From 60d5bc546799ef274670ba5478484fe9af892a7d Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Wed, 12 Dec 2018 00:26:52 +0800
Subject: [PATCH] add eval hooks for VOC dataset
---
mmdet/apis/train.py | 12 ++++++---
mmdet/core/evaluation/__init__.py | 4 +--
mmdet/core/evaluation/eval_hooks.py | 39 +++++++++++++++++++++++++++++
3 files changed, 49 insertions(+), 6 deletions(-)
diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py
index 2a58972..38bafa4 100644
--- a/mmdet/apis/train.py
+++ b/mmdet/apis/train.py
@@ -6,8 +6,8 @@ import torch
from mmcv.runner import Runner, DistSamplerSeedHook
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmdet.core import (DistOptimizerHook, CocoDistEvalRecallHook,
- CocoDistEvalmAPHook)
+from mmdet.core import (DistOptimizerHook, DistEvalmAPHook,
+ CocoDistEvalRecallHook, CocoDistEvalmAPHook)
from mmdet.datasets import build_dataloader
from mmdet.models import RPN
from .env import get_root_logger
@@ -81,9 +81,13 @@ def _dist_train(model, dataset, cfg, validate=False):
# register eval hooks
if validate:
if isinstance(model.module, RPN):
+ # TODO: implement recall hooks for other datasets
runner.register_hook(CocoDistEvalRecallHook(cfg.data.val))
- elif cfg.data.val.type == 'CocoDataset':
- runner.register_hook(CocoDistEvalmAPHook(cfg.data.val))
+ else:
+ if cfg.data.val.type == 'CocoDataset':
+ runner.register_hook(CocoDistEvalmAPHook(cfg.data.val))
+ else:
+ runner.register_hook(DistEvalmAPHook(cfg.data.val))
if cfg.resume_from:
runner.resume(cfg.resume_from)
diff --git a/mmdet/core/evaluation/__init__.py b/mmdet/core/evaluation/__init__.py
index 026234f..4585c23 100644
--- a/mmdet/core/evaluation/__init__.py
+++ b/mmdet/core/evaluation/__init__.py
@@ -2,7 +2,7 @@ from .class_names import (voc_classes, imagenet_det_classes,
imagenet_vid_classes, coco_classes, dataset_aliases,
get_classes)
from .coco_utils import coco_eval, fast_eval_recall, results2json
-from .eval_hooks import (DistEvalHook, CocoDistEvalRecallHook,
+from .eval_hooks import (DistEvalHook, DistEvalmAPHook, CocoDistEvalRecallHook,
CocoDistEvalmAPHook)
from .mean_ap import average_precision, eval_map, print_map_summary
from .recall import (eval_recalls, print_recall_summary, plot_num_recall,
@@ -11,7 +11,7 @@ from .recall import (eval_recalls, print_recall_summary, plot_num_recall,
__all__ = [
'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',
'coco_classes', 'dataset_aliases', 'get_classes', 'coco_eval',
- 'fast_eval_recall', 'results2json', 'DistEvalHook',
+ 'fast_eval_recall', 'results2json', 'DistEvalHook', 'DistEvalmAPHook',
'CocoDistEvalRecallHook', 'CocoDistEvalmAPHook', 'average_precision',
'eval_map', 'print_map_summary', 'eval_recalls', 'print_recall_summary',
'plot_num_recall', 'plot_iou_recall'
diff --git a/mmdet/core/evaluation/eval_hooks.py b/mmdet/core/evaluation/eval_hooks.py
index 1402f7f..ba6adbb 100644
--- a/mmdet/core/evaluation/eval_hooks.py
+++ b/mmdet/core/evaluation/eval_hooks.py
@@ -12,6 +12,7 @@ from pycocotools.cocoeval import COCOeval
from torch.utils.data import Dataset
from .coco_utils import results2json, fast_eval_recall
+from .mean_ap import eval_map
from mmdet import datasets
@@ -102,6 +103,44 @@ class DistEvalHook(Hook):
raise NotImplementedError
+class DistEvalmAPHook(DistEvalHook):
+
+ def evaluate(self, runner, results):
+ gt_bboxes = []
+ gt_labels = []
+ gt_ignore = [] if self.dataset.with_crowd else None
+ for i in range(len(self.dataset)):
+ ann = self.dataset.get_ann_info(i)
+ bboxes = ann['bboxes']
+ labels = ann['labels']
+ if gt_ignore is not None:
+ ignore = np.concatenate([
+ np.zeros(bboxes.shape[0], dtype=np.bool),
+ np.ones(ann['bboxes_ignore'].shape[0], dtype=np.bool)
+ ])
+ gt_ignore.append(ignore)
+ bboxes = np.vstack([bboxes, ann['bboxes_ignore']])
+ labels = np.concatenate([labels, ann['labels_ignore']])
+ gt_bboxes.append(bboxes)
+ gt_labels.append(labels)
+ # If the dataset is VOC2007, then use 11 points mAP evaluation.
+ if hasattr(self.dataset, 'year') and self.dataset.year == 2007:
+ ds_name = 'voc07'
+ else:
+ ds_name = self.dataset.CLASSES
+ mean_ap, eval_results = eval_map(
+ results,
+ gt_bboxes,
+ gt_labels,
+ gt_ignore=gt_ignore,
+ scale_ranges=None,
+ iou_thr=0.5,
+ dataset=ds_name,
+ print_summary=True)
+ runner.log_buffer.output['mAP'] = mean_ap
+ runner.log_buffer.ready = True
+
+
class CocoDistEvalRecallHook(DistEvalHook):
def __init__(self,
--
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