='gaussian', fwd_model=fwd_model, noise_std=noise_std) + print(dataset.shape) + + main_model = get_main_model(num_layers) + post_model = get_posterior_model(num_layers_post) + + main_model = main_model.to(device) + post_model = post_model.to(device) + + main_trainable_parameters = [p for p in main_model.parameters() if p.requires_grad] + post_trainable_parameters = [p for p in post_model.parameters() if p.requires_grad] + main_optimizer = torch.optim.Adam(main_trainable_parameters, lr=2e-04) + post_optimizer = torch.optim.Adam(post_trainable_parameters, lr=2e-04) + tot_optimizer = torch.optim.Adam(main_trainable_parameters + post_trainable_parameters, lr=2e-04) + + ## Pretrain the main model with noisy data + main_loss_fn = get_model_loss(main_model) + + train_dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(dataset), batch_size=batch_size, shuffle=True, drop_last=True) + dloader = iter(train_dataloader) + + # Saving + folder = '../results/toy/toy_ambient2' + folder_no = len(glob.glob(f'{folder}/*')) + folder = f'../results/toy/toy_ambient2/{folder_no:03}-{main_model.block_type}-{num_layers}/pre-main' + os.makedirs(folder, exist_ok=True) + scatterplot_saver(dataset[:1000], path=f'{folder}/reals.png') + + sampler = sample_from(main_model) + + # Pretrain the main model with noisy data + print("Pretraining the main model with noisy data") + idx = 0 + while True: + try: y = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + y = next(dloader)[0] + + y = y.to(device) + y = fwdinv(y) + main_optimizer.zero_grad() + loss = main_loss_fn(y) + loss.backward() + main_optimizer.step() + + if idx % 50 == 0: + print(f"Idx. : {idx}, Curr. loss : {loss}") + if idx % 50 == 0: + x_samp = sampler(1000) + scatterplot_saver(x_samp, path=f'{folder}/fakes_{idx:06}.png') + torch.save(main_model, f'{folder}/network_{idx:06}.pth', pickle_module=dill) + + idx += 1 + if idx >= num_iters_pre: + break + + + ## Pretrain the posterior model + post_loss_fn = get_posterior_loss(post_model, latent_bs=batch_size, reg_parameter=reg_parameter, num_z_per_data=num_z_per_data, fwd_model=fwd_model) + + dloader = iter(train_dataloader) + + # Saving + folder = f'../results/toy/toy_ambient2/{folder_no:03}-{main_model.block_type}-{num_layers}/pre-post' + os.makedirs(folder, exist_ok=True) + y_tests,_ = data.generate('all', 8, noise='gaussian', shuffle=False, seed=1, fwd_model=fwd_model, noise_std=noise_std) + real_post_samples = [data.generate_posterior_samples(200, yt, noise_std=noise_std) for yt in y_tests] + scatterplot_saver(dataset[:100], *real_post_samples, path=f'{folder}/reals.png') + # scatterplot_saver(dataset[:100], path=f'{folder}/reals.png') + + sampler = sample_from_cond(post_model, fwd_model=fwd_model) + + # Pretrain the posterior model + print("Pretraining the posterior model") + idx = 0 + while True: + try: y = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + y = next(dloader)[0] + + y = y.to(device) + post_optimizer.zero_grad() + loss, _ = post_loss_fn(y) + loss.backward() + post_optimizer.step() + + if idx % 50 == 0: + print(f"Idx. : {idx}, Curr. loss : {loss}") + if idx % 50 == 0: + x_samp = [sampler(200, yt) for yt in