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dextr-0.1.2


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توضیحات

PyTorch Deep Extreme Cut library
ویژگی مقدار
سیستم عامل -
نام فایل dextr-0.1.2
نام dextr
نسخه کتابخانه 0.1.2
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Geoff French
ایمیل نویسنده -
آدرس صفحه اصلی https://github.com/Britefury/dextr
آدرس اینترنتی https://pypi.org/project/dextr/
مجوز 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.


نیازمندی

مقدار نام
- numpy
- scipy
- Pillow
- scikit-image
- torch
- torchvision


نحوه نصب


نصب پکیج whl dextr-0.1.2:

    pip install dextr-0.1.2.whl


نصب پکیج tar.gz dextr-0.1.2:

    pip install dextr-0.1.2.tar.gz