MLBEDSW-4022: support PAD followed by pool operator
PAD followed by max/average pool is run on NPU if NPU
padding can be used. Average pool is converted to depthwise.
Change-Id: Icc3652e6d9ecff5ac3dc7d92080313d90c245404
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index 40b8cd5..285b3ac 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -1,4 +1,4 @@
-# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
+# Copyright (C) 2020-2021 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
@@ -157,6 +157,53 @@
assert pad_op not in op.ifm.ops
+def test_optimise_pad_followed_by_avg_pool():
+ """
+ Tests that the PAD operator is bypassed when followed by a average pool operator,
+ and that the average pool is converted to a depthwise
+ """
+ # Create Pad operation followed by AvgPool
+ quant = testutil.default_quant_params()
+ in_tens = Tensor([1, 76, 75, 64], DataType.uint8, "input")
+ in_tens.quantization = quant
+ pad_input = create_const_tensor("pad_input", [4, 2], DataType.int32, [[0, 0], [2, 1], [1, 1], [0, 0]])
+ temp_tens = Tensor([1, 79, 77, 64], DataType.uint8, "pad_out")
+ temp_tens.quantization = quant.clone()
+ out_tens = Tensor([1, 76, 75, 64], DataType.uint8, "output")
+ out_tens.quantization = quant.clone()
+
+ pad_op = testutil.create_op(Op.Pad, [in_tens, pad_input], temp_tens)
+ attrs = {
+ "padding": Padding.VALID,
+ "ksize": [1, 5, 3, 1],
+ "stride_w": 2,
+ "stride_h": 2,
+ "dilation_w_factor": 1,
+ "dilation_h_factor": 1,
+ }
+ attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1)
+ pad_op.run_on_npu = True
+ conv2d_op = testutil.create_op(Op.AvgPool, [temp_tens], out_tens, attrs)
+ conv2d_op.run_on_npu = True
+ nng = Graph()
+ sg = testutil.create_subgraph([pad_op, conv2d_op])
+ nng.subgraphs.append(sg)
+ arch = testutil.create_arch()
+
+ optimise_pad(conv2d_op, nng, arch)
+
+ op = sg.output_tensors[0].ops[0]
+ assert op.type == Op.DepthwiseConv2DBias
+ assert op.attrs["padding"] == Padding.EXPLICIT
+ assert op.attrs["explicit_padding"] == (2, 1, 1, 1)
+ assert op.ifm.shape == [1, 76, 75, 64]
+ assert pad_op not in op.ifm.ops
+ # Check that bias and weight tensors have been added
+ assert op.bias.shape == [64]
+ print("op.weights:", op.weights)
+ assert op.weights.shape == [5, 3, 1, 64]
+
+
def test_remove_reshape():
"""
Tests that the expected reshape are removed in graph_optimisation