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import json |
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from typing import Any, List, NamedTuple, Optional, Tuple |
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class Annotation(NamedTuple): |
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area: float |
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iscrowd: int |
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category_id: int |
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bbox: List[float] |
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giou_friendly_bbox: List[float] |
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tokens_positive: List[Tuple[int, int]] |
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class Datapoint(NamedTuple): |
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image_id: int |
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dataset_name: str |
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tokens_negative: List[Tuple[int, int]] |
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original_id: int |
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caption: str |
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annotations: List[Annotation] |
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def convert2dataset_combined( |
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datapoint_list_coco: List[Datapoint], |
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datapoint_list_vg: List[Datapoint], |
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imgid2imginfo_coco, |
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imgid2imginfo_vg, |
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output_path, |
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): |
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"""""" |
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print(f"Dumping combined coco and vg images related all training examples...") |
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next_img_id = 0 |
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next_id = 0 |
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annotations = [] |
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images = [] |
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for datapoint in datapoint_list_coco: |
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img_id = datapoint.image_id |
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filename = imgid2imginfo_coco[img_id]["file_name"] |
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cur_img = { |
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"file_name": filename, |
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"height": imgid2imginfo_coco[img_id]["height"], |
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"width": imgid2imginfo_coco[img_id]["width"], |
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"id": next_img_id, |
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"original_id": img_id, |
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"caption": datapoint.caption, |
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"tokens_negative": datapoint.tokens_negative, |
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"data_source": "coco", |
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"dataset_name": datapoint.dataset_name, |
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} |
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for anns in datapoint.annotations: |
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cur_obj = { |
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"area": float(anns.area), |
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"iscrowd": anns.iscrowd, |
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"image_id": next_img_id, |
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"category_id": anns.category_id, |
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"id": next_id, |
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"bbox": anns.bbox, |
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"tokens_positive": anns.tokens_positive, |
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} |
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next_id += 1 |
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annotations.append(cur_obj) |
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next_img_id += 1 |
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images.append(cur_img) |
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for datapoint in datapoint_list_vg: |
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img_id = datapoint.image_id |
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filename = f"{img_id}.jpg" |
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cur_img = { |
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"file_name": filename, |
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"height": imgid2imginfo_vg[img_id]["height"], |
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"width": imgid2imginfo_vg[img_id]["width"], |
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"id": next_img_id, |
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"original_id": img_id, |
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"caption": datapoint.caption, |
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"tokens_negative": datapoint.tokens_negative, |
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"data_source": "vg", |
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"dataset_name": datapoint.dataset_name, |
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} |
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for anns in datapoint.annotations: |
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cur_obj = { |
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"area": float(anns.area), |
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"iscrowd": anns.iscrowd, |
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"image_id": next_img_id, |
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"category_id": anns.category_id, |
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"id": next_id, |
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"bbox": anns.bbox, |
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"tokens_positive": anns.tokens_positive, |
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} |
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next_id += 1 |
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annotations.append(cur_obj) |
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next_img_id += 1 |
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images.append(cur_img) |
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ds = {"info": [], "licenses": [], "images": images, "annotations": annotations, "categories": []} |
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with open(output_path / f"final_mixed_train.json", "w") as j_file: |
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json.dump(ds, j_file) |
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return next_img_id, next_id |
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