Feat CI Pipeline

.github/workflows/code_check.yml

@@ -0,0 +1,48 @@
+name: Code Checking
+
+on: 
+  push:
+    paths:
+      - '**/*.py'
+  pull_request:
+    branches:
+      - master
+
+env:
+  singularity_image: oras://ghcr.io/berenslab/retinal-rl:singularity-image-latest
+  sif_file: retinal-rl_singularity-image-latest.sif
+
+jobs:
+  check:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+    - name: Fetch all branches
+      run: git fetch --all
+
+    - name: Setup Apptainer
+      uses: eWaterCycle/setup-apptainer@v2
+      with:
+        apptainer-version: 1.3.0
+    - name: Cache Singularity Image
+      id: cache-singularity
+      uses: actions/cache@v3
+      with:
+        path: ${{ env.sif_file }}
+        key: ${{ runner.os }}-singularity-${{ hashFiles('~/resources/retinal-rl.def') }}
+        restore-keys: |
+          ${{ runner.os }}-singularity-${{ hashFiles('~/resources/retinal-rl.def') }}
+          ${{ runner.os }}-singularity-
+    - name: Pull Singularity container
+      if: steps.cache-singularity.outputs.cache-hit != 'true'
+      run: |
+        singularity registry login --username ${{ github.actor }} --password ${{ secrets.GITHUB_TOKEN }} oras://ghcr.io
+        singularity pull ${{ env.sif_file }} ${{ env.singularity_image }}
+
+    - name: Run Pylint
+      run: |
+        singularity exec ${{ env.sif_file }} \
+        pylint $(git diff --name-only origin/master...HEAD -- '*.py')

.github/workflows/config_scan.yml

@@ -0,0 +1,36 @@
+name: Scan Configs
+on: [pull_request,workflow_dispatch]
+
+env:
+  singularity_image: oras://ghcr.io/berenslab/retinal-rl:singularity-image-latest
+  sif_file: retinal-rl_singularity-image-latest.sif
+
+jobs:
+  scan:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - name: Setup Apptainer
+      uses: eWaterCycle/setup-apptainer@v2
+      with:
+        apptainer-version: 1.3.0
+    - name: Cache Singularity Image
+      id: cache-singularity
+      uses: actions/cache@v3
+      with:
+        path: ${{ env.sif_file }}
+        key: ${{ runner.os }}-singularity-${{ hashFiles('~/resources/retinal-rl.def') }}
+        restore-keys: |
+          ${{ runner.os }}-singularity-${{ hashFiles('~/resources/retinal-rl.def') }}
+          ${{ runner.os }}-singularity-
+    - name: Pull Singularity container
+      if: steps.cache-singularity.outputs.cache-hit != 'true'
+      run: |
+        singularity registry login --username ${{ github.actor }} --password ${{ secrets.GITHUB_TOKEN }} oras://ghcr.io
+        singularity pull ${{ env.sif_file }} ${{ env.singularity_image }}
+
+    - name: Scan classification config
+      run: |
+        cp -r resources/config_templates/* config/
+        singularity exec ${{ env.sif_file }} \
+        python main.py -m +experiment=cifar10-class-recon command=scan system.device=cpu

.github/workflows/container_build.yml

@@ -0,0 +1,35 @@
+name: Build Singularity Container
+
+on: 
+  schedule:
+    - cron: '0 2 1 * *'
+  push:
+    paths:
+      - 'resources/retinal-rl.def'
+  pull_request:
+    branches:
+      - master
+    paths:
+      - 'resources/retinal-rl.def'
+
+jobs:
+  singularity-build:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: eWaterCycle/setup-apptainer@v2
+      with:
+        apptainer-version: 1.3.0
+
+    - name: Build Singularity container
+      run: apptainer build retinal-rl.sif resources/retinal-rl.def
+
+    - name: Scan classification config / ensure minimal functionality
+      run: |
+        cp -r resources/config_templates/* config/
+        singularity exec retinal-rl.sif python main.py -m +experiment=cifar10-class-recon command=scan system.device=cpu
+
+    - name: Push to ghcr.io
+      run: |
+        singularity registry login --username ${{ github.actor }} --password ${{ secrets.GITHUB_TOKEN }} oras://ghcr.io
+        singularity push retinal-rl.sif oras://ghcr.io/berenslab/retinal-rl:singularity-image-latest

