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Repository: bailvwangzi/repulsion_loss_ssd
Branch: master
Commit: fcf6c14be7e6
Files: 30
Total size: 1.4 MB

Directory structure:
gitextract_6znju64_/

├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── data/
│   ├── __init__.py
│   ├── coco.py
│   ├── coco_labels.txt
│   ├── config.py
│   ├── scripts/
│   │   ├── COCO2014.sh
│   │   ├── VOC2007.sh
│   │   └── VOC2012.sh
│   └── voc0712.py
├── demo/
│   ├── __init__.py
│   ├── demo.ipynb
│   └── live.py
├── eval.py
├── layers/
│   ├── __init__.py
│   ├── box_utils.py
│   ├── functions/
│   │   ├── __init__.py
│   │   ├── detection.py
│   │   └── prior_box.py
│   └── modules/
│       ├── __init__.py
│       ├── l2norm.py
│       ├── multibox_loss.py
│       └── repulsion_loss.py
├── ssd.py
├── test.py
├── train.py
└── utils/
    ├── __init__.py
    └── augmentations.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitattributes
================================================
*.ipynb linguist-language=Python
.ipynb_checkpoints/* linguist-documentation
dev.ipynb linguist-documentation


================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*,cover
.hypothesis/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# IPython Notebook
.ipynb_checkpoints

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule

# dotenv
.env

# virtualenv
venv/
ENV/

# Spyder project settings
.spyderproject

# Rope project settings
.ropeproject

# atom remote-sync package
.remote-sync.json

# weights
weights/

#DS_Store
.DS_Store

# dev stuff
eval/
eval.ipynb
dev.ipynb
.vscode/

# not ready
videos/
templates/
data/ssd_dataloader.py
data/datasets/
doc/visualize.py
read_results.py
ssd300_120000/
demos/live
webdemo.py
test_data_aug.py

# attributes

# pycharm
.idea/
.settings/
.project
.project
.pydevproject

# temp checkout soln
data/datasets/
data/ssd_dataloader.py

# pylint
.pylintrc


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2017 Max deGroot, Ellis Brown

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# Repulsion Loss implemented with SSD
Forked from [PyTorch-SSD](https://github.com/amdegroot/ssd.pytorch), which is a [PyTorch](http://pytorch.org/) implementation of [Single Shot MultiBox Detector](http://arxiv.org/abs/1512.02325) from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg.  The official and original Caffe code can be found [here](https://github.com/weiliu89/caffe/tree/ssd).


<img align="right" src= "https://github.com/amdegroot/ssd.pytorch/blob/master/doc/ssd.png" height = 400/>

### Table of Contents
- <a href='#installation'>Installation</a>
- <a href='#datasets'>Datasets</a>
- <a href='#training-ssd'>Train</a>
- <a href='#evaluation'>Evaluate</a>
- <a href='#performance'>Performance</a>
- <a href='#demos'>Demos</a>
- <a href='#todo'>Future Work</a>
- <a href='#references'>Reference</a>

&nbsp;
&nbsp;
&nbsp;
&nbsp;

## Installation
- Install [PyTorch](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.
- Clone this repository.
  * Note: We currently only support Python 3+.
- Then download the dataset by following the [instructions](#datasets) below.
- We now support [Visdom](https://github.com/facebookresearch/visdom) for real-time loss visualization during training!
  * To use Visdom in the browser:
  ```Shell
  # First install Python server and client
  pip install visdom
  # Start the server (probably in a screen or tmux)
  python -m visdom.server
  ```
  * Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).
- Note: For training, we currently support [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [COCO](http://mscoco.org/), and aim to add [ImageNet](http://www.image-net.org/) support soon.

## Datasets
To make things easy, we provide bash scripts to handle the dataset downloads and setup for you.  We also provide simple dataset loaders that inherit `torch.utils.data.Dataset`, making them fully compatible with the `torchvision.datasets` [API](http://pytorch.org/docs/torchvision/datasets.html).


### COCO
Microsoft COCO: Common Objects in Context

##### Download COCO 2014
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh
```

### VOC Dataset
PASCAL VOC: Visual Object Classes

##### Download VOC2007 trainval & test
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>
```

##### Download VOC2012 trainval
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>
```

## Training SSD
- First download the fc-reduced [VGG-16](https://arxiv.org/abs/1409.1556) PyTorch base network weights at:              https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
- By default, we assume you have downloaded the file in the `ssd.pytorch/weights` dir:

```Shell
mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
```

- To train SSD using the train script simply specify the parameters listed in `train.py` as a flag or manually change them.

```Shell
python train.py
```

- Note:
  * For training, an NVIDIA GPU is strongly recommended for speed.
  * For instructions on Visdom usage/installation, see the <a href='#installation'>Installation</a> section.
  * You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see `train.py` for options)

## Evaluation
To evaluate a trained network:

```Shell
python eval.py
```

You can specify the parameters listed in the `eval.py` file by flagging them or manually changing them.  

## Example
SSD:

![](doc/003534.jpg)

SSD + repulsion loss:

![](doc/003534_repul.jpg)


## Performance

#### VOC2007 Test

##### mAP

| Method | mAP | mAP on Crowd | 
|:-:|:-:|:-:|
| SSD | <b>77.52%</b> | 48.24% | 
| SSD+RepGT | 77.43% | <b>50.12%</b> | 



## Demos

### Use a pre-trained SSD network for detection

#### Download a pre-trained network
- We are trying to provide PyTorch `state_dicts` (dict of weight tensors) of the latest SSD model definitions trained on different datasets.  
- Currently, we provide the following PyTorch models:
    * SSD300 trained on VOC0712 (newest PyTorch weights)
      - https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth
    * SSD300 trained on VOC0712 (original Caffe weights)
      - https://s3.amazonaws.com/amdegroot-models/ssd_300_VOC0712.pth
- Our goal is to reproduce this table from the [original paper](http://arxiv.org/abs/1512.02325)
<p align="left">
<img src="http://www.cs.unc.edu/~wliu/papers/ssd_results.png" alt="SSD results on multiple datasets" width="800px"></p>

### Try the demo notebook
- Make sure you have [jupyter notebook](http://jupyter.readthedocs.io/en/latest/install.html) installed.
- Two alternatives for installing jupyter notebook:
    1. If you installed PyTorch with [conda](https://www.continuum.io/downloads) (recommended), then you should already have it.  (Just  navigate to the ssd.pytorch cloned repo and run):
    `jupyter notebook`

    2. If using [pip](https://pypi.python.org/pypi/pip):

