Python utils.load_images() Examples

The following are 2 code examples of utils.load_images(). 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 utils , or try the search function .
Example #1
Source File: birads_prediction_torch.py    From BIRADS_classifier with BSD 2-Clause "Simplified" License 4 votes vote down vote up 
def inference(parameters, verbose=True):
    """
    Function that creates a model, loads the parameters, and makes a prediction
    :param parameters: dictionary of parameters
    :param verbose: Whether to print predicted probabilities
    :return: Predicted probabilities for each class
    """
    # resolve device
    device = torch.device(
        "cuda:{}".format(parameters["gpu_number"]) if parameters["device_type"] == "gpu"
        else "cpu"
    )

    # construct models
    model = models.BaselineBreastModel(device, nodropout_probability=1.0, gaussian_noise_std=0.0).to(device)
    model.load_state_dict(torch.load(parameters["model_path"]))

    # load input images and prepare data
    datum_l_cc = utils.load_images(parameters['image_path'], 'L-CC')
    datum_r_cc = utils.load_images(parameters['image_path'], 'R-CC')
    datum_l_mlo = utils.load_images(parameters['image_path'], 'L-MLO')
    datum_r_mlo = utils.load_images(parameters['image_path'], 'R-MLO')
    x = {
        "L-CC": torch.Tensor(datum_l_cc).permute(0, 3, 1, 2).to(device),
        "L-MLO": torch.Tensor(datum_l_mlo).permute(0, 3, 1, 2).to(device),
        "R-CC": torch.Tensor(datum_r_cc).permute(0, 3, 1, 2).to(device),
        "R-MLO": torch.Tensor(datum_r_mlo).permute(0, 3, 1, 2).to(device),
    }

    # run prediction
    with torch.no_grad():
        prediction_birads = model(x).cpu().numpy()

    if verbose:
        # nicely prints out the predictions
        birads0_prob = prediction_birads[0][0]
        birads1_prob = prediction_birads[0][1]
        birads2_prob = prediction_birads[0][2]
        print('BI-RADS prediction:\n' +
              '\tBI-RADS 0:\t' + str(birads0_prob) + '\n' +
              '\tBI-RADS 1:\t' + str(birads1_prob) + '\n' +
              '\tBI-RADS 2:\t' + str(birads2_prob))

    return prediction_birads[0]
Example #2
Source File: density_model_torch.py    From breast_density_classifier with BSD 2-Clause "Simplified" License 4 votes vote down vote up 
def inference(parameters, verbose=True):

    # resolve device
    device = torch.device(
        "cuda:{}".format(parameters["gpu_number"]) if parameters["device_type"] == "gpu"
        else "cpu"
    )

    # load input images
    datum_l_cc = utils.load_images(parameters['image_path'], 'L-CC')
    datum_r_cc = utils.load_images(parameters['image_path'], 'R-CC')
    datum_l_mlo = utils.load_images(parameters['image_path'], 'L-MLO')
    datum_r_mlo = utils.load_images(parameters['image_path'], 'R-MLO')

    # construct models and prepare data
    if parameters["model_type"] == 'cnn':
        model = models.BaselineBreastModel(device, nodropout_probability=1.0, gaussian_noise_std=0.0).to(device)
        model.load_state_dict(torch.load(parameters["model_path"]))
        x = {
            "L-CC": torch.Tensor(datum_l_cc).permute(0, 3, 1, 2).to(device),
            "L-MLO": torch.Tensor(datum_l_mlo).permute(0, 3, 1, 2).to(device),
            "R-CC": torch.Tensor(datum_r_cc).permute(0, 3, 1, 2).to(device),
            "R-MLO": torch.Tensor(datum_r_mlo).permute(0, 3, 1, 2).to(device),
        }
    elif parameters["model_type"] == 'histogram':
        model = models.BaselineHistogramModel(num_bins=parameters["bins_histogram"]).to(device)
        model.load_state_dict(torch.load(parameters["model_path"]))
        x = torch.Tensor(utils.histogram_features_generator([
            datum_l_cc, datum_r_cc, datum_l_mlo, datum_r_mlo
        ], parameters)).to(device)
    else:
        raise RuntimeError(parameters["model_type"])

    # run prediction
    with torch.no_grad():
        prediction_density = model(x).cpu().numpy()

    if verbose:
        # nicely prints out the predictions
        print('Density prediction:\n'
              '\tAlmost entirely fatty (0):\t\t\t' + str(prediction_density[0, 0]) + '\n'
              '\tScattered areas of fibroglandular density (1):\t' + str(prediction_density[0, 1]) + '\n'
              '\tHeterogeneously dense (2):\t\t\t' + str(prediction_density[0, 2]) + '\n'
              '\tExtremely dense (3):\t\t\t\t' + str(prediction_density[0, 3]) + '\n')

    return prediction_density[0]