Added input normalization

Author: Lucas-Steinmann

car_dataset.py

@@ -4,10 +4,11 @@
 from warnings import warn
 
 import numpy as np
+import torch
 import torch.utils.data as data
 from PIL import Image
 from sklearn.model_selection import train_test_split
-from torch.utils.data import Subset, Dataset
+from torch.utils.data import Subset, Dataset, DataLoader
 
 
 def train_val_datasets(dataset: Dataset, val_split: float = 0.5, shuffle: bool = True) -> Tuple[Dataset, Dataset]:
@@ -234,3 +235,23 @@ def statistics(self) -> str:
         fmt_str += "Avg Height: {}\n".format(sum([img.height for img, gt in self]) / float(len(self)))
         fmt_str += "Avg Aspect: {}\n".format(sum([img.width / img.height for img, gt in self]) / float(len(self)))
         return fmt_str
+
+    def mean_and_std(self) -> Tuple[float, float]:
+        loader = DataLoader(
+            self.subsets['train'],
+            batch_size=10,
+            num_workers=1,
+            shuffle=False
+        )
+        mean = torch.full((3,), 0.0)
+        std = torch.full((3,), 0.0)
+        nb_samples = 0.
+        for data, gt in loader:
+            batch_samples = data.size(0)
+            data = data.view(batch_samples, data.size(1), -1)
+            mean += data.mean(2).sum(0)
+            std += data.std(2).sum(0)
+            nb_samples += batch_samples
+        mean /= nb_samples
+        std /= nb_samples
+        return mean, std

config.yml

@@ -1,7 +1,7 @@
 data : "ORAND-CAR-2014/CAR-A/"
-train_val_split : 0.008
-epochs : 5
-batch_size : 8
+train_val_split: 0.8
+epochs: 1000
+batch_size: 16
 lr : 1.0e-4
-verbose : True
+verbose: False
 log: train_log.txt

train.py

@@ -77,6 +77,7 @@ def create_dataloader(args: Namespace, verbose: bool = False) -> Dict[str, DataL
         'train': transforms.Compose([
             transforms.Resize((width, height)),
             transforms.ToTensor(),
+            transforms.Normalize([0.6205, 0.6205, 0.6205], [0.1343, 0.1343, 0.1343])
         ]),
         'test': transforms.Compose([
             transforms.Resize((width, height)),