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Diffstat (limited to 'python/pyarmnn/test/test_caffe_parser.py')
-rw-r--r-- | python/pyarmnn/test/test_caffe_parser.py | 131 |
1 files changed, 0 insertions, 131 deletions
diff --git a/python/pyarmnn/test/test_caffe_parser.py b/python/pyarmnn/test/test_caffe_parser.py deleted file mode 100644 index d744b907d4..0000000000 --- a/python/pyarmnn/test/test_caffe_parser.py +++ /dev/null @@ -1,131 +0,0 @@ -# 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 caffe parser - parser = ann.ICaffeParser() - - # Specify path to model - path_to_model = os.path.join(shared_data_folder, 'mock_model.caffemodel') - - # Specify the tensor shape relative to the input [1, 1, 28, 28] - tensor_shape = {'Placeholder': ann.TensorShape((1, 1, 28, 28))} - - # Specify the requested_outputs - requested_outputs = ["output"] - - # Parse caffe binary & create network - parser.CreateNetworkFromBinaryFile(path_to_model, tensor_shape, requested_outputs) - - yield parser - - -def test_caffe_parser_swig_destroy(): - assert ann.ICaffeParser.__swig_destroy__, "There is a swig python destructor defined" - assert ann.ICaffeParser.__swig_destroy__.__name__ == "delete_ICaffeParser" - - -def test_check_caffe_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_get_network_input_binding_info(parser): - input_binding_info = parser.GetNetworkInputBindingInfo("Placeholder") - - tensor = input_binding_info[1] - assert tensor.GetDataType() == 1 - assert tensor.GetNumDimensions() == 4 - assert tensor.GetNumElements() == 784 - - -def test_get_network_output_binding_info(parser): - output_binding_info1 = parser.GetNetworkOutputBindingInfo("output") - - # Check the tensor info retrieved from GetNetworkOutputBindingInfo - tensor1 = output_binding_info1[1] - - assert tensor1.GetDataType() == 1 - assert tensor1.GetNumDimensions() == 2 - assert tensor1.GetNumElements() == 10 - - -def test_filenotfound_exception(shared_data_folder): - parser = ann.ICaffeParser() - - # path to model - path_to_model = os.path.join(shared_data_folder, 'some_unknown_network.caffemodel') - - # generic tensor shape [1, 1, 1, 1] - tensor_shape = {'data': ann.TensorShape((1, 1, 1, 1))} - - # requested_outputs - requested_outputs = [""] - - with pytest.raises(RuntimeError) as err: - parser.CreateNetworkFromBinaryFile(path_to_model, tensor_shape, 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 graph file' in str(err.value) - - -def test_caffe_parser_end_to_end(shared_data_folder): - parser = ann.ICaffeParser = ann.ICaffeParser() - - # Load the network specifying the inputs and outputs - input_name = "Placeholder" - tensor_shape = {input_name: ann.TensorShape((1, 1, 28, 28))} - requested_outputs = ["output"] - - network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.caffemodel'), - tensor_shape, requested_outputs) - - # Specify preferred backend - preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')] - - input_binding_info = parser.GetNetworkInputBindingInfo(input_name) - - 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_caffe.npy - input_tensor_data = np.load(os.path.join(shared_data_folder, 'caffe_parser/input_caffe.npy')).astype(np.float32) - input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data]) - - # Load output binding info and - 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. - expected_output = np.load(os.path.join(shared_data_folder, 'caffe_parser/golden_output_caffe.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], expected_output, 4) |