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import torch
import torch.distributed as dist
import abc
import json
import logging
import time
import numpy as np
from .distributed import synchronize, is_main_process, all_gather_container
from pycocotools.cocoeval import COCOeval
# FIXME experimenting with speedups for OpenImages eval, it's slow
#import pyximport; py_importer, pyx_importer = pyximport.install(pyimport=True)
import effdet.evaluation.detection_evaluator as tfm_eval
#pyximport.uninstall(py_importer, pyx_importer)
_logger = logging.getLogger(__name__)
__all__ = ['CocoEvaluator', 'PascalEvaluator', 'OpenImagesEvaluator', 'create_evaluator']
class Evaluator:
def __init__(self, distributed=False, pred_yxyx=False):
self.distributed = distributed
self.distributed_device = None
self.pred_yxyx = pred_yxyx
self.img_indices = []
self.predictions = []
def add_predictions(self, detections, target):
if self.distributed:
if self.distributed_device is None:
# cache for use later to broadcast end metric
self.distributed_device = detections.device
synchronize()
detections = all_gather_container(detections)
img_indices = all_gather_container(target['img_idx'])
if not is_main_process():
return
else:
img_indices = target['img_idx']
detections = detections.cpu().numpy()
img_indices = img_indices.cpu().numpy()
for img_idx, img_dets in zip(img_indices, detections):
self.img_indices.append(img_idx)
self.predictions.append(img_dets)
def _coco_predictions(self):
# generate coco-style predictions
coco_predictions = []
coco_ids = []
for img_idx, img_dets in zip(self.img_indices, self.predictions):
img_id = self._dataset.img_ids[img_idx]
coco_ids.append(img_id)
if self.pred_yxyx:
# to xyxy
img_dets[:, 0:4] = img_dets[:, [1, 0, 3, 2]]
# to xywh
img_dets[:, 2] -= img_dets[:, 0]
img_dets[:, 3] -= img_dets[:, 1]
for det in img_dets:
score = float(det[4])
if score < .001: # stop when below this threshold, scores in descending order
break
coco_det = dict(
image_id=int(img_id),
bbox=det[0:4].tolist(),
score=score,
category_id=int(det[5]))
coco_predictions.append(coco_det)
return coco_predictions, coco_ids
@abc.abstractmethod
def evaluate(self):
pass
def save(self, result_file):
# save results in coco style, override to save in a alternate form
if not self.distributed or dist.get_rank() == 0:
assert len(self.predictions)
coco_predictions, coco_ids = self._coco_predictions()
json.dump(coco_predictions, open(result_file, 'w'), indent=4)
class CocoEvaluator(Evaluator):
def __init__(self, dataset, neptune=None, distributed=False, pred_yxyx=False):
super().__init__(distributed=distributed, pred_yxyx=pred_yxyx)
self._dataset = dataset.parser
self.coco_api = dataset.parser.coco
self.neptune = neptune
def reset(self):
self.img_indices = []
self.predictions = []
def evaluate(self):
if not self.distributed or dist.get_rank() == 0:
assert len(self.predictions)
coco_predictions, coco_ids = self._coco_predictions()
json.dump(coco_predictions, open('./temp.json', 'w'), indent=4)
results = self.coco_api.loadRes('./temp.json')
coco_eval = COCOeval(self.coco_api, results, 'bbox')
coco_eval.params.imgIds = coco_ids # score only ids we've used
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
metric = coco_eval.stats[0] # mAP 0.5-0.95
if self.neptune:
self.neptune.log_metric('valid/mAP/0.5-0.95IOU', metric)
self.neptune.log_metric('valid/mAP/0.5IOU', coco_eval.stats[1])
if self.distributed:
dist.broadcast(torch.tensor(metric, device=self.distributed_device), 0)
else:
metric = torch.tensor(0, device=self.distributed_device)
dist.broadcast(metric, 0)
metric = metric.item()
self.reset()
return metric
class TfmEvaluator(Evaluator):
""" Tensorflow Models Evaluator Wrapper """
def __init__(
self, dataset, neptune=None, distributed=False, pred_yxyx=False,
evaluator_cls=tfm_eval.ObjectDetectionEvaluator):
super().__init__(distributed=distributed, pred_yxyx=pred_yxyx)
self._evaluator = evaluator_cls(categories=dataset.parser.cat_dicts)
self._eval_metric_name = self._evaluator._metric_names[0]
self._dataset = dataset.parser
self.neptune = neptune
def reset(self):
self._evaluator.clear()
self.img_indices = []
self.predictions = []
def evaluate(self):
if not self.distributed or dist.get_rank() == 0:
for img_idx, img_dets in zip(self.img_indices, self.predictions):
gt = self._dataset.get_ann_info(img_idx)
self._evaluator.add_single_ground_truth_image_info(img_idx, gt)
bbox = img_dets[:, 0:4] if self.pred_yxyx else img_dets[:, [1, 0, 3, 2]]
det = dict(bbox=bbox, score=img_dets[:, 4], cls=img_dets[:, 5])
self._evaluator.add_single_detected_image_info(img_idx, det)
metrics = self._evaluator.evaluate()
_logger.info('Metrics:')
for k, v in metrics.items():
_logger.info(f'{k}: {v}')
if self.neptune:
key = 'valid/mAP/' + str(k).split('/')[-1]
self.neptune.log_metric(key, v)
map_metric = metrics[self._eval_metric_name]
if self.distributed:
dist.broadcast(torch.tensor(map_metric, device=self.distributed_device), 0)
else:
map_metric = torch.tensor(0, device=self.distributed_device)
wait = dist.broadcast(map_metric, 0, async_op=True)
while not wait.is_completed():
# wait without spinning the cpu @ 100%, no need for low latency here
time.sleep(0.5)
map_metric = map_metric.item()
self.reset()
return map_metric
class PascalEvaluator(TfmEvaluator):
def __init__(self, dataset, neptune=None, distributed=False, pred_yxyx=False):
super().__init__(
dataset, neptune, distributed=distributed, pred_yxyx=pred_yxyx, evaluator_cls=tfm_eval.PascalDetectionEvaluator)
class OpenImagesEvaluator(TfmEvaluator):
def __init__(self, dataset, distributed=False, pred_yxyx=False):
super().__init__(
dataset, distributed=distributed, pred_yxyx=pred_yxyx, evaluator_cls=tfm_eval.OpenImagesDetectionEvaluator)
def create_evaluator(name, dataset, neptune=None, distributed=False, pred_yxyx=False):
# FIXME support OpenImages Challenge2019 metric w/ image level label consideration
if 'coco' in name:
return CocoEvaluator(dataset, neptune, distributed=distributed, pred_yxyx=pred_yxyx)
elif 'openimages' in name:
return OpenImagesEvaluator(dataset, distributed=distributed, pred_yxyx=pred_yxyx)
else:
return CocoEvaluator(dataset, neptune, distributed=distributed, pred_yxyx=pred_yxyx)
#return PascalEvaluator(dataset, neptune, distributed=distributed, pred_yxyx=pred_yxyx)
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