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import copy
from collections import defaultdict
from pathlib import Path
import torch
import torch.utils.data
import maskrcnn_benchmark.utils.dist as dist
from maskrcnn_benchmark.layers.set_loss import generalized_box_iou
from .modulated_coco import ModulatedDataset
class RefExpDataset(ModulatedDataset):
pass
class RefExpEvaluator(object):
def __init__(self, refexp_gt, iou_types, k=(1, 5, 10), thresh_iou=0.5):
assert isinstance(k, (list, tuple))
refexp_gt = copy.deepcopy(refexp_gt)
self.refexp_gt = refexp_gt
self.iou_types = iou_types
self.img_ids = self.refexp_gt.imgs.keys()
self.predictions = {}
self.k = k
self.thresh_iou = thresh_iou
def accumulate(self):
pass
def update(self, predictions):
self.predictions.update(predictions)
def synchronize_between_processes(self):
all_predictions = dist.all_gather(self.predictions)
merged_predictions = {}
for p in all_predictions:
merged_predictions.update(p)
self.predictions = merged_predictions
def summarize(self):
if dist.is_main_process():
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 image_id in self.img_ids:
ann_ids = self.refexp_gt.getAnnIds(imgIds=image_id)
assert len(ann_ids) == 1
img_info = self.refexp_gt.loadImgs(image_id)[0]
target = self.refexp_gt.loadAnns(ann_ids[0])
prediction = self.predictions[image_id]
assert prediction is not None
sorted_scores_boxes = sorted(
zip(prediction["scores"].tolist(), prediction["boxes"].tolist()), reverse=True
)
sorted_scores, sorted_boxes = zip(*sorted_scores_boxes)
sorted_boxes = torch.cat([torch.as_tensor(x).view(1, 4) for x in sorted_boxes])
target_bbox = target[0]["bbox"]
converted_bbox = [
target_bbox[0],
target_bbox[1],
target_bbox[2] + target_bbox[0],
target_bbox[3] + target_bbox[1],
]
giou = generalized_box_iou(sorted_boxes, torch.as_tensor(converted_bbox).view(-1, 4))
for k in self.k:
if max(giou[:k]) >= self.thresh_iou:
dataset2score[img_info["dataset_name"]][k] += 1.0
dataset2count[img_info["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, 5, 10: {results[key]} \n")
return results
return None
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