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# Copyright © 2020 Arm Ltd. All rights reserved.
# SPDX-License-Identifier: MIT
import os
import pytest
import pyarmnn as ann
import numpy as np
def test_TfLiteParserOptions_default_values():
parserOptions = ann.TfLiteParserOptions()
assert parserOptions.m_InferAndValidate == False
assert parserOptions.m_StandInLayerForUnsupported == False
@pytest.fixture()
def parser(shared_data_folder):
"""
Parse and setup the test network to be used for the tests below
"""
parser = ann.ITfLiteParser()
parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.tflite'))
yield parser
def test_tflite_parser_swig_destroy():
assert ann.ITfLiteParser.__swig_destroy__, "There is a swig python destructor defined"
assert ann.ITfLiteParser.__swig_destroy__.__name__ == "delete_ITfLiteParser"
def test_check_tflite_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_tflite_parser_with_optional_options():
parserOptions = ann.TfLiteParserOptions()
parserOptions.m_InferAndValidate = True
parser = ann.ITfLiteParser(parserOptions)
assert parser.thisown
def create_with_opt() :
parserOptions = ann.TfLiteParserOptions()
parserOptions.m_InferAndValidate = True
return ann.ITfLiteParser(parserOptions)
def test_tflite_parser_with_optional_options_out_of_scope(shared_data_folder):
parser = create_with_opt()
network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, "mock_model.tflite"))
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
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
def test_tflite_get_sub_graph_count(parser):
graphs_count = parser.GetSubgraphCount()
assert graphs_count == 1
def test_tflite_get_network_input_binding_info(parser):
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
tensor = input_binding_info[1]
assert tensor.GetDataType() == 2
assert tensor.GetNumDimensions() == 4
assert tensor.GetNumElements() == 784
assert tensor.GetQuantizationOffset() == 128
assert tensor.GetQuantizationScale() == 0.007843137718737125
def test_tflite_get_network_output_binding_info(parser):
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
output_binding_info1 = parser.GetNetworkOutputBindingInfo(graph_id, output_names[0])
# Check the tensor info retrieved from GetNetworkOutputBindingInfo
tensor1 = output_binding_info1[1]
assert tensor1.GetDataType() == 2
assert tensor1.GetNumDimensions() == 2
assert tensor1.GetNumElements() == 10
assert tensor1.GetQuantizationOffset() == 0
assert tensor1.GetQuantizationScale() == 0.00390625
def test_tflite_get_subgraph_input_tensor_names(parser):
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
assert input_names == ('input_1',)
def test_tflite_get_subgraph_output_tensor_names(parser):
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
assert output_names[0] == 'dense/Softmax'
def test_tflite_filenotfound_exception(shared_data_folder):
parser = ann.ITfLiteParser()
with pytest.raises(RuntimeError) as err:
parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'some_unknown_network.tflite'))
# Only check for part of the exception since the exception returns
# absolute path which will change on different machines.
assert 'Cannot find the file' in str(err.value)
def test_tflite_parser_end_to_end(shared_data_folder):
parser = ann.ITfLiteParser()
network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, "mock_model.tflite"))
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
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
# Load test image data stored in input_lite.npy
input_tensor_data = np.load(os.path.join(shared_data_folder, 'tflite_parser/input_lite.npy'))
input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
output_tensors = []
for index, output_name in enumerate(output_names):
out_bind_info = parser.GetNetworkOutputBindingInfo(graph_id, output_name)
out_tensor_info = out_bind_info[1]
out_tensor_id = out_bind_info[0]
output_tensors.append((out_tensor_id,
ann.Tensor(out_tensor_info)))
runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
output_vectors = []
for index, out_tensor in enumerate(output_tensors):
output_vectors.append(out_tensor[1].get_memory_area())
# Load golden output file for result comparison.
expected_outputs = np.load(os.path.join(shared_data_folder, 'tflite_parser/golden_output_lite.npy'))
# Check that output matches golden output
assert (expected_outputs == output_vectors[0]).all()
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