diff --git a/tests/fpgadataflow/test_fpgadataflow_upsampler.py b/tests/fpgadataflow/test_fpgadataflow_upsampler.py
index cc398887f944e507ee804c6e859eace70041663b..64a0519c92c30cfa40f21db54b70f833dd7f2f1d 100644
--- a/tests/fpgadataflow/test_fpgadataflow_upsampler.py
+++ b/tests/fpgadataflow/test_fpgadataflow_upsampler.py
@@ -131,8 +131,11 @@ def test_fpgadataflow_upsampler(dt, IFMDim, OFMDim, NumChannels, exec_mode):
     torch_model = PyTorchTestModel(upscale_factor=OFMDim / IFMDim)
     input_shape = (1, NumChannels, IFMDim, IFMDim)
     test_in = torch.arange(0, np.prod(np.asarray(input_shape)))
+    # Limit the input to values valid for the given datatype
     test_in %= dt.max() - dt.min() + 1
     test_in += dt.min()
+    # Additionally make sure we always start with 0, for convenience purposes.
+    test_in = torch.roll(test_in, dt.min())
     test_in = test_in.view(*input_shape).type(torch.float32)
 
     # Get golden PyTorch and ONNX inputs