```Shell
# make sure pip is upgraded
pip3 install --upgrade pip
# install jupyter notebook
pip install jupyter
# Run this inside ssd.pytorch
jupyter notebook
```

- Now navigate to `demo/demo.ipynb` at http://localhost:8888 (by default) and have at it!

### Try the webcam demo
- Works on CPU (may have to tweak `cv2.waitkey` for optimal fps) or on an NVIDIA GPU
- This demo currently requires opencv2+ w/ python bindings and an onboard webcam
  * You can change the default webcam in `demo/live.py`
- Install the [imutils](https://github.com/jrosebr1/imutils) package to leverage multi-threading on CPU:
  * `pip install imutils`
- Running `python -m demo.live` opens the webcam and begins detecting!

## TODO
We have accumulated the following to-do list, which we hope to complete in the near future
- Still to come:
  * [x] Support for the MS COCO dataset
  * [ ] Support for SSD512 training and testing
  * [ ] Support for training on custom datasets
  * [ ] Support for RepBox term
  * [ ] Support for selecting the second largest IoU from the same class

## Authors


## References
- Xinlong Wang, et al. "Repulsion Loss: Detecting Pedestrians in a Crowd." [CVPR2018](https://arxiv.org/abs/1711.07752).
- Wei Liu, et al. "SSD: Single Shot MultiBox Detector." [ECCV2016](http://arxiv.org/abs/1512.02325).
- [Pytorch-SSD](https://github.com/amdegroot/ssd.pytorch).
- [Original Implementation (CAFFE)](https://github.com/weiliu89/caffe/tree/ssd).


================================================
FILE: data/__init__.py
================================================
from .voc0712 import VOCDetection, VOCAnnotationTransform, VOC_CLASSES, VOC_ROOT

#from .coco import COCODetection, COCOAnnotationTransform, COCO_CLASSES, COCO_ROOT, get_label_map
from .config import *
import torch
import cv2
import numpy as np

def detection_collate(batch):
    """Custom collate fn for dealing with batches of images that have a different
    number of associated object annotations (bounding boxes).

    Arguments:
        batch: (tuple) A tuple of tensor images and lists of annotations

    Return:
        A tuple containing:
            1) (tensor) batch of images stacked on their 0 dim
            2) (list of tensors) annotations for a given image are stacked on
                                 0 dim
    """
    targets = []
    imgs = []
    for sample in batch:
        imgs.append(sample[0])
        targets.append(torch.FloatTensor(sample[1]))
    return torch.stack(imgs, 0), targets


def base_transform(image, size, mean):
    x = cv2.resize(image, (size, size)).astype(np.float32)
    x -= mean
    x = x.astype(np.float32)
    return x


class BaseTransform:
    def __init__(self, size, mean):
        self.size = size
        self.mean = np.array(mean, dtype=np.float32)

    def __call__(self, image, boxes=None, labels=None):
        return base_transform(image, self.size, self.mean), boxes, labels


================================================
FILE: data/coco.py
================================================
from .config import HOME
import os
import os.path as osp
import sys
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import cv2
import numpy as np

COCO_ROOT = osp.join(HOME, 'data/coco/')
IMAGES = 'images'
ANNOTATIONS = 'annotations'
COCO_API = 'PythonAPI'
INSTANCES_SET = 'instances_{}.json'
COCO_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
                'train', 'truck', 'boat', 'traffic light', 'fire', 'hydrant',
                'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
                'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
                'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
                'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
                'kite', 'baseball bat', 'baseball glove', 'skateboard',
                'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
                'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
                'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
                'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
                'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
                'keyboard', 'cell phone', 'microwave oven', 'toaster', 'sink',
                'refrigerator', 'book', 'clock', 'vase', 'scissors',
                'teddy bear', 'hair drier', 'toothbrush')


def get_label_map(label_file):
    label_map = {}
    labels = open(label_file, 'r')
    for line in labels:
        ids = line.split(',')
        label_map[int(ids[0])] = int(ids[1])
    return label_map


class COCOAnnotationTransform(object):
    """Transforms a COCO annotation into a Tensor of bbox coords and label index
    Initilized with a dictionary lookup of classnames to indexes
    """
    def __init__(self):
        self.label_map = get_label_map(osp.join(COCO_ROOT, 'coco_labels.txt'))

    def __call__(self, target, width, height):
        """
        Args:
            target (dict): COCO target json annotation as a python dict
            height (int): height
            width (int): width
        Returns:
            a list containing lists of bounding boxes  [bbox coords, class idx]
        """
        scale = np.array([width, height, width, height])
        res = []
        for obj in target:
            if 'bbox' in obj:
                bbox = obj['bbox']
                bbox[2] += bbox[0]
                bbox[3] += bbox[1]
                label_idx = self.label_map[obj['category_id']] - 1
                final_box = list(np.array(bbox)/scale)
                final_box.append(label_idx)
                res += [final_box]  # [xmin, ymin, xmax, ymax, label_idx]
            else:
                print("no bbox problem!")

        return res  # [[xmin, ymin, xmax, ymax, label_idx], ... ]


class COCODetection(data.Dataset):
    """`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset.
    Args:
        root (string): Root directory where images are downloaded to.
