Пример #1
0
    # synthtextloader = Synth80k('/home/jiachx/publicdatasets/SynthText/SynthText', target_size=768, viz=True, debug=True)
    # train_loader = torch.utils.data.DataLoader(
    #     synthtextloader,
    #     batch_size=1,
    #     shuffle=False,
    #     num_workers=0,
    #     drop_last=True,
    #     pin_memory=True)
    # train_batch = iter(train_loader)
    # image_origin, target_gaussian_heatmap, target_gaussian_affinity_heatmap, mask = next(train_batch)
    from craft import CRAFT
    from torchutil import copyStateDict

    net = CRAFT(freeze=True)
    net.load_state_dict(
        copyStateDict(torch.load('/data/CRAFT-pytorch/1-7.pth')))
    net = net.cuda()
    net = torch.nn.DataParallel(net)
    net.eval()
    dataloader = ICDAR2015(net,
                           '/data/CRAFT-pytorch/icdar2015',
                           target_size=768,
                           viz=True)
    train_loader = torch.utils.data.DataLoader(dataloader,
                                               batch_size=1,
                                               shuffle=False,
                                               num_workers=0,
                                               drop_last=True,
                                               pin_memory=True)
    total = 0
    total_sum = 0
Пример #2
0
    #     pin_memory=True)
    # train_batch = iter(train_loader)
    # image_origin, target_gaussian_heatmap, target_gaussian_affinity_heatmap, mask = next(train_batch)
    from craft import CRAFT
    from torchutil import copyStateDict
    import argparse
    parser = argparse.ArgumentParser(description='123')
    parser.add_argument('--load_model',
                        default='',
                        type=str,
                        help='folder path to input images')

    args = parser.parse_args()

    net = CRAFT(freeze=True)
    net.load_state_dict(copyStateDict(torch.load(args.load_model)))
    net = net.cuda()
    net = torch.nn.DataParallel(net)
    net.eval()
    dataloader = ICDAR2015(net,
                           './data/icdar15/train_images/',
                           target_size=768,
                           viz=True)
    train_loader = torch.utils.data.DataLoader(dataloader,
                                               batch_size=1,
                                               shuffle=False,
                                               num_workers=0,
                                               drop_last=True,
                                               pin_memory=True)
    total = 0
    total_sum = 0
Пример #3
0
if __name__ == '__main__':
    # synthtextloader = Synth80k('/home/jiachx/publicdatasets/SynthText/SynthText', target_size=768, viz=True, debug=True)
    # train_loader = torch.utils.data.DataLoader(
    #     synthtextloader,
    #     batch_size=1,
    #     shuffle=False,
    #     num_workers=0,
    #     drop_last=True,
    #     pin_memory=True)
    # train_batch = iter(train_loader)
    # image_origin, target_gaussian_heatmap, target_gaussian_affinity_heatmap, mask = next(train_batch)
    from detector import Detector
    from torchutil import copyStateDict

    net = Detector(freeze=True)
    net.load_state_dict(copyStateDict(torch.load('./pretrain/1-7.pth')))
    net = net.cuda()
    net = torch.nn.DataParallel(net)
    net.eval()
    dataloader = ICDAR2015(net, './data/icdar2015', target_size=768, viz=True)
    train_loader = torch.utils.data.DataLoader(dataloader,
                                               batch_size=1,
                                               shuffle=False,
                                               num_workers=0,
                                               drop_last=True,
                                               pin_memory=True)
    total = 0
    total_sum = 0
    for index, (opimage, region_scores, affinity_scores, confidence_mask,
                confidences_mean) in enumerate(train_loader):
        total += 1
Пример #4
0
if __name__ == '__main__':
    # synthtextloader = Synth80k('/home/jiachx/publicdatasets/SynthText/SynthText', target_size=768, viz=True, debug=True)
    # train_loader = torch.utils.data.DataLoader(
    #     synthtextloader,
    #     batch_size=1,
    #     shuffle=False,
    #     num_workers=0,
    #     drop_last=True,
    #     pin_memory=True)
    # train_batch = iter(train_loader)
    # image_origin, target_gaussian_heatmap, target_gaussian_affinity_heatmap, mask = next(train_batch)
    from craft import CRAFT
    from torchutil import copyStateDict

    net = CRAFT(freeze=True)
    net.load_state_dict(copyStateDict(torch.load('/ic15_iter_1300.pth')))
    net = net.cuda()
    net = torch.nn.DataParallel(net)
    net.eval()
    dataloader = ICDAR2015(net,
                           '/icdar2015/icdar2015train',
                           target_size=640,
                           viz=True)
    train_loader = torch.utils.data.DataLoader(dataloader,
                                               batch_size=1,
                                               shuffle=False,
                                               num_workers=0,
                                               drop_last=True,
                                               pin_memory=True)
    total = 0
    total_sum = 0
    # image_origin, target_gaussian_heatmap, target_gaussian_affinity_heatmap, mask = next(train_batch)
    from craft import CRAFT
    from torchutil import copyStateDict
    import argparse
    parser = argparse.ArgumentParser(description='123')
    parser.add_argument('--load_model', default='', type=str, help='folder path to input images')


    args = parser.parse_args()




    net = CRAFT(freeze=True)
    net.load_state_dict(
        copyStateDict(torch.load(args.load_model)))
    net = net.cuda()
    net = torch.nn.DataParallel(net)
    net.eval()
    dataloader = ICDAR2015(net, './data/icdar15/train_images/', target_size=768, viz=True)
    train_loader = torch.utils.data.DataLoader(
        dataloader,
        batch_size=1,
        shuffle=False,
        num_workers=0,
        drop_last=True,
        pin_memory=True)
    total = 0
    total_sum = 0
    for index, (opimage, region_scores, affinity_scores, confidence_mask, confidences_mean) in enumerate(train_loader):
        total += 1