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import os
import contextlib
import copy
import numpy as np
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
from .data import ImageData, ReactionImageData, CorefImageData
class CocoEvaluator(object):
def __init__(self, coco_gt):
coco_gt = copy.deepcopy(coco_gt)
self.coco_gt = coco_gt
def evaluate(self, predictions):
img_ids, results = self.prepare(predictions, 'bbox')
if len(results) == 0:
return np.zeros((12,))
coco_dt = self.coco_gt.loadRes(results)
cocoEval = COCOeval(self.coco_gt, coco_dt, 'bbox')
cocoEval.params.imgIds = img_ids
cocoEval.params.catIds = [1]
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
self.cocoEval = cocoEval
return cocoEval.stats
def prepare(self, predictions, iou_type):
if iou_type == "bbox":
return self.prepare_for_coco_detection(predictions)
else:
raise ValueError("Unknown iou type {}".format(iou_type))
def prepare_for_coco_detection(self, predictions):
img_ids = []
coco_results = []
for idx, prediction in enumerate(predictions):
if len(prediction) == 0:
continue
image = self.coco_gt.dataset['images'][idx]
img_ids.append(image['id'])
width = image['width']
height = image['height']
coco_results.extend(
[
{
"image_id": image['id'],
"category_id": pred['category_id'],
"bbox": convert_to_xywh(pred['bbox'], width, height),
"score": pred['score'],
}
for pred in prediction
]
)
return img_ids, coco_results
def convert_to_xywh(box, width, height):
xmin, ymin, xmax, ymax = box
return [xmin * width, ymin * height, (xmax - xmin) * width, (ymax - ymin) * height]
EMPTY_STATS = {'gold_hits': 0, 'gold_total': 0, 'pred_hits': 0, 'pred_total': 0, 'image': 0}
class ReactionEvaluator(object):
def evaluate_image(self, gold_image, pred_image, **kwargs):
data = ReactionImageData(gold_image, pred_image)
return data.evaluate(**kwargs)
def compute_metrics(self, gold_hits, gold_total, pred_hits, pred_total):
precision = pred_hits / max(pred_total, 1)
recall = gold_hits / max(gold_total, 1)
f1 = precision * recall * 2 / max(precision + recall, 1e-6)
return {'precision': precision, 'recall': recall, 'f1': f1}
def evaluate(self, groundtruths, predictions, **kwargs):
gold_hits, gold_total, pred_hits, pred_total = 0, 0, 0, 0
for gold_image, pred_image in zip(groundtruths, predictions):
gh, ph = self.evaluate_image(gold_image, pred_image, **kwargs)
gold_hits += sum(gh)
gold_total += len(gh)
pred_hits += sum(ph)
pred_total += len(ph)
return self.compute_metrics(gold_hits, gold_total, pred_hits, pred_total)
def evaluate_by_size(self, groundtruths, predictions, **kwargs):
group_stats = {}
for gold_image, pred_image in zip(groundtruths, predictions):
gh, ph = self.evaluate_image(gold_image, pred_image, **kwargs)
gtotal = len(gh)
if gtotal not in group_stats:
group_stats[gtotal] = copy.deepcopy(EMPTY_STATS)
group_stats[gtotal]['gold_hits'] += sum(gh)
group_stats[gtotal]['gold_total'] += len(gh)
group_stats[gtotal]['pred_hits'] += sum(ph)
group_stats[gtotal]['pred_total'] += len(ph)
group_stats[gtotal]['image'] += 1
group_scores = {}
for gtotal, stats in group_stats.items():
group_scores[gtotal] = self.compute_metrics(
stats['gold_hits'], stats['gold_total'], stats['pred_hits'], stats['pred_total'])
return group_scores, group_stats
def evaluate_by_group(self, groundtruths, predictions, **kwargs):
group_stats = {}
for gold_image, pred_image in zip(groundtruths, predictions):
gh, ph = self.evaluate_image(gold_image, pred_image, **kwargs)
diagram_type = gold_image['diagram_type']
if diagram_type not in group_stats:
group_stats[diagram_type] = copy.deepcopy(EMPTY_STATS)
group_stats[diagram_type]['gold_hits'] += sum(gh)
group_stats[diagram_type]['gold_total'] += len(gh)
group_stats[diagram_type]['pred_hits'] += sum(ph)
group_stats[diagram_type]['pred_total'] += len(ph)
group_stats[diagram_type]['image'] += 1
group_scores = {}
for group, stats in group_stats.items():
group_scores[group] = self.compute_metrics(
stats['gold_hits'], stats['gold_total'], stats['pred_hits'], stats['pred_total'])
return group_scores, group_stats
def evaluate_summarize(self, groundtruths, predictions, **kwargs):
size_scores, size_stats = self.evaluate_by_size(groundtruths, predictions, **kwargs)
summarize = {
'overall': copy.deepcopy(EMPTY_STATS),
# 'single': copy.deepcopy(EMPTY_STATS),
# 'multiple': copy.deepcopy(EMPTY_STATS)
}
for size, stats in size_stats.items():
if type(size) is int:
# output = summarize['single'] if size <= 1 else summarize['multiple']
for key in stats:
# output[key] += stats[key]
summarize['overall'][key] += stats[key]
scores = {}
for key, val in summarize.items():
scores[key] = self.compute_metrics(val['gold_hits'], val['gold_total'], val['pred_hits'], val['pred_total'])
return scores, summarize, size_stats
class CorefEvaluator(object):
def evaluate_image(self, gold_image, pred_image, **kwargs):
data = CorefImageData(gold_image, predictions = pred_image)
return data.evaluate()
def evaluate(self, groundtruths, predictions):
hits, gold_total, pred_total = 0, 0, 0
counter = 0
print(len(predictions))
for gold_image, pred_image in zip(groundtruths, predictions):
try: hit, gold_pairs, pred_pairs = self.evaluate_image(gold_image, pred_image)
except: print(counter)
hits += hit
gold_total += gold_pairs
pred_total += pred_pairs
counter += 1
return hits, gold_total, pred_total
def evaluate_summarize(self, groundtruths, predictions):
hits, gold_total, pred_total = self.evaluate(groundtruths, predictions)
precision = hits/max(pred_total, 1)
recall = hits/max(gold_total, 1)
f1 = precision * recall * 2 / max(precision + recall, 1e-6)
return (precision, recall, f1)
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