--- a
+++ b/inference.py
@@ -0,0 +1,119 @@
+import math
+import torch
+import torch.nn.functional as F
+import numpy as np
+import h5py
+import nibabel as nib
+from medpy import metric
+from networks.vnet import VNet
+
+
+def calculate_metric_percase(pred, gt):
+    dice = metric.binary.dc(pred, gt)
+    jc = metric.binary.jc(pred, gt)
+    hd = metric.binary.hd95(pred, gt)
+    asd = metric.binary.asd(pred, gt)
+
+    return dice, jc, hd, asd
+
+
+def test_single_case(net, image, stride_xy, stride_z, patch_size, num_classes=1):
+    w, h, d = image.shape
+
+    # if the size of image is less than patch_size, then padding it
+    add_pad = False
+    if w < patch_size[0]:
+        w_pad = patch_size[0]-w
+        add_pad = True
+    else:
+        w_pad = 0
+    if h < patch_size[1]:
+        h_pad = patch_size[1]-h
+        add_pad = True
+    else:
+        h_pad = 0
+    if d < patch_size[2]:
+        d_pad = patch_size[2]-d
+        add_pad = True
+    else:
+        d_pad = 0
+    wl_pad, wr_pad = w_pad//2,w_pad-w_pad//2
+    hl_pad, hr_pad = h_pad//2,h_pad-h_pad//2
+    dl_pad, dr_pad = d_pad//2,d_pad-d_pad//2
+    if add_pad:
+        image = np.pad(image, [(wl_pad,wr_pad),(hl_pad,hr_pad), (dl_pad, dr_pad)], mode='constant', constant_values=0)
+    ww,hh,dd = image.shape
+
+    sx = math.ceil((ww - patch_size[0]) / stride_xy) + 1
+    sy = math.ceil((hh - patch_size[1]) / stride_xy) + 1
+    sz = math.ceil((dd - patch_size[2]) / stride_z) + 1
+    # print("{}, {}, {}".format(sx, sy, sz))
+    score_map = np.zeros((num_classes, ) + image.shape).astype(np.float32)
+    cnt = np.zeros(image.shape).astype(np.float32)
+
+    for x in range(0, sx):
+        xs = min(stride_xy*x, ww-patch_size[0])
+        for y in range(0, sy):
+            ys = min(stride_xy * y,hh-patch_size[1])
+            for z in range(0, sz):
+                zs = min(stride_z * z, dd-patch_size[2])
+                test_patch = image[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]]
+                test_patch = np.expand_dims(np.expand_dims(test_patch,axis=0),axis=0).astype(np.float32)
+                test_patch = torch.from_numpy(test_patch).cuda()
+                y1 = net(test_patch)
+                y = F.softmax(y1, dim=1)
+                y = y.cpu().data.numpy()
+                y = y[0,:,:,:,:]
+                score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
+                  = score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y
+                cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
+                  = cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + 1
+    score_map = score_map/np.expand_dims(cnt,axis=0)
+    label_map = np.argmax(score_map, axis = 0)
+    if add_pad:
+        label_map = label_map[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
+        score_map = score_map[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
+    return label_map, score_map
+
+def test_all_case(net, image_list, num_classes=2, patch_size=(112, 112, 80), stride_xy=18, stride_z=4, save_result=True, test_save_path=None, preproc_fn=None):
+    total_metric = 0.0
+    for ith,image_path in enumerate(image_list):
+        h5f = h5py.File(image_path, 'r')
+        image = h5f['image'][:]
+        label = h5f['label'][:]
+        if preproc_fn is not None:
+            image = preproc_fn(image)
+        prediction, score_map = test_single_case(net, image, stride_xy, stride_z, patch_size, num_classes=num_classes)
+
+        if np.sum(prediction)==0:
+            single_metric = (0,0,0,0)
+        else:
+            single_metric = calculate_metric_percase(prediction, label[:])
+        print('%02d,\t%.5f, %.5f, %.5f, %.5f' % (ith, single_metric[0], single_metric[1], single_metric[2], single_metric[3]))
+        total_metric += np.asarray(single_metric)
+
+        if save_result:
+            nib.save(nib.Nifti1Image(prediction.astype(np.float32), np.eye(4)), test_save_path + "%02d_pred.nii.gz"%(ith))
+            nib.save(nib.Nifti1Image(image[:].astype(np.float32), np.eye(4)), test_save_path + "%02d_img.nii.gz"%(ith))
+            nib.save(nib.Nifti1Image(label[:].astype(np.float32), np.eye(4)), test_save_path + "%02d_gt.nii.gz"%(ith))
+    avg_metric = total_metric / len(image_list)
+    print('average metric is {}'.format(avg_metric))
+
+    return avg_metric
+
+
+if __name__ == '__main__':
+    data_path = '/***/LASet/data/'
+    test_save_path = 'predictions/supervised'
+    save_mode_path = 'model/LA_vnet_25_labeled/supervised/supervised_best_model.pth'
+    net = VNet(n_channels=1,n_classes=2, normalization='batchnorm').cuda()
+    net.load_state_dict(torch.load(save_mode_path))
+    print("init weight from {}".format(save_mode_path))
+    net.eval()
+    with open(data_path + '/../test.list', 'r') as f:
+        image_list = f.readlines()
+    image_list = [data_path +item.replace('\n', '')+"/mri_norm2.h5" for item in image_list]
+    # 滑动窗口法
+    avg_metric = test_all_case(net, image_list, num_classes=2,
+                                patch_size=(112, 112, 80), stride_xy=18, stride_z=4,
+                                save_result=False,test_save_path=test_save_path)   
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