我想请问一下，deformable detr怎么直接使用GitHub上训练好的保存的那个权重的文件来进行物体检测，谢谢，非常想得到你的回复
编写一个detect.py文件，使用预训练模型。
https://www.jianshu.com/p/b364534fd0a7
上面时原作者的内容，可以进行参考，感觉很不错，代码可能需要改一点点，不多，很简单，希望可以帮到你
```python
import cv2
from PIL import Image
import numpy as np
import os
import time
import torch
from torch import nn
# from torchvision.models import resnet50
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])
])
# 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)
# 将0-1映射到图像
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
# plot box by opencv
def plot_result(pil_img, prob, boxes, save_name=None, imshow=False, imwrite=False):
LABEL = ['all','hat', 'person', 'groundrod', 'vest', 'workclothes_clothes', 'workclothes_trousers', 'winter_clothes',
'winter_trousers', 'noworkclothes_clothes', 'noworkclothes_trousers', 'height', 'safteybelt', 'smoking',
'noheight', 'fire', 'extinguisher', 'roll_workclothes', 'roll_noworkclothes', 'insulating_gloves', 'car',
'fence', 'bottle', 'shorts', 'holes', 'single_ladder', 'down', 'double_ladder', 'oxygen_horizontally',
'oxygen_vertically', 'acetylene_vertically', 'acetylene_horizontally']
len(prob)
opencvImage = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
if len(prob) == 0:
print("[INFO] NO box detect !!! ")
if imwrite:
if not os.path.exists("./result/pred_no"):
os.makedirs("./result/pred_no")
cv2.imwrite(os.path.join("./result/pred_no", save_name), opencvImage)
return
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)
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
# 单张图像的推断
def detect(im, model, transform, prob_threshold=0.7):
# mean-std normalize the input image (batch-size: 1)
img = transform(im).unsqueeze(0)
# demo model only support by default images with aspect ratio between 0.5 and 2
# if you want to use images with an aspect ratio outside this range
# rescale your image so that the maximum size is at most 1333 for best results
#assert img.shape[-2] <= 1600 and img.shape[
# -1] <= 1600, 'demo model only supports images up to 1600 pixels on each side'
# propagate through the model
img = img.to(device)
start = time.time()
outputs = model(img)
#end = time.time()
# 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
#end = time.time()
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('exps/r50_deformable_detr/checkpoint0049.pth',main_args)
files = os.listdir("coco/testdata/test2017")
cn = 0
waste=0
for file in files:
img_path = os.path.join("coco/testdata/test2017", file)
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)
```