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Repository: valeoai/LOST
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Directory structure:
gitextract_b6vyv_32/

├── .gitignore
├── LICENSE
├── README.md
├── __init__.py
├── cluster_for_OD.py
├── data/
│   ├── CAD_predictions/
│   │   ├── LOST_plus_CAD_COCO20k.json
│   │   ├── LOST_plus_CAD_VOC07.json
│   │   └── LOST_plus_CAD_VOC12.json
│   └── LOST_predictions/
│       └── LOST_VOC07.pkl
├── datasets/
│   └── coco_20k_filenames.txt
├── datasets.py
├── main_corloc_evaluation.py
├── main_lost.py
├── networks.py
├── object_discovery.py
├── requirements.txt
├── tools/
│   ├── configs/
│   │   ├── RN50_DINO_FRCNN_COCO20k_CAD.yaml
│   │   ├── RN50_DINO_FRCNN_VOC07_CAD.yaml
│   │   ├── RN50_DINO_FRCNN_VOC07_OD.yaml
│   │   ├── RN50_DINO_FRCNN_VOC12_CAD.yaml
│   │   └── RN50_DINO_FRCNN_VOC12_OD.yaml
│   ├── convert_pretrained_to_detectron_format.py
│   ├── evaluate_unsupervised_detection_voc.py
│   ├── prepare_coco_CAD_gt.py
│   ├── prepare_coco_LOST_CAD_pseudo_boxes_in_detectron2_format.py
│   ├── prepare_voc_LOST_CAD_pseudo_boxes_in_detectron2_format.py
│   ├── prepare_voc_LOST_OD_pseudo_boxes_in_detectron2_format.py
│   ├── prepare_voc_data_in_coco_style.py
│   ├── train_net_for_LOST_CAD.py
│   └── train_net_for_LOST_OD.py
└── visualizations.py

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FILE CONTENTS
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FILE: .gitignore
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outputs/*
*.pyc


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FILE: LICENSE
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   LOST
   
   Copyright 2021 Valeo

   Licensed under the Apache License, Version 2.0 (the "License");
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       https://www.apache.org/licenses/LICENSE-2.0

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================================================
FILE: README.md
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# LOST 
Pytorch implementation of the unsupervised object discovery method **LOST**. More details can be found in the paper:

**Localizing Objects with Self-Supervised Transformers and no Labels**, BMVC 2021 [[arXiv](https://arxiv.org/abs/2109.14279)]  
by *Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet and Jean Ponce*

<div>
  <img width="25.3%" alt="LOST visualizations" src="examples/LOST_ex0.png">
  <img width="27.5%" alt="LOST visualizations" src="examples/LOST_ex1.png">
  <img width="27.5%" alt="LOST visualizations" src="examples/LOST_ex2.png">
</div>  


\
If you use the **LOST** code or framework in your research, please consider citing:


```
@inproceedings{LOST,
   title = {Localizing Objects with Self-Supervised Transformers and no Labels},
   author = {Oriane Sim\'eoni and Gilles Puy and Huy V. Vo and Simon Roburin and Spyros Gidaris and Andrei Bursuc and Patrick P\'erez and Renaud Marlet and Jean Ponce},
   journal = {Proceedings of the British Machine Vision Conference (BMVC)},
   month = {November},
   year = {2021}
}
```

## Content
#### LOST
- [Installation of LOST](#installation-of-lost) 
- [Apply LOST to one image](#apply-lost-to-one-image)
- [Launching LOST on datasets](#launching-lost-on-datasets)

#### Towards unsupervised object detection
- [Installation of LOST+CAD/OD](#installation-for-cad-and-od-trainings)
- [Training LOST+CAD](#training-a-class-agnostic-detector-cad-with-lost-pseudo-annotations)
- [Training LOST+OD](#training-a-class-aware-detector-od-with-lost-pseudo-annotations)


## Installation of LOST
### Dependencies

This code was implemented with python 3.7, PyTorch 1.7.1 and CUDA 10.2. Please install [PyTorch](https://pytorch.org/). In order to install the additionnal dependencies, please launch the following command:

```
pip install -r requirements.txt
```

### Install DINO
This method is based on DINO [paper](https://arxiv.org/pdf/2104.14294.pdf). The framework can be installed using the following commands:
```
git clone https://github.com/facebookresearch/dino.git
cd dino; 
touch __init__.py
echo -e "import sys\nfrom os.path import dirname, join\nsys.path.insert(0, join(dirname(__file__), '.'))" >> __init__.py; cd ../;
```

The code was made using the commit ba9edd1 of DINO repo (please rebase if breakage).

