feat: new paste dataset

Former-commit-id: 039874208d5a27bf01beb2746a77502fd836ae5c [formerly 66638fcabaea1044d9a2fd48e6ffb20f149ebf47]
Former-commit-id: 6bdf8bba0b3cbd8706337aa3167c36fba8855a4c

comp.ipynb.REMOVED.git-id

@@ -1 +1 @@
-9cbd3cff7e664a80a5a1fa1404898b7bba3cae0d
+3c9a34f197340a6051eb34d11695c7d6b72164f0

src/train.py

@@ -15,16 +15,22 @@ CONFIG = {
     "DIR_TEST_IMG": "/home/lilian/data_disk/lfainsin/test/",
     "DIR_SPHERE_IMG": "/home/lilian/data_disk/lfainsin/spheres/Images/",
     "DIR_SPHERE_MASK": "/home/lilian/data_disk/lfainsin/spheres/Masks/",
-    "FEATURES": [16, 32, 64, 128],
+    # "FEATURES": [1, 2, 4, 8],
+    # "FEATURES": [4, 8, 16, 32],
+    # "FEATURES": [8, 16, 32, 64],
+    # "FEATURES": [4, 8, 16, 32, 64],
+    "FEATURES": [8, 16, 32, 64, 128],
+    # "FEATURES": [16, 32, 64, 128],
+    # "FEATURES": [64, 128, 256, 512],
     "N_CHANNELS": 3,
     "N_CLASSES": 1,
     "AMP": True,
     "PIN_MEMORY": True,
     "BENCHMARK": True,
     "DEVICE": "gpu",
-    "WORKERS": 8,
+    "WORKERS": 10,
-    "EPOCHS": 10,
+    "EPOCHS": 1,
-    "BATCH_SIZE": 16,
+    "BATCH_SIZE": 32,
     "LEARNING_RATE": 1e-4,
     "WEIGHT_DECAY": 1e-8,
     "MOMENTUM": 0.9,

src/unet/model.py

@@ -82,7 +82,7 @@ class UNet(pl.LightningModule):
         )
 
         ds_train = SphereDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=tf_train)
-        ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 5000)))
+        # ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 10000)))
 
         return DataLoader(
             ds_train,
@@ -178,6 +178,8 @@ class UNet(pl.LightningModule):
                                 },
                             },
                         ),
+                        dice,
+                        dice_bin,
                     ]
                 )
 
@@ -199,7 +201,7 @@ class UNet(pl.LightningModule):
         mae = torch.stack([d["mae"] for d in validation_outputs]).mean()
 
         # table unpacking
-        columns = ["ID", "image", "ground truth", "prediction"]
+        columns = ["ID", "image", "ground truth", "prediction", "dice", "dice_bin"]
         rowss = [d["table_rows"] for d in validation_outputs]
         rows = list(itertools.chain.from_iterable(rowss))
 

src/utils/paste.py

@@ -1,5 +1,6 @@
 import os
 import random as rd
+from pathlib import Path
 
 import albumentations as A
 import numpy as np
@@ -22,15 +23,15 @@ class RandomPaste(A.DualTransform):
     def __init__(
         self,
         nb,
-        path_paste_img_dir,
+        image_dir,
-        path_paste_mask_dir,
         scale_range=(0.1, 0.2),
         always_apply=True,
         p=1.0,
     ):
         super().__init__(always_apply, p)
-        self.path_paste_img_dir = path_paste_img_dir
+        self.images = []
-        self.path_paste_mask_dir = path_paste_mask_dir
+        self.images.extend(list(Path(image_dir).glob("**/*.jpg")))
+        self.images.extend(list(Path(image_dir).glob("**/*.png")))
         self.scale_range = scale_range
         self.nb = nb
 
@@ -69,14 +70,15 @@ class RandomPaste(A.DualTransform):
         return False
 
     def get_params_dependent_on_targets(self, params):
-        # choose a random image inside the image folder
+        # choose a random image and its corresponding mask
-        filename = rd.choice(os.listdir(self.path_paste_img_dir))
+        img_path = rd.choice(self.images)
+        mask_path = img_path.parent.joinpath("MASK.PNG")
 
         # load the "paste" image
         paste_img = Image.open(
             os.path.join(
                 self.path_paste_img_dir,
-                filename,
+                img_path,
             )
         ).convert("RGBA")
 
@@ -84,25 +86,23 @@ class RandomPaste(A.DualTransform):
         paste_mask = Image.open(
             os.path.join(
                 self.path_paste_mask_dir,
-                filename,
+                mask_path,
             )
         ).convert("LA")
 
         # load the target image
         target_img = params["image"]
+
+        # compute shapes, for easier computations
         target_shape = np.array(target_img.shape[:2], dtype=np.uint)
         paste_shape = np.array(paste_img.size, dtype=np.uint)
 
-        # change paste_img's brightness randomly
-        filter = ImageEnhance.Brightness(paste_img)
-        paste_img = filter.enhance(rd.uniform(0.5, 1.5))
-
         # change paste_img's contrast randomly
         filter = ImageEnhance.Contrast(paste_img)
         paste_img = filter.enhance(rd.uniform(0.5, 1.5))
 
-        # change paste_img's sharpness randomly
+        # change paste_img's brightness randomly
-        filter = ImageEnhance.Sharpness(paste_img)
+        filter = ImageEnhance.Brightness(paste_img)
         paste_img = filter.enhance(rd.uniform(0.5, 1.5))
 
         # compute the minimum scaling to fit inside target image