add experiments launching

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/data/base.py

@@ -13,9 +13,13 @@
 
 @dataclass
 class DataModule(LightningDataModule):
-    batch_size: int = context.config.target_network_training.batch_size
+    batch_size: int = field(
+        default_factory=lambda: context.config.target_network_training.batch_size
+    )
     evaluation_batch_size: int = 1000
-    validation_ratio = context.config.validation_ratio
+    validation_ratio: float = field(
+        default_factory=lambda: context.config.validation_ratio
+    )
     train: data.Dataset[Any] = field(init=False)
     validation: data.Dataset[Any] = field(init=False)
     train_validation: data.Dataset[Any] = field(init=False)

src/revnets/evaluations/__init__.py

@@ -1,4 +1,3 @@
-from . import analysis, attack, outputs, weights
 from .base import Evaluator
 from .evaluate import evaluate
 from .evaluation import Evaluation

src/revnets/evaluations/analysis/activations.py

@@ -9,8 +9,8 @@
 from torch.nn import Module
 from torch.utils.data import TensorDataset
 
-from revnets.reconstructions.queries.data import QueryDataSet
 from revnets.reconstructions.queries.random import Reconstructor
+from revnets.utils.data import compute_targets
 
 from .. import base
 
@@ -48,7 +48,7 @@ def visualize_network(self, network: nn.Module, name: str) -> None:
 
     def visualize_model_outputs(self, model: Module, name: str) -> None:
         inputs = Reconstructor(self.pipeline).create_queries(self.n_inputs)
-        outputs = QueryDataSet(model).compute_targets(inputs)
+        outputs = compute_targets(inputs, model)
         if self.activation:
             outputs = F.relu(outputs)  # pragma: nocover
         ActivationsVisualizer(outputs, name).run()

src/revnets/evaluations/analysis/trained_target.py

@@ -10,7 +10,7 @@
 class Evaluator(base.Evaluator):
     def evaluate(self) -> None:
         dataset = self.pipeline.load_prepared_data()
-        network = Network(self.pipeline.target, learning_rate=0)
+        network = Network(self.pipeline.target)
         dataloaders = (
             dataset.train_dataloader(),
             dataset.val_dataloader(),

src/revnets/evaluations/analysis/weights.py

@@ -7,10 +7,10 @@
 from torch.nn import Module
 
 from revnets.context import context
+from revnets.standardization import extract_layer_weights, generate_layers
 
 from ...utils.colors import get_colors
 from ..weights import layers_mae
-from ..weights.standardize import extract_layer_weights, generate_layers
 
 cpu = torch.device("cpu")
 

src/revnets/evaluations/evaluate.py

@@ -16,9 +16,8 @@ def apply(evaluation_module: ModuleType) -> Any:
         evaluator: Evaluator = evaluation_module.Evaluator(reconstruction, pipeline)
         return evaluator.get_evaluation()
 
-    analysis_modules = (analysis.weights,)
-
     if context.config.evaluation.run_analysis:
+        analysis_modules = (analysis.weights,)
         for analysis_module in analysis_modules:
             apply(analysis_module)
 

src/revnets/evaluations/weights/layers_mae.py

@@ -2,8 +2,9 @@
 
 import torch
 
+from revnets.standardization import extract_layer_weights, generate_layers
+
 from . import mae
-from .standardize import extract_layer_weights, generate_layers
 
 
 class Evaluator(mae.Evaluator):

src/revnets/evaluations/weights/mse.py

@@ -4,9 +4,10 @@
 
 import torch
 
+from revnets.standardization import Standardizer, align
+
 from ...context import context
 from .. import base
-from .standardize import Standardizer, align
 
 
 @dataclass

src/revnets/launching/lauch_plan.py

@@ -10,17 +10,15 @@
 class LaunchPlan:
     @classmethod
     def reconstruction_techniques(cls) -> Iterator[ModuleType]:
-        yield reconstructions.cheat
+        yield reconstructions.queries.random
 
     @classmethod
     def pipelines(cls) -> Iterator[ModuleType]:
-        # yield pipelines.mininet_images.mininet_100
         yield pipelines.mininet.mininet
 