## Apply LOST to one image
Following are scripts to apply LOST to an image defined via the `image_path` parameter and visualize the predictions (`pred`), the maps of the Figure 2 in the paper (`fms`) and the visulization of the seed expansion (`seed_expansion`). Box predictions are also stored in the output directory given by parameter `output_dir`.

```
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize pred
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize fms
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize seed_expansion
```

## Launching LOST on datasets
Following are the different steps to reproduce the results of **LOST** presented in the paper. 

### PASCAL-VOC
Please download the PASCAL VOC07 and PASCAL VOC12 datasets ([link](http://host.robots.ox.ac.uk/pascal/VOC/)) and put the data in the folder `datasets`. There should be the two subfolders: `datasets/VOC2007` and `datasets/VOC2012`. In order to apply lost and compute corloc results (VOC07 61.9, VOC12 64.0), please launch:
```
python main_lost.py --dataset VOC07 --set trainval
python main_lost.py --dataset VOC12 --set trainval
```

### COCO
Please download the [COCO dataset](https://cocodataset.org/#home) and put the data in  `datasets/COCO`. Results are provided given the 2014 annotations following previous works. The following command line allows you to get results on the subset of 20k images of the COCO dataset (corloc 50.7), following previous litterature. To be noted that the 20k images are a subset of the `train` set.
```
python main_lost.py --dataset COCO20k --set train
```

### Different models
We have tested the method on different setups of the VIT model, corloc results are presented in the following table (more can be found in the paper). 

<table>
  <tr>
    <th>arch</th>
    <th>pre-training</th>
    <th colspan="3">dataset</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th>VOC07</th>
    <th>VOC12</th>
    <th>COCO20k</th>
  </tr>
  <tr>
    <td>ViT-S/16</td>
    <td>DINO</td>
    <td>61.9</td>
    <td>64.0</td>
    <td>50.7</td>
  <tr>
  <tr>
    <td>ViT-S/8</td>
    <td>DINO</td>
    <td>55.5</td>
    <td>57.0</td>
    <td>49.5</td>
  <tr>
  <tr>
    <td>ViT-B/16</td>
    <td>DINO</td>
    <td>60.1</td>
    <td>63.3</td>
    <td>50.0</td>
  <tr>
  <tr>
    <td>ResNet50</td>
    <td>DINO</td>
    <td>36.8</td>
    <td>42.7</td>
    <td>26.5</td>
  <tr>
  <tr>
    <td>ResNet50</td>
    <td>Imagenet</td>
    <td>33.5</td>
    <td>39.1</td>
    <td>25.5</td>
  <tr>
</table>

\
Previous results on the dataset `VOC07` can be obtained by launching: 
```
python main_lost.py --dataset VOC07 --set trainval #VIT-S/16
python main_lost.py --dataset VOC07 --set trainval --patch_size 8 #VIT-S/8
python main_lost.py --dataset VOC07 --set trainval --arch vit_base #VIT-B/16
python main_lost.py --dataset VOC07 --set trainval --arch resnet50 #Resnet50/DINO
python main_lost.py --dataset VOC07 --set trainval --arch resnet50_imagenet #Resnet50/imagenet
```

## Towards unsupervised object detection
In this work, we additionally use LOST predictions to train object detection models without any human supervision. We explore two scenarios: class-agnostic (CAD) and (pseudo) class-aware training of object detectors (OD). The next section present the different steps to reproduce our results.

### Installation for CAD and OD trainings
We use the [detectron2](https://github.com/facebookresearch/detectron2) framework to train a Faster R-CNN model with LOST predictions as pseudo-gt. The code was developped with the version [v0.5](https://github.com/facebookresearch/detectron2/releases) of the framework. In order to reproduce our results, please install detectron2 using the next commands. In case of failure, you can find the installation corresponding to your version of pytorch/CUDA [here](https://github.com/facebookresearch/detectron2/releases).
```bash
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install detectron2==0.5
```

Set global variables for ease of usage. 
```bash
export LOST=$(pwd)
cd detectron2; export D2=$(pwd);
```

Then please copy LOST-specific files to detectron2 framework, following:
```bash
ln -s $LOST/tools/*.py $D2/tools/. # Move LOST tools to D2
mkdir $D2/configs/LOST
ln -s $LOST/tools/configs/* $D2/configs/LOST/. # Move LOST configs to D2
```

### Training a Class-Agnostic Detector (CAD) with LOST pseudo-annotations

Before launching a training, data must be formated to fit detectron2 and COCO styles. Following are the command lines to do this formatting for boxes predicted with LOST.
```bash
cd $D2; 

# Format DINO weights to fit detectron2
wget https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth -P ./data # Download the model from DINO
python tools/convert_pretrained_to_detectron_format.py --input ./data/dino_resnet50_pretrain.pth --output ./data/dino_RN50_pretrain_d2_format.pkl

# Format pseudo-boxes data to fit detectron2
python tools/prepare_voc_LOST_CAD_pseudo_boxes_in_detectron2_format.py --year 2007 --pboxes $LOST/data/LOST_predictions/LOST_VOC07.pkl

# Format VOC data to fit COCO style
python tools/prepare_voc_data_in_coco_style.py --is_CAD --voc07_dir $LOST/datasets/VOC2007 --voc12_dir $LOST/datasets/VOC2012
```

