fix tests

src/revnets/context/context.py

@@ -1,4 +1,5 @@
 from functools import cached_property
+from typing import cast
 
 import torch
 from package_utils.context import Context as Context_
@@ -22,8 +23,10 @@ def output_path(self) -> Path:
 
     @property
     def results_path(self) -> Path:
-        path = self.output_path / "results.yaml"
-        return path.with_nonexistent_name()
+        relative_path = self.output_path.relative_to(Path.config)
+        path = Path.results / relative_path / "results.yaml"
+        path = path.with_nonexistent_name()
+        return cast(Path, path)
 
     @property
     def log_path(self) -> Path:

src/revnets/evaluations/evaluate.py

@@ -6,7 +6,7 @@
 from revnets.context import context
 
 from ..pipelines import Pipeline
-from . import outputs, weights
+from . import analysis, outputs, weights
 from .base import Evaluator
 from .evaluation import Evaluation
 
@@ -17,8 +17,6 @@ def apply(evaluation_module: ModuleType) -> Any:
         return evaluator.get_evaluation()
 
     if context.config.evaluation.run_analysis:
-        from . import analysis
-
         analysis_modules = (analysis.weights,)
         for analysis_module in analysis_modules:
             apply(analysis_module)

src/revnets/main/main.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass
 from types import ModuleType
+from typing import Any
 
 import cli
 import numpy as np
@@ -28,15 +29,15 @@ def config(self) -> Config:
     def run(self) -> None:
         set_seed()
         cli.console.rule(self.config.title)
-        self.run_experiment()
+        results = self.run_experiment()
+        context.results_path.yaml = results
 
-    def run_experiment(self) -> None:
+    def run_experiment(self) -> dict[str, Any]:
         pipeline: Pipeline = extract_module(pipelines, self.config.pipeline).Pipeline()
         reconstruction = self.create_reconstruction(pipeline)
         evaluation = evaluations.evaluate(reconstruction, pipeline)
         evaluation.show()
-        results = {"metrics": evaluation.dict(), "config": context.config.dict()}
-        context.results_path.yaml = results
+        return {"metrics": evaluation.dict(), "config": context.config.dict()}
 
     def create_reconstruction(self, pipeline: Pipeline) -> nn.Module:
         module = extract_module(reconstructions, self.config.reconstruction_technique)

src/revnets/reconstructions/queries/data.py

@@ -16,7 +16,9 @@ def __init__(
     ) -> None:
         self.target = target
         if evaluation_batch_size is None:
-            evaluation_batch_size = context.config.evaluation_batch_size
+            evaluation_batch_size = (
+                context.config.evaluation_batch_size
+            )  # pragma: nocover
         self.evaluation_batch_size = evaluation_batch_size
         tensors = torch.Tensor([]), torch.Tensor([])
         super().__init__(*tensors)

src/revnets/training/targets/network.py

@@ -9,11 +9,7 @@
 
 
 class Network(network.Network[Metrics]):
-    def __init__(
-        self,
-        model: nn.Module,
-        do_log: bool = True,
-    ) -> None:
+    def __init__(self, model: nn.Module, do_log: bool = True) -> None:
         learning_rate = context.config.target_network_training.learning_rate
         super().__init__(model, learning_rate, do_log)
 

tests/test_cli_entry_points.py

@@ -5,7 +5,7 @@
 
 
 @no_cli_args
-@patch("revnets.main.main.Experiment.run_experiment")
+@patch("revnets.main.main.Experiment.run_experiment", return_value={})
 def test_main(run: MagicMock) -> None:
     entry_point.entry_point()
     run.assert_called_once()

tests/test_pipelines.py

@@ -11,10 +11,26 @@
     pipelines.images.mediumnet_small,
 )
 
+all_pipeline_modules = (
+    *pipeline_modules,
+    pipelines.mininet.mininet_40,
+    pipelines.mininet.mininet_100,
+    pipelines.images.mininet_100,
+    pipelines.images.mininet_128,
+    pipelines.images.mininet_200,
+    pipelines.mediumnet.mediumnet_40,
+)
+
 
 @pytest.mark.parametrize("pipeline_module", pipeline_modules)
 def test_target_network_training(
     pipeline_module: ModuleType, test_context: None
 ) -> None:
     pipeline: Pipeline = pipeline_module.Pipeline()
     pipeline.create_target_network()
+
+
+@pytest.mark.parametrize("pipeline_module", all_pipeline_modules)
+def test_network_factory(pipeline_module: ModuleType, test_context: None) -> None:
+    pipeline: Pipeline = pipeline_module.Pipeline()
+    pipeline.create_network_factory()