Fix test

Author: jroesch

python/tvm/relay/expr.py

@@ -27,6 +27,7 @@
 from .._ffi import base as _base
 from .. import nd as _nd
 from .. import convert
+from ..ndarray import NDArray
 
 # will be registered afterwards
 _op_make = None
@@ -305,10 +306,15 @@ def __call__(self, *args):
         """
         return Call(self, args, None, None)
 
-    def get_params(self, params):
-        return _expr.FunctionGet(self, params)
+    def get_params(self):
+        return _expr.FunctionGetParams(self)
 
     def set_params(self, params):
+        for key in params:
+            value = params[key]
+            if isinstance(value, NDArray):
+                params[key] = Constant(value)
+
         return _expr.FunctionSetParams(self, params)
 
 

src/relay/ir/expr.cc

@@ -163,7 +163,7 @@ Function FunctionNode::SetParams(const tvm::Map<Var, Constant>& parameters) cons
   return FunctionSetAttr(GetRef<Function>(this), "__params__", parameters);
 }
 
-TVM_REGISTER_API("relay._expr.FunctionSetParms")
+TVM_REGISTER_API("relay._expr.FunctionSetParams")
 .set_body_typed<Function(const Function&, const tvm::Map<Var, Constant>&)>(
   [](const Function& func, const tvm::Map<Var, Constant>& parameters) {
     return func->SetParams(parameters);
@@ -174,7 +174,7 @@ tvm::Map<Var, Constant> FunctionNode::GetParams() const {
   return Downcast<tvm::Map<Var, Constant>>(node_ref);
 }
 
-TVM_REGISTER_API("relay._expr.FunctionGetParms")
+TVM_REGISTER_API("relay._expr.FunctionGetParams")
 .set_body_typed<tvm::Map<Var, Constant>(const Function&)>([](const Function& func) {
   return func->GetParams();
 });

tests/python/relay/test_ir_nodes.py

@@ -160,7 +160,6 @@ def test_global_var():
     str(gv)
     check_json_roundtrip(gv)
 
-
 def test_function():
     param_names = ['a', 'b', 'c', 'd']
     params = tvm.convert([relay.Var(n) for n in param_names])
@@ -184,7 +183,8 @@ def test_function_attrs():
     fn = relay.Function(params, body, ret_type, type_params)
     model_params = {}
     for param in params[:1]:
-        tensor = np.random.rand(*param.shape).astype(param.dtype)
+        cty = param.type_annotation
+        tensor = np.random.rand(*[int(sh) for sh in cty.shape]).astype(cty.dtype)
         model_params[param] = tvm.nd.array(tensor)
     fn = fn.set_params(model_params)
     assert fn.params == params
@@ -196,8 +196,12 @@ def test_function_attrs():
     json_str = tvm.save_json(fn)
     fn_after = tvm.load_json(json_str)
     model_params_after = fn_after.get_params()
-    for p1, p2 in zip(model_params, model_params_after):
-        assert p1.asnumpy() == p2.asnumpy()
+    after_keys = [item[0] for item in model_params_after.items()]
+    for key1, key2 in zip(model_params, after_keys):
+        assert key1.name_hint == key2.name_hint
+        p1 = model_params[key1]
+        p2 = model_params_after[key2]
+        np.testing.assert_allclose(p1.data.asnumpy(), p2.data.asnumpy())
 
 def test_call():
     op = relay.Var('f')
@@ -280,9 +284,11 @@ def test_conv2d_attrs():
     test_local_var()
     test_global_var()
     test_function()
+    test_function_attrs()
     test_call()
     test_let()
     test_if()
     test_tuple_get_item()
     test_op()
     test_conv2d_attrs()
+