The next command line allows you to launch a CAD training with 4 gpus on the VOC2007 dataset. The batch size is set to 16, 4 to 8 GPUs may be needed depending on your machines. Please make sure to change the argument value `MODEL.WEIGHTS` to the correct path of DINO weights.
```bash
python tools/train_net_for_LOST_CAD.py --num-gpus 4 --config-file ./configs/LOST/RN50_DINO_FRCNN_VOC07_CAD.yaml DATALOADER.NUM_WORKERS 8 OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_VOC07_CAD MODEL.WEIGHTS ./data/dino_RN50_pretrain_d2_format.pkl
```

Inference results of the model will be stored in `$OUTPUT_DIR/inference`. In order to produce results on the `train+val` dataset, please use the following command:
```
python tools/train_net_for_LOST_CAD.py --resume --eval-only --num-gpus 4 --config-file ./configs/LOST/RN50_DINO_FRCNN_VOC07_CAD.yaml DATALOADER.NUM_WORKERS 6 MODEL.WEIGHTS ./outputs/RN50_DINO_FRCNN_VOC07_CAD/model_final.pth OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_VOC07_CAD/ DATASETS.TEST '("voc_2007_trainval_CAD_coco_style", )'
cd $LOST;
python main_corloc_evaluation.py --dataset VOC07 --set trainval --type_pred detectron --pred_file $D2/outputs/RN50_DINO_FRCNN_VOC07_CAD/inference/coco_instances_results.json
```

#### Training LOST+CAD on COCO20k dataset
Following are the command lines allowing to train a detector in a class-agnostic fashion on the COCO20k subset of COCO dataset.

```bash
cd $D2;

# Format pseudo-boxes data to fit detectron2
python tools/prepare_coco_LOST_CAD_pseudo_boxes_in_detectron2_format.py --pboxes $LOST/outputs/COCO20k_train/LOST-vit_small16_k/preds.pkl

# Generate COCO20k CAD gt annotations
python tools/prepare_coco_CAD_gt.py --coco_dir $LOST/datasets/COCO

# Train detector (evaluation done on COCO20k CAD training set)
python tools/train_net_for_LOST_CAD.py --num-gpus 4 --config-file ./configs/LOST/RN50_DINO_FRCNN_COCO20k_CAD.yaml DATALOADER.NUM_WORKERS 8 OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_COCO20k_CAD MODEL.WEIGHTS ./data/dino_RN50_pretrain_d2_format.pkl

# Corloc evaluation
python main_corloc_evaluation.py --dataset COCO20k --type_pred detectron --pred_file $D2/outputs/RN50_DINO_FRCNN_COCO20k_CAD/inference/coco_instances_results.json
```


#### Evaluating LOST+CAD (corloc results)

We have provided predictions of a class-agnostic Faster R-CNN model trained using LOST boxes as pseudo-gt; they are stored in the folder `data/CAD_predictions`. In order to launch the corloc evaluation, please launch the following scripts. It is to be noted that in this evaluation, only the box with the highest confidence score is considered per image. 

```bash
python main_corloc_evaluation.py --dataset VOC07 --set trainval --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_VOC07.json
python main_corloc_evaluation.py --dataset VOC12 --set trainval --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_VOC12.json
python main_corloc_evaluation.py --dataset COCO20k --set train --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_COCO20k.json
```

The following table presents the obtained corloc results.

<table>
  <tr>
    <th>method</th>
    <th colspan="3">dataset</th>
  </tr>
  <tr>
    <th></th>
    <th>VOC07</th>
    <th>VOC12</th>
    <th>COCO20k</th>
  </tr>
  <tr>
    <td>LOST</td>
    <td>61.9</td>
    <td>64.0</td>
    <td>50.7</td>
  <tr>
  <tr>
    <td>LOST+CAD</td>
    <td>65.7</td>
    <td>70.4</td>
    <td>57.5</td>
  <tr>
</table>

