Open-sourcing the code

.gitattributes

+*.pth filter=lfs diff=lfs merge=lfs -text

Dockerfile

+ARG PYTORCH="1.6.0"
+ARG CUDA="10.1"
+ARG CUDNN="7"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
+ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
+ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"
+
+RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    bzip2 \
+    cmake \
+    curl \
+    git \
+    g++ \
+    libboost-all-dev \
+    pkg-config \
+    rsync \
+    software-properties-common \
+    sudo \
+    tar \
+    timidity \
+    unzip \
+    wget \
+    locales \
+    zlib1g-dev \
+    python3-dev \
+    python3 \
+    python3-pip \
+    python3-tk \
+    libjpeg-dev \
+    libpng-dev \
+    libgl1-mesa-glx
+
+# Install MMCV
+RUN pip install mmcv-full==latest+torch1.6.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
+
+# Install MMDetection
+RUN conda clean --all
+RUN git clone https://github.com/open-mmlab/mmdetection.git mmdetection
+WORKDIR mmdetection
+ENV FORCE_CUDA="1"
+RUN pip install -r requirements/build.txt
+RUN pip install --no-cache-dir -e .
+
+# Python3
+RUN pip install pip --upgrade
+RUN pip install cython aicrowd_api timeout_decorator \
+  numpy \
+  pandas \
+  aicrowd-repo2docker \
+  pillow
+RUN pip install albumentations==0.5.1
+
+RUN conda install cython -y && conda clean --all
+RUN python3.6 -m pip install aicrowd_api   aicrowd-repo2docker
+# Unicode support:
+RUN locale-gen en_US.UTF-8
+
+ENV LANG en_US.UTF-8
+ENV LANGUAGE en_US:en
+ENV LC_ALL en_US.UTF-8
+
+# Enables X11 sharing and creates user home directory
+ENV USER_NAME aicrowd
+ENV HOME_DIR /home/$USER_NAME
+#
+# Replace HOST_UID/HOST_GUID with your user / group id (needed for X11)
+ENV HOST_UID 1000
+ENV HOST_GID 1000
+
+RUN export uid=${HOST_UID} gid=${HOST_GID} && \
+    mkdir -p ${HOME_DIR} && \
+    echo "$USER_NAME:x:${uid}:${gid}:$USER_NAME,,,:$HOME_DIR:/bin/bash" >> /etc/passwd && \
+    echo "$USER_NAME:x:${uid}:" >> /etc/group && \
+    echo "$USER_NAME ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/$USER_NAME && \
+    chmod 0440 /etc/sudoers.d/$USER_NAME && \
+    chown ${uid}:${gid} -R ${HOME_DIR}
+
+USER ${USER_NAME}
+WORKDIR ${HOME_DIR}
+
+COPY --chown=1000:1000 . .
+# COPY . .
+
+# RUN sudo chown ${HOST_UID}:${HOST_GID} -R *
+# RUN sudo chmod 775 -R *
+

