SAMatch/val_2D.py at main · YZST/SAMatch · GitHub

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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, 10000

import pywt
def test_single_volume(image, label, net, classes, patch_size=[256, 256], cuda_num= "cuda:0"):
    # image = image.permute(1,0,2,3)
    # label = label.permute(1,0,2,3)
    image, label = image.squeeze(0).cpu().detach(
    ).numpy(), label.squeeze(0).cpu().detach().numpy()

    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)
        ##################################
        # coeffs = pywt.dwt2(slice, 'bior1.1')
        # cA, (cH, cV, cD) = coeffs
        # l = cA
        # h = cH+ cV+ cD
        # l = zoom(l, (256 / 128, 256 / 128), order=0)
        # h = zoom(h, (256 / 128, 256 / 128), order=0)
        # # input_1 = torch.from_numpy(l).float()
        # # input_2 = torch.from_numpy(h).float()
        # input_1 = torch.from_numpy(l).unsqueeze(
        #     0).unsqueeze(0).float().cuda()
        # input_2 = torch.from_numpy(h).unsqueeze(
        #     0).unsqueeze(0).float().cuda()
        ######################################
        input = torch.from_numpy(slice).unsqueeze(
            0).unsqueeze(0).float().to(cuda_num)
        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
    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_synapse(image, label, net, classes, patch_size=[256, 256]):
    image, label = image.squeeze(0).cpu().detach(
    ).numpy(), label.squeeze(0).cpu().detach().numpy()
    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
    metric_list = []
    for i in range(0, classes):
        metric_list.append(calculate_metric_percase(
            prediction == i, label == i))
    return metric_list

def test_single_volume_BUSI(image, label, net, classes, patch_size=[256, 256], cuda_num="cuda:0"):
    image, label = image.squeeze(0).cpu().detach().numpy(), label.squeeze(0).cpu().detach().numpy()
    prediction = np.zeros_like(label)
    #for ind in range(image.shape[0]):
    slice = image
    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().to(cuda_num)
    net.eval()
    with torch.no_grad():
        out_put = net(input)
        if len(out_put) != 1:
            out_put = out_put[0]

        out = torch.argmax(torch.softmax(
                out_put, 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 = pred
    metric_list = []
    for i in range(1, classes): # ignore the background
        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()
    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
    metric_list = []
    for i in range(1, classes):
        metric_list.append(calculate_metric_percase(
            prediction == i, label == i))
    return metric_list