### Training a Class-Aware Detector (OD) with LOST pseudo-annotations

Following are the different steps to train a class-aware detector using LOST peusdo-boxes for the dataset VOC07. We provide LOST boxes correspoding to the dataset VOC07 in `$LOST/data/LOST_predictions/LOST_VOC07.pkl`.

```bash
cd $LOST;
# Cluster features of LOST boxes
python cluster_for_OD.py --pred_file $LOST/data/LOST_predictions/LOST_VOC07.pkl --nb_clusters 20 --dataset VOC07 --set trainval

cd $D2;
# Format DINO weights to fit detectron2
wget https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth -P ./data # Download the model from DINO
python tools/convert_pretrained_to_detectron_format.py --input ./data/dino_resnet50_pretrain.pth --output ./data/dino_RN50_pretrain_d2_format.pkl

# Prepare the clustered LOST pseudo-box data for training
python tools/prepare_voc_LOST_OD_pseudo_boxes_in_detectron2_format.py --year 2007 --pboxes $LOST/data/LOST_predictions/LOST_VOC07_clustered_20clu.pkl

# Format VOC data to fit COCO style
python tools/prepare_voc_data_in_coco_style.py --voc07_dir  $LOST/datasets/VOC2007 --voc12_dir $LOST/datasets/VOC2012

# Train the detector on VOC2007 trainval set -- please be aware that no hungarian matching is used during training, so validation restuls are not meaningful (will be close to 0). Please use command bellow in order to evaluate results correctly. 
python tools/train_net_for_LOST_OD.py --num-gpus 8 --config-file ./configs/LOST/RN50_DINO_FRCNN_VOC07_OD.yaml DATALOADER.NUM_WORKERS 8 OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_VOC07_OD MODEL.WEIGHTS ./data/dino_RN50_pretrain_d2_format.pkl

# Evaluate the detector results using hungarian matching -- allows to reproduce results from the paper
cd $LOST;
python tools/evaluate_unsupervised_detection_voc.py --results ./detectron2/outputs/RN50_DINO_FRCNN_VOC07_OD/inference/coco_instances_results.json
```

### Training details

We use the `R50-C4` model of Detectron2 with ResNet50 pre-trained with DINO self-supervision [model](https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth). 

Details: 
- mini-batches of size 16 across 8 GPUs using SyncBatchNorm 
- extra BatchNorm layer for the RoI head after conv5, i.e., `Res5ROIHeadsExtraNorm` layer in Detectron2
- frozen first two convolutional blocks of ResNet-50, i.e., `conv1` and `conv2` in Detectron2.
- learning rate is first warmed-up for 100 steps to 0.02 and then reduced by a factor of 10 after 18K and 22K training steps 
- we use in total 24K training steps for all the experiments, except when training class-agnostic detectors on
the pseudo-boxes of the VOC07 trainval set, in which case we use 10K steps. 

## License
LOST is released under the [Apache 2.0 license](./LICENSE).


================================================
FILE: __init__.py
================================================


================================================
FILE: cluster_for_OD.py
================================================
# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import argparse
import pickle
from tqdm import tqdm
from collections import defaultdict

import torch
import torch.nn as nn
import torchvision
from torchvision import transforms as pth_transforms
import numpy as np
import scipy.cluster.vq as vq

from networks import get_model
from datasets import Dataset

if __name__ == "__main__":
    parser = argparse.ArgumentParser("Cluster LOST predictions.")
    
    # Model
    parser.add_argument(
        "--arch",
        default="vit_small",
        type=str,
        choices=[
            "vit_small",
        ],
        help="Model architecture.",
    )
    parser.add_argument(
        "--patch_size", 
        default=16, 
        type=int, 
        help="Patch resolution of the model."
    )
    
    # Dataset
    parser.add_argument(
        "--dataset",
        default="VOC07",
        type=str,
        choices=[None, "VOC07", "VOC12", "COCO20k"],
        help="Dataset name.",
    )
    parser.add_argument(
        "--set",
        default="train",
        type=str,
        choices=["val", "train", "trainval", "test"],
        help="Path of the image to load.",
    )
    parser.add_argument(
        "--no_hard",
        action="store_true",
        help="Only used in the case of the VOC_all setup (see the paper).",
    )

    # Prediction files
    parser.add_argument(
        "--pred_file",
        type=str,
        default="outputs/VOC07_trainval/LOST-vit_small16_k/preds.pkl",
        help="Predicted boxes.",
    )

    # Clustering specific
    parser.add_argument(
        "--nb_clusters", 
        type=int, 
        default=20, 
        help="Number of clusters used for kmeans clustering.")

    parser.add_argument("--random_seed", 
        type=int, 
        default=123, 
        help="K-means random seed.")

