First commit

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+*.mdb
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+
+### Linux ###
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+# Linux trash folder which might appear on any partition or disk
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+# .nfs files are created when an open file is removed but is still being accessed
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+### Python Patch ###
+.venv/
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+### Python.VirtualEnv Stack ###
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+[Bb]in
+[Ii]nclude
+[Ll]ib64
+[Ll]ocal
+[Ss]cripts
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+
+### Windows ###
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+$RECYCLE.BIN/
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+data/
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+*.zip
\ No newline at end of file

LICENSE

+MIT License
+
+Copyright (c) 2022 HDETR-group (Yuhui Yuan,Ding Jia,Haodi He,Xiaopei Wu,Haojun 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.

README.md

+# HDETR
+## 论文
+[DETRs with Hybrid Matching](https://arxiv.org/abs/2207.13080)
+
+## 模型结构
+基于DETR结构，在匹配阶段加入一对多的匹配分支。
+
+<div align=center>
+    <img src="./doc/hybrid.png"/>
+</div>
+
+## 算法原理
+H-DETR引入一对多匹配分支，将原始的一对一匹配分支与一个辅助的一对多匹配分支结合起来，允许多个查询分配给每个正样本，增加正样本查询数量，提高训练效果。此外，H-DETR在推理过程中仍然使用原始的一对一匹配分支，以保持DETR的优势。
+
+<div align=center>
+    <img src="./doc/methods.png"/>
+</div>
+
+## 环境配置
+
+注意:requirements.txt安装完成后,还需要额外安装下列包
+
+```
+pip install openmim
+mim install mmcv-full  （注意版本是不是1.7.1）
+pip install mmdet==2.26.0 （对应mmcv 1.7.1）
+```
+
+-v 路径、docker_name和imageID根据实际情况修改
+
+### Docker（方法一）
+
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py38-latest
+docker run -it -v /path/your_code_data/:/path/ your_code_data/ --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+
+cd /your_code_path/HDETR_pytorch
+pip install -r requirements.txt
+```
+
+### Dockerfile（方法二）
+
+```
+cd ./docker
+cp ../requirements.txt requirements.txt
+
+docker build --no-cache -t hdetr:latest .
+docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+```
+
+### Anaconda（方法三）
+
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
+
+```
+DTK软件栈：dtk23.04.1
+python：python3.8
+torch：1.13.1
+torchvision：0.14.1
+```
+
+Tips：以上dtk软件栈、python、torch等DCU相关工具版本需要严格一一对应
+
+2、其他非特殊库直接按照requirements.txt安装
+
+```
+pip3 install -r requirements.txt
+```
+
+## 数据集
+
+COCO2017
+
+[训练数据](http://images.cocodataset.org/zips/train2017.zip)
+
+[验证数据](http://images.cocodataset.org/zips/val2017.zip)
+
+[测试数据](http://images.cocodataset.org/zips/test2017.zip)
+
+[标签数据](https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip)
+
+数据集的目录结构如下：
+
+```
+├── COCO2017
+│   ├── images
+│       ├── train2017
+│       ├── val2017
+│       └── test2017
+│   ├── annotations
+│       ├── instances_train2017.json
+│       └── instances_val2017.json
+```
+
+训练/验证集数据准备：
+
+训练/验证集都是采用的COCO的数据格式，如果使用自己的标注数据，请先将标注数据转换成COCO的格式，并按照上面的目录结构进行存放。
+
+## 训练
+
+训练前的准备工作：
+
+1. 进行下面步骤编译
+
+```
+cd ./models/ops
+bash ./make.sh
+```
+
+2. 进行单元测试，需要所有的结果都为TRUE
+
+```
+python test.py
+cd ../../
+```
+
+3. 选择需要训练的模型的config, 设置<config path>为需要训练的模型属性, 设置<coco path>为当前环境中训练数据对应地址。
+
+Tips:
+
+1. 如果有预训练模型，修改config中的 --pretrained_backbone_path 为保存的预训练模型地址；
+
+2. 如果使用backbone为swin,可前往 https://github.com/microsoft/Swin-Transformer 选择对应的预训练模型后再进行训练步骤。
+
+### 单机单卡
+
+```
+bash train.sh
+```
+
+### 单机多卡
+
+```
+bash train_multi.sh
+```
+
+### 多机多卡
+
+#### slurm cluster训练方式
+
+<partition> 分区名称
+<job_name> 本次执行的任务名称，建议可以使用{模型}_卡数_单卡bs_日期进行命名
+<config path> 需要训练的模型属性, 参见configs文件夹下选择
+
+1 node with 4 DCUs:
+
+```
+GPUS_PER_NODE=4 ./tools/run_dist_slurm.sh <partition> <job_name> 4 <config path>
+```
+
+2 nodes(example is 2) of each with 4 DCUs:
+
+```
+GPUS_PER_NODE=4 ./tools/run_dist_slurm.sh <partition> <job_name> 8 <config path>
+```
+
+## 推理
+
+验证前需提前准备好预训练模型，<checkpoint path>设置为模型地址，<coco path>为当前环境中推理数据的对应地址，数据应为COCO数据格式。
+
+如没有预训练模型，可从 参考资料 中提供的模型下载，选择模型对应的config后进行效果验证。
+
+如果想要查看预测效果（预测结果输出到图片上），请执行：
+
+```
+python test.py --pre_trained_model <checkpoint path> --coco_path <coco path>
+```
+
+其余对应参数与训练模型参数需一致，详情请参考代码里面的参数配置：
+
+#### 单卡推理
+
+```
+bash val.sh
+```
+
+#### 多卡推理
+
+```
+bash val_multi.sh
+```
+
+## result
+
+COCO2017测试集上的单张图像结果展示：
+
+<div align=center>
+    <img src="./doc/results.jpg"/>
+</div>
+
+### 精度
+
+在COCO2017的测试集上进行单卡测试，结果如下表所示
+
+根据测试结果情况填写表格：
+| Name     | Backbone | query | epochs | AP |
+| :--------: | :------: | :------: | :------: | :------: |
+| H-Deformable-DETR + tricks(our) | R50 | 300 | 12 | xxx  |
+| H-Deformable-DETR + tricks | R50 | 300 | 12 | 48.7 |
+
+
+## 应用场景
+### 算法类别
+目标检测
+
+### 热点应用行业
+网安,交通,政府
+
+
+## 源码仓库及问题反馈
+https://developer.hpccube.com/codes/modelzoo/hdetr_pytorch
+
+## 参考资料
+https://github.com/HDETR/H-Deformable-DETR