     @classmethod
     def seeds(cls) -> Iterator[int]:
-        yield 77
-        # yield from (77, 78, 79)
+        yield from range(5)
 
     @classmethod
     def experiments_to_launch(cls) -> Iterator[models.Experiment]:

src/revnets/launching/main.py

@@ -19,6 +19,13 @@ def main() -> None:
 def launch(experiment: Experiment) -> None:
     config_dict = cast(dict[str, Any], context.options.config_path.yaml)
     experiment.prepare_config(config_dict)
-    print(experiment.config_path)
-    cli.run("ls")
-    # cli.run("revnets", "--config-path", experiment.config_path)
+    command = "revnets", "--config-path", str(experiment.config_path)
+    title = "_".join(experiment.names)
+    launch_command(command, title)
+
+
+def launch_command(command: tuple[str, ...], title: str) -> None:
+    command_str = " ".join(command)
+    shell_command = f"{command_str}; fish"
+    launch_command = ("tmux", "new-session", "-s", title, "-d", shell_command)
+    cli.run(launch_command)

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/models/config.py

@@ -34,10 +34,10 @@ class Evaluation:
 class Config(SerializationMixin):
     sampling_data_size: int = 20000
     reconstruction_training: HyperParameters = HyperParameters(
-        epochs=300, learning_rate=3e-2, batch_size=256
+        epochs=300, learning_rate=1e-1, batch_size=256
     )
-    reconstruct_from_checkpoint: bool = True
-    always_train: bool = False
+    reconstruct_from_checkpoint: bool = False
+    always_train: bool = True
     n_rounds: int = 2
     experiment: Experiment = field(default_factory=Experiment)
 
@@ -70,14 +70,14 @@ class Config(SerializationMixin):
     console_metrics_refresh_interval: float = 0.5
     max_difficult_inputs_epochs: int = 100
 
+    limit_batches: int | None = None
+
     @property
     def number_of_validation_sanity_steps(self) -> int | None:
         return 0 if self.debug else 0
 
-    @property
-    def limit_batches(self) -> int | None:
-        return self.debug_batch_limit if self.debug else None
-
     def __post_init__(self) -> None:
         if self.evaluation.run_analysis:
             self.always_train = False
+        if self.debug:
+            self.limit_batches = self.debug_batch_limit  # pragma: nocover

src/revnets/models/experiment.py

@@ -43,7 +43,7 @@ def generate_title_parts(self) -> Iterator[str]:
 
     @property
     def path(self) -> Path:
-        path = Path.results
+        path = Path.config
         for name in self.names:
             path /= name
         return cast(Path, path)

src/revnets/networks/base.py

@@ -1,5 +1,5 @@
 from collections.abc import Iterable
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 import torch
 from torch import nn
@@ -11,10 +11,12 @@
 from ..utils import NamedClass
 
 
-@dataclass(frozen=True)
+@dataclass
 class NetworkFactory(NamedClass):
     output_size: int = 10
-    activation: Activation = context.config.target_network_training.activation
+    activation: Activation = field(
+        default_factory=lambda: context.config.target_network_training.activation
+    )
 
     def create_activation_layer(self) -> Module:
         activation_layer: Module

src/revnets/networks/images/mediumnet.py

@@ -6,7 +6,7 @@
 from .. import mediumnet
 
 
-@dataclass(frozen=True)
+@dataclass
 class NetworkFactory(mediumnet.NetworkFactory):
     input_size: int = 784
     hidden_size1: int = 512

src/revnets/networks/images/mininet.py

@@ -6,7 +6,7 @@
 from .. import mininet
 
 
-@dataclass(frozen=True)
+@dataclass
 class NetworkFactory(mininet.NetworkFactory):
     input_size: int = 784
     hidden_size: int = 40

src/revnets/networks/mediumnet.py

@@ -6,7 +6,7 @@
 from . import base
 
 
-@dataclass(frozen=True)
+@dataclass
 class NetworkFactory(base.NetworkFactory):
     input_size: int = 40
     hidden_size1: int = 20

