add reference of some code and remove some code (#4468)

docs/tutorials/PrepareDataSet.md

@@ -436,7 +436,5 @@ python tools/anchor_cluster.py -c configs/ppyolo/ppyolo.yml -n 9 -s 608 -m v2 -i
 | -c/--config | 模型的配置文件 | 无默认值 | 必须指定 |
 | -n/--n | 聚类的簇数 | 9 | Anchor的数目 |
 | -s/--size | 图片的输入尺寸 | None | 若指定，则使用指定的尺寸，如果不指定, 则尝试从配置文件中读取图片尺寸 |
-|  -m/--method  |  使用的Anchor聚类方法  |  v2  |  目前只支持yolov2/v5的聚类算法  |
+|  -m/--method  |  使用的Anchor聚类方法  |  v2  |  目前只支持yolov2的聚类算法  |
 |  -i/--iters  |  kmeans聚类算法的迭代次数  |  1000  | kmeans算法收敛或者达到迭代次数后终止 |
-| -gi/--gen_iters |  遗传算法的迭代次数  | 1000 |  该参数只用于yolov5的Anchor聚类算法  |
-| -t/--thresh|  Anchor尺度的阈值  | 0.25 | 该参数只用于yolov5的Anchor聚类算法 |

ppdet/data/transform/op_helper.py

@@ -464,65 +464,6 @@ def gaussian2D(shape, sigma_x=1, sigma_y=1):
     return h
 
 
-def transform_bbox(sample,
-                   M,
-                   w,
-                   h,
-                   area_thr=0.25,
-                   wh_thr=2,
-                   ar_thr=20,
-                   perspective=False):
-    """
-    transfrom bbox according to tranformation matrix M,
-    refer to https://github.com/ultralytics/yolov5/blob/develop/utils/datasets.py
-    """
-    bbox = sample['gt_bbox']
-    label = sample['gt_class']
-    # rotate bbox
-    n = len(bbox)
-    xy = np.ones((n * 4, 3), dtype=np.float32)
-    xy[:, :2] = bbox[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2)
-    # xy = xy @ M.T
-    xy = np.matmul(xy, M.T)
-    if perspective:
-        xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)
-    else:
-        xy = xy[:, :2].reshape(n, 8)
-    # get new bboxes
-    x = xy[:, [0, 2, 4, 6]]
-    y = xy[:, [1, 3, 5, 7]]
-    bbox = np.concatenate(
-        (x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-    # clip boxes
-    mask = filter_bbox(bbox, w, h, area_thr)
-    sample['gt_bbox'] = bbox[mask]
-    sample['gt_class'] = sample['gt_class'][mask]
-    if 'is_crowd' in sample:
-        sample['is_crowd'] = sample['is_crowd'][mask]
-    if 'difficult' in sample:
-        sample['difficult'] = sample['difficult'][mask]
-    return sample
-
-
-def filter_bbox(bbox, w, h, area_thr=0.25, wh_thr=2, ar_thr=20):
-    """
-    filter bbox, refer to https://github.com/ultralytics/yolov5/blob/develop/utils/datasets.py
-    """
-    # clip boxes
-    area1 = (bbox[:, 2:4] - bbox[:, 0:2]).prod(1)
-    bbox[:, [0, 2]] = bbox[:, [0, 2]].clip(0, w)
-    bbox[:, [1, 3]] = bbox[:, [1, 3]].clip(0, h)
-    # compute
-    area2 = (bbox[:, 2:4] - bbox[:, 0:2]).prod(1)
-    area_ratio = area2 / (area1 + 1e-16)
-    wh = bbox[:, 2:4] - bbox[:, 0:2]
-    ar_ratio = np.maximum(wh[:, 1] / (wh[:, 0] + 1e-16),
-                          wh[:, 0] / (wh[:, 1] + 1e-16))
-    mask = (area_ratio > area_thr) & (
-        (wh > wh_thr).all(1)) & (ar_ratio < ar_thr)
-    return mask
-
-
 def draw_umich_gaussian(heatmap, center, radius, k=1):
     """
     draw_umich_gaussian, refer to https://github.com/xingyizhou/CenterNet/blob/master/src/lib/utils/image.py#L126

ppdet/data/transform/operators.py

@@ -48,7 +48,7 @@ from .op_helper import (satisfy_sample_constraint, filter_and_process,
                         generate_sample_bbox, clip_bbox, data_anchor_sampling,
                         satisfy_sample_constraint_coverage, crop_image_sampling,
                         generate_sample_bbox_square, bbox_area_sampling,
-                        is_poly, transform_bbox, get_border)
+                        is_poly, get_border)
 
 from ppdet.utils.logger import setup_logger
 from ppdet.modeling.keypoint_utils import get_affine_transform, affine_transform
@@ -2476,6 +2476,9 @@ class RandomSelect(BaseOperator):
     """
     Randomly choose a transformation between transforms1 and transforms2,
     and the probability of choosing transforms1 is p.
+
+    The code is based on https://github.com/facebookresearch/detr/blob/main/datasets/transforms.py
+
     """
 
     def __init__(self, transforms1, transforms2, p=0.5):
@@ -2833,6 +2836,10 @@ class WarpAffine(BaseOperator):
                  shift=0.1):
         """WarpAffine
         Warp affine the image
+
+        The code is based on https://github.com/xingyizhou/CenterNet/blob/master/src/lib/datasets/sample/ctdet.py
+
+
         """
         super(WarpAffine, self).__init__()
         self.keep_res = keep_res

ppdet/ext_op/rbox_iou_op.cc

-/* Copyright (c) 2021 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. */
+//   Copyright (c) 2021 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.
+//
+// The code is based on https://github.com/csuhan/s2anet/blob/master/mmdet/ops/box_iou_rotated
 
 #include "rbox_iou_op.h"
 #include "paddle/extension.h"

ppdet/ext_op/rbox_iou_op.cu

-/* Copyright (c) 2021 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. */
+//   Copyright (c) 2021 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.
+//
+// The code is based on https://github.com/csuhan/s2anet/blob/master/mmdet/ops/box_iou_rotated
 
 #include "rbox_iou_op.h"
 #include "paddle/extension.h"

ppdet/ext_op/rbox_iou_op.h

-/* Copyright (c) 2021 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. */
+//   Copyright (c) 2021 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.
+//
+// The code is based on https://github.com/csuhan/s2anet/blob/master/mmdet/ops/box_iou_rotated
 
 #pragma once
 

ppdet/modeling/heads/s2anet_head.py

@@ -11,6 +11,9 @@
 # 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.
+#
+# The code is based on https://github.com/csuhan/s2anet/blob/master/mmdet/models/anchor_heads_rotated/s2anet_head.py
+
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
@@ -625,7 +628,8 @@ class S2ANetHead(nn.Layer):
                     fam_bbox_total = self.gwd_loss(fam_bbox_decode,
                                                    bbox_gt_bboxes_level)
                     fam_bbox_total = fam_bbox_total * feat_bbox_weights
-                    fam_bbox_total = paddle.sum(fam_bbox_total) / num_total_samples
+                    fam_bbox_total = paddle.sum(
+                        fam_bbox_total) / num_total_samples
 
             fam_bbox_losses.append(fam_bbox_total)
             st_idx += feat_anchor_num
@@ -739,7 +743,8 @@ class S2ANetHead(nn.Layer):
                     odm_bbox_total = self.gwd_loss(odm_bbox_decode,
                                                    bbox_gt_bboxes_level)
                     odm_bbox_total = odm_bbox_total * feat_bbox_weights
-                    odm_bbox_total = paddle.sum(odm_bbox_total) / num_total_samples
+                    odm_bbox_total = paddle.sum(
+                        odm_bbox_total) / num_total_samples
 
             odm_bbox_losses.append(odm_bbox_total)
             st_idx += feat_anchor_num

ppdet/modeling/necks/yolo_fpn.py

@@ -180,7 +180,7 @@ class CoordConv(nn.Layer):
                  name='',
                  data_format='NCHW'):
         """
-        CoordConv layer
+        CoordConv layer, see https://arxiv.org/abs/1807.03247
 
         Args:
             ch_in (int): input channel

static/configs/yolov4/README.md

@@ -31,10 +31,8 @@ python tools/anchor_cluster.py -c ${config} -m ${method} -s ${size}
 | -c/--config | 模型的配置文件 | 无默认值 | 必须指定 |
 | -n/--n | 聚类的簇数 | 9 | Anchor的数目 |
 | -s/--size | 图片的输入尺寸 | None | 若指定，则使用指定的尺寸，如果不指定, 则尝试从配置文件中读取图片尺寸 |
-|  -m/--method  |  使用的Anchor聚类方法  |  v2  |  目前只支持yolov2/v5的聚类算法  |
+|  -m/--method  |  使用的Anchor聚类方法  |  v2  |  目前只支持yolov2的聚类算法  |
 |  -i/--iters  |  kmeans聚类算法的迭代次数  |  1000  | kmeans算法收敛或者达到迭代次数后终止 |
-| -gi/--gen_iters |  遗传算法的迭代次数  | 1000 |  该参数只用于yolov5的Anchor聚类算法  |
-| -t/--thresh|  Anchor尺度的阈值  | 0.25 | 该参数只用于yolov5的Anchor聚类算法 |
 
