Fix version 0.2.0-dev3 and later

In this MR, version 0.2.0-dev3 (and later) is fixed:

• We use DPIAdjusting transform as it is a key component when training on images of very different sizes
• We compute the real mean and std values on the train set (we do not use default values from ImageNet = revert a5f7b5ab)
• The padding is on the right and bottom of the image (= revert part of f57bc667 for now)
• Two parameters files are created during training: one for training details (used to reproduce/continue the experiment), one for inference (that can be directly used by Arkindex DAN worker)

dan/transforms.py

@@ -119,30 +119,64 @@ class Erosion:
         return erode(np.array(x), self.kernel, iterations=self.iterations)
 
 
-class ErosionDilation:
+class ErosionDilation(ImageOnlyTransform):
     """
     Random erosion or dilation
     """
 
-    def __init__(self, min_kernel, max_kernel, iterations, p=1.0):
+    def __init__(
+        self,
+        min_kernel: int,
+        max_kernel: int,
+        iterations: int,
+        always_apply: bool = False,
+        p: float = 1.0,
+    ):
+        super(ErosionDilation, self).__init__(always_apply, p)
         self.min_kernel = min_kernel
         self.max_kernel = max_kernel
         self.iterations = iterations
         self.p = p
         self.always_apply = False
 
-    def __call__(self, image, force_apply=False):
-        if not (random.random() <= self.p or self.always_apply or force_apply):
-            return {"image": image}
+    def apply(self, img: np.ndarray, **params):
         kernel_h = randint(self.min_kernel, self.max_kernel)
         kernel_w = randint(self.min_kernel, self.max_kernel)
         kernel = np.ones((kernel_h, kernel_w), np.uint8)
         augmented_image = (
-            Erosion(kernel, iterations=self.iterations)(image)
+            Erosion(kernel, iterations=self.iterations)(img)
             if random.random() < 0.5
-            else Dilation(kernel=kernel, iterations=self.iterations)(image)
+            else Dilation(kernel=kernel, iterations=self.iterations)(img)
         )
-        return {"image": augmented_image}
+        return augmented_image
+
+
+class DPIAdjusting(ImageOnlyTransform):
+    """
+    Resolution modification
+    """
+
+    def __init__(
+        self,
+        min_factor: float = 0.75,
+        max_factor: float = 1,
+        always_apply: bool = False,
+        p: float = 1.0,
+    ):
+        super(DPIAdjusting, self).__init__(always_apply, p)
+        self.min_factor = min_factor
+        self.max_factor = max_factor
+        self.p = p
+        self.always_apply = False
+
+    def apply(self, img: np.ndarray, **params):
+        factor = float(Uniform(self.min_factor, self.max_factor).sample())
+        img = Image.fromarray(img)
+        augmented_image = img.resize(
+            (int(np.ceil(img.width * factor)), int(np.ceil(img.height * factor))),
+            Image.BILINEAR,
+        )
+        return np.array(augmented_image)
 
 
 def get_preprocessing_transforms(
@@ -194,6 +228,9 @@ def get_augmentation_transforms() -> SomeOf:
         ],
         n=2,
         p=0.9,
+        )
+    ],
+    p=0.9
     )