diff --git a/src/finn/transformation/fpgadataflow/create_stitched_ip.py b/src/finn/transformation/fpgadataflow/create_stitched_ip.py
index c88c65fc89bfb695662478248f52a2bf632b278d..933cb2757bb9cfc696debc6265443ccc5ce3a617 100644
--- a/src/finn/transformation/fpgadataflow/create_stitched_ip.py
+++ b/src/finn/transformation/fpgadataflow/create_stitched_ip.py
@@ -64,7 +64,9 @@ def is_external_output(model, node, i):
# indicate whether output i of node should be made external
# True only if output is unconnected
consumers = model.find_consumers(node.output[i])
- if consumers is None:
+ if consumers == []:
+ # TODO should ideally check if tensor is in top-level
+ # outputs
return True
return False
@@ -270,7 +272,7 @@ class CreateStitchedIP(Transformation):
for input in model.graph.input:
inp_name = input.name
inp_cons = model.find_consumers(inp_name)
- assert inp_cons is not None, "No consumer for input " + inp_name
+ assert inp_cons != [], "No consumer for input " + inp_name
assert len(inp_cons) == 1, "Multiple consumers for input " + inp_name
node = inp_cons[0]
node_inst = getCustomOp(node)
diff --git a/src/finn/transformation/fpgadataflow/insert_dwc.py b/src/finn/transformation/fpgadataflow/insert_dwc.py
index 4a0d0a89c4a6bb5809887ffcfffb2068ccebaa48..afc889f5bc90a551efddc6232f5689504fe1bb29 100644
--- a/src/finn/transformation/fpgadataflow/insert_dwc.py
+++ b/src/finn/transformation/fpgadataflow/insert_dwc.py
@@ -45,7 +45,7 @@ class InsertDWC(Transformation):
if _suitable_node(n):
for output_name in n.output:
consumers = model.find_consumers(output_name)
- if consumers is None:
+ if consumers == []:
continue
assert len(consumers) == 1, (
n.name
diff --git a/src/finn/transformation/fpgadataflow/insert_fifo.py b/src/finn/transformation/fpgadataflow/insert_fifo.py
index b5ae2da47a19af5b6bbf44a2a65cbef4c3bbc4dd..26613849060e361a6bc93483e3e1d8416e1fd97f 100644
--- a/src/finn/transformation/fpgadataflow/insert_fifo.py
+++ b/src/finn/transformation/fpgadataflow/insert_fifo.py
@@ -62,7 +62,7 @@ class InsertFIFO(Transformation):
if _suitable_node(first_node):
for n_output in first_node.output:
consumers = model.find_consumers(n_output)
- if consumers is None:
+ if consumers == []:
continue
if len(consumers) > 1:
warnings.warn(
diff --git a/src/finn/transformation/fpgadataflow/insert_tlastmarker.py b/src/finn/transformation/fpgadataflow/insert_tlastmarker.py
index 34cb61346dcd5bcd6f41a4272748764cf385a524..a33cee4640a7498f478962767ac4260d9c2bed90 100644
--- a/src/finn/transformation/fpgadataflow/insert_tlastmarker.py
+++ b/src/finn/transformation/fpgadataflow/insert_tlastmarker.py
@@ -97,7 +97,7 @@ class InsertTLastMarker(Transformation):
first_node = model.find_consumers(graph_in_name)
# skip if no consumers (this may be the case for unused initializers)
# TODO: fix this with a cleanup transform
- if first_node is None:
+ if first_node == []:
continue
assert len(first_node) == 1, "Input fans out to multiple nodes"
first_node = first_node[0]
diff --git a/src/finn/transformation/fpgadataflow/make_zynq_proj.py b/src/finn/transformation/fpgadataflow/make_zynq_proj.py
index 80ce8f0163a23293423ac208451c901eb645643c..da620766a3f3cc5dcdc67fb80f3c4dcc500a475f 100644
--- a/src/finn/transformation/fpgadataflow/make_zynq_proj.py
+++ b/src/finn/transformation/fpgadataflow/make_zynq_proj.py
@@ -152,11 +152,13 @@ class MakeZYNQProject(Transformation):
# define kernel instances
# name kernels connected to graph inputs as idmaxx
# name kernels connected to graph outputs as odmaxx
- if producer is None or consumer is None:
+ if (producer is None) or (consumer == []):
+ # TODO not a good way of checking for external inp&out
+ # should look at the list of top-level in/out instead
if producer is None:
instance_names[node.name] = "idma" + str(idma_idx)
idma_idx += 1
