Further bugfixes and improved readability of dynapcnn network repr.

sinabs/backend/dynapcnn/dynapcnn_layer.py

@@ -96,7 +96,7 @@ def __init__(
         super().__init__()
 
         self.in_shape = in_shape
-        self._pool = pool
+        self.pool = pool
         self._discretize = discretize
         self._rescale_weights = rescale_weights
 
@@ -135,20 +135,16 @@ def __init__(
         if self._discretize:
             conv, spk = discretize_conv_spike_(conv, spk, to_int=False)
 
-        self._conv = conv
-        self._spk = spk
+        self.conv = conv
+        self.spk = spk
 
     @property
     def conv_layer(self):
-        return self._conv
+        return self.conv
 
     @property
     def spk_layer(self):
-        return self._spk
-
-    @property
-    def pool(self):
-        return self._pool
+        return self.spk
 
     @property
     def discretize(self):
@@ -175,7 +171,7 @@ def forward(self, x) -> List[torch.Tensor]:
         x = self.conv_layer(x)
         x = self.spk_layer(x)
 
-        for pool in self._pool:
+        for pool in self.pool:
 
             if pool == 1:
                 # no pooling is applied.
@@ -215,7 +211,7 @@ def get_output_shape(self) -> List[Tuple[int, int, int]]:
         neuron_shape = self.get_neuron_shape()
         # this is the actual output shape, including pooling
         output_shape = []
-        for pool in self._pool:
+        for pool in self.pool:
             output_shape.append(
                 neuron_shape[0],
                 neuron_shape[1] // pool,
@@ -227,7 +223,7 @@ def summary(self) -> dict:
         """Returns a summary of the convolution's/pooling's kernel sizes and the output shape of the spiking layer."""
 
         return {
-            "pool": (self._pool),
+            "pool": (self.pool),
             "kernel": list(self.conv_layer.weight.data.shape),
             "neuron": self._get_conv_output_shape(),  # neuron layer output has the same shape as the convolution layer ouput.
         }

sinabs/backend/dynapcnn/dynapcnn_network.py

@@ -132,6 +132,13 @@ def exit_layers(self):
             self.dynapcnn_layers[i] for i in self._dynapcnn_module.get_exit_layers()
         ]
 
+    @property
+    def is_deployed_on_dynapcnn_device(self):
+        return (
+            hasattr(self, "device")
+            and parse_device_id(self.device)[0] in ChipFactory.supported_devices
+        )
+
     @property
     def layer_destination_map(self):
         return self._dynapcnn_module.destination_map
@@ -212,10 +219,7 @@ def forward(
             structure as if `return_complete` is `True`, but only with entries
             where the destination is marked as final.
         """
-        if (
-            hasattr(self, "device")
-            and parse_device_id(self.device)[0] in ChipFactory.supported_devices
-        ):
+        if self.is_deployed_on_dynapcnn_device:
             return self.hw_forward(x)
         else:
             # Forward pass through software DynapcnnLayer instance
@@ -654,12 +658,37 @@ def _to_device(self, device: torch.device) -> None:
 
     def __str__(self):
         pretty_print = ""
+        if self.dvs_layer is not None:
+            pretty_print += (
+                "-------------------------- [ DVSLayer ] --------------------------\n"
+            )
+            pretty_print += f"{self.dvs_layer}\n\n"
         for idx, layer_data in self.dynapcnn_layers.items():
             pretty_print += f"----------------------- [ DynapcnnLayer {idx} ] -----------------------\n"
+            if self.is_deployed_on_dynapcnn_device:
+                pretty_print += f"Core {self.layer2core_map[idx]}\n"
             pretty_print += f"{layer_data}\n\n"
 
         return pretty_print
 
+    def __repr__(self):
+        if self.is_deployed_on_dynapcnn_device:
+            layer_info = "\n\n".join(
+                f"{idx} - core: {self.layer2core_map[idx]}\n{pformat(layer)}"
+                for idx, layer in self.dynapcnn_layers.items()
+            )
+            device_info = f" deployed on {self.device},"
+        else:
+            layer_info = "\n\n".join(
+                f"Index: {idx}\n{pformat(layer)}"
+                for idx, layer in self.dynapcnn_layers.items()
+            )
+            device_info = f" on {self.device}," if hasattr(self, "device") else ""
+        return (
+            f"DynapCNN Network{device_info} containing:\nDVS Layer: {pformat(self.dvs_layer)}"
+            "\n\nDynapCNN Layers:\n\n" + layer_info
+        )
+
 
 class DynapcnnCompatibleNetwork(DynapcnnNetwork):
     """Deprecated class, use DynapcnnNetwork instead."""

sinabs/backend/dynapcnn/dynapcnnnetwork_module.py

@@ -1,7 +1,7 @@
 # author    : Willian Soares Girao
 # contact   : [email protected]
 
