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Update main.py
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main.py
CHANGED
@@ -73,24 +73,23 @@ annotation_folder = '/annotations/'
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if not os.path.exists(os.path.abspath('.') + annotation_folder):
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annotation_zip = tf.keras.utils.get_file('captions.zip',
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cache_subdir=os.path.abspath('.'),
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origin='http://images.cocodataset.org/annotations/
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extract=True)
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annotation_file = os.path.dirname(annotation_zip)+'/annotations/
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os.remove(annotation_zip)
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# Download image files
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image_folder = '/
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if not os.path.exists(os.path.abspath('.') + image_folder):
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image_zip = tf.keras.utils.get_file('
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cache_subdir=os.path.abspath('.'),
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origin='http://images.cocodataset.org/zips/
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extract=True)
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PATH = os.path.dirname(image_zip) + image_folder
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os.remove(image_zip)
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else:
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PATH = os.path.abspath('.') + image_folder
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PATH
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"""## Optional: limit the size of the training set
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To speed up training for this tutorial, you'll use a subset of 30,000 captions and their corresponding images to train your model. Choosing to use more data would result in improved captioning quality.
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if not os.path.exists(os.path.abspath('.') + annotation_folder):
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annotation_zip = tf.keras.utils.get_file('captions.zip',
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cache_subdir=os.path.abspath('.'),
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origin='http://images.cocodataset.org/annotations/annotations_trainval2014.zip',
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extract=True)
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annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json'
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os.remove(annotation_zip)
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# Download image files
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image_folder = '/train2014/'
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if not os.path.exists(os.path.abspath('.') + image_folder):
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image_zip = tf.keras.utils.get_file('train2014.zip',
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cache_subdir=os.path.abspath('.'),
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origin='http://images.cocodataset.org/zips/train2014.zip',
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extract=True)
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PATH = os.path.dirname(image_zip) + image_folder
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os.remove(image_zip)
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else:
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PATH = os.path.abspath('.') + image_folder
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"""## Optional: limit the size of the training set
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To speed up training for this tutorial, you'll use a subset of 30,000 captions and their corresponding images to train your model. Choosing to use more data would result in improved captioning quality.
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