    # Keep?
    parser.add_argument("--visualize", type=str, default=None, help="Visualize")


    args = parser.parse_args()

    # -------------------------------------------------------------------------------------------------------
    # Dataset
    dataset = Dataset(args.dataset, args.set, args.no_hard)

    # -------------------------------------------------------------------------------------------------------
    # Model
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = get_model(args.arch, args.patch_size, -1, device)

    # -------------------------------------------------------------------------------------------------------
    # Load predictions
    print(f'Extract features corresponding to the boxes {args.pred_file}.')

    with open(args.pred_file, "rb") as f:
        predictions = pickle.load(f)

    # -------------------------------------------------------------------------------------------------------
    # Extract CLS token

    # Features location
    out_path = f'{args.pred_file.split(".pkl")[0]}_cropped_feats_{args.arch}.pkl'

    if not os.path.exists(out_path):

        feats = defaultdict(defaultdict)

        pbar = tqdm(dataset.dataloader)
        for im_id, inp in enumerate(pbar):

            # ------------ Image processing ---------------------------------------
            img = inp[0]
            init_image_size = img.shape

            # Get the name of the image
            im_name = dataset.get_image_name(inp[1])

            # Pass in case of no gt boxes in the image
            if im_name is None:
                continue

            # Prediction
            pred = np.asarray(predictions[im_name])
            xmin, xmax = round(pred[1]), round(pred[3])
            ymin, ymax = round(pred[0]), round(pred[2])

            # Crop the image
            cropped = img[:, xmin:xmax, ymin:ymax]

            # Resize cropped region
            resize_f = pth_transforms.Resize(256, interpolation=3)
            cropped_im = resize_f(cropped)

            # move to gpu
            cropped_im = cropped_im.cuda(non_blocking=True)
            # Size for transformers
            w_featmap = cropped_im.shape[-2] // args.patch_size
            h_featmap = cropped_im.shape[-1] // args.patch_size

            # Forward pass
            with torch.no_grad():
                f = model(cropped_im[None, :, :, :])
                norm_f = nn.functional.normalize(f, dim=1, p=2)
                feats[im_name]["cropped_feat"] = np.array(norm_f.to("cpu"))
                feats[im_name]["predicted_bb"] = predictions[im_name]

        with open(out_path, "wb") as handle:
            pickle.dump(feats, handle, protocol=pickle.HIGHEST_PROTOCOL)

        print(f'Cropped features saved at {out_path}.')

    else:
        with open(out_path, "rb") as f:
            feats = pickle.load(f)
        print(f'Cropped features loaded from {out_path}.')

    # -------------------------------------------------------------------------------------------------------
    # Apply clustering
    seed_ = f'_seed-{args.random_seed}' if args.random_seed != 123 else ""
    clustering_path = f'{args.pred_file.split(".pkl")[0]}_clustered_{args.nb_clusters}clu{seed_}.pkl'

    np.random.seed(seed=args.random_seed)
    all_feats = []
    pred_bbx = []

    keys = sorted(feats.keys())
    for key in keys:
        if feats[key]["cropped_feat"].squeeze().shape == (384,):
            all_feats.append(feats[key]["cropped_feat"].squeeze())
            pred_bbx.append(feats[key]["predicted_bb"])
            
    # Cluster whitened features
    x = np.array(all_feats)
    c, clusters = vq.kmeans2(data=vq.whiten(x) /  np.linalg.norm(vq.whiten(x), axis=1)[:, None], 
                             k=args.nb_clusters)

    pseudo_labels = defaultdict(defaultdict)
    for i in range(len(keys)):
        k = keys[i]
        pseudo_labels[k]["pseudo_label"] = clusters[i]
        pseudo_labels[k]["predicted_bb"] = pred_bbx[i]

    with open(clustering_path, "wb") as f:
        pickle.dump(pseudo_labels, f, protocol=pickle.HIGHEST_PROTOCOL)
    print(f'Pseudo-labels saved at {clustering_path}.')

================================================
FILE: data/CAD_predictions/LOST_plus_CAD_COCO20k.json
================================================
[File too large to display: 20.9 MB]

================================================
FILE: data/CAD_predictions/LOST_plus_CAD_VOC07.json
================================================
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Download .txt
gitextract_b6vyv_32/

├── .gitignore
├── LICENSE
├── README.md
├── __init__.py
├── cluster_for_OD.py
├── data/
│   ├── CAD_predictions/
│   │   ├── LOST_plus_CAD_COCO20k.json
│   │   ├── LOST_plus_CAD_VOC07.json
│   │   └── LOST_plus_CAD_VOC12.json
│   └── LOST_predictions/
│       └── LOST_VOC07.pkl
├── datasets/
│   └── coco_20k_filenames.txt
├── datasets.py
├── main_corloc_evaluation.py
├── main_lost.py
├── networks.py
├── object_discovery.py
├── requirements.txt
├── tools/
│   ├── configs/
│   │   ├── RN50_DINO_FRCNN_COCO20k_CAD.yaml
│   │   ├── RN50_DINO_FRCNN_VOC07_CAD.yaml
│   │   ├── RN50_DINO_FRCNN_VOC07_OD.yaml
│   │   ├── RN50_DINO_FRCNN_VOC12_CAD.yaml
│   │   └── RN50_DINO_FRCNN_VOC12_OD.yaml
│   ├── convert_pretrained_to_detectron_format.py
│   ├── evaluate_unsupervised_detection_voc.py
│   ├── prepare_coco_CAD_gt.py
│   ├── prepare_coco_LOST_CAD_pseudo_boxes_in_detectron2_format.py
│   ├── prepare_voc_LOST_CAD_pseudo_boxes_in_detectron2_format.py
│   ├── prepare_voc_LOST_OD_pseudo_boxes_in_detectron2_format.py
│   ├── prepare_voc_data_in_coco_style.py
│   ├── train_net_for_LOST_CAD.py
│   └── train_net_for_LOST_OD.py
└── visualizations.py
Download .txt
SYMBOL INDEX (70 symbols across 13 files)

