feat: split two types of datasets

Former-commit-id: 2609316692d315f4b0df614c533bf28d20ffaf21 [formerly c1a425cb33fefa2809e591f0fe527236f6386863]
Former-commit-id: c3f96d3f272652a6162b17112be0e722c99eef57

src/data/dataloader.py

@@ -1,12 +1,11 @@
 import albumentations as A
 import pytorch_lightning as pl
-from albumentations.pytorch import ToTensorV2
 from torch.utils.data import DataLoader
 
 import wandb
 from utils import RandomPaste
 
-from .dataset import SphereDataset
+from .dataset import LabeledDataset, SyntheticDataset
 
 
 class SyntheticSphere(pl.LightningDataModule):
@@ -14,7 +13,7 @@ class SyntheticSphere(pl.LightningDataModule):
         super().__init__()
 
     def train_dataloader(self):
-        tf_train = A.Compose(
+        transform = A.Compose(
             [
                 A.Resize(wandb.config.IMG_SIZE, wandb.config.IMG_SIZE),
                 A.Flip(),
@@ -22,16 +21,14 @@ class SyntheticSphere(pl.LightningDataModule):
                 RandomPaste(wandb.config.SPHERES, wandb.config.DIR_SPHERE),
                 A.GaussianBlur(),
                 A.ISONoise(),
-                A.ToFloat(max_value=255),
-                ToTensorV2(),
             ],
         )
 
-        ds_train = SphereDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=tf_train)
+        dataset = SyntheticDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=transform)
         # ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 10000)))
 
         return DataLoader(
-            ds_train,
+            dataset,
             shuffle=True,
             batch_size=wandb.config.BATCH_SIZE,
             num_workers=wandb.config.WORKERS,
@@ -39,10 +36,10 @@ class SyntheticSphere(pl.LightningDataModule):
         )
 
     def val_dataloader(self):
-        ds_valid = SphereDataset(image_dir=wandb.config.DIR_VALID_IMG)
+        dataset = LabeledDataset(image_dir=wandb.config.DIR_VALID_IMG)
 
         return DataLoader(
-            ds_valid,
+            dataset,
             shuffle=False,
             batch_size=1,
             num_workers=wandb.config.WORKERS,

src/data/dataset.py

@@ -7,8 +7,8 @@ from PIL import Image
 from torch.utils.data import Dataset
 
 
-class SphereDataset(Dataset):
+class SyntheticDataset(Dataset):
-    def __init__(self, image_dir, transform=None):
+    def __init__(self, image_dir, transform):
         self.images = list(Path(image_dir).glob("**/*.jpg"))
         self.transform = transform
 
@@ -16,30 +16,60 @@ class SphereDataset(Dataset):
         return len(self.images)
 
     def __getitem__(self, index):
+        # open and convert image
         image = np.array(Image.open(self.images[index]).convert("RGB"), dtype=np.uint8)
 
-        if self.transform is not None:
+        # create empty mask of same size
-            mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
+        mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
-            augmentations = self.transform(image=image, mask=mask)
-            image = augmentations["image"]
-            mask = augmentations["mask"]
-        else:
-            mask_path = self.images[index].parent.joinpath("MASK.PNG")
-            mask = np.array(Image.open(mask_path).convert("L"), dtype=np.uint8) / 255
 
-            preprocess = A.Compose(
+        # augment image and mask
-                [
+        augmentations = self.transform(image=image, mask=mask)
-                    A.SmallestMaxSize(1024),
+        image = augmentations["image"]
-                    A.ToFloat(max_value=255),
+        mask = augmentations["mask"]
-                    ToTensorV2(),
+
-                ],
+        # convert image & mask to Tensor float in [0, 1]
-            )
+        post_process = A.Compose(
-            augmentations = preprocess(image=image, mask=mask)
+            [
-            image = augmentations["image"]
+                A.ToFloat(max_value=255),
-            mask = augmentations["mask"]
+                ToTensorV2(),
+            ],
+        )
+        augmentations = post_process(image=image, mask=mask)
+        image = augmentations["image"]
+        mask = augmentations["mask"]
 
         # make sure image and mask are floats
         image = image.float()
         mask = mask.float()
 
         return image, mask
+
+
+class LabeledDataset(Dataset):
+    def __init__(self, image_dir):
+        self.images = list(Path(image_dir).glob("**/*.jpg"))
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, index):
+        # open and convert image
+        image = np.array(Image.open(self.images[index]).convert("RGB"), dtype=np.uint8)
+
+        # open and convert mask
+        mask_path = self.images[index].parent.joinpath("MASK.PNG")
+        mask = np.array(Image.open(mask_path).convert("L"), dtype=np.uint8) / 255
+
+        # convert image & mask to Tensor float in [0, 1]
+        post_process = A.Compose(
+            [
+                # A.SmallestMaxSize(1024),
+                A.ToFloat(max_value=255),
+                ToTensorV2(),
+            ],
+        )
+        augmentations = post_process(image=image, mask=mask)
+        image = augmentations["image"]
+        mask = augmentations["mask"]
+
+        return image, mask