test=dygraph sync reader from static ppdet (#1084)

sync reader from static ppdet

ppdet/data/reader.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import copy
 import functools
 import collections
@@ -167,6 +168,8 @@ class Reader(object):
             Default True.
         mixup_epoch (int): mixup epoc number. Default is -1, meaning
             not use mixup.
+        cutmix_epoch (int): cutmix epoc number. Default is -1, meaning
+            not use cutmix.
         class_aware_sampling (bool): whether use class-aware sampling or not.
             Default False.
         worker_num (int): number of working threads/processes.
@@ -191,6 +194,7 @@ class Reader(object):
                  drop_last=False,
                  drop_empty=True,
                  mixup_epoch=-1,
+                 cutmix_epoch=-1,
                  class_aware_sampling=False,
                  worker_num=-1,
                  use_process=False,
@@ -241,6 +245,7 @@ class Reader(object):
 
         # sampling
         self._mixup_epoch = mixup_epoch
+        self._cutmix_epoch = cutmix_epoch
         self._class_aware_sampling = class_aware_sampling
 
         self._load_img = False
@@ -253,6 +258,8 @@ class Reader(object):
         self._pos = -1
         self._epoch = -1
 
+        self._curr_iter = 0
+
         # multi-process
         self._worker_num = worker_num
         self._parallel = None
@@ -274,6 +281,11 @@ class Reader(object):
     def reset(self):
         """implementation of Dataset.reset
         """
+        if self._epoch < 0:
+            self._epoch = 0
+        else:
+            self._epoch += 1
+
         self.indexes = [i for i in range(self.size())]
         if self._class_aware_sampling:
             self.indexes = np.random.choice(
@@ -283,17 +295,18 @@ class Reader(object):
                 p=self.img_weights)
 
         if self._shuffle:
+            trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
+            np.random.seed(self._epoch + trainer_id)
             np.random.shuffle(self.indexes)
 
         if self._mixup_epoch > 0 and len(self.indexes) < 2:
             logger.debug("Disable mixup for dataset samples "
                          "less than 2 samples")
             self._mixup_epoch = -1
-
-        if self._epoch < 0:
-            self._epoch = 0
-        else:
-            self._epoch += 1
+        if self._cutmix_epoch > 0 and len(self.indexes) < 2:
+            logger.info("Disable cutmix for dataset samples "
+                        "less than 2 samples")
+            self._cutmix_epoch = -1
 
         self._pos = 0
 
@@ -306,6 +319,7 @@ class Reader(object):
         if self.drained():
             raise StopIteration
         batch = self._load_batch()
+        self._curr_iter += 1
         if self._drop_last and len(batch) < self._batch_size:
             raise StopIteration
         if self._worker_num > -1:
@@ -321,6 +335,7 @@ class Reader(object):
                 break
             pos = self.indexes[self._pos]
             sample = copy.deepcopy(self._roidbs[pos])
+            sample["curr_iter"] = self._curr_iter
             self._pos += 1
 
             if self._drop_empty and self._fields and 'gt_mask' in self._fields:
@@ -343,9 +358,18 @@ class Reader(object):
                 mix_idx = np.random.randint(1, num)
                 mix_idx = self.indexes[(mix_idx + self._pos - 1) % num]
                 sample['mixup'] = copy.deepcopy(self._roidbs[mix_idx])
+                sample['mixup']["curr_iter"] = self._curr_iter
                 if self._load_img:
                     sample['mixup']['image'] = self._load_image(sample['mixup'][
                         'im_file'])
+            if self._epoch < self._cutmix_epoch:
+                num = len(self.indexes)
+                mix_idx = np.random.randint(1, num)
+                sample['cutmix'] = copy.deepcopy(self._roidbs[mix_idx])
+                sample['cutmix']["curr_iter"] = self._curr_iter
+                if self._load_img:
+                    sample['cutmix']['image'] = self._load_image(sample[
+                        'cutmix']['im_file'])
 
             batch.append(sample)
             bs += 1

ppdet/data/source/widerface.py

@@ -41,7 +41,8 @@ class WIDERFaceDataSet(DataSet):
                  image_dir=None,
                  anno_path=None,
                  sample_num=-1,
-                 with_background=True):
+                 with_background=True,
+                 with_lmk=False):
         super(WIDERFaceDataSet, self).__init__(
             image_dir=image_dir,
             anno_path=anno_path,
@@ -53,6 +54,7 @@ class WIDERFaceDataSet(DataSet):
         self.with_background = with_background
         self.roidbs = None
         self.cname2cid = None
+        self.with_lmk = with_lmk
 