FILE: datasets.py
  class ImageDataset (line 34) | class ImageDataset:
    method __init__ (line 35) | def __init__(self, image_path):
    method get_image_name (line 49) | def get_image_name(self, *args, **kwargs):
    method load_image (line 52) | def load_image(self, *args, **kwargs):
  class Dataset (line 55) | class Dataset:
    method __init__ (line 56) | def __init__(self, dataset_name, dataset_set, remove_hards):
    method load_image (line 111) | def load_image(self, im_name):
    method get_image_name (line 124) | def get_image_name(self, inp):
    method extract_gt (line 135) | def extract_gt(self, targets, im_name):
    method extract_classes (line 143) | def extract_classes(self):
    method extract_classes_VOC (line 168) | def extract_classes_VOC(self):
    method extract_classes_COCO (line 179) | def extract_classes_COCO(self):
    method get_hards (line 190) | def get_hards(self):
  function discard_hard_voc (line 211) | def discard_hard_voc(dataloader):
  function extract_gt_COCO (line 231) | def extract_gt_COCO(targets, remove_iscrowd=True):
  function extract_gt_VOC (line 251) | def extract_gt_VOC(targets, remove_hards=False):
  function bbox_iou (line 282) | def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=Fal...
  function select_coco_20k (line 336) | def select_coco_20k(sel_file, all_annotations_file):

FILE: main_lost.py
  function hook_fn_forward_qkv (line 217) | def hook_fn_forward_qkv(module, input, output):

FILE: networks.py
  function get_model (line 23) | def get_model(arch, patch_size, resnet_dilate, device):
  class ResNet50Bottom (line 96) | class ResNet50Bottom(nn.Module):
    method __init__ (line 98) | def __init__(self, original_model):
    method forward (line 103) | def forward(self, x):
  class vgg16Bottom (line 108) | class vgg16Bottom(nn.Module):
    method __init__ (line 110) | def __init__(self, original_model):
    method forward (line 117) | def forward(self, x):

FILE: object_discovery.py
  function lost (line 23) | def lost(feats, dims, scales, init_image_size, k_patches=100):
  function patch_scoring (line 60) | def patch_scoring(M, threshold=0.):
  function detect_box (line 81) | def detect_box(A, seed, dims, initial_im_size=None, scales=None):
  function dino_seg (line 123) | def dino_seg(attn, dims, patch_size, head=0):

FILE: tools/convert_pretrained_to_detectron_format.py
  function _load_pytorch_weights (line 8) | def _load_pytorch_weights(file_path):

FILE: tools/evaluate_unsupervised_detection_voc.py
  function parse_rec (line 23) | def parse_rec(filename):
  function voc_ap (line 46) | def voc_ap(rec, prec, use_07_metric=False):
  function voc_eval (line 78) | def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, u...
  function hungarian_matching (line 199) | def hungarian_matching(reward_matrix):
  function load_predictions (line 222) | def load_predictions(results_file):
  function sort_detections (line 250) | def sort_detections(detections):

FILE: tools/prepare_coco_LOST_CAD_pseudo_boxes_in_detectron2_format.py
  function get_img_size (line 24) | def get_img_size(ann_file):
  function prepare_annotation_data (line 35) | def prepare_annotation_data(loc_object):

FILE: tools/prepare_voc_LOST_CAD_pseudo_boxes_in_detectron2_format.py
  function get_img_size (line 30) | def get_img_size(ann_file):
  function prepare_annotation_data (line 41) | def prepare_annotation_data(loc_object):

FILE: tools/prepare_voc_LOST_OD_pseudo_boxes_in_detectron2_format.py
  function get_img_size (line 34) | def get_img_size(ann_file):
  function prepare_annotation_data (line 45) | def prepare_annotation_data(loc_object, cluster_to_cls):

FILE: tools/prepare_voc_data_in_coco_style.py
  function get_img_size (line 31) | def get_img_size(ann_file):
  function prepare_annotation_data (line 42) | def prepare_annotation_data(ann_file, class_agnostic=False):