- elif consumer is None:
+ elif consumer == []:
instance_names[node.name] = "odma" + str(odma_idx)
odma_idx += 1
config.append(
diff --git a/src/finn/transformation/fpgadataflow/set_fifo_depths.py b/src/finn/transformation/fpgadataflow/set_fifo_depths.py
index ce7cf7bc589fae7fb6c8785b51cf45514f49c5a0..28f74b5292d14947f4f8a27f2723af4f04590ec8 100644
--- a/src/finn/transformation/fpgadataflow/set_fifo_depths.py
+++ b/src/finn/transformation/fpgadataflow/set_fifo_depths.py
@@ -99,7 +99,7 @@ class RemoveShallowFIFOs(Transformation):
# bypass shallow fifos
shallow_fifos.append(node)
consumers = model.find_consumers(node.output[0])
- if consumers is None:
+ if consumers == []:
producer = model.find_producer(node.input[0])
for idx, inp in enumerate(producer.output):
if inp == node.input[0]:
diff --git a/src/finn/transformation/fpgadataflow/vitis_build.py b/src/finn/transformation/fpgadataflow/vitis_build.py
index 365632cd5a02eae6e19e670e0b676c521e460507..4dce3ab16c38bfe5dd43f3e23b14ea2ec571f68c 100644
--- a/src/finn/transformation/fpgadataflow/vitis_build.py
+++ b/src/finn/transformation/fpgadataflow/vitis_build.py
@@ -213,11 +213,13 @@ class VitisLink(Transformation):
# define kernel instances
# name kernels connected to graph inputs as idmaxx
# name kernels connected to graph inputs as odmaxx
+ # TODO not a good way of checking for external in/out
+ # check top-level in/out list instead
if producer is None:
instance_names[node.name] = "idma" + str(idma_idx)
config.append("nk=%s:1:%s" % (node.name, instance_names[node.name]))
idma_idx += 1
- elif consumer is None:
+ elif consumer == []:
instance_names[node.name] = "odma" + str(odma_idx)
config.append("nk=%s:1:%s" % (node.name, instance_names[node.name]))
odma_idx += 1
diff --git a/src/finn/transformation/qonnx/fold_quant_weights.py b/src/finn/transformation/qonnx/fold_quant_weights.py
index c81085e7a74eb3e08fbee447d7b9b027a436671e..e8a0f418ae5eb587d6aabae57d8b379357d3a0ca 100644
--- a/src/finn/transformation/qonnx/fold_quant_weights.py
+++ b/src/finn/transformation/qonnx/fold_quant_weights.py
@@ -146,11 +146,14 @@ class FoldQuantWeights(Transformation):
model.set_initializer(mul_tensor.name, scale)
successor = model.find_consumers(node_out)
- if successor is None:
+ if successor == []:
raise RuntimeError(
"Can only constant fold scaled Quant weights "
"if a successor exists."
)
+ assert (
+ len(successor) == 1
+ ), "Only implemented for a single consumer"
successor = successor[0]
succ_output_name = successor.output[0]
diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index 97ae3b51a849a4174c9853cb41c0d6d72bdf8dad..32e539d87045520044378b94fd0e3c71486990c7 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -627,10 +627,9 @@ class AbsorbTransposeIntoResize(Transformation):
graph.node.insert(node_ind + 1, new_transpose)
# rewire nodes
final_t_cands = model.find_consumers(mt_cand.output[0])
- if final_t_cands is not None:
- # rewire next nodes' inputs
- for final_t_cand in final_t_cands:
- final_t_cand.input[0] = trans_output
+ # rewire next nodes' inputs
+ for final_t_cand in final_t_cands:
+ final_t_cand.input[0] = trans_output
mt_cand.output[0] = trans_input
graph_modified = True
if graph_modified:
diff --git a/src/finn/transformation/streamline/reorder.py b/src/finn/transformation/streamline/reorder.py
index 9751f41d7eb1d82229a3409f74ea6ef7441b4729..e922dffe37691a39434e9ebafa5df6d1a11d389e 100644
--- a/src/finn/transformation/streamline/reorder.py
+++ b/src/finn/transformation/streamline/reorder.py
@@ -755,6 +755,7 @@ class MoveOpPastFork(Transformation):
# Check case when branches are empty and go
# to the same node
consumers = model.find_consumers(n.output[0])
+ assert len(consumers) > 1, "Must have >1 consumer"
unique_consumer = True
for consum_node in consumers[1:]:
if consumers[0] != consum_node: