Add PyArmNN to work with ArmNN API of 20.02
* Add Swig rules for generating python wrapper
* Add documentation
* Add tests and testing data
Change-Id: If48eda08931514fa21e72214dfead2835f07237c
Signed-off-by: Richard Burton <richard.burton@arm.com>
Signed-off-by: Derek Lamberti <derek.lamberti@arm.com>
diff --git a/python/pyarmnn/test/test_network.py b/python/pyarmnn/test/test_network.py
new file mode 100644
index 0000000..fc2591c
--- /dev/null
+++ b/python/pyarmnn/test/test_network.py
@@ -0,0 +1,288 @@
+# Copyright © 2020 Arm Ltd. All rights reserved.
+# SPDX-License-Identifier: MIT
+import os
+import stat
+
+import pytest
+import pyarmnn as ann
+
+
+@pytest.fixture(scope="function")
+def get_runtime(shared_data_folder, network_file):
+ parser= ann.ITfLiteParser()
+ preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
+ network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, network_file))
+ options = ann.CreationOptions()
+ runtime = ann.IRuntime(options)
+
+ yield preferred_backends, network, runtime
+
+
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite',
+ ],
+ ids=['mock_model'])
+def test_optimize_executes_successfully(network_file, get_runtime):
+ preferred_backends = [ann.BackendId('CpuRef')]
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+
+ opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+
+ assert len(messages) == 0, 'With only CpuRef, there should be no warnings irrelevant of architecture.'
+ assert opt_network
+
+
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite',
+ ],
+ ids=['mock_model'])
+def test_optimize_owned_by_python(network_file, get_runtime):
+ preferred_backends = get_runtime[0]
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+
+ opt_network, _ = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+ assert opt_network.thisown
+
+
+@pytest.mark.aarch64
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite'
+ ],
+ ids=['mock_model'])
+def test_optimize_executes_successfully_for_neon_backend_only(network_file, get_runtime):
+ preferred_backends = [ann.BackendId('CpuAcc')]
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+
+ opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+ assert 0 == len(messages)
+ assert opt_network
+
+
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite'
+ ],
+ ids=['mock_model'])
+def test_optimize_fails_for_invalid_backends(network_file, get_runtime):
+ invalid_backends = [ann.BackendId('Unknown')]
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+
+ with pytest.raises(RuntimeError) as err:
+ ann.Optimize(network, invalid_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+
+ expected_error_message = "None of the preferred backends [Unknown ] are supported."
+ assert expected_error_message in str(err.value)
+
+
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite'
+ ],
+ ids=['mock_model'])
+def test_optimize_fails_for_no_backends_specified(network_file, get_runtime):
+ empty_backends = []
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+
+ with pytest.raises(RuntimeError) as err:
+ ann.Optimize(network, empty_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+
+ expected_error_message = "Invoked Optimize with no backends specified"
+ assert expected_error_message in str(err.value)
+
+
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite'
+ ],
+ ids=['mock_model'])
+def test_serialize_to_dot(network_file, get_runtime, tmpdir):
+ preferred_backends = get_runtime[0]
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+ opt_network, _ = ann.Optimize(network, preferred_backends,
+ runtime.GetDeviceSpec(), ann.OptimizerOptions())
+ dot_file_path = os.path.join(tmpdir, 'mock_model.dot')
+ """Check that serialized file does not exist at the start, gets created after SerializeToDot and is not empty"""
+ assert not os.path.exists(dot_file_path)
+ opt_network.SerializeToDot(dot_file_path)
+
+ assert os.path.exists(dot_file_path)
+
+ with open(dot_file_path) as res_file:
+ expected_data = res_file.read()
+ assert len(expected_data) > 1
+ assert '[label=< [1,28,28,1] >]' in expected_data
+
+
+@pytest.mark.x86_64
