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Repository: lichengunc/MAttNet
Branch: master
Commit: 3204d07d4b79
Files: 46
Total size: 3.0 MB
Directory structure:
gitextract_p3dpb7wy/
├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── cv/
│ ├── example_demo.ipynb
│ ├── inspect_cv.ipynb
│ └── mrcn_detection.ipynb
├── data/
│ └── README.md
├── experiments/
│ ├── det_results.txt
│ ├── easy_results.txt
│ ├── mask_results.txt
│ └── scripts/
│ ├── eval_dets.sh
│ ├── eval_easy.sh
│ ├── eval_masks.sh
│ └── train_mattnet.sh
├── lib/
│ ├── .gitignore
│ ├── crits/
│ │ ├── __init__.py
│ │ └── max_margin_crit.py
│ ├── layers/
│ │ ├── __init__.py
│ │ ├── joint_match.py
│ │ ├── lang_encoder.py
│ │ └── visual_encoder.py
│ ├── loaders/
│ │ ├── __init__.py
│ │ ├── dets_loader.py
│ │ ├── gt_mrcn_loader.py
│ │ └── loader.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── eval_dets_utils.py
│ │ ├── eval_easy_utils.py
│ │ └── utils.py
│ └── mrcn/
│ ├── __init__.py
│ ├── inference.py
│ └── inference_no_imdb.py
└── tools/
├── _init_paths.py
├── eval_dets.py
├── eval_easy.py
├── eval_masks.py
├── extract_mrcn_ann_feats.py
├── extract_mrcn_det_feats.py
├── extract_mrcn_head_feats.py
├── mattnet.py
├── opt.py
├── prepro.py
├── run_detect.py
├── run_detect_to_mask.py
└── train.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
.ipynb_checkpoints
*.pyc
================================================
FILE: .gitmodules
================================================
[submodule "pyutils/mask-faster-rcnn"]
path = pyutils/mask-faster-rcnn
url = https://github.com/lichengunc/mask-faster-rcnn
[submodule "pyutils/refer"]
path = pyutils/refer
url = https://github.com/lichengunc/refer
[submodule "pyutils/refer-parser2"]
path = pyutils/refer-parser2
url = https://github.com/lichengunc/refer-parser2
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2018 Licheng Yu
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
================================================
# PyTorch Implementation of MAttNet
## Introduction
This repository is Pytorch implementation of [MAttNet: Modular Attention Network for Referring Expression Comprehension](https://arxiv.org/pdf/1801.08186.pdf) in [CVPR 2018](http://cvpr2018.thecvf.com/).
Refering Expressions are natural language utterances that indicate particular objects within a scene, e.g., "the woman in red sweater", "the man on the right", etc.
For robots or other intelligent agents communicating with people in the world, the ability to accurately comprehend such expressions will be a necessary component for natural interactions.
In this project, we address referring expression comprehension: localizing an image region described by a natural language expression.
Check our [paper](https://arxiv.org/pdf/1801.08186.pdf) and [online demo](http://vision2.cs.unc.edu/refer/comprehension) for more details.
Examples are shown as follows:
<p align="center">
<img src="http://bvisionweb1.cs.unc.edu/licheng/MattNet/mattnet_example.jpg" width="75%"/>
</p>
## Prerequisites
* Python 2.7
* Pytorch 0.2 (may not work with 1.0 or higher)
* CUDA 8.0
## Installation
1. Clone the MAttNet repository
```
git clone --recursive https://github.com/lichengunc/MAttNet
```
2. Prepare the submodules and associated data
* Mask R-CNN: Follow the instructions of my [mask-faster-rcnn](https://github.com/lichengunc/mask-faster-rcnn) repo, preparing everything needed for `pyutils/mask-faster-rcnn`.
You could use `cv/mrcn_detection.ipynb` to test if you've get Mask R-CNN ready.
* REFER API and data: Use the download links of [REFER](https://github.com/lichengunc/refer) and go to the foloder running `make`. Follow `data/README.md` to prepare images and refcoco/refcoco+/refcocog annotations.
* refer-parser2: Follow the instructions of [refer-parser2](https://github.com/lichengunc/refer-parser2) to extract the parsed expressions using [Vicente's R1-R7 attributes](http://tamaraberg.com/papers/referit.pdf). **Note** this sub-module is only used if you want to train the models by yourself.
## Training
1. Prepare the training and evaluation data by running `tools/prepro.py`:
```
python tools/prepro.py --dataset refcoco --splitBy unc
```
2. Extract features using Mask R-CNN, where the `head_feats` are used in subject module training and `ann_feats` is used in relationship module training.
```bash
CUDA_VISIBLE_DEVICES=gpu_id python tools/extract_mrcn_head_feats.py --dataset refcoco --splitBy unc
CUDA_VISIBLE_DEVICES=gpu_id python tools/extract_mrcn_ann_feats.py --dataset refcoco --splitBy unc
```
3. Detect objects/masks and extract features (only needed if you want to evaluate the automatic comprehension). We empirically set the confidence threshold of Mask R-CNN as 0.65.
```bash
CUDA_VISIBLE_DEVICES=gpu_id python tools/run_detect.py --dataset refcoco --splitBy unc --conf_thresh 0.65
CUDA_VISIBLE_DEVICES=gpu_id python tools/run_detect_to_mask.py --dataset refcoco --splitBy unc
CUDA_VISIBLE_DEVICES=gpu_id python tools/extract_mrcn_det_feats.py --dataset refcoco --splitBy unc
```
4. Train MAttNet with ground-truth annotation:
```bash
./experiments/scripts/train_mattnet.sh GPU_ID refcoco unc
```
During training, you may want to use `cv/inpect_cv.ipynb` to check the training/validation curves and do cross validation.
