Face det (#9179)

* add "WIDERFaceEvalDataset" and "WiderFaceOnlineMetric"

* add "support widerface eval online"

Author: leo-q8

configs/datasets/wider_face.yml

@@ -9,11 +9,12 @@ TrainDataset:
     data_fields: ['image', 'gt_bbox', 'gt_class']
 
 EvalDataset:
-  !WIDERFaceDataSet
+  !WIDERFaceValDataset
     dataset_dir: dataset/wider_face
-    anno_path: wider_face_split/wider_face_val_bbx_gt.txt
     image_dir: WIDER_val/images
-    data_fields: ['image']
+    anno_path: wider_face_split/wider_face_val_bbx_gt.txt
+    gt_mat_path: WIDER_val/ground_truth
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'ori_gt_bbox']
 
 TestDataset:
   !ImageFolder

configs/face_detection/_base_/face_reader.yml

@@ -1,4 +1,4 @@
-worker_num: 2
+worker_num: 8
 TrainReader:
   inputs_def:
     num_max_boxes: 90
@@ -23,7 +23,7 @@ TrainReader:
   batch_transforms:
     - NormalizeImage: {mean:  [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false}
     - Permute: {}
-  batch_size: 8
+  batch_size: 16
   shuffle: true
   drop_last: true
 
@@ -34,6 +34,9 @@ EvalReader:
     - NormalizeImage: {mean:  [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false}
     - Permute: {}
   batch_size: 1
+  collate_samples: false
+  shuffle: false
+  drop_last: false
 
 
 TestReader:

configs/face_detection/blazeface_1000e.yml

@@ -6,4 +6,4 @@ _BASE_: [
   '_base_/face_reader.yml',
 ]
 weights: output/blazeface_1000e/model_final
-multi_scale_eval: True
+snapshot_epoch: 10

configs/face_detection/blazeface_fpn_ssh_1000e.yml

@@ -6,4 +6,4 @@ _BASE_: [
   '_base_/face_reader.yml',
 ]
 weights: output/blazeface_fpn_ssh_1000e/model_final
-multi_scale_eval: True
+snapshot_epoch: 10

ppdet/data/source/widerface.py

@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import defaultdict
 import os
 import numpy as np
+from scipy.io import loadmat
 
