diff --git a/src/finn/util/test.py b/src/finn/util/test.py
index e070f8d89d668f3d068cb75df83e5700a24e49b0..0b7284d76650757f4db2187a8cdaa9beb83dfc52 100644
--- a/src/finn/util/test.py
+++ b/src/finn/util/test.py
@@ -145,7 +145,6 @@ def get_example_input(topology):
     elif topology == "cnv":
         fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
         input_tensor = np.load(fn)["arr_0"].astype(np.float32)
-        input_tensor = input_tensor / 255
         return input_tensor
     else:
         raise Exception("Unknown topology, can't return example input")
diff --git a/tests/end2end/test_end2end_bnn_pynq.py b/tests/end2end/test_end2end_bnn_pynq.py
index ff97b722e8c01b8304afaad6ee5d5607dceef508..e2edbaa80ab0fd61d1d5396846142733df4afc6c 100644
--- a/tests/end2end/test_end2end_bnn_pynq.py
+++ b/tests/end2end/test_end2end_bnn_pynq.py
@@ -195,10 +195,12 @@ def get_folding_function(topology, wbits, abits):
         raise Exception("Unknown topology/quantization combo for predefined folding")
 
 
-def get_golden_io_pair(topology, wbits, abits, return_topk=None):
+def get_golden_io_pair(topology, wbits, abits, preproc=ToTensor(), return_topk=None):
     (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
     input_tensor_npy = get_example_input(topology)
     input_tensor_torch = torch.from_numpy(input_tensor_npy).float()
+    if preproc is not None:
+        input_tensor_torch = preproc.forward(input_tensor_torch).detach()
     output_tensor_npy = model.forward(input_tensor_torch).detach().numpy()
     if return_topk is not None:
         output_tensor_npy = get_topk(output_tensor_npy, k=return_topk)
@@ -284,6 +286,8 @@ class TestEnd2End:
             model = model.transform(to_hls.InferConvInpGen())
             model = model.transform(to_hls.InferStreamingMaxPool())
             model = model.transform(RemoveCNVtoFCFlatten())
+        # get rid of Tranpose -> Tranpose identity seq
+        model = model.transform(absorb.AbsorbConsecutiveTransposes())
         model = model.transform(GiveUniqueNodeNames())
         model = model.transform(InferDataLayouts())
         model.save(get_checkpoint_name(topology, wbits, abits, "convert_to_hls_layers"))