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import copy
from collections import defaultdict
from pathlib import Path
from tqdm import tqdm
import torch
import torch.utils.data
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from prettytable import PrettyTable
import re
import json
from box_ops import generalized_box_iou, box_iou
from decode_string import decode_bbox_from_caption
import pdb
class RefExpEvaluatorFromTxt(object):
def __init__(self, refexp_gt_path, k=(1, -1), thresh_iou=0.5):
assert isinstance(k, (list, tuple))
with open(refexp_gt_path, 'r') as f:
self.refexp_gt = json.load(f)
self.img_ids = [item['id'] for item in self.refexp_gt['images']]
print(f"Load {len(self.img_ids)} images")
print(f"Load {len(self.refexp_gt['annotations'])} annotations")
self.k = k
self.thresh_iou = thresh_iou
def summarize(self,
prediction_file: str,
quantized_size: int = 32,
verbose: bool = False,):
# get the predictions
with open(prediction_file, 'r', encoding='utf-8') as f:
predict_all_lines = f.readlines()
# filter the invaild lines for predict_all_lines
filter_prediction_lines = []
for line in predict_all_lines:
line_pieces = line.strip('\n').split('\t')
if 'H-' in line_pieces[0]:
if line_pieces[0].split('-')[-1].isdigit():
filter_prediction_lines.append(line)
predict_all_lines = filter_prediction_lines
predict_index = 0
dataset2score = {
"refcoco": {k: 0.0 for k in self.k},
"refcoco+": {k: 0.0 for k in self.k},
"refcocog": {k: 0.0 for k in self.k},
}
dataset2count = {"refcoco": 0.0, "refcoco+": 0.0, "refcocog": 0.0}
for item_img, item_ann in tqdm(zip(self.refexp_gt['images'], self.refexp_gt['annotations'])):
# quit when evaluating all predictions
if predict_index == len(predict_all_lines):
break
if item_img['id'] != item_ann['image_id']:
raise ValueError(f"Ann\n{item_ann} \nis not matched\n {item_img}")
dataset_name = item_img['dataset_name']
img_height = item_img['height']
img_width = item_img['width']
caption = item_img['caption']
target_bbox = item_ann["bbox"]
converted_bbox = [
target_bbox[0],
target_bbox[1],
target_bbox[2] + target_bbox[0],
target_bbox[3] + target_bbox[1],
]
target_bbox = torch.as_tensor(converted_bbox).view(-1, 4)
prediction_line = predict_all_lines[predict_index].split('</image>')[-1]
predict_index += 1
collect_entity_location = decode_bbox_from_caption(prediction_line, quantized_size=quantized_size, verbose=verbose)
predict_boxes = []
for (p_pred, p_x1, p_y1, p_x2, p_y2) in collect_entity_location:
if p_pred.strip() != caption.strip():
continue
else:
pred_box = [p_x1 * img_width, p_y1 * img_height, p_x2 * img_width, p_y2 * img_height]
predict_boxes.append(pred_box)
if len(predict_boxes) == 0:
print(f"Can't find valid bbox for the given phrase {caption}, \n{collect_entity_location}")
print(f"We set a 0-area box to calculate result")
predict_boxes = [[0., 0., 0., 0.]]
predict_boxes = torch.as_tensor(predict_boxes).view(-1, 4)
iou, _ = box_iou(predict_boxes, target_bbox)
mean_iou, _ = box_iou(predict_boxes.mean(0).view(-1, 4), target_bbox)
for k in self.k:
if k == 'upper bound':
if max(iou) >= self.thresh_iou:
dataset2score[dataset_name][k] += 1.0
elif k == 'mean':
if max(mean_iou) >= self.thresh_iou:
dataset2score[dataset_name][k] += 1.0
else:
if max(iou[0, :k]) >= self.thresh_iou:
dataset2score[dataset_name][k] += 1.0
dataset2count[dataset_name] += 1.0
for key, value in dataset2score.items():
for k in self.k:
try:
value[k] /= dataset2count[key]
except:
pass
results = {}
for key, value in dataset2score.items():
results[key] = sorted([v for k, v in value.items()])
print(f" Dataset: {key} - Precision @ 1, mean, all: {results[key]} \n")
return results
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('prediction_file', help='prediction_file')
parser.add_argument('annotation_file', default='/path/to/mdetr_processed_json_annotations', help='annotation_file')
parser.add_argument('--quantized_size', default=32, type=int)
args = parser.parse_args()
evaluator = RefExpEvaluatorFromTxt(
refexp_gt_path=args.annotation_file,
k=(1, 'mean', 'upper bound'),
thresh_iou=0.5,
)
evaluator.summarize(args.prediction_file, args.quantized_size, verbose=False)
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