## Evaluation
Evaluate MAttNet with ground-truth annotation:
```bash
./experiments/scripts/eval_easy.sh GPUID refcoco unc
```
If you detected/extracted the Mask R-CNN results already (step 3 above), now you can evaluate the automatic comprehension accuracy using Mask R-CNN detection and segmentation:
```bash
./experiments/scripts/eval_dets.sh GPU_ID refcoco unc
./experiments/scripts/eval_masks.sh GPU_ID refcoco unc
```
## Pre-trained Models
In order to get the results in our paper, please follow [Training Step 1-3](#training) for data and feature preparation then run [Evaluation Step 1](#evaluation).
We provide the pre-trained models for RefCOCO, RefCOCO+ and RefCOCOg. Download and put them under `./output` folder.
1) RefCOCO: [Pre-trained model (56M)](http://bvisionweb1.cs.unc.edu/licheng/MattNet/pretrained/refcoco_unc.zip)
<table>
<tr><th> Localization (gt-box) </th><th> Localization (Mask R-CNN) </th><th> Segmentation (Mask R-CNN) </th></tr>
<tr><td>
| val | test A | test B |
|--|--|--|
| 85.57\% | 85.95\% | 84.36\% |
</td><td>
| val | test A | test B |
|--|--|--|
| 76.65\% | 81.14\% | 69.99\% |
</td><td>
| val | test A | test B |
|--|--|--|
| 75.16\% | 79.55\% | 68.87\% |
</td></tr> </table>
2) RefCOCO+: [Pre-trained model (56M)](http://bvisionweb1.cs.unc.edu/licheng/MattNet/pretrained/refcoco+_unc.zip)
<table>
<tr><th> Localization (gt-box) </th><th> Localization (Mask R-CNN) </th><th> Segmentation (Mask R-CNN) </th></tr>
<tr><td>
| val | test A | test B |
|--|--|--|
| 71.71\% | 74.28\% | 66.27\% |
</td><td>
| val | test A | test B |
|--|--|--|
| 65.33\% | 71.62\% | 56.02\% |
</td><td>
| val | test A | test B |
|--|--|--|
| 64.11\% | 70.12\% | 54.82\% |
</td></tr> </table>
3) RefCOCOg: [Pre-trained model (58M)](http://bvisionweb1.cs.unc.edu/licheng/MattNet/pretrained/refcocog_umd.zip)
<table>
<tr><th> Localization (gt-box) </th><th> Localization (Mask R-CNN) </th><th> Segmentation (Mask R-CNN) </th></tr>
<tr><td>
| val | test |
|--|--|
| 78.96\% | 78.51\% |
</td><td>
| val | test |
|--|--|
| 66.58\% | 67.27\% |
</td><td>
| val | test |
|--|--|
| 64.48\% | 65.60\% |
</td></tr> </table>
## Pre-computed detections/masks
We provide the [detected boxes/masks](http://bvisionweb1.cs.unc.edu/licheng/MattNet/detections.zip) for those who are interested in automatic comprehension.
This was done using [Training Step 3](#training).
**Note** our Mask R-CNN is trained on COCO’s training images, **excluding** those in RefCOCO, RefCOCO+, and RefCOCOg’s validation+testing.
That said it is unfair to use the other off-the-shelf detectors trained on whole COCO set for this task.
## Demo
Run `cv/example_demo.ipynb` for demo example.
You can also check our [Online Demo](http://vision2.cs.unc.edu/refer/comprehension).
## Citation
@inproceedings{yu2018mattnet,
title={MAttNet: Modular Attention Network for Referring Expression Comprehension},
author={Yu, Licheng and Lin, Zhe and Shen, Xiaohui and Yang, Jimei and Lu, Xin and Bansal, Mohit and Berg, Tamara L},
booktitle={CVPR},
year={2018}
}
## License
MAttNet is released under the MIT License (refer to the LICENSE file for details).
## A few notes
I'd like to share several thoughts after working on Referring Expressions for 3 years (since 2015):
* **Model Improvement**: I'm satisfied with this model architecture but still feel the context information is not fully exploited. We tried the context of visual comparison in our [ECCV2016](https://arxiv.org/pdf/1608.00272.pdf). It worked well but relied too much on the detector. That's why I removed the appearance difference in this paper. (Location comparison still remains as it's too important.) I'm looking forward to seeing more robust and interesting context proposed in the future.
Another direction is the end-to-end multi-task training. Current model loses some concepts after going through Mask R-CNN. For example, Mask R-CNN can perfectly detect (big) ``sports ball`` in an image but MAttNet can no longer recognize it. The reason is we are training the two models seperately and our RefCOCO dataset do not have ball-related expressions.
* **Borrowing External Concepts**: Current datasets (RefCOCO, RefCOCO+, RefCOCOg) have bias toward ``person`` category. Around half of the expressions are related to person. However, in real life people may also be interested in referring other common objects (cup, bottle, book) or even stuff (sky, tree or building). As RefCOCO already provides common referring expression structure, the (only) piece left is getting the universal objects/stuff concepts, which could be borrowed from external datasets/tasks.
* **Referring Expression Generation (REG)**: Surprisingly few paper works on referring expression generation task so far! Dialogue is important. Referring to things is always the first step for computer-to-human interaction.
(I don't think people would love to use a passive computer or robot which cannot talk.)
In our [CVPR2017](https://arxiv.org/pdf/1612.09542.pdf), we actually collected more testing expressions for better REG evaluation. (Check [REFER2](https://github.com/lichengunc/refer2) for the data. The only difference with [REFER](https://github.com/lichengunc/refer) is it contains more testing expressions on RefCOCO and RefCOCO+.)
