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# Copyright © 2020 Arm Ltd and Contributors. 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
"""
parser = ann.IDeserializer()
parser.CreateNetworkFromBinary(os.path.join(shared_data_folder, 'mock_model.armnn'))
yield parser
def test_deserializer_swig_destroy():
assert ann.IDeserializer.__swig_destroy__, "There is a swig python destructor defined"
assert ann.IDeserializer.__swig_destroy__.__name__ == "delete_IDeserializer"
def test_check_deserializer_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_deserializer_get_network_input_binding_info(parser):
# use 0 as a dummy value for layer_id, which is unused in the actual implementation
layer_id = 0
input_name = 'input_1'
input_binding_info = parser.GetNetworkInputBindingInfo(layer_id, input_name)
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_deserializer_get_network_output_binding_info(parser):
# use 0 as a dummy value for layer_id, which is unused in the actual implementation
layer_id = 0
output_name = "dense/Softmax"
output_binding_info1 = parser.GetNetworkOutputBindingInfo(layer_id, output_name)
# 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_deserializer_filenotfound_exception(shared_data_folder):
parser = ann.IDeserializer()
with pytest.raises(RuntimeError) as err:
parser.CreateNetworkFromBinary(os.path.join(shared_data_folder, 'some_unknown_network.armnn'))
# Only check for part of the exception since the exception returns
# absolute path which will change on different machines.
assert 'Cannot read the file' in str(err.value)
def test_deserializer_end_to_end(shared_data_folder):
parser = ann.IDeserializer()
network = parser.CreateNetworkFromBinary(os.path.join(shared_data_folder, "mock_model.armnn"))
# use 0 as a dummy value for layer_id, which is unused in the actual implementation
layer_id = 0
input_name = 'input_1'
output_name = 'dense/Softmax'
input_binding_info = parser.GetNetworkInputBindingInfo(layer_id, input_name)
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, 'deserializer/input_lite.npy'))
input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
output_tensors = []
out_bind_info = parser.GetNetworkOutputBindingInfo(layer_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, 'deserializer/golden_output_lite.npy'))
# Check that output matches golden output
assert (expected_outputs == output_vectors[0]).all()
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