add onehot and getattr

dataset/consts.py

@@ -0,0 +1,40 @@
+PLANT_CLASSES = [
+    "Tomato",
+    "Potato",
+    "Corn_(maize)",
+    "Apple",
+    "Blueberry",
+    "Soybean",
+    "Cherry_(including_sour)",
+    "Squash",
+    "Strawberry",
+    "Pepper,_bell",
+    "Peach",
+    "Grape",
+    "Orange",
+    "Raspberry",
+]
+
+DISEASE_CLASSES = [
+    "healthy",
+    "Northern_Leaf_Blight",
+    "Tomato_mosaic_virus",
+    "Early_blight",
+    "Leaf_scorch",
+    "Tomato_Yellow_Leaf_Curl_Virus",
+    "Cedar_apple_rust",
+    "Late_blight",
+    "Spider_mites Two-spotted_spider_mite",
+    "Black_rot",
+    "Bacterial_spot",
+    "Apple_scab",
+    "Powdery_mildew",
+    "Esca_(Black_Measles)",
+    "Haunglongbing_(Citrus_greening)",
+    "Leaf_Mold",
+    "Common_rust_",
+    "Target_Spot",
+    "Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "Septoria_leaf_spot",
+    "Cercospora_leaf_spot Gray_leaf_spot",
+]

dataset/dataset.py

@@ -3,6 +3,8 @@ from pathlib import Path
 
 import tensorflow as tf
 
+from .consts import DISEASE_CLASSES, PLANT_CLASSES
+
 
 class Dataset:
     ''' Class to load and preprocess the dataset. 
@@ -50,16 +52,23 @@ class Dataset:
         path = tf.strings.split(image_path, os.path.sep)[-2]
         plant = tf.strings.split(path, '___')[0]
         disease = tf.strings.split(path, '___')[1]
-        return tf.cast(plant, dtype=tf.string, name=None), tf.cast(disease, dtype=tf.string, name=None)
+
+        one_hot_plant = plant == PLANT_CLASSES
+        one_hot_disease = disease == DISEASE_CLASSES
+
+        return tf.cast(one_hot_plant, dtype=tf.uint8, name=None), tf.cast(one_hot_disease, dtype=tf.uint8, name=None)
 
     def _get_image(self, image_path):
         img = tf.io.read_file(image_path)
-        img = tf.io.decode_jpeg(img, channels=3) / 255.
-        return tf.cast(img, dtype=tf.float32, name=None)
+        img = tf.io.decode_jpeg(img, channels=3)
+        return tf.cast(img, dtype=tf.float32, name=None) / 255.
 
     def _preprocess(self, image_path):
         labels = self._get_labels(image_path)
         image = self._get_image(image_path)
 
         # returns X, Y1, Y2
-        return image, labels
+        return image, labels[0], labels[1]
+
+    def __getattr__(self, attr):
+        return getattr(self.dataset, attr)

test.py

@@ -6,5 +6,5 @@ from dataset.dataset import Dataset
 train_dataset = Dataset(Path('data/resized_dataset/train'))
 valid_dataset = Dataset(Path('data/resized_dataset/valid'))
 
-for image, labels in train_dataset.dataset.take(1):
-    print(image, labels)
+for i in train_dataset.take(1):
+    print(i)