While we achieved the SOA results in the paper, there should be plentiful space for further improvement.
Our speaker model can only utter "boring" and "safe" expressions, thus cannot well specify every object in an image.
GAN or a Modular Speaker might be effective weapons as future work.
* **Data Collection**: Larger Referring Expressions dataset is apparently the most straight-forward way to improve the performance of any model. You might have two questions: 1) What data should we collect? 2) How do we collect the dataset?
A larger Referring Expression dataset covering the whole MS COCO is expected (of course).
This will also make end-to-end learning possible in the future.
Task-specific dataset is also interesting.
Since [ReferIt Game](http://tamaraberg.com/referitgame/), there have been several datasets in different domains, e.g., [video](https://arxiv.org/pdf/1708.01641v1.pdf), [dialogue](https://arxiv.org/pdf/1611.08481v2.pdf) and [spoken language](https://arxiv.org/pdf/1711.03800v2.pdf).
Note you may be careful about the problem setting.
Randomly fitting referring expressions into a task (just for paper publication) is boring.
As for the collection method, I prefer the way used in our ealy work [ReferIt Game](http://tamaraberg.com/referitgame/). The collected expressions might be slightly short (compared with image captioning datasets), but that is how we refer things naturally in daily life.
## Authorship
This project is maintained by [Licheng Yu](http://cs.unc.edu/~licheng/).
================================================
FILE: cv/example_demo.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import argparse\n",
"import os\n",
"import os.path as osp\n",
"import numpy as np\n",
"import torch # put this before scipy import\n",
"from scipy.misc import imread, imresize\n",
"import sys\n",
"sys.path.insert(0, '../tools')\n",
"\n",
"from mattnet import MattNet"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# box functions\n",
"def xywh_to_xyxy(boxes):\n",
" \"\"\"Convert [x y w h] box format to [x1 y1 x2 y2] format.\"\"\"\n",
" return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))\n",
"\n",
"def show_attn(img_path, box, attn):\n",
" \"\"\"\n",
" box : [xywh]\n",
" attn: 49\n",
" \"\"\"\n",
" img = imread(img_path)\n",
" attn = np.array(attn).reshape(7,7)\n",
" x,y,w,h = int(box[0]), int(box[1]), int(box[2]), int(box[3])\n",
" roi = img[y:y+h-1, x:x+w-1]\n",
" attn = imresize(attn, [h,w])\n",
" plt.imshow(roi)\n",
" plt.imshow(attn, alpha=0.7)\n",
" \n",
"def show_boxes(img_path, boxes, colors, texts=None, masks=None):\n",
" # boxes [[xyxy]]\n",
" img = imread(img_path)\n",
" plt.imshow(img)\n",
" ax = plt.gca()\n",
" for k in range(boxes.shape[0]):\n",
" box = boxes[k]\n",
" xmin, ymin, xmax, ymax = list(box)\n",
" coords = (xmin, ymin), xmax - xmin + 1, ymax - ymin + 1\n",
" color = colors[k]\n",
" ax.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))\n",
" if texts is not None:\n",
" ax.text(xmin, ymin, texts[k], bbox={'facecolor':color, 'alpha':0.5})\n",
" # show mask\n",
" if masks is not None:\n",
" for k in range(len(masks)):\n",
" mask = masks[k]\n",
" m = np.zeros( (mask.shape[0], mask.shape[1], 3))\n",
" m[:,:,0] = 0; m[:,:,1] = 0; m[:,:,2] = 1.\n",
" ax.imshow(np.dstack([m*255, mask*255*0.4]).astype(np.uint8)) "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# arguments\n",
"parser = argparse.ArgumentParser()\n",
"parser.add_argument('--dataset', type=str, default='refcoco', help='dataset name: refclef, refcoco, refcoco+, refcocog')\n",
"parser.add_argument('--splitBy', type=str, default='unc', help='splitBy: unc, google, berkeley')\n",
"parser.add_argument('--model_id', type=str, default='mrcn_cmr_with_st', help='model id name')\n",
"args = parser.parse_args('')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MatNet [refcoco_unc's mrcn_cmr_with_st] loaded in 1.27 seconds.\n",
"Using config:\n",
"{'ANCHOR_RATIOS': [0.5, 1, 2],\n",
" 'ANCHOR_SCALES': [4, 8, 16, 32],\n",
" 'DATA_DIR': '/playpen10/licheng/Documents/refer_cvpr2018/MattNet/pyutils/mask-faster-rcnn/data',\n",
" 'EXP_DIR': 'res101',\n",
" 'MASK_SIZE': 14,\n",
" 'MATLAB': 'matlab',\n",
" 'MOBILENET': {'DEPTH_MULTIPLIER': 1.0,\n",
" 'FIXED_LAYERS': 5,\n",
" 'REGU_DEPTH': False,\n",
" 'WEIGHT_DECAY': 4e-05},\n",
" 'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),\n",
" 'POOLING_ALIGN': False,\n",
" 'POOLING_MODE': 'crop',\n",
" 'POOLING_SIZE': 7,\n",
" 'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False},\n",
" 'RNG_SEED': 3,\n",
" 'ROOT_DIR': '/playpen10/licheng/Documents/refer_cvpr2018/MattNet/pyutils/mask-faster-rcnn',\n",
" 'TEST': {'BBOX_REG': True,\n",
" 'HAS_RPN': True,\n",
" 'MAX_SIZE': 1000,\n",
" 'MODE': 'nms',\n",
" 'NMS': 0.3,\n",
" 'PROPOSAL_METHOD': 'gt',\n",
" 'RPN_NMS_THRESH': 0.7,\n",
" 'RPN_POST_NMS_TOP_N': 300,\n",
" 'RPN_PRE_NMS_TOP_N': 6000,\n",
" 'RPN_TOP_N': 5000,\n",
" 'SCALES': [600],\n",
" 'SVM': False},\n",
" 'TRAIN': {'ASPECT_GROUPING': False,\n",
