def test_fullyconnected_layer_optional_none():
net = ann.INetwork()
layer = net.AddFullyConnectedLayer(
fullyConnectedDescriptor=ann.FullyConnectedDescriptor(),
weights=ann.ConstTensor())
assert layer
def test_fullyconnected_layer_all_args():
net = ann.INetwork()
layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(),
weights=ann.ConstTensor(),
biases=ann.ConstTensor(),
name='NAME1')
assert layer
assert 'NAME1' == layer.GetName()
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_fully_connected_descriptor_default_values():
desc = ann.FullyConnectedDescriptor()
assert desc.m_BiasEnabled == False
assert desc.m_TransposeWeightMatrix == False