     def load_roidb_and_cname2cid(self):
         anno_path = os.path.join(self.dataset_dir, self.anno_path)
@@ -62,33 +64,23 @@ class WIDERFaceDataSet(DataSet):
 
         records = []
         ct = 0
-        file_lists = _load_file_list(txt_file)
+        file_lists = self._load_file_list(txt_file)
         cname2cid = widerface_label(self.with_background)
 
         for item in file_lists:
             im_fname = item[0]
             im_id = np.array([ct])
-            gt_bbox = np.zeros((len(item) - 2, 4), dtype=np.float32)
-            gt_class = np.ones((len(item) - 2, 1), dtype=np.int32)
+            gt_bbox = np.zeros((len(item) - 1, 4), dtype=np.float32)
+            gt_class = np.ones((len(item) - 1, 1), dtype=np.int32)
+            gt_lmk_labels = np.zeros((len(item) - 1, 10), dtype=np.float32)
+            lmk_ignore_flag = np.zeros((len(item) - 1, 1), dtype=np.int32)
             for index_box in range(len(item)):
-                if index_box >= 2:
-                    temp_info_box = item[index_box].split(' ')
-                    xmin = float(temp_info_box[0])
-                    ymin = float(temp_info_box[1])
-                    w = float(temp_info_box[2])
-                    h = float(temp_info_box[3])
-                    # Filter out wrong labels
-                    if w < 0 or h < 0:
-                        logger.warn('Illegal box with w: {}, h: {} in '
-                                    'img: {}, and it will be ignored'.format(
-                                        w, h, im_fname))
+                if index_box < 1:
                     continue
-                    xmin = max(0, xmin)
-                    ymin = max(0, ymin)
-                    xmax = xmin + w
-                    ymax = ymin + h
-                    gt_bbox[index_box - 2] = [xmin, ymin, xmax, ymax]
-
+                gt_bbox[index_box - 1] = item[index_box][0]
+                if self.with_lmk:
+                    gt_lmk_labels[index_box - 1] = item[index_box][1]
+                    lmk_ignore_flag[index_box - 1] = item[index_box][2]
             im_fname = os.path.join(image_dir,
                                     im_fname) if image_dir else im_fname
             widerface_rec = {
@@ -97,7 +89,10 @@ class WIDERFaceDataSet(DataSet):
                 'gt_bbox': gt_bbox,
                 'gt_class': gt_class,
             }
-            # logger.debug
+            if self.with_lmk:
+                widerface_rec['gt_keypoint'] = gt_lmk_labels
+                widerface_rec['keypoint_ignore'] = lmk_ignore_flag
+
             if len(item) != 0:
                 records.append(widerface_rec)
 
@@ -108,8 +103,7 @@ class WIDERFaceDataSet(DataSet):
         logger.debug('{} samples in file {}'.format(ct, anno_path))
         self.roidbs, self.cname2cid = records, cname2cid
 
-
-def _load_file_list(input_txt):
+    def _load_file_list(self, input_txt):
         with open(input_txt, 'r') as f_dir:
             lines_input_txt = f_dir.readlines()
 
