Set user defined spike_exp level globally when creating netx network

src/lava/lib/dl/netx/hdf5.py

@@ -53,6 +53,8 @@ class Network(AbstractProcess):
         neuron's reset parameter. None means no reset. Defaults to None.
     reset_offset: int
         determines the phase shift of network reset if enabled. Defaults to 0.
+    spike_exp: int
+        determines the decimal place of graded spike. Defaults to 6.
     """
 
     def __init__(self,
@@ -62,7 +64,8 @@ def __init__(self,
                  input_message_bits: Optional[int] = 0,
                  input_shape: Optional[Tuple[int, ...]] = None,
                  reset_interval: Optional[int] = None,
-                 reset_offset: int = 0) -> None:
+                 reset_offset: int = 0,
+                 spike_exp: int = 6) -> None:
         super().__init__(net_config=net_config,
                          num_layers=num_layers,
                          input_message_bits=input_message_bits)
@@ -75,6 +78,7 @@ def __init__(self,
         self.input_shape = input_shape
         self.reset_interval = reset_interval
         self.reset_offset = reset_offset
+        self.spike_exp = spike_exp
 
         self.net_str = ''
         self.layers = self._create()
@@ -102,7 +106,8 @@ def __len__(self) -> int:
     def get_neuron_params(neuron_config: h5py.Group,
                           input: bool = False,
                           reset_interval: Optional[int] = None,
-                          reset_offset: int = 0) -> AbstractProcess:
+                          reset_offset: int = 0,
+                          spike_exp: int = 6) -> AbstractProcess:
         """Provides the correct neuron configuration process and parameters
         from the neuron description in hdf5 config.
 
@@ -118,6 +123,8 @@ def get_neuron_params(neuron_config: h5py.Group,
         reset_offset: int
             the offset/phase of reset. It is only valid of reset_interval is
             not None.
+        spike_exp: int
+            determines the decimal place of graded spike. Defaults to 6.
 
         Returns
         -------
@@ -159,7 +166,7 @@ def get_neuron_params(neuron_config: h5py.Group,
                 neuron_process = Delta
                 neuron_params = {'neuron_proc': neuron_process,
                                  'vth': neuron_config['vThMant'],
-                                 'spike_exp': 6,
+                                 'spike_exp': spike_exp,
                                  'state_exp': 6,
                                  'num_message_bits': num_message_bits}
             elif 'sigma_output' in neuron_config.keys():
@@ -170,7 +177,7 @@ def get_neuron_params(neuron_config: h5py.Group,
                 neuron_process = SigmaDelta
                 neuron_params = {'neuron_proc': neuron_process,
                                  'vth': neuron_config['vThMant'],
-                                 'spike_exp': 6,
+                                 'spike_exp': spike_exp,
                                  'state_exp': 6,
                                  'num_message_bits': num_message_bits}
             return neuron_params
@@ -233,7 +240,8 @@ def array_entry(data: Union[int, Tuple[int, int], None]) -> str:
     @staticmethod
     def create_input(layer_config: h5py.Group,
                      reset_interval: Optional[int] = None,
-                     reset_offset: int = 0) -> Tuple[Input, str]:
+                     reset_offset: int = 0,
+                     spike_exp: int = 6) -> Tuple[Input, str]:
         """Creates input layer from layer configuration.
 
         Parameters
@@ -245,6 +253,8 @@ def create_input(layer_config: h5py.Group,
         reset_offset: int
             the offset/phase of reset. It is only valid of reset_interval is
             not None.
+        spike_exp: int
+            determines the decimal place of graded spike. Defaults to 6.
 
         Returns
         -------
@@ -257,7 +267,8 @@ def create_input(layer_config: h5py.Group,
         neuron_params = Network.get_neuron_params(layer_config['neuron'],
                                                   reset_interval=reset_interval,
                                                   reset_offset=reset_offset,
-                                                  input=True)
+                                                  input=True,
+                                                  spike_exp=spike_exp)
 
         if 'weight' in layer_config.keys():
             weight = int(layer_config['weight'])
@@ -298,7 +309,8 @@ def create_input(layer_config: h5py.Group,
     def create_dense(layer_config: h5py.Group,
                      input_message_bits: int = 0,
                      reset_interval: Optional[int] = None,
-                     reset_offset: int = 0) -> Tuple[Dense, str]:
+                     reset_offset: int = 0,
+                     spike_exp: int = 6) -> Tuple[Dense, str]:
         """Creates dense layer from layer configuration
 
         Parameters
@@ -313,6 +325,8 @@ def create_dense(layer_config: h5py.Group,
         reset_offset: int
             the offset/phase of reset. It is only valid of reset_interval is
             not None.
+        spike_exp: int
+            determines the decimal place of graded spike. Defaults to 6.
 
