训练好的模型

[ForeUP]

您好，请问方便分享一些训练好的模型吗？我想先测试一下效果，非常感谢！

[cwhgn]

您好，如果您是需要预训练模型，可以在SOLIDER上下载，用SOLIDER-REID的训练命令直接训练即可得到finetune后的ReID模型。如果您希望直接使用训练好的ReID模型，我们争取近期更新一版，把模型放上去。

[ForeUP]

好的，非常感谢！

[cwhgn]

您好，训练好的ReID模型链接我们已经更新到Readme中了，欢迎试用。

[deep-practice]

用训练好的模型提取特征，相同的人和不同的人相似度都很高

[cwhgn]

可以用runtest.sh确认下效果。

[deep-practice]

`import torch
import torch.nn as nn
import torchvision.transforms as T
import cv2
from config import cfg
from model import make_model
import torch.nn.functional as F
import numpy as np
from PIL import Image, ImageFile
import os.path as osp

def read_image(img_path):
got_img = False
if not osp.exists(img_path): raise IOError("{} does not exist".format(img_path))
while not got_img:
try:
img = Image.open(img_path).convert('RGB')
got_img = True
except IOError:
print("IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.".format(img_path))
pass
return img

@torch.no_grad()
def get_feature(img,model,device,normalize=False):
input = val_transforms(img).unsqueeze(0)
input = input.to(device)
output, _ = model(input)
if normalize:
output = F.normalize(output)
return output

val_transforms = T.Compose([
T.Resize(cfg.INPUT.SIZE_TEST),
T.ToTensor(),
T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
config_file = "configs/msmt17/swin_small.yml"
cfg.merge_from_file(config_file)
cfg.freeze()
model = make_model(cfg, num_class=1000, camera_num=0, view_num = 0, semantic_weight = 0.2)
model.load_param("weights/swin_small_msmt17.pth")
device = "cuda"

if device:
if torch.cuda.device_count() > 1:
print('Using {} GPUs for inference'.format(torch.cuda.device_count()))
model = nn.DataParallel(model)
model.to(device)

model.eval()

img1 = read_image("test_imgs/p5.jpg")
img2 = read_image("test_imgs/p7.jpg")
feature1 = get_feature(img1,model,device,normalize=True)
feature2 = get_feature(img2,model,device,normalize=True)
feature1,feature2 = F.normalize(feature1),F.normalize(feature2)
sim = torch.mm(feature1,feature2.t())
print(sim)

@cwhgn 这是我参考test.py写的代码，能帮忙看看问题在哪儿吗？图片之前相似度高达0.9+

`

[deep-practice]

模型用的官方提供的swin_small_msmt17.pth

[cwhgn]

目前看我暂时也没找到您代码的问题。几点建议哈：1）可以先check下swin_small_msmt17.pth载入是否成功；2）分步check下图片特征和提供的test.py的输出是否一致；3）如果和test.py一致，则有没有可能两者相似度就是很高。

[deep-practice]

1）模型载入成功
2）对比了test.py提取的特征和我demo里面提取的特征，数值是一样的
3）使用欧式距离，相同图片和不同图片能区分开，但换成余弦相似度，任何图片的相似度都在0.9+

[cwhgn]

针对3，我理解是因为训练时采用的是欧式距离，见

SOLIDER-REID/loss/triplet_loss.py

Line 124 in c35c076

dist_mat = euclidean_dist(global_feat, global_feat)

如果你希望余弦距离可分的话，可以尝试用余弦距离进行训练。

[deep-practice]

明白了，谢谢

[MyraBaba]

@deep-practice

A silly newbee question.

this p5 and p7 image is person cropped image. What should be the dimesion of the image ?

is 384 x 192 ?

Is there any newbee document to make inference and compare distance of two detected person image bow.

Detected person could be from yolo or from solider

Best