def test_create_tensor_from_tensor(dt):
tensor_info = __get_tensor_info(dt)
tensor = ann.Tensor(tensor_info)
copied_tensor = ann.Tensor(tensor)
assert copied_tensor != tensor, "Different objects"
assert copied_tensor.GetInfo() != tensor.GetInfo(), "Different objects"
assert copied_tensor.get_memory_area(
).ctypes.data == tensor.get_memory_area().ctypes.data, "Same memory area"
assert copied_tensor.GetNumElements() == tensor.GetNumElements()
assert copied_tensor.GetNumBytes() == tensor.GetNumBytes()
assert copied_tensor.GetDataType() == tensor.GetDataType()
def test_const_tensor_from_tensor_has_memory_area_access_after_deletion_of_original_tensor(
):
tensor_info = ann.TensorInfo(ann.TensorShape((2, 3)), ann.DataType_Float32)
tensor = ann.Tensor(tensor_info)
tensor.get_memory_area()[0] = 100
copied_mem = tensor.get_memory_area().copy()
assert 100 == copied_mem[0], "Memory was copied correctly"
copied_tensor = ann.ConstTensor(tensor)
tensor.get_memory_area()[0] = 200
assert 200 == tensor.get_memory_area(
)[0], "Tensor and copied Tensor point to the same memory"
assert 200 == copied_tensor.get_memory_area(
)[0], "Tensor and copied Tensor point to the same memory"
assert 100 == copied_mem[0], "Copied test memory not affected"
copied_mem[0] = 200 # modify test memory to equal copied Tensor
del tensor
np.testing.assert_array_equal(copied_tensor.get_memory_area(), copied_mem), "After initial tensor was deleted, " \
"copied Tensor still has " \
"its memory as expected"
def test_tensor_memory_output(dt):
tensor_info = __get_tensor_info(dt)
tensor = ann.Tensor(tensor_info)
# empty memory area because inference has not yet been run.
assert tensor.get_memory_area().tolist() # has random stuff
assert 4 == tensor.get_memory_area().itemsize, "it is float32"
def test_tensor_memory_output_fp16(dt):
# Check Tensor with float16
tensor_info = __get_tensor_info(dt)
tensor = ann.Tensor(tensor_info)
assert tensor.GetNumElements() == 6
assert tensor.GetNumBytes() == 12
assert tensor.GetDataType() == ann.DataType_Float16
def test_copied_tensor_has_memory_area_access_after_deletion_of_original_tensor(
dt):
tensor = ann.Tensor(__get_tensor_info(dt))
tensor.get_memory_area()[0] = 100
initial_mem_copy = np.array(tensor.get_memory_area())
assert 100 == initial_mem_copy[0]
copied_tensor = ann.Tensor(tensor)
del tensor
np.testing.assert_array_equal(copied_tensor.get_memory_area(),
initial_mem_copy)
assert 100 == copied_tensor.get_memory_area()[0]
def test_create_tensor_undefined_datatype():
tensor_info = ann.TensorInfo()
tensor_info.SetDataType(99)
with pytest.raises(ValueError) as err:
ann.Tensor(tensor_info)
assert 'The data type provided for this Tensor is not supported.' in str(
err.value)
def test_tflite_parser_end_to_end(shared_data_folder):
parser = ann.ITfLiteParser()
network = parser.CreateNetworkFromBinaryFile(
os.path.join(shared_data_folder, "mock_model.tflite"))
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
input_binding_info = parser.GetNetworkInputBindingInfo(
graph_id, input_names[0])
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
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, 'tflite_parser/input_lite.npy'))
input_tensors = ann.make_input_tensors([input_binding_info],
[input_tensor_data])
output_tensors = []
for index, output_name in enumerate(output_names):
out_bind_info = parser.GetNetworkOutputBindingInfo(
graph_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,
'tflite_parser/golden_output_lite.npy'))
# Check that output matches golden output
assert (expected_outputs == output_vectors[0]).all()
def test_tensor__str__(dt):
tensor_info = __get_tensor_info(dt)
elements = tensor_info.GetNumElements()
num_bytes = tensor_info.GetNumBytes()
d_type = dt
dimensions = tensor_info.GetNumDimensions()
tensor = ann.Tensor(tensor_info)
assert str(tensor) == "Tensor{{DataType: {}, NumBytes: {}, NumDimensions: " \
"{}, NumElements: {}}}".format(d_type, num_bytes, dimensions, elements)
def test_create_const_tensor_from_tensor():
tensor_info = ann.TensorInfo(ann.TensorShape((2, 3)), ann.DataType_Float32)
tensor = ann.Tensor(tensor_info)
copied_tensor = ann.ConstTensor(tensor)
assert copied_tensor != tensor, "Different objects"
assert copied_tensor.GetInfo() != tensor.GetInfo(), "Different objects"
assert copied_tensor.get_memory_area(
).ctypes.data == tensor.get_memory_area().ctypes.data, "Same memory area"
assert copied_tensor.GetNumElements() == tensor.GetNumElements()
assert copied_tensor.GetNumBytes() == tensor.GetNumBytes()
assert copied_tensor.GetDataType() == tensor.GetDataType()
def test_create_tensor_with_info(dt):
tensor_info = __get_tensor_info(dt)
elements = tensor_info.GetNumElements()
num_bytes = tensor_info.GetNumBytes()
