use summary.display() for nicer test logs

Author: FynnBe

tests/conftest.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
 import subprocess
-import warnings
 from itertools import chain
 from typing import Dict, List
 
+from loguru import logger
 from pytest import FixtureRequest, fixture
 
 from bioimageio.spec import __version__ as bioimageio_spec_version
@@ -13,7 +13,7 @@
     import torch
 
     torch_version = tuple(map(int, torch.__version__.split(".")[:2]))
-    warnings.warn(f"detected torch version {torch.__version__}")
+    logger.warning("detected torch version {}", torch.__version__)
 except ImportError:
     torch = None
     torch_version = None
@@ -40,7 +40,7 @@
 
 skip_tensorflow = tensorflow is None
 
-warnings.warn(f"testing with bioimageio.spec {bioimageio_spec_version}")
+logger.warning("testing with bioimageio.spec {}", bioimageio_spec_version)
 
 # TODO: use models from new collection on S3
 MODEL_SOURCES: Dict[str, str] = {

tests/test_any_model_fixture.py

@@ -3,4 +3,4 @@
 
 def test_model(any_model: str):
     summary = load_description_and_validate_format_only(any_model)
-    assert summary.status == "passed", summary.format()
+    assert summary.status == "passed", summary.display()

tests/test_resource_tests.py

@@ -41,7 +41,7 @@ def test_test_model(any_model: str):
     with ValidationContext(raise_errors=True):
         summary = test_model(any_model)
 
-    assert summary.status == "passed", summary.format()
+    assert summary.status == "passed", summary.display()
 
 
 def test_loading_description_multiple_times(unet2d_nuclei_broad_model: str):

tests/test_weight_converters.py

@@ -21,7 +21,7 @@ def test_pytorch_to_torchscript(any_torch_model, tmp_path):
     assert ret_val.source == out_path
     model_descr.weights.torchscript = ret_val
     summary = test_model(model_descr, weight_format="torchscript")
-    assert summary.status == "passed", summary.format()
+    assert summary.status == "passed", summary.display()
 
 
 def test_pytorch_to_onnx(convert_to_onnx, tmp_path):
@@ -42,7 +42,7 @@ def test_pytorch_to_onnx(convert_to_onnx, tmp_path):
 
     model_descr.weights.onnx = ret_val
     summary = test_model(model_descr, weight_format="onnx")
-    assert summary.status == "passed", summary.format()
+    assert summary.status == "passed", summary.display()
 
 
 def test_keras_to_tensorflow(any_keras_model: Path, tmp_path: Path):
@@ -57,7 +57,7 @@ def test_keras_to_tensorflow(any_keras_model: Path, tmp_path: Path):
 
     model_descr.weights.keras = ret_val
     summary = test_model(model_descr, weight_format="keras_hdf5")
-    assert summary.status == "passed", summary.format()
+    assert summary.status == "passed", summary.display()
 
 
 @pytest.mark.skip()
@@ -78,7 +78,7 @@ def test_keras_to_tensorflow_zipped(any_keras_model: Path, tmp_path: Path):
 
     model_descr.weights.keras = ret_val
     summary = test_model(model_descr, weight_format="keras_hdf5")
-    assert summary.status == "passed", summary.format()
+    assert summary.status == "passed", summary.display()
 
 
 # TODO: add tensorflow_to_keras converter
@@ -94,7 +94,7 @@ def test_keras_to_tensorflow_zipped(any_keras_model: Path, tmp_path: Path):
 
 #     model_descr.weights.keras = ret_val
 #     summary = test_model(model_descr, weight_format="keras_hdf5")
-#     assert summary.status == "passed", summary.format()
+#     assert summary.status == "passed", summary.display()
 
 
 # @pytest.mark.skip()
@@ -115,4 +115,4 @@ def test_keras_to_tensorflow_zipped(any_keras_model: Path, tmp_path: Path):
 
 #     model_descr.weights.keras = ret_val
 #     summary = test_model(model_descr, weight_format="keras_hdf5")
-#     assert summary.status == "passed", summary.format()
+#     assert summary.status == "passed", summary.display()