[cherry-pick] refine ppdet file name (#2533)

* refine ppdet file name

* unify bbox_utils

ppdet/metrics/coco_utils.py

@@ -21,7 +21,7 @@ import sys
 import numpy as np
 import itertools
 
-from ppdet.metrics.post_process import get_det_res, get_seg_res, get_solov2_segm_res
+from ppdet.metrics.json_results import get_det_res, get_seg_res, get_solov2_segm_res
 from ppdet.metrics.map_utils import draw_pr_curve
 
 from ppdet.utils.logger import setup_logger

ppdet/modeling/__init__.py

@@ -7,7 +7,6 @@ from . import losses
 from . import architectures
 from . import post_process
 from . import layers
-from . import utils
 
 from .ops import *
 from .backbones import *
@@ -18,4 +17,3 @@ from .losses import *
 from .architectures import *
 from .post_process import *
 from .layers import *
-from .utils import *

ppdet/modeling/bbox_utils.py

@@ -14,6 +14,8 @@
 
 import math
 import paddle
+import paddle.nn.functional as F
+import math
 
 
 def bbox2delta(src_boxes, tgt_boxes, weights):
@@ -111,6 +113,16 @@ def bbox_area(boxes):
 
 
 def bbox_overlaps(boxes1, boxes2):
+    """
+    Calculate overlaps between boxes1 and boxes2
+
+    Args:
+        boxes1 (Tensor): boxes with shape [M, 4]
+        boxes2 (Tensor): boxes with shape [N, 4]
+
+    Return:
+        overlaps (Tensor): overlaps between boxes1 and boxes2 with shape [M, N]
+    """
     area1 = bbox_area(boxes1)
     area2 = bbox_area(boxes2)
 
