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#
# 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 os
import sys
# add python path of PaddleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)
from ppdet.utils.logger import setup_logger
logger = setup_logger('ppdet.anchor_cluster')
from scipy.cluster.vq import kmeans
import numpy as np
from tqdm import tqdm
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu, check_version, check_config
from ppdet.core.workspace import load_config, merge_config
class BaseAnchorCluster(object):
def __init__(self, n, cache_path, cache, verbose=True):
"""
Base Anchor Cluster
Args:
n (int): number of clusters
cache_path (str): cache directory path
cache (bool): whether using cache
verbose (bool): whether print results
"""
super(BaseAnchorCluster, self).__init__()
self.n = n
self.cache_path = cache_path
self.cache = cache
self.verbose = verbose
def print_result(self, centers):
raise NotImplementedError('%s.print_result is not available' %
self.__class__.__name__)
def get_whs(self):
whs_cache_path = os.path.join(self.cache_path, 'whs.npy')
shapes_cache_path = os.path.join(self.cache_path, 'shapes.npy')
if self.cache and os.path.exists(whs_cache_path) and os.path.exists(
shapes_cache_path):
self.whs = np.load(whs_cache_path)
self.shapes = np.load(shapes_cache_path)
return self.whs, self.shapes
whs = np.zeros((0, 2))
shapes = np.zeros((0, 2))
self.dataset.parse_dataset()
roidbs = self.dataset.roidbs
for rec in tqdm(roidbs):
h, w = rec['h'], rec['w']
bbox = rec['gt_bbox']
wh = bbox[:, 2:4] - bbox[:, 0:2] + 1
wh = wh / np.array([[w, h]])
shape = np.ones_like(wh) * np.array([[w, h]])
whs = np.vstack((whs, wh))
shapes = np.vstack((shapes, shape))
if self.cache:
os.makedirs(self.cache_path, exist_ok=True)
np.save(whs_cache_path, whs)
np.save(shapes_cache_path, shapes)
self.whs = whs
self.shapes = shapes
return self.whs, self.shapes
def calc_anchors(self):
raise NotImplementedError('%s.calc_anchors is not available' %
self.__class__.__name__)
def __call__(self):
self.get_whs()
centers = self.calc_anchors()
if self.verbose:
self.print_result(centers)
return centers
class YOLOv2AnchorCluster(BaseAnchorCluster):
def __init__(self,
n,
dataset,
size,
cache_path,
cache,
iters=1000,
verbose=True):
super(YOLOv2AnchorCluster, self).__init__(
n, cache_path, cache, verbose=verbose)
"""
YOLOv2 Anchor Cluster
The code is based on https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.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): kmeans algorithm iters
verbose (bool): whether print results
"""
self.dataset = dataset
self.size = size
self.iters = iters
def print_result(self, centers):
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):
wh1 = whs[:, None]
wh2 = centers[None]
inter = np.minimum(wh1, wh2).prod(2)
return inter / (wh1.prod(2) + wh2.prod(2) - inter)
def kmeans_expectation(self, whs, centers, assignments):
dist = self.metric(whs, centers)
new_assignments = dist.argmax(1)
converged = (new_assignments == assignments).all()
return converged, new_assignments
def kmeans_maximizations(self, whs, centers, assignments):
new_centers = np.zeros_like(centers)
for i in range(centers.shape[0]):
mask = (assignments == i)
if mask.sum():
new_centers[i, :] = whs[mask].mean(0)
return new_centers
def calc_anchors(self):
self.whs = self.whs * np.array([self.size])
# random select k centers
whs, n, iters = self.whs, self.n, self.iters
logger.info('Running kmeans for %d anchors on %d points...' %
(n, len(whs)))
idx = np.random.choice(whs.shape[0], size=n, replace=False)
centers = whs[idx]
assignments = np.zeros(whs.shape[0:1]) * -1
# kmeans
if n == 1:
return self.kmeans_maximizations(whs, centers, assignments)
pbar = tqdm(range(iters), desc='Cluster anchors with k-means algorithm')
for _ in pbar:
# E step
converged, assignments = self.kmeans_expectation(whs, centers,
assignments)
if converged:
logger.info('kmeans algorithm has converged')
break
# M step
centers = self.kmeans_maximizations(whs, centers, assignments)
ious = self.metric(whs, centers)
pbar.desc = 'avg_iou: %.4f' % (ious.max(1).mean())
centers = sorted(centers, key=lambda x: x[0] * x[1])
return centers
def main():
parser = ArgsParser()
parser.add_argument(
'--n', '-n', default=9, type=int, help='num of clusters')
parser.add_argument(
'--iters',
'-i',
default=1000,
type=int,
help='num of iterations for kmeans')
parser.add_argument(
'--verbose', '-v', default=True, type=bool, help='whether print result')
parser.add_argument(
'--size',
'-s',
default=None,
type=str,
help='image size: w,h, using comma as delimiter')
parser.add_argument(
'--method',
'-m',
default='v2',
type=str,
help='cluster method, v2 is only supported now')
parser.add_argument(
'--cache_path', default='cache', type=str, help='cache path')
parser.add_argument(
'--cache', action='store_true', help='whether use cache')
FLAGS = parser.parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
if 'use_gpu' not in cfg:
cfg.use_gpu = False
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version('develop')
# get dataset
dataset = cfg['TrainDataset']
if FLAGS.size:
if ',' in FLAGS.size:
size = list(map(int, FLAGS.size.split(',')))
assert len(size) == 2, "the format of size is incorrect"
else:
size = int(FLAGS.size)
size = [size, size]
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)
else:
raise ValueError('cluster method: %s is not supported' % FLAGS.method)
anchors = cluster()
if __name__ == "__main__":
main()
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