Merge pull request #114 from quetric/feature/add_past_conv_check_padding

Feature/add past conv check padding

src/finn/transformation/streamline/reorder.py

@@ -244,7 +244,12 @@ class MoveScalarAddPastConv(Transformation):
                     start_name = n.input[0]
                     end_name = consumer.output[0]
                     conv_out_shape = model.get_tensor_shape(end_name)
-                    if all(x == 1 for x in A.shape):
+
+                    using_padding = True
+                    pads = list(get_by_name(consumer.attribute, "pads").ints)
+                    if sum(pads) == 0:
+                        using_padding = False
+                    if all(x == 1 for x in A.shape) and not using_padding:
                         # create a tensor filled with the add constant, in
                         # the shape expected by the convolution
                         conv_in_const = np.zeros(conv_in_shape, dtype=np.float32)
@@ -256,7 +261,8 @@ class MoveScalarAddPastConv(Transformation):
                         execute_node(conv_node, exec_ctx, model.graph)
                         # retrieve the conv output
                         Anew = exec_ctx[end_name]
-                        # strip out repetition
+
+                        # strip out repetition if no padding
                         Anew = Anew[0, :, 0, 0].reshape(1, -1, 1, 1)
                         # update the add weight
                         model.set_initializer(add_weight_name, Anew)
@@ -274,6 +280,7 @@ class MoveScalarAddPastConv(Transformation):
                         graph.node.remove(add_node)
                         graph.node.insert(node_ind, add_node)
                         graph_modified = True
+
         model = model.transform(InferShapes())
         return (model, graph_modified)
 

tests/transformation/test_move_scalar_past_conv.py

@@ -12,6 +12,85 @@ from finn.transformation.streamline import (
 )
 
 
+@pytest.mark.parametrize("padding", [False, True])
+@pytest.mark.parametrize(
+    "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
+)
+def test_move_scalar_past_conv(test_args, padding):
+    scalar_op = test_args[0]
+    transf_fxn = test_args[1]
+
+    in_feature_dim = 7
+    in_chn = 3
+
+    stages = 2
+    kernel_size = 3
+
+    out_feature_dim = (
+        in_feature_dim if padding else in_feature_dim - (kernel_size // 2 * 2) * stages
+    )
+
+    input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
+    output_shape = [1, in_chn, out_feature_dim, out_feature_dim]
+
+    conv_param_shape = [in_chn, in_chn, kernel_size, kernel_size]
+
+    conv_config = {}
+    conv_config["dilations"] = [1, 1]
+    conv_config["group"] = 1
+    conv_config["kernel_shape"] = [kernel_size, kernel_size]
+    if padding:
+        conv_config["pads"] = [1, 1, 1, 1]
+    else:
+        conv_config["pads"] = [0, 0, 0, 0]
+    conv_config["strides"] = [1, 1]
+
+    top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+    top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+
+    value_info = [oh.make_tensor_value_info("p1", TensorProto.FLOAT, [1])]
+    value_info += [oh.make_tensor_value_info("p2", TensorProto.FLOAT, conv_param_shape)]
+    value_info += [oh.make_tensor_value_info("p3", TensorProto.FLOAT, conv_param_shape)]
+
+    modelproto = oh.make_model(
+        oh.make_graph(
+            name="test",
+            inputs=[top_in],
+            outputs=[top_out],
+            value_info=value_info,
+            nodes=[
+                oh.make_node(scalar_op, ["top_in", "p1"], ["t1"]),
+                oh.make_node("Conv", ["t1", "p2"], ["t2"], **conv_config),
+                oh.make_node("Conv", ["t2", "p3"], ["top_out"], **conv_config),
+            ],
+        )
+    )
+    model = ModelWrapper(modelproto)
+    model = model.transform(InferShapes())
+
+    np.random.seed(0)
+    model.set_initializer("p1", *np.random.rand(1).astype(np.float32))
+    model.set_initializer("p2", np.random.rand(*conv_param_shape).astype(np.float32))
+    model.set_initializer("p3", np.random.rand(*conv_param_shape).astype(np.float32))
+    new_model = model.transform(transf_fxn)
+    inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+    assert ox.compare_execution(model, new_model, inp_dict)
+    if scalar_op == "Add":
+        if padding:
+            assert new_model.graph.node[0].op_type == scalar_op
+            assert new_model.graph.node[1].op_type == "Conv"
+            assert new_model.graph.node[2].op_type == "Conv"
+        else:
+            assert new_model.graph.node[0].op_type == "Conv"
+            assert new_model.graph.node[1].op_type == scalar_op
+            assert new_model.graph.node[2].op_type == "Conv"
+    else:
+        assert new_model.graph.node[0].op_type == "Conv"
+        assert new_model.graph.node[1].op_type == "Conv"
+        assert new_model.graph.node[2].op_type == scalar_op
+
+
 @pytest.mark.parametrize(
     "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
 )