+@pytest.mark.parametrize("network_file",
+ [
+ 'mock_model.tflite'
+ ],
+ ids=['mock_model'])
+def test_serialize_to_dot_mode_readonly(network_file, get_runtime, tmpdir):
+ preferred_backends = get_runtime[0]
+ network = get_runtime[1]
+ runtime = get_runtime[2]
+ opt_network, _ = ann.Optimize(network, preferred_backends,
+ runtime.GetDeviceSpec(), ann.OptimizerOptions())
+ """Create file, write to it and change mode to read-only"""
+ dot_file_path = os.path.join(tmpdir, 'mock_model.dot')
+ f = open(dot_file_path, "w+")
+ f.write("test")
+ f.close()
+ os.chmod(dot_file_path, stat.S_IREAD)
+ assert os.path.exists(dot_file_path)
+
+ with pytest.raises(RuntimeError) as err:
+ opt_network.SerializeToDot(dot_file_path)
+
+ expected_error_message = "Failed to open dot file"
+ assert expected_error_message in str(err.value)
+
+
+@pytest.mark.parametrize("method", [
+ 'AddActivationLayer',
+ 'AddAdditionLayer',
+ 'AddArgMinMaxLayer',
+ 'AddBatchNormalizationLayer',
+ 'AddBatchToSpaceNdLayer',
+ 'AddComparisonLayer',
+ 'AddConcatLayer',
+ 'AddConstantLayer',
+ 'AddConvolution2dLayer',
+ 'AddDepthToSpaceLayer',
+ 'AddDepthwiseConvolution2dLayer',
+ 'AddDequantizeLayer',
+ 'AddDetectionPostProcessLayer',
+ 'AddDivisionLayer',
+ 'AddElementwiseUnaryLayer',
+ 'AddFloorLayer',
+ 'AddFullyConnectedLayer',
+ 'AddGatherLayer',
+ 'AddInputLayer',
+ 'AddInstanceNormalizationLayer',
+ 'AddLogSoftmaxLayer',
+ 'AddL2NormalizationLayer',
+ 'AddLstmLayer',
+ 'AddMaximumLayer',
+ 'AddMeanLayer',
+ 'AddMergeLayer',
+ 'AddMinimumLayer',
+ 'AddMultiplicationLayer',
+ 'AddNormalizationLayer',
+ 'AddOutputLayer',
+ 'AddPadLayer',
+ 'AddPermuteLayer',
+ 'AddPooling2dLayer',
+ 'AddPreluLayer',
+ 'AddQuantizeLayer',
+ 'AddQuantizedLstmLayer',
+ 'AddReshapeLayer',
+ 'AddResizeLayer',
+ 'AddSliceLayer',
+ 'AddSoftmaxLayer',
+ 'AddSpaceToBatchNdLayer',
+ 'AddSpaceToDepthLayer',
+ 'AddSplitterLayer',
+ 'AddStackLayer',
+ 'AddStandInLayer',
+ 'AddStridedSliceLayer',
+ 'AddSubtractionLayer',
+ 'AddSwitchLayer',
+ 'AddTransposeConvolution2dLayer'
+])
+def test_network_method_exists(method):
+ assert getattr(ann.INetwork, method, None)
+
+
+def test_fullyconnected_layer_optional_none():
+ net = ann.INetwork()
+ layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(),
+ weights=ann.ConstTensor())
+
+ assert layer
+
+
+def test_fullyconnected_layer_optional_provided():
+ net = ann.INetwork()
+ layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(),
+ weights=ann.ConstTensor(),
+ biases=ann.ConstTensor())
+
+ assert layer
+
+
+def test_fullyconnected_layer_all_args():
+ net = ann.INetwork()
+ layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(),
+ weights=ann.ConstTensor(),
+ biases=ann.ConstTensor(),
+ name='NAME1')
+
+ assert layer
+ assert 'NAME1' == layer.GetName()
+
+
+def test_DepthwiseConvolution2d_layer_optional_none():
+ net = ann.INetwork()
+ layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(),
+ weights=ann.ConstTensor())
+
+ assert layer
+
+
+def test_DepthwiseConvolution2d_layer_optional_provided():
+ net = ann.INetwork()
+ layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(),
+ weights=ann.ConstTensor(),
+ biases=ann.ConstTensor())
+
+ assert layer
+
+
+def test_DepthwiseConvolution2d_layer_all_args():
+ net = ann.INetwork()
+ layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(),
+ weights=ann.ConstTensor(),
+ biases=ann.ConstTensor(),
+ name='NAME1')
+
+ assert layer
+ assert 'NAME1' == layer.GetName()
+
+
+def test_Convolution2d_layer_optional_none():
+ net = ann.INetwork()
+ layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(),
+ weights=ann.ConstTensor())
+
+ assert layer
+
+
+def test_Convolution2d_layer_optional_provided():
+ net = ann.INetwork()
+ layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(),
+ weights=ann.ConstTensor(),
+ biases=ann.ConstTensor())
+
+ assert layer
+
+
+def test_Convolution2d_layer_all_args():
+ net = ann.INetwork()
+ layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(),
+ weights=ann.ConstTensor(),
+ biases=ann.ConstTensor(),
+ name='NAME1')
+
+ assert layer
+ assert 'NAME1' == layer.GetName()