 ## 模型库
 下表中展示了当前支持的网络结构。

static/docs/tutorials/Custom_DataSet.md

@@ -139,10 +139,8 @@ python tools/anchor_cluster.py -c configs/ppyolo/ppyolo.yml -n 9 -s 608 -m v2 -i
 | -c/--config | 模型的配置文件 | 无默认值 | 必须指定 |
 | -n/--n | 聚类的簇数 | 9 | Anchor的数目 |
 | -s/--size | 图片的输入尺寸 | None | 若指定，则使用指定的尺寸，如果不指定, 则尝试从配置文件中读取图片尺寸 |
-|  -m/--method  |  使用的Anchor聚类方法  |  v2  |  目前只支持yolov2/v5的聚类算法  |
+|  -m/--method  |  使用的Anchor聚类方法  |  v2  |  目前只支持yolov2的聚类算法  |
 |  -i/--iters  |  kmeans聚类算法的迭代次数  |  1000  | kmeans算法收敛或者达到迭代次数后终止 |
-| -gi/--gen_iters |  遗传算法的迭代次数  | 1000 |  该参数只用于yolov5的Anchor聚类算法  |
-| -t/--thresh|  Anchor尺度的阈值  | 0.25 | 该参数只用于yolov5的Anchor聚类算法 |
 
 ## 4.修改参数配置
 

static/tools/anchor_cluster.py

@@ -126,8 +126,7 @@ class YOLOv2AnchorCluster(BaseAnchorCluster):
         """
         YOLOv2 Anchor Cluster
 
-        Reference:
-            https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.py
+        The code is based on https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.py
 
         Args:
             n (int): number of clusters
@@ -196,103 +195,6 @@ class YOLOv2AnchorCluster(BaseAnchorCluster):
         return centers
 
 
-class YOLOv5AnchorCluster(BaseAnchorCluster):
-    def __init__(self,
-                 n,
-                 dataset,
-                 size,
-                 cache_path,
-                 cache,
-                 iters=300,
-                 gen_iters=1000,
-                 thresh=0.25,
-                 verbose=True):
-        super(YOLOv5AnchorCluster, self).__init__(
-            n, cache_path, cache, verbose=verbose)
-        """
-        YOLOv5 Anchor Cluster
-
-        Reference:
-            https://github.com/ultralytics/yolov5/blob/master/utils/general.py
-
-        Args:
-            n (int): number of clusters
-            dataset (DataSet): DataSet instance, VOC or COCO
-            size (list): [w, h]
-            cache_path (str): cache directory path
-            cache (bool): whether using cache
-            iters (int): iters of kmeans algorithm
-            gen_iters (int): iters of genetic algorithm
-            threshold (float): anchor scale threshold
-            verbose (bool): whether print results
-        """
-        self.dataset = dataset
-        self.size = size
-        self.iters = iters
-        self.gen_iters = gen_iters
-        self.thresh = thresh
-
-    def print_result(self, centers):
-        whs = self.whs
-        centers = centers[np.argsort(centers.prod(1))]
-        x, best = self.metric(whs, centers)
-        bpr, aat = (
-            best > self.thresh).mean(), (x > self.thresh).mean() * self.n
-        logger.info(
-            'thresh=%.2f: %.4f best possible recall, %.2f anchors past thr' %
-            (self.thresh, bpr, aat))
-        logger.info(
-            'n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thresh=%.3f-mean: '
-            % (self.n, self.size, x.mean(), best.mean(),
-               x[x > self.thresh].mean()))
-        logger.info('%d anchor cluster result: [w, h]' % self.n)
-        for w, h in centers:
-            logger.info('[%d, %d]' % (round(w), round(h)))
-
-    def metric(self, whs, centers):
-        r = whs[:, None] / centers[None]
-        x = np.minimum(r, 1. / r).min(2)
-        return x, x.max(1)
-
-    def fitness(self, whs, centers):
-        _, best = self.metric(whs, centers)
-        return (best * (best > self.thresh)).mean()
-
-    def calc_anchors(self):
-        self.whs = self.whs * self.shapes / self.shapes.max(
-            1, keepdims=True) * np.array([self.size])
-        wh0 = self.whs
-        i = (wh0 < 3.0).any(1).sum()
-        if i:
-            logger.warning('Extremely small objects found. %d of %d'
-                           'labels are < 3 pixels in width or height' %
-                           (i, len(wh0)))
-
-        wh = wh0[(wh0 >= 2.0).any(1)]
-        logger.info('Running kmeans for %g anchors on %g points...' %
-                    (self.n, len(wh)))
-        s = wh.std(0)
-        centers, dist = kmeans(wh / s, self.n, iter=self.iters)
-        centers *= s
-
-        f, sh, mp, s = self.fitness(wh, centers), centers.shape, 0.9, 0.1
-        pbar = tqdm(
-            range(self.gen_iters),
-            desc='Evolving anchors with Genetic Algorithm')
-        for _ in pbar:
-            v = np.ones(sh)
-            while (v == 1).all():
-                v = ((np.random.random(sh) < mp) * np.random.random() *
-                     np.random.randn(*sh) * s + 1).clip(0.3, 3.0)
-            new_centers = (centers.copy() * v).clip(min=2.0)
-            new_f = self.fitness(wh, new_centers)
-            if new_f > f:
-                f, centers = new_f, new_centers.copy()
-                pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
-
-        return centers
-
-
 def main():
     parser = ArgsParser()
     parser.add_argument(
@@ -303,18 +205,6 @@ def main():
         default=1000,
         type=int,
         help='num of iterations for kmeans')
-    parser.add_argument(
-        '--gen_iters',
-        '-gi',
-        default=1000,
-        type=int,
-        help='num of iterations for genetic algorithm')
-    parser.add_argument(
-        '--thresh',
-        '-t',
-        default=0.25,
-        type=float,
-        help='anchor scale threshold')
     parser.add_argument(
         '--verbose', '-v', default=True, type=bool, help='whether print result')
     parser.add_argument(
@@ -328,7 +218,7 @@ def main():
         '-m',
         default='v2',
         type=str,
-        help='cluster method, [v2, v5] are supported now')
+        help='cluster method, v2 is only supported now')
     parser.add_argument(
         '--cache_path', default='cache', type=str, help='cache path')
     parser.add_argument(
@@ -353,18 +243,14 @@ def main():
             size = int(FLAGS.size)
             size = [size, size]
 
