[CustomOp] add MaxPoolNHWC

src/finn/custom_op/maxpoolnhwc.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.
+
+from finn.custom_op import CustomOp
+import numpy as np
+from onnx import helper, TensorProto
+from finn.core.modelwrapper import ModelWrapper
+
+
+class MaxPoolNHWC(CustomOp):
+    # a MaxPool node, but using the NHWC data layout
+
+    def get_nodeattr_types(self):
+        # no specific attributes for MaxPoolNHWC
+        return {}
+
+    def make_shape_compatible_op(self):
+        pass
+
+    def infer_node_datatype(self, model):
+        pass
+
+    def execute_node(self, context, graph):
+        node = self.onnx_node
+        inp_name = node.input[0]
+        out_name = node.output[0]
+        inp = context[inp_name]
+        dummy_out = context[out_name]
+        # convert i/o NHWC -> NCHW
+        inp = np.transpose(inp, (0, 3, 1, 2))
+        dummy_out = np.transpose(dummy_out, (0, 3, 1, 2))
+        # execute as regular MaxPool
+        node.domain = ""
+        node.op_type = "MaxPool"
+        inp_vi = helper.make_tensor_value_info(inp_name, TensorProto.FLOAT, inp.shape)
+        out_vi = helper.make_tensor_value_info(
+            out_name, TensorProto.FLOAT, dummy_out.shape
+        )
+        tmp_graph = helper.make_graph(
+            nodes=[node], name="tmp_graph", inputs=[inp_vi], outputs=[out_vi]
+        )
+        tmp_model = helper.make_model(tmp_graph, producer_name="finn")
+        tmp_model = ModelWrapper(tmp_model)
+        new_ctx = {inp_name: inp}
+        from finn.core.onnx_exec import execute_onnx
+
+        ret = execute_onnx(tmp_model, new_ctx)
+        # restore original node props
+        node.domain = "finn"
+        node.op_type = "MaxPoolNHWC"
+        outp = ret[out_name]
+        # convert output NCHW -> NHWC
+        outp = np.transpose(outp, (0, 2, 3, 1))
+        context[out_name] = outp
+
+    def verify_node(self):
+        info_messages = []
+        # verify that "domain" is set to "finn"
+        domain_value = self.onnx_node.domain
+        if domain_value == "finn":
+            info_messages.append("Attribute domain is set correctly")
+        else:
+            info_messages.append('Attribute domain should be set to "finn"')
+        return info_messages

src/finn/custom_op/registry.py

@@ -38,6 +38,7 @@ from finn.custom_op.fpgadataflow.tlastmarker import TLastMarker
 from finn.custom_op.multithreshold import MultiThreshold
 from finn.custom_op.streamingdataflowpartition import StreamingDataflowPartition
 from finn.custom_op.xnorpopcount import XnorPopcountMatMul
+from finn.custom_op.maxpoolnhwc import MaxPoolNHWC
 
 # create a mapping of all known CustomOp names and classes
 custom_op = {}
@@ -50,6 +51,7 @@ custom_op["StreamingFCLayer_Batch"] = StreamingFCLayer_Batch
 custom_op["ConvolutionInputGenerator"] = ConvolutionInputGenerator
 custom_op["TLastMarker"] = TLastMarker
 custom_op["StreamingDataflowPartition"] = StreamingDataflowPartition
+custom_op["MaxPoolNHWC"] = MaxPoolNHWC
 
 
 def getCustomOp(node):