updated basic tutorials, better comments, code revision, checked it works with latest pytorch version

ML/Pytorch/Basics/albumentations_tutorial/classification.py

@@ -3,6 +3,7 @@ import albumentations as A
 import numpy as np
 from utils import plot_examples
 from PIL import Image
+from tqdm import tqdm
 
 image = Image.open("images/elon.jpeg")
 
@@ -14,18 +15,20 @@ transform = A.Compose(
         A.HorizontalFlip(p=0.5),
         A.VerticalFlip(p=0.1),
         A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
-        A.OneOf([
-            A.Blur(blur_limit=3, p=0.5),
-            A.ColorJitter(p=0.5),
-        ], p=1.0),
+        A.OneOf(
+            [
+                A.Blur(blur_limit=3, p=0.5),
+                A.ColorJitter(p=0.5),
+            ],
+            p=1.0,
+        ),
     ]
 )
 
 images_list = [image]
 image = np.array(image)
-for i in range(15):
+for i in tqdm(range(15)):
     augmentations = transform(image=image)
     augmented_img = augmentations["image"]
     images_list.append(augmented_img)
 plot_examples(images_list)
-

ML/Pytorch/Basics/albumentations_tutorial/full_pytorch_example.py

@@ -8,6 +8,7 @@ from albumentations.pytorch import ToTensorV2
 from torch.utils.data import Dataset
 import os
 
+
 class ImageFolder(Dataset):
     def __init__(self, root_dir, transform=None):
         super(ImageFolder, self).__init__()
@@ -18,7 +19,7 @@ class ImageFolder(Dataset):
 
         for index, name in enumerate(self.class_names):
             files = os.listdir(os.path.join(root_dir, name))
-            self.data += list(zip(files, [index]*len(files)))
+            self.data += list(zip(files, [index] * len(files)))
 
     def __len__(self):
         return len(self.data)
@@ -43,10 +44,13 @@ transform = A.Compose(
         A.HorizontalFlip(p=0.5),
         A.VerticalFlip(p=0.1),
         A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
-        A.OneOf([
-            A.Blur(blur_limit=3, p=0.5),
-            A.ColorJitter(p=0.5),
-        ], p=1.0),
+        A.OneOf(
+            [
+                A.Blur(blur_limit=3, p=0.5),
+                A.ColorJitter(p=0.5),
+            ],
+            p=1.0,
+        ),
         A.Normalize(
             mean=[0, 0, 0],
             std=[1, 1, 1],
@@ -58,5 +62,5 @@ transform = A.Compose(
 
 dataset = ImageFolder(root_dir="cat_dogs", transform=transform)
 
-for x,y in dataset:
+for x, y in dataset:
     print(x.shape)

ML/Pytorch/Basics/albumentations_tutorial/utils.py

@@ -8,7 +8,7 @@ import albumentations as A
 
 def visualize(image):
     plt.figure(figsize=(10, 10))
-    plt.axis('off')
+    plt.axis("off")
     plt.imshow(image)
     plt.show()
 
@@ -22,7 +22,7 @@ def plot_examples(images, bboxes=None):
         if bboxes is not None:
             img = visualize_bbox(images[i - 1], bboxes[i - 1], class_name="Elon")
         else:
-            img = images[i-1]
+            img = images[i - 1]
         fig.add_subplot(rows, columns, i)
         plt.imshow(img)
     plt.show()