-    elif 'image_shape' in cfg['TrainReader']['inputs_def']:
-        size = cfg['TrainReader']['inputs_def']['image_shape'][1:]
+    elif 'image_shape' in cfg['TestReader']['inputs_def']:
+        size = cfg['TestReader']['inputs_def']['image_shape'][1:]
     else:
         raise ValueError('size is not specified')
 
     if FLAGS.method == 'v2':
         cluster = YOLOv2AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
                                       FLAGS.cache, FLAGS.iters, FLAGS.verbose)
-    elif FLAGS.method == 'v5':
-        cluster = YOLOv5AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
-                                      FLAGS.cache, FLAGS.iters, FLAGS.gen_iters,
-                                      FLAGS.thresh, FLAGS.verbose)
     else:
         raise ValueError('cluster method: %s is not supported' % FLAGS.method)
 

tools/anchor_cluster.py

@@ -111,8 +111,7 @@ class YOLOv2AnchorCluster(BaseAnchorCluster):
         """
         YOLOv2 Anchor Cluster
 
-        Reference:
-            https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.py
+        The code is based on https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.py
 
         Args:
             n (int): number of clusters
@@ -182,103 +181,6 @@ class YOLOv2AnchorCluster(BaseAnchorCluster):
         return centers
 
 
-class YOLOv5AnchorCluster(BaseAnchorCluster):
-    def __init__(self,
-                 n,
-                 dataset,
-                 size,
-                 cache_path,
-                 cache,
-                 iters=300,
-                 gen_iters=1000,
-                 thresh=0.25,
-                 verbose=True):
-        super(YOLOv5AnchorCluster, self).__init__(
-            n, cache_path, cache, verbose=verbose)
-        """
-        YOLOv5 Anchor Cluster
-
-        Reference:
-            https://github.com/ultralytics/yolov5/blob/master/utils/general.py
-
-        Args:
-            n (int): number of clusters
-            dataset (DataSet): DataSet instance, VOC or COCO
-            size (list): [w, h]
-            cache_path (str): cache directory path
-            cache (bool): whether using cache
-            iters (int): iters of kmeans algorithm
-            gen_iters (int): iters of genetic algorithm
-            threshold (float): anchor scale threshold
-            verbose (bool): whether print results
-        """
-        self.dataset = dataset
-        self.size = size
-        self.iters = iters
-        self.gen_iters = gen_iters
-        self.thresh = thresh
-
-    def print_result(self, centers):
-        whs = self.whs
-        centers = centers[np.argsort(centers.prod(1))]
-        x, best = self.metric(whs, centers)
-        bpr, aat = (
-            best > self.thresh).mean(), (x > self.thresh).mean() * self.n
-        logger.info(
-            'thresh=%.2f: %.4f best possible recall, %.2f anchors past thr' %
-            (self.thresh, bpr, aat))
-        logger.info(
-            'n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thresh=%.3f-mean: '
-            % (self.n, self.size, x.mean(), best.mean(),
-               x[x > self.thresh].mean()))
-        logger.info('%d anchor cluster result: [w, h]' % self.n)
-        for w, h in centers:
-            logger.info('[%d, %d]' % (round(w), round(h)))
-
-    def metric(self, whs, centers):
-        r = whs[:, None] / centers[None]
-        x = np.minimum(r, 1. / r).min(2)
-        return x, x.max(1)
-
-    def fitness(self, whs, centers):