README.md

+# <center>Food Recognition Challenge - Starter Kit</center>
+
+![Food-Challenge](https://i.imgur.com/0G3PEc7.png)
+
+<p align="center">
+ <a href="https://discord.gg/GTckBMx"><img src="https://img.shields.io/discord/657211973435392011?style=for-the-badge" alt="chat on Discord"></a>
+</p>
+
+# Table of contents
+- [💪 Getting Started](#-getting-started)
+  * [Using this repository](#using-this-repository)
+  * [Using colab starter kit](#using-colab-starter-kit)
+  * [Running the code locally](#running-the-code-locally)
+- [🧩 Repository structure](#-repository-structure)
+  * [Required files](#required-files)
+  * [Other files](#other-files)
+- [🚀 Submission](#-submission)
+  * [Prepare your environment](#prepare-your-environment)
+    + [`Dockerfile`](#dockerfile)
+    + [`apt.txt`](#apttxt)
+    + [`requirements.txt`](#requirementstxt)
+  * [Initial setup](#initial-setup)
+  * [Submit to AIcrowd](#submit-to-aicrowd)
+- [🛠 Troubleshooting](#-troubleshooting)
+  * [My submission failed. How do I know what happened?](#my-submission-failed-how-do-i-know-what-happened)
+  * [My docker builds fail. Can I reproduce this locally?](#my-docker-builds-fail-can-i-reproduce-this-locally)
+- [📎 Important links](#-important-links)
+- [✍️ Author](#-author)
+  * [✨ Contributors](#-contributors)
+
+
+
+# 💪 Getting Started
+
+The dataset for this challenge is available on the [challenge's resources page](https://www.aicrowd.com/challenges/food-recognition-challenge/dataset_files).
+
+## Using this repository
+This repository contains the code for a random agent. To run the code locally,
+
+- Clone the repository
+- Install dependencies
+- Execute `run.sh`
+
+Clone the repository
+```bash
+git clone https://github.com/AIcrowd/food-recognition-challenge-starter-kit
+cd food-recognition-challenge-starter-kit
+```
+
+Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+Update the value for `AICROWD_TEST_IMAGES_PATH` to point to your validation dataset
+and run `debug.sh`
+```bash
+./debug.sh
+```
+
+During the evaluation, we will execute `run.sh`. You can add any additional commands
+as per your needs.
+
+## Using colab starter kit
+
+We prepared a colab notebook that uses `detectron2`. You can train your agent and make
+a submission from your notebook!
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1F2IbqpI0ecbRDCNiBLPFCCOVEK0cgbH7?usp=sharing)
+
+## Running the code locally
+
+- First, make sure that you have the dataset downloaded.
+- Update the value of `AICROWD_TEST_IMAGES_PATH` in `debug.sh` file to point to your dataset
+- Execute `debug.sh`
+
+```bash
+./debug.sh
+```
+
+This will generate `predictions.json` file in your current directory.
+
+# 🧩 Repository structure
+
+## Required files
+
+**File** | **Description**
+--- | ---
+`Dockerfile` | Configuration file to build docker images used during evaluation
+`aicrowd.json` | A configuration file used to identify the challenge and resources needed for evaluation
+`aicrowd_helpers.py`<sup>#</sup> | Helpers file used to relay evaluation progress
+`apt.txt` | List of packages that should be installed (via `apt`) for your code to run
+`requirements.txt` | List of python packages that should be installed (via `pip`) for your code to run
+`run.sh` | Entry point to your code
+
+<sup>#</sup> Do not edit these files.
+
+## Other files
+
+**File** | **Description**
+--- | ---
+`run.py` | Python script to generate random predictions
+`debug.sh` | Helps your run your code locally
+`utils/` | Directory containing some useful scripts and notebooks
+`utils/requirements_detectron2.txt` | A sample `requirements.txt` file for using `detectron2`
+`utils/requirements_mmdetection.txt` | A sample `requirements.txt` file for using `mmdetection`
+
+
+# 🚀 Submission
+
+## Prepare your environment
+
+There are three files that help you setup your environment.
+1. `Dockerfile`
+2. `apt.txt`
+3. `requirements.txt`
+
+### `Dockerfile`
+If you plan to use GPU, please make sure that you are using an appropriate `CUDA` and
+`CUDNN` versions. You can specify these at the top of your [`Docerfile`](Dockerfile#L1).
+
+### `apt.txt`
+If there are certain system level packages that you need, you can specify them in your
+`apt.txt`. If you are familiar with ubuntu/debian, this is same as installing these
+packages using `apt-get install` command.
+
+### `requirements.txt`
+You can specify the list of python packages that need to be installed in your
