add loader

dataset/dataset.py

@@ -0,0 +1,66 @@
+import os
+from pathlib import Path
+
+import tensorflow as tf
+
+
+class Dataset:
+    ''' Class to load and preprocess the dataset. 
+    Loads images and labels from the given directory to tf.data.Dataset.
+    
+    
+    Args:
+        `data_dir (Path)`: Path to the dataset directory.
+        `seed (int)`: Seed for shuffling the dataset.
+        `repeat (int)`: Number of times to repeat the dataset.
+        `shuffle_buffer_size (int)`: Size of the buffer for shuffling the dataset.
+        `batch_size (int)`: Batch size for the dataset.
+    '''
+    def __init__(self,
+                 data_dir: Path,
+                 seed: int = 42,
+                 repeat: int = 1,
+                 shuffle_buffer_size: int = 10_000,
+                 batch_size: int = 64) -> None:
+        self.data_dir = data_dir
+        self.seed = seed
+        self.repeat = repeat
+        self.batch_size = batch_size
+
+        self.dataset = self._load_dataset()\
+            .shuffle(shuffle_buffer_size, seed=self.seed)\
+            .repeat(self.repeat)\
+            .prefetch(tf.data.experimental.AUTOTUNE)
+
+    def _load_dataset(self) -> tf.data.Dataset:
+        # check if path has 'test' word in it
+        dataset = tf.data.Dataset.list_files(str(self.data_dir / '*/*'))
+        if 'test' in str(self.data_dir).lower():
+            # file names issue - labels have camel case (regex?) and differs from the train/valid sets
+            pass
+        else:
+            dataset = dataset.map(
+                _preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+
+        return dataset
+
+
+def _get_labels(image_path):
+    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)
+
+
+def _get_image(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)
+
+
+def _preprocess(image_path):
+    labels = _get_labels(image_path)
+    image = _get_image(image_path)
+
+    # returns X, Y1, Y2
+    return image, labels

file_manager/shard_files.py

@@ -0,0 +1,19 @@
+from pathlib import Path
+
+# TODO: split the files into smaller dirs and make list of them
+class FileSharder:
+    def __init__(self,
+                 train_dir: Path = Path('./data/resized_dataset/train'),
+                 valid_dir: Path = Path('./data/resized_dataset/valid'),
+                 test_dir: Path = Path('./data/resized_dataset/test'),
+                 shard_size = 5_000) -> None:
+        self.shard_size = shard_size
+
+        self.train_dir = train_dir
+        self.valid_dir = valid_dir
+        self.test_dir = test_dir
+
+        self.shard()
+
+    def shard(self):
+        pass

test.py

@@ -0,0 +1,10 @@
+
+from pathlib import Path
+
+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)