[Relay][UnitTest] Removed redundant unit test.

test_op_level2.py::test_conv2d should have been removed in the
refactoring in #8947.  All functionality tested by it is in
test_op_level2.py::TestConv2D.

tests/python/relay/test_op_level2.py

@@ -357,121 +357,6 @@ def test_run(
         tvm.testing.assert_allclose(op_res1.numpy(), ref_res, rtol=1e-4, atol=1e-4)
 
 
-def test_conv2d_run(target, dev):
-    def run_test_conv2d(
-        dtype,
-        out_dtype,
-        scale,
-        dshape,
-        kshape,
-        padding=(1, 1),
-        fref=None,
-        groups=1,
-        dilation=(1, 1),
-        channels=32,
-        kernel_size=(3, 3),
-    ):
-        x = relay.var("x", shape=dshape, dtype=dtype)
-        w = relay.var("w", shape=kshape, dtype=dtype)
-        y = relay.nn.conv2d(
-            x,
-            w,
-            padding=padding,
-            dilation=dilation,
-            groups=groups,
-            channels=channels,
-            kernel_size=kernel_size,
-        )
-        func = relay.Function([x, w], y)
-        data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
-        kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
-        dkernel = tvm.topi.testing.dilate_python(kernel, (1, 1) + dilation)
-        ref_res = tvm.topi.testing.conv2d_nchw_python(
-            data.astype(out_dtype), dkernel.astype(out_dtype), 1, padding, groups=groups
-        )
-
-        op_res1 = relay.create_executor("graph", device=dev, target=target).evaluate(func)(
-            data, kernel
-        )
-        tvm.testing.assert_allclose(op_res1.numpy(), ref_res, rtol=1e-4, atol=1e-4)
-
-    # group conv2d
-    run_test_conv2d(
-        dtype="float32",
-        out_dtype="float32",
-        scale=1,
-        dshape=(1, 32, 18, 18),
-        kshape=(32, 4, 3, 3),
-        padding=(1, 1),
-        channels=32,
-        groups=8,
-        kernel_size=(3, 3),
-        dilation=(1, 1),
-    )
-    # also group conv2d
-    run_test_conv2d(
-        dtype="float32",
-        out_dtype="float32",
-        scale=1,
-        dshape=(1, 32, 18, 18),
-        kshape=(64, 1, 3, 3),
-        padding=(1, 1),
-        channels=64,
-        groups=32,
-        kernel_size=(3, 3),
-        dilation=(1, 1),
-    )
-
-    # normal conv2d
-    run_test_conv2d(
-        dtype="float32",
-        out_dtype="float32",
-        scale=1,
-        dshape=(1, 3, 224, 224),
-        kshape=(10, 3, 3, 3),
-        padding=(1, 1),
-        channels=10,
-        kernel_size=(3, 3),
-        dilation=(1, 1),
-    )
-    # mixed precision
-    run_test_conv2d(
-        dtype="int8",
-        out_dtype="int32",
-        scale=1,
-        dshape=(1, 3, 224, 224),
-        kshape=(10, 3, 3, 3),
-        padding=(1, 1),
-        channels=10,
-        kernel_size=(3, 3),
-        dilation=(1, 1),
-    )
-    # mixed precision.
-    run_test_conv2d(
-        dtype="int8",
-        out_dtype="int32",
-        scale=1,
-        dshape=(1, 3, 224, 224),
-        kshape=(10, 3, 1, 3),
-        padding=(0, 1),
-        channels=10,
-        kernel_size=(1, 3),
-        dilation=(1, 1),
-    )
-    # dilated conv2d
-    run_test_conv2d(
-        dtype="float32",
-        out_dtype="float32",
-        scale=1,
-        dshape=(1, 3, 18, 18),
-        kshape=(10, 3, 3, 3),
-        padding=(1, 1),
-        channels=10,
-        kernel_size=(3, 3),
-        dilation=(3, 3),
-    )
-
-
 def test_compile_depthwise_conv2d_arm_cpu():
     dtype = "float32"
     out_dtype = "float32"