y_tests] + scatterplot_saver(dataset[:100], *x_samp, path=f'{folder}/fakes_{idx:06}.png') + torch.save(post_model, f'{folder}/network_{idx:06}.pth', pickle_module=dill) + + idx += 1 + if idx >= num_iters_pre: + break + + + ## Train both models jointly + dloader = iter(train_dataloader) + main_loss_fn = get_model_loss(main_model, importance_weighting=importance_weighting) + post_loss_fn = get_posterior_loss(post_model, latent_bs=batch_size, reg_parameter=reg_parameter2, num_z_per_data=num_z_per_data, importance_weighting=importance_weighting, fwd_model=fwd_model) + + # Saving + folder = f'../results/toy/toy_ambient2/{folder_no:03}-{main_model.block_type}-{num_layers}/main' + os.makedirs(folder, exist_ok=True) + data_gt,_ = data.generate('all', 1000, shuffle=True, seed=1) + data_gt = data_gt.numpy() + scatterplot_saver(dataset[:1000], data_gt, path=f'{folder}/reals.png') + + sampler = sample_from(main_model) + cond_sampler = sample_from_cond(post_model, fwd_model=fwd_model) + + # Train both models jointly + print("Training both models jointly") + idx = 0 + while True: + try: y = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + y = next(dloader)[0] + + y = y.to(device) + tot_optimizer.zero_grad() + loss_post, x_posts = post_loss_fn(y) + + if importance_weighting == 0: + loss_main = torch.zeros(size=()).to(device) + for x_post in x_posts: + loss_main += main_loss_fn(x_post) / len(x_posts) + else: + loss_main = [] + for x_post in x_posts: + loss_main.append( main_loss_fn(x_post) ) + loss_main = torch.stack(loss_main, dim=0) + loss = loss_main + loss_post + if importance_weighting: + importance_weights = torch.softmax(loss, dim=0).detach() + loss = loss * importance_weights + loss = torch.mean(torch.sum(loss, dim=0)) + loss.backward() + tot_optimizer.step() + + if idx % 50 == 0: + print(f"Idx. : {idx}, Curr. loss : {loss}") + if idx % 50 == 0: + + # save samples from main model + x_samp = sampler(1000, temp=0.8) + scatterplot_saver(dataset[:1000], data_gt, x_samp, path=f'{folder}/fakes_{idx:06}.png') + + # save samples from posterior model + x_post = [cond_sampler(200, yt) for yt in y_tests] + scatterplot_saver(dataset[:100], *x_post, path=f'{folder}/posts_{idx:06}.png') + + torch.save(post_model, f'{folder}/post-network_{idx:06}.pth', pickle_module=dill) + torch.save(main_model, f'{folder}/main-network_{idx:06}.pth', pickle_module=dill) + + idx += 1 + if idx >= num_iters: + break diff --git a/toy/data.py b/toy/data.py new file mode 100644 index 0000000..e13c392 --- /dev/null +++ b/toy/data.py @@ -0,0 +1,137 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch +import torch.utils.data + +verts = [ + (-2.4142, 1.), + (-1., 2.4142), + (1., 2.4142), + (2.4142, 1.), + (2.4142, -1.), + (1., -2.4142), + (-1., -2.4142), + (-2.4142, -1.) + ] + +label_maps = { + 'all': [0, 1, 2, 3, 4, 5, 6, 7], + 'some': [0, 0, 0, 0, 1, 1, 2, 3], + 'none': [0, 0, 0, 0, 0, 0, 0, 0], + } + + +def generate(labels, tot_dataset_size, fwd_model=None, noise=None, shuffle=True, seed=0, **kwargs): + # print('Generating artifical data for setup "%s"' % (labels)) + + np.random.seed(seed) + N = tot_dataset_size + mapping = label_maps[labels] + + pos = np.random.normal(size=(N, 2), scale=0.1) + labels = np.zeros((N, 8)) + n = N//8 + + for i, v in enumerate(verts): + pos[i*n:(i+1)*n, :] += v + labels[i*n:(i+1)*n, mapping[i]] = 1. + + if shuffle: + shuffling = np.random.permutation(N) + else: shuffling = np.arange(N) + + if fwd_model == None: pass + + if fwd_model == 'simple_slant_squish': + R = np.array([ + [1/np.sqrt(2), -1/np.sqrt(2)], + [1/np.sqrt(2), 1/np.sqrt(2)] + ]) + A = R.T @ np.diag([np.sqrt(3), 1/np.sqrt(3)]) @ R + pos = (A @ pos.T).T + + if noise == None: pass + + elif noise == 'gaussian': + pos_noise = np.random.randn(*pos.shape) * kwargs['noise_std'] + pos += pos_noise + + pos = torch.tensor(pos[shuffling], dtype=torch.float) + labels = torch.tensor(labels[shuffling], dtype=torch.float) + + return pos, labels + + # test_loader = torch.utils.data.DataLoader( + # torch.utils.data.TensorDataset(pos[:test_split], labels[:test_split]), + # batch_size=batch_size, shuffle=True, drop_last=True) + + # train_loader = torch.utils.data.DataLoader( + # torch.utils.data.TensorDataset(pos[test_split:], labels[test_split:]), + # batch_size=batch_size, shuffle=True, drop_last=True) + + # return test_loader, train_loader + + +def generate_posterior_samples(tot_dataset_size, cond, **kwargs): + + samples = [] + cond = cond.numpy() + while True: + sample_pxgy = cond + np.random.randn(*cond.shape) * kwargs['noise_std'] + + acceptance_prob = np.mean([ + np.exp( + - (sample_pxgy - vert)**2 / 2 / (0.1**2) + ) + for vert in np.array(verts)]) / np.mean([ + np.exp( + - (verts[0] - vert)**2 / 2 / (0.1**2) + ) + for vert in np.array(verts)]) + + if np.random.uniform(0,1) >= acceptance_prob: + samples.append(sample_pxgy) + + if len(samples) >= tot_dataset_size: + break + + return np.array(samples) + + +if __name__ == '__main__': + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + temp = '/home/vak2/Documents/temp.png' + + N = 10000 + idx = np.random.randint(10000) + dat,lab = generate('all', N) + dat_n = dat + np.random.randn(*dat.shape) * 0.1 + postsamp = generate_posterior_samples(1000, dat_n[idx], noise_std=0.2) + + print(lab[idx]) + + plt.scatter(dat_n[:,0], dat_n[:,1], alpha=0.01, edgecolor='none') + plt.scatter(dat[:,0], dat[:,1], alpha=0.01, edgecolor='none') + plt.scatter(postsamp[:,0], postsamp[:,1], alpha=0.01, edgecolor='none') + plt.xlim([-5,5]) + plt.ylim([-5,5]) + plt.savefig(temp) + plt.close() + + + + + + + + + diff --git a/toy/plotting.py b/toy/plotting.py new file mode 100644 index 0000000..2e6594f --- /dev/null +++ b/toy/plotting.py @@ -0,0 +1,121 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +cmap = 'gist_heat' + +def plot_true_distribution(scale=0.15): + verts = [ + (-2.4142, 1.), + (-1., 2.4142), + (1., 2.4142), + (2.4142, 1.), + (2.4142, -1.), + (1., -2.4142), + (-1., -2.4142), + (-2.4142, -1.) + ] + + x,y = np.meshgrid(np.linspace(-4,4,128), np.linspace(-4,4,128)) + + pdf = np.zeros_like(x) + for v in verts: + pdf += np.exp( - ( (x - v[0])**2 + (y - v[1])**2 ) / 2 / scale**2 ) + + plt.imshow(pdf, cmap=cmap) + plt.xlim([-4,4]) + plt.ylim([-4,4]) + plt.axis('equal') + plt.axis('off') + plt.title("True distribution") + plt.savefig('../results/toy/true_distribution.svg', bbox_inches='tight', pad=0) + plt.savefig('../results/toy/true_distribution.png', bbox_inches='tight', pad=0) + plt.close() + + +def plot_noisy_distribution(scale=0.15, noise_std=0.45): + verts = [ + (-2.4142, 1.), + (-1., 2.4142), + (1., 2.4142), + (2.4142, 1.), + (2.4142, -1.), + (1., -2.4142), + (-1., -2.4142), + (-2.4142, -1.) + ] + + x,y = np.meshgrid(np.linspace(-4,4,128), np.linspace(-4,4,128)) + + pdf = np.zeros_like(x) + for v in verts: + pdf += np.exp( - ( (x - v[0])**2 + (y - v[1])**2 ) / 2 / (scale**2 + noise_std**2) ) + + plt.imshow(pdf, cmap=cmap) + plt.xlim([-4,4]) + plt.ylim([-4,4]) + plt.axis('equal') + plt.axis('off') + plt.title("Measurement distribution") + plt.savefig('../results/toy/meas_distribution.svg', bbox_inches='tight', pad=0) + plt.savefig('../results/toy/meas_distribution.png', bbox_inches='tight', pad=0) + plt.close() + + +def plot_inn_distribution(scale=0.15, path='', name=''): + + import scipy.stats as st + from skimage.transform import resize + + data = np.load(path)[:2000] + # bins = [ + # np.linspace(-4, 4, 128), + # np.linspace(-4, 4, 128), + # ] + # kernel = st.gaussian_kde(data.T) + # x,y = np.meshgrid(bins[0], bins[1]) + # positions = np.vstack([x.ravel(), y.ravel()]) + # counts = np.reshape(kernel(positions).T, x.shape) + counts,_,_ = np.histogram2d(data[...,0], data[...,1], bins=64, range=[[-4,4],[-4,4]]) + counts = resize(counts, (128,128), anti_aliasing=True) + + plt.imshow(counts, cmap=cmap) + # plt.scatter(data[...,0], data[...,1], alpha=0.3, edgecolor='none') + # plt.xlim([-4,4]) + # plt.ylim([-4,4]) + plt.axis('equal') + plt.axis('off') + plt.title(f"{name} distribution") + plt.savefig(f'../results/toy/{name}_distribution.svg', bbox_inches='tight', pad=0) + plt.savefig(f'../results/toy/{name}_distribution.png', bbox_inches='tight', pad=0) + plt.close() + + + + +if __name__ == '__main__': + + vanilla_path_0p7 = '/home/vak2/Documents/ambientflow/results/toy/toy_vanilla2/007-AllInOneBlock-24/data-029000-0.7.npy' + vanilla_path_1p0 = '/home/vak2/Documents/ambientflow/results/toy/toy_vanilla2/007-AllInOneBlock-24/data-029000-1.0.npy' + ambient_path_0p7 = '/home/vak2/Documents/ambientflow/results/toy/toy_ambient2/029-AllInOneBlock-24/data-027250-0.7.npy' + ambient_path_1p0 = '/home/vak2/Documents/ambientflow/results/toy/toy_ambient2/029-AllInOneBlock-24/data-027250-1.0.npy' + + plot_true_distribution() + plot_noisy_distribution() + plot_inn_distribution( + path = ambient_path_1p0, + name = 'ambient-1.0' + ) + plot_inn_distribution( + path = vanilla_path_1p0, + name = 'vanilla-1.0' + ) \ No newline at end of file diff --git a/toy/vanilla_model.py b/toy/vanilla_model.py new file mode 100644 index 0000000..b1d2b10 --- /dev/null +++ b/toy/vanilla_model.py @@ -0,0 +1,161 @@ +""" Copyright (c) 2022-2023 authors +Author : Varun A. Kelkar +Email : vak2@illinois.edu + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from time import time + +import torch +import torch.nn as nn +import torch.optim +import numpy as np +# from tqdm import tqdm + +import FrEIA.framework as Ff +import FrEIA.modules as Fm + +torch.manual_seed(1234) +np.random.seed(42) + +# from FrEIA.framework import InputNode, OutputNode, Node, ReversibleGraphNet +# from FrEIA.modules import GLOWCouplingBlock, PermuteRandom + +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +def subnet_fc(c_in, c_out): + return nn.Sequential(nn.Linear(c_in, 