[Transform] introduce the InferDataLayouts transformation

src/finn/transformation/infer_data_layouts.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 finn.custom_op.registry as registry
+import finn.core.data_layout as DataLayout
+from finn.transformation import Transformation
+import warnings
+from finn.util.basic import get_by_name
+
+
+def _dims_to_layout(node, ndims):
+    if node.domain == "finn":
+        if node.op_type == "MultiThreshold":
+            mt_inst = registry.getCustomOp(node)
+            layout = mt_inst.get_nodeattr("data_layout")
+            if ndims == 2:
+                return DataLayout.NC
+            elif layout == "NHWC" and ndims == 4:
+                return DataLayout.NHWC
+            elif layout == "NCHW" and ndims == 4:
+                return DataLayout.NCHW
+            else:
+                return DataLayout.UNKNOWN
+        else:
+            if ndims == 2:
+                return DataLayout.NC
+            elif ndims == 4:
+                return DataLayout.NHWC
+            else:
+                return DataLayout.UNKNOWN
+    else:
+        if ndims == 2:
+            return DataLayout.NC
+        elif ndims == 4:
+            return DataLayout.NCHW
+        else:
+            return DataLayout.UNKNOWN
+
+
+def _infer_node_data_layout(model, node):
+    """Infer output data layout annotation(s) for a particular node.
+    Returns True if any changes were made."""
+    old_layouts = list(map(lambda x: model.get_tensor_layout(x), node.output))
+    if node.domain == "finn":
+        # try to guess based on number of output dims
+        for o in node.output:
+            ndims = len(model.get_tensor_shape(o))
+            new_layout = _dims_to_layout(node, ndims)
+            model.set_tensor_layout(o, new_layout)
+    else:
+        if node.op_type == "Transpose":
+            # grab input annotation and switch it around using perm
+            perm = get_by_name(node.attribute, "perm").ints
+            inp_layout = model.get_tensor_layout(node.input[0])
+            out_layout = [x for _, x in sorted(zip(perm, inp_layout))]
+            model.set_tensor_layout(node.output[0], out_layout)
+        else:
+            # try to guess based on number of output dims
+            for o in node.output:
+                ndims = len(model.get_tensor_shape(o))
+                model.set_tensor_layout(o, _dims_to_layout(node, ndims))
+    # compare old and new output dtypes to see if anything changed
+    new_layouts = list(map(lambda x: model.get_tensor_layout(x), node.output))
+    graph_modified = new_layouts != old_layouts
+    return graph_modified
+
+
+class InferDataLayouts(Transformation):
+    """Try to infer data layout annotations info for all input/intermediate/output
+    tensors based on inputs and node type."""
+
+    def apply(self, model):
+        graph = model.graph
+        graph_modified = False
+        # first, make sure that the global input has an annotation
+        # this is really hard to do in general, so we do some bad guesswork
+        inp_name = graph.input[0].name
+        if model.get_tensor_layout(inp_name) is None:
+            inp_shape = model.get_tensor_shape(inp_name)
+            if len(inp_shape) == 4:
+                warnings.warn("Assuming 4D input is NCHW")
+                model.set_tensor_layout(inp_name, DataLayout.NCHW)
+                graph_modified = True
+            elif len(inp_shape) == 2:
+                graph_modified = True
+                warnings.warn("Assuming 2D input is NC")
+                model.set_tensor_layout(inp_name, DataLayout.NC)
+            else:
+                raise Exception(
+                    """Unknown number of dims for input, don't know
+                how to annotate"""
+                )
+        for node in graph.node:
+            graph_modified |= _infer_node_data_layout(model, node)
+        return (model, graph_modified)