Python dataset.resizeNormalize() Examples

The following are 2 code examples of dataset.resizeNormalize(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module dataset , or try the search function .
Example #1
Source File: train.py    From crnn-pytorch with MIT License 7 votes vote down vote up 
def data_loader():
    # train
    train_dataset = dataset.lmdbDataset(root=args.trainroot)
    assert train_dataset
    if not params.random_sample:
        sampler = dataset.randomSequentialSampler(train_dataset, params.batchSize)
    else:
        sampler = None
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=params.batchSize, \
            shuffle=True, sampler=sampler, num_workers=int(params.workers), \
            collate_fn=dataset.alignCollate(imgH=params.imgH, imgW=params.imgW, keep_ratio=params.keep_ratio))
    
    # val
    val_dataset = dataset.lmdbDataset(root=args.valroot, transform=dataset.resizeNormalize((params.imgW, params.imgH)))
    assert val_dataset
    val_loader = torch.utils.data.DataLoader(val_dataset, shuffle=True, batch_size=params.batchSize, num_workers=int(params.workers))
    
    return train_loader, val_loader
Example #2
Source File: predict.py    From ctpn-crnn with MIT License 6 votes vote down vote up 
def crnn_recognition(cropped_image, model):
    converter = utils.strLabelConverter(alphabet)

    image = cropped_image.convert('L')

    ##
    # w = int(image.size[0] / (280 * 1.0 / 160))
    transformer = dataset.resizeNormalize((280, 32))
    image = transformer(image)
    # if torch.cuda.is_available():
    #     image = image.cuda()
    image = image.view(1, *image.size())
    image = Variable(image)

    model.eval()
    preds = model(image)

    _, preds = preds.max(2)
    preds = preds.transpose(1, 0).contiguous().view(-1)

    preds_size = Variable(torch.IntTensor([preds.size(0)]))
    sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
    print('results: {0}'.format(sim_pred))
    return sim_pred