dextr 0.1.2

PyTorch Deep Extreme Cut library

PyTorch implementation of DEXTR

An implementation of DEXTR. The original implementation can be found at 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.

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

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:

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:

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 with a ResNet-101 based encoder. It should take several hours on an nVidia 1080Ti GPU.

• Download the Pascal VOC 2012 dataset development kit
• Create a file called dextr.cfg with the following contents:

[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.