+`requirements.txt`.
+
+Please note that we are using `apt.txt` and `requirements.txt` in the `Dockerfile` to
+install required packages. We believe that this makes it easier for you to add the
+required packages without much hassle. If you are comfortable with docker, you are
+free to edit the `Dockerfile` as needed.
+
+## Initial setup
+
+Before you submit to AIcrowd, you need to setup SSH access to our GitLab instance.
+This is a one-time requirement to setup your repository.
+
+This process involves
+1. Cloning the repository
+2. Replace git origin to point to your personal repository
+3. Setup SSH key
+
+To clone the repository, please refer getting started section.
+
+Now, you need to point the repository to your personal repository on AIcrowd GitLab.
+
+```bash
+git remote set-url origin git@gitlab.aicrowd.com:<your-aicrowd-username>/food-recognition-challenge-starter-kit.git
+```
+
+To be able to push your code to GitLab, you should setup SSH keys first. Please
+follow the instructions at
+[https://discourse.aicrowd.com/t/how-to-add-ssh-key-to-gitlab/2603](https://discourse.aicrowd.com/t/how-to-add-ssh-key-to-gitlab/2603)
+
+## Submit to AIcrowd
+
+To submit to AIcrowd, you need to push a tag starting with `submission-` to GitLab.
+
+Add the changes to git.
+
+```bash
+git add --all
+git commit -m "<brief summary of changes>"
+```
+
+You need to add large files via `git-lfs`.
+
+```bash
+git lfs install
+
+# Add all the files larger than 5 MB to LFS
+find . -type f -size +5M -exec git lfs migrate import --include={} &> /dev/null \;
+```
+
+For more information on using LFS, please refer
+[uploading large files to GitLab](https://discourse.aicrowd.com/t/how-to-upload-large-files-size-to-your-submission/2304).
+
+Create and push the tag
+
+```bash
+# You can replace "-initial-version" with something that describes your submission
+git tag -am "submission-initial-version" "submission-initial-version"
+git lfs push origin master
+git push origin master
+git push origin submission-initial-version
+```
+
+# 🛠 Troubleshooting
+
+## My submission failed. How do I know what happened?
+
+If you make a submission in `debug` mode, we provide the outputs from your code on the
+GitLab issue page corresponding to your submission. To make a submission in `debug`
+mode, you need to add `"debug": true` in your `aicrowd.json`. Please note that the
+debug mode submission will not be considered for leaderboard.
+
+## My docker builds fail. Can I reproduce this locally?
+
+You can build the images locally by running the following
+
+```bash
+docker build .
+```
+
+# 📎 Important links
+
+
+- 💪 &nbsp;Challenge Page: https://www.aicrowd.com/challenges/food-recognition-challenge
+- 🗣️ &nbsp;Discussion Forum: https://www.aicrowd.com/challenges/food-recognition-challenge/discussion
+- 🏆 &nbsp;Leaderboard: https://www.aicrowd.com/challenges/food-recognition-challenge/leaderboards
+- Resources - Round 1
+  * [Colab Notebook for Data Analysis and Tutorial](https://colab.research.google.com/drive/1A5p9GX5X3n6OMtLjfhnH6Oeq13tWNtFO#scrollTo=ok54AWT_VoWV)
+  * [Baseline with `mmdetection` (pytorch)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-pytorch-baseline)
+  * [Baseline with `matterport-maskrcnn` (keras - tensorflow)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-recognition)
+- Resources - Round 2
+  * [Colab Notebook for Data Analysis and Tutorial](https://colab.research.google.com/drive/1vXdv9quZ7CXO5lLCjhyz3jtejRzDq221)
+  * [Baseline with `mmdetection` (pytorch)](https://gitlab.aicrowd.com/nikhil_rayaprolu/food-round2)
+- Resources - Round 3
+  * [Colab Notebook for data exploration](https://discourse.aicrowd.com/t/detectron2-colab-notebook-from-data-exploration-to-training-the-model/3691)
+- [Participant contributions](https://discourse.aicrowd.com/tags/c/food-recognition-challenge/112/explainer)
+- External resources:
+  * [Convert Annotations from MS COCO format to PascalVOC format](https://github.com/CasiaFan/Dataset_to_VOC_converter/blob/master/anno_coco2voc.py)
+  
+
+# ✍️ Author   
+**[Sharada Mohanty](https://twitter.com/memohanty?lang=en)**
+
+## ✨ Contributors
+
+* [Nikhil Rayaprolu](https://github.com/nikhilrayaprolu)
+* [Pulkit Gera](https://github.com/darthgera123)
+* [Shivam Khandelwal](https://twitter.com/skbly7?lang=en)
+* [Jyotish P](https://github.com/jyotishp)
\ No newline at end of file

