add documentation and tests.

python/jittor/nn.py

@@ -1770,6 +1770,9 @@ def resize(img, size, mode="nearest", align_corners=False, tf_mode=False):
         x = hid * (h / H)
         y = wid * (w / W)
     elif mode == "area":
+        '''
+        Area interpolation uses AdaptivePool2D to resize origin images.
+        '''
         stride = (h // H, w // W)
         assert stride[0] > 0 and stride[1] > 0
         x, y = jt.meshgrid(jt.arange(0, H, 1), jt.arange(0, W, 1))

python/jittor/test/test_interpolation.py

@@ -0,0 +1,34 @@
+# ***************************************************************
+# Copyright (c) 2022 Jittor. All Rights Reserved.
+# Maintainers:
+#     Haoyang Peng <2247838039@qq.com>
+#     Guowei Yang <471184555@qq.com>
+#     Dun Liang <randonlang@gmail.com>.
+#
+# This file is subject to the terms and conditions defined in
+# file 'LICENSE.txt', which is part of this source code package.
+# ***************************************************************
+import jittor as jt
+from jittor import nn
+import numpy as np
+import unittest
+
+try:
+    import torch
+    has_torch = True
+except:
+    has_torch = False
+
+@unittest.skipIf(not has_torch, "No pytorch installation found.")
+class TestInterpolation(unittest.TestCase):
+    def test_interpolation_area(self):
+        img = np.random.uniform(0, 1, (1, 3, 24, 10))
+        output_shape = (12, 5)
+        jimg = jt.array(img)
+        timg = torch.from_numpy(img)
+        joutput = nn.interpolate(jimg, output_shape, mode="area")
+        toutput = torch.nn.functional.interpolate(timg, output_shape, mode="area")
+        np.testing.assert_allclose(joutput.numpy(), toutput.numpy(), rtol=1e-7, atol=1e-7)
+
+if __name__ == "__main__":
+    unittest.main()