From 2a93fdcfd443b21d51b79be76e26685a48673a5f Mon Sep 17 00:00:00 2001
From: daveabiy <dawitk27@gmail.com>
Date: Sun, 2 May 2021 14:17:53 +0200
Subject: [PATCH] Our master branch
---
LICENSE | 201 --------------------------------
README.md | 64 ++--------
project/__init__.py | 0
project/lit_autoencoder.py | 88 --------------
project/lit_image_classifier.py | 109 -----------------
project/lit_mnist.py | 96 ---------------
setup.py | 6 +-
7 files changed, 15 insertions(+), 549 deletions(-)
delete mode 100644 LICENSE
delete mode 100644 project/__init__.py
delete mode 100644 project/lit_autoencoder.py
delete mode 100644 project/lit_image_classifier.py
delete mode 100644 project/lit_mnist.py
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 261eeb9..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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- http://www.apache.org/licenses/
-
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diff --git a/README.md b/README.md
index 63571ba..c76af1e 100644
--- a/README.md
+++ b/README.md
@@ -1,24 +1,12 @@
-### Deep learning project seed
-Use this seed to start new deep learning / ML projects.
-
-- Built in setup.py
-- Built in requirements
-- Examples with MNIST
-- Badges
-- Bibtex
-
-#### Goals
-The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.
-
-### DELETE EVERYTHING ABOVE FOR YOUR PROJECT
-
----
-
<div align="center">
-# Your Project Name
+# Deep-Inverse
+
-[](https://www.nature.com/articles/nature14539)
+[](https://papers.nips.cc/paper/2018/file/d903e9608cfbf08910611e4346a0ba44-Paper.pdf)
+[](https://iopscience.iop.org/article/10.1088/1361-6420/aaf14a)
+[](https://arxiv.org/pdf/2008.02839.pdf)
+[](https://www.nature.com/articles/s41597-021-00893--z)
[](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
[](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
[](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
@@ -34,49 +22,21 @@ Conference
-->
</div>
-## Description
-What it does
+## GOALS OF THE PROJECT
+We would like to build a data-driven model to reconstruct CT images. The model should be evaluated on the LoDoPaB challenge. (https://lodopab.grand-challenge.org/)
## How to run
-First, install dependencies
-```bash
+
# clone project
-git clone https://github.com/YourGithubName/deep-learning-project-template
+git clone https://gitlab.rrz.uni-hamburg.de/BAT9096/deepinverse.git
# install project
-cd deep-learning-project-template
-pip install -e .
-pip install -r requirements.txt
- ```
- Next, navigate to any file and run it.
- ```bash
-# module folder
-cd project
# run module (example: mnist as your main contribution)
-python lit_classifier_main.py
-```
-## Imports
-This project is setup as a package which means you can now easily import any file into any other file like so:
-```python
-from project.datasets.mnist import mnist
-from project.lit_classifier_main import LitClassifier
-from pytorch_lightning import Trainer
-
-# model
-model = LitClassifier()
-
-# data
-train, val, test = mnist()
-# train
-trainer = Trainer()
-trainer.fit(model, train, val)
-
-# test using the best model!
-trainer.test(test_dataloaders=test)
-```
+## Imports
+Not yet completed
### Citation
```
diff --git a/project/__init__.py b/project/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/project/lit_autoencoder.py b/project/lit_autoencoder.py
deleted file mode 100644
index 3f9ff0c..0000000
--- a/project/lit_autoencoder.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from argparse import ArgumentParser
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import pytorch_lightning as pl
-from torch.utils.data import random_split
-
-from torchvision.datasets.mnist import MNIST
-from torchvision import transforms
-
-
-class LitAutoEncoder(pl.LightningModule):
-
- def __init__(self):
- super().__init__()
- self.encoder = nn.Sequential(
- nn.Linear(28 * 28, 64),
- nn.ReLU(),
- nn.Linear(64, 3)
- )
- self.decoder = nn.Sequential(
- nn.Linear(3, 64),
- nn.ReLU(),
- nn.Linear(64, 28 * 28)
- )
-
- def forward(self, x):
- # in lightning, forward defines the prediction/inference actions
- embedding = self.encoder(x)
- return embedding
-
- def training_step(self, batch, batch_idx):
- x, y = batch
- x = x.view(x.size(0), -1)
- z = self.encoder(x)
- x_hat = self.decoder(z)
- loss = F.mse_loss(x_hat, x)
- return loss
-
- def configure_optimizers(self):
- optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
- return optimizer
-
-
-def cli_main():
- pl.seed_everything(1234)
-
- # ------------
- # args
- # ------------
- parser = ArgumentParser()
- parser.add_argument('--batch_size', default=32, type=int)
- parser.add_argument('--hidden_dim', type=int, default=128)
- parser = pl.Trainer.add_argparse_args(parser)
- args = parser.parse_args()
-
- # ------------
- # data
- # ------------
- dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
- mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
- mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
- train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
- val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
- test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
- # ------------
- # model
- # ------------
- model = LitAutoEncoder()
-
- # ------------
- # training
- # ------------
- trainer = pl.Trainer.from_argparse_args(args)
- trainer.fit(model, train_loader, val_loader)
-
- # ------------
- # testing
- # ------------
- result = trainer.test(test_dataloaders=test_loader)
- print(result)
-
-
-if __name__ == '__main__':
- cli_main()
diff --git a/project/lit_image_classifier.py b/project/lit_image_classifier.py
deleted file mode 100644
index 1296a3f..0000000
--- a/project/lit_image_classifier.py
+++ /dev/null
@@ -1,109 +0,0 @@
-from argparse import ArgumentParser
-
-import torch
-import pytorch_lightning as pl
-from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
-
-from torchvision.datasets.mnist import MNIST
-from torchvision import transforms
-
-