        set_name (string): Name of the specific set of COCO images.
        transform (callable, optional): A function/transform that augments the
                                        raw images`
        target_transform (callable, optional): A function/transform that takes
        in the target (bbox) and transforms it.
    """

    def __init__(self, root, image_set='trainval35k', transform=None,
                 target_transform=COCOAnnotationTransform(), dataset_name='MS COCO'):
        sys.path.append(osp.join(root, COCO_API))
        from pycocotools.coco import COCO
        self.root = osp.join(root, IMAGES, image_set)
        self.coco = COCO(osp.join(root, ANNOTATIONS,
                                  INSTANCES_SET.format(image_set)))
        self.ids = list(self.coco.imgToAnns.keys())
        self.transform = transform
        self.target_transform = target_transform
        self.name = dataset_name

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: Tuple (image, target).
                   target is the object returned by ``coco.loadAnns``.
        """
        im, gt, h, w = self.pull_item(index)
        return im, gt

    def __len__(self):
        return len(self.ids)

    def pull_item(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: Tuple (image, target, height, width).
                   target is the object returned by ``coco.loadAnns``.
        """
        img_id = self.ids[index]
        target = self.coco.imgToAnns[img_id]
        ann_ids = self.coco.getAnnIds(imgIds=img_id)

        target = self.coco.loadAnns(ann_ids)
        path = osp.join(self.root, self.coco.loadImgs(img_id)[0]['file_name'])
        assert osp.exists(path), 'Image path does not exist: {}'.format(path)
        img = cv2.imread(osp.join(self.root, path))
        height, width, _ = img.shape
        if self.target_transform is not None:
            target = self.target_transform(target, width, height)
        if self.transform is not None:
            target = np.array(target)
            img, boxes, labels = self.transform(img, target[:, :4],
                                                target[:, 4])
            # to rgb
            img = img[:, :, (2, 1, 0)]

            target = np.hstack((boxes, np.expand_dims(labels, axis=1)))
        return torch.from_numpy(img).permute(2, 0, 1), target, height, width

    def pull_image(self, index):
        '''Returns the original image object at index in PIL form

        Note: not using self.__getitem__(), as any transformations passed in
        could mess up this functionality.

        Argument:
            index (int): index of img to show
        Return:
            cv2 img
        '''
        img_id = self.ids[index]
        path = self.coco.loadImgs(img_id)[0]['file_name']
        return cv2.imread(osp.join(self.root, path), cv2.IMREAD_COLOR)

    def pull_anno(self, index):
        '''Returns the original annotation of image at index

        Note: not using self.__getitem__(), as any transformations passed in
        could mess up this functionality.

        Argument:
            index (int): index of img to get annotation of
        Return:
            list:  [img_id, [(label, bbox coords),...]]
                eg: ('001718', [('dog', (96, 13, 438, 332))])
        '''
        img_id = self.ids[index]
        ann_ids = self.coco.getAnnIds(imgIds=img_id)
        return self.coco.loadAnns(ann_ids)

    def __repr__(self):
        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
        fmt_str += '    Root Location: {}\n'.format(self.root)
        tmp = '    Transforms (if any): '
        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
        tmp = '    Target Transforms (if any): '
        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
        return fmt_str


================================================
FILE: data/coco_labels.txt
================================================
1,1,person
2,2,bicycle
3,3,car
4,4,motorcycle
5,5,airplane
6,6,bus
7,7,train
8,8,truck
9,9,boat
10,10,traffic light
11,11,fire hydrant
13,12,stop sign
14,13,parking meter
15,14,bench
16,15,bird
17,16,cat
18,17,dog
19,18,horse
20,19,sheep
21,20,cow
22,21,elephant
23,22,bear
24,23,zebra
25,24,giraffe
27,25,backpack
28,26,umbrella
31,27,handbag
32,28,tie
33,29,suitcase
34,30,frisbee
35,31,skis
36,32,snowboard
37,33,sports ball
38,34,kite
39,35,baseball bat
40,36,baseball glove
41,37,skateboard
42,38,surfboard
43,39,tennis racket
44,40,bottle
46,41,wine glass
47,42,cup
48,43,fork
49,44,knife
50,45,spoon
51,46,bowl
52,47,banana
53,48,apple
54,49,sandwich
55,50,orange
56,51,broccoli
57,52,carrot
58,53,hot dog
59,54,pizza
60,55,donut
61,56,cake
62,57,chair
63,58,couch
64,59,potted plant
65,60,bed
67,61,dining table
70,62,toilet
72,63,tv
73,64,laptop
74,65,mouse
75,66,remote
76,67,keyboard
77,68,cell phone
78,69,microwave
79,70,oven
80,71,toaster
81,72,sink
82,73,refrigerator
84,74,book
85,75,clock
86,76,vase
87,77,scissors
88,78,teddy bear
89,79,hair drier
90,80,toothbrush


================================================
FILE: data/config.py
================================================
# config.py
import os.path

# gets home dir cross platform
HOME = os.path.expanduser("~")

# for making bounding boxes pretty
COLORS = ((255, 0, 0, 128), (0, 255, 0, 128), (0, 0, 255, 128),
          (0, 255, 255, 128), (255, 0, 255, 128), (255, 255, 0, 128))

MEANS = (104, 117, 123)

# SSD300 CONFIGS
voc = {
    'num_classes': 21,
    'lr_steps': (80000, 100000, 120000),
    'max_iter': 120000,
    'feature_maps': [38, 19, 10, 5, 3, 1],
    'min_dim': 300,
    'steps': [8, 16, 32, 64, 100, 300],
    'min_sizes': [30, 60, 111, 162, 213, 264],
    'max_sizes': [60, 111, 162, 213, 264, 315],
    'aspect_ratios': [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
    'variance': [0.1, 0.2],
    'clip': True,
    'name': 'VOC',
}

coco = {
    'num_classes': 201,
    'lr_steps': (280000, 360000, 400000),
    'max_iter': 400000,
    'feature_maps': [38, 19, 10, 5, 3, 1],
    'min_dim': 300,