@@ -126,3 +138,125 @@ def bbox_overlaps(boxes1, boxes2):
                             (paddle.unsqueeze(area1, 1) + area2 - inter),
                             paddle.zeros_like(inter))
     return overlaps
+
+
+def xywh2xyxy(box):
+    x, y, w, h = box
+    x1 = x - w * 0.5
+    y1 = y - h * 0.5
+    x2 = x + w * 0.5
+    y2 = y + h * 0.5
+    return [x1, y1, x2, y2]
+
+
+def make_grid(h, w, dtype):
+    yv, xv = paddle.meshgrid([paddle.arange(h), paddle.arange(w)])
+    return paddle.stack((xv, yv), 2).cast(dtype=dtype)
+
+
+def decode_yolo(box, anchor, downsample_ratio):
+    """decode yolo box
+
+    Args:
+        box (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
+        anchor (list): anchor with the shape [na, 2]
+        downsample_ratio (int): downsample ratio, default 32
+        scale (float): scale, default 1.
+
+    Return:
+        box (list): decoded box, [x, y, w, h], all have the shape [b, na, h, w, 1]
+    """
+    x, y, w, h = box
+    na, grid_h, grid_w = x.shape[1:4]
+    grid = make_grid(grid_h, grid_w, x.dtype).reshape((1, 1, grid_h, grid_w, 2))
+    x1 = (x + grid[:, :, :, :, 0:1]) / grid_w
+    y1 = (y + grid[:, :, :, :, 1:2]) / grid_h
+
+    anchor = paddle.to_tensor(anchor)
+    anchor = paddle.cast(anchor, x.dtype)
+    anchor = anchor.reshape((1, na, 1, 1, 2))
+    w1 = paddle.exp(w) * anchor[:, :, :, :, 0:1] / (downsample_ratio * grid_w)
+    h1 = paddle.exp(h) * anchor[:, :, :, :, 1:2] / (downsample_ratio * grid_h)
+
+    return [x1, y1, w1, h1]
+
+
+def iou_similarity(box1, box2, eps=1e-9):
+    """Calculate iou of box1 and box2
+
+    Args:
+        box1 (Tensor): box with the shape [N, M1, 4]
+        box2 (Tensor): box with the shape [N, M2, 4]
+
+    Return:
+        iou (Tensor): iou between box1 and box2 with the shape [N, M1, M2]
+    """
+    box1 = box1.unsqueeze(2)  # [N, M1, 4] -> [N, M1, 1, 4]
+    box2 = box2.unsqueeze(1)  # [N, M2, 4] -> [N, 1, M2, 4]
+    px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
+    gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
+    x1y1 = paddle.maximum(px1y1, gx1y1)
+    x2y2 = paddle.minimum(px2y2, gx2y2)
+    overlap = (x2y2 - x1y1).clip(0).prod(-1)
+    area1 = (px2y2 - px1y1).clip(0).prod(-1)
+    area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
+    union = area1 + area2 - overlap + eps
+    return overlap / union
+
+
+def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
+    """calculate the iou of box1 and box2
+
+    Args:
+        box1 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
+        box2 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
+        giou (bool): whether use giou or not, default False
+        diou (bool): whether use diou or not, default False
+        ciou (bool): whether use ciou or not, default False
+        eps (float): epsilon to avoid divide by zero
+
+    Return:
+        iou (Tensor): iou of box1 and box1, with the shape [b, na, h, w, 1]
+    """
+    px1, py1, px2, py2 = box1
+    gx1, gy1, gx2, gy2 = box2
+    x1 = paddle.maximum(px1, gx1)
+    y1 = paddle.maximum(py1, gy1)
+    x2 = paddle.minimum(px2, gx2)
+    y2 = paddle.minimum(py2, gy2)
+
+    overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0))
+
+    area1 = (px2 - px1) * (py2 - py1)
+    area1 = area1.clip(0)
+
+    area2 = (gx2 - gx1) * (gy2 - gy1)
+    area2 = area2.clip(0)
+
+    union = area1 + area2 - overlap + eps
+    iou = overlap / union
+
+    if giou or ciou or diou:
+        # convex w, h
+        cw = paddle.maximum(px2, gx2) - paddle.minimum(px1, gx1)
+        ch = paddle.maximum(py2, gy2) - paddle.minimum(py1, gy1)
+        if giou:
+            c_area = cw * ch + eps
+            return iou - (c_area - union) / c_area
+        else:
+            # convex diagonal squared
+            c2 = cw**2 + ch**2 + eps
+            # center distance
+            rho2 = ((px1 + px2 - gx1 - gx2)**2 + (py1 + py2 - gy1 - gy2)**2) / 4
+            if diou:
+                return iou - rho2 / c2
+            else:
+                w1, h1 = px2 - px1, py2 - py1 + eps
+                w2, h2 = gx2 - gx1, gy2 - gy1 + eps
+                delta = paddle.atan(w1 / h1) - paddle.atan(w2 / h2)
+                v = (4 / math.pi**2) * paddle.pow(delta, 2)
+                alpha = v / (1 + eps - iou + v)
+                alpha.stop_gradient = True
+                return iou - (rho2 / c2 + v * alpha)
+    else:
+        return iou

ppdet/modeling/losses/iou_aware_loss.py

@@ -20,7 +20,7 @@ import paddle
 import paddle.nn.functional as F
 from ppdet.core.workspace import register, serializable
 from .iou_loss import IouLoss
-from ..utils import xywh2xyxy, bbox_iou, decode_yolo
+from ..bbox_utils import xywh2xyxy, bbox_iou
 
 
 @register

ppdet/modeling/losses/iou_loss.py

@@ -19,7 +19,7 @@ from __future__ import print_function
 import paddle
 import paddle.nn.functional as F
 from ppdet.core.workspace import register, serializable
-from ..utils import xywh2xyxy, bbox_iou, decode_yolo
+from ..bbox_utils import xywh2xyxy, bbox_iou
 
 __all__ = ['IouLoss', 'GIoULoss']
 

ppdet/modeling/losses/yolo_loss.py

@@ -21,7 +21,7 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 from ppdet.core.workspace import register
 
-from ..utils import decode_yolo, xywh2xyxy, iou_similarity
+from ..bbox_utils import decode_yolo, xywh2xyxy, iou_similarity
 