FILE: tools/train_net_for_LOST_CAD.py
  function register_voc_in_coco_style (line 36) | def register_voc_in_coco_style(
  function register_CAD_LOST_pseudo_boxes_for_the_voc2007_trainval_dataset (line 86) | def register_CAD_LOST_pseudo_boxes_for_the_voc2007_trainval_dataset(
  function register_CAD_objects_coco_train_dataset (line 100) | def register_CAD_objects_coco_train_dataset(image_root=None):
  function register_CAD_objects_coco_val_dataset (line 120) | def register_CAD_objects_coco_val_dataset(image_root=None):
  function register_CAD_coco20k_train_gt_dataset (line 140) | def register_CAD_coco20k_train_gt_dataset(
  function register_CAD_LOST_pseudo_boxes_for_the_coco20k_trainval_dataset (line 154) | def register_CAD_LOST_pseudo_boxes_for_the_coco20k_trainval_dataset(
  class Res5ROIHeadsExtraNorm (line 189) | class Res5ROIHeadsExtraNorm(Res5ROIHeads):
    method _build_res5_block (line 194) | def _build_res5_block(self, cfg):
  class Trainer (line 202) | class Trainer(DefaultTrainer):
    method build_evaluator (line 211) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
    method test_with_TTA (line 259) | def test_with_TTA(cls, cfg, model):
  function setup (line 276) | def setup(args):
  function main (line 288) | def main(args):

FILE: tools/train_net_for_LOST_OD.py
  function register_voc_in_coco_style (line 35) | def register_voc_in_coco_style(
  function register_clustered_LOST_pseudo_boxes_for_the_voc2007_trainval_dataset (line 88) | def register_clustered_LOST_pseudo_boxes_for_the_voc2007_trainval_dataset(
  class Res5ROIHeadsExtraNorm (line 109) | class Res5ROIHeadsExtraNorm(Res5ROIHeads):
    method _build_res5_block (line 114) | def _build_res5_block(self, cfg):
  class Trainer (line 122) | class Trainer(DefaultTrainer):
    method build_evaluator (line 131) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
    method test_with_TTA (line 179) | def test_with_TTA(cls, cfg, model):
  function setup (line 196) | def setup(args):
  function main (line 208) | def main(args):

