Store test results in temporary folder.

tests/fpgadataflow/test_fpgadataflow_upsampler.py

@@ -29,6 +29,7 @@
 import pytest
 
 import numpy as np
+import tempfile
 import torch
 from brevitas.export import FINNManager
 from torch import nn
@@ -124,6 +125,8 @@ class PyTorchTestModel(nn.Module):
 @pytest.mark.vivado
 @pytest.mark.slow
 def test_fpgadataflow_upsampler(dt, IFMDim, OFMDim, NumChannels, exec_mode):
+    tmpdir = tempfile.mkdtemp()
+    atol = 1e-3
     # Create the test model and inputs for it
     torch_model = PyTorchTestModel(upscale_factor=OFMDim / IFMDim)
     input_shape = (1, NumChannels, IFMDim, IFMDim)
@@ -132,7 +135,7 @@ def test_fpgadataflow_upsampler(dt, IFMDim, OFMDim, NumChannels, exec_mode):
 
     # Get golden PyTorch and ONNX inputs
     golden_torch_float = torch_model(test_in)
-    export_path = "Upsample_exported.onnx"
+    export_path = f"{tmpdir}/Upsample_exported.onnx"
     FINNManager.export(
         torch_model, input_shape=input_shape, export_path=export_path, opset_version=11
     )
@@ -140,19 +143,19 @@ def test_fpgadataflow_upsampler(dt, IFMDim, OFMDim, NumChannels, exec_mode):
     input_dict = {model.graph.input[0].name: test_in.numpy().astype(np.int8)}
     input_dict = {model.graph.input[0].name: test_in.numpy()}
     golden_output_dict = oxe.execute_onnx(model, input_dict, True)
-    golden_result_float = golden_output_dict[model.graph.output[0].name]
+    golden_result = golden_output_dict[model.graph.output[0].name]
 
     # Make sure PyTorch and ONNX match
-    pyTorch_onnx_match = np.isclose(golden_result_float, golden_torch_float).all()
+    pyTorch_onnx_match = np.isclose(golden_result, golden_torch_float).all()
     assert pyTorch_onnx_match, "ONNX and PyTorch upsampling output don't match."
 
     # Prep model for execution
     model = ModelWrapper(export_path)
-    transpose_path = "Upsample_transposed.onnx"
+    transpose_path = f"{tmpdir}/Upsample_transposed.onnx"
     model = model.transform(TransposeUpsampleIO())
     model.save(transpose_path)
 
-    hls_upsample_path = "Upsample_hls.onnx"
+    hls_upsample_path = f"{tmpdir}/Upsample_hls.onnx"
     model = ModelWrapper(transpose_path)
     model = model.transform(ForceDataTypeForTensors(dType=dt))
     model = model.transform(GiveUniqueNodeNames())
@@ -188,8 +191,8 @@ def test_fpgadataflow_upsampler(dt, IFMDim, OFMDim, NumChannels, exec_mode):
     output_dict = oxe.execute_onnx(model, input_dict, True)
     test_result = output_dict[model.graph.output[0].name]
 
-    test_restuls_transposed = test_result.transpose(_to_chan_first_args)
-    output_matches = np.isclose(golden_result_float, test_restuls_transposed).all()
+    test_result_transposed = test_result.transpose(_to_chan_first_args)
+    output_matches = np.isclose(golden_result, test_result_transposed, atol=atol).all()
 
     if exec_mode == "cppsim":
         assert output_matches, "Cppsim output doesn't match ONNX/PyTorch."
@@ -197,8 +200,7 @@ def test_fpgadataflow_upsampler(dt, IFMDim, OFMDim, NumChannels, exec_mode):
         # os.environ["LIVENESS_THRESHOLD"] = str(liveness)
         assert output_matches, "Rtlsim output doesn't match ONNX/PyTorch."
 
-    # Should this be done as well?
-
+    # ToDo: Should this be done as well?
     # if exec_mode == "rtlsim":
     #     hls_synt_res_est = model.analysis(hls_synth_res_estimation)
     #     assert "ChannelwiseOp_Batch_0" in hls_synt_res_est