132 lines
4.5 KiB
Python
132 lines
4.5 KiB
Python
# Copyright © 2020 Arm Ltd. All rights reserved.
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# SPDX-License-Identifier: MIT
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import os
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import pytest
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import pyarmnn as ann
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import numpy as np
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@pytest.fixture()
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def parser(shared_data_folder):
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"""
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Parse and setup the test network to be used for the tests below
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"""
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# Create caffe parser
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parser = ann.ICaffeParser()
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# Specify path to model
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path_to_model = os.path.join(shared_data_folder, 'mock_model.caffemodel')
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# Specify the tensor shape relative to the input [1, 1, 28, 28]
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tensor_shape = {'Placeholder': ann.TensorShape((1, 1, 28, 28))}
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# Specify the requested_outputs
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requested_outputs = ["output"]
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# Parse caffe binary & create network
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parser.CreateNetworkFromBinaryFile(path_to_model, tensor_shape, requested_outputs)
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yield parser
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def test_caffe_parser_swig_destroy():
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assert ann.ICaffeParser.__swig_destroy__, "There is a swig python destructor defined"
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assert ann.ICaffeParser.__swig_destroy__.__name__ == "delete_ICaffeParser"
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def test_check_caffe_parser_swig_ownership(parser):
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# Check to see that SWIG has ownership for parser. This instructs SWIG to take
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# ownership of the return value. This allows the value to be automatically
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# garbage-collected when it is no longer in use
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assert parser.thisown
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def test_get_network_input_binding_info(parser):
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input_binding_info = parser.GetNetworkInputBindingInfo("Placeholder")
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tensor = input_binding_info[1]
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assert tensor.GetDataType() == 1
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assert tensor.GetNumDimensions() == 4
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assert tensor.GetNumElements() == 784
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def test_get_network_output_binding_info(parser):
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output_binding_info1 = parser.GetNetworkOutputBindingInfo("output")
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# Check the tensor info retrieved from GetNetworkOutputBindingInfo
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tensor1 = output_binding_info1[1]
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assert tensor1.GetDataType() == 1
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assert tensor1.GetNumDimensions() == 2
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assert tensor1.GetNumElements() == 10
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def test_filenotfound_exception(shared_data_folder):
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parser = ann.ICaffeParser()
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# path to model
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path_to_model = os.path.join(shared_data_folder, 'some_unknown_network.caffemodel')
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# generic tensor shape [1, 1, 1, 1]
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tensor_shape = {'data': ann.TensorShape((1, 1, 1, 1))}
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# requested_outputs
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requested_outputs = [""]
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with pytest.raises(RuntimeError) as err:
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parser.CreateNetworkFromBinaryFile(path_to_model, tensor_shape, requested_outputs)
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# Only check for part of the exception since the exception returns
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# absolute path which will change on different machines.
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assert 'Failed to open graph file' in str(err.value)
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def test_caffe_parser_end_to_end(shared_data_folder):
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parser = ann.ICaffeParser = ann.ICaffeParser()
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# Load the network specifying the inputs and outputs
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input_name = "Placeholder"
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tensor_shape = {input_name: ann.TensorShape((1, 1, 28, 28))}
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requested_outputs = ["output"]
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network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.caffemodel'),
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tensor_shape, requested_outputs)
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# Specify preferred backend
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preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
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input_binding_info = parser.GetNetworkInputBindingInfo(input_name)
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options = ann.CreationOptions()
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runtime = ann.IRuntime(options)
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opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
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assert 0 == len(messages)
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net_id, messages = runtime.LoadNetwork(opt_network)
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assert "" == messages
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# Load test image data stored in input_caffe.npy
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input_tensor_data = np.load(os.path.join(shared_data_folder, 'caffe_parser/input_caffe.npy')).astype(np.float32)
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input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
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# Load output binding info and
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outputs_binding_info = []
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for output_name in requested_outputs:
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outputs_binding_info.append(parser.GetNetworkOutputBindingInfo(output_name))
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output_tensors = ann.make_output_tensors(outputs_binding_info)
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runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
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output_vectors = ann.workload_tensors_to_ndarray(output_tensors)
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# Load golden output file for result comparison.
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expected_output = np.load(os.path.join(shared_data_folder, 'caffe_parser/golden_output_caffe.npy'))
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# Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
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np.testing.assert_almost_equal(output_vectors[0], expected_output, 4)
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