feat: show augmentation

src/dataset.py

@@ -14,28 +14,23 @@ train_ds = splits["train"]
 val_ds = splits["test"]
 test_ds = dataset["test"]
 
-labels = train_ds.features['label'].names
+labels = ["Not AI", "AI"]
 id2label = {k: v for k, v in enumerate(labels)}
 label2id = {v: k for k, v in enumerate(labels)}
 
 if __name__ == '__main__':
 
-    import cv2
-    import numpy as np
+    import matplotlib.pyplot as plt
 
     print(f"labels:\n  {labels}")
     print(f"label-id correspondances:\n  {label2id}\n  {id2label}")
 
     idx = 0
     label = id2label[dataset['train'][idx]['label']]
-    image = cv2.cvtColor(
-        np.array(dataset['train'][idx]['image']),
-        cv2.COLOR_BGR2RGB)
-    cv2.namedWindow(label, cv2.WINDOW_NORMAL)
-    cv2.imshow(label, image)
-    image = cv2.cvtColor(
-        np.array(dataset['test'][idx]['image']),
-        cv2.COLOR_BGR2RGB)
-    cv2.namedWindow("Test", cv2.WINDOW_NORMAL)
-    cv2.imshow("Test", image)
-    cv2.waitKey(0)
+    plt.subplot(1, 2, 1)
+    plt.imshow(dataset['train'][idx]['image'])
+    plt.title(f"Label: {label}")
+    plt.subplot(1, 2, 2)
+    plt.imshow(dataset['test'][idx]['image'])
+    plt.title("Test")
+    plt.show()

src/transform.py

@@ -6,6 +6,7 @@ from torchvision.transforms import (
     RandomVerticalFlip,
     ToTensor,
 )
+from imgaug.augmenters import JpegCompression
 
 # get feature extractor (to normalize images)
 normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
@@ -13,9 +14,10 @@ normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 # define train transform
 _train_transforms = Compose(
     [
-        # AugMix(),
+        AugMix(),
         RandomHorizontalFlip(),
         RandomVerticalFlip(),
+        # lambda img : JpegCompression(compression=(0, 30))(image=img),
         ToTensor(),
         normalize,
     ]
@@ -42,3 +44,24 @@ def val_transforms(examples):
     """Transforms for validation."""
     examples["pixel_values"] = [_val_transforms(image.convert("RGB")) for image in examples["image"]]
     return examples
+
+
+if __name__ == '__main__':
+
+    from dataset import train_ds
+    import matplotlib.pyplot as plt
+    import numpy as np 
+
+    idx = 0
+    img = train_ds[idx]['image']
+    plt.subplot(1, 2, 1)
+    plt.imshow(img)
+    plt.title("Original")
+    img = _train_transforms(img.convert("RGB"))
+    img = np.array(img.permute(1, 2, 0))
+    img -= img.min()
+    img /= img.max()
+    plt.subplot(1, 2, 2)
+    plt.imshow(img)
+    plt.title("Augmented")
+    plt.show()