refactor FaCoRNetLightning module

Add FaCoRNet lightning config, among other things.

ours/configs/facornet.yml

@@ -0,0 +1,39 @@
+seed_everything: 100
+trainer:
+  num_sanity_val_steps: 1
+  log_every_n_steps: 10
+  accelerator: "gpu"
+  deterministic: yes
+  fast_dev_run: no
+  max_epochs: 53
+  limit_train_batches: 100
+  callbacks:
+    class_path: lightning.pytorch.callbacks.ModelCheckpoint
+    init_args:
+      dirpath: ./
+      filename: '{epoch}-{auc/val:.3f}-{auc/train:.3f}'
+      monitor: auc/val
+      verbose: no
+      save_last: yes
+      save_top_k: 1
+      save_weights_only: no
+      auto_insert_metric_name: no
+      mode: max
+
+
+data:
+   class_path: datasets.facornet.FaCoRNetDataModule
+   init_args:
+     batch_size: 20
+     root_dir: data/facornet
+
+model:
+  class_path: models.facornet.FaCoRNetLightning
+  init_args:
+    lr: 1e-4
+    momentum: 0.9
+    weight_decay: 0
+    weights_path: null
+    threshold: null
+    model:
+      class_path: models.facornet.FaCoR

ours/datasets/facornet.py

@@ -1,8 +1,8 @@
 from pathlib import Path
 
-import lightning as pl
+import lightning as L
 from torch.utils.data import DataLoader
-from torchvision import transforms
+from torchvision import transforms as T
 
 from .fiw import FIW
 
@@ -16,7 +16,8 @@ class FIWFaCoRNet(FIW):
 
     # AdaFace uses BGR -- should I revert conversion read_image here?
 
-    def __init__(self, **kwargs):
+    def __init__(self, batch_size: int, **kwargs):
+        self.batch_size = batch_size
         super().__init__(**kwargs)
 
     def __getitem__(self, item):
@@ -27,40 +28,49 @@ def __getitem__(self, item):
         return img1, img2, labels
 
 
-class FaCoRNetDataModule(pl.LightningDataModule):
+class FaCoRNetDataModule(L.LightningDataModule):
     def __init__(self, batch_size=20, root_dir=".", transform=None):
         super().__init__()
         self.batch_size = batch_size
         self.root_dir = root_dir
-        self.transform = transform or transforms.Compose([transforms.ToTensor()])
+        self.transform = transform or T.Compose([T.ToTensor()])
 
     def setup(self, stage=None):
         if stage == "fit" or stage is None:
             self.train_dataset = FIW(
-                root_dir=self.root_dir, sample_path=Path(FIWFaCoRNet.TRAIN_PAIRS), transform=self.transform
+                root_dir=self.root_dir,
+                sample_path=Path(FIWFaCoRNet.TRAIN_PAIRS),
+                batch_size=self.batch_size,
+                biased=True,
+                transform=self.transform,
             )
             self.val_dataset = FIW(
-                root_dir=self.root_dir, sample_path=Path(FIWFaCoRNet.VAL_PAIRS_MODEL_SEL), transform=self.transform
+                root_dir=self.root_dir,
+                sample_path=Path(FIWFaCoRNet.VAL_PAIRS_MODEL_SEL),
+                batch_size=self.batch_size,
+                transform=self.transform,
             )
         if stage == "validate" or stage is None:
             self.val_dataset = FIW(
-                root_dir=self.root_dir, sample_path=Path(FIWFaCoRNet.VAL_PAIRS_THRES_SEL), transform=self.transform
+                root_dir=self.root_dir,
+                sample_path=Path(FIWFaCoRNet.VAL_PAIRS_THRES_SEL),
+                batch_size=self.batch_size,
+                transform=self.transform,
             )
         if stage == "test" or stage is None:
             self.test_dataset = FIW(
-                root_dir=self.root_dir, sample_path=Path(FIWFaCoRNet.TEST_PAIRS), transform=self.transform
+                root_dir=self.root_dir,
+                sample_path=Path(FIWFaCoRNet.TEST_PAIRS),
+                batch_size=self.batch_size,
+                transform=self.transform,
             )
         print(f"Setup {stage} datasets")
 
     def train_dataloader(self):
-        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=4, pin_memory=True)
+        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=False, num_workers=4, pin_memory=True)
 
     def val_dataloader(self):
         return DataLoader(self.val_dataset, batch_size=self.batch_size, shuffle=False, num_workers=4, pin_memory=True)
 
     def test_dataloader(self):
         return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=4, pin_memory=True)
-
-
-if __name__ == "__main__":
-    fiw = FIW(root_dir="../../datasets/", sample_path=FIWFaCoRNet.TRAIN_PAIRS)

ours/datasets/fiw.py

@@ -8,12 +8,14 @@
 
 
 class FIW(Dataset):
-    def __init__(self, root_dir, sample_path, transform=None):
+    def __init__(self, root_dir: str, sample_path: Path, batch_size: int = 20, biased: bool = False, transform=None):
         self.root_dir = Path(root_dir)
         self.images_dir = "images"
         self.sample_path = sample_path
+        self.batch_size = batch_size
         self.transform = transform
         self.bias = 0
+        self.biased = biased
         self.sample_cls = Sample
         self.sample_list = self.load_sample()
         print(f"Loaded {len(self.sample_list)} samples from {sample_path}")
@@ -35,7 +37,7 @@ def load_sample(self):
         return sample_list
 
     def __len__(self):
-        return len(self.sample_list)
+        return len(self.sample_list) // self.batch_size if self.biased else len(self.sample_list)
 
     def read_image(self, path):
         # TODO: add to utils.py

ours/datasets/utils.py

@@ -7,7 +7,7 @@
 class Sample:
     # TODO: move to utils.py
     NAME2LABEL = {
-        # "non-kin": 0,
+        "non-kin": 0,
         "md": 1,
         "ms": 2,
         "sibs": 3,

ours/guild.yml

@@ -122,14 +122,16 @@
         - datasets/utils.py
         - tasks/facornet.py
       flags-import: all
-      flags-dest: config:facornet.yml
-      output-scalars: '(\key):\s+(\value)'
+      flags-dest: config:configs/facornet.yml
+      output-scalars: '(\key)=(\value)'
       requires:
         - file: weights
           target-type: link
         - file: ../datasets/
           target-type: link
           rename: data
+        - file: configs/facornet.yml
+          target-type: link
     val:
       description: Reproduction of Kinship Representation Learning with Face Componential Relation (2023)
       main: tasks.facornet validate