From dd1348e5d3f062c3818174be610ed02e35b4cf5b Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Wed, 20 May 2020 01:34:39 +0100
Subject: [PATCH] [Transform] introduce the InferDataLayouts transformation
---
src/finn/transformation/infer_data_layouts.py | 120 ++++++++++++++++++
1 file changed, 120 insertions(+)
create mode 100644 src/finn/transformation/infer_data_layouts.py
diff --git a/src/finn/transformation/infer_data_layouts.py b/src/finn/transformation/infer_data_layouts.py
new file mode 100644
index 000000000..bb06f6abc
--- /dev/null
+++ b/src/finn/transformation/infer_data_layouts.py
@@ -0,0 +1,120 @@
+# 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)
--
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