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2f26016 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | import os
import json
import argparse
import torch
from torchvision.ops import box_iou
import sys
import logging
import warnings
from typing import Dict, Any, Sequence
from PIL import Image
from tqdm import tqdm
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def eval_rec(answers, labels):
preds = []
targets = []
# for answer, annotation in tqdm(zip(answers, labels)):
for answer, annotation in zip(answers, labels):
text = answer['text']
label = annotation['label']
#"text": "[0.09, 0.29, 0.37, 0.98]\n\nThe woman is wearing black pants."
# remove suffix :"\n\nThe woman is wearing black pants." of text, and prserve "[0.09, 0.29, 0.37, 0.98]"
text = text.split('\n\n')[0]
# remove []
text = text.replace('[', '')
text = text.replace(']', '')
label = label.replace('[', '')
label = label.replace(']', '')
# crop the coord
coords = text.strip(' ').split(',')
try:
xmin, ymin, xmax, ymax = coords
except:
continue
pred = torch.as_tensor([float(xmin), float(ymin),
float(xmax), float(ymax)])
preds.append(pred)
coords = label.strip(' ').split(',')
xmin, ymin, xmax, ymax = coords
target = torch.as_tensor([float(xmin), float(ymin),
float(xmax), float(ymax)])
img = Image.open('./playground/data/eval/rec/images/train2017/' + annotation['image'])
width_ori, height_ori = img.size
xmin, ymin, xmax, ymax = target
# print(annotation['text'].split(':')[-1], xmin, ymin, xmax, ymax)
xmin, ymin, xmax, ymax = xmin * width_ori, ymin * height_ori, xmax * width_ori, ymax * height_ori
# import matplotlib.pyplot as plt
# plt.figure(annotation['text'].split(':')[-1])
# plt.axis('off')
# plt.imshow(img)
# plt.gca().add_patch(
# plt.Rectangle(
# (xmin, ymin), xmax - xmin, ymax - ymin, color='red', fill=False
# )
# )
# plt.savefig('image1.png')
if 0:
if width_ori > height_ori:
ymin += (width_ori - height_ori) // 2
ymax += (width_ori - height_ori) // 2
width = width_ori
height = height_ori + width_ori - height_ori
else:
xmin += (height_ori - width_ori) // 2
xmax += (height_ori - width_ori) // 2
width = width_ori + height_ori - width_ori
height = height_ori
else:
width = width_ori
height = height_ori
# import matplotlib.pyplot as plt
# plt.figure(annotation['text'] + '1'.split(':')[-1])
# plt.axis('off')
# img_pad = expand2square(img, (0,0,0))
# plt.imshow(img_pad)
# plt.gca().add_patch(
# plt.Rectangle(
# (xmin, ymin), xmax - xmin, ymax - ymin, color='red', fill=False
# )
# )
# plt.savefig('image2.png')
# import pdb; pdb.set_trace()
target = torch.as_tensor([float(xmin / width), float(ymin / height),
float(xmax / width), float(ymax / height)])
targets.append(target)
pred_boxes = torch.stack(preds, dim=0)
target_boxes = torch.stack(targets, dim=0)
# normalized box value is too small, so that the area is 0.
ious = box_iou(pred_boxes * 1000, target_boxes * 1000)
ious = torch.einsum('i i -> i', ious) # take diag elem
# NOTE: please note iou only calculate for success target
iou = ious.mean().item()
correct = (ious > 0.5).sum().item()
# HACK: currently we expand image to square. so this iou is the real iou.
warn_message = "this iou is calculate on normalized box. just for non-rigorous training progress checking." \
"the value is consistent with real iou only if image.width == image.height."
warnings.warn(warn_message)
return {
'accuracy': 1.0 * correct / len(targets),
'iou': iou,
'warning': warn_message,
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--annotation-file", type=str)
parser.add_argument("--question-file", type=str)
parser.add_argument("--result-file", type=str)
args = parser.parse_args()
questions = [json.loads(line) for line in open(args.question_file)]
questions = {question['question_id']: question for question in questions}
answers = [json.loads(q) for q in open(args.result_file)]
annotations = [json.loads(a) for a in open(args.annotation_file)]
val_splits = ['REC_refcoco_unc_val',
'REC_refcoco_unc_testA',
'REC_refcoco_unc_testB',
'REC_refcoco+_unc_val',
'REC_refcoco+_unc_testA',
'REC_refcoco+_unc_testB',
'REC_refcocog_umd_val',
'REC_refcocog_umd_test',]
# val_splits = ['REC_refcoco+_unc_val']
for category in val_splits:
cur_answers = [x for x in answers if questions[x['question_id']]['category'] == category]
cur_labels = [x for x in annotations if questions[x['question_id']]['category'] == category]
if len(cur_answers) == 0:
continue
print('split: {}, # samples answer: {}, # samples target {}'.format(category, len(cur_answers), len(cur_labels)))
# align the targe and label
align_answers = []
align_labels = []
for cur_answer in cur_answers:
for cur_label in cur_labels:
if cur_answer['question_id'] == cur_label['question_id']:
align_answers.append(cur_answer)
align_labels.append(cur_label)
break
# eval_info = eval_rec(cur_answers, cur_labels)
eval_info = eval_rec(align_answers, align_labels)
print("=================={}==================".format(category))
print(eval_info)
print("======================================")
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