--- a
+++ b/inpainting/test.py
@@ -0,0 +1,71 @@
+import numpy as np
+import cv2
+import os
+import subprocess
+import glob
+from options.test_options import TestOptions
+from model.net import InpaintingModel_GMCNN
+from util.utils import generate_rect_mask, generate_stroke_mask, getLatest
+
+os.environ['CUDA_VISIBLE_DEVICES']=str(np.argmax([int(x.split()[2]) for x in subprocess.Popen(
+        "nvidia-smi -q -d Memory | grep -A4 GPU | grep Free", shell=True, stdout=subprocess.PIPE).stdout.readlines()]
+        ))
+
+config = TestOptions().parse()
+
+if os.path.isfile(config.dataset_path):
+    pathfile = open(config.dataset_path, 'rt').read().splitlines()
+elif os.path.isdir(config.dataset_path):
+    pathfile = glob.glob(os.path.join(config.dataset_path, '*.png'))
+else:
+    print('Invalid testing data file/folder path.')
+    exit(1)
+total_number = len(pathfile)
+test_num = total_number if config.test_num == -1 else min(total_number, config.test_num)
+print('The total number of testing images is {}, and we take {} for test.'.format(total_number, test_num))
+
+print('configuring model..')
+ourModel = InpaintingModel_GMCNN(in_channels=4, opt=config)
+ourModel.print_networks()
+if config.load_model_dir != '':
+    print('Loading pretrained model from {}'.format(config.load_model_dir))
+    ourModel.load_networks(getLatest(os.path.join(config.load_model_dir, '*.pth')))
+    print('Loading done.')
+
+if config.random_mask:
+    np.random.seed(config.seed)
+
+for i in range(test_num):
+    if config.mask_type == 'rect':
+        mask, _ = generate_rect_mask(config.img_shapes, config.mask_shapes, config.random_mask)
+    else:
+        mask = generate_stroke_mask(im_size=(config.img_shapes[0], config.img_shapes[1]),
+                                    parts=8, maxBrushWidth=20, maxLength=100, maxVertex=20)
+    image = cv2.imread(pathfile[i])
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    h, w = image.shape[:2]
+
+    if h >= config.img_shapes[0] and w >= config.img_shapes[1]:
+        h_start = (h-config.img_shapes[0]) // 2
+        w_start = (w-config.img_shapes[1]) // 2
+        image = image[h_start: h_start+config.img_shapes[0], w_start: w_start+config.img_shapes[1], :]
+    else:
+        t = min(h, w)
+        image = image[(h-t)//2:(h-t)//2+t, (w-t)//2:(w-t)//2+t, :]
+        image = cv2.resize(image, (config.img_shapes[1], config.img_shapes[0]))
+
+    image = np.transpose(image, [2, 0, 1])
+    image = np.expand_dims(image, axis=0)
+    image_vis = image * (1-mask) + 255 * mask
+    image_vis = np.transpose(image_vis[0][::-1,:,:], [1, 2, 0])
+    cv2.imwrite(os.path.join(config.saving_path, 'input_{:03d}.png'.format(i)), image_vis.astype(np.uint8))
+
+    h, w = image.shape[2:]
+    grid = 4
+    image = image[:, :, :h // grid * grid, :w // grid * grid]
+    mask = mask[:, :, :h // grid * grid, :w // grid * grid]
+    result = ourModel.evaluate(image, mask)
+    result = np.transpose(result[0][::-1,:,:], [1, 2, 0])
+    cv2.imwrite(os.path.join(config.saving_path, '{:03d}.png'.format(i)), result)
+    print(' > {} / {}'.format(i+1, test_num))
+print('done.')