" 'BATCH_SIZE': 256,\n",
" 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],\n",
" 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],\n",
" 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],\n",
" 'BBOX_NORMALIZE_TARGETS': True,\n",
" 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True,\n",
" 'BBOX_REG': True,\n",
" 'BBOX_THRESH': 0.5,\n",
" 'BG_THRESH_HI': 0.5,\n",
" 'BG_THRESH_LO': 0.0,\n",
" 'BIAS_DECAY': False,\n",
" 'DISPLAY': 20,\n",
" 'DOUBLE_BIAS': False,\n",
" 'FG_FRACTION': 0.25,\n",
" 'FG_THRESH': 0.5,\n",
" 'FROM_FRCN': False,\n",
" 'GAMMA': 0.1,\n",
" 'HAS_RPN': True,\n",
" 'IMS_PER_BATCH': 1,\n",
" 'LEARNING_RATE': 0.001,\n",
" 'MAX_SIZE': 1000,\n",
" 'MOMENTUM': 0.9,\n",
" 'PROPOSAL_METHOD': 'gt',\n",
" 'RPN_BATCHSIZE': 256,\n",
" 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],\n",
" 'RPN_CLOBBER_POSITIVES': False,\n",
" 'RPN_FG_FRACTION': 0.5,\n",
" 'RPN_NEGATIVE_OVERLAP': 0.3,\n",
" 'RPN_NMS_THRESH': 0.7,\n",
" 'RPN_POSITIVE_OVERLAP': 0.7,\n",
" 'RPN_POSITIVE_WEIGHT': -1.0,\n",
" 'RPN_POST_NMS_TOP_N': 2000,\n",
" 'RPN_PRE_NMS_TOP_N': 12000,\n",
" 'SCALES': [600],\n",
" 'SNAPSHOT_ITERS': 5000,\n",
" 'SNAPSHOT_KEPT': 3,\n",
" 'SNAPSHOT_PREFIX': 'res101_mask_rcnn',\n",
" 'STEPSIZE': [30000],\n",
" 'SUMMARY_INTERVAL': 180,\n",
" 'TRUNCATED': False,\n",
" 'USE_ALL_GT': True,\n",
" 'USE_FLIPPED': True,\n",
" 'USE_GT': False,\n",
" 'WEIGHT_DECAY': 0.0001},\n",
" 'USE_GPU_NMS': True}\n",
"loading annotations into memory...\n",
"Done (t=0.76s)\n",
"creating index...\n",
"index created!\n",
"pretrained-model loaded from [../tools/../lib/mrcn/../../pyutils/mask-faster-rcnn/output/res101/coco_2014_train_minus_refer_valtest+coco_2014_valminusminival/notime/res101_mask_rcnn_iter_1250000.pth].\n",
"Mask R-CNN: imdb[coco_minus_refer], tag[notime], id[res101_mask_rcnn_iter_1250000] loaded in 4.23 seconds.\n"
]
}
],
"source": [
"# MattNet\n",
"mattnet = MattNet(args)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# image path\n",
"IMAGE_DIR = '../data/images/mscoco/images/train2014'\n",
"img_path = osp.join(IMAGE_DIR, 'COCO_train2014_'+str(229598).zfill(12)+'.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dP8DZ8jZu3n4Rj139WnTtAWKMePza1+L627+K5578Njz12Dfj7/7fP4Sv/NLv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w7ryjNkHLmr7zqNjGM89jsDrYF3Z2d+t0qxot/vM7obMx1PGQ1vwa5IUkOrlbK73Wd4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HuH82YKSNzvBrRUB/YKIstHiHuqI5NwyNifZIOAg++mz8G+6jHZsHQvOdjIGZ/8ZxAFRFN4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\n33bcfxD7GEoTC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MNWKCq3p+sHPyqf+5ElDOMcURLvPMaU+MEilER4RkmS3Tjf/SRIm+ZJ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\nKMm+G0jDVze6vJwevJyeW0pllgLvy6/P4ylmoKMUMc7MU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5
gitextract_p3dpb7wy/
├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── cv/
│ ├── example_demo.ipynb
│ ├── inspect_cv.ipynb
│ └── mrcn_detection.ipynb
├── data/
│ └── README.md
├── experiments/
│ ├── det_results.txt
│ ├── easy_results.txt
│ ├── mask_results.txt
│ └── scripts/
│ ├── eval_dets.sh
│ ├── eval_easy.sh
│ ├── eval_masks.sh
│ └── train_mattnet.sh
├── lib/
│ ├── .gitignore
│ ├── crits/
│ │ ├── __init__.py
│ │ └── max_margin_crit.py
│ ├── layers/
│ │ ├── __init__.py
│ │ ├── joint_match.py
│ │ ├── lang_encoder.py
│ │ └── visual_encoder.py
│ ├── loaders/
│ │ ├── __init__.py
│ │ ├── dets_loader.py
│ │ ├── gt_mrcn_loader.py
│ │ └── loader.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── eval_dets_utils.py
│ │ ├── eval_easy_utils.py
│ │ └── utils.py
│ └── mrcn/
│ ├── __init__.py
│ ├── inference.py
│ └── inference_no_imdb.py
└── tools/
├── _init_paths.py
├── eval_dets.py
├── eval_easy.py
├── eval_masks.py
├── extract_mrcn_ann_feats.py
├── extract_mrcn_det_feats.py
├── extract_mrcn_head_feats.py
├── mattnet.py
├── opt.py
├── prepro.py
├── run_detect.py
├── run_detect_to_mask.py
└── train.py
SYMBOL INDEX (168 symbols across 24 files)
FILE: lib/crits/max_margin_crit.py
class MaxMarginCriterion (line 9) | class MaxMarginCriterion(nn.Module):
method __init__ (line 11) | def __init__(self, visual_rank_weight, lang_rank_weight, margin):
method forward (line 19) | def forward(self, cossim):
FILE: lib/layers/joint_match.py
class Matching (line 19) | class Matching(nn.Module):
method __init__ (line 21) | def __init__(self, vis_dim, lang_dim, jemb_dim, jemb_drop_out):
method forward (line 38) | def forward(self, visual_input, lang_input):
class RelationMatching (line 65) | class RelationMatching(nn.Module):
method __init__ (line 66) | def __init__(self, vis_dim, lang_dim, jemb_dim, jemb_drop_out):
method forward (line 83) | def forward(self, visual_input, lang_input, masks):
class JointMatching (line 113) | class JointMatching(nn.Module):
method __init__ (line 115) | def __init__(self, opt):
method forward (line 158) | def forward(self, pool5, fc7, lfeats, dif_lfeats, cxt_fc7, cxt_lfeats,...