 from ppdet.core.workspace import register, serializable
 from .dataset import DetDataset
@@ -178,3 +180,82 @@ def _load_file_list(self, input_txt):
 def widerface_label():
     labels_map = {'face': 0}
     return labels_map
+
+
+@register
+@serializable
+class WIDERFaceValDataset(WIDERFaceDataSet):
+    def __init__(self,
+                 dataset_dir=None,
+                 image_dir=None,
+                 anno_path=None,
+                 gt_mat_path=None,
+                 data_fields=['image'],
+                 sample_num=-1,
+                 with_lmk=False):
+        super().__init__(
+            dataset_dir=dataset_dir,
+            image_dir=image_dir,
+            anno_path=anno_path,
+            data_fields=data_fields,
+            sample_num=sample_num,
+            with_lmk=with_lmk)
+        self.gt_mat_path = gt_mat_path
+        self.val_mat = os.path.join(self.dataset_dir, self.gt_mat_path, 'wider_face_val.mat')
+        self.hard_mat_path = os.path.join(self.dataset_dir, self.gt_mat_path, 'wider_hard_val.mat')
+        self.medium_mat_path = os.path.join(self.dataset_dir, self.gt_mat_path, 'wider_medium_val.mat')
+        self.easy_mat_path = os.path.join(self.dataset_dir, self.gt_mat_path, 'wider_easy_val.mat')
+
+        assert os.path.exists(self.val_mat), f'{self.val_mat} not exist'
+        assert os.path.exists(self.hard_mat_path), f'{self.hard_mat_path} not exist'
+        assert os.path.exists(self.medium_mat_path), f'{self.medium_mat_path} not exist'
+        assert os.path.exists(self.easy_mat_path), f'{self.easy_mat_path} not exist'
+
+    def parse_dataset(self):
+        super().parse_dataset()
+
+        box_list, flie_list, event_list, hard_info_list, medium_info_list, \
+            easy_info_list = self.get_gt_infos()
+        setting_infos = [easy_info_list, medium_info_list, hard_info_list]
+        settings = ['easy', 'medium', 'hard']
+        info_by_name = defaultdict(dict)
+        for setting_id in range(3):
+            info_list = setting_infos[setting_id]
+            setting = settings[setting_id]
+            for i in range(len(event_list)):
+                img_list = flie_list[i][0]
+                gt_box_list = box_list[i][0]
+                sub_info_list = info_list[i][0]
+                for j in range(len(img_list)):
+                    img_name = str(img_list[j][0][0])
+                    gt_boxes = gt_box_list[j][0].astype(np.float32)
+                    info_by_name[img_name]['gt_ori_bbox'] = gt_boxes
+
+                    keep_index = sub_info_list[j][0]
+                    ignore = np.zeros(gt_boxes.shape[0])
+                    if len(keep_index) != 0:
+                        ignore[keep_index-1] = 1
+                    info_by_name[img_name][f'gt_{setting}_ignore'] = ignore
+
+        for roidb in self.roidbs:
+            img_file = roidb['im_file'].split('/')[-1]
+            img_name = ".".join(img_file.split(".")[:-1])
+            roidb.update(info_by_name[img_name])
+
+    def get_gt_infos(self):
+        """ gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)"""
+
+        val_mat = loadmat(self.val_mat)
+        hard_mat = loadmat(self.hard_mat_path)
+        medium_mat = loadmat(self.medium_mat_path)
+        easy_mat = loadmat(self.easy_mat_path)
+
+        box_list = val_mat['face_bbx_list']
+        file_list = val_mat['file_list']
+        event_list = val_mat['event_list']
+
+        hard_info_list = hard_mat['gt_list']
+        medium_info_list = medium_mat['gt_list']
+        easy_info_list = easy_mat['gt_list']
+
+        return box_list, file_list, event_list, hard_info_list, medium_info_list, easy_info_list

ppdet/engine/trainer.py

@@ -276,8 +276,8 @@ def _init_callbacks(self):
             self._compose_callback = ComposeCallback(self._callbacks)
         elif self.mode == 'eval':
             self._callbacks = [LogPrinter(self)]
-            if self.cfg.metric == 'WiderFace':
-                self._callbacks.append(WiferFaceEval(self))
+            # if self.cfg.metric == 'WiderFace':
+            #     self._callbacks.append(WiferFaceEval(self))
             self._compose_callback = ComposeCallback(self._callbacks)
         elif self.mode == 'test' and self.cfg.get('use_vdl', False):
             self._callbacks = [VisualDLWriter(self)]
@@ -381,13 +381,8 @@ def _init_metrics(self, validate=False):
                     save_prediction_only=save_prediction_only)
             ]
         elif self.cfg.metric == 'WiderFace':
-            multi_scale = self.cfg.multi_scale_eval if 'multi_scale_eval' in self.cfg else True
             self._metrics = [
-                WiderFaceMetric(
-                    image_dir=os.path.join(self.dataset.dataset_dir,
-                                           self.dataset.image_dir),
-                    anno_file=self.dataset.get_anno(),
-                    multi_scale=multi_scale)
+                WiderFaceMetric()
             ]
         elif self.cfg.metric == 'KeyPointTopDownCOCOEval':
             eval_dataset = self.cfg['EvalDataset']

ppdet/metrics/metrics.py

@@ -27,7 +27,8 @@
 
 from .map_utils import prune_zero_padding, DetectionMAP
 from .coco_utils import get_infer_results, cocoapi_eval
-from .widerface_utils import face_eval_run
+from .widerface_utils import (face_eval_run, image_eval, img_pr_info,
+                              dataset_pr_info, voc_ap)
 from ppdet.data.source.category import get_categories
 from ppdet.modeling.rbox_utils import poly2rbox_np
 
