diff options
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 19 | ||||
| -rw-r--r-- | ethosu/vela/test/test_graph_optimiser.py | 61 |
2 files changed, 16 insertions, 64 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index 7401927..5889905 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -317,7 +317,7 @@ def fixup_fully_connected_input(op, arch, nng): return op -def convert_batched_fc_shape(op, arch, nng): +def convert_batched_fc_to_conv(op, arch, nng): if op.type == Op.FullyConnected: ifm = op.inputs[0] ofm = op.outputs[0] @@ -327,6 +327,19 @@ def convert_batched_fc_shape(op, arch, nng): batching_split = {4: (2, 2), 8: (2, 4), 16: (4, 4)} h, w = batching_split.get(n, (1, n)) + # Convert to convolution + op.name += "_conv" + op.type = Op.Conv2DBias + op.attrs = { + "dilation": (1, 1, 1, 1), + "dilation_h_factor": 1, + "dilation_w_factor": 1, + "padding": b"SAME", + "stride_h": 1, + "stride_w": 1, + "strides": (1, 1, 1, 1), + } + prev_op = ifm.ops[0] desired_shape = [1, h, w, ifm.shape[-1]] if len(ifm.consumer_list) == 1 and prev_op is not None and prev_op.type == Op.Reshape: @@ -367,7 +380,7 @@ def convert_batched_fc_shape(op, arch, nng): else: op.outputs[0].set_all_shapes(desired_shape) else: - # Add reshape op to the output + # Add rehape op to the output op.set_output_tensor(create_reshape_tensor(ofm, desired_shape, False)) return op @@ -1082,7 +1095,7 @@ def optimise_graph_a(nng, arch, verbose_graph=False): convert_conv_to_fc, convert_softmax, fixup_fully_connected_input, - convert_batched_fc_shape, + convert_batched_fc_to_conv, fixup_pack_input, unfuse_activation_function, fixup_conv2d_backprop, diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py deleted file mode 100644 index 62a1b76..0000000 --- a/ethosu/vela/test/test_graph_optimiser.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the License); you may -# not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an AS IS BASIS, WITHOUT -# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Description: -# Unit tests for graph_optimiser -import numpy as np - -from ethosu.vela.graph_optimiser import convert_batched_fc_shape -from ethosu.vela.operation import Op -from ethosu.vela.tensor import create_const_tensor -from ethosu.vela.tensor import Tensor -from ethosu.vela.test import testutil - - -def test_convert_batched_fc(): - """Tests shape conversion of batched fully connected""" - shape = [4, 8] - ifm = create_const_tensor("test_in", shape, np.uint8, np.zeros(shape)) - weights = create_const_tensor("weight_in", shape, np.uint8, np.zeros(shape)) - ofm = Tensor(ifm.shape, np.uint8, "test_out") - op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) - ifm.consumer_list.append(op) - - prev_op = op.clone() - conv_op = convert_batched_fc_shape(op, None, None) - - assert conv_op.ifm != prev_op.ifm - assert conv_op.ofm != prev_op.ofm - assert conv_op.type == Op.FullyConnected - assert len(conv_op.ifm.shape) == 4 - assert conv_op.ifm.shape == conv_op.ofm.shape - assert conv_op.ifm.ops[0].type == Op.Reshape - - shape = [1, 8] - ifm.shape = shape - weights.shape = shape - ofm.shape = shape - op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) - ifm.consumer_list.append(op) - - prev_op = op.clone() - conv_op = convert_batched_fc_shape(op, None, None) - - assert conv_op.ifm == prev_op.ifm - assert conv_op.ofm == prev_op.ofm - assert conv_op.type == Op.FullyConnected - assert len(conv_op.ifm.shape) == 2 - assert conv_op.ifm.shape == conv_op.ofm.shape |