.pylintrc

@@ -0,0 +1,2 @@
+[MASTER]
+init-hook='import sys; sys.path.append(".")'

doom_creator/compile-scenario.py → doom_creator/compile_scenario.py

@@ -1,3 +1,15 @@
+"""Scenario Compiler for Retinal-RL
+
+This module provides a utility to construct scenarios for the retinal-rl project.
+It merges YAML files from a specified directory into a scenario specification and
+subsequently compiles the scenario defined through those yaml files.
+
+Usage:
+    python -m exec.compile-scenario [options] [yaml_files...]
+
+Example:
+    python -m exec.compile-scenario gathering apples"""
+
 import argparse
 import os
 import sys
@@ -11,6 +23,13 @@
 
 
 def make_parser():
+    """
+    Create and configure an argument parser for the scenario compiler.
+    Check the parsers description and arguments help for further information.
+
+    Returns:
+        argparse.ArgumentParser: Configured argument parser for the scenario compiler.
+    """
     # Initialize parser
     Directories()
     parser = argparse.ArgumentParser(
@@ -22,7 +41,7 @@ def make_parser():
         running the first time one should use the --preload flag to download the
         necessary resources into the --out_dir ('{Directories().CACHE_DIR}').
         """,
-        epilog="Example: python -m exec.compile-scenario gathering apples",
+        epilog="Example: python -m exec.compile_scenario gathering apples",
     )
     # Positional argument for scenario yaml files (required, can be multiple)
     parser.add_argument(
@@ -44,7 +63,7 @@ def make_parser():
     parser.add_argument(
         "--dataset_dir",
         default=None,
-        help="source directory of a dataset (for preloading), if you already downloaded it somewhere",
+        help="source directory of a dataset (for preloading), if already downloaded somewhere",
     )
     parser.add_argument(
         "--resource_dir",
@@ -72,6 +91,15 @@ def make_parser():
 
 
 def main():
+    """
+    Main function to parse arguments and execute scenario compilation tasks.
+
+    The function supports various modes of operation including preloading resources,
+    listing available YAML files, and creating scenarios. It also handles error
+    checking and warns the user if no actions are specified.
+
+    For a more detailed documentation, check the parser.
+    """
     # Parse args
     argv = sys.argv[1:]
     parser = make_parser()
@@ -92,7 +120,7 @@ def main():
         print(f"Listing contents of {dirs.SCENARIO_YAML_DIR}:")
         for flnm in os.listdir(dirs.SCENARIO_YAML_DIR):
             print(flnm)
-        print(f"If you want to load from a different folder, change this to")
+        print("If you want to load from a different folder, change this to")
     if do_make:
         cfg, needed_types = check_preload(cfg, args.test)
         any_dataset = False

doom_creator/util/_templates/vizdoom.py

@@ -1,5 +1,13 @@
-def config(scenario_name):
-    return """\
+"""
+This module contains a dummy for a vizdoom config.
+"""
+
+
+def config(scenario_name: str):
+    """Returns a config for a vizdoom game,
+    referencing {scenario_name}.zip as the scenario."""
+
+    return f"""\
 doom_scenario_path = {scenario_name}.zip
 
 living_reward = 0.0
@@ -32,6 +40,4 @@ def config(scenario_name):
 available_game_variables = {{ HEALTH }}
 
 mode = PLAYER
-""".format(
-        scenario_name=scenario_name
-    )
+"""

doom_creator/util/templates.py

@@ -1 +1,8 @@
+"""Templates for doom scenario creation
+
+This module provides the templates for acs scripts, decorate
+definitions and the overall config (vizdoom)
+"""
 from doom_creator.util._templates import acs, decorate, vizdoom
+
+__all__ = ['acs', 'decorate', 'vizdoom']

resources/config_templates/user/brain/retinal-classifier.yaml → resources/config_templates/user/brain/shallow-autoencoder.yaml

@@ -24,8 +24,8 @@ circuits:
     _target_: retinal_rl.models.circuits.convolutional.ConvolutionalEncoder
     num_layers: 2
     num_channels: [16,32]  # Two layers with 16 and 32 channels
-    kernel_size: ${kernel_size}
-    stride: ${stride}
+    kernel_size: 8
+    stride: 2
     act_name: ${activation}
     layer_names: ["bipolar", "retinal_ganglion"]  # Names inspired by retinal cell types
 