@@ -123,17 +117,48 @@ def _load_file_list(input_txt):
                 file_dict[num_class] = []
                 file_dict[num_class].append(line_txt)
             if '.jpg' not in line_txt:
-            if len(line_txt) > 6:
+                if len(line_txt) <= 6:
+                    continue
+                result_boxs = []
                 split_str = line_txt.split(' ')
-                x1_min = float(split_str[0])
-                y1_min = float(split_str[1])
-                x2_max = float(split_str[2])
-                y2_max = float(split_str[3])
-                line_txt = str(x1_min) + ' ' + str(y1_min) + ' ' + str(
-                    x2_max) + ' ' + str(y2_max)
-                file_dict[num_class].append(line_txt)
-            else:
-                file_dict[num_class].append(line_txt)
+                xmin = float(split_str[0])
+                ymin = float(split_str[1])
+                w = float(split_str[2])
+                h = float(split_str[3])
+                # Filter out wrong labels
+                if w < 0 or h < 0:
+                    logger.warn('Illegal box with w: {}, h: {} in '
+                                'img: {}, and it will be ignored'.format(
+                                    w, h, file_dict[num_class][0]))
+                    continue
+                xmin = max(0, xmin)
+                ymin = max(0, ymin)
+                xmax = xmin + w
+                ymax = ymin + h
+                gt_bbox = [xmin, ymin, xmax, ymax]
+                result_boxs.append(gt_bbox)
+                if self.with_lmk:
+                    assert len(split_str) > 18, 'When `with_lmk=True`, the number' \
+                            'of characters per line in the annotation file should' \
+                            'exceed 18.'
+                    lmk0_x = float(split_str[5])
+                    lmk0_y = float(split_str[6])
+                    lmk1_x = float(split_str[8])
+                    lmk1_y = float(split_str[9])
+                    lmk2_x = float(split_str[11])
+                    lmk2_y = float(split_str[12])
+                    lmk3_x = float(split_str[14])
+                    lmk3_y = float(split_str[15])
+                    lmk4_x = float(split_str[17])
+                    lmk4_y = float(split_str[18])
+                    lmk_ignore_flag = 0 if lmk0_x == -1 else 1
+                    gt_lmk_label = [
+                        lmk0_x, lmk0_y, lmk1_x, lmk1_y, lmk2_x, lmk2_y, lmk3_x,
+                        lmk3_y, lmk4_x, lmk4_y
+                    ]
+                    result_boxs.append(gt_lmk_label)
+                    result_boxs.append(lmk_ignore_flag)
+                file_dict[num_class].append(result_boxs)
 
         return list(file_dict.values())
 

ppdet/data/transform/batch_operators.py

@@ -26,13 +26,17 @@ import cv2
 import numpy as np
 
 from .operators import register_op, BaseOperator
-from .op_helper import jaccard_overlap
+from .op_helper import jaccard_overlap, gaussian2D
 
 logger = logging.getLogger(__name__)
 
 __all__ = [
-    'PadBatch', 'RandomShape', 'PadMultiScaleTest', 'Gt2YoloTarget',
-    'Gt2FCOSTarget'
+    'PadBatch',
+    'RandomShape',
+    'PadMultiScaleTest',
+    'Gt2YoloTarget',
+    'Gt2FCOSTarget',
+    'Gt2TTFTarget',
 ]
 
 
@@ -41,17 +45,15 @@ class PadBatch(BaseOperator):
     """
     Pad a batch of samples so they can be divisible by a stride.
     The layout of each image should be 'CHW'.
-
     Args:
         pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
             height and width is divisible by `pad_to_stride`.
     """
 
-    def __init__(self, pad_to_stride=0, use_padded_im_info=True, pad_gt=False):
+    def __init__(self, pad_to_stride=0, use_padded_im_info=True):
         super(PadBatch, self).__init__()
         self.pad_to_stride = pad_to_stride
         self.use_padded_im_info = use_padded_im_info
-        self.pad_gt = pad_gt
 
