Merge pull request #163 from fastmachinelearning/fix/chanlast_forking…

…_transpose

Don't remove opposing transposes if first one is forking

src/qonnx/transformation/channels_last.py

@@ -301,7 +301,10 @@ def apply(self, model):
                 ndim = len(input_shape)
                 if list(to_channels_first_args(ndim)) == perm_1:
                     successor_nodes = model.find_direct_successors(n)
-                    if successor_nodes is None:
+                    # skip if:
+                    # - this Transpose has no successors (nothing to do)
+                    # - this Transpose output is forking (cannot remove)
+                    if successor_nodes is None or len(successor_nodes) > 1:
                         continue
                     successor_node = successor_nodes[0]
 

tests/transformation/test_channelslast_residual.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2024 Advanced Micro Devices, Inc.
+# 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 qonnx 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 numpy as np
+from pkgutil import get_data
+
+import qonnx.core.onnx_exec as oxe
+from qonnx.core.modelwrapper import ModelWrapper
+from qonnx.transformation.channels_last import ConvertToChannelsLastAndClean
+from qonnx.util.basic import gen_finn_dt_tensor
+
+
+def test_channelslast_residual():
+    raw_m = get_data("qonnx.data", "onnx/residual_block_clean.onnx")
+    model = ModelWrapper(raw_m)
+    iname = model.graph.input[0].name
+    idt = model.get_tensor_datatype(iname)
+    ishape = model.get_tensor_shape(iname)
+    idict = {iname: gen_finn_dt_tensor(idt, ishape)}
+    oname = model.graph.output[0].name
+    expected_out = oxe.execute_onnx(model, idict)[oname]
+    model = model.transform(ConvertToChannelsLastAndClean(make_input_channels_last=False))
+    expected_ops = ["Transpose", "Conv", "Conv", "Relu", "Conv", "Relu", "Add", "MaxPool", "Transpose"]
+    ops = [x.op_type for x in model.graph.node]
+    assert ops == expected_ops, "Did not found expected op sequence after lowering and channels-last"
+    for node in model.graph.node:
+        if node.op_type in ["Conv", "MaxPool"]:
+            assert node.domain == "qonnx.custom_op.channels_last"
+    out = oxe.execute_onnx(model, idict)[oname]
+    assert np.isclose(expected_out, out, atol=1e-4).all()