[Test] update avgpool export test for newest Brevitas

tests/brevitas/test_brevitas_avg_pool_export.py

@@ -5,7 +5,7 @@ import torch
 import numpy as np
 import brevitas.onnx as bo
 from brevitas.nn import QuantAvgPool2d
-from brevitas.quant_tensor import pack_quant_tensor
+from brevitas.quant_tensor import QuantTensor
 from brevitas.core.quant import QuantType
 from finn.core.modelwrapper import ModelWrapper
 from finn.core.datatype import DataType
@@ -41,11 +41,18 @@ def test_brevitas_avg_pool_export(
     # call forward pass manually once to cache scale factor and bitwidth
     input_tensor = torch.from_numpy(np.zeros(ishape)).float()
     scale = np.ones((1, channels, 1, 1))
+    zpt = torch.from_numpy(np.zeros((1))).float()
     output_scale = torch.from_numpy(scale).float()
-    input_quant_tensor = pack_quant_tensor(
-        tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor, signed=signed
+    input_quant_tensor = QuantTensor(
+        value=input_tensor,
+        scale=output_scale,
+        bit_width=ibw_tensor,
+        signed=signed,
+        zero_point=zpt,
+    )
+    bo.export_finn_onnx(
+        b_avgpool, export_path=export_onnx_path, input_t=input_quant_tensor
     )
-    bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
     model = ModelWrapper(export_onnx_path)
 
     # determine input FINN datatype
@@ -62,8 +69,12 @@ def test_brevitas_avg_pool_export(
     # calculate golden output
     inp = gen_finn_dt_tensor(dtype, ishape)
     input_tensor = torch.from_numpy(inp).float()
-    input_quant_tensor = pack_quant_tensor(
-        tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor, signed=signed
+    input_quant_tensor = QuantTensor(
+        value=input_tensor,
+        scale=output_scale,
+        bit_width=ibw_tensor,
+        signed=signed,
+        zero_point=zpt,
     )
     b_avgpool.eval()
     expected = b_avgpool.forward(input_quant_tensor).tensor.detach().numpy()
@@ -81,11 +92,17 @@ def test_brevitas_avg_pool_export(
     inp_tensor = inp * scale
     input_tensor = torch.from_numpy(inp_tensor).float()
     input_scale = torch.from_numpy(scale).float()
-    input_quant_tensor = pack_quant_tensor(
-        tensor=input_tensor, scale=input_scale, bit_width=ibw_tensor, signed=signed
+    input_quant_tensor = QuantTensor(
+        value=input_tensor,
+        scale=input_scale,
+        bit_width=ibw_tensor,
+        signed=signed,
+        zero_point=zpt,
     )
     # export again to set the scale values correctly
-    bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
+    bo.export_finn_onnx(
+        b_avgpool, export_path=export_onnx_path, input_t=input_quant_tensor
+    )
     model = ModelWrapper(export_onnx_path)
     model = model.transform(InferShapes())
     model = model.transform(InferDataTypes())