forked from p71924506/PulseFocusPlatform
376 lines
13 KiB
Python
376 lines
13 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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# add python path of PadleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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if parent_path not in sys.path:
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sys.path.append(parent_path)
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import logging
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FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
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logging.basicConfig(level=logging.INFO, format=FORMAT)
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logger = logging.getLogger(__name__)
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from scipy.cluster.vq import kmeans
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import numpy as np
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from tqdm import tqdm
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try:
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from ppdet.utils.cli import ArgsParser
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from ppdet.utils.check import check_gpu, check_version, check_config
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from ppdet.core.workspace import load_config, merge_config, create
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except ImportError as e:
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if sys.argv[0].find('static') >= 0:
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logger.error("Importing ppdet failed when running static model "
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"with error: {}\n"
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"please try:\n"
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"\t1. run static model under PaddleDetection/static "
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"directory\n"
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"\t2. run 'pip uninstall ppdet' to uninstall ppdet "
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"dynamic version firstly.".format(e))
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sys.exit(-1)
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else:
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raise e
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class BaseAnchorCluster(object):
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def __init__(self, n, cache_path, cache, verbose=True):
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"""
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Base Anchor Cluster
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Args:
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n (int): number of clusters
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cache_path (str): cache directory path
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cache (bool): whether using cache
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verbose (bool): whether print results
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"""
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super(BaseAnchorCluster, self).__init__()
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self.n = n
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self.cache_path = cache_path
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self.cache = cache
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self.verbose = verbose
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def print_result(self, centers):
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raise NotImplementedError('%s.print_result is not available' %
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self.__class__.__name__)
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def get_whs(self):
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whs_cache_path = os.path.join(self.cache_path, 'whs.npy')
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shapes_cache_path = os.path.join(self.cache_path, 'shapes.npy')
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if self.cache and os.path.exists(whs_cache_path) and os.path.exists(
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shapes_cache_path):
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self.whs = np.load(whs_cache_path)
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self.shapes = np.load(shapes_cache_path)
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return self.whs, self.shapes
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whs = np.zeros((0, 2))
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shapes = np.zeros((0, 2))
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roidbs = self.dataset.get_roidb()
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for rec in tqdm(roidbs):
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h, w = rec['h'], rec['w']
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bbox = rec['gt_bbox']
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wh = bbox[:, 2:4] - bbox[:, 0:2] + 1
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wh = wh / np.array([[w, h]])
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shape = np.ones_like(wh) * np.array([[w, h]])
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whs = np.vstack((whs, wh))
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shapes = np.vstack((shapes, shape))
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if self.cache:
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os.makedirs(self.cache_path, exist_ok=True)
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np.save(whs_cache_path, whs)
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np.save(shapes_cache_path, shapes)
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self.whs = whs
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self.shapes = shapes
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return self.whs, self.shapes
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def calc_anchors(self):
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raise NotImplementedError('%s.calc_anchors is not available' %
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self.__class__.__name__)
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def __call__(self):
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self.get_whs()
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centers = self.calc_anchors()
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if self.verbose:
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self.print_result(centers)
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return centers
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class YOLOv2AnchorCluster(BaseAnchorCluster):
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def __init__(self,
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n,
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dataset,
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size,
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cache_path,
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cache,
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iters=1000,
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verbose=True):
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super(YOLOv2AnchorCluster, self).__init__(
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n, cache_path, cache, verbose=verbose)
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"""
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YOLOv2 Anchor Cluster
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Reference:
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https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.py
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Args:
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n (int): number of clusters
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dataset (DataSet): DataSet instance, VOC or COCO
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size (list): [w, h]
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cache_path (str): cache directory path
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cache (bool): whether using cache
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iters (int): kmeans algorithm iters
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verbose (bool): whether print results
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"""
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self.dataset = dataset
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self.size = size
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self.iters = iters
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def print_result(self, centers):
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logger.info('%d anchor cluster result: [w, h]' % self.n)
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for w, h in centers:
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logger.info('[%d, %d]' % (round(w), round(h)))
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def metric(self, whs, centers):
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wh1 = whs[:, None]
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wh2 = centers[None]
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inter = np.minimum(wh1, wh2).prod(2)
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return inter / (wh1.prod(2) + wh2.prod(2) - inter)
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def kmeans_expectation(self, whs, centers, assignments):
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dist = self.metric(whs, centers)
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new_assignments = dist.argmax(1)
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converged = (new_assignments == assignments).all()
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return converged, new_assignments
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def kmeans_maximizations(self, whs, centers, assignments):
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new_centers = np.zeros_like(centers)
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for i in range(centers.shape[0]):
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mask = (assignments == i)
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if mask.sum():
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new_centers[i, :] = whs[mask].mean(0)
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return new_centers
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def calc_anchors(self):
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self.whs = self.whs * np.array([self.size])
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# random select k centers
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whs, n, iters = self.whs, self.n, self.iters
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logger.info('Running kmeans for %d anchors on %d points...' %
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(n, len(whs)))
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idx = np.random.choice(whs.shape[0], size=n, replace=False)
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centers = whs[idx]
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assignments = np.zeros(whs.shape[0:1]) * -1
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# kmeans
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if n == 1:
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return self.kmeans_maximizations(whs, centers, assignments)
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pbar = tqdm(range(iters), desc='Cluster anchors with k-means algorithm')
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for _ in pbar:
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# E step
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converged, assignments = self.kmeans_expectation(whs, centers,
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assignments)
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if converged:
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break
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# M step
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centers = self.kmeans_maximizations(whs, centers, assignments)
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ious = self.metric(whs, centers)
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pbar.desc = 'avg_iou: %.4f' % (ious.max(1).mean())
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centers = sorted(centers, key=lambda x: x[0] * x[1])
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return centers
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class YOLOv5AnchorCluster(BaseAnchorCluster):