d_type = dt
tensor = ann.Tensor(tensor_info)
assert tensor_info != tensor.GetInfo(), "Different objects"
assert elements == tensor.GetNumElements()
assert num_bytes == tensor.GetNumBytes()
assert d_type == tensor.GetDataType()
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()
def random_runtime(shared_data_folder):
parser = ann.ITfLiteParser()
network = parser.CreateNetworkFromBinaryFile(
os.path.join(shared_data_folder, 'mock_model.tflite'))
preferred_backends = [ann.BackendId('CpuRef')]
options = ann.CreationOptions()
runtime = ann.IRuntime(options)
graphs_count = parser.GetSubgraphCount()
graph_id = graphs_count - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
input_binding_info = parser.GetNetworkInputBindingInfo(
graph_id, input_names[0])
input_tensor_id = input_binding_info[0]
input_tensor_info = input_binding_info[1]
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
input_data = np.random.randint(255,
size=input_tensor_info.GetNumElements(),
dtype=np.uint8)
const_tensor_pair = (input_tensor_id,
ann.ConstTensor(input_tensor_info, input_data))
input_tensors = [const_tensor_pair]
output_tensors = []
for index, output_name in enumerate(output_names):
out_bind_info = parser.GetNetworkOutputBindingInfo(
graph_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)))
yield preferred_backends, network, runtime, input_tensors, output_tensors
def test_add_constant_layer_to_fully_connected():
inputWidth = 1
inputHeight = 1
inputChannels = 5
inputNum = 2
outputChannels = 3
outputNum = 2
inputShape = (inputNum, inputChannels, inputHeight, inputWidth)
outputShape = (outputNum, outputChannels)
weightsShape = (inputChannels, outputChannels)
biasShape = (outputChannels, )
input = np.array([[1.0, 2.0, 3.0, 4.0, 5.0], [5.0, 4.0, 3.0, 2.0, 1.0]],
dtype=np.float32)
weights = np.array(
[[.5, 2., .5], [.5, 2., 1.], [.5, 2., 2.], [.5, 2., 3.], [.5, 2., 4.]],
dtype=np.float32)
biasValues = np.array([10, 20, 30], dtype=np.float32)
expectedOutput = np.array([[
0.5 + 1.0 + 1.5 + 2.0 + 2.5 + biasValues[0], 2.0 + 4.0 + 6.0 + 8.0 +
10. + biasValues[1], 0.5 + 2.0 + 6.0 + 12. + 20. + biasValues[2]
],
[
2.5 + 2.0 + 1.5 + 1.0 + 0.5 + biasValues[0],
10.0 + 8.0 + 6.0 + 4.0 + 2. + biasValues[1],
2.5 + 4.0 + 6.0 + 6. + 4. + biasValues[2]
]],
dtype=np.float32)
network = ann.INetwork()
input_info = ann.TensorInfo(ann.TensorShape(inputShape),
ann.DataType_Float32, 0, 0, True)
input_tensor = ann.ConstTensor(input_info, input)
input_layer = network.AddInputLayer(0, "input")
w_info = ann.TensorInfo(ann.TensorShape(weightsShape),
ann.DataType_Float32, 0, 0, True)
w_tensor = ann.ConstTensor(w_info, weights)
w_layer = network.AddConstantLayer(w_tensor, "weights")
b_info = ann.TensorInfo(ann.TensorShape(biasShape), ann.DataType_Float32,
0, 0, True)
b_tensor = ann.ConstTensor(b_info, biasValues)
b_layer = network.AddConstantLayer(b_tensor, "bias")
fc_descriptor = ann.FullyConnectedDescriptor()
fc_descriptor.m_BiasEnabled = True
fc_descriptor.m_ConstantWeights = True
fully_connected = network.AddFullyConnectedLayer(fc_descriptor, "fc")
output_info = ann.TensorInfo(ann.TensorShape(outputShape),
ann.DataType_Float32)
output_tensor = ann.Tensor(output_info, np.zeros([1, 1], dtype=np.float32))
output = network.AddOutputLayer(0, "output")
input_layer.GetOutputSlot(0).Connect(fully_connected.GetInputSlot(0))
w_layer.GetOutputSlot(0).Connect(fully_connected.GetInputSlot(1))
b_layer.GetOutputSlot(0).Connect(fully_connected.GetInputSlot(2))
fully_connected.GetOutputSlot(0).Connect(output.GetInputSlot(0))
input_layer.GetOutputSlot(0).SetTensorInfo(input_info)
w_layer.GetOutputSlot(0).SetTensorInfo(w_info)
b_layer.GetOutputSlot(0).SetTensorInfo(b_info)
fully_connected.GetOutputSlot(0).SetTensorInfo(output_info)
preferred_backends = [ann.BackendId('CpuRef')]
options = ann.CreationOptions()
runtime = ann.IRuntime(options)
opt_network, messages = ann.Optimize(network, preferred_backends,
runtime.GetDeviceSpec(),
ann.OptimizerOptions())
net_id, messages = runtime.LoadNetwork(opt_network)
input_tensors = [(0, input_tensor)]
output_tensors = [(0, output_tensor)]
runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
output_vectors = ann.workload_tensors_to_ndarray(output_tensors)
assert (output_vectors == expectedOutput).all()
def test_create_empty_tensor():
tensor = ann.Tensor()
assert 0 == tensor.GetNumElements()
assert 0 == tensor.GetNumBytes()
assert tensor.get_memory_area() is None
def test_create_const_tensor_incorrect_args():
with pytest.raises(ValueError) as err:
ann.Tensor('something', 'something')
expected_error_message = "Incorrect number of arguments or type of arguments provided to create Tensor."
assert expected_error_message in str(err.value)