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# Copyright © 2019 Arm Ltd. All rights reserved.
# SPDX-License-Identifier: MIT
import os
import pytest
import pyarmnn as ann
import numpy as np
@pytest.fixture(scope="function")
def get_tensor_info_input(shared_data_folder):
"""
Sample input tensor information.
"""
parser = ann.ITfLiteParser()
parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'ssd_mobilenetv1.tflite'))
graph_id = 0
input_binding_info = [parser.GetNetworkInputBindingInfo(graph_id, 'normalized_input_image_tensor')]
yield input_binding_info
@pytest.fixture(scope="function")
def get_tensor_info_output(shared_data_folder):
"""
Sample output tensor information.
"""
parser = ann.ITfLiteParser()
parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'ssd_mobilenetv1.tflite'))
graph_id = 0
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
outputs_binding_info = []
for output_name in output_names:
outputs_binding_info.append(parser.GetNetworkOutputBindingInfo(graph_id, output_name))
yield outputs_binding_info
def test_make_input_tensors(get_tensor_info_input):
input_tensor_info = get_tensor_info_input
input_data = []
for tensor_id, tensor_info in input_tensor_info:
input_data.append(np.random.randint(0, 255, size=(1, tensor_info.GetNumElements())).astype(np.uint8))
input_tensors = ann.make_input_tensors(input_tensor_info, input_data)
assert len(input_tensors) == 1
for tensor, tensor_info in zip(input_tensors, input_tensor_info):
# Because we created ConstTensor function, we cannot check type directly.
assert type(tensor[1]).__name__ == 'ConstTensor'
assert str(tensor[1].GetInfo()) == str(tensor_info[1])
def test_make_output_tensors(get_tensor_info_output):
output_binding_info = get_tensor_info_output
output_tensors = ann.make_output_tensors(output_binding_info)
assert len(output_tensors) == 4
for tensor, tensor_info in zip(output_tensors, output_binding_info):
assert type(tensor[1]) == ann.Tensor
assert str(tensor[1].GetInfo()) == str(tensor_info[1])
def test_workload_tensors_to_ndarray(get_tensor_info_output):
output_binding_info = get_tensor_info_output
output_tensors = ann.make_output_tensors(output_binding_info)
data = ann.workload_tensors_to_ndarray(output_tensors)
for i in range(0, len(output_tensors)):
assert len(data[i]) == output_tensors[i][1].GetNumElements()
def test_make_input_tensors_fp16(get_tensor_info_input):
# Check ConstTensor with float16
input_tensor_info = get_tensor_info_input
input_data = []
for tensor_id, tensor_info in input_tensor_info:
input_data.append(np.random.randint(0, 255, size=(1, tensor_info.GetNumElements())).astype(np.float16))
tensor_info.SetDataType(ann.DataType_Float16) # set datatype to float16
input_tensors = ann.make_input_tensors(input_tensor_info, input_data)
assert len(input_tensors) == 1
for tensor, tensor_info in zip(input_tensors, input_tensor_info):
# Because we created ConstTensor function, we cannot check type directly.
assert type(tensor[1]).__name__ == 'ConstTensor'
assert str(tensor[1].GetInfo()) == str(tensor_info[1])
assert tensor[1].GetDataType() == ann.DataType_Float16
assert tensor[1].GetNumElements() == 270000
assert tensor[1].GetNumBytes() == 540000 # check each element is two byte
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