method sub_rel_kl (line 205) | def sub_rel_kl(self, sub_attn, rel_attn, input_labels):
FILE: lib/layers/lang_encoder.py
class RNNEncoder (line 11) | class RNNEncoder(nn.Module):
method __init__ (line 12) | def __init__(self, vocab_size, word_embedding_size, word_vec_size, hid...
method forward (line 27) | def forward(self, input_labels):
class PhraseAttention (line 85) | class PhraseAttention(nn.Module):
method __init__ (line 86) | def __init__(self, input_dim):
method forward (line 91) | def forward(self, context, embedded, input_labels):
FILE: lib/layers/visual_encoder.py
class Normalize_Scale (line 11) | class Normalize_Scale(nn.Module):
method __init__ (line 12) | def __init__(self, dim, init_norm=20):
method forward (line 17) | def forward(self, bottom):
class LocationEncoder (line 28) | class LocationEncoder(nn.Module):
method __init__ (line 29) | def __init__(self, opt):
method forward (line 36) | def forward(self, lfeats, dif_lfeats):
class SubjectEncoder (line 51) | class SubjectEncoder(nn.Module):
method __init__ (line 53) | def __init__(self, opt):
method forward (line 73) | def forward(self, pool5, fc7, phrase_emb):
method extract_subj_feats (line 117) | def extract_subj_feats(self, pool5, fc7):
class RelationEncoder (line 153) | class RelationEncoder(nn.Module):
method __init__ (line 154) | def __init__(self, opt):
method forward (line 160) | def forward(self, cxt_feats, cxt_lfeats):
FILE: lib/loaders/dets_loader.py
function xywh_to_xyxy (line 37) | def xywh_to_xyxy(boxes):
function xyxy_to_xywh (line 41) | def xyxy_to_xywh(boxes):
class DetsLoader (line 47) | class DetsLoader(Loader):
method __init__ (line 49) | def __init__(self, data_json, data_h5, dets_json):
method prepare_mrcn (line 83) | def prepare_mrcn(self, head_feats_dir, args):
method loadFeats (line 99) | def loadFeats(self, Feats):
method resetIterator (line 112) | def resetIterator(self, split):
method expand_list (line 116) | def expand_list(self, L, n):
method image_to_head (line 122) | def image_to_head(self, image_id):
method fetch_neighbour_ids (line 131) | def fetch_neighbour_ids(self, ref_det_id):
method image_to_head (line 167) | def image_to_head(self, image_id):
method fetch_grid_feats (line 177) | def fetch_grid_feats(self, boxes, net_conv, im_info):
method compute_lfeats (line 185) | def compute_lfeats(self, det_ids):
method compute_dif_lfeats (line 196) | def compute_dif_lfeats(self, det_ids, topK=5):
method fetch_cxt_feats (line 213) | def fetch_cxt_feats(self, det_ids, opt):
method getTestBatch (line 248) | def getTestBatch(self, split, opt):
method getImageBatch (line 305) | def getImageBatch(self, image_id, sent_ids=None, opt={}):
FILE: lib/loaders/gt_mrcn_loader.py
function xywh_to_xyxy (line 35) | def xywh_to_xyxy(boxes):
function xyxy_to_xywh (line 39) | def xyxy_to_xywh(boxes):
class GtMRCNLoader (line 43) | class GtMRCNLoader(Loader):
method __init__ (line 45) | def __init__(self, data_json, data_h5):
method prepare_mrcn (line 68) | def prepare_mrcn(self, head_feats_dir, args):
method loadFeats (line 81) | def loadFeats(self, Feats):
method shuffle (line 94) | def shuffle(self, split):
method resetIterator (line 98) | def resetIterator(self, split):
method expand_list (line 102) | def expand_list(self, L, n):
method getBatch (line 109) | def getBatch(self, split, opt):
method sample_neg_ids (line 234) | def sample_neg_ids(self, ann_id, seq_per_ref, sample_ratio):
method fetch_neighbour_ids (line 265) | def fetch_neighbour_ids(self, ref_ann_id):
method fetch_sent_ids_by_ref_id (line 305) | def fetch_sent_ids_by_ref_id(self, ref_id, num_sents):
method combine_feats (line 319) | def combine_feats(self, feats0, feats1):
method image_to_head (line 325) | def image_to_head(self, image_id):
method fetch_grid_feats (line 336) | def fetch_grid_feats(self, boxes, net_conv, im_info):
method compute_lfeats (line 344) | def compute_lfeats(self, ann_ids):
method compute_dif_lfeats (line 355) | def compute_dif_lfeats(self, ann_ids, topK=5):
method fetch_cxt_feats (line 372) | def fetch_cxt_feats(self, ann_ids, opt):
method get_attribute_weights (line 407) | def get_attribute_weights(self, scale=10):
method fetch_attribute_label (line 416) | def fetch_attribute_label(self, ref_ann_ids):
method decode_attribute_label (line 432) | def decode_attribute_label(self, scores):
method getAttributeBatch (line 448) | def getAttributeBatch(self, split):
method getSentBatch (line 486) | def getSentBatch(self, sent_id, opt):
method getTestBatch (line 524) | def getTestBatch(self, split, opt):
method getImageBatch (line 582) | def getImageBatch(self, image_id, sent_ids=None, opt={}):
FILE: lib/loaders/loader.py