    'steps': [8, 16, 32, 64, 100, 300],
    'min_sizes': [21, 45, 99, 153, 207, 261],
    'max_sizes': [45, 99, 153, 207, 261, 315],
    'aspect_ratios': [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
    'variance': [0.1, 0.2],
    'clip': True,
    'name': 'COCO',
}


================================================
FILE: data/scripts/COCO2014.sh
================================================
#!/bin/bash

start=`date +%s`

# handle optional download dir
if [ -z "$1" ]
  then
    # navigate to ~/data
    echo "navigating to ~/data/ ..."
    mkdir -p ~/data
    cd ~/data/
    mkdir -p ./coco
    cd ./coco
    mkdir -p ./images
    mkdir -p ./annotations
  else
    # check if specified dir is valid
    if [ ! -d $1 ]; then
        echo $1 " is not a valid directory"
        exit 0
    fi
    echo "navigating to " $1 " ..."
    cd $1
fi

if [ ! -d images ]
  then
    mkdir -p ./images
fi

# Download the image data.
cd ./images
echo "Downloading MSCOCO train images ..."
curl -LO http://images.cocodataset.org/zips/train2014.zip
echo "Downloading MSCOCO val images ..."
curl -LO http://images.cocodataset.org/zips/val2014.zip

cd ../
if [ ! -d annotations]
  then
    mkdir -p ./annotations
fi

# Download the annotation data.
cd ./annotations
echo "Downloading MSCOCO train/val annotations ..."
curl -LO http://images.cocodataset.org/annotations/annotations_trainval2014.zip
echo "Finished downloading. Now extracting ..."

# Unzip data
echo "Extracting train images ..."
unzip ../images/train2014.zip -d ../images
echo "Extracting val images ..."
unzip ../images/val2014.zip -d ../images
echo "Extracting annotations ..."
unzip ./annotations_trainval2014.zip

echo "Removing zip files ..."
rm ../images/train2014.zip
rm ../images/val2014.zip
rm ./annotations_trainval2014.zip

echo "Creating trainval35k dataset..."

# Download annotations json
echo "Downloading trainval35k annotations from S3"
curl -LO https://s3.amazonaws.com/amdegroot-datasets/instances_trainval35k.json.zip

# combine train and val 
echo "Combining train and val images"
mkdir ../images/trainval35k
cd ../images/train2014
find -maxdepth 1 -name '*.jpg' -exec cp -t ../trainval35k {} + # dir too large for cp
cd ../val2014
find -maxdepth 1 -name '*.jpg' -exec cp -t ../trainval35k {} +


end=`date +%s`
runtime=$((end-start))

echo "Completed in " $runtime " seconds"


================================================
FILE: data/scripts/VOC2007.sh
================================================
#!/bin/bash
# Ellis Brown

start=`date +%s`

# handle optional download dir
if [ -z "$1" ]
  then
    # navigate to ~/data
    echo "navigating to ~/data/ ..." 
    mkdir -p ~/data
    cd ~/data/
  else
    # check if is valid directory
    if [ ! -d $1 ]; then
        echo $1 "is not a valid directory"
        exit 0
    fi
    echo "navigating to" $1 "..."
    cd $1
fi

echo "Downloading VOC2007 trainval ..."
# Download the data.
curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
echo "Downloading VOC2007 test data ..."
curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
echo "Done downloading."

# Extract data
echo "Extracting trainval ..."
tar -xvf VOCtrainval_06-Nov-2007.tar
echo "Extracting test ..."
tar -xvf VOCtest_06-Nov-2007.tar
echo "removing tars ..."
rm VOCtrainval_06-Nov-2007.tar
rm VOCtest_06-Nov-2007.tar

end=`date +%s`
runtime=$((end-start))

echo "Completed in" $runtime "seconds"

================================================
FILE: data/scripts/VOC2012.sh
================================================
#!/bin/bash
# Ellis Brown

start=`date +%s`

# handle optional download dir
if [ -z "$1" ]
  then
    # navigate to ~/data
    echo "navigating to ~/data/ ..." 
    mkdir -p ~/data
    cd ~/data/
  else
    # check if is valid directory
    if [ ! -d $1 ]; then
        echo $1 "is not a valid directory"
        exit 0
    fi
    echo "navigating to" $1 "..."
    cd $1
fi

echo "Downloading VOC2012 trainval ..."
# Download the data.
curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
echo "Done downloading."


# Extract data
echo "Extracting trainval ..."
tar -xvf VOCtrainval_11-May-2012.tar
echo "removing tar ..."
rm VOCtrainval_11-May-2012.tar

end=`date +%s`
runtime=$((end-start))

echo "Completed in" $runtime "seconds"

================================================
FILE: data/voc0712.py
================================================
"""VOC Dataset Classes

Original author: Francisco Massa
https://github.com/fmassa/vision/blob/voc_dataset/torchvision/datasets/voc.py

Updated by: Ellis Brown, Max deGroot
"""
from .config import HOME
import os.path as osp
import sys
import torch
import torch.utils.data as data
import cv2
import numpy as np
if sys.version_info[0] == 2:
    import xml.etree.cElementTree as ET
else:
    import xml.etree.ElementTree as ET

VOC_CLASSES = (  # always index 0
    'aeroplane', 'bicycle', 'bird', 'boat',
    'bottle', 'bus', 'car', 'cat', 'chair',
    'cow', 'diningtable', 'dog', 'horse',
    'motorbike', 'person', 'pottedplant',
    'sheep', 'sofa', 'train', 'tvmonitor')

# note: if you used our download scripts, this should be right
VOC_ROOT = osp.join(HOME, "data/VOCdevkit/")

class VOCAnnotationTransform(object):
    """Transforms a VOC annotation into a Tensor of bbox coords and label index
    Initilized with a dictionary lookup of classnames to indexes

    Arguments:
        class_to_ind (dict, optional): dictionary lookup of classnames -> indexes
            (default: alphabetic indexing of VOC's 20 classes)
        keep_difficult (bool, optional): keep difficult instances or not
            (default: False)
        height (int): height
        width (int): width
    """

    def __init__(self, class_to_ind=None, keep_difficult=False):
        self.class_to_ind = class_to_ind or dict(
            zip(VOC_CLASSES, range(len(VOC_CLASSES))))
        self.keep_difficult = keep_difficult

    def __call__(self, target, width, height):
        """
        Arguments:
            target (annotation) : the target annotation to be made usable
                will be an ET.Element
        Returns:
            a list containing lists of bounding boxes  [bbox coords, class name]
        """
        res = []
        for obj in target.iter('object'):
            difficult = int(obj.find('difficult').text) == 1
            if not self.keep_difficult and difficult:
                continue
            name = obj.find('name').text.lower().strip()
            bbox = obj.find('bndbox')

            pts = ['xmin', 'ymin', 'xmax', 'ymax']
            bndbox = []
            for i, pt in enumerate(pts):
                cur_pt = int(bbox.find(pt).text) - 1
                # scale height or width
                cur_pt = cur_pt / width if i % 2 == 0 else cur_pt / height
                bndbox.append(cur_pt)
            label_idx = self.class_to_ind[name]
            bndbox.append(label_idx)
            res += [bndbox]  # [xmin, ymin, xmax, ymax, label_ind]
            # img_id = target.find('filename').text[:-4]

        return res  # [[xmin, ymin, xmax, ymax, label_ind], ... ]


class VOCDetection(data.Dataset):
    """VOC Detection Dataset Object

    input is image, target is annotation

    Arguments:
        root (string): filepath to VOCdevkit folder.
        image_set (string): imageset to use (eg. 'train', 'val', 'test')
        transform (callable, optional): transformation to perform on the
            input image
        target_transform (callable, optional): transformation to perform on the
            target `annotation`
            (eg: take in caption string, return tensor of word indices)
        dataset_name (string, optional): which dataset to load
            (default: 'VOC2007')
    """

    def __init__(self, root,
                 image_sets=[('2007', 'trainval'), ('2012', 'trainval')],
                 transform=None, target_transform=VOCAnnotationTransform(),
                 dataset_name='VOC0712'):
        self.root = root
        self.image_set = image_sets
        self.transform = transform
        self.target_transform = target_transform
        self.name = dataset_name
        self._annopath = osp.join('%s', 'Annotations', '%s.xml')
        self._imgpath = osp.join('%s', 'JPEGImages', '%s.jpg')
        self.ids = list()
        for (year, name) in image_sets:
            rootpath = osp.join(self.root, 'VOC' + year)
            for line in open(osp.join(rootpath, 'ImageSets', 'Main', name + '.txt')):
                self.ids.append((rootpath, line.strip()))

    def __getitem__(self, index):
        im, gt, h, w = self.pull_item(index)

        return im, gt

    def __len__(self):
        return len(self.ids)

    def pull_item(self, index):
        img_id = self.ids[index]
        target = ET.parse(self._annopath % img_id).getroot()
        img = cv2.imread(self._imgpath % img_id)
        height, width, channels = img.shape

        if self.target_transform is not None:
            target = self.target_transform(target, width, height)

        if self.transform is not None:
            target = np.array(target)
            img, boxes, labels = self.transform(img, target[:, :4], target[:, 4])
            # to rgb
            img = img[:, :, (2, 1, 0)]
            # img = img.transpose(2, 0, 1)
            target = np.hstack((boxes, np.expand_dims(labels, axis=1)))
        return torch.from_numpy(img).permute(2, 0, 1), target, height, width
        # return torch.from_numpy(img), target, height, width

    def pull_image(self, index):
        '''Returns the original image object at index in PIL form

        Note: not using self.__getitem__(), as any transformations passed in
        could mess up this functionality.

        Argument:
            index (int): index of img to show
        Return:
            PIL img
        '''
        img_id = self.ids[index]
        return cv2.imread(self._imgpath % img_id, cv2.IMREAD_COLOR)

    def pull_anno(self, index):
        '''Returns the original annotation of image at index

        Note: not using self.__getitem__(), as any transformations passed in
        could mess up this functionality.

        Argument:
            index (int): index of img to get annotation of
        Return:
            list:  [img_id, [(label, bbox coords),...]]
                eg: ('001718', [('dog', (96, 13, 438, 332))])
        '''
        img_id = self.ids[index]
        anno = ET.parse(self._annopath % img_id).getroot()
        gt = self.target_transform(anno, 1, 1)
        return img_id[1], gt

    def pull_tensor(self, index):
        '''Returns the original image at an index in tensor form

        Note: not using self.__getitem__(), as any transformations passed in
        could mess up this functionality.

        Argument:
            index (int): index of img to show
        Return:
            tensorized version of img, squeezed
        '''
        return torch.Tensor(self.pull_image(index)).unsqueeze_(0)


================================================
FILE: demo/__init__.py
================================================


================================================
FILE: demo/demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Detection with SSD\n",
    "### Here we demostrate detection on example images using SSD with PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.backends.cudnn as cudnn\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import cv2\n",
    "if torch.cuda.is_available():\n",
    "    torch.set_default_tensor_type('torch.cuda.FloatTensor')\n",
    "\n",
    "from ssd import build_ssd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build SSD300 in Test Phase\n",
    "1. Build the architecture, specifyingsize of the input image (300),\n",
    "    and number of object classes to score (21 for VOC dataset)\n",
    "2. Next we load pretrained weights on the VOC0712 trainval dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading weights into state dict...\n",
      "Finished!\n"
     ]
    }