     def __call__(self, samples, context=None):
         """
@@ -61,9 +63,9 @@ class PadBatch(BaseOperator):
         coarsest_stride = self.pad_to_stride
         if coarsest_stride == 0:
             return samples
-
         max_shape = np.array([data['image'].shape for data in samples]).max(
             axis=0)
+
         if coarsest_stride > 0:
             max_shape[1] = int(
                 np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
@@ -80,52 +82,6 @@ class PadBatch(BaseOperator):
             data['image'] = padding_im
             if self.use_padded_im_info:
                 data['im_info'][:2] = max_shape[1:3]
-
-        if self.pad_gt:
-            gt_num = []
-            if data['gt_poly'] is not None and len(data['gt_poly']) > 0:
-                pad_mask = True
-            else:
-                pad_mask = False
-
-            if pad_mask:
-                poly_num = []
-                poly_part_num = []
-                point_num = []
-            for data in samples:
-                gt_num.append(data['gt_bbox'].shape[0])
-                if pad_mask:
-                    poly_num.append(len(data['gt_poly']))
-                    for poly in data['gt_poly']:
-                        poly_part_num.append(int(len(poly)))
-                        for p_p in poly:
-                            point_num.append(int(len(p_p) / 2))
-            gt_num_max = max(gt_num)
-            gt_box_data = np.zeros([gt_num_max, 4])
-            gt_class_data = np.zeros([gt_num_max])
-            is_crowd_data = np.ones([gt_num_max])
-
-            if pad_mask:
-                poly_num_max = max(poly_num)
-                poly_part_num_max = max(poly_part_num)
-                point_num_max = max(point_num)
-                gt_masks_data = -np.ones(
-                    [poly_num_max, poly_part_num_max, point_num_max, 2])
-
-            for i, data in enumerate(samples):
-                gt_num = data['gt_bbox'].shape[0]
-                gt_box_data[0:gt_num, :] = data['gt_bbox']
-                gt_class_data[0:gt_num] = np.squeeze(data['gt_class'])
-                is_crowd_data[0:gt_num] = np.squeeze(data['is_crowd'])
-                if pad_mask:
-                    for j, poly in enumerate(data['gt_poly']):
-                        for k, p_p in enumerate(poly):
-                            pp_np = np.array(p_p).reshape(-1, 2)
-                            gt_masks_data[j, k, :pp_np.shape[0], :] = pp_np
-                    data['gt_poly'] = gt_masks_data
-                data['gt_bbox'] = gt_box_data
-                data['gt_class'] = gt_class_data
-                data['is_crowd_data'] = is_crowd_data
         return samples
 
 
@@ -136,13 +92,12 @@ class RandomShape(BaseOperator):
     select one an interpolation algorithm [cv2.INTER_NEAREST, cv2.INTER_LINEAR,
     cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4]. If random_inter is
     False, use cv2.INTER_NEAREST.
-
     Args:
         sizes (list): list of int, random choose a size from these
         random_inter (bool): whether to randomly interpolation, defalut true.
     """
 
-    def __init__(self, sizes=[], random_inter=False):
+    def __init__(self, sizes=[], random_inter=False, resize_box=False):
         super(RandomShape, self).__init__()
         self.sizes = sizes
         self.random_inter = random_inter
@@ -153,6 +108,7 @@ class RandomShape(BaseOperator):
             cv2.INTER_CUBIC,
             cv2.INTER_LANCZOS4,
         ] if random_inter else []
+        self.resize_box = resize_box
 
     def __call__(self, samples, context=None):
         shape = np.random.choice(self.sizes)
@@ -166,6 +122,12 @@ class RandomShape(BaseOperator):
             im = cv2.resize(
                 im, None, None, fx=scale_x, fy=scale_y, interpolation=method)
             samples[i]['image'] = im
+            if self.resize_box and 'gt_bbox' in samples[i] and len(samples[0][
+                    'gt_bbox']) > 0:
+                scale_array = np.array([scale_x, scale_y] * 2, dtype=np.float32)
+                samples[i]['gt_bbox'] = np.clip(samples[i]['gt_bbox'] *
+                                                scale_array, 0,
+                                                float(shape) - 1)
         return samples
 
