Change support level to 10

python/tvm/relay/op/nn/nn.py

@@ -393,7 +393,7 @@ def global_avg_pool2d(data,
 def adaptive_max_pool2d(data,
                         output_size=None,
                         layout="NCHW"):
-    r"""2D adaptive max pooling operator.
+    r"""2D adaptive max pooling operator. This operator is experimental.
 
     This operator takes data as input and does 2D max value calculation
     across each window represented by WxH.
@@ -439,7 +439,7 @@ def adaptive_max_pool2d(data,
 def adaptive_avg_pool2d(data,
                         output_size=None,
                         layout="NCHW"):
-    r"""2D adaptive average pooling operator.
+    r"""2D adaptive average pooling operator. This operator is experimental.
 
     This operator takes data as input and does 2D average value calculation
     across each window represented by WxH.

src/relay/op/nn/pooling.cc

@@ -511,7 +511,7 @@ RELAY_REGISTER_OP("nn.adaptive_avg_pool2d")
 .set_attrs_type_key("relay.attrs.AdaptivePool2DAttrs")
 .set_num_inputs(1)
 .add_argument("data", "Tensor", "The input tensor.")
-.set_support_level(2)
+.set_support_level(10)
 .add_type_rel("AdaptiveAvgPool2D", AdaptivePool2DRel)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
                                Pool2DInferCorrectLayout<AdaptivePool2DAttrs>)
@@ -550,7 +550,7 @@ RELAY_REGISTER_OP("nn.adaptive_max_pool2d")
 .set_attrs_type_key("relay.attrs.AdaptivePool2DAttrs")
 .set_num_inputs(1)
 .add_argument("data", "Tensor", "The input tensor.")
-.set_support_level(2)
+.set_support_level(10)
 .add_type_rel("AdaptiveMaxPool2D", AdaptivePool2DRel)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
                                Pool2DInferCorrectLayout<AdaptivePool2DAttrs>)

tests/python/relay/test_op_level10.py

@@ -208,7 +208,50 @@ def test_shape_of():
             tvm.testing.assert_allclose(op_res.asnumpy(),
                                         np.array(shape).astype('int32'))
 
+def verify_adaptive_pool2d(dshape, out_size, pool_type, layout="NCHW", dtype="float32"):
+    def start_index(index, odim, idim):
+        return int(np.floor(index * idim / odim))
+
+    def end_index(index, odim, idim):
+        return int(np.ceil((index + 1) * idim / odim))
+
+    np_data = np.random.uniform(low=0, high=255, size=dshape).astype(dtype)
+    n, c, h, w = dshape
+    oh, ow = out_size
+    oshape = (n, c) + out_size
+    np_out = np.zeros(oshape).astype(dtype)
+    np_op = np.mean if pool_type == "avg" else np.max
+    for i in range(n):
+        for j in range(c):
+            for k in range(oh):
+                k_start = start_index(k, oh, h)
+                k_end = end_index(k, oh, h)
+                k_sl = slice(k_start, k_end)
+                for l in range(ow):
+                    l_start = start_index(l, ow, w)
+                    l_end = end_index(l, ow, w)
+                    l_sl = slice(l_start, l_end)
+                    np_out[i, j, k, l] = np_op(np_data[i, j, k_sl, l_sl])
+
+    opfunc = relay.nn.adaptive_avg_pool2d if pool_type == "avg" else relay.nn.adaptive_max_pool2d
+    x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
+    y = opfunc(x, out_size, layout)
+    func = relay.Function([x], y)
+
+    for target, ctx in ctx_list():
+        intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
+        relay_out = intrp1.evaluate(func)(np_data)
+        tvm.testing.assert_allclose(relay_out.asnumpy(), np_out, rtol=1e-5, atol=1e-5)
+
+def test_adaptive_pool2d():
+    verify_adaptive_pool2d((1, 9, 224, 224), (1, 1), "max")
+    verify_adaptive_pool2d((1, 3, 224, 224), (2, 3), "avg")
+    verify_adaptive_pool2d((1, 14, 56, 78), (34, 13), "max")
+    verify_adaptive_pool2d((1, 5, 46, 97), (4, 96), "avg")
+
+
 if __name__ == "__main__":
+    test_adaptive_pool2d()
     test_collapse_sum_like()
     test_broadcast_to_like()
     test_slice_like()

tests/python/relay/test_op_level2.py

@@ -293,49 +293,6 @@ def test_avg_pool2d_no_count_pad():
         op_res1 = intrp1.evaluate(func)(data)
         tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
 
-
-def verify_adaptive_pool2d(dshape, out_size, pool_type, layout="NCHW", dtype="float32"):
-    def start_index(index, odim, idim):
-        return int(np.floor(index * idim / odim))
-
-    def end_index(index, odim, idim):
-        return int(np.ceil((index + 1) * idim / odim))
-
-    np_data = np.random.uniform(low=0, high=255, size=dshape).astype(dtype)
-    n, c, h, w = dshape
-    oh, ow = out_size
-    oshape = (n, c) + out_size
-    np_out = np.zeros(oshape).astype(dtype)
-    np_op = np.mean if pool_type == "avg" else np.max
-    for i in range(n):
-        for j in range(c):
-            for k in range(oh):
-                k_start = start_index(k, oh, h)
-                k_end = end_index(k, oh, h)
-                k_sl = slice(k_start, k_end)
-                for l in range(ow):
-                    l_start = start_index(l, ow, w)
-                    l_end = end_index(l, ow, w)
-                    l_sl = slice(l_start, l_end)
-                    np_out[i, j, k, l] = np_op(np_data[i, j, k_sl, l_sl])
-
-    opfunc = relay.nn.adaptive_avg_pool2d if pool_type == "avg" else relay.nn.adaptive_max_pool2d
-    x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
-    y = opfunc(x, out_size, layout)
-    func = relay.Function([x], y)
-
-    for target, ctx in ctx_list():
-        intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
-        relay_out = intrp1.evaluate(func)(np_data)
-        tvm.testing.assert_allclose(relay_out.asnumpy(), np_out, rtol=1e-5, atol=1e-5)
-
-def test_adaptive_pool2d():
-    verify_adaptive_pool2d((1, 9, 224, 224), (1, 1), "max")
-    verify_adaptive_pool2d((1, 3, 224, 224), (2, 3), "avg")
-    verify_adaptive_pool2d((1, 14, 56, 78), (34, 13), "max")
-    verify_adaptive_pool2d((1, 5, 46, 97), (4, 96), "avg")
-
-
 def test_flatten_infer_type():
     d1, d2, d3, d4 = tvm.var("d1"), tvm.var("d2"), tvm.var("d3"), tvm.var("d4")
     x = relay.var("x", relay.TensorType((d1, d2, d3, d4), "float32"))
@@ -524,7 +481,6 @@ def test_upsampling():
 if __name__ == "__main__":
     test_pool2d()
     test_avg_pool2d_no_count_pad()
-    test_adaptive_pool2d()
     test_lrn()
     test_l2_normalize()
     test_conv2d_infer_type()