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def __init__(self,
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n,
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dataset,
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size,
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cache_path,
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cache,
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iters=300,
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gen_iters=1000,
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thresh=0.25,
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verbose=True):
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super(YOLOv5AnchorCluster, self).__init__(
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n, cache_path, cache, verbose=verbose)
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"""
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YOLOv5 Anchor Cluster
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Reference:
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https://github.com/ultralytics/yolov5/blob/master/utils/general.py
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Args:
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n (int): number of clusters
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dataset (DataSet): DataSet instance, VOC or COCO
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size (list): [w, h]
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cache_path (str): cache directory path
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cache (bool): whether using cache
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iters (int): iters of kmeans algorithm
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gen_iters (int): iters of genetic algorithm
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threshold (float): anchor scale threshold
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verbose (bool): whether print results
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"""
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self.dataset = dataset
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self.size = size
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self.iters = iters
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self.gen_iters = gen_iters
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self.thresh = thresh
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def print_result(self, centers):
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whs = self.whs
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centers = centers[np.argsort(centers.prod(1))]
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x, best = self.metric(whs, centers)
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bpr, aat = (
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best > self.thresh).mean(), (x > self.thresh).mean() * self.n
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logger.info(
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'thresh=%.2f: %.4f best possible recall, %.2f anchors past thr' %
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(self.thresh, bpr, aat))
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logger.info(
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'n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thresh=%.3f-mean: '
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% (self.n, self.size, x.mean(), best.mean(),
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x[x > self.thresh].mean()))
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logger.info('%d anchor cluster result: [w, h]' % self.n)
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for w, h in centers:
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logger.info('[%d, %d]' % (round(w), round(h)))
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def metric(self, whs, centers):
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r = whs[:, None] / centers[None]
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x = np.minimum(r, 1. / r).min(2)
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return x, x.max(1)
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def fitness(self, whs, centers):
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_, best = self.metric(whs, centers)
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return (best * (best > self.thresh)).mean()
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def calc_anchors(self):
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self.whs = self.whs * self.shapes / self.shapes.max(
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1, keepdims=True) * np.array([self.size])
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wh0 = self.whs
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i = (wh0 < 3.0).any(1).sum()
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if i:
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logger.warning('Extremely small objects found. %d of %d'
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'labels are < 3 pixels in width or height' %
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(i, len(wh0)))
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wh = wh0[(wh0 >= 2.0).any(1)]
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logger.info('Running kmeans for %g anchors on %g points...' %
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(self.n, len(wh)))
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s = wh.std(0)
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centers, dist = kmeans(wh / s, self.n, iter=self.iters)
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centers *= s
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f, sh, mp, s = self.fitness(wh, centers), centers.shape, 0.9, 0.1
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pbar = tqdm(
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range(self.gen_iters),
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desc='Evolving anchors with Genetic Algorithm')
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for _ in pbar:
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v = np.ones(sh)
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while (v == 1).all():
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v = ((np.random.random(sh) < mp) * np.random.random() *
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np.random.randn(*sh) * s + 1).clip(0.3, 3.0)
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new_centers = (centers.copy() * v).clip(min=2.0)
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new_f = self.fitness(wh, new_centers)
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if new_f > f:
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f, centers = new_f, new_centers.copy()
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pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
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return centers
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def main():
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parser = ArgsParser()
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parser.add_argument(
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'--n', '-n', default=9, type=int, help='num of clusters')
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parser.add_argument(
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'--iters',
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'-i',
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default=1000,
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type=int,
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help='num of iterations for kmeans')
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parser.add_argument(
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'--gen_iters',
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'-gi',
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default=1000,
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type=int,
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help='num of iterations for genetic algorithm')
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parser.add_argument(
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'--thresh',
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'-t',
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default=0.25,
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type=float,
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help='anchor scale threshold')
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parser.add_argument(
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'--verbose', '-v', default=True, type=bool, help='whether print result')
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parser.add_argument(
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'--size',
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'-s',
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default=None,
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type=str,
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help='image size: w,h, using comma as delimiter')
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parser.add_argument(
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'--method',
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'-m',
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default='v2',
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type=str,
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help='cluster method, [v2, v5] are supported now')
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parser.add_argument(
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'--cache_path', default='cache', type=str, help='cache path')
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parser.add_argument(
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'--cache', action='store_true', help='whether use cache')
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FLAGS = parser.parse_args()
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cfg = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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check_config(cfg)
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# check if set use_gpu=True in paddlepaddle cpu version
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check_gpu(cfg.use_gpu)
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# check if paddlepaddle version is satisfied
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check_version()
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# get dataset
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dataset = cfg['TrainReader']['dataset']
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if FLAGS.size:
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if ',' in FLAGS.size:
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size = list(map(int, FLAGS.size.split(',')))
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assert len(size) == 2, "the format of size is incorrect"
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else:
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size = int(FLAGS.size)
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size = [size, size]
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elif 'image_shape' in cfg['TrainReader']['inputs_def']:
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size = cfg['TrainReader']['inputs_def']['image_shape'][1:]
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else:
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raise ValueError('size is not specified')
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if FLAGS.method == 'v2':
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cluster = YOLOv2AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
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FLAGS.cache, FLAGS.iters, FLAGS.verbose)
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elif FLAGS.method == 'v5':
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cluster = YOLOv5AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
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FLAGS.cache, FLAGS.iters, FLAGS.gen_iters,
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FLAGS.thresh, FLAGS.verbose)
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else:
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raise ValueError('cluster method: %s is not supported' % FLAGS.method)
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anchors = cluster()
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if __name__ == "__main__":
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main()
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