val_2D.py

import numpy as np
import torch
from medpy import metric
from scipy.ndimage import zoom


def calculate_metric_percase(pred, gt):
    pred[pred > 0] = 1
    gt[gt > 0] = 1
    if pred.sum() > 0:
        dice = metric.binary.dc(pred, gt)
        hd95 = metric.binary.hd95(pred, gt)
        return dice, hd95
    else:
        return 0, 0


def test_single_volume(image, label, net, classes, patch_size=[256, 256]):
    image, label = image.squeeze(0).cpu().detach(
    ).numpy(), label.squeeze(0).cpu().detach().numpy()
    if len(image.shape) == 3:
        prediction = np.zeros_like(label)
        for ind in range(image.shape[0]):
            slice = image[ind, :, :]
            x, y = slice.shape[0], slice.shape[1]
            slice = zoom(
                slice, (patch_size[0] / x, patch_size[1] / y), order=0)
            input = torch.from_numpy(slice).unsqueeze(
                0).unsqueeze(0).float().cuda()
            net.eval()
            with torch.no_grad():
                out = torch.argmax(torch.softmax(
                    net(input), dim=1), dim=1).squeeze(0)
                out = out.cpu().detach().numpy()
                pred = zoom(
                    out, (x / patch_size[0], y / patch_size[1]), order=0)
                prediction[ind] = pred
    else:
        input = torch.from_numpy(image).unsqueeze(
            0).unsqueeze(0).float().cuda()
        net.eval()
        with torch.no_grad():
            out = torch.argmax(torch.softmax(
                net(input), dim=1), dim=1).squeeze(0)
            prediction = out.cpu().detach().numpy()
    metric_list = []
    for i in range(1, classes):
        metric_list.append(calculate_metric_percase(
            prediction == i, label == i))
    return metric_list


def test_single_volume_ds(image, label, net, classes, patch_size=[256, 256]):
    image, label = image.squeeze(0).cpu().detach(
    ).numpy(), label.squeeze(0).cpu().detach().numpy()
    if len(image.shape) == 3:
        prediction = np.zeros_like(label)
        for ind in range(image.shape[0]):
            slice = image[ind, :, :]
            x, y = slice.shape[0], slice.shape[1]
            slice = zoom(
                slice, (patch_size[0] / x, patch_size[1] / y), order=0)
            input = torch.from_numpy(slice).unsqueeze(
                0).unsqueeze(0).float().cuda()
            net.eval()
            with torch.no_grad():
                output_main, _, _, _ = net(input)
                out = torch.argmax(torch.softmax(
                    output_main, dim=1), dim=1).squeeze(0)
                out = out.cpu().detach().numpy()
                pred = zoom(
                    out, (x / patch_size[0], y / patch_size[1]), order=0)
                prediction[ind] = pred
    else:
        input = torch.from_numpy(image).unsqueeze(
            0).unsqueeze(0).float().cuda()
        net.eval()
        with torch.no_grad():
            output_main, _, _, _ = net(input)
            out = torch.argmax(torch.softmax(
                output_main, dim=1), dim=1).squeeze(0)
            prediction = out.cpu().detach().numpy()
    metric_list = []
    for i in range(1, classes):
        metric_list.append(calculate_metric_percase(
            prediction == i, label == i))
    return metric_list


def test_single_volume_cct(image, label, net, classes, patch_size=[256, 256]):
    image, label = image.squeeze(0).cpu().detach(
    ).numpy(), label.squeeze(0).cpu().detach().numpy()
    if len(image.shape) == 3:
        prediction = np.zeros_like(label)
        for ind in range(image.shape[0]):
            slice = image[ind, :, :]
            x, y = slice.shape[0], slice.shape[1]
            slice = zoom(
                slice, (patch_size[0] / x, patch_size[1] / y), order=0)
            input = torch.from_numpy(slice).unsqueeze(
                0).unsqueeze(0).float().cuda()
            net.eval()
            with torch.no_grad():
                output_main = net(input)[0]
                out = torch.argmax(torch.softmax(
                    output_main, dim=1), dim=1).squeeze(0)
                out = out.cpu().detach().numpy()
                pred = zoom(
                    out, (x / patch_size[0], y / patch_size[1]), order=0)
                prediction[ind] = pred
    else:
        input = torch.from_numpy(image).unsqueeze(
            0).unsqueeze(0).float().cuda()
        net.eval()
        with torch.no_grad():
            output_main, _, _, _ = net(input)
            out = torch.argmax(torch.softmax(
                output_main, dim=1), dim=1).squeeze(0)
            prediction = out.cpu().detach().numpy()
    metric_list = []
    for i in range(1, classes):
        metric_list.append(calculate_metric_percase(
            prediction == i, label == i))
    return metric_list