@@ -34,8 +34,8 @@ circuits:
     _target_: retinal_rl.models.circuits.convolutional.ConvolutionalEncoder
     num_layers: 1
     num_channels: 64
-    kernel_size: ${kernel_size}
-    stride: ${stride}
+    kernel_size: 5
+    stride: 1
     act_name: ${activation}
     layer_names: ["lgn"]  # Lateral Geniculate Nucleus
 
@@ -44,27 +44,27 @@ circuits:
     _target_: retinal_rl.models.circuits.convolutional.ConvolutionalEncoder
     num_layers: 1
     num_channels: 64
-    kernel_size: ${kernel_size}
-    stride: ${stride} 
+    kernel_size: 8
+    stride: 2
     act_name: ${activation}
     layer_names: ["v1"]  # Primary Visual Cortex
 
   # Prefrontal Cortex: high-level cognitive processing
   pfc:
     _target_: retinal_rl.models.circuits.fully_connected.FullyConnectedEncoder
     output_shape:
-      - ${latent_size}  # Size of the latent representation
+      - 128  # Size of the latent representation
     hidden_units: 
-      - ${hidden_units}  # Number of hidden units
+      - 64  # Number of hidden units
     act_name: ${activation}
 
   # Decoder: for reconstructing the input from the latent representation
   decoder:
     _target_: retinal_rl.models.circuits.convolutional.ConvolutionalDecoder
     num_layers: 3
     num_channels: [32,16,3]  # For a symmetric encoder, this should be the reverse of the num_channels in the CNN layers up to the point of decoding (in this case, the thalamus)
-    kernel_size: ${kernel_size}
-    stride: ${stride}
+    kernel_size: [5,8,8]
+    stride: [1,2,2]
     act_name: ${activation}
 
   # Classifier: for categorizing the input into classes

resources/config_templates/user/dataset/cifar10-large.yaml → resources/config_templates/user/dataset/cifar10-decontrast.yaml

@@ -11,11 +11,9 @@ imageset:
       image_rescale_range: [1, 5]
   noise_transforms:
     - _target_: retinal_rl.datasets.transforms.ShotNoiseTransform
-      lambda_range: [0.8, 1.2]
+      lambda_range: [0.5, 1.5]
     - _target_: retinal_rl.datasets.transforms.ContrastTransform
-      contrast_range: [0.5, 1.5]
+      contrast_range: [0.01, 1.2]
   apply_normalization: true
-  # normalization_mean: [0.4914, 0.4822, 0.4465]
-  # normalization_std: [0.2023, 0.1994, 0.2010]
   fixed_transformation: false
   multiplier: 1

resources/config_templates/user/experiment/cifar10-class-recon.yaml

@@ -1,25 +1,22 @@
-# This is the main entry point for users to specify their config parameters, and
-# should be freely copied and edited.
-
-# Defaults for the various subconfigs. Can be overriden from the commandline
-# with e.g. experiment/brain=new_brain, where new_brain.yaml lives in the brain
-# subdirectory
+# @package _global_
 defaults:
   - _self_
-  - sweep: kernel-size
-  - dataset: cifar10-large
-  - brain: retinal-classifier
-  - optimizer: class-recon
+  - override /dataset: cifar10-decontrast
+  - override /brain: shallow-autoencoder
+  - override /optimizer: recon-weight
 
-# This *must* match the experiment file name
-name: cifar10-class-recon
+# This is the main entry point for control of a retinal-rl experiment. Variables
+# created here will be top-level, and defaults can be set for the various parts
+# of an experiment (NB: do not add comments above the defaults list or it will
+# break the config system.)
 framework: classification
 
 # This is a free list of parameters that can be interpolated by the subconfigs
 # in sweep, dataset, brain, and optimizer. A major use for this is interpolating
 # values in the subconfigs, and then looping over them in a sweep.
-latent_size: 128
-hidden_units: 64
 activation: "elu"
-kernel_size: 8
-stride: 2
+activation_sparsity: 0.0001
+weight_decay: 0.0001
+sparse_objective: retinal_rl.models.objective.L1Sparsity
+recon_weight_retina: 1
+recon_weight_thalamus: 0.99