   ],
   "source": [
    "net = build_ssd('test', 300, 21)    # initialize SSD\n",
    "net.load_weights('../weights/ssd300_mAP_77.43_v2.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Image \n",
    "### Here we just load a sample image from the VOC07 dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XK37/q9f8/a9+i1mvcWbPsAuYmHVVu2p48vyKzYVwffkcE9f89ne/5O13b9lu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YamaQalduCGfiNwo0iztkUINaBgQohhIKr3Qtz1P4SIvPXvtSv6GRMxh5KKTBmEljRNLKplAK1z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kNEhyoD8+UJUhuqXJ5f6RJrDNgl7bqnESvefW+f73/vCmZ/h/7jlGL8g//pDqPgufndCW9d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\nkdStfpiTSKsxC4uTEVH0V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Download .txt
gitextract_6znju64_/

├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── data/
│   ├── __init__.py
│   ├── coco.py
│   ├── coco_labels.txt
│   ├── config.py
│   ├── scripts/
│   │   ├── COCO2014.sh
│   │   ├── VOC2007.sh
│   │   └── VOC2012.sh
│   └── voc0712.py
├── demo/
│   ├── __init__.py
│   ├── demo.ipynb
│   └── live.py
├── eval.py
├── layers/
│   ├── __init__.py
│   ├── box_utils.py
│   ├── functions/
│   │   ├── __init__.py
│   │   ├── detection.py
│   │   └── prior_box.py
│   └── modules/
│       ├── __init__.py
│       ├── l2norm.py
│       ├── multibox_loss.py
│       └── repulsion_loss.py
├── ssd.py
├── test.py
├── train.py
└── utils/
    ├── __init__.py
    └── augmentations.py
Download .txt
SYMBOL INDEX (147 symbols across 15 files)

FILE: data/__init__.py
  function detection_collate (line 9) | def detection_collate(batch):
  function base_transform (line 30) | def base_transform(image, size, mean):
  class BaseTransform (line 37) | class BaseTransform:
    method __init__ (line 38) | def __init__(self, size, mean):
    method __call__ (line 42) | def __call__(self, image, boxes=None, labels=None):

FILE: data/coco.py
  function get_label_map (line 33) | def get_label_map(label_file):
  class COCOAnnotationTransform (line 42) | class COCOAnnotationTransform(object):
    method __init__ (line 46) | def __init__(self):
    method __call__ (line 49) | def __call__(self, target, width, height):
  class COCODetection (line 75) | class COCODetection(data.Dataset):
    method __init__ (line 86) | def __init__(self, root, image_set='trainval35k', transform=None,
    method __getitem__ (line 98) | def __getitem__(self, index):
    method __len__ (line 109) | def __len__(self):
    method pull_item (line 112) | def pull_item(self, index):
    method pull_image (line 141) | def pull_image(self, index):
    method pull_anno (line 156) | def pull_anno(self, index):
    method __repr__ (line 172) | def __repr__(self):

FILE: data/voc0712.py
  class VOCAnnotationTransform (line 30) | class VOCAnnotationTransform(object):
    method __init__ (line 43) | def __init__(self, class_to_ind=None, keep_difficult=False):
    method __call__ (line 48) | def __call__(self, target, width, height):
  class VOCDetection (line 79) | class VOCDetection(data.Dataset):
    method __init__ (line 96) | def __init__(self, root,
    method __getitem__ (line 113) | def __getitem__(self, index):
    method __len__ (line 118) | def __len__(self):
    method pull_item (line 121) | def pull_item(self, index):
    method pull_image (line 140) | def pull_image(self, index):
    method pull_anno (line 154) | def pull_anno(self, index):
    method pull_tensor (line 171) | def pull_tensor(self, index):

FILE: demo/live.py
  function cv2_demo (line 20) | def cv2_demo(net, transform):

FILE: eval.py
  function str2bool (line 32) | def str2bool(v):
  class Timer (line 79) | class Timer(object):
    method __init__ (line 81) | def __init__(self):
    method tic (line 88) | def tic(self):
    method toc (line 93) | def toc(self, average=True):
  function parse_rec (line 104) | def parse_rec(filename):
  function get_output_dir (line 124) | def get_output_dir(name, phase):
  function get_voc_results_file_template (line 136) | def get_voc_results_file_template(image_set, cls):
  function write_voc_results_file (line 146) | def write_voc_results_file(all_boxes, dataset):
  function do_python_eval (line 163) | def do_python_eval(output_dir='output', use_07=True):
  function voc_ap (line 194) | def voc_ap(rec, prec, use_07_metric=True):
  function voc_eval (line 228) | def voc_eval(detpath,
  function test_net (line 364) | def test_net(save_folder, net, cuda, dataset, transform, top_k,
  function evaluate_detections (line 416) | def evaluate_detections(box_list, output_dir, dataset):

FILE: layers/box_utils.py
  function point_form (line 6) | def point_form(boxes):
  function center_size (line 18) | def center_size(boxes):
  function intersect (line 30) | def intersect(box_a, box_b):
  function jaccard (line 51) | def jaccard(box_a, box_b):
  function IoG (line 71) | def IoG(box_a, box_b):
  function match (line 91) | def match(threshold, predicts, truths, priors, variances, labels, loc_t,...
  function encode (line 154) | def encode(matched, priors, variances):
  function decode (line 179) | def decode(loc, priors, variances):
  function decode_new (line 199) | def decode_new(loc, priors, variances):
  function log_sum_exp (line 218) | def log_sum_exp(x):
  function nms (line 232) | def nms(boxes, scores, overlap=0.5, top_k=200):

FILE: layers/functions/detection.py
  class Detect (line 7) | class Detect(Function):
    method __init__ (line 13) | def __init__(self, num_classes, bkg_label, top_k, conf_thresh, nms_thr...
    method forward (line 24) | def forward(self, loc_data, conf_data, prior_data):

FILE: layers/functions/prior_box.py
  class PriorBox (line 7) | class PriorBox(object):
    method __init__ (line 11) | def __init__(self, cfg):
    method forward (line 28) | def forward(self):

FILE: layers/modules/l2norm.py
  class L2Norm (line 7) | class L2Norm(nn.Module):
    method __init__ (line 8) | def __init__(self,n_channels, scale):
    method reset_parameters (line 16) | def reset_parameters(self):