 
@@ -525,3 +487,99 @@ class Gt2FCOSTarget(BaseOperator):
                 sample['centerness{}'.format(lvl)] = np.reshape(
                     ctn_targets_by_level[lvl], newshape=[grid_h, grid_w, 1])
         return samples
+
+
+@register_op
+class Gt2TTFTarget(BaseOperator):
+    """
+    Gt2TTFTarget
+    Generate TTFNet targets by ground truth data
+    
+    Args:
+        num_classes(int): the number of classes.
+        down_ratio(int): the down ratio from images to heatmap, 4 by default.
+        alpha(float): the alpha parameter to generate gaussian target.
+            0.54 by default.
+    """
+
+    def __init__(self, num_classes, down_ratio=4, alpha=0.54):
+        super(Gt2TTFTarget, self).__init__()
+        self.down_ratio = down_ratio
+        self.num_classes = num_classes
+        self.alpha = alpha
+
+    def __call__(self, samples, context=None):
+        output_size = samples[0]['image'].shape[1]
+        feat_size = output_size // self.down_ratio
+        for sample in samples:
+            heatmap = np.zeros(
+                (self.num_classes, feat_size, feat_size), dtype='float32')
+            box_target = np.ones(
+                (4, feat_size, feat_size), dtype='float32') * -1
+            reg_weight = np.zeros((1, feat_size, feat_size), dtype='float32')
+
+            gt_bbox = sample['gt_bbox']
+            gt_class = sample['gt_class']
+
+            bbox_w = gt_bbox[:, 2] - gt_bbox[:, 0] + 1
+            bbox_h = gt_bbox[:, 3] - gt_bbox[:, 1] + 1
+            area = bbox_w * bbox_h
+            boxes_areas_log = np.log(area)
+            boxes_ind = np.argsort(boxes_areas_log, axis=0)[::-1]
+            boxes_area_topk_log = boxes_areas_log[boxes_ind]
+            gt_bbox = gt_bbox[boxes_ind]
+            gt_class = gt_class[boxes_ind]
+
+            feat_gt_bbox = gt_bbox / self.down_ratio
+            feat_gt_bbox = np.clip(feat_gt_bbox, 0, feat_size - 1)
+            feat_hs, feat_ws = (feat_gt_bbox[:, 3] - feat_gt_bbox[:, 1],
+                                feat_gt_bbox[:, 2] - feat_gt_bbox[:, 0])
+
+            ct_inds = np.stack(
+                [(gt_bbox[:, 0] + gt_bbox[:, 2]) / 2,
+                 (gt_bbox[:, 1] + gt_bbox[:, 3]) / 2],
+                axis=1) / self.down_ratio
+
+            h_radiuses_alpha = (feat_hs / 2. * self.alpha).astype('int32')
+            w_radiuses_alpha = (feat_ws / 2. * self.alpha).astype('int32')
+
+            for k in range(len(gt_bbox)):
+                cls_id = gt_class[k]
+                fake_heatmap = np.zeros((feat_size, feat_size), dtype='float32')
+                self.draw_truncate_gaussian(fake_heatmap, ct_inds[k],
+                                            h_radiuses_alpha[k],
+                                            w_radiuses_alpha[k])
+
+                heatmap[cls_id] = np.maximum(heatmap[cls_id], fake_heatmap)
+                box_target_inds = fake_heatmap > 0
+                box_target[:, box_target_inds] = gt_bbox[k][:, None]
+
+                local_heatmap = fake_heatmap[box_target_inds]
+                ct_div = np.sum(local_heatmap)
+                local_heatmap *= boxes_area_topk_log[k]
+                reg_weight[0, box_target_inds] = local_heatmap / ct_div
+            sample['ttf_heatmap'] = heatmap
+            sample['ttf_box_target'] = box_target
+            sample['ttf_reg_weight'] = reg_weight
+        return samples
+
+    def draw_truncate_gaussian(self, heatmap, center, h_radius, w_radius):
+        h, w = 2 * h_radius + 1, 2 * w_radius + 1
+        sigma_x = w / 6
+        sigma_y = h / 6
+        gaussian = gaussian2D((h, w), sigma_x, sigma_y)
+
+        x, y = int(center[0]), int(center[1])
+
+        height, width = heatmap.shape[0:2]
+
+        left, right = min(x, w_radius), min(width - x, w_radius + 1)
+        top, bottom = min(y, h_radius), min(height - y, h_radius + 1)
+
+        masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
+        masked_gaussian = gaussian[h_radius - top:h_radius + bottom, w_radius -
+                                   left:w_radius + right]
+        if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
+            heatmap[y - top:y + bottom, x - left:x + right] = np.maximum(
+                masked_heatmap, masked_gaussian)
+        return heatmap

ppdet/data/transform/gridmask_utils.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import numpy as np
+from PIL import Image
+
+
+class GridMask(object):
+    def __init__(self,
+                 use_h=True,
+                 use_w=True,
+                 rotate=1,
+                 offset=False,
+                 ratio=0.5,
+                 mode=1,
+                 prob=0.7,
+                 upper_iter=360000):
+        super(GridMask, self).__init__()
+        self.use_h = use_h
+        self.use_w = use_w
+        self.rotate = rotate
+        self.offset = offset
+        self.ratio = ratio
+        self.mode = mode
+        self.prob = prob
+        self.st_prob = prob
+        self.upper_iter = upper_iter
+
+    def __call__(self, x, curr_iter):
+        self.prob = self.st_prob * min(1, 1.0 * curr_iter / self.upper_iter)
+        if np.random.rand() > self.prob:
+            return x
+        _, h, w = x.shape
+        hh = int(1.5 * h)
+        ww = int(1.5 * w)
+        d = np.random.randint(2, h)
+        self.l = min(max(int(d * self.ratio + 0.5), 1), d - 1)
+        mask = np.ones((hh, ww), np.float32)
+        st_h = np.random.randint(d)
+        st_w = np.random.randint(d)
+        if self.use_h:
+            for i in range(hh // d):
+                s = d * i + st_h
+                t = min(s + self.l, hh)
+                mask[s:t, :] *= 0
+        if self.use_w:
+            for i in range(ww // d):
+                s = d * i + st_w
+                t = min(s + self.l, ww)
+                mask[:, s:t] *= 0
+
+        r = np.random.randint(self.rotate)
+        mask = Image.fromarray(np.uint8(mask))
+        mask = mask.rotate(r)
+        mask = np.asarray(mask)
+        mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) // 2
+                    + w].astype(np.float32)
+
+        if self.mode == 1:
+            mask = 1 - mask
+        mask = np.expand_dims(mask, axis=0)
+        if self.offset:
+            offset = (2 * (np.random.rand(h, w) - 0.5)).astype(np.float32)
+            x = (x * mask + offset * (1 - mask)).astype(x.dtype)
+        else:
+            x = (x * mask).astype(x.dtype)
+
+        return x

ppdet/data/transform/op_helper.py

@@ -61,10 +61,13 @@ def is_overlap(object_bbox, sample_bbox):
         return True
 
 
-def filter_and_process(sample_bbox, bboxes, labels, scores=None):
+def filter_and_process(sample_bbox, bboxes, labels, scores=None,
+                       keypoints=None):
     new_bboxes = []
     new_labels = []
     new_scores = []
+    new_keypoints = []
+    new_kp_ignore = []
     for i in range(len(bboxes)):
         new_bbox = [0, 0, 0, 0]
         obj_bbox = [bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3]]
@@ -84,9 +87,24 @@ def filter_and_process(sample_bbox, bboxes, labels, scores=None):
             new_labels.append([labels[i][0]])
             if scores is not None:
                 new_scores.append([scores[i][0]])
+            if keypoints is not None:
+                sample_keypoint = keypoints[0][i]
+                for j in range(len(sample_keypoint)):
+                    kp_len = sample_height if j % 2 else sample_width
+                    sample_coord = sample_bbox[1] if j % 2 else sample_bbox[0]
+                    sample_keypoint[j] = (
+                        sample_keypoint[j] - sample_coord) / kp_len
+                    sample_keypoint[j] = max(min(sample_keypoint[j], 1.0), 0.0)
+                new_keypoints.append(sample_keypoint)
+                new_kp_ignore.append(keypoints[1][i])
+
     bboxes = np.array(new_bboxes)
     labels = np.array(new_labels)
     scores = np.array(new_scores)
+    if keypoints is not None:
+        keypoints = np.array(new_keypoints)
+        new_kp_ignore = np.array(new_kp_ignore)
+        return bboxes, labels, scores, (keypoints, new_kp_ignore)
     return bboxes, labels, scores
 
 
@@ -420,7 +438,8 @@ def gaussian_radius(bbox_size, min_overlap):
 
 def draw_gaussian(heatmap, center, radius, k=1, delte=6):
     diameter = 2 * radius + 1
-    gaussian = gaussian2D((diameter, diameter), sigma=diameter / delte)
+    sigma = diameter / delte
+    gaussian = gaussian2D((diameter, diameter), sigma_x=sigma, sigma_y=sigma)
 
     x, y = center
 
@@ -435,10 +454,11 @@ def draw_gaussian(heatmap, center, radius, k=1, delte=6):
     np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
 
 
-def gaussian2D(shape, sigma=1):
+def gaussian2D(shape, sigma_x=1, sigma_y=1):
     m, n = [(ss - 1.) / 2. for ss in shape]
     y, x = np.ogrid[-m:m + 1, -n:n + 1]
 
-    h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
+    h = np.exp(-(x * x / (2 * sigma_x * sigma_x) + y * y / (2 * sigma_y *
+                                                            sigma_y)))
     h[h < np.finfo(h.dtype).eps * h.max()] = 0
     return h