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author | Michael McGeagh <michael.mcgeagh@arm.com> | 2020-09-08 11:09:48 +0100 |
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committer | Michael McGeagh <michael.mcgeagh@arm.com> | 2020-09-11 13:29:06 +0100 |
commit | 8dbf8cfefa1feea6598f5f4864657ba6b6ad60ed (patch) | |
tree | e55debe4a80b01a79381a6aca378a9c6a7da5447 /ethosu/vela/pass_packing.py | |
parent | fa4cb29996ffe1e64e39655c2195af6ff02e887a (diff) | |
download | ethos-u-vela-8dbf8cfefa1feea6598f5f4864657ba6b6ad60ed.tar.gz |
MLBEDSW-2745 Support relus with differing scales
In the event we have a relu op with different input and output scales,
we need to fuse it with a nop avgpool.
Also refactor the existing avgpool nop code to a common function.
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: Iedf4513e7595ee4ee1777ba0b1eb38a8df8aed5e
Diffstat (limited to 'ethosu/vela/pass_packing.py')
-rw-r--r-- | ethosu/vela/pass_packing.py | 19 |
1 files changed, 4 insertions, 15 deletions
diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py index 9e36cd62..a1b03fe2 100644 --- a/ethosu/vela/pass_packing.py +++ b/ethosu/vela/pass_packing.py @@ -20,8 +20,8 @@ import enum from .nn_graph import Pass from .nn_graph import PassPlacement +from .operation import create_avgpool_nop from .operation import NpuBlockType -from .operation import Operation from .tensor import TensorPurpose @@ -455,20 +455,9 @@ def pack_into_passes(nng, arch, verbose_packing=False): # Configure a 1x1 AvgPool and attach the op onto it op = op_list[0] inp = op.inputs[0] - avgpool_name = op.name + "_avgpool" - avgpool_op = Operation("AvgPool", avgpool_name) - avgpool_op.inputs = [inp] - avgpool_op.inputs[0].consumer_list.append(avgpool_op) - avgpool_op.attrs["padding"] = b"VALID" - avgpool_op.attrs["npu_block_type"] = NpuBlockType.Pooling - avgpool_op.attrs["stride_w"] = 1 - avgpool_op.attrs["stride_h"] = 1 - avgpool_op.attrs["filter_width"] = 1 - avgpool_op.attrs["filter_height"] = 1 - avgpool_op.attrs["strides"] = [1, 1, 1, 1] - avgpool_op.attrs["ksize"] = [1, 1, 1, 1] - avgpool_op.attrs["skirt"] = [0, 0, 0, 0] - avgpool_op.attrs["explicit_padding"] = [0, 0, 0, 0] + + avgpool_op = create_avgpool_nop(op.name + "_avgpool") + avgpool_op.add_input_tensor(inp) avgpool_out = inp.clone("_avgpooled") avgpool_out.consumer_list.append(op) avgpool_op.set_output_tensor(avgpool_out) |