 __all__ = ['YOLOv3Loss']
 

ppdet/modeling/utils/__init__.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 . import bbox_util
-
-from .bbox_util import *

ppdet/modeling/utils/bbox_util.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 division
-from __future__ import print_function
-
-import paddle
-import paddle.nn.functional as F
-import math
-
-
-def xywh2xyxy(box):
-    x, y, w, h = box
-    x1 = x - w * 0.5
-    y1 = y - h * 0.5
-    x2 = x + w * 0.5
-    y2 = y + h * 0.5
-    return [x1, y1, x2, y2]
-
-
-def make_grid(h, w, dtype):
-    yv, xv = paddle.meshgrid([paddle.arange(h), paddle.arange(w)])
-    return paddle.stack((xv, yv), 2).cast(dtype=dtype)
-
-
-def decode_yolo(box, anchor, downsample_ratio):
-    """decode yolo box
-
-    Args:
-        box (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
-        anchor (list): anchor with the shape [na, 2]
-        downsample_ratio (int): downsample ratio, default 32
-        scale (float): scale, default 1.
-
-    Return:
-        box (list): decoded box, [x, y, w, h], all have the shape [b, na, h, w, 1]
-    """
-    x, y, w, h = box
-    na, grid_h, grid_w = x.shape[1:4]
-    grid = make_grid(grid_h, grid_w, x.dtype).reshape((1, 1, grid_h, grid_w, 2))
-    x1 = (x + grid[:, :, :, :, 0:1]) / grid_w
-    y1 = (y + grid[:, :, :, :, 1:2]) / grid_h
-
-    anchor = paddle.to_tensor(anchor)
-    anchor = paddle.cast(anchor, x.dtype)
-    anchor = anchor.reshape((1, na, 1, 1, 2))
-    w1 = paddle.exp(w) * anchor[:, :, :, :, 0:1] / (downsample_ratio * grid_w)
-    h1 = paddle.exp(h) * anchor[:, :, :, :, 1:2] / (downsample_ratio * grid_h)
-
-    return [x1, y1, w1, h1]
-
-
-def iou_similarity(box1, box2, eps=1e-9):
-    """Calculate iou of box1 and box2
-
-    Args:
-        box1 (Tensor): box with the shape [N, M1, 4]
-        box2 (Tensor): box with the shape [N, M2, 4]
-
-    Return:
-        iou (Tensor): iou between box1 and box2 with the shape [N, M1, M2]
-    """
-    box1 = box1.unsqueeze(2)  # [N, M1, 4] -> [N, M1, 1, 4]
-    box2 = box2.unsqueeze(1)  # [N, M2, 4] -> [N, 1, M2, 4]
-    px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
-    gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
-    x1y1 = paddle.maximum(px1y1, gx1y1)
-    x2y2 = paddle.minimum(px2y2, gx2y2)
-    overlap = (x2y2 - x1y1).clip(0).prod(-1)
-    area1 = (px2y2 - px1y1).clip(0).prod(-1)
-    area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
-    union = area1 + area2 - overlap + eps
-    return overlap / union
-
-
-def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
-    """calculate the iou of box1 and box2
-
-    Args:
-        box1 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
-        box2 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
-        giou (bool): whether use giou or not, default False
-        diou (bool): whether use diou or not, default False
-        ciou (bool): whether use ciou or not, default False
-        eps (float): epsilon to avoid divide by zero
-
-    Return:
-        iou (Tensor): iou of box1 and box1, with the shape [b, na, h, w, 1]
-    """
-    px1, py1, px2, py2 = box1
-    gx1, gy1, gx2, gy2 = box2
-    x1 = paddle.maximum(px1, gx1)
-    y1 = paddle.maximum(py1, gy1)
-    x2 = paddle.minimum(px2, gx2)
-    y2 = paddle.minimum(py2, gy2)
-
-    overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0))
-
-    area1 = (px2 - px1) * (py2 - py1)
-    area1 = area1.clip(0)
-
-    area2 = (gx2 - gx1) * (gy2 - gy1)
-    area2 = area2.clip(0)
-
-    union = area1 + area2 - overlap + eps
-    iou = overlap / union
-
-    if giou or ciou or diou:
-        # convex w, h
-        cw = paddle.maximum(px2, gx2) - paddle.minimum(px1, gx1)
-        ch = paddle.maximum(py2, gy2) - paddle.minimum(py1, gy1)
-        if giou:
-            c_area = cw * ch + eps
-            return iou - (c_area - union) / c_area
-        else:
-            # convex diagonal squared
-            c2 = cw**2 + ch**2 + eps
-            # center distance
-            rho2 = ((px1 + px2 - gx1 - gx2)**2 + (py1 + py2 - gy1 - gy2)**2) / 4
-            if diou:
-                return iou - rho2 / c2
-            else:
-                w1, h1 = px2 - px1, py2 - py1 + eps
-                w2, h2 = gx2 - gx1, gy2 - gy1 + eps
-                delta = paddle.atan(w1 / h1) - paddle.atan(w2 / h2)
-                v = (4 / math.pi**2) * paddle.pow(delta, 2)
-                alpha = v / (1 + eps - iou + v)
-                alpha.stop_gradient = True
-                return iou - (rho2 / c2 + v * alpha)
-    else:
-        return iou

