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  • 【pytorch-ssd目标检测】测试自己创建的数据集

    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

    直接看修改后的text.py:

    from __future__ import print_function
    import sys
    import os
    import argparse
    import torch
    import torch.nn as nn
    import torch.backends.cudnn as cudnn
    import torchvision.transforms as transforms
    from torch.autograd import Variable
    #from data import VOC_ROOT, VOC_CLASSES as labelmap
    from data import MASK_ROOT, MASK_CLASSES as labelmap
    from PIL import Image
    #from data import VOCAnnotationTransform, VOCDetection, BaseTransform, VOC_CLASSES
    from data import MASKAnnotationTransform, MASKDetection, BaseTransform, MASK_CLASSES
    import torch.utils.data as data
    from ssd import build_ssd
    
    parser = argparse.ArgumentParser(description='Single Shot MultiBox Detection')
    parser.add_argument('--trained_model', default='weights/ssd300_MASK_5000.pth',
                        type=str, help='Trained state_dict file path to open')
    parser.add_argument('--save_folder', default='eval/', type=str,
                        help='Dir to save results')
    parser.add_argument('--visual_threshold', default=0.6, type=float,
                        help='Final confidence threshold')
    parser.add_argument('--cuda', default=True, type=bool,
                        help='Use cuda to train model')
    #parser.add_argument('--voc_root', default=VOC_ROOT, help='Location of VOC root directory')
    parser.add_argument('-f', default=None, type=str, help="Dummy arg so we can load in Jupyter Notebooks")
    args = parser.parse_args()
    
    if args.cuda and torch.cuda.is_available():
        torch.set_default_tensor_type('torch.cuda.FloatTensor')
    else:
        torch.set_default_tensor_type('torch.FloatTensor')
    
    if not os.path.exists(args.save_folder):
        os.mkdir(args.save_folder)
    
    
    def test_net(save_folder, net, cuda, testset, transform, thresh):
        # dump predictions and assoc. ground truth to text file for now
        filename = save_folder+'test1.txt'
        num_images = len(testset)
        for i in range(num_images):
            print('Testing image {:d}/{:d}....'.format(i+1, num_images))
            img = testset.pull_image(i)
            img_id, annotation = testset.pull_anno(i)
            x = torch.from_numpy(transform(img)[0]).permute(2, 0, 1)
            x = Variable(x.unsqueeze(0))
    
            with open(filename, mode='a') as f:
                f.write('
    GROUND TRUTH FOR: '+img_id+'
    ')
                for box in annotation:
                    f.write('label: '+' || '.join(str(b) for b in box)+'
    ')
            if cuda:
                x = x.cuda()
    
            y = net(x)      # forward pass
            detections = y.data
            # scale each detection back up to the image
            scale = torch.Tensor([img.shape[1], img.shape[0],
                                 img.shape[1], img.shape[0]])
            pred_num = 0
            for i in range(detections.size(1)):
                j = 0
                while detections[0, i, j, 0] >= 0.6:
                    if pred_num == 0:
                        with open(filename, mode='a') as f:
                            f.write('PREDICTIONS: '+'
    ')
                    score = detections[0, i, j, 0]
                    label_name = labelmap[i-1]
                    pt = (detections[0, i, j, 1:]*scale).cpu().numpy()
                    coords = (pt[0], pt[1], pt[2], pt[3])
                    pred_num += 1
                    with open(filename, mode='a') as f:
                        f.write(str(pred_num)+' label: '+label_name+' score: ' +
                                str(score) + ' '+' || '.join(str(c) for c in coords) + '
    ')
                    j += 1
    
    
    def test_voc():
        # load net
        num_classes = len(MASK_CLASSES) + 1 # +1 background
        net = build_ssd('test', 300, num_classes) # initialize SSD
        net.load_state_dict(torch.load(args.trained_model))
        net.eval()
        print('Finished loading model!')
        # load data
        mask_root="/content/drive/My Drive/pytorch_ssd"
        testset = MASKDetection(mask_root, "test", None, MASKAnnotationTransform())
        if args.cuda:
            net = net.cuda()
            cudnn.benchmark = True
        # evaluation
        test_net(args.save_folder, net, args.cuda, testset,
                 BaseTransform(net.size, (104, 117, 123)),
                 thresh=args.visual_threshold)
    
    if __name__ == '__main__':
        test_voc()

    开始执行:

    !python test.py --trained_model weights/ssd300_MASK_5000.pth

    运行结果:

    Finished loading model!
    Testing image 1/80....
    /pytorch/torch/csrc/autograd/python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
    Testing image 2/80....
    。。。。。。
    /pytorch/torch/csrc/autograd/python_function.cpp:648: UserWarning: Legacy autograd function object was called twice.  You will probably get incorrect gradients from this computation, as the saved tensors from the second invocation will clobber the saved tensors from the first invocation.  Please consider rewriting your autograd function in the modern style; for information on the new format, please see: https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd
    Testing image 80/80....

    看下生成了的文件:

    看下test1.py中是什么:

    GROUND TRUTH FOR: test_00000007
    label: 46.0 || 0.0 || 139.0 || 128.0 || 0
    PREDICTIONS: 
    1 label: mask score: tensor(0.9097) 31.465145 || 5.5611525 || 149.25903 || 86.10434
    
    GROUND TRUTH FOR: test_00000010
    label: 24.0 || 9.0 || 113.0 || 133.0 || 0
    PREDICTIONS: 
    1 label: mask score: tensor(0.8791) 21.426735 || 17.9471 || 112.9484 || 122.676765
    
    GROUND TRUTH FOR: test_00000015
    label: 407.0 || 37.0 || 486.0 || 143.0 || 0
    PREDICTIONS: 
    1 label: mask score: tensor(0.8441) 403.54123 || 42.476467 || 487.46075 || 146.36295
    
    GROUND TRUTH FOR: test_00000016
    label: 156.0 || 135.0 || 277.0 || 265.0 || 0
    PREDICTIONS: 
    1 label: mask score: tensor(0.9541) 159.74387 || 109.33117 || 284.67053 || 264.61325
    。。。。。。

    每一张图片的坐标、置信度。

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  • 原文地址:https://www.cnblogs.com/xiximayou/p/12550566.html
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