    method forward (line 19) | def forward(self, x):

FILE: layers/modules/multibox_loss.py
  class MultiBoxLoss (line 12) | class MultiBoxLoss(nn.Module):
    method __init__ (line 35) | def __init__(self, num_classes, overlap_thresh, prior_for_matching,
    method forward (line 50) | def forward(self, predictions, targets):

FILE: layers/modules/repulsion_loss.py
  class RepulsionLoss (line 12) | class RepulsionLoss(nn.Module):
    method __init__ (line 14) | def __init__(self, use_gpu=True, sigma=0.):
    method smoothln (line 21) | def smoothln(self, x, sigma=0.):
    method forward (line 24) | def forward(self, loc_data, ground_data, prior_data):

FILE: ssd.py
  class SSD (line 10) | class SSD(nn.Module):
    method __init__ (line 28) | def __init__(self, phase, size, base, extras, head, num_classes):
    method forward (line 50) | def forward(self, x):
    method load_weights (line 113) | def load_weights(self, base_file):
  function vgg (line 126) | def vgg(cfg, i, batch_norm=False):
  function add_extras (line 149) | def add_extras(cfg, i, batch_norm=False):
  function multibox (line 166) | def multibox(vgg, extra_layers, cfg, num_classes):
  function build_ssd (line 198) | def build_ssd(phase, size=300, num_classes=21):

FILE: test.py
  function test_net (line 38) | def test_net(save_folder, net, cuda, testset, transform, thresh):
  function test_voc (line 81) | def test_voc():

FILE: train.py
  function str2bool (line 20) | def str2bool(v):
  function train (line 74) | def train():
  function adjust_learning_rate (line 213) | def adjust_learning_rate(optimizer, gamma, step):
  function xavier (line 224) | def xavier(param):
  function weights_init (line 228) | def weights_init(m):
  function create_vis_plot (line 234) | def create_vis_plot(_xlabel, _ylabel, _title, _legend):
  function update_vis_plot (line 247) | def update_vis_plot(iteration, loc, conf, window1, window2, update_type,

FILE: utils/augmentations.py
  function intersect (line 9) | def intersect(box_a, box_b):
  function jaccard_numpy (line 16) | def jaccard_numpy(box_a, box_b):
  class Compose (line 36) | class Compose(object):
    method __init__ (line 47) | def __init__(self, transforms):
    method __call__ (line 50) | def __call__(self, img, boxes=None, labels=None):
  class Lambda (line 56) | class Lambda(object):
    method __init__ (line 59) | def __init__(self, lambd):
    method __call__ (line 63) | def __call__(self, img, boxes=None, labels=None):
  class ConvertFromInts (line 67) | class ConvertFromInts(object):
    method __call__ (line 68) | def __call__(self, image, boxes=None, labels=None):
  class SubtractMeans (line 72) | class SubtractMeans(object):
    method __init__ (line 73) | def __init__(self, mean):
    method __call__ (line 76) | def __call__(self, image, boxes=None, labels=None):
  class ToAbsoluteCoords (line 82) | class ToAbsoluteCoords(object):
    method __call__ (line 83) | def __call__(self, image, boxes=None, labels=None):
  class ToPercentCoords (line 93) | class ToPercentCoords(object):
    method __call__ (line 94) | def __call__(self, image, boxes=None, labels=None):
  class Resize (line 104) | class Resize(object):
    method __init__ (line 105) | def __init__(self, size=300):
    method __call__ (line 108) | def __call__(self, image, boxes=None, labels=None):
  class RandomSaturation (line 114) | class RandomSaturation(object):
    method __init__ (line 115) | def __init__(self, lower=0.5, upper=1.5):
    method __call__ (line 121) | def __call__(self, image, boxes=None, labels=None):
  class RandomHue (line 128) | class RandomHue(object):
    method __init__ (line 129) | def __init__(self, delta=18.0):
    method __call__ (line 133) | def __call__(self, image, boxes=None, labels=None):
  class RandomLightingNoise (line 141) | class RandomLightingNoise(object):
    method __init__ (line 142) | def __init__(self):
    method __call__ (line 147) | def __call__(self, image, boxes=None, labels=None):
  class ConvertColor (line 155) | class ConvertColor(object):
    method __init__ (line 156) | def __init__(self, current='BGR', transform='HSV'):
    method __call__ (line 160) | def __call__(self, image, boxes=None, labels=None):
  class RandomContrast (line 170) | class RandomContrast(object):
    method __init__ (line 171) | def __init__(self, lower=0.5, upper=1.5):
    method __call__ (line 178) | def __call__(self, image, boxes=None, labels=None):
  class RandomBrightness (line 185) | class RandomBrightness(object):
    method __init__ (line 186) | def __init__(self, delta=32):
    method __call__ (line 191) | def __call__(self, image, boxes=None, labels=None):
  class ToCV2Image (line 198) | class ToCV2Image(object):
    method __call__ (line 199) | def __call__(self, tensor, boxes=None, labels=None):
  class ToTensor (line 203) | class ToTensor(object):
    method __call__ (line 204) | def __call__(self, cvimage, boxes=None, labels=None):
  class RandomSampleCrop (line 208) | class RandomSampleCrop(object):
    method __init__ (line 221) | def __init__(self):
    method __call__ (line 234) | def __call__(self, image, boxes=None, labels=None):
  class Expand (line 312) | class Expand(object):
    method __init__ (line 313) | def __init__(self, mean):
    method __call__ (line 316) | def __call__(self, image, boxes, labels):
  class RandomMirror (line 340) | class RandomMirror(object):
    method __call__ (line 341) | def __call__(self, image, boxes, classes):
  class SwapChannels (line 350) | class SwapChannels(object):
    method __init__ (line 358) | def __init__(self, swaps):
    method __call__ (line 361) | def __call__(self, image):
  class PhotometricDistort (line 376) | class PhotometricDistort(object):
    method __init__ (line 377) | def __init__(self):
    method __call__ (line 389) | def __call__(self, image, boxes, labels):
  class SSDAugmentation (line 400) | class SSDAugmentation(object):
    method __init__ (line 401) | def __init__(self, size=300, mean=(104, 117, 123)):
