Fix (tests): fix TruncAvgPoolTest

tests/brevitas_finn/brevitas/test_brevitas_avg_pool_export.py

@@ -5,17 +5,16 @@
 
 import numpy as np
 import pytest
-from qonnx.core.datatype import DataType
 from qonnx.core.modelwrapper import ModelWrapper
 import qonnx.core.onnx_exec as oxe
 from qonnx.transformation.infer_datatypes import InferDataTypes
 from qonnx.transformation.infer_shapes import InferShapes
-from qonnx.util.basic import gen_finn_dt_tensor
 import torch
 
 from brevitas.export import export_qonnx
 from brevitas.nn import TruncAvgPool2d
-from brevitas.quant_tensor import QuantTensor
+from brevitas.nn.quant_activation import QuantIdentity
+from brevitas.nn.quant_activation import QuantReLU
 
 export_onnx_path = "test_brevitas_avg_pool_export.onnx"
 
@@ -29,36 +28,38 @@
 @pytest.mark.parametrize("idim", [7, 8])
 def test_brevitas_avg_pool_export(
         kernel_size, stride, signed, bit_width, input_bit_width, channels, idim, request):
+    if signed:
+        quant_node = QuantIdentity(
+            bit_width=input_bit_width,
+            return_quant_tensor=True,
+        )
+    else:
+        quant_node = QuantReLU(
+            bit_width=input_bit_width,
+            return_quant_tensor=True,
+        )
 
     quant_avgpool = TruncAvgPool2d(
         kernel_size=kernel_size, stride=stride, bit_width=bit_width, float_to_int_impl_type='floor')
-    quant_avgpool.eval()
+    model_brevitas = torch.nn.Sequential(quant_node, quant_avgpool)
+    model_brevitas.eval()
 
     # determine input
-    prefix = 'INT' if signed else 'UINT'
-    dt_name = prefix + str(input_bit_width)
-    dtype = DataType[dt_name]
     input_shape = (1, channels, idim, idim)
-    input_array = gen_finn_dt_tensor(dtype, input_shape)
-    scale_array = np.random.uniform(low=0, high=1, size=(1, channels, 1, 1)).astype(np.float32)
-    input_tensor = torch.from_numpy(input_array * scale_array).float()
-    scale_tensor = torch.from_numpy(scale_array).float()
-    zp = torch.tensor(0.)
-    input_quant_tensor = QuantTensor(
-        input_tensor, scale_tensor, zp, input_bit_width, signed, training=False)
+    inp = torch.randn(input_shape)
 
     # export
     test_id = request.node.callspec.id
     export_path = test_id + '_' + export_onnx_path
-    export_qonnx(quant_avgpool, export_path=export_path, input_t=input_quant_tensor)
+    export_qonnx(model_brevitas, export_path=export_path, input_t=inp)
     model = ModelWrapper(export_path)
     model = model.transform(InferShapes())
     model = model.transform(InferDataTypes())
 
     # reference brevitas output
-    ref_output_array = quant_avgpool(input_quant_tensor).tensor.detach().numpy()
+    ref_output_array = model_brevitas(inp).tensor.detach().numpy()
     # finn output
-    idict = {model.graph.input[0].name: input_array}
+    idict = {model.graph.input[0].name: inp.detach().numpy()}
     odict = oxe.execute_onnx(model, idict, True)
     finn_output = odict[model.graph.output[0].name]
     # compare outputs