-        _, best = self.metric(whs, centers)
-        return (best * (best > self.thresh)).mean()
-
-    def calc_anchors(self):
-        self.whs = self.whs * self.shapes / self.shapes.max(
-            1, keepdims=True) * np.array([self.size])
-        wh0 = self.whs
-        i = (wh0 < 3.0).any(1).sum()
-        if i:
-            logger.warning('Extremely small objects found. %d of %d'
-                           'labels are < 3 pixels in width or height' %
-                           (i, len(wh0)))
-
-        wh = wh0[(wh0 >= 2.0).any(1)]
-        logger.info('Running kmeans for %g anchors on %g points...' %
-                    (self.n, len(wh)))
-        s = wh.std(0)
-        centers, dist = kmeans(wh / s, self.n, iter=self.iters)
-        centers *= s
-
-        f, sh, mp, s = self.fitness(wh, centers), centers.shape, 0.9, 0.1
-        pbar = tqdm(
-            range(self.gen_iters),
-            desc='Evolving anchors with Genetic Algorithm')
-        for _ in pbar:
-            v = np.ones(sh)
-            while (v == 1).all():
-                v = ((np.random.random(sh) < mp) * np.random.random() *
-                     np.random.randn(*sh) * s + 1).clip(0.3, 3.0)
-            new_centers = (centers.copy() * v).clip(min=2.0)
-            new_f = self.fitness(wh, new_centers)
-            if new_f > f:
-                f, centers = new_f, new_centers.copy()
-                pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
-
-        return centers
-
-
 def main():
     parser = ArgsParser()
     parser.add_argument(
@@ -289,18 +191,6 @@ def main():
         default=1000,
         type=int,
         help='num of iterations for kmeans')
-    parser.add_argument(
-        '--gen_iters',
-        '-gi',
-        default=1000,
-        type=int,
-        help='num of iterations for genetic algorithm')
-    parser.add_argument(
-        '--thresh',
-        '-t',
-        default=0.25,
-        type=float,
-        help='anchor scale threshold')
     parser.add_argument(
         '--verbose', '-v', default=True, type=bool, help='whether print result')
     parser.add_argument(
@@ -314,7 +204,7 @@ def main():
         '-m',
         default='v2',
         type=str,
-        help='cluster method, [v2, v5] are supported now')
+        help='cluster method, v2 is only supported now')
     parser.add_argument(
         '--cache_path', default='cache', type=str, help='cache path')
     parser.add_argument(
@@ -338,19 +228,15 @@ def main():
         else:
             size = int(FLAGS.size)
             size = [size, size]
-    elif 'inputs_def' in cfg['TrainReader'] and 'image_shape' in cfg[
-            'TrainReader']['inputs_def']:
-        size = cfg['TrainReader']['inputs_def']['image_shape'][1:]
+    elif 'inputs_def' in cfg['TestReader'] and 'image_shape' in cfg[
+            'TestReader']['inputs_def']:
+        size = cfg['TestReader']['inputs_def']['image_shape'][1:]
     else:
         raise ValueError('size is not specified')
 
     if FLAGS.method == 'v2':
         cluster = YOLOv2AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
                                       FLAGS.cache, FLAGS.iters, FLAGS.verbose)
-    elif FLAGS.method == 'v5':
-        cluster = YOLOv5AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
-                                      FLAGS.cache, FLAGS.iters, FLAGS.gen_iters,
-                                      FLAGS.thresh, FLAGS.verbose)
     else:
         raise ValueError('cluster method: %s is not supported' % FLAGS.method)