标签：ade rds ase 测试 path mod var 使用 blog
制作类似pascal voc格式的目标检测数据集：https://www.cnblogs.com/xiximayou/p/12546061.html
训练自己创建的数据集：https://www.cnblogs.com/xiximayou/p/12546556.html
验证自己创建的数据集：https://www.cnblogs.com/xiximayou/p/12550471.html
测试自己创建的数据集：https://www.cnblogs.com/xiximayou/p/12550566.html
还是以在谷歌colab上为例：
cd /content/drive/My Drive/pytorch_ssd
导入相应的包：
import os import sys module_path = os.path.abspath(os.path.join(‘..‘)) if module_path not in sys.path: sys.path.append(module_path) import torch import torch.nn as nn import torch.backends.cudnn as cudnn from torch.autograd import Variable import numpy as np import cv2 if torch.cuda.is_available(): torch.set_default_tensor_type(‘torch.cuda.FloatTensor‘) from ssd import build_ssd
加载谷歌网盘：
from google.colab import drive drive.mount(‘/content/drive‘)
加载模型：
net = build_ssd(‘test‘, 300, 3) # initialize SSD net.load_weights(‘weights/ssd300_MASK_5000.pth‘)
可视化要检测的图像：
# image = cv2.imread(‘./data/example.jpg‘, cv2.IMREAD_COLOR) # uncomment if dataset not downloaded %matplotlib inline from matplotlib import pyplot as plt from data import MASKDetection, MASK_ROOT, MASKAnnotationTransform # here we specify year (07 or 12) and dataset (‘test‘, ‘val‘, ‘train‘) mask_root="/content/drive/My Drive/pytorch_ssd" testset = MASKDetection(mask_root, "val", None, MASKAnnotationTransform()) img_id = 2 image = testset.pull_image(img_id) rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # View the sampled input image before transform plt.figure(figsize=(10,10)) plt.imshow(rgb_image) plt.show()
调整图片的格式：
x = cv2.resize(image, (300, 300)).astype(np.float32) x -= (104.0, 117.0, 123.0) x = x.astype(np.float32) x = x[:, :, ::-1].copy() plt.imshow(x) x = torch.from_numpy(x).permute(2, 0, 1)
使用模型进行预测：
xx = Variable(x.unsqueeze(0)) # wrap tensor in Variable if torch.cuda.is_available(): xx = xx.cuda() y = net(xx)
输出结果：
from data import MASK_CLASSES as labels top_k=3 plt.figure(figsize=(10,10)) colors = plt.cm.hsv(np.linspace(0, 1, 3)).tolist() plt.imshow(rgb_image) # plot the image for matplotlib currentAxis = plt.gca() detections = y.data # scale each detection back up to the image scale = torch.Tensor(rgb_image.shape[1::-1]).repeat(2) for i in range(detections.size(1)): j = 0 while detections[0,i,j,0] >= 0.6: score = detections[0,i,j,0] label_name = labels[i-1] display_txt = ‘%s: %.2f‘%(label_name, score) pt = (detections[0,i,j,1:]*scale).cpu().numpy() coords = (pt[0], pt[1]), pt[2]-pt[0]+1, pt[3]-pt[1]+1 color = colors[i] currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2)) currentAxis.text(pt[0], pt[1], display_txt, bbox={‘facecolor‘:color, ‘alpha‘:0.5}) j+=1
由于我的数据集中很少没有戴口罩的样本，因此没有戴口罩的AP较低。
至此，使用pytorch-ssd训练测试自己数据集就全部完成啦。
标签：ade rds ase 测试 path mod var 使用 blog
原文地址：https://www.cnblogs.com/xiximayou/p/12552854.html
