feature/load-dataset #2

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s495733 merged 7 commits from feature/load-dataset into main 2024-05-05 19:42:12 +02:00
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dataset/__init__.py Normal file
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dataset/dataset.py Normal file
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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

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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

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test.py Normal file
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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)