benchmark.py

+# ------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+"""
+Benchmark inference speed of Deformable DETR.
+"""
+
+import os
+import time
+import argparse
+import torch
+
+from main import get_args_parser as get_main_args_parser
+from models import build_model
+from datasets import build_dataset
+from util.misc import nested_tensor_from_tensor_list
+
+
+def get_benckmark_arg_parser():
+    parser = argparse.ArgumentParser("Benchmark inference speed of Deformable DETR.")
+    parser.add_argument(
+        "--num_iters", type=int, default=300, help="total iters to benchmark speed"
+    )
+    parser.add_argument(
+        "--warm_iters",
+        type=int,
+        default=5,
+        help="ignore first several iters that are very slow",
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=1, help="batch size in inference"
+    )
+    parser.add_argument("--resume", type=str, help="load the pre-trained checkpoint")
+    return parser
+
+
+@torch.no_grad()
+def measure_average_inference_time(model, inputs, num_iters=100, warm_iters=5):
+    ts = []
+    for iter_ in range(num_iters):
+        torch.cuda.synchronize()
+        t_ = time.perf_counter()
+        model(inputs)
+        torch.cuda.synchronize()
+        t = time.perf_counter() - t_
+        if iter_ >= warm_iters:
+            ts.append(t)
+    print(ts)
+    return sum(ts) / len(ts)
+
+
+def benchmark():
+    args, _ = get_benckmark_arg_parser().parse_known_args()
+    main_args = get_main_args_parser().parse_args(_)
+    assert (
+        args.warm_iters < args.num_iters and args.num_iters > 0 and args.warm_iters >= 0
+    )
+    assert args.batch_size > 0
+    assert args.resume is None or os.path.exists(args.resume)
+    dataset = build_dataset("val", main_args)
+    model, _, _ = build_model(main_args)
+    model.cuda()
+    model.eval()
+    if args.resume is not None:
+        ckpt = torch.load(args.resume, map_location=lambda storage, loc: storage)
+        model.load_state_dict(ckpt["model"])
+    inputs = nested_tensor_from_tensor_list(
+        [dataset.__getitem__(0)[0].cuda() for _ in range(args.batch_size)]
+    )
+    t = measure_average_inference_time(model, inputs, args.num_iters, args.warm_iters)
+    return 1.0 / t * args.batch_size
+
+
+if __name__ == "__main__":
+    fps = benchmark()
+    print(f"Inference Speed: {fps:.1f} FPS")
+

