feat(mge): do not export F.nn.interpolate

GitOrigin-RevId: 031c6555c0a4c190594d96e47d989da5cd5df62f

imperative/python/megengine/functional/nn.py

@@ -36,7 +36,6 @@ __all__ = [
     "dot",
     "dropout",
     "indexing_one_hot",
-    "interpolate",
     "leaky_relu",
     "linear",
     "local_conv2d",
@@ -1112,9 +1111,9 @@ def interpolate(
         import megengine.functional as F
 
         x = tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2))
-        out = F.interpolate(x, [4, 4], align_corners=False)
+        out = F.nn.interpolate(x, [4, 4], align_corners=False)
         print(out.numpy())
-        out2 = F.interpolate(x, scale_factor=2.)
+        out2 = F.nn.interpolate(x, scale_factor=2.)
         np.testing.assert_allclose(out.numpy(), out2.numpy())
 
     Outputs:

imperative/python/test/unit/functional/test_functional.py

@@ -101,8 +101,8 @@ def test_interpolate():
     def linear_interpolate():
         inp = tensor(np.arange(1, 3, dtype=np.float32).reshape(1, 1, 2))
 
-        out = F.interpolate(inp, scale_factor=2.0, mode="LINEAR")
-        out2 = F.interpolate(inp, 4, mode="LINEAR")
+        out = F.nn.interpolate(inp, scale_factor=2.0, mode="LINEAR")
+        out2 = F.nn.interpolate(inp, 4, mode="LINEAR")
 
         np.testing.assert_allclose(
             out.numpy(), np.array([[[1.0, 1.25, 1.75, 2.0]]], dtype=np.float32)
@@ -114,16 +114,16 @@ def test_interpolate():
     def many_batch_interpolate():
         inp = tensor(np.arange(1, 9, dtype=np.float32).reshape(2, 1, 2, 2))
 
-        out = F.interpolate(inp, [4, 4])
-        out2 = F.interpolate(inp, scale_factor=2.0)
+        out = F.nn.interpolate(inp, [4, 4])
+        out2 = F.nn.interpolate(inp, scale_factor=2.0)
 
         np.testing.assert_allclose(out.numpy(), out2.numpy())
 
     def assign_corner_interpolate():
         inp = tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2))
 
-        out = F.interpolate(inp, [4, 4], align_corners=True)
-        out2 = F.interpolate(inp, scale_factor=2.0, align_corners=True)
+        out = F.nn.interpolate(inp, [4, 4], align_corners=True)
+        out2 = F.nn.interpolate(inp, scale_factor=2.0, align_corners=True)
 
         np.testing.assert_allclose(out.numpy(), out2.numpy())
 
@@ -131,13 +131,13 @@ def test_interpolate():
         inp = tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2))
 
         with pytest.raises(ValueError):
-            F.interpolate(inp, scale_factor=2.0, mode="LINEAR")
+            F.nn.interpolate(inp, scale_factor=2.0, mode="LINEAR")
 
     def inappropriate_scale_linear_interpolate():
         inp = tensor(np.arange(1, 3, dtype=np.float32).reshape(1, 1, 2))
 
         with pytest.raises(ValueError):
-            F.interpolate(inp, scale_factor=[2.0, 3.0], mode="LINEAR")
+            F.nn.interpolate(inp, scale_factor=[2.0, 3.0], mode="LINEAR")
 
     linear_interpolate()
     many_batch_interpolate()