-class Backbone(torch.nn.Module):
- def __init__(self, hidden_dim=128):
- super().__init__()
- self.l1 = torch.nn.Linear(28 * 28, hidden_dim)
- self.l2 = torch.nn.Linear(hidden_dim, 10)
-
- def forward(self, x):
- x = x.view(x.size(0), -1)
- x = torch.relu(self.l1(x))
- x = torch.relu(self.l2(x))
- return x
-
-
-class LitClassifier(pl.LightningModule):
- def __init__(self, backbone, learning_rate=1e-3):
- super().__init__()
- self.save_hyperparameters()
- self.backbone = backbone
-
- def forward(self, x):
- # use forward for inference/predictions
- embedding = self.backbone(x)
- return embedding
-
- def training_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self.backbone(x)
- loss = F.cross_entropy(y_hat, y)
- self.log('train_loss', loss, on_epoch=True)
- return loss
-
- def validation_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self.backbone(x)
- loss = F.cross_entropy(y_hat, y)
- self.log('valid_loss', loss, on_step=True)
-
- def test_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self.backbone(x)
- loss = F.cross_entropy(y_hat, y)
- self.log('test_loss', loss)
-
- def configure_optimizers(self):
- # self.hparams available because we called self.save_hyperparameters()
- return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
- @staticmethod
- def add_model_specific_args(parent_parser):
- parser = ArgumentParser(parents=[parent_parser], add_help=False)
- parser.add_argument('--learning_rate', type=float, default=0.0001)
- return parser
-
-
-def cli_main():
- pl.seed_everything(1234)
-
- # ------------
- # args
- # ------------
- parser = ArgumentParser()
- parser.add_argument('--batch_size', default=32, type=int)
- parser.add_argument('--hidden_dim', type=int, default=128)
- parser = pl.Trainer.add_argparse_args(parser)
- parser = LitClassifier.add_model_specific_args(parser)
- args = parser.parse_args()
-
- # ------------
- # data
- # ------------
- dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
- mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
- mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
- train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
- val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
- test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
- # ------------
- # model
- # ------------
- model = LitClassifier(Backbone(hidden_dim=args.hidden_dim), args.learning_rate)
-
- # ------------
- # training
- # ------------
- trainer = pl.Trainer.from_argparse_args(args)
- trainer.fit(model, train_loader, val_loader)
-
- # ------------
- # testing
- # ------------
- result = trainer.test(test_dataloaders=test_loader)
- print(result)
-
-
-if __name__ == '__main__':
- cli_main()
diff --git a/project/lit_mnist.py b/project/lit_mnist.py
deleted file mode 100644
index 8733378..0000000
--- a/project/lit_mnist.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from argparse import ArgumentParser
-
-import torch
-import pytorch_lightning as pl
-from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
-
-from torchvision.datasets.mnist import MNIST
-from torchvision import transforms
-
-
-class LitClassifier(pl.LightningModule):
- def __init__(self, hidden_dim=128, learning_rate=1e-3):
- super().__init__()
- self.save_hyperparameters()
-
- self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
- self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
-
- def forward(self, x):
- x = x.view(x.size(0), -1)
- x = torch.relu(self.l1(x))
- x = torch.relu(self.l2(x))
- return x
-
- def training_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self(x)
- loss = F.cross_entropy(y_hat, y)
- return loss
-
- def validation_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self(x)
- loss = F.cross_entropy(y_hat, y)
- self.log('valid_loss', loss)
-
- def test_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self(x)
- loss = F.cross_entropy(y_hat, y)
- self.log('test_loss', loss)
-
- def configure_optimizers(self):
- return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
- @staticmethod
- def add_model_specific_args(parent_parser):
- parser = ArgumentParser(parents=[parent_parser], add_help=False)
- parser.add_argument('--hidden_dim', type=int, default=128)
- parser.add_argument('--learning_rate', type=float, default=0.0001)
- return parser
-
-
-def cli_main():
- pl.seed_everything(1234)
-
- # ------------
- # args
- # ------------
- parser = ArgumentParser()
- parser.add_argument('--batch_size', default=32, type=int)
- parser = pl.Trainer.add_argparse_args(parser)
- parser = LitClassifier.add_model_specific_args(parser)
- args = parser.parse_args()
-
- # ------------
- # data
- # ------------
- dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
- mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
- mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
- train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
- val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
- test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
- # ------------
- # model
- # ------------
- model = LitClassifier(args.hidden_dim, args.learning_rate)
-
- # ------------
- # training
- # ------------
- trainer = pl.Trainer.from_argparse_args(args)
- trainer.fit(model, train_loader, val_loader)
-
- # ------------
- # testing
- # ------------
- trainer.test(test_dataloaders=test_loader)
-
-
-if __name__ == '__main__':
- cli_main()
diff --git a/setup.py b/setup.py
index 3de44dd..8b71410 100644
--- a/setup.py
+++ b/setup.py
@@ -3,13 +3,13 @@
from setuptools import setup, find_packages
setup(
- name='project',
+ name='DeepInverse',
version='0.0.0',
- description='Describe Your Cool Project',
+ description='Deep Learning in Inverse Problems',
author='',
author_email='',
# REPLACE WITH YOUR OWN GITHUB PROJECT LINK
- url='https://github.com/PyTorchLightning/pytorch-lightning-conference-seed',
+ url='https://gitlab.rrz.uni-hamburg.de/BAT9096/deepinverse.git',
install_requires=['pytorch-lightning'],
packages=find_packages(),
)
--
GitLab