diff --git a/src/eval.py b/src/eval.py
new file mode 100644
index 0000000..e9e5305
--- /dev/null
+++ b/src/eval.py
@@ -0,0 +1,72 @@
+import numpy as np
+from glob import glob
+from src.models.modnet import MODNet
+from PIL import Image
+from src.infer import predit_matte
+import torch.nn as nn
+import torch
+
+
+def cal_mad(pred, gt):
+    diff = pred - gt
+    diff = np.abs(diff)
+    mad = np.mean(diff)
+    return mad
+
+
+def cal_mse(pred, gt):
+    diff = pred - gt
+    diff = diff ** 2
+    mse = np.mean(diff)
+    return mse
+
+
+def load_eval_dataset(dataset_root_dir='src/datasets/PPM-100'):
+    image_path = dataset_root_dir + '/image/*'
+    matte_path = dataset_root_dir + '/matte/*'
+    image_file_name_list = glob(image_path)
+    image_file_name_list = sorted(image_file_name_list)
+    matte_file_name_list = glob(matte_path)
+    matte_file_name_list = sorted(matte_file_name_list)
+
+    return image_file_name_list, matte_file_name_list
+
+
+def eval(modnet: MODNet, dataset):
+    mse = total_mse = 0.0
+    mad = total_mad = 0.0
+    cnt = 0
+
+    for im_pth, mt_pth in zip(dataset[0], dataset[1]):
+        im = Image.open(im_pth)
+        pd_matte = predit_matte(modnet, im)
+
+        gt_matte = Image.open(mt_pth)
+        gt_matte = np.asarray(gt_matte) / 255
+
+        total_mse += cal_mse(pd_matte, gt_matte)
+        total_mad += cal_mad(pd_matte, gt_matte)
+
+        cnt += 1
+    if cnt > 0:
+        mse = total_mse / cnt
+        mad = total_mad / cnt
+
+    return mse, mad
+
+
+if __name__ == '__main__':
+    # create MODNet and load the pre-trained ckpt
+    modnet = MODNet(backbone_pretrained=False)
+    modnet = nn.DataParallel(modnet)
+
+    ckp_pth = 'pretrained/modnet_photographic_portrait_matting.ckpt'
+    if torch.cuda.is_available():
+        modnet = modnet.cuda()
+        weights = torch.load(ckp_pth)
+    else:
+        weights = torch.load(ckp_pth, map_location=torch.device('cpu'))
+    modnet.load_state_dict(weights)
+    dataset = load_eval_dataset('src/datasets/PPM-100')
+    mse, mad = eval(modnet, dataset)
+    print(f'mse: {mse:6f}, mad: {mad:6f}')
diff --git a/src/infer.py b/src/infer.py
new file mode 100644
index 0000000..1fe99cc
--- /dev/null
+++ b/src/infer.py
@@ -0,0 +1,85 @@
+from src.models.modnet import MODNet
+from PIL import Image
+import numpy as np
+from torchvision import transforms
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+
+def predit_matte(modnet: MODNet, im: Image):
+    # define image to tensor transform
+    im_transform = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+        ]
+    )
+
+    # define hyper-parameters
+    ref_size = 512
+
+    modnet.eval()
+
+    # unify image channels to 3
+    im = np.asarray(im)
+    if len(im.shape) == 2:
+        im = im[:, :, None]
+    if im.shape[2] == 1:
+        im = np.repeat(im, 3, axis=2)
+    elif im.shape[2] == 4:
+        im = im[:, :, 0:3]
+
+    im = Image.fromarray(im)
+    # convert image to PyTorch tensor
+    im = im_transform(im)
+
+    # add mini-batch dim
+    im = im[None, :, :, :]
+
+    # resize image for input
+    im_b, im_c, im_h, im_w = im.shape
+    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
+        if im_w >= im_h:
+            im_rh = ref_size
+            im_rw = int(im_w / im_h * ref_size)
+        elif im_w < im_h:
+            im_rw = ref_size
+            im_rh = int(im_h / im_w * ref_size)
+    else:
+        im_rh = im_h
+        im_rw = im_w
+
+    im_rw = im_rw - im_rw % 32
+    im_rh = im_rh - im_rh % 32
+    im = F.interpolate(im, size=(im_rh, im_rw), mode='area')
+
+    # inference
+    _, _, matte = modnet(im.cuda() if torch.cuda.is_available() else im, True)
+
+    # resize and save matte
+    matte = F.interpolate(matte, size=(im_h, im_w), mode='area')
+    matte = matte[0][0].data.cpu().numpy()
+    return matte
+
+
+if __name__ == '__main__':
+    # create MODNet and load the pre-trained ckpt
+    modnet = MODNet(backbone_pretrained=False)
+    modnet = nn.DataParallel(modnet)
+
+    ckp_pth = 'pretrained/modnet_photographic_portrait_matting.ckpt'
+    if torch.cuda.is_available():
+        modnet = modnet.cuda()
+        weights = torch.load(ckp_pth)
+    else:
+        weights = torch.load(ckp_pth, map_location=torch.device('cpu'))
+    modnet.load_state_dict(weights)
+
+    pth = 'src/datasets/PPM-100/image/13179159164_1a4ae8d085_o.jpg'
+    img = Image.open(pth)
+
+    matte = predit_matte(modnet, img)
+    prd_img = Image.fromarray(((matte * 255).astype('uint8')), mode='L')
+    prd_img.save('test_predic.jpg')
+