aicrowd.json

+{
+  "challenge_id" : "aicrowd-food-recognition-challenge",
+  "grader_id": "aicrowd-food-recognition-challenge",
+  "authors" : ["gaurav_singhal"],
+  "description" : "Food Recognition Challenge Submission",
+  "license" : "MIT",
+  "gpu": true,
+  "debug": false
+}
\ No newline at end of file

aicrowd_helpers.py

+#!/usr/bin/env python
+import aicrowd_api
+import os
+
+########################################################################
+# Instatiate Event Notifier
+########################################################################
+aicrowd_events = aicrowd_api.events.AIcrowdEvents()
+
+
+def execution_start():
+    ########################################################################
+    # Register Evaluation Start event
+    ########################################################################
+    aicrowd_events.register_event(
+        event_type=aicrowd_events.AICROWD_EVENT_INFO,
+        message="execution_started",
+        payload={  # Arbitrary Payload
+            "event_type": "food_recognition_challenge:execution_started"
+        },
+    )
+
+
+def execution_progress(progress_payload):
+    image_ids = progress_payload["image_ids"]
+    ########################################################################
+    # Register Evaluation Progress event
+    ########################################################################
+    aicrowd_events.register_event(
+        event_type=aicrowd_events.AICROWD_EVENT_INFO,
+        message="execution_progress",
+        payload={  # Arbitrary Payload
+            "event_type": "food_recognition_challenge:execution_progress",
+            "image_ids": image_ids,
+        },
+    )
+
+
+def execution_success(payload):
+    predictions_output_path = payload["predictions_output_path"]
+    ########################################################################
+    # Register Evaluation Complete event
+    ########################################################################
+    expected_output_path = os.getenv("AICROWD_PREDICTIONS_OUTPUT_PATH", False)
+    if expected_output_path != predictions_output_path:
+        raise Exception(
+            "Please write the output to the path specified in the environment variable : AICROWD_PREDICTIONS_OUTPUT_PATH instead of {}".format(
+                predictions_output_path
+            )
+        )
+
+    aicrowd_events.register_event(
+        event_type=aicrowd_events.AICROWD_EVENT_SUCCESS,
+        message="execution_success",
+        payload={  # Arbitrary Payload
+            "event_type": "food_recognition_challenge:execution_success",
+            "predictions_output_path": predictions_output_path,
+        },
+        blocking=True,
+    )
+
+
+def execution_error(error):
+    ########################################################################
+    # Register Evaluation Complete event
+    ########################################################################
+    aicrowd_events.register_event(
+        event_type=aicrowd_events.AICROWD_EVENT_ERROR,
+        message="execution_error",
+        payload={  # Arbitrary Payload
+            "event_type": "food_recognition_challenge:execution_error",
+            "error": error,
+        },
+        blocking=True,
+    )

apt.txt

+wget
+ca-certificates
+g++
+libjpeg-dev
+libpng-dev
+zlib1g-dev
+libgl1-mesa-glx
+libglib2.0-0
+git

bayes_postprocess.py

+import pandas as pd
+import numpy as np
+import json
+
+with open("prob_matrix.json") as f:
+    d = json.loads(f.read())
+df_csr = pd.read_json(d)
+df_csr.index = df_csr.index.astype(int)
+df_csr.columns = df_csr.columns.astype(int)
+
+def get_prior_prob(A):
+    '''
+    P(A) = num_images(A)/total_images
+    '''
+    total_images = 24093
+    return df_csr.loc[A].sum()/total_images
+
+def get_posterior(A, events):
+    '''
+    Get posterior probability of category. P(A/events). Expects co-occurence dataframe
+    '''
+    numerator = np.prod(
+        (
+            df_csr.loc[A, [j for j in events]] / \
+            df_csr.loc[[j for j in events]].sum(axis=1)
+        ).to_list()
+    ) * get_prior_prob(A)
+    denominator = np.prod([get_prior_prob(j) for j in events])
+    if (numerator/denominator) > 0.99:
+        #print("{}, {}, {}".format(numerator, denominator, numerator/denominator))
+        # Something bad
+        return 0.99
+    return numerator/denominator
+        
+def posterior_util(cats):
+    '''
+    Get Posterior probability of all the categories
+    
+    @input
+    cats -> List of category ids
+    '''
+    probs = []
+    for i in range(len(cats)):
+        events = cats[:i]+cats[i+1:]
+        probs.append(get_posterior(cats[i], events))
+    return probs
\ No newline at end of file

