[StreamingFC] check num inputs before trying to grab thresholds

src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py

@@ -185,35 +185,36 @@ class StreamingFCLayer_Batch(HLSCustomOp):
         f_weights.write(weight_hls_code)
         f_weights.close()
         # thresholds
-        thresholds = model.get_initializer(self.onnx_node.input[2])
-        if thresholds is not None:
-            threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
-            tdt = DataType.INT32
-            # use UINT32 threshold export for bipolar times bipolar
-            inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
-            wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
-            if inp_is_bipolar and wt_is_bipolar:
-                tdt = DataType.UINT32
-            thresholds_hls_code = numpy_to_hls_code(
-                threshold_tensor, tdt, "thresholds", False, True
-            )
-            # write thresholds into thresh.h
-            code_gen_dir = self.get_nodeattr("code_gen_dir")
-            f_thresh = open("{}/thresh.h".format(code_gen_dir), "w")
-            tdt_hls = tdt.get_hls_datatype_str()
-            odt_hls = self.get_output_datatype().get_hls_datatype_str()
-            f_thresh.write(
-                "static ThresholdsActivation<{},{},{},{},{},{}> threshs = ".format(
-                    self.get_nodeattr("TMEM"),
-                    self.get_nodeattr("PE"),
-                    threshold_tensor.shape[-1],
-                    tdt_hls,
-                    odt_hls,
-                    self.get_nodeattr("ActVal"),
+        if len(self.onnx_node.input) > 2:
+            thresholds = model.get_initializer(self.onnx_node.input[2])
+            if thresholds is not None:
+                threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
+                tdt = DataType.INT32
+                # use UINT32 threshold export for bipolar times bipolar
+                inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
+                wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
+                if inp_is_bipolar and wt_is_bipolar:
+                    tdt = DataType.UINT32
+                thresholds_hls_code = numpy_to_hls_code(
+                    threshold_tensor, tdt, "thresholds", False, True
                 )
-            )
-            f_thresh.write(thresholds_hls_code)
-            f_thresh.close()
+                # write thresholds into thresh.h
+                code_gen_dir = self.get_nodeattr("code_gen_dir")
+                f_thresh = open("{}/thresh.h".format(code_gen_dir), "w")
+                tdt_hls = tdt.get_hls_datatype_str()
+                odt_hls = self.get_output_datatype().get_hls_datatype_str()
+                f_thresh.write(
+                    "static ThresholdsActivation<{},{},{},{},{},{}> threshs = ".format(
+                        self.get_nodeattr("TMEM"),
+                        self.get_nodeattr("PE"),
+                        threshold_tensor.shape[-1],
+                        tdt_hls,
+                        odt_hls,
+                        self.get_nodeattr("ActVal"),
+                    )
+                )
+                f_thresh.write(thresholds_hls_code)
+                f_thresh.close()
 
     def execute_node(self, context, graph):
         node = self.onnx_node
@@ -247,7 +248,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
         # execute the precompiled model
         super().exec_precompiled_singlenode_model()
         # load output npy file
-        super().npy_to_dynamic_output()
+        super().npy_to_dynamic_output(context)
 
     def global_includes(self):
         self.code_gen_dict["$GLOBALS$"] = ['#include "weights.hpp"']