         Returns
         -------
@@ -335,7 +349,8 @@ def create_dense(layer_config: h5py.Group,
                              'gradedSpike': False}
         neuron_params = Network.get_neuron_params(neuron_config,
                                                   reset_interval=reset_interval,
-                                                  reset_offset=reset_offset)
+                                                  reset_offset=reset_offset,
+                                                  spike_exp=spike_exp)
         if "weight/imag" in layer_config.f:
             weight_real = layer_config['weight/real']
             weight_imag = layer_config['weight/imag']
@@ -418,7 +433,8 @@ def create_conv(layer_config: h5py.Group,
                     input_shape: Tuple[int, int, int],
                     input_message_bits: int = 0,
                     reset_interval: Optional[int] = None,
-                    reset_offset: int = 0) -> Tuple[Conv, str]:
+                    reset_offset: int = 0,
+                    spike_exp: int = 6) -> Tuple[Conv, str]:
         """Creates conv layer from layer configuration
 
         Parameters
@@ -435,6 +451,8 @@ def create_conv(layer_config: h5py.Group,
         reset_offset: int
             the offset/phase of reset. It is only valid of reset_interval is
             not None.
+        spike_exp: int
+            determines the decimal place of graded spike. Defaults to 6.
 
         Returns
         -------
@@ -450,7 +468,8 @@ def expand(x):
         shape = tuple(layer_config['shape'][::-1])  # WHC (XYZ)
         neuron_params = Network.get_neuron_params(layer_config['neuron'],
                                                   reset_interval=reset_interval,
-                                                  reset_offset=reset_offset)
+                                                  reset_offset=reset_offset,
+                                                  spike_exp=spike_exp)
         weight = layer_config['weight'][:, :, ::-1, ::-1]
         weight = weight.reshape(weight.shape[:4]).transpose((0, 3, 2, 1))
         stride = expand(layer_config['stride'])
@@ -534,7 +553,8 @@ def _create(self) -> List[AbstractProcess]:
                     layer, table = self.create_input(
                         layer_config[i],
                         reset_interval=reset_interval,
-                        reset_offset=reset_offset)
+                        reset_offset=reset_offset,
+                        spike_exp=self.spike_exp)
                     if i >= self.skip_layers:
                         layers.append(layer)
                         reset_offset += 1
@@ -559,7 +579,8 @@ def _create(self) -> List[AbstractProcess]:
                     input_shape=input_shape,
                     input_message_bits=input_message_bits,
                     reset_interval=reset_interval,
-                    reset_offset=reset_offset)
+                    reset_offset=reset_offset,
+                    spike_exp=self.spike_exp)
                 if i >= self.skip_layers:
                     layers.append(layer)
                     reset_offset += 1
@@ -587,7 +608,8 @@ def _create(self) -> List[AbstractProcess]:
                     layer_config=layer_config[i],
                     input_message_bits=input_message_bits,
                     reset_interval=reset_interval,
-                    reset_offset=reset_offset)
+                    reset_offset=reset_offset,
+                    spike_exp=self.spike_exp)
                 if i >= self.skip_layers:
                     layers.append(layer)
                     reset_offset += 1

tests/lava/lib/dl/netx/test_hdf5.py

@@ -174,6 +174,40 @@ def test_pilotnet_sdnn(self) -> None:
             f'Error was {error}.'
         )
 
+    def test_pilotnet_sdnn_spike_exp(self) -> None:
+        """Tests the output of pilotnet sdnn with spike exp."""
+        net_config = root + '/gts/pilotnet_sdnn/network.net'
+        net = netx.hdf5.Network(net_config=net_config, spike_exp=0)
+        input = np.load(root + '/gts/pilotnet_sdnn/input.npy')
+        source = io.source.RingBuffer(data=input)
+        sink = io.sink.RingBuffer(shape=net.out_layer.shape,
+                                  buffer=len(net.layers))
+        source.s_out.connect(net.in_layer.neuron.a_in)
+        net.out_layer.out.connect(sink.a_in)
+
+        num_steps = len(net.layers)
+        run_condition = RunSteps(num_steps=num_steps)
+        run_config = TestRunConfig(select_tag='fixed_pt')
+        net.run(condition=run_condition, run_cfg=run_config)
+        output = sink.data.get()
+        net.stop()
+
+        scale = (1 << (6 - net.spike_exp))
+        gt = np.load(root + '/gts/pilotnet_sdnn/output.npy') / scale
+        error = np.abs(output - gt).mean()
+        if verbose:
+            print('Network:')
+            print(net)
+            print(f'{output=}')
+            print('PilotNet SDNN spike error:', error)
+
+        self.assertTrue(
+            error < 2 * scale,
+            f'Output spike and ground truth do not match for PilotNet SDNN. '
+            f'Found {output[output != gt] = } and {gt[output != gt] = }. '
+            f'Error was {error}.'
+        )
+
     def test_axonal_delay_ntidigits(self) -> None:
         """Tests the output of ntidigits hdf5 description. This network
         consists of axonal delay. So this tests specifically tests for