    method __call__ (line 416) | def __call__(self, img, boxes, labels):
Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,448K chars).
[
  {
    "path": ".gitattributes",
    "chars": 110,
    "preview": "*.ipynb linguist-language=Python\n.ipynb_checkpoints/* linguist-documentation\ndev.ipynb linguist-documentation\n"
  },
  {
    "path": ".gitignore",
    "chars": 1493,
    "preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
  },
  {
    "path": "LICENSE",
    "chars": 1081,
    "preview": "MIT License\n\nCopyright (c) 2017 Max deGroot, Ellis Brown\n\nPermission is hereby granted, free of charge, to any person ob"
  },
  {
    "path": "README.md",
    "chars": 6731,
    "preview": "# Repulsion Loss implemented with SSD\nForked from [PyTorch-SSD](https://github.com/amdegroot/ssd.pytorch), which is a [P"
  },
  {
    "path": "data/__init__.py",
    "chars": 1343,
    "preview": "from .voc0712 import VOCDetection, VOCAnnotationTransform, VOC_CLASSES, VOC_ROOT\n\n#from .coco import COCODetection, COCO"
  },
  {
    "path": "data/coco.py",
    "chars": 7168,
    "preview": "from .config import HOME\nimport os\nimport os.path as osp\nimport sys\nimport torch\nimport torch.utils.data as data\nimport "
  },
  {
    "path": "data/coco_labels.txt",
    "chars": 1083,
    "preview": "1,1,person\n2,2,bicycle\n3,3,car\n4,4,motorcycle\n5,5,airplane\n6,6,bus\n7,7,train\n8,8,truck\n9,9,boat\n10,10,traffic light\n11,1"
  },
  {
    "path": "data/config.py",
    "chars": 1152,
    "preview": "# config.py\nimport os.path\n\n# gets home dir cross platform\nHOME = os.path.expanduser(\"~\")\n\n# for making bounding boxes p"
  },
  {
    "path": "data/scripts/COCO2014.sh",
    "chars": 1955,
    "preview": "#!/bin/bash\n\nstart=`date +%s`\n\n# handle optional download dir\nif [ -z \"$1\" ]\n  then\n    # navigate to ~/data\n    echo \"n"
  },
  {
    "path": "data/scripts/VOC2007.sh",
    "chars": 971,
    "preview": "#!/bin/bash\n# Ellis Brown\n\nstart=`date +%s`\n\n# handle optional download dir\nif [ -z \"$1\" ]\n  then\n    # navigate to ~/da"
  },
  {
    "path": "data/scripts/VOC2012.sh",
    "chars": 763,
    "preview": "#!/bin/bash\n# Ellis Brown\n\nstart=`date +%s`\n\n# handle optional download dir\nif [ -z \"$1\" ]\n  then\n    # navigate to ~/da"
  },
  {
    "path": "data/voc0712.py",
    "chars": 6570,
    "preview": "\"\"\"VOC Dataset Classes\n\nOriginal author: Francisco Massa\nhttps://github.com/fmassa/vision/blob/voc_dataset/torchvision/d"
  },
  {
    "path": "demo/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "demo/demo.ipynb",
    "chars": 1317480,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Object Detection with SSD\\n\",\n   "
  },
  {
    "path": "demo/live.py",
    "chars": 3072,
    "preview": "from __future__ import print_function\nimport torch\nfrom torch.autograd import Variable\nimport cv2\nimport time\nfrom imuti"
  },
  {
    "path": "eval.py",
    "chars": 15869,
    "preview": "\"\"\"Adapted from:\n    @longcw faster_rcnn_pytorch: https://github.com/longcw/faster_rcnn_pytorch\n    @rbgirshick py-faste"
  },
  {
    "path": "layers/__init__.py",
    "chars": 48,
    "preview": "from .functions import *\nfrom .modules import *\n"
  },
  {
    "path": "layers/box_utils.py",
    "chars": 11832,
    "preview": "# -*- coding: utf-8 -*-\nfrom __future__ import division\nimport torch\nimport time\n\ndef point_form(boxes):\n    \"\"\" Convert"
  },
  {
    "path": "layers/functions/__init__.py",
    "chars": 97,
    "preview": "from .detection import Detect\nfrom .prior_box import PriorBox\n\n\n__all__ = ['Detect', 'PriorBox']\n"
  },
  {
    "path": "layers/functions/detection.py",
    "chars": 2693,
    "preview": "import torch\nfrom torch.autograd import Function\nfrom ..box_utils import decode, nms\nfrom data import voc as cfg\n\n\nclass"
  },
  {
    "path": "layers/functions/prior_box.py",
    "chars": 2001,
    "preview": "from __future__ import division\nfrom math import sqrt as sqrt\nfrom itertools import product as product\nimport torch\n\n\ncl"
  },
  {
    "path": "layers/modules/__init__.py",
    "chars": 164,
    "preview": "from .l2norm import L2Norm\nfrom .multibox_loss import MultiBoxLoss\nfrom .repulsion_loss import RepulsionLoss\n\n__all__ = "
  },
  {
    "path": "layers/modules/l2norm.py",
    "chars": 758,
    "preview": "import torch\nimport torch.nn as nn\nfrom torch.autograd import Function\nfrom torch.autograd import Variable\nimport torch."
  },
  {
    "path": "layers/modules/multibox_loss.py",
    "chars": 5371,
    "preview": "# -*- coding: utf-8 -*-\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as"
  },
  {
    "path": "layers/modules/repulsion_loss.py",
    "chars": 921,
    "preview": "# -*- coding: utf-8 -*-\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as"
  },
  {
    "path": "ssd.py",
    "chars": 7667,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom layers impor"
  },
  {
    "path": "test.py",
    "chars": 3925,
    "preview": "from __future__ import print_function\nimport sys\nimport os\nimport argparse\nimport torch\nimport torch.nn as nn\nimport tor"
  },
  {
    "path": "train.py",
    "chars": 9712,
    "preview": "from __future__ import division\nfrom data import *\nfrom utils.augmentations import SSDAugmentation\nfrom layers.modules i"
  },
  {
    "path": "utils/__init__.py",
    "chars": 42,
    "preview": "from .augmentations import SSDAugmentation"
  },
  {
    "path": "utils/augmentations.py",
    "chars": 13453,
    "preview": "import torch\nfrom torchvision import transforms\nimport cv2\nimport numpy as np\nimport types\nfrom numpy import random\n\n\nde"
  }
]

About this extraction

This page contains the full source code of the bailvwangzi/repulsion_loss_ssd GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (1.4 MB), approximately 917.9k tokens, and a symbol index with 147 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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