1 files changed, 133 insertions, 0 deletions
diff --git a/python/pyarmnn/test/test_tf_parser.py b/python/pyarmnn/test/test_tf_parser.py
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+++ b/python/pyarmnn/test/test_tf_parser.py
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+# Copyright © 2019 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 (mobilenetv1) 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, 'mobilenet_v1_1.0_224.pb')
+
+    # tensor shape [1, 224, 224, 3]
+    tensorshape = {'input': ann.TensorShape((1, 224, 224, 3))}
+
+    # requested_outputs
+    requested_outputs = ["MobilenetV1/Predictions/Reshape_1"]
+
+    # 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() == 150528
+    assert tensor.GetQuantizationOffset() == 0
+    assert tensor.GetQuantizationScale() == 0
+
+
+def test_tf_parser_get_network_output_binding_info(parser):
+    output_binding_info = parser.GetNetworkOutputBindingInfo("MobilenetV1/Predictions/Reshape_1")
+
+    tensor = output_binding_info[1]
+    assert tensor.GetDataType() == 1
+    assert tensor.GetNumDimensions() == 2
+    assert tensor.GetNumElements() == 1001
+    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, 224, 224, 3))}
+    requested_outputs = ["MobilenetV1/Predictions/Reshape_1"]
+
+    network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mobilenet_v1_1.0_224.pb'),
+                                                 tensorshape, requested_outputs)
+
+    input_binding_info = parser.GetNetworkInputBindingInfo("input")
+
+    # load test image data stored in input.npy
+    input_tensor_data = np.load(os.path.join(shared_data_folder, 'tf_parser/input.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 to compare the output results with
+    golden_output = np.load(os.path.join(shared_data_folder, 'tf_parser/golden_output.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, golden_output, decimal=4)