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val_2d.py
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39 lines (35 loc) · 1.35 KB
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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, 0
def test_single_volume(image, label, model, 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()
model.eval()
with torch.no_grad():
output = model(input)
if len(output)>1:
output = output[0]
out = torch.argmax(torch.softmax(output, 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