ppdet/utils/bbox_utils.py

-# Copyright (c) 2019 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 division
-from __future__ import print_function
-
-import numpy as np
-
-from .logger import setup_logger
-logger = setup_logger(__name__)
-
-__all__ = ["bbox_overlaps", "box_to_delta"]
-
-
-def bbox_overlaps(boxes_1, boxes_2):
-    '''
-    bbox_overlaps
-        boxes_1: x1, y, x2, y2
-        boxes_2: x1, y, x2, y2
-    '''
-    assert boxes_1.shape[1] == 4 and boxes_2.shape[1] == 4
-
-    num_1 = boxes_1.shape[0]
-    num_2 = boxes_2.shape[0]
-
-    x1_1 = boxes_1[:, 0:1]
-    y1_1 = boxes_1[:, 1:2]
-    x2_1 = boxes_1[:, 2:3]
-    y2_1 = boxes_1[:, 3:4]
-    area_1 = (x2_1 - x1_1 + 1) * (y2_1 - y1_1 + 1)
-
-    x1_2 = boxes_2[:, 0].transpose()
-    y1_2 = boxes_2[:, 1].transpose()
-    x2_2 = boxes_2[:, 2].transpose()
-    y2_2 = boxes_2[:, 3].transpose()
-    area_2 = (x2_2 - x1_2 + 1) * (y2_2 - y1_2 + 1)
-
-    xx1 = np.maximum(x1_1, x1_2)
-    yy1 = np.maximum(y1_1, y1_2)
-    xx2 = np.minimum(x2_1, x2_2)
-    yy2 = np.minimum(y2_1, y2_2)
-
-    w = np.maximum(0.0, xx2 - xx1 + 1)
-    h = np.maximum(0.0, yy2 - yy1 + 1)
-    inter = w * h
-
-    ovr = inter / (area_1 + area_2 - inter)
-    return ovr
-
-
-def box_to_delta(ex_boxes, gt_boxes, weights):
-    """ box_to_delta """
-    ex_w = ex_boxes[:, 2] - ex_boxes[:, 0] + 1
-    ex_h = ex_boxes[:, 3] - ex_boxes[:, 1] + 1
-    ex_ctr_x = ex_boxes[:, 0] + 0.5 * ex_w
-    ex_ctr_y = ex_boxes[:, 1] + 0.5 * ex_h
-
-    gt_w = gt_boxes[:, 2] - gt_boxes[:, 0] + 1
-    gt_h = gt_boxes[:, 3] - gt_boxes[:, 1] + 1
-    gt_ctr_x = gt_boxes[:, 0] + 0.5 * gt_w
-    gt_ctr_y = gt_boxes[:, 1] + 0.5 * gt_h
-
-    dx = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0]
-    dy = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1]
-    dw = (np.log(gt_w / ex_w)) / weights[2]
-    dh = (np.log(gt_h / ex_h)) / weights[3]
-
-    targets = np.vstack([dx, dy, dw, dh]).transpose()
-    return targets

