FIX running CI+ linter

.github/workflows/benchopt_test.yml

@@ -19,12 +19,44 @@ jobs:
       benchmark-dirs: ${{ steps.find-dirs.outputs.dirs }}
     steps:
       - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
       - name: Find benchmark directories
         id: find-dirs
         run: |
-          # Find all directories containing an objective.py file
-          dirs=$(find . -maxdepth 2 -name "objective.py" -type f | xargs dirname | sed 's|./||' | jq -R -s -c 'split("\n")[:-1]')
-          echo "dirs=$dirs" >> $GITHUB_OUTPUT
+          # List all benchmark directories containing an objective.py file (plain list)
+          dirs_list=$(find . -maxdepth 2 -name "objective.py" -type f | xargs dirname | sed 's|./||')
+
+          # Compute a single reference range per event
+          ref_range=""
+          if [ "${{ github.event_name }}" = "pull_request" ]; then
+            # Ensure base branch is available and compare base...HEAD
+            git fetch --no-tags --prune --depth=1 origin +refs/heads/${{ github.event.pull_request.base.ref }}:refs/remotes/origin/${{ github.event.pull_request.base.ref }}
+            ref_range="origin/${{ github.event.pull_request.base.ref }}...HEAD"
+          elif [ "${{ github.event_name }}" = "push" ]; then
+            # Compare before..sha for the push
+            if [ -n "${{ github.event.before }}" ] && [ -n "${{ github.sha }}" ]; then
+              ref_range="${{ github.event.before }}..${{ github.sha }}"
+            fi
+          fi
+
+          # Filter directories based on changes relative to ref_range
+          filtered=""
+          if [ -n "$ref_range" ]; then
+            # Include only directories that have changes relative to ref_range
+            for d in $dirs_list; do
+              if git diff --name-only "$ref_range" -- "$d" | grep -q .; then
+                filtered="${filtered}${d}\n"
+              fi
+            done
+          else
+            # No ref_range (e.g., schedule/tag/create): include all benchmarks
+            filtered=$(printf "%s\n" "$dirs_list")
+          fi
+
+          # Build JSON array from filtered list using jq and output in GitHub Actions format
+          dirs_json=$(printf "%b" "$filtered" | sed '/^$/d' | jq -R -s -c 'split("\n")[:-1]')
+          echo "dirs=$dirs_json" >> $GITHUB_OUTPUT
 
   benchopt_dev:
     needs: find-benchmarks

benchmark_template/config.yml

@@ -10,3 +10,4 @@ plot_configs: # add here configurations for plotting each metric
     metric: NIQE
     scale: linear
   # add more plot configurations as needed
+data_home: "../data"

benchmark_template/datasets/dataset.py

@@ -14,29 +14,29 @@ class Dataset(BaseDataset):
     # IMPORTANT: the names of physics and noise should match
     # the ones defined in deepinv.physics with exact same spelling
     parameters = {
-        'physics' : ['physics_name'],
-        'noise' : ['noise_name'],
+        'physics': ['physics_name'],
+        'noise': ['noise_name'],
         'img_size': [256],
         # add any other parameter you might need
     }
 
-    requirements = ["datasets"]
-
     def get_data(self):
-        root = get_data_path("DIV2K")
+        root = get_data_path("Set14_HR")
 
         transform = transforms.Compose([
             transforms.Resize((self.img_size, self.img_size)),
             transforms.ToTensor()
         ])
 
         # load the dataset
-        dataset = dinv.datasets.DIV2K(
-            root, mode="val", download=True, transform=transform
+        dataset = dinv.datasets.Set14HR(
+            root, download=True, transform=transform
         )
 
         # define the physics according to the parameters
-        physics = dinv.physics.Denoising(noise_model=dinv.physics.GaussianNoise())
+        physics = dinv.physics.Denoising(
+            noise_model=dinv.physics.GaussianNoise()
+        )
 
         return dict(
             dataset=dataset,

benchmark_template/objective.py

@@ -11,7 +11,7 @@ class Objective(BaseObjective):
 
     url = "https://github.com/deep-inverse/benchmarks"
 
-    requirements = ["deepinv"]
+    requirements = ["deepinv", "datasets", "pip:pyiqa"]
 
     # Minimal version of benchopt required to run this benchmark.
     # Bump it up if the benchmark depends on a new feature of benchopt.
@@ -46,7 +46,13 @@ def evaluate_result(self, model):
         return results
 
     def get_one_result(self):
-        return dict(model=lambda x: x)
+        class DummyModel:
+            def eval(self): pass
+
+            def __call__(self, x, physics=None):
+                return physics.A_adjoint(x)
+
+        return dict(model=DummyModel())
 
     def get_objective(self):
         return dict(

benchmark_template/solvers/solver1.py

@@ -16,7 +16,7 @@ def set_objective(self, train_dataset=None, physics=None):
         )
 
         # replace by your model, should take (y, physics) as input
-        self.model = dinv.models.RAM(device=device)
+        self.model = dinv.models.DnCNN(device=device)
         self.model.device = device
 
     def run(self, _):

cbsd500_gaussian_denoising/config.yml

@@ -9,3 +9,4 @@ plot_configs:
     plot_kind: deepinv_boxplot
     metric: NIQE
     scale: linear
+data_home: "../data"

cbsd500_gaussian_denoising/datasets/cbsd68.py

@@ -14,8 +14,8 @@ class Dataset(BaseDataset):
 
     name = "CBSD68"
     parameters = {
-        'physics' : ['Denoising'],
-        'noise' : ['GaussianNoise'],
+        'physics': ['Denoising'],
+        'noise': ['GaussianNoise'],
         'sigma': [0.1],
         'img_size': [256],
         'debug': [False],
@@ -25,8 +25,6 @@ class Dataset(BaseDataset):
         "debug": [True]
     }
 
-    requirements = ["datasets"]
-
     def get_data(self):
 
         root = get_data_path("CBSD68")

cbsd500_gaussian_denoising/objective.py

@@ -11,7 +11,7 @@ class Objective(BaseObjective):
 
     url = "https://github.com/deep-inverse/benchmarks"
 
-    requirements = ["deepinv"]
+    requirements = ["deepinv", "datasets", "pip:pyiqa"]
 
     # Minimal version of benchopt required to run this benchmark.
     # Bump it up if the benchmark depends on a new feature of benchopt.

div2k_gaussian_deblurring/config.yml

@@ -9,3 +9,4 @@ plot_configs:
     plot_kind: deepinv_boxplot
     metric: NIQE
     scale: linear
+data_home: "../data"

div2k_gaussian_deblurring/datasets/div2k.py

@@ -14,8 +14,8 @@ class Dataset(BaseDataset):
 
     name = "DIV2K"
     parameters = {
-        'physics' : ['Blur'],
-        'noise' : ['GaussianNoise'],
+        'physics': ['Blur'],
+        'noise': ['GaussianNoise'],
         'sigma': [0.1],
         'img_size': [256],
         'debug': [False],
@@ -25,8 +25,6 @@ class Dataset(BaseDataset):
         "debug": [True]
     }
 
-    requirements = ["datasets"]
-
     def get_data(self):
         root = get_data_path("DIV2K")
         transform = transforms.Compose([

div2k_gaussian_deblurring/objective.py

@@ -11,7 +11,7 @@ class Objective(BaseObjective):
 
     url = "https://github.com/deep-inverse/benchmarks"
 
-    requirements = ["deepinv"]
+    requirements = ["deepinv", "datasets", "pip:pyiqa"]
 
     # Minimal version of benchopt required to run this benchmark.
     # Bump it up if the benchmark depends on a new feature of benchopt.