From 9c134a5116c6bf5c30e7a255a6e56217b54c41db Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Tue, 3 Dec 2019 00:54:56 +0000
Subject: [PATCH] [CustomOp] generalize param gen fxn to a single optional one
---
src/finn/custom_op/fpgadataflow/__init__.py | 10 ++--------
.../custom_op/fpgadataflow/streamingfclayer_batch.py | 6 +++---
2 files changed, 5 insertions(+), 11 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index 0d9a306ba..f2733ee76 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -42,8 +42,7 @@ class HLSCustomOp(CustomOp):
def code_generation(self, model):
node = self.onnx_node
- self.generate_weights(model)
- self.generate_thresholds(model)
+ self.generate_params(model)
self.global_includes()
self.defines()
self.read_npy_data()
@@ -78,12 +77,7 @@ class HLSCustomOp(CustomOp):
builder.build(code_gen_dir)
self.set_nodeattr("executable_path", builder.executable_path)
- @abstractmethod
- def generate_weights(self, context):
- pass
-
- @abstractmethod
- def generate_thresholds(self, context):
+ def generate_params(self, model):
pass
@abstractmethod
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index fdc66ce2b..58d60e7c4 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -149,7 +149,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
assert ret.shape[2] == n_thres_steps
return ret
- def generate_weights(self, model):
+ def generate_params(self, model):
+ # weights
weights = model.get_initializer(self.onnx_node.input[1])
# convert weights into hlslib-compatible format
weight_tensor = self.get_hls_compatible_weight_tensor(weights)
@@ -184,8 +185,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
f_weights.write(weight_hls_code)
f_weights.close()
-
- def generate_thresholds(self, model):
+ # thresholds
thresholds = model.get_initializer(self.onnx_node.input[2])
if thresholds is not None:
threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
--
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