ppdet/utils/post_process.py

-# Copyright (c) 2019 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 division
-from __future__ import print_function
-
-import numpy as np
-import cv2
-
-from .logger import setup_logger
-logger = setup_logger(__name__)
-
-__all__ = ['nms']
-
-
-def box_flip(boxes, im_shape):
-    im_width = im_shape[0][1]
-    flipped_boxes = boxes.copy()
-
-    flipped_boxes[:, 0::4] = im_width - boxes[:, 2::4] - 1
-    flipped_boxes[:, 2::4] = im_width - boxes[:, 0::4] - 1
-    return flipped_boxes
-
-
-def nms(dets, thresh):
-    """Apply classic DPM-style greedy NMS."""
-    if dets.shape[0] == 0:
-        return dets[[], :]
-    scores = dets[:, 0]
-    x1 = dets[:, 1]
-    y1 = dets[:, 2]
-    x2 = dets[:, 3]
-    y2 = dets[:, 4]
-
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-    order = scores.argsort()[::-1]
-
-    ndets = dets.shape[0]
-    suppressed = np.zeros((ndets), dtype=np.int)
-
-    # nominal indices
-    # _i, _j
-    # sorted indices
-    # i, j
-    # temp variables for box i's (the box currently under consideration)
-    # ix1, iy1, ix2, iy2, iarea
-
-    # variables for computing overlap with box j (lower scoring box)
-    # xx1, yy1, xx2, yy2
-    # w, h
-    # inter, ovr
-
-    for _i in range(ndets):
-        i = order[_i]
-        if suppressed[i] == 1:
-            continue
-        ix1 = x1[i]
-        iy1 = y1[i]
-        ix2 = x2[i]
-        iy2 = y2[i]
-        iarea = areas[i]
-        for _j in range(_i + 1, ndets):
-            j = order[_j]
-            if suppressed[j] == 1:
-                continue
-            xx1 = max(ix1, x1[j])
-            yy1 = max(iy1, y1[j])
-            xx2 = min(ix2, x2[j])
-            yy2 = min(iy2, y2[j])
-            w = max(0.0, xx2 - xx1 + 1)
-            h = max(0.0, yy2 - yy1 + 1)
-            inter = w * h
-            ovr = inter / (iarea + areas[j] - inter)
-            if ovr >= thresh:
-                suppressed[j] = 1
-    keep = np.where(suppressed == 0)[0]
-    dets = dets[keep, :]
-    return dets
-
-
-def soft_nms(dets, sigma, thres):
-    dets_final = []
-    while len(dets) > 0:
-        maxpos = np.argmax(dets[:, 0])
-        dets_final.append(dets[maxpos].copy())
-        ts, tx1, ty1, tx2, ty2 = dets[maxpos]
-        scores = dets[:, 0]
-        # force remove bbox at maxpos
-        scores[maxpos] = -1
-        x1 = dets[:, 1]
-        y1 = dets[:, 2]
-        x2 = dets[:, 3]
-        y2 = dets[:, 4]
-        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-        xx1 = np.maximum(tx1, x1)
-        yy1 = np.maximum(ty1, y1)
-        xx2 = np.minimum(tx2, x2)
-        yy2 = np.minimum(ty2, y2)
-        w = np.maximum(0.0, xx2 - xx1 + 1)
-        h = np.maximum(0.0, yy2 - yy1 + 1)
-        inter = w * h
-        ovr = inter / (areas + areas[maxpos] - inter)
-        weight = np.exp(-(ovr * ovr) / sigma)
-        scores = scores * weight
-        idx_keep = np.where(scores >= thres)
-        dets[:, 0] = scores
-        dets = dets[idx_keep]
-    dets_final = np.array(dets_final).reshape(-1, 5)
-    return dets_final
-
-
-def bbox_area(box):
-    w = box[2] - box[0] + 1
-    h = box[3] - box[1] + 1
-    return w * h
-
-
-def bbox_overlaps(x, y):
-    N = x.shape[0]
-    K = y.shape[0]
-    overlaps = np.zeros((N, K), dtype=np.float32)
-    for k in range(K):
-        y_area = bbox_area(y[k])
-        for n in range(N):
-            iw = min(x[n, 2], y[k, 2]) - max(x[n, 0], y[k, 0]) + 1
-            if iw > 0:
-                ih = min(x[n, 3], y[k, 3]) - max(x[n, 1], y[k, 1]) + 1
-                if ih > 0:
-                    x_area = bbox_area(x[n])
-                    ua = x_area + y_area - iw * ih
-                    overlaps[n, k] = iw * ih / ua
-    return overlaps
-
-
-def box_voting(nms_dets, dets, vote_thresh):
-    top_dets = nms_dets.copy()
-    top_boxes = nms_dets[:, 1:]
-    all_boxes = dets[:, 1:]
-    all_scores = dets[:, 0]
-    top_to_all_overlaps = bbox_overlaps(top_boxes, all_boxes)
-    for k in range(nms_dets.shape[0]):
-        inds_to_vote = np.where(top_to_all_overlaps[k] >= vote_thresh)[0]
-        boxes_to_vote = all_boxes[inds_to_vote, :]
-        ws = all_scores[inds_to_vote]
-        top_dets[k, 1:] = np.average(boxes_to_vote, axis=0, weights=ws)
-
-    return top_dets
-
-
-def get_nms_result(boxes,
-                   scores,
-                   config,
-                   num_classes,
-                   background_label=0,
-                   labels=None):
-    has_labels = labels is not None
-    cls_boxes = [[] for _ in range(num_classes)]
-    start_idx = 1 if background_label == 0 else 0
-    for j in range(start_idx, num_classes):
-        inds = np.where(labels == j)[0] if has_labels else np.where(
-            scores[:, j] > config['score_thresh'])[0]
-        scores_j = scores[inds] if has_labels else scores[inds, j]
-        boxes_j = boxes[inds, :] if has_labels else boxes[inds, j * 4:(j + 1) *
-                                                          4]
-        dets_j = np.hstack((scores_j[:, np.newaxis], boxes_j)).astype(
-            np.float32, copy=False)
-        if config.get('use_soft_nms', False):
-            nms_dets = soft_nms(dets_j, config['sigma'], config['nms_thresh'])
-        else:
-            nms_dets = nms(dets_j, config['nms_thresh'])
-        if config.get('enable_voting', False):
-            nms_dets = box_voting(nms_dets, dets_j, config['vote_thresh'])
-        #add labels
-        label = np.array([j for _ in range(len(nms_dets))])
-        nms_dets = np.hstack((label[:, np.newaxis], nms_dets)).astype(
-            np.float32, copy=False)
-        cls_boxes[j] = nms_dets
-    # Limit to max_per_image detections **over all classes**
-    image_scores = np.hstack(
-        [cls_boxes[j][:, 1] for j in range(start_idx, num_classes)])
-    if len(image_scores) > config['detections_per_im']:
-        image_thresh = np.sort(image_scores)[-config['detections_per_im']]
-        for j in range(start_idx, num_classes):
-            keep = np.where(cls_boxes[j][:, 1] >= image_thresh)[0]
-            cls_boxes[j] = cls_boxes[j][keep, :]
-
-    im_results = np.vstack(
-        [cls_boxes[j] for j in range(start_idx, num_classes)])
-    return im_results
-
-
-def mstest_box_post_process(result, config, num_classes):
-    """
-    Multi-scale Test
-    Only available for batch_size=1 now.
-    """
-    post_bbox = {}
-    use_flip = False
-    ms_boxes = []
-    ms_scores = []
-    im_shape = result['im_shape'][0]
-    for k in result.keys():
-        if 'bbox' in k:
-            boxes = result[k][0]
-            boxes = np.reshape(boxes, (-1, 4 * num_classes))
-            scores = result['score' + k[4:]][0]
-            if 'flip' in k:
-                boxes = box_flip(boxes, im_shape)
-                use_flip = True
-            ms_boxes.append(boxes)
-            ms_scores.append(scores)
-
-    ms_boxes = np.concatenate(ms_boxes)
-    ms_scores = np.concatenate(ms_scores)