class Loader (line 24) | class Loader(object):
method __init__ (line 26) | def __init__(self, data_json, data_h5=None):
method vocab_size (line 63) | def vocab_size(self):
method label_length (line 67) | def label_length(self):
method encode_labels (line 70) | def encode_labels(self, sent_str_list):
method decode_labels (line 84) | def decode_labels(self, labels):
method fetch_label (line 97) | def fetch_label(self, ref_id, num_sents):
method fetch_seq (line 117) | def fetch_seq(self, sent_id):
FILE: lib/models/eval_dets_utils.py
function computeIoU (line 18) | def computeIoU(box1, box2):
function eval_split (line 33) | def eval_split(loader, model, crit, split, opt):
FILE: lib/models/eval_easy_utils.py
function compute_overall (line 17) | def compute_overall(predictions):
function eval_attributes (line 41) | def eval_attributes(loader, model, split, opt):
function eval_split (line 83) | def eval_split(loader, model, crit, split, opt):
function eval_dets_split (line 200) | def eval_dets_split(loader, model, crit, split, opt):
FILE: lib/models/utils.py
function clip_gradient (line 10) | def clip_gradient(optimizer, grad_clip):
function set_lr (line 15) | def set_lr(optimizer, lr):
FILE: lib/mrcn/inference.py
function get_imdb_name (line 36) | def get_imdb_name(imdb_name):
class Inference (line 44) | class Inference:
method __init__ (line 46) | def __init__(self, args):
method load_net (line 67) | def load_net(self):
method predict (line 91) | def predict(self, img_path):
method boxes_to_masks (line 116) | def boxes_to_masks(self, img_path, boxes, labels):
method extract_head (line 152) | def extract_head(self, img_path):
method head_to_prediction (line 161) | def head_to_prediction(self, net_conv, im_info):
method box_to_fc7 (line 208) | def box_to_fc7(self, net_conv, im_info, ori_boxes):
method box_to_spatial_fc7 (line 236) | def box_to_spatial_fc7(self, net_conv, im_info, ori_boxes):
method spatial_fc7_to_prediction (line 264) | def spatial_fc7_to_prediction(self, spatial_fc7, im_info, ori_boxes):
method _get_image_blob (line 298) | def _get_image_blob(self, im):
method _get_blobs (line 332) | def _get_blobs(self, im):
method _clip_boxes (line 339) | def _clip_boxes(self, boxes, im_shape):
FILE: lib/mrcn/inference_no_imdb.py
function get_imdb_name (line 36) | def get_imdb_name(imdb_name):
class Inference (line 44) | class Inference:
method __init__ (line 46) | def __init__(self, args):
method load_net (line 65) | def load_net(self):
method predict (line 89) | def predict(self, img_path):
method boxes_to_masks (line 115) | def boxes_to_masks(self, img_path, boxes, labels):
method extract_head (line 151) | def extract_head(self, img_path):
method head_to_prediction (line 160) | def head_to_prediction(self, net_conv, im_info):
method box_to_spatial_fc7 (line 207) | def box_to_spatial_fc7(self, net_conv, im_info, ori_boxes):
method box_to_pool5_fc7 (line 235) | def box_to_pool5_fc7(self, net_conv, im_info, ori_boxes):
method box_to_fc7 (line 265) | def box_to_fc7(self, net_conv, im_info, ori_boxes):
method spatial_fc7_to_prediction (line 293) | def spatial_fc7_to_prediction(self, spatial_fc7, im_info, ori_boxes):
method _get_image_blob (line 327) | def _get_image_blob(self, im):
method _get_blobs (line 361) | def _get_blobs(self, im):
method _clip_boxes (line 368) | def _clip_boxes(self, boxes, im_shape):
FILE: tools/eval_dets.py
function load_model (line 25) | def load_model(checkpoint_path, opt):
function evaluate (line 35) | def evaluate(params):
FILE: tools/eval_easy.py
function load_model (line 25) | def load_model(checkpoint_path, opt):
function evaluate (line 35) | def evaluate(params):
FILE: tools/eval_masks.py
function computeIoU (line 25) | def computeIoU(pred_rle, gd_rle):
function annToRLE (line 33) | def annToRLE(ann, h, w):
function main (line 49) | def main(args):
FILE: tools/extract_mrcn_ann_feats.py
function xywh_to_xyxy (line 29) | def xywh_to_xyxy(boxes):
function xyxy_to_xywh (line 33) | def xyxy_to_xywh(boxes):
function image_to_head (line 37) | def image_to_head(head_feats_dir, image_id):
function ann_to_fc7 (line 47) | def ann_to_fc7(mrcn, ann, net_conv, im_info):
function ann_to_pool5_fc7 (line 61) | def ann_to_pool5_fc7(mrcn, ann, net_conv, im_info):
function main (line 76) | def main(args):
FILE: tools/extract_mrcn_det_feats.py
function xywh_to_xyxy (line 28) | def xywh_to_xyxy(boxes):
function xyxy_to_xywh (line 32) | def xyxy_to_xywh(boxes):
function image_to_head (line 36) | def image_to_head(head_feats_dir, image_id):
function det_to_pool5_fc7 (line 46) | def det_to_pool5_fc7(mrcn, det, net_conv, im_info):
function main (line 62) | def main(args):
FILE: tools/extract_mrcn_head_feats.py
