[TEST] Add idt== DataType.INT32 test case

tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py

@@ -52,9 +52,9 @@ def make_single_maxpool_modelwrapper(onnx_op_name, ishape, idt, pdt, pshape):
 
 
 # parameter datatype
-@pytest.mark.parametrize("pdt", [DataType.BIPOLAR, DataType.UINT4, DataType.INT4])
+@pytest.mark.parametrize("pdt", [DataType.BIPOLAR, DataType.UINT4, DataType.INT2])
 # input datatype
-@pytest.mark.parametrize("idt", [DataType.UINT4, DataType.INT4])
+@pytest.mark.parametrize("idt", [DataType.INT32, DataType.UINT4, DataType.INT4])
 # function
 @pytest.mark.parametrize("onnx_op_name", ["Add", "Mul"])
 # vector parameter or scalar parameter (broadcast)
@@ -78,7 +78,15 @@ def test_convert_to_hls_channelwise_layer(
     np.random.seed(0)
     model = make_single_maxpool_modelwrapper(onnx_op_name, ishape, idt, pdt, pshape)
 
-    x = gen_finn_dt_tensor(idt, (1, ifm_ch, ifm_dim, ifm_dim))
+    # Since the aren't Data types with a bit width of a non power of 2,
+    # there are cases where the input won't use it full range.
+    if idt == DataType.INT32:
+        x = gen_finn_dt_tensor(DataType.INT16, (1, ifm_ch, ifm_dim, ifm_dim))
+    elif idt == DataType.UINT32:
+        x = gen_finn_dt_tensor(DataType.UINT16, (1, ifm_ch, ifm_dim, ifm_dim))
+    else:
+        x = gen_finn_dt_tensor(idt, (1, ifm_ch, ifm_dim, ifm_dim))
+
     input_dict = prepare_inputs(x)
     y_expected = oxe.execute_onnx(model, input_dict)["outp"]