FILE: visualizations.py
  function visualize_predictions (line 24) | def visualize_predictions(image, pred, seed, scales, dims, vis_folder, i...
  function visualize_fms (line 53) | def visualize_fms(A, seed, scores, dims, scales, output_folder, im_name):
  function visualize_seed_expansion (line 98) | def visualize_seed_expansion(image, pred, seed, pred_seed, scales, dims,...
Copy disabled (too large) Download .json
Condensed preview — 31 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (15,198K chars).
[
  {
    "path": ".gitignore",
    "chars": 16,
    "preview": "outputs/*\n*.pyc\n"
  },
  {
    "path": "LICENSE",
    "chars": 10778,
    "preview": "   LOST\n   \n   Copyright 2021 Valeo\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not u"
  },
  {
    "path": "README.md",
    "chars": 14185,
    "preview": "# LOST \r\nPytorch implementation of the unsupervised object discovery method **LOST**. More details can be found in the p"
  },
  {
    "path": "__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "cluster_for_OD.py",
    "chars": 6801,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\n#\n# Licensed under the Apache Licens"
  },
  {
    "path": "data/CAD_predictions/LOST_plus_CAD_VOC07.json",
    "chars": 4301744,
    "preview": "[{\"image_id\": \"000005\", \"category_id\": 0, \"bbox\": [65.1786117553711, 144.56309509277344, 395.58087158203125, 208.8906402"
  },
  {
    "path": "data/CAD_predictions/LOST_plus_CAD_VOC12.json",
    "chars": 9055419,
    "preview": "[{\"image_id\": \"2008_000002\", \"category_id\": 0, \"bbox\": [96.18538665771484, 25.250545501708984, 345.051513671875, 247.455"
  },
  {
    "path": "datasets/coco_20k_filenames.txt",
    "chars": 832314,
    "preview": "train2014/COCO_train2014_000000560992.jpg\ntrain2014/COCO_train2014_000000396626.jpg\ntrain2014/COCO_train2014_00000053723"
  },
  {
    "path": "datasets.py",
    "chars": 12993,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\n#\n# Licensed under the Apache Licens"
  },
  {
    "path": "main_corloc_evaluation.py",
    "chars": 4963,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\r\n#\r\n# Licensed under the Apache Lice"
  },
  {
    "path": "main_lost.py",
    "chars": 13292,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\r\n#\r\n# Licensed under the Apache Lice"
  },
  {
    "path": "networks.py",
    "chars": 4638,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\r\n#\r\n# Licensed under the Apache Lice"
  },
  {
    "path": "object_discovery.py",
    "chars": 5776,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\r\n#\r\n# Licensed under the Apache Lice"
  },
  {
    "path": "requirements.txt",
    "chars": 86,
    "preview": "scipy>=1.4.1\nmatplotlib>=3.2.2\nopencv-python>=4.1.2\ntqdm>=4.41.0\nscikit-image\ncatalyst"
  },
  {
    "path": "tools/configs/RN50_DINO_FRCNN_COCO20k_CAD.yaml",
    "chars": 1017,
    "preview": "MODEL:\n  META_ARCHITECTURE: \"GeneralizedRCNN\"\n  RPN:\n    PRE_NMS_TOPK_TEST: 6000\n    POST_NMS_TOPK_TEST: 1000\n  WEIGHTS:"
  },
  {
    "path": "tools/configs/RN50_DINO_FRCNN_VOC07_CAD.yaml",
    "chars": 1056,
    "preview": "MODEL:\r\n  META_ARCHITECTURE: \"GeneralizedRCNN\"\r\n  RPN:\r\n    PRE_NMS_TOPK_TEST: 6000\r\n    POST_NMS_TOPK_TEST: 1000\r\n  WEI"
  },
  {
    "path": "tools/configs/RN50_DINO_FRCNN_VOC07_OD.yaml",
    "chars": 969,
    "preview": "MODEL:\n  META_ARCHITECTURE: \"GeneralizedRCNN\"\n  RPN:\n    PRE_NMS_TOPK_TEST: 6000\n    POST_NMS_TOPK_TEST: 1000\n  WEIGHTS:"
  },
  {
    "path": "tools/configs/RN50_DINO_FRCNN_VOC12_CAD.yaml",
    "chars": 1001,
    "preview": "MODEL:\r\n  META_ARCHITECTURE: \"GeneralizedRCNN\"\r\n  RPN:\r\n    PRE_NMS_TOPK_TEST: 6000\r\n    POST_NMS_TOPK_TEST: 1000\r\n  WEI"
  },
  {
    "path": "tools/configs/RN50_DINO_FRCNN_VOC12_OD.yaml",
    "chars": 969,
    "preview": "MODEL:\n  META_ARCHITECTURE: \"GeneralizedRCNN\"\n  RPN:\n    PRE_NMS_TOPK_TEST: 6000\n    POST_NMS_TOPK_TEST: 1000\n  WEIGHTS:"
  },
  {
    "path": "tools/convert_pretrained_to_detectron_format.py",
    "chars": 2689,
    "preview": "# disclaimer: inspired by MoCo and PyContrast official repos.\r\n\r\nimport pickle as pkl\r\nimport torch\r\nimport argparse\r\n\r\n"
  },
  {
    "path": "tools/evaluate_unsupervised_detection_voc.py",
    "chars": 12979,
    "preview": "import argparse\r\n\r\nimport io\r\nimport json\r\n\r\nimport numpy as np\r\nimport os\r\nimport os.path\r\nimport tempfile\r\nimport xml."
  },
  {
    "path": "tools/prepare_coco_CAD_gt.py",
    "chars": 3112,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance\n#\n# Licensed under the Apache License, Version 2.0 (the \"License"
  },
  {
    "path": "tools/prepare_coco_LOST_CAD_pseudo_boxes_in_detectron2_format.py",
    "chars": 4139,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance\n#\n# Licensed under the Apache License, Version 2.0 (the \"License"
  },
  {
    "path": "tools/prepare_voc_LOST_CAD_pseudo_boxes_in_detectron2_format.py",
    "chars": 3855,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"Licen"
  },
  {
    "path": "tools/prepare_voc_LOST_OD_pseudo_boxes_in_detectron2_format.py",
    "chars": 4389,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance\n#\n# Licensed under the Apache License, Version 2.0 (the \"License"
  },
  {
    "path": "tools/prepare_voc_data_in_coco_style.py",
    "chars": 4670,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"Licen"
  },
  {
    "path": "tools/train_net_for_LOST_CAD.py",
    "chars": 15662,
    "preview": "#!/usr/bin/env python\r\n# Copyright (c) Facebook, Inc. and its affiliates.\r\n# Copyright 2021 Valeo Comfort and Driving As"
  },
  {
    "path": "tools/train_net_for_LOST_OD.py",
    "chars": 11168,
    "preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Copyright 2021 Valeo Comfort and Driving Assi"
  },
  {
    "path": "visualizations.py",
    "chars": 4505,
    "preview": "# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai\r\n#\r\n# Licensed under the Apache Lice"
  }
]

// ... and 2 more files (download for full content)

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This page contains the full source code of the valeoai/LOST GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 31 files (34.6 MB), approximately 3.6M tokens, and a symbol index with 70 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.

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