src/revnets/networks/mininet.py

@@ -7,7 +7,7 @@
 from . import base
 
 
-@dataclass(frozen=True)
+@dataclass
 class NetworkFactory(base.NetworkFactory):
     input_size: int = 40
     hidden_size: int = 20

src/revnets/pipelines/images/mediumnet/mediumnet.py

@@ -5,4 +5,6 @@
 
 
 class Pipeline(mininet.Pipeline):
-    network_factory: NetworkFactory = networks.images.mediumnet.NetworkFactory()
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.images.mediumnet.NetworkFactory()

src/revnets/pipelines/images/mediumnet/mediumnet_small.py

@@ -8,6 +8,8 @@
 
 @dataclass
 class Pipeline(mediumnet.Pipeline):
-    network_factory: NetworkFactory = networks.images.mediumnet.NetworkFactory(
-        hidden_size1=100, hidden_size2=50
-    )
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.images.mediumnet.NetworkFactory(
+            hidden_size1=100, hidden_size2=50
+        )

src/revnets/pipelines/images/mininet/mininet_100.py

@@ -5,6 +5,6 @@
 
 
 class Pipeline(mininet_small.Pipeline):
-    network_factory: NetworkFactory = networks.images.mininet.NetworkFactory(
-        hidden_size=100
-    )
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.images.mininet.NetworkFactory(hidden_size=100)

src/revnets/pipelines/images/mininet/mininet_128.py

@@ -5,6 +5,6 @@
 
 
 class Pipeline(mininet_small.Pipeline):
-    network_factory: NetworkFactory = networks.images.mininet.NetworkFactory(
-        hidden_size=128
-    )
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.images.mininet.NetworkFactory(hidden_size=128)

src/revnets/pipelines/images/mininet/mininet_200.py

@@ -5,6 +5,6 @@
 
 
 class Pipeline(mininet_small.Pipeline):
-    network_factory: NetworkFactory = networks.images.mininet.NetworkFactory(
-        hidden_size=200
-    )
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.images.mininet.NetworkFactory(hidden_size=200)

src/revnets/pipelines/images/mininet/mininet_small.py

@@ -8,9 +8,9 @@
 
 @dataclass
 class Pipeline(train.Pipeline):
-    network_factory: NetworkFactory = networks.images.mininet.NetworkFactory(
-        hidden_size=40
-    )
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.images.mininet.NetworkFactory(hidden_size=40)
 
     @classmethod
     def load_data(cls) -> data.mnist.DataModule:

src/revnets/pipelines/mediumnet/mediumnet.py

@@ -5,4 +5,6 @@
 
 
 class Pipeline(mininet.Pipeline):
-    network_factory: NetworkFactory = networks.mediumnet.NetworkFactory()
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.mediumnet.NetworkFactory()

src/revnets/pipelines/mediumnet/mediumnet_40.py

@@ -5,6 +5,6 @@
 
 
 class Pipeline(mininet.Pipeline):
-    network_factory: NetworkFactory = networks.mediumnet.NetworkFactory(
-        hidden_size1=40, hidden_size2=20
-    )
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.mediumnet.NetworkFactory(hidden_size1=40, hidden_size2=20)

src/revnets/pipelines/mininet/mininet.py

@@ -8,7 +8,9 @@
 
 @dataclass
 class Pipeline(train.Pipeline):
-    network_factory: NetworkFactory = mininet.NetworkFactory()
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return mininet.NetworkFactory()
 
     @classmethod
     def load_data(cls) -> mnist1d.DataModule:

src/revnets/pipelines/mininet/mininet_100.py

@@ -8,4 +8,6 @@
 
 @dataclass
 class Pipeline(mininet.Pipeline):
-    network_factory: NetworkFactory = networks.mininet.NetworkFactory(hidden_size=100)
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.mininet.NetworkFactory(hidden_size=100)

src/revnets/pipelines/mininet/mininet_40.py

@@ -8,4 +8,6 @@
 
 @dataclass
 class Pipeline(mininet.Pipeline):
-    network_factory: NetworkFactory = networks.mininet.NetworkFactory(hidden_size=40)
+    @classmethod
+    def create_network_factory(cls) -> NetworkFactory:
+        return networks.mininet.NetworkFactory(hidden_size=40)