From dc0c6ed9f8b993e63f492f203d7d7080ab4c835c Mon Sep 17 00:00:00 2001 From: Richard Burton Date: Wed, 8 Apr 2020 16:39:05 +0100 Subject: 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 Signed-off-by: Derek Lamberti --- python/pyarmnn/test/test_tf_parser.py | 133 ++++++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 python/pyarmnn/test/test_tf_parser.py (limited to 'python/pyarmnn/test/test_tf_parser.py') diff --git a/python/pyarmnn/test/test_tf_parser.py b/python/pyarmnn/test/test_tf_parser.py new file mode 100644 index 0000000000..796dd71e7b --- /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) -- cgit v1.2.1