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)