-from collections import defaultdict
+from pprint import pformat
 from typing import Dict, List, Optional, Set, Union
 from warnings import warn
 
@@ -372,3 +372,8 @@ def remap(key):
             remap(node): [remap(src) for src in sources]
             for node, sources in self._node_source_map.items()
         }
+
+    def __repr__(self):
+        return f"DVS Layer: {pformat(self.dvs_layer)}\n\nDynapCNN Layers:\n" + pformat(
+            self.dynapcnn_layers
+        )

sinabs/backend/dynapcnn/nir_graph_extractor.py

@@ -119,6 +119,9 @@ def __init__(
         # retrieves what the I/O shape for each node's module is.
         self._nodes_io_shapes = self._get_nodes_io_shapes(dummy_input)
 
+        # Verify that graph is compatible
+        self.verify_graph_integrity()
+
     ####################################################### Publich Methods #######################################################
 
     @property
@@ -180,11 +183,9 @@ def get_dynapcnn_network_module(
         - The DynapcnnNetworkModule based on graph representation of this `GraphExtractor`
 
         """
-        # Make sure all nodes are supported
+        # Make sure all nodes are supported and there are no isolated nodes.
         self.verify_node_types()
-
-        # Verify that graph is compatible
-        self.verify_graph_integrity()
+        self.verify_no_isolated_nodes()
 
         # create a dict holding the data necessary to instantiate a `DynapcnnLayer`.
         self.dcnnl_map, self.dvs_layer_info = collect_dynapcnn_layer_info(
@@ -281,7 +282,6 @@ def verify_graph_integrity(self):
 
         Check that:
         - Only nodes of specific classes have multiple sources or targets.
-        - There are no disconnected nodes except for `DVSLayer` instances.
 
         Raises
         ------
@@ -379,6 +379,26 @@ def verify_node_types(self):
                 f"{pformat(SupportedNodeTypes)}."
             )
 
+    def verify_no_isolated_nodes(self):
+        """Verify that there are no disconnected nodes except for `DVSLayer` instances.
+
+        Raises
+        ------
+        - InvalidGraphStructure when disconnected nodes are detected
+        """
+        for node, module in self.indx_2_module_map.items():
+            # Make sure there are no individual, unconnected nodes
+            edges_with_node = {e for e in self.edges if node in e}
+            if not edges_with_node and not isinstance(module, DVSLayer):
+                raise InvalidGraphStructure(
+                    f"There is an isolated module of type {type(module)}. Only "
+                    "`DVSLayer` instances can be completely disconnected from "
+                    "any other module. Other than that, layers for DynapCNN "
+                    "consist of groups of weight layers (`Linear` or `Conv2d`), "
+                    "spiking layers (`IAF` or `IAFSqueeze`), and optioanlly "
+                    "pooling layers (`SumPool2d`, `AvgPool2d`)."
+                )
+
     ####################################################### Pivate Methods #######################################################
 
     def _handle_dvs_input(

tests/test_dynapcnn/test_doorbell.py

@@ -75,10 +75,8 @@ def test_same_result():
 
 
 def test_auto_config():
-    # - Should give an error with the normal layer ordering
     dynapcnn_net = DynapcnnNetwork(snn, input_shape=input_shape, discretize=True)
-    with pytest.raises(ValueError):
-        dynapcnn_net.make_config(chip_layers_ordering=[0, 1, 2, 3, 4])
+    dynapcnn_net.make_config(chip_layers_ordering=[0, 1, 2, 3, 4])
     dynapcnn_net.make_config(layer2core_map="auto")
 
 

tests/test_dynapcnnnetwork/test_failcases.py

@@ -47,14 +47,14 @@ def test_missing_spiking_layer():
     in_shape = (2, 28, 28)
     snn = nn.Sequential(
         nn.Conv2d(2, 8, kernel_size=3, stride=1, bias=False),
-        sl.IAF(),
+        sl.IAFSqueeze(batch_size=1),
         sl.SumPool2d(2),
         nn.AvgPool2d(2),
         nn.Conv2d(8, 16, kernel_size=3, stride=1, bias=False),
-        sl.IAF(),
+        sl.IAFSqueeze(batch_size=1),
         nn.Dropout2d(),
         nn.Conv2d(16, 2, kernel_size=3, stride=1, bias=False),
-        sl.IAF(),
+        sl.IAFSqueeze(batch_size=1),
         nn.Flatten(),
         nn.Linear(8, 5),
     )
@@ -66,7 +66,7 @@ def test_missing_spiking_layer():
 def test_incorrect_model_start():
     in_shape = (2, 28, 28)
     snn = nn.Sequential(
-        sl.IAF(),
+        sl.IAFSqueeze(batch_size=1),
         sl.SumPool2d(2),
         nn.AvgPool2d(2),
     )