开源地址：
https://github.com/fundamentalvision/deformable-detr

超级小白，摸索了几天，感谢批评指正！！！

一、数据集准备

1.下载数据集：

train_2017：

http://images.cocodataset.org/zips/train2017.zip

val_2017：

http://images.cocodataset.org/zips/val2017.zip

2.下载标注文件（instances_train2017.json    instances_val2017.json）

http://images.cocodataset.org/annotations/annotations_trainval2017.zip

3.数据集文件夹

二、环境配置（命令）

1. 创建python环境：

conda create -n deformable_detr python=3.7 pip

2. 激活环境：

conda activate deformable_detr

PyTorch>=1.5.1, torchvision>=0.6.1，自行配置，不赘述

3. 安装必要的包：

pip install -r requirements.txt

4. 编译cuda操作：

cd ./models/ops

sh ./make.sh

编译成功后可 pip list 结果如下：

 

 有 MultiScaleDeformableAttention 包

5. 测试 python test.py （可省略）：

运行test.py的时间太长，我直接Kill了

6. 运行 python main.py

也可以使用官方给的命令：

GPUS_PER_NODE=8 ./tools/run_dist_launch.sh 8 ./configs/r50_deformable_detr.sh

进行修改，如两张卡进行训练：

GPUS_PER_NODE=2 ./tools/run_dist_launch.sh 2 ./configs/r50_deformable_detr.sh

（我的环境是Linux，此处会出现chmod文件权限问题，百度即可自行解决，用到了chmod 777）

7. 训练过程：训练Epoch：[0] 结束后会进行Test，然后接着Epoch：[1]训练

三、预测

由于训练时间太长，我直接Kill了，使用官方给的权重进行预测

1. 下载权重文件：r50_deformable_detr-checkpoint.pth

如图点击model下载（需要梯子）

https://drive.google.com/file/d/1nDWZWHuRwtwGden77NLM9JoWe-YisJnA/view

 2. 待预测图片及其位置：

(我自己从COCO数据集随机复制的几张图片)

3. 运行如下代码 predict.py（代码非原创，参考网上修改）：

import cv2
from PIL import Image
import numpy as np
import os
import time

import torch
from torch import nn
import torchvision.transforms as T
from main import get_args_parser as get_main_args_parser
from models import build_model

torch.set_grad_enabled(False)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("[INFO] 当前使用{}做推断".format(device))

# 图像数据处理
transform = T.Compose([
    T.Resize(800),
    T.ToTensor(),
    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# plot box by opencv
def plot_result(pil_img, prob, boxes, save_name=None, imshow=False, imwrite=False):
    opencvImage = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
    LABEL =['N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
            'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
            'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
            'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
            'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
            'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
            'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
            'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
            'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
            'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
            'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
            'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
            'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
    for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes):
        cl = p.argmax()
        label_text = '{}: {}%'.format(LABEL[cl], round(p[cl] * 100, 2))

        cv2.rectangle(opencvImage, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (255, 255, 0), 2)
        cv2.putText(opencvImage, label_text, (int(xmin) + 10, int(ymin) + 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
                    (255, 255, 0), 2)
    
    if imshow:
        cv2.imshow('detect', opencvImage)
        cv2.waitKey(0)

    if imwrite:
        if not os.path.exists("./result/pred"):
            os.makedirs('./result/pred')
        cv2.imwrite('./result/pred/{}'.format(save_name), opencvImage)

# 将xywh转xyxy
def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=1)

def rescale_bboxes(out_bbox, size):
    img_w, img_h = size
    b = box_cxcywh_to_xyxy(out_bbox)
    b = b.cpu().numpy()
    b = b * np.array([img_w, img_h, img_w, img_h], dtype=np.float32)
    return b

def load_model(model_path , args):
    model, _, _ = build_model(args)
    model.cuda()
    model.eval()
    state_dict = torch.load(model_path) # <-----------修改加载模型的路径
    model.load_state_dict(state_dict["model"])
    model.to(device)
    print("load model sucess")
    return model

# 图像的推断
def detect(im, model, transform, prob_threshold=0.7):
    # mean-std normalize the input image (batch-size: 1)
    img = transform(im).unsqueeze(0)


    # propagate through the model
    img = img.to(device)
    start = time.time()
    outputs = model(img)
   
    # keep only predictions with 0.7+ confidence
    print(outputs['pred_logits'].softmax(-1)[0, :, :-1])
    probas = outputs['pred_logits'].softmax(-1)[0, :, :-1]
    keep = probas.max(-1).values > prob_threshold

    probas = probas.cpu().detach().numpy()
    keep = keep.cpu().detach().numpy()

    # convert boxes from [0; 1] to image scales
    bboxes_scaled = rescale_bboxes(outputs['pred_boxes'][0, keep], im.size)
    end = time.time()
    return probas[keep], bboxes_scaled, end - start


if __name__ == "__main__":
    
    main_args = get_main_args_parser().parse_args()
    # 加载模型
    dfdetr = load_model('DDETR/r50_deformable_detr-checkpoint.pth',main_args) # <--修改为自己加载模型的路径

    files = os.listdir("DDETR/data/coco/mytestdata/") # <--修改为待预测图片所在文件夹路径

    cn = 0
    waste=0
    for file in files:
        img_path = os.path.join("DDETR/data/coco/mytestdata/", file) # <--修改为待预测图片所在文件夹路径
        im = Image.open(img_path)

        scores, boxes, waste_time = detect(im, dfdetr, transform)
        plot_result(im, scores, boxes, save_name=file, imshow=False, imwrite=True)
        print("{} [INFO] {} time: {} done!!!".format(cn,file, waste_time))

        cn+=1
        waste+=waste_time
        waste_avg = waste/cn
        print(waste_avg)

 4. 预测结果及预览：

使用Deformable DETR进行预测：

参考：

https://www.jianshu.com/p/b364534fd0a7

Windows下运行Deformable-DETR_harold_du的博客-CSDN博客_deformable detr

Deformable DETR环境配置和应用_Alaso_soso的博客-CSDN博客

DETR导出onnx模型，并进行推理（cpu环境）_athrunsunny的博客-CSDN博客