@@ -337,22 +338,93 @@ def get_results(self):
 
 
 class WiderFaceMetric(Metric):
-    def __init__(self, image_dir, anno_file, multi_scale=True):
-        self.image_dir = image_dir
-        self.anno_file = anno_file
-        self.multi_scale = multi_scale
-        self.clsid2catid, self.catid2name = get_categories('widerface')
-
-    def update(self, model):
-
-        face_eval_run(
-            model,
-            self.image_dir,
-            self.anno_file,
-            pred_dir='output/pred',
-            eval_mode='widerface',
-            multi_scale=self.multi_scale)
+    def __init__(self, iou_thresh=0.5):
+        self.iou_thresh = iou_thresh
+        self.reset()
 
+    def reset(self):
+        self.pred_boxes_list = []
+        self.gt_boxes_list = []
+        self.aps = []
+
+        self.hard_ignore_list = []
+        self.medium_ignore_list = []
+        self.easy_ignore_list = []
+
+    def update(self, data, outs):
+        batch_pred_bboxes = outs['bbox']
+        batch_pred_bboxes_num = outs['bbox_num']
+        assert len(batch_pred_bboxes_num) == len(data['gt_bbox'])
+        batch_size = len(data['gt_bbox'])
+        box_cnt = 0
+        for batch_id in range(batch_size):
+            pred_bboxes_num = batch_pred_bboxes_num[batch_id]
+            pred_bboxes = batch_pred_bboxes[box_cnt: box_cnt + 
+                                            pred_bboxes_num].numpy() 
+            box_cnt += pred_bboxes_num
+
+            det_conf = pred_bboxes[:, 1]
+            det_xmin = pred_bboxes[:, 2]
+            det_ymin = pred_bboxes[:, 3]
+            det_xmax = pred_bboxes[:, 4]
+            det_ymax = pred_bboxes[:, 5]
+            det = np.column_stack((det_xmin, det_ymin, det_xmax, 
+                                   det_ymax, det_conf))
+            self.pred_boxes_list.append(det) # xyxy conf
+            self.gt_boxes_list.append(data['gt_ori_bbox'][batch_id].numpy()) # xywh
+            self.hard_ignore_list.append(
+                data['gt_hard_ignore'][batch_id].numpy())
+            self.medium_ignore_list.append(
+                data['gt_medium_ignore'][batch_id].numpy())
+            self.easy_ignore_list.append(
+                data['gt_easy_ignore'][batch_id].numpy())
+    
+    def accumulate(self):
+        total_num = len(self.gt_boxes_list)
+        settings = ['easy', 'medium', 'hard']
+        setting_ingores = [self.easy_ignore_list, 
+                           self.medium_ignore_list, 
+                           self.hard_ignore_list]
+        thresh_num = 1000
+        aps = []
+        for setting_id in range(3):
+            count_face = 0
+            pr_curve = np.zeros((thresh_num, 2)).astype(np.float32)
+            gt_ignore_list = setting_ingores[setting_id]
+            for i in range(total_num):
+                pred_boxes = self.pred_boxes_list[i] # xyxy conf
+                gt_boxes = self.gt_boxes_list[i] # xywh
+                ignore = gt_ignore_list[i]
+                count_face += np.sum(ignore)
+
+                if len(gt_boxes) == 0 or len(pred_boxes) == 0:
+                    continue
+                pred_recall, proposal_list = image_eval(pred_boxes, gt_boxes, 
+                                                        ignore, self.iou_thresh)
+                _img_pr_info = img_pr_info(thresh_num, pred_boxes,
+                                           proposal_list, pred_recall)
+                pr_curve += _img_pr_info
+            pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)
+
+            propose = pr_curve[:, 0]
+            recall = pr_curve[:, 1]
+
+            ap = voc_ap(recall, propose)
+            aps.append(ap)
+        self.aps = aps
+    
+    def log(self):
+        logger.info("==================== Results ====================")
+        logger.info("Easy   Val AP: {}".format(self.aps[0]))
+        logger.info("Medium Val AP: {}".format(self.aps[1]))
+        logger.info("Hard   Val AP: {}".format(self.aps[2]))
+        logger.info("=================================================")
+    
+    def get_results(self):
+        return {
+            'easy_ap': self.aps[0],
+            'medium_ap': self.aps[1],
+            'hard_ap': self.aps[2]}
 
 class RBoxMetric(Metric):
     def __init__(self, anno_file, **kwargs):