dextr 0.1.2
PyTorch Deep Extreme Cut library
License: MIT

# PyTorch implementation of DEXTR

An implementation of [DEXTR](http://people.ee.ethz.ch/~cvlsegmentation/dextr/).
The original implementation can be found at [https://github.com/scaelles/DEXTR-PyTorch](https://github.com/scaelles/DEXTR-PyTorch).

This implementation is intended for use as a library.

### Installation

`> pip install dextr`


## Python Inference API

See `demo.py` for an example of using the `dextr` inference API.

We have trained a ResNet-101 based U-Net DEXTR model on the Pascal VOC 2012 training set. You can download it
[here](https://storage.googleapis.com/dextr_pytorch_models_public/dextr_pascalvoc_resunet101-a2d81727.pth).

You can load this model -- downloading it automatically -- like so:

```py3
from dextr.model import DextrModel

# Load the model (automatically downloads if necessary)
# You can also provide a `map_location` paramter to load it onto a specific device
model = DextrModel.pascalvoc_resunet101()
```

Alternatively you can load a model that you have trained yourself from a file:
```py3
MODEL_PATH = '...'
dextr_model = torch.load(MODEL_PATH, map_location='cuda:0')
```

Use the `predict` method to predict a mask for an object in an image, identified by its extreme points:

```py3
mask = dextr_model.predict([image], [extreme_points])[0]
```

You can perform inference on multiple images with one call.
The `DextrModel.predict` method takes a list
of images and extreme points as either a list of `(4, [y, x])` NumPy arrays or
one `(N, 4, [y, x])` shaped NumPy array. 

The images that you use as input can take the form of either NumPy arrays or PIL Images. Each image should
have a corresponding list of four extreme points. It returns a list of masks; each
mask is the same size as the corresponding input image:


## Training using the command line `train_dextr.py` program

### Train a DEXTR network using the Pascal VOC dataset

This will train a DEXTR model using a [U-Net](https://arxiv.org/abs/1505.04597) with a ResNet-101 based encoder.
It should take several hours on an nVidia 1080Ti GPU. 

- Download the Pascal VOC 2012 dataset [development kit](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/)
- Create a file called `dextr.cfg` with the following contents:
```cfg
[paths]
pascal_voc=<path to VOC2012 diretory>
```  
- Train the DEXTR model by running:

`> python train_dextr.py pascal_resunet101 --dataset=pascal_voc --arch=resunet101`

The name `pascal_resunet101` is the name of the job; STDOUT will be logged to `logs/log_pascal_resunet101.txt` and the model
file will be saved to `checkpoints/pascal_resunet101.pth`. You can give the job any name you like.


### Fine tuning a DEXTR network using a custom data set

There are two types of data set you can use:
1. Each input image has a corresponding label image, where label images have an integer pixel type such that each
   pixel gives the index of the object that covers it, or 0 for background. The Pascal VOC dataset is arranged in
   this way.
2. Each input image has a corresponding set of mask images that form a stack. Each mask image is an 8-bit greyscale
   image that corresponds to an object/instance and identifies the pixels covered by it.

Please arrange your custom data set so that the image file names (excluding extension) match or are a prefix
to the label/mask image file names. E.g. the image `img0.jpg` will match the label file `img0.png` or `img0_labels.png`.
For mask stack datasets `img0.jpg` would match to the mask images `img0_mask0.png`, ... `img0_maskN.png`.
The images and labels can live in separate directories; they are matched by filename *only*.

In these examples, we assume that you have downloaded the pre-trained DEXTR model linked above.

##### Training using a label image data set

`> python train_dextr.py my_model_from_labels --dataset=custom_label
--train_image_pat=/mydataset/train/input/*.jpg --train_target_pat=/mydataset/train/labels/*.png
--arch=resunet101 --load_model=dextr_pascalvoc_resunet101-a2d81727.pth`

The input and label images are given to the `--train_image_pat` and `--train_target_pat` options.
You can specify validation images using the `--val_image_pat` and `--val_target_pat` options in a similar way.

`--load_model=dextr_pascalvoc_resunet101-a2d81727.pth` indicates that we should start by loading the
model trained on Pascal VOC above and fine-tune it, rather than starting from an ImageNet classifier.

You can specify that the label index 255 should be ignore by adding `--label_ignore_index=255`.

You could train using the entire (train and validation) Pascal VOC data set using:

`> python train_dextr.py my_model_from_pascal --dataset=custom_label
--train_image_pat=/pascal/VOC2012/JPEGImages/*.jpg --train_target_pat=/pascal/VOC2012/SegmentationObjects/*.png
--label_ignore_index=255 --arch=resunet101`

##### Training using a mask stack data set

`> python train_dextr.py my_model_from_masks --dataset=custom_mask
--train_image_pat=/mydataset/train/input/*.jpg --train_target_pat=/mydataset/train/masks/*.png
--arch=resunet101 --load_model=dextr_pascalvoc_resunet101-a2d81727.pth`



## Python training API

The `training_loop` function within the `dextr.model` module provides a simple training loop that can be used
for training or fine-tuning models. See `train_dextr.py` for usage.