[code generation] Added suffix "_npysim" to slidingwindow involved files

src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py

@@ -57,7 +57,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
             idt = DataType.BINARY
 
         # TODO ensure codegen dir exists
-        code_gen_dir = self.get_nodeattr("code_gen_dir")
+        code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
         # create a npy file for input of the node
 
         inp = context[node.input[0]]
@@ -100,7 +100,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
         ]
 
     def read_npy_data(self):
-        code_gen_dir = self.get_nodeattr("code_gen_dir")
+        code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
         dtype = self.get_input_datatype()
         if dtype == DataType.BIPOLAR:
             # use binary for bipolar storage
@@ -136,7 +136,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
         ]
 
     def dataoutstrm(self):
-        code_gen_dir = self.get_nodeattr("code_gen_dir")
+        code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
         dtype = self.get_output_datatype()
         if dtype == DataType.BIPOLAR:
             # use binary for bipolar storage

tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py

@@ -8,7 +8,7 @@ from finn.core.datatype import DataType
 from finn.core.modelwrapper import ModelWrapper
 from finn.core.utils import gen_finn_dt_tensor
 from finn.transformation.fpgadataflow.cleanup import CleanUp
-from finn.transformation.fpgadataflow.codegen import CodeGen
+from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
 from finn.transformation.fpgadataflow.compile import Compile
 
 
@@ -140,12 +140,11 @@ def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride):
     ofm_dim = int(((ifm_dim - k) / stride) + 1)
 
     x = gen_finn_dt_tensor(idt, (1, ifm_ch, ifm_dim, ifm_dim))
-    # x_values = np
     model = make_single_slidingwindow_modelwrapper(
         k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
     )
 
-    model = model.transform(CodeGen())
+    model = model.transform(CodeGen_npysim())
     model = model.transform(Compile())
 
     # prepare input data