commit 95b279f1454d58a93238851cb5ff394c7782ad32
author Dwight Lidman <dwight.lidman@arm.com> Fri Mar 26 10:53:28 2021 +0100
committer patrik.gustavsson <patrik.gustavsson@arm.com> Wed Apr 07 10:50:39 2021 +0000
tree eb2e8f4db229f0581a894084c75f44b55877a05b
parent fe368bc231fb680ebfa48e2c35e92dec5639df5e

MEAN implementation changed to Average Pool

This is a small commit which changes one of
the four MEAN implementations to a simpler
one, using an AvgPool instead of a
DepthwiseConv.

Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: I9e8af071e8b820796577ee4792b4812a1212602b

ethosu/vela/supported_operators.py

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 777e9c7..5bf2c45 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -122,6 +122,7 @@
     filter_product_range = (1, 256 * 256)
     mean_kernel_product = 64 * 64
     mean_kernel_product_int8 = 16 * 16
+    mean_kernel_product_avgpool = 256 * 256
     # Supported consumers
     supported_pad_consumers = convolution_ops | depthwise_convolution_ops | pooling_ops
 
@@ -272,6 +273,7 @@
         self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_input_8bit)
         self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_input_dims)
         self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_axis)
+        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product_avgpool)
         self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product)
         self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product_int8)
 
@@ -1028,6 +1030,7 @@
             valid = len(op.ifm.shape) == len(op.ofm.shape)
         return valid, f"Op has ifm shape={op.ifm.shape} and ofm shape={op.ofm.shape}"
 
+    @staticmethod
     def constraint_mean_input_dims(op):
         "Input tensor must be at least 2D"
         dims = len(op.inputs[0].shape)
@@ -1045,9 +1048,25 @@
         return valid, f"Axis is {axis}"
 
     @classmethod
+    @docstring_format_args([mean_kernel_product_avgpool])
+    def constraint_mean_height_width_product_avgpool(cls, op):
+        """Product of height and width can be at most {}"""
+        shape = op.inputs[0].shape
+        hi = 0 if len(shape) < 4 else 1
+        h, w = shape[hi : hi + 2]
+        max_prod = cls.mean_kernel_product_avgpool
+        return h * w <= max_prod, f"Product of height and width is {h * w}"
+
+    @classmethod
     @docstring_format_args([mean_kernel_product])
     def constraint_mean_height_width_product(cls, op):
-        "Product of height and width can be at most {}"
+        """Product of height and width can be at most {} when IFM and OFM have different scale or zero point,
+        or keep_dims is True"""
+        ifmq, ofmq = op.ifm.quantization, op.ofm.quantization
+        keep_dims = op.attrs.get("keep_dims")
+        # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool
+        if not keep_dims and ifmq.scale_f32 == ofmq.scale_f32 and ifmq.zero_point == ofmq.zero_point:
+            return True, ""
         shape = op.inputs[0].shape
         hi = 0 if len(shape) < 4 else 1
         h, w = shape[hi : hi + 2]
@@ -1064,6 +1083,8 @@
         IFM datatype is int8"""
         shape = op.ifm.shape
         axis = op.inputs[1].values if op.inputs[1].shape == [] else list(op.inputs[1].values)
+        # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool
+        # and constraint_mean_height_width_product
         if (
             len(shape) != 4
             or op.ifm.dtype != DataType.int8