function main (line 29) | def main(args):
FILE: tools/mattnet.py
function xywh_to_xyxy (line 36) | def xywh_to_xyxy(boxes):
function show_boxes (line 40) | def show_boxes(img, boxes, colors, texts=None):
function show_mask (line 57) | def show_mask(img, mask, color):
class MattNet (line 70) | class MattNet():
method __init__ (line 72) | def __init__(self, args):
method load_matnet_model (line 97) | def load_matnet_model(self, checkpoint_path, opt):
method forward_image (line 107) | def forward_image(self, img_path, nms_thresh=.3, conf_thresh=.65):
method comprehend (line 190) | def comprehend(self, img_data, expr):
method encode_labels (line 259) | def encode_labels(self, sent_str_list, word_to_ix):
method compute_lfeats (line 275) | def compute_lfeats(self, det_ids, Dets, im):
method fetch_neighbour_ids (line 286) | def fetch_neighbour_ids(self, ref_det_id, Dets):
method compute_dif_lfeats (line 320) | def compute_dif_lfeats(self, det_ids, Dets, topK=5):
method fetch_cxt_feats (line 337) | def fetch_cxt_feats(self, det_ids, Dets, spatial_fc7, opt):
method cls_to_detections (line 376) | def cls_to_detections(self, scores, boxes, nms_thresh, conf_thresh):
FILE: tools/opt.py
function parse_opt (line 4) | def parse_opt():
FILE: tools/prepro.py
function build_vocab (line 46) | def build_vocab(refer, params):
function check_sentLength (line 94) | def check_sentLength(sentToFinal):
function encode_captions (line 108) | def encode_captions(sentences, wtoi, params):
function check_encoded_labels (line 124) | def check_encoded_labels(sentences, labels, itow):
function prepare_json (line 135) | def prepare_json(refer, sentToFinal, ref_to_att_wds, params):
function build_att_vocab (line 185) | def build_att_vocab(refer, params, att_types=['r1', 'r2', 'r7']):
function main (line 226) | def main(params):
FILE: tools/run_detect.py
function cls_to_detections (line 26) | def cls_to_detections(scores, boxes, imdb, nms_thresh, conf_thresh):
function main (line 46) | def main(args):
FILE: tools/run_detect_to_mask.py
function xywh_to_xyxy (line 29) | def xywh_to_xyxy(boxes):
function xyxy_to_xywh (line 33) | def xyxy_to_xywh(boxes):
function main (line 38) | def main(args):
FILE: tools/train.py
function lossFun (line 30) | def lossFun(loader, optimizer, model, mm_crit, att_crit, opt, iter):
function main (line 82) | def main(args):
Condensed preview — 46 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (3,196K chars).
[
{
"path": ".gitignore",
"chars": 24,
"preview": ".ipynb_checkpoints\n*.pyc"
},
{
"path": ".gitmodules",
"chars": 336,
"preview": "[submodule \"pyutils/mask-faster-rcnn\"]\n\tpath = pyutils/mask-faster-rcnn\n\turl = https://github.com/lichengunc/mask-faster"
},
{
"path": "LICENSE",
"chars": 1067,
"preview": "MIT License\n\nCopyright (c) 2018 Licheng Yu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy"
},
{
"path": "README.md",
"chars": 10156,
"preview": "# PyTorch Implementation of MAttNet\n\n## Introduction\n\nThis repository is Pytorch implementation of [MAttNet: Modular Att"
},
{
"path": "cv/example_demo.ipynb",
"chars": 822761,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "cv/inspect_cv.ipynb",
"chars": 98233,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outp"
},
{
"path": "cv/mrcn_detection.ipynb",
"chars": 2026856,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Test mrcn inference\"\n ]\n },\n "
},
{
"path": "data/README.md",
"chars": 378,
"preview": "This directory should contain the following data:\n```\n$COCO_PATH\n├── images\n│ ├── mscoco\n│ │ └──train2014 (contain"
},
{
"path": "experiments/det_results.txt",
"chars": 472,
"preview": "[refcoco_unc][val], id[mrcn_cmr_with_st]'s acc is 76.65%\n[refcoco_unc][testA], id[mrcn_cmr_with_st]'s acc is 81.14%\n[ref"
},
{
"path": "experiments/easy_results.txt",
"chars": 473,
"preview": "\n[refcoco_unc][val], id[mrcn_cmr_with_st]'s acc is 85.57%\n[refcoco_unc][testA], id[mrcn_cmr_with_st]'s acc is 85.95%\n[re"
},
{
"path": "experiments/mask_results.txt",
"chars": 1668,
"preview": "[refcoco_unc][val], id[mrcn_cmr_with_st]'s iou is:\n precision@0.5 = 75.16\n precision@0.6 = 72.55\n precision@0.7"
},
{
"path": "experiments/scripts/eval_dets.sh",
"chars": 1059,
"preview": "\n\nGPU_ID=$1\nDATASET=$2\nSPLITBY=$3\n\n# IMDB=\"coco_minus_refer\"\n# ITERS=1150000\n# TAG=\"notime\"\n# NET=\"res101\"\nID=\"mrcn_cmr_"
},
{
"path": "experiments/scripts/eval_easy.sh",
"chars": 1030,
"preview": "\n\nGPU_ID=$1\nDATASET=$2\nSPLITBY=$3\n\n# IMDB=\"coco_minus_refer\"\n# ITERS=1150000\n# TAG=\"notime\"\n# NET=\"res101\"\nID=\"mrcn_cmr_"
},
{
"path": "experiments/scripts/eval_masks.sh",
"chars": 940,