build.sh

+#!/bin/bash
+docker build -t $IMAGE_NAME .
\ No newline at end of file

classwise_map.json

+{"keep":{"2578":"drs","1198":"drs","2099":"drs","2530":"htc","1026":"drs","1505":"htc","1731":"drs","1538":"htc","1022":"htc","2620":"drs","1074":"drs","1533":"drs","1084":"drs","2730":"htc","1210":"drs","1308":"drs","1156":"htc","1108":"drs","1143":"drs","2413":"drs","2512":"drs","2836":"drs","1482":"drs","3080":"drs","1013":"htc","1942":"drs","2952":"drs","1214":"drs","1889":"drs","2729":"drs","1065":"drs","2742":"drs","2841":"htc","387":"htc","1483":"drs","2973":"htc","1789":"drs","1228":"drs","2501":"drs","1788":"drs","6404":"drs","2237":"htc","1138":"drs","2923":"htc","1967":"drs","1038":"drs","2524":"drs","1607":"drs","2736":"drs","1144":"drs","1119":"drs","1004":"drs","2711":"drs","2498":"drs","1050":"htc","2073":"drs","1321":"drs","2930":"drs","2454":"htc","1614":"drs","1727":"drs","1383":"htc","1070":"htc","1919":"drs","1561":"htc","1948":"drs","2446":"htc","1568":"drs","1280":"htc","2905":"drs","1730":"drs","1186":"htc","2495":"drs","1620":"htc","2743":"drs","1748":"htc","1924":"htc","3262":"drs","1856":"drs","2967":"drs","1223":"drs","1328":"drs","1463":"drs","2320":"drs","1199":"drs","1075":"drs","1153":"drs","1163":"drs","1123":"htc","1157":"drs","2053":"drs","1554":"htc","2521":"drs","1311":"drs","1078":"drs","1453":"htc","2504":"drs","1565":"drs","1853":"drs","1310":"drs","2747":"drs","1009":"drs","1490":"drs","2131":"drs","1588":"htc","1082":"htc","1032":"drs","1150":"drs","1587":"drs","1560":"drs","1831":"drs","1670":"drs","1098":"htc","1007":"drs","1956":"drs","2939":"drs","2303":"drs","1469":"drs","1033":"drs","1496":"htc","732":"drs","1061":"drs","1728":"drs","1068":"drs","1478":"drs","1569":"drs","1327":"drs","1980":"drs","1014":"htc","1094":"htc","1221":"htc","1056":"drs","630":"drs","2970":"drs","1107":"drs","1915":"drs","1102":"drs","2961":"drs","3220":"drs","1113":"htc","1166":"htc","3115":"drs","1557":"drs","1916":"drs","3630":"htc","2376":"htc","1794":"drs","2741":"htc","2954":"htc","3332":"htc","1793":"drs","2269":"drs","2580":"drs","2873":"drs","1547":"drs","1019":"drs","1724":"htc","2172":"drs","2470":"drs","1184":"htc","3306":"htc","2949":"drs","1402":"htc","2555":"htc","2254":"drs","1162":"htc","2362":"htc","1264":"htc","2543":"drs","1494":"htc","1209":"htc","1215":"htc","1089":"htc","1376":"htc","1249":"htc","2616":"drs","1124":"drs","2022":"drs","1566":"drs","1151":"htc","2534":"drs","2738":"htc","1116":"drs","1040":"drs","1154":"drs","2895":"drs","1300":"htc","1893":"drs","1069":"htc","2618":"drs","1058":"drs","1180":"htc","2944":"drs","1384":"htc","1126":"drs","1468":"htc","2350":"drs","633":"drs","2934":"drs","1203":"drs","1024":"htc","5641":"drs","2634":"htc","1060":"drs","1520":"drs","3358":"drs","3532":"drs","2562":"drs","1181":"drs","1536":"htc","1506":"htc","1352":"htc","2807":"drs","1545":"drs","1323":"drs","2750":"htc","1187":"htc","1348":"drs","1085":"drs","2734":"drs","1294":"htc","2728":"drs","1307":"drs","1627":"drs","3100":"htc","1879":"drs","2935":"drs","2898":"drs","50":"drs","1237":"drs","1551":"drs","1092":"drs","1229":"drs","1152":"drs","2103":"htc","1220":"drs","1170":"drs","1986":"drs","1213":"drs","1523":"htc","2920":"htc","3042":"htc","1487":"htc","1467":"drs","3249":"drs","2134":"htc","727":"htc","1556":"drs","1985":"htc","2132":"htc","2749":"drs","3308":"drs","1200":"drs","1580":"drs","1055":"htc","2278":"drs","1371":"drs","1054":"htc","2964":"drs","1176":"drs","1191":"drs","1522":"htc","578":"drs","1190":"htc"}}
\ No newline at end of file

debug.sh

+#!/bin/bash
+nvidia-docker run -it \
+  --net=host \
+  -v ${TEST_IMAGES_PATH}:/test_images \
+  -v /tmp:/tmp_host \
+  -e AICROWD_IS_GRADING=True \
+  -e AICROWD_TEST_IMAGES_PATH="/test_images" \
+  -e AICROWD_PREDICTIONS_OUTPUT_PATH="/tmp/output.json" \
+  $IMAGE_NAME \
+  /home/aicrowd/run.sh
\ No newline at end of file

