[Bugfix][Relay][Frontend] Fix bug in mxnet converter for slick_like (#2744)

* Fix bug in mxnet converter for slick_like

* More tolerance for topi_conv2d_NCHWc

python/tvm/relay/frontend/mxnet.py

@@ -194,6 +194,13 @@ def _mx_slice(inputs, attrs):
     return _op.strided_slice(inputs[0], **new_attrs)
 
 
+def _mx_slice_like(inputs, attrs):
+    assert len(inputs) == 2
+    new_attrs = {}
+    new_attrs["axes"] = attrs.get_int_tuple("axes", None)
+    return _op.slice_like(*inputs, **new_attrs)
+
+
 def _mx_slice_axis(inputs, attrs):
     assert len(inputs) == 1
     shape = ir_pass.infer_type(inputs[0]).checked_type.shape
@@ -383,7 +390,6 @@ _identity_list = [
     "exp",
     "negative",
     "reshape_like",
-    "slice_like",
     "zeros_like",
     "ones_like",
     "where",
@@ -473,6 +479,7 @@ _convert_map = {
     "BatchNorm_v1"  : _mx_batch_norm,
     "LRN"           : _mx_lrn,
     "slice"         : _mx_slice,
+    "slice_like"    : _mx_slice_like,
     "slice_axis"    : _mx_slice_axis,
     "SliceChannel"  : _mx_split,
     "split"         : _mx_split,

tests/python/frontend/mxnet/test_forward.py

@@ -336,7 +336,6 @@ def test_forward_scalar_ops():
                 op_res = intrp.evaluate(new_sym)(a_np)
                 tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())
 
-
 def test_forward_slice_axis():
     def verify(shape, axis, begin, end):
         data_np = np.random.uniform(size=shape).astype("float32")
@@ -354,6 +353,27 @@ def test_forward_slice_axis():
     verify((3, 4), 1, -3, -1)
     verify((3, 4), -1, -3, -1)
 
+def test_forward_slice_like():
+    def verify(x_shape, y_shape, axes):
+        x_np = np.random.uniform(size=x_shape).astype("float32")
+        y_np = np.random.uniform(size=y_shape).astype("float32")
+        if axes is None:
+            ref_res = mx.nd.slice_like(mx.nd.array(x_np), mx.nd.array(y_np))
+            mx_sym = mx.sym.slice_like(mx.sym.var("x"), mx.sym.var("y"))
+        else:
+            ref_res = mx.nd.slice_like(mx.nd.array(x_np), mx.nd.array(y_np), axes=axes)
+            mx_sym = mx.sym.slice_like(mx.sym.var("x"), mx.sym.var("y"), axes=axes)
+        new_sym, _ = relay.frontend.from_mxnet(mx_sym, {"x": x_shape, "y": y_shape})
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, ctx=ctx, target=target)
+                op_res = intrp.evaluate(new_sym)(x_np, y_np)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())
+    verify((3, 4), (2, 3), None)
+    verify((3, 4), (2, 3), (0, 1))
+    verify((3, 4), (2, 3), (0))
+    verify((3, 4), (2, 3), (-1))
+
 
 if __name__ == '__main__':
     test_forward_mlp()
@@ -382,3 +402,4 @@ if __name__ == '__main__':
     test_forward_elemwise_ops()
     test_forward_scalar_ops()
     test_forward_slice_axis()
+    test_forward_slice_like()

topi/tests/python/test_topi_conv2d_NCHWc.py

@@ -105,7 +105,7 @@ def verify_conv2d_NCHWc(batch, in_channel, in_size, num_filter, kernel, stride,
                              name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
                                   (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
             func(a, w, c)
-        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
+        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-3)
 
     # test llvm only for now since conv2d_NCHWc implement is missing in other backend.
     for device in ["llvm"]:
@@ -202,4 +202,4 @@ def test_conv2d_NCHWc():
     verify_conv2d_NCHWc(1,  256,   3, 126, 3, 1, 1)
 
 if __name__ == "__main__":
-    test_conv2d_NCHWc()
\ No newline at end of file
+    test_conv2d_NCHWc()