"preview": "\n\nGPU_ID=$1\nDATASET=$2\nSPLITBY=$3\n\n# IMDB=\"coco_minus_refer\"\n# ITERS=1150000\n# TAG=\"notime\"\n# NET=\"res101\"\nID=\"mrcn_cmr_"
},
{
"path": "experiments/scripts/train_mattnet.sh",
"chars": 382,
"preview": "\n\nGPU_ID=$1\nDATASET=$2\nSPLITBY=$3\n\nIMDB=\"coco_minus_refer\"\nITERS=1250000\nTAG=\"notime\"\nNET=\"res101\"\nID=\"mrcn_cmr_with_st\""
},
{
"path": "lib/.gitignore",
"chars": 0,
"preview": ""
},
{
"path": "lib/crits/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "lib/crits/max_margin_crit.py",
"chars": 2016,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tor"
},
{
"path": "lib/layers/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "lib/layers/joint_match.py",
"chars": 8959,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tor"
},
{
"path": "lib/layers/lang_encoder.py",
"chars": 4843,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport num"
},
{
"path": "lib/layers/visual_encoder.py",
"chars": 7559,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tor"
},
{
"path": "lib/loaders/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "lib/loaders/dets_loader.py",
"chars": 12432,
"preview": "\"\"\"\ndata_json has \n0. refs : list of {ref_id, ann_id, box, image_id, split, category_id, sent_ids}\n1. images "
},
{
"path": "lib/loaders/gt_mrcn_loader.py",
"chars": 23872,
"preview": "\"\"\"\ndata_json has \n0. refs: [{ref_id, ann_id, box, image_id, split, category_id, sent_ids, att_wds}]\n1. images: "
},
{
"path": "lib/loaders/loader.py",
"chars": 4807,
"preview": "\"\"\"\ndata_json has \n0. refs : list of {ref_id, ann_id, box, image_id, split, category_id, sent_ids}\n1. images "
},
{
"path": "lib/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "lib/models/eval_dets_utils.py",
"chars": 4750,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n"
},
{
"path": "lib/models/eval_easy_utils.py",
"chars": 10833,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n"
},
{
"path": "lib/models/utils.py",
"chars": 481,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport col"
},
{
"path": "lib/mrcn/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "lib/mrcn/inference.py",
"chars": 12318,
"preview": "\"\"\"\nargs: imdb_name, net, iters, tag\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __f"
},
{
"path": "lib/mrcn/inference_no_imdb.py",
"chars": 13248,
"preview": "\"\"\"\nargs: net, iters, tag\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ imp"
},
{
"path": "tools/_init_paths.py",
"chars": 483,
"preview": "import os.path as osp\nimport sys \n\n# mrcn path\nthis_dir = osp.dirname(__file__)\nmrcn_dir = osp.join(this_dir, '..', 'pyu"
},
{
"path": "tools/eval_dets.py",
"chars": 4117,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n"
},
{
"path": "tools/eval_easy.py",
"chars": 4163,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n"
},
{
"path": "tools/eval_masks.py",
"chars": 4661,
"preview": "\"\"\"\n1) masks information: cache/detections/dataset_splitBy/res101_coco_minus_refer_notime_masks.json\n contains [{det_"
},
{
"path": "tools/extract_mrcn_ann_feats.py",
"chars": 4626,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport arg"
},
{
"path": "tools/extract_mrcn_det_feats.py",
"chars": 4315,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport arg"
},
{
"path": "tools/extract_mrcn_head_feats.py",
"chars": 2664,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport arg"
},
{
"path": "tools/mattnet.py",
"chars": 15548,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# add matp"
},
{
"path": "tools/opt.py",
"chars": 6264,
"preview": "from pprint import pprint\nimport argparse\n\ndef parse_opt():\n\n parser = argparse.ArgumentParser()\n # Data input set"
},
{
"path": "tools/prepro.py",
"chars": 12572,
"preview": "\"\"\"\nPreprocess a raw json dataset into hdf5 and json files for use in lib/loaders\n\nInput: refer loader\nOutput: json file"
},
{
"path": "tools/run_detect.py",
"chars": 4585,
"preview": "\"\"\"\nRun detection on all images and save detected bounding box (with category_name and score).\n\ncache/detections/refcoco"
},
{
"path": "tools/run_detect_to_mask.py",
"chars": 3575,
"preview": "\"\"\"\nGiven detected results from:\ncache/detections/refcoco_unc/{net}_{imdb}_{tag}_dets.json has\n0. dets: list of {det_id,"
},
{
"path": "tools/train.py",
"chars": 7805,
"preview": "from __future__ import absolute_import \nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os"
}
]
About this extraction
This page contains the full source code of the lichengunc/MAttNet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 46 files (3.0 MB), approximately 788.9k tokens, and a symbol index with 168 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.