-    bbox_pred = get_nms_result(ms_boxes, ms_scores, config, num_classes)
-    post_bbox.update({'bbox': (bbox_pred, [[len(bbox_pred)]])})
-    if use_flip:
-        bbox = bbox_pred[:, 2:]
-        bbox_flip = np.append(
-            bbox_pred[:, :2], box_flip(bbox, im_shape), axis=1)
-        post_bbox.update({'bbox_flip': (bbox_flip, [[len(bbox_flip)]])})
-    return post_bbox
-
-
-def mstest_mask_post_process(result, cfg):
-    mask_list = []
-    im_shape = result['im_shape'][0]
-    M = cfg.FPNRoIAlign['mask_resolution']
-    for k in result.keys():
-        if 'mask' in k:
-            masks = result[k][0]
-            if len(masks.shape) != 4:
-                masks = np.zeros((0, M, M))
-                mask_list.append(masks)
-                continue
-            if 'flip' in k:
-                masks = masks[:, :, :, ::-1]
-            mask_list.append(masks)
-
-    mask_pred = np.mean(mask_list, axis=0)
-    return {'mask': (mask_pred, [[len(mask_pred)]])}
-
-
-def mask_encode(results, resolution, thresh_binarize=0.5):
-    import pycocotools.mask as mask_util
-    from ppdet.utils.coco_eval import expand_boxes
-    scale = (resolution + 2.0) / resolution
-    bboxes = results['bbox'][0]
-    masks = results['mask'][0]
-    lengths = results['mask'][1][0]
-    im_shapes = results['im_shape'][0]
-    segms = []
-    if bboxes.shape == (1, 1) or bboxes is None:
-        return segms
-    if len(bboxes.tolist()) == 0:
-        return segms
-
-    s = 0
-    # for each sample
-    for i in range(len(lengths)):
-        num = lengths[i]
-        im_shape = im_shapes[i]
-
-        bbox = bboxes[s:s + num][:, 2:]
-        clsid_scores = bboxes[s:s + num][:, 0:2]
-        mask = masks[s:s + num]
-        s += num
-
-        im_h = int(im_shape[0])
-        im_w = int(im_shape[1])
-        expand_bbox = expand_boxes(bbox, scale)
-        expand_bbox = expand_bbox.astype(np.int32)
-        padded_mask = np.zeros(
-            (resolution + 2, resolution + 2), dtype=np.float32)
-
-        for j in range(num):
-            xmin, ymin, xmax, ymax = expand_bbox[j].tolist()
-            clsid, score = clsid_scores[j].tolist()
-            clsid = int(clsid)
-            padded_mask[1:-1, 1:-1] = mask[j, clsid, :, :]
-
-            w = xmax - xmin + 1
-            h = ymax - ymin + 1
-            w = np.maximum(w, 1)
-            h = np.maximum(h, 1)
-            resized_mask = cv2.resize(padded_mask, (w, h))
-            resized_mask = np.array(
-                resized_mask > thresh_binarize, dtype=np.uint8)
-            im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
-
-            x0 = min(max(xmin, 0), im_w)
-            x1 = min(max(xmax + 1, 0), im_w)
-            y0 = min(max(ymin, 0), im_h)
-            y1 = min(max(ymax + 1, 0), im_h)
-
-            im_mask[y0:y1, x0:x1] = resized_mask[(y0 - ymin):(y1 - ymin), (
-                x0 - xmin):(x1 - xmin)]
-            segm = mask_util.encode(
-                np.array(
-                    im_mask[:, :, np.newaxis], order='F'))[0]
-            segms.append(segm)
-    return segms
-
-
-def corner_post_process(results, config, num_classes):
-    detections = results['bbox'][0]
-    keep_inds = (detections[:, 1] > -1)
-    detections = detections[keep_inds]
-    labels = detections[:, 0]
-    scores = detections[:, 1]
-    boxes = detections[:, 2:6]
-    cls_boxes = get_nms_result(
-        boxes, scores, config, num_classes, background_label=-1, labels=labels)
-    results.update({'bbox': (cls_boxes, [[len(cls_boxes)]])})