configs/one_stage/deformable-detr-baseline/50eps/r50_deformable_detr.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/one_stage/deformable-detr-baseline/12eps/r50_deformable_detr
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 50 \
+    --lr_drop 40 \
+    ${PY_ARGS}

configs/one_stage/deformable-detr-baseline/50eps/r50_deformable_detr_plus_iterative_bbox_refinement.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/one_stage/deformable-detr-baseline/12eps/r50_deformable_detr_plus_iterative_bbox_refinement
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 50 \
+    --lr_drop 40 \
+    ${PY_ARGS}

configs/one_stage/deformable-detr-baseline/50eps/r50_deformable_detr_single_scale.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/one_stage/deformable-detr-baseline/12eps/r50_deformable_detr_single_scale
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --num_feature_levels 1 \
+    --output_dir ${EXP_DIR} \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 50 \
+    --lr_drop 40 \
+    ${PY_ARGS}

configs/one_stage/deformable-detr-baseline/50eps/r50_deformable_detr_single_scale_dc5.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/one_stage/deformable-detr-baseline/12eps/r50_deformable_detr_single_scale_dc5
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --num_feature_levels 1 \
+    --dilation \
+    --output_dir ${EXP_DIR} \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 50 \
+    --lr_drop 40 \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/r50_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/r50_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    --dropout 0.0 \
+    --mixed_selection \
+    --look_forward_twice \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/r50_n1800_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/r50_n1800_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 1800 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/swin/swin_large_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/swin/swin_large_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    --backbone swin_large \
+    --pretrained_backbone_path /mnt/pretrained_backbone/swin_large_patch4_window7_224_22k.pth \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/swin/swin_large_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/swin/swin_large_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    --dropout 0.0 \
+    --mixed_selection \
+    --look_forward_twice \
+    --backbone swin_large \
+    --pretrained_backbone_path /mnt/pretrained_backbone/swin_large_patch4_window7_224_22k.pth \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/swin/swin_tiny_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/swin/swin_tiny_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    --backbone swin_tiny \
+    --pretrained_backbone_path /mnt/pretrained_backbone/swin_tiny_patch4_window7_224.pth \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/12eps/swin/swin_tiny_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/12eps/swin/swin_tiny_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 12 \
+    --lr_drop 11 \
+    --dropout 0.0 \
+    --mixed_selection \
+    --look_forward_twice \
+    --backbone swin_tiny \
+    --pretrained_backbone_path /mnt/pretrained_backbone/swin_tiny_patch4_window7_224.pth \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/24eps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/24eps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 24 \
+    --lr_drop 20 \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/24eps/r50_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/24eps/r50_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 24 \
+    --lr_drop 20 \
+    --dropout 0.0 \
+    --mixed_selection \
+    --look_forward_twice \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/24eps/r50_n1800_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/24eps/r50_n1800_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 1800 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 24 \
+    --lr_drop 20 \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/36eps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/36eps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 36 \
+    --lr_drop 30 \
+    ${PY_ARGS}

configs/two_stage/deformable-detr-baseline/36eps/r50_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh

+#!/usr/bin/env bash
+
+set -x
+
+EXP_DIR=exps/two_stage/deformable-detr-baseline/36eps/r50_dp0_mqs_lft_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
+PY_ARGS=${@:1}
+
+python -u main.py \
+    --output_dir ${EXP_DIR} \
+    --with_box_refine \
+    --two_stage \
+    --dim_feedforward 2048 \
+    --num_queries_one2one 300 \
+    --num_queries_one2many 0 \
+    --k_one2many 0 \
+    --epochs 36 \
+    --lr_drop 30 \
+    --dropout 0.0 \
+    --mixed_selection \
+    --look_forward_twice \
+    ${PY_ARGS}