infer.py

+import argparse
+import glob
+import json
+import os
+import sys
+from merge_results import consolidate_results, write_data
+import aicrowd_helpers
+import mmcv
+
+TEST_IMAGES_PATH = os.getenv("AICROWD_TEST_IMAGES_PATH")
+OUT_FILE = os.getenv("AICROWD_PREDICTIONS_OUTPUT_PATH")
+
+def filter_predictions(file_path, map_name):
+    # Load the predictions
+    with open(file_path) as f:
+        preds = json.loads(f.read())
+
+    # Load the classwise mAP
+    with open("classwise_map.json") as f:
+        d = json.loads(f.read())
+    class_map = d['keep']
+
+    new_predictions = []
+    for pred in preds:
+        cat_id = str(pred['category_id'])
+        # if (cat_id in class_map) and (class_map[cat_id]==map_name):
+        #     new_predictions.append(pred)
+        if cat_id in class_map:
+            new_predictions.append(pred)
+    write_data(new_predictions, file_path)
+
+
+def create_test_predictions():
+    annotations = {
+        'categories': list(), 
+        'info': dict(), 
+        'images': list()
+    }
+    all_images = f"{TEST_IMAGES_PATH}/*.jpg"
+    for item in glob.glob(all_images):
+        img = mmcv.imread(item)
+        annotations['images'].append({
+            "id": int(os.path.basename(item).split('.')[0]),
+            "file_name": os.path.basename(item),
+            'width': img.shape[1],
+            'height': img.shape[0]
+        })
+    classes = open("classes.json").read()
+    annotations.update({'categories': json.loads(classes)})
+    
+    with open('test_predictions.json', 'w') as fout:
+        json.dump(annotations, fout)
+
+
+if __name__ == '__main__':
+    # Create blank predictions for testing. Same as in predict_model.py
+    create_test_predictions()
+
+    aicrowd_helpers.execution_start()
+    parser = argparse.ArgumentParser(
+        description='MMDet test (and eval) a model')
+    parser.add_argument('--out_file', help='output result file')
+    
+    args = parser.parse_args()
+    os.environ["IMAGE_COUNT"] = "0"
+
+    ## Ensemble
+    output_files = []
+    from models import *
+
+    model_config = get_model() 
+    for config in model_config:
+        script_descriptor = open("predict.py")
+        model_script = script_descriptor.read()
+        sys.argv = ["predict_model.py", "--config", config['config'], "--checkpoint", config['weights'] \
+            , "--eval-options", "--format-only", \
+            , "jsonfile_prefix=predictions", "--out_file", config['output_file']]
+        exec(model_script)
+        script_descriptor.close()
+        output_files.append(config['output_file']) 
+
+        # # Filter-out predictions
+        # if "name" in config:
+        #     filter_predictions(config['output_file'], config['name'])
+        
+    # Consolidate the results
+    consolidate_results(output_files, 'test_predictions.json', args.out_file)
\ No newline at end of file

