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@ -31,12 +31,12 @@ class Dataset:
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self.shuffle_buffer_size = shuffle_buffer_size
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self.batch_size = batch_size
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self.dataset = self._load_dataset()\
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self.dataset = self.__load_dataset()\
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.shuffle(self.shuffle_buffer_size, seed=self.seed)\
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.repeat(self.repeat)\
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.prefetch(tf.data.experimental.AUTOTUNE)
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def _load_dataset(self) -> tf.data.Dataset:
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def __load_dataset(self) -> tf.data.Dataset:
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# check if path has 'test' word in it
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dataset = tf.data.Dataset.list_files(str(self.data_dir / '*/*'))
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if 'test' in str(self.data_dir).lower():
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@ -44,11 +44,11 @@ class Dataset:
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pass
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else:
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dataset = dataset.map(
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self._preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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self.__preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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return dataset
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def _get_labels(self, image_path):
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def __get_labels(self, image_path):
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path = tf.strings.split(image_path, os.path.sep)[-2]
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plant = tf.strings.split(path, '___')[0]
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disease = tf.strings.split(path, '___')[1]
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@ -58,14 +58,14 @@ class Dataset:
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return tf.cast(one_hot_plant, dtype=tf.uint8, name=None), tf.cast(one_hot_disease, dtype=tf.uint8, name=None)
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def _get_image(self, image_path):
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def __get_image(self, image_path):
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img = tf.io.read_file(image_path)
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img = tf.io.decode_jpeg(img, channels=3)
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return tf.cast(img, dtype=tf.float32, name=None) / 255.
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def _preprocess(self, image_path):
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labels = self._get_labels(image_path)
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image = self._get_image(image_path)
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def __preprocess(self, image_path):
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labels = self.__get_labels(image_path)
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image = self.__get_image(image_path)
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# returns X, Y1, Y2
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return image, labels[0], labels[1]
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