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_tf_parser.py b/python/pyarmnn/test/test_tf_parser.py
new file mode 100644
index 0000000..796dd71
--- /dev/null
+++ b/python/pyarmnn/test/test_tf_parser.py
@@ -0,0 +1,133 @@
+# Copyright © 2020 Arm Ltd. All rights reserved.
+# SPDX-License-Identifier: MIT
+import os
+
+import pytest
+import pyarmnn as ann
+import numpy as np
+
+
+@pytest.fixture()
+def parser(shared_data_folder):
+ """
+ Parse and setup the test network to be used for the tests below
+ """
+
+ # create tf parser
+ parser = ann.ITfParser()
+
+ # path to model
+ path_to_model = os.path.join(shared_data_folder, 'mock_model.pb')
+
+ # tensor shape [1, 28, 28, 1]
+ tensorshape = {'input': ann.TensorShape((1, 28, 28, 1))}
+
+ # requested_outputs
+ requested_outputs = ["output"]
+
+ # parse tf binary & create network
+ parser.CreateNetworkFromBinaryFile(path_to_model, tensorshape, requested_outputs)
+
+ yield parser
+
+
+def test_tf_parser_swig_destroy():
+ assert ann.ITfParser.__swig_destroy__, "There is a swig python destructor defined"
+ assert ann.ITfParser.__swig_destroy__.__name__ == "delete_ITfParser"
+
+
+def test_check_tf_parser_swig_ownership(parser):
+ # Check to see that SWIG has ownership for parser. This instructs SWIG to take
+ # ownership of the return value. This allows the value to be automatically
+ # garbage-collected when it is no longer in use
+ assert parser.thisown
+
+
+def test_tf_parser_get_network_input_binding_info(parser):
+ input_binding_info = parser.GetNetworkInputBindingInfo("input")
+
+ tensor = input_binding_info[1]
+ assert tensor.GetDataType() == 1
+ assert tensor.GetNumDimensions() == 4
+ assert tensor.GetNumElements() == 28*28*1
+ assert tensor.GetQuantizationOffset() == 0
+ assert tensor.GetQuantizationScale() == 0
+
+
+def test_tf_parser_get_network_output_binding_info(parser):
+ output_binding_info = parser.GetNetworkOutputBindingInfo("output")
+
+ tensor = output_binding_info[1]
+ assert tensor.GetDataType() == 1
+ assert tensor.GetNumDimensions() == 2
+ assert tensor.GetNumElements() == 10
+ assert tensor.GetQuantizationOffset() == 0
+ assert tensor.GetQuantizationScale() == 0
+
+
+def test_tf_filenotfound_exception(shared_data_folder):
+ parser = ann.ITfParser()
+
+ # path to model
+ path_to_model = os.path.join(shared_data_folder, 'some_unknown_model.pb')
+
+ # tensor shape [1, 1, 1, 1]
+ tensorshape = {'input': ann.TensorShape((1, 1, 1, 1))}
+
+ # requested_outputs
+ requested_outputs = [""]
+
+ # parse tf binary & create network
+
+ with pytest.raises(RuntimeError) as err:
+ parser.CreateNetworkFromBinaryFile(path_to_model, tensorshape, requested_outputs)
+
+ # Only check for part of the exception since the exception returns
+ # absolute path which will change on different machines.
+ assert 'failed to open' in str(err.value)
+
+
+def test_tf_parser_end_to_end(shared_data_folder):
+ parser = ann.ITfParser = ann.ITfParser()
+
+ tensorshape = {'input': ann.TensorShape((1, 28, 28, 1))}
+ requested_outputs = ["output"]
+
+ network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.pb'),
+ tensorshape, requested_outputs)
+
+ input_binding_info = parser.GetNetworkInputBindingInfo("input")
+
+ # load test image data stored in input_tf.npy
+ input_tensor_data = np.load(os.path.join(shared_data_folder, 'tf_parser/input_tf.npy')).astype(np.float32)
+
+ preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
+
+ options = ann.CreationOptions()
+ runtime = ann.IRuntime(options)
+
+ opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+
+ assert 0 == len(messages)
+
+ net_id, messages = runtime.LoadNetwork(opt_network)
+
+ assert "" == messages
+
+ input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
+
+ outputs_binding_info = []
+
+ for output_name in requested_outputs:
+ outputs_binding_info.append(parser.GetNetworkOutputBindingInfo(output_name))
+
+ output_tensors = ann.make_output_tensors(outputs_binding_info)
+
+ runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+ output_vectors = ann.workload_tensors_to_ndarray(output_tensors)
+
+ # Load golden output file for result comparison.
+ golden_output = np.load(os.path.join(shared_data_folder, 'tf_parser/golden_output_tf.npy'))
+
+ # Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
+ np.testing.assert_almost_equal(output_vectors[0], golden_output, decimal=4)