merge_results.py

+import json
+import argparse
+import os
+import numpy as np
+from pycocotools import mask as maskUtils 
+
+# For bayesian statistics
+# import pandas as pd
+# import bayes_postprocess as bayes
+
+def calculate_iou(box1, box2):
+    '''
+    Calculate IoU of two bounding boxes.
+    
+    @input
+    bb1 : dict{'x1', 'x2', 'y1', 'y2'}
+    bb2 : dict{'x1', 'x2', 'y1', 'y2'}
+
+    @output
+    IoU in float range[0, 1]
+    '''
+    bb1 = {
+        "x1": box1[0],
+        "y1": box1[1],
+        "x2": box1[0] + box1[2],
+        "y2": box1[1] + box1[3],
+    }
+    bb2 = {
+        "x1": box2[0],
+        "y1": box2[1],
+        "x2": box2[0] + box2[2],
+        "y2": box2[1] + box2[3],
+    }
+    # Get intersection coordinates
+    x_left = max(bb1['x1'], bb2['x1'])
+    y_top = max(bb1['y1'], bb2['y1'])
+    x_right = min(bb1['x2'], bb2['x2'])
+    y_bottom = min(bb1['y2'], bb2['y2'])
+
+    if x_right < x_left or y_bottom < y_top:
+        return 0.0
+
+    # Intersection of 2 boxes
+    intersection_area = (x_right - x_left + 1) * (y_bottom - y_top + 1)
+    # Calculate the areas of both AABBs
+    box1area = (bb1['x2'] - bb1['x1'] + 1) * (bb1['y2'] - bb1['y1'] + 1)
+    box2area = (bb2['x2'] - bb2['x1'] + 1) * (bb2['y2'] - bb2['y1'] + 1)
+
+    IoU = intersection_area / float(box1area + box2area - intersection_area)
+    return IoU
+
+def uneBoundingBoxes(boxes_xmls, by_class=True, threshold=0.5):
+    boundingBoxes=[]
+    total_boxes=len(boxes_xmls)
+    while(total_boxes>0):
+        boxPrim=boxes_xmls[0]
+        listBox = []
+        listBox.append(boxPrim)
+        boxes_xmls_temp=boxes_xmls[1:]
+        boxes_xmls.remove(boxPrim)
+        for box in boxes_xmls_temp:
+            if by_class and boxPrim['category_id']==box['category_id']:
+                if calculate_iou(boxPrim['bbox'], box['bbox']) > threshold:
+                    boxes_xmls.remove(box)
+                    listBox.append(box)
+            elif not by_class and calculate_iou(boxPrim['bbox'], box['bbox']) > threshold:
+                boxes_xmls.remove(box)
+                listBox.append(box)    
+        boundingBoxes.append(listBox)
+        total_boxes=len(boxes_xmls)
+    return boundingBoxes
+
+def group_by_iou(all_predictions, data, by_class=True, threshold = 0.5):
+    iou_groups = dict()
+    for img in data['images']:
+        # All the predictions for an image
+        preds = [item for item in all_predictions if item['image_id'] == img['id']]
+        iou_groups[img['id']] = uneBoundingBoxes(preds, by_class, threshold)
+    return iou_groups
+
+def load_file(file):
+    with open(file) as f:
+        data = json.loads(f.read())
+    return data
+
+def merge_predictions(file_list, annotations):
+    # load the annotations
+    data = load_file(annotations)
+
+    # load all predictions into one big list
+    all_predictions = list()
+    for file_name in file_list:
+        all_predictions.extend(load_file(file_name))
+    return all_predictions, data
+
+def group_predictions_by_cat(predictions, data):
+    '''
+    Group the predictions by image id and class
+    '''
+    grouped_preds = {item["id"]: {} for item in data['images']}
+    for pred in predictions:
+        if pred['image_id'] not in grouped_preds:
+            grouped_preds[pred['image_id']] = dict()
+        if pred['category_id'] not in grouped_preds[pred['image_id']]:
+            grouped_preds[pred['image_id']][pred['category_id']] = []
+        grouped_preds[pred['image_id']][pred['category_id']].append(pred)
+    return grouped_preds
+
+def remove_redundant_predictions(grouped_preds):
+    '''
+    Remove the predictions which are redundant
+    '''
+    new_preds = list()
+    # loop through the images
+    for img_id, cat_preds in grouped_preds.items():
+        for cat_id, preds in cat_preds.items():
+            delete_items = set()
+            # if we have more than one prediction we will see how much they overlap
+            if len(preds) > 1:
+                for i, pred1 in enumerate(preds):
+                    for j, pred2 in enumerate(preds):
+                        if j > i:
+                            overlap = calculate_iou(pred1['bbox'], pred2['bbox'])
+                            # if the overlap is greater than 40 % we will reduce the score of the lower scoring one
+                            if overlap > 0.4 and pred1['score'] > pred2['score']:
+                                delete_items.add(j)
+                            elif overlap > 0.4 and pred2['score'] > pred1['score']:
+                                delete_items.add(i)
+                            
+                # delete the items with the lower scores
+                delete_items = sorted(delete_items)
+                delete_items.reverse()
+                for idx in delete_items:
+                    del preds[idx]
+            new_preds.extend(preds)
+    return new_preds
+
+def select_max_score(all_predictions, data, multi_model: bool = True):
+    '''
+    Group the results by IOU and class
+    '''
+    iou_groups = group_by_iou(all_predictions, data, by_class=True)
+    new_predictions = list()
+    for img_id, pred_groups in iou_groups.items():
+        for i, group in enumerate(pred_groups):
+            if multi_model:
+                group[0]['score'] *= 0.5
+            elif len(group) > 1:
+                # sort the predictions by descending score
+                group = sorted(group, key=lambda x: x['score'], reverse=True)
+            # append the highest score for each group to the new predictions
+            new_predictions.append(group[0])
+    
+    # group the predictions by category
+    grouped_preds = group_predictions_by_cat(new_predictions, data)
+    
+    # delete redundant predictions
+    if multi_model:
+        return remove_redundant_predictions(grouped_preds)
+    return new_predictions
+
+def remove_duplicated_categories(predictions: list) -> list:
+    '''
+    Remove the predictions with duplicated categories and IOU > .2
+    '''
+    grouped_preds = group_predictions(predictions, "image_id")
+    new_predictions = list()
+    for img_id, img_preds in grouped_preds.items():
+        # Prediction for an image > 10
+        if len(img_preds) > 10:
+            # Group by category_id the predictions 
+            cat_grouped_preds = group_predictions(img_preds, "category_id")
+            # loop over predicted categories
+            for cat_id, cat_preds in cat_grouped_preds.items():
+                # Group the predicted categories with a smaller IOU threshold
+                cat_groups = uneBoundingBoxes(cat_preds.copy(), threshold=0.5)
+                for i, group in enumerate(cat_groups):
+                    group = sorted(group, key=lambda x: x['score'], reverse=True)
+                    # Add the best score for each group to new predictions
+                    new_predictions.append(group[0])
+        else:
+            new_predictions.extend(img_preds)
+    return new_predictions
+
+def group_predictions(predictions, key) -> dict:
+    '''
+    Group predictions by key
+
+    @input
+    predictions -> List of predictions
+    key -> String by which predictions are to be grouped
+
+    @output
+    Group the predictions by the given key
+    '''
+    grouped_predictions = dict()
+    for pred in predictions:
+        if pred[key] not in grouped_predictions:
+            grouped_predictions[pred[key]] = list()
+        grouped_predictions[pred[key]].append(pred)
+    return grouped_predictions
+
+def apply_bayesian_stats(new_preds, img_ids):
+    df_new_preds = pd.DataFrame(new_preds)
+    for img_id in img_ids:
+        temp = df_new_preds.query("image_id==@img_id")[['category_id', 'score']]
+        temp['prob'] = bayes.posterior_util(temp.category_id.to_list())
+        mid_score = temp.prob.sort_values(ascending=False).median()
+        remove_index = temp.query("prob<=@mid_score and score<0.001").index
+        df_new_preds.drop(index=remove_index, inplace=True)
+    
+    return df_new_preds.to_dict("records")
+
+def consolidate_results(models, annotations, out_file, remove_duplicates=True, reduce_score=True):
+    all_predictions, data = merge_predictions(models, annotations)
+    # group the results by IOU and class
+    new_preds = select_max_score(all_predictions, data, multi_model=reduce_score)
+    # remove duplicated overlapping categories
+    if remove_duplicates:
+        new_preds = remove_duplicated_categories(new_preds)
+
+    # img_ids = [d['id'] for d in data['images']]
+    # new_preds = apply_bayesian_stats(new_preds, img_ids)
+
+    # write the data
+    out_file_ = os.getenv("AICROWD_PREDICTIONS_OUTPUT_PATH")
+    write_data(new_preds, out_file_)
+
+def write_data(data, out_file):
+    '''
+    Writes the data in the json file
+    '''
+    with open(out_file, "w") as file:
+        json_dump = json.dumps(data)
+        file.write(json_dump)
\ No newline at end of file

