[Test] Add depthwise convolution cppsim/rtlsim test

tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py

@@ -78,7 +78,7 @@ def make_single_im2col_modelwrapper(k, ifm_ch, ifm_dim, ofm_dim, simd, stride, i
 
 
 def make_single_slidingwindow_modelwrapper(
-    k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
+    k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt, dw=0
 ):
     odt = idt
     inp = helper.make_tensor_value_info(
@@ -102,6 +102,7 @@ def make_single_slidingwindow_modelwrapper(
         Stride=stride,
         inputDataType=idt.name,
         outputDataType=odt.name,
+        depthwise=dw
     )
     graph = helper.make_graph(
         nodes=[SlidingWindow_node],
@@ -128,9 +129,9 @@ def prepare_inputs(input_tensor):
 # kernel size
 @pytest.mark.parametrize("k", [2, 4])
 # input dimension
-@pytest.mark.parametrize("ifm_dim", [4, 6, 8])
+@pytest.mark.parametrize("ifm_dim", [6, 8])
 # input channels
-@pytest.mark.parametrize("ifm_ch", [2, 4])  # , 2, 3, 4])
+@pytest.mark.parametrize("ifm_ch", [2, 4])
 # Stride
 @pytest.mark.parametrize("stride", [1, 2])
 # execution mode
@@ -168,6 +169,50 @@ def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride, exec_mode,
         k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
     )
     y_expected = oxe.execute_onnx(golden, input_dict)["outp"]
-    # if idt == DataType.BIPOLAR:
-    #     y_expected = 2 * y_expected - 1
     assert (y_produced == y_expected).all()
+    
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.BIPOLAR, DataType.INT2])
+# kernel size
+@pytest.mark.parametrize("k", [2, 4])
+# input dimension
+@pytest.mark.parametrize("ifm_dim", [4, 6, 8])
+# input channels
+@pytest.mark.parametrize("ifm_ch", [2, 4]) 
+# Stride
+@pytest.mark.parametrize("stride", [1, 2])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+# input channel parallelism ("SIMD")
+@pytest.mark.parametrize("simd", [1, 2])
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_fpgadataflow_slidingwindow_dw(idt, k, ifm_dim, ifm_ch, stride, exec_mode, simd):
+    # set node attribute depthwise
+    dw = 1
+    ofm_dim = int(((ifm_dim - k) / stride) + 1)
+
+    x = gen_finn_dt_tensor(idt, (1, ifm_dim, ifm_dim, ifm_ch))
+    model = make_single_slidingwindow_modelwrapper(
+        k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt, dw
+    )
+
+    if exec_mode == "cppsim":
+        model = model.transform(SetExecMode("cppsim"))
+        model = model.transform(PrepareCppSim())
+        model = model.transform(CompileCppSim())
+    elif exec_mode == "rtlsim":
+        model = model.transform(SetExecMode("rtlsim"))
+        model = model.transform(GiveUniqueNodeNames())
+        model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+        model = model.transform(HLSSynthIP())
+        model = model.transform(PrepareRTLSim())
+    else:
+        raise Exception("Unknown exec_mode in test_fpgadataflow_slidingwindow")
+
+    # prepare input data
+    input_dict = prepare_inputs(x)
+    # execute model
+    y_produced = oxe.execute_onnx(model, input_dict)["outp"]
+    y_shape = (1, ofm_dim, ofm_dim, k*k*ifm_ch)
+    assert y_produced.shape == y_shape