ONNX export: Test for fully connected

tests/python-pytest/onnx/export/mxnet_export_test.py

@@ -260,6 +260,52 @@ def test_square():
     npt.assert_almost_equal(result, numpy_op)
 
 
+@with_seed()
+def test_fully_connected():
+    def random_arrays(*shapes):
+        """Generate some random numpy arrays."""
+        arrays = [np.random.randn(*s).astype("float32")
+                  for s in shapes]
+        if len(arrays) == 1:
+            return arrays[0]
+        return arrays
+
+    data_names = ['x', 'w', 'b']
+
+    dim_in, dim_out = (3, 4)
+    input_data = random_arrays((4, dim_in), (dim_out, dim_in), (dim_out,))
+
+    ipsym = []
+    data_shapes = []
+    data_forward = []
+    for idx in range(len(data_names)):
+        val = input_data[idx]
+        data_shapes.append((data_names[idx], np.shape(val)))
+        data_forward.append(mx.nd.array(val))
+        ipsym.append(mx.sym.Variable(data_names[idx]))
+
+    op = mx.sym.FullyConnected(data=ipsym[0], weight=ipsym[1], bias=ipsym[2], num_hidden=dim_out, name='FC')
+
+    model = mx.mod.Module(op, data_names=data_names, label_names=None)
+    model.bind(for_training=False, data_shapes=data_shapes, label_shapes=None)
+
+    model.init_params()
+
+    args, auxs = model.get_params()
+    params = {}
+    params.update(args)
+    params.update(auxs)
+
+    converted_model = onnx_mxnet.export_model(op, params, [shape[1] for shape in data_shapes], np.float32, "fc.onnx")
+
+    sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model)
+    result = forward_pass(sym, arg_params, aux_params, data_names, input_data)
+
+    numpy_op = np.dot(input_data[0], input_data[1].T) + input_data[2]
+
+    npt.assert_almost_equal(result, numpy_op)
+
+
 def test_softmax():
     input1 = np.random.rand(1000, 1000).astype("float32")
     label1 = np.random.rand(1000)