models.py

+def get_model():
+    return [
+        {
+            "config" : "./models/htc/htc_x101_3.py",
+            "weights" : "./models/htc/epoch_52.pth",
+            "output_file" : "predictions_htc.json"
+            # "name": "htc"
+        },
+        # {
+        #     "config" : "./models/detectoRS/detectors_htc_r50_2.py",
+        #     "weights" : "./models/detectoRS/epoch_20.pth",
+        #     "output_file" : "predictions_det.json"
+        # },
+        {
+            "config" : "./models/detectoRS_4/detectors_htc_r50_4.py",
+            "weights" : "./models/detectoRS_4/epoch_35.pth",
+            "output_file" : "predictions_det_5.json"
+            # "name": "drs"
+        },
+        # {
+        #     "config" : "./models/detectoRS_4_1/detectors_htc_r50_4_1.py",
+        #     "weights" : "./models/detectoRS_4_1/epoch_39.pth",
+        #     "output_file" : "predictions_det_41.json"
+        # },
+        # {
+        #     "config" : "./models/detectoRS_4/detectors_htc_r50_4.py",
+        #     "weights" : "./models/detectoRS_4/epoch_40_ssl.pth",
+        #     "output_file" : "predictions_det_5.json"
+        # }
+    ]
\ No newline at end of file