[Test] Add test for move channelwise add past conv

tests/transformation/test_move_chw_add_past_conv.py

+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import pytest
+
+import numpy as np
+from onnx import helper, TensorProto
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline.reorder import MoveAddPastConv
+from finn.custom_op.im2col import compute_conv_output_dim
+import finn.core.onnx_exec as oxe
+
+
+# input dimension
+@pytest.mark.parametrize("idim", [4, 7])
+# kernel size
+@pytest.mark.parametrize("k", [2, 3])
+# stride
+@pytest.mark.parametrize("s", [1, 2])
+# input channels
+@pytest.mark.parametrize("ich", [2, 4])
+# output channels
+@pytest.mark.parametrize("och", [2, 3])
+def test_move_chw_add_past_conv(idim, k, s, ich, och):
+    odim = compute_conv_output_dim(idim, k, s)
+
+    ishape = [1, ich, idim, idim]
+    oshape = [1, och, odim, odim]
+    add_param_shape = [1, ich, 1, 1]
+    conv_param_shape = [och, ich, k, k]
+
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+    a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, add_param_shape)
+    a1 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, conv_param_shape)
+
+    conv_config = {}
+    conv_config["dilations"] = [1, 1]
+    conv_config["group"] = 1
+    conv_config["kernel_shape"] = [k, k]
+    conv_config["pads"] = [0, 0, 0, 0]
+    conv_config["strides"] = [s, s]
+
+    add_node = helper.make_node("Add", ["inp", "a0"], ["add_out"])
+    conv_node = helper.make_node("Conv", ["add_out", "a1"], ["outp"], **conv_config)
+
+    model = helper.make_model(
+        helper.make_graph(
+            nodes=[add_node, conv_node],
+            name="move-add-graph",
+            inputs=[inp],
+            outputs=[outp],
+            value_info=[a0, a1],
+        )
+    )
+
+    model = ModelWrapper(model)
+    # initialize model
+    a0_values = np.random.uniform(low=0, high=1, size=tuple(add_param_shape)).astype(
+        np.float32
+    )
+    model.set_initializer("a0", a0_values)
+    a1_values = np.random.uniform(low=0, high=1, size=tuple(conv_param_shape)).astype(
+        np.float32
+    )
+    model.set_initializer("a1", a1_values)
+
+    model = model.transform(InferShapes())
+
+    # execution before transformation
+    inp_values = np.random.uniform(low=0, high=1, size=tuple(ishape)).astype(np.float32)
+    idict = {model.graph.input[0].name: inp_values}
+    odict = oxe.execute_onnx(model, idict)
+    y_before = odict[model.graph.output[0].name]
+
+    model = model.transform(MoveAddPastConv())
+    odict = oxe.execute_onnx(model, idict)
+    y_after = odict[model.graph.output[0].name]
+
+    assert np.isclose(y_before, y_after).all()
+    assert model.graph.node[0].op_type == "Conv"
+    assert model.graph.node[1].op_type == "Add"