512), nn.ReLU(), + nn.Linear(512, c_out)) + +def get_main_model(num_layers=16, block_type='AllInOneBlock'): + + # Define the main flow + mainflow = Ff.SequenceINN(2) + for k in range(num_layers): + mainflow.append(Fm.AllInOneBlock, subnet_constructor=subnet_fc, permute_soft=True) + + mainflow.block_type = block_type + + return mainflow + +def get_main_model2(num_layers=8, block_type='GLOWCouplingBlock'): + + nodes = [Ff.InputNode(2, name='input')] + + for k in range(num_layers): + nodes.append(Ff.Node(nodes[-1], + Fm.GLOWCouplingBlock, + {'subnet_constructor':subnet_fc, 'clamp':2.0}, + name=F'coupling_{k}')) + nodes.append(Ff.Node(nodes[-1], + Fm.PermuteRandom, + {'seed':k}, + name=F'permute_{k}')) + + nodes.append(Ff.OutputNode(nodes[-1], name='output')) + # TODO: Define the posterior flow + # This will be a conditional INN, use the conditional INN paper arch for this. + model = Ff.ReversibleGraphNet(nodes, verbose=False) + model.block_type = block_type + return model + + + +def log_normal(var): + K = np.prod(var.shape[1:]) + var = torch.reshape(var, [var.shape[0], -1]) + return -K/2 * np.log(2*np.pi) - 0.5 * torch.norm(var, dim=-1)**2 + +def get_loss(model): + + def loss_fn(data_batch): + zbatch, jac = model([data_batch], jac=True) + return - torch.mean(log_normal(zbatch) + jac) + + return loss_fn + +def sample_from(model): + + def sampler(num_samples): + z = np.random.randn(num_samples, 2) + z = torch.Tensor(z).to(device) + x,_ = model(z, rev=True) + x = x.detach().cpu().numpy() + return x + + return sampler + +def scatterplot_saver(data, path): + plt.scatter(data[:,0], data[:,1], alpha=0.3, edgecolor='none') + plt.xlim([-5,5]) + plt.ylim([-5,5]) + plt.savefig(path) + plt.close() + + + +if __name__ == '__main__': + import data + import glob + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + import os + import dill + + N = 5000000 + batch_size = 16384 + num_iters = 50000 + num_layers = 24 + + dataset, _ = data.generate('all', N) + print(dataset.shape) + + model = get_main_model(num_layers) + + model = model.to(device) + + trainable_parameters = [p for p in model.parameters() if p.requires_grad] + optimizer = torch.optim.Adam(trainable_parameters, lr=5e-04) + + loss_fn = get_loss(model) + + train_dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(dataset), batch_size=batch_size, shuffle=True, drop_last=True) + dloader = iter(train_dataloader) + + # Saving + folder_no = len(glob.glob('../results/toy/toy_vanilla2/*')) + folder = f'../results/toy/toy_vanilla2/{folder_no:03}-{model.block_type}-{num_layers}' + os.makedirs(folder, exist_ok=True) + scatterplot_saver(dataset[:1000], f'{folder}/reals.png') + + sampler = sample_from(model) + + print("Training vanilla model") + idx = 0 + while True: + try: x = next(dloader)[0] + except StopIteration: + dloader = iter(train_dataloader) + x = next(dloader)[0] + + x = x.to(device) + optimizer.zero_grad() + loss = loss_fn(x) + loss.backward() + optimizer.step() + + if idx % 50 == 0: + print(f"Idx. : {idx}, Curr. loss : {loss}") + if idx % 100 == 0: + x_samp = sampler(1000) + scatterplot_saver(x_samp, f'{folder}/fakes_{idx:06}.png') + torch.save(model, f'{folder}/network_{idx:06}.pth', pickle_module=dill) + + + idx += 1 + if idx >= num_iters: + break