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# 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.
import sys
import json
import logging
import numpy as np
from ppdet.utils.logger import setup_logger
logger = setup_logger('sniper_params_stats')
def get_default_params(architecture):
"""get_default_params"""
if architecture == "FasterRCNN":
anchor_range = np.array([64., 512.]) # for frcnn-fpn
# anchor_range = np.array([16., 373.]) # for yolov3
# anchor_range = np.array([32., 373.]) # for yolov3
default_crop_size = 1536 # mod 32 for frcnn-fpn
default_max_bbox_size = 352
elif architecture == "YOLOv3":
anchor_range = np.array([32., 373.]) # for yolov3
default_crop_size = 800 # mod 32 for yolov3
default_max_bbox_size = 352
else:
raise NotImplementedError
return anchor_range, default_crop_size, default_max_bbox_size
def get_box_ratios(anno_file):
"""
get_size_ratios
:param anno_file: coco anno flile
:return: size_ratio: (box_long_size / pic_long_size)
"""
coco_dict = json.load(open(anno_file))
image_list = coco_dict['images']
anno_list = coco_dict['annotations']
image_id2hw = {}
for im_dict in image_list:
im_id = im_dict['id']
h, w = im_dict['height'], im_dict['width']
image_id2hw[im_id] = (h, w)
box_ratios = []
for a_dict in anno_list:
im_id = a_dict['image_id']
im_h, im_w = image_id2hw[im_id]
bbox = a_dict['bbox']
x1, y1, w, h = bbox
pic_long = max(im_h, im_w)
box_long = max(w, h)
box_ratios.append(box_long / pic_long)
return np.array(box_ratios)
def get_target_size_and_valid_box_ratios(anchor_range, box_ratio_p2, box_ratio_p98):
"""get_scale_and_ratios"""
anchor_better_low, anchor_better_high = anchor_range # (60., 512.)
anchor_center = np.sqrt(anchor_better_high * anchor_better_low)
anchor_log_range = np.log10(anchor_better_high) - np.log10(anchor_better_low)
box_ratio_log_range = np.log10(box_ratio_p98) - np.log10(box_ratio_p2)
logger.info("anchor_log_range:{}, box_ratio_log_range:{}".format(anchor_log_range, box_ratio_log_range))
box_cut_num = int(np.ceil(box_ratio_log_range / anchor_log_range))
box_ratio_log_window = box_ratio_log_range / box_cut_num
logger.info("box_cut_num:{}, box_ratio_log_window:{}".format(box_cut_num, box_ratio_log_window))
image_target_sizes = []
valid_ratios = []
for i in range(box_cut_num):
# # method1: align center
# box_ratio_log_center = np.log10(p2) + 0.5 * box_ratio_log_window + i * box_ratio_log_window
# box_ratio_center = np.power(10, box_ratio_log_center)
# scale = anchor_center / box_ratio_center
# method2: align left low
box_ratio_low = np.power(10, np.log10(box_ratio_p2) + i * box_ratio_log_window)
image_target_size = anchor_better_low / box_ratio_low
image_target_sizes.append(int(image_target_size))
valid_ratio = anchor_range / image_target_size
valid_ratios.append(valid_ratio.tolist())
logger.info("Box cut {}".format(i))
logger.info("box_ratio_low: {}".format(box_ratio_low))
logger.info("image_target_size: {}".format(image_target_size))
logger.info("valid_ratio: {}".format(valid_ratio))
return image_target_sizes, valid_ratios
def get_valid_ranges(valid_ratios):
"""
get_valid_box_ratios_range
:param valid_ratios:
:return:
"""
valid_ranges = []
if len(valid_ratios) == 1:
valid_ranges.append([-1, -1])
else:
for i, vratio in enumerate(valid_ratios):
if i == 0:
valid_ranges.append([-1, vratio[1]])
elif i == len(valid_ratios) - 1:
valid_ranges.append([vratio[0], -1])
else:
valid_ranges.append(vratio)
return valid_ranges
def get_percentile(a_array, low_percent, high_percent):
"""
get_percentile
:param low_percent:
:param high_percent:
:return:
"""
array_p0 = min(a_array)
array_p100 = max(a_array)
array_plow = np.percentile(a_array, low_percent)
array_phigh = np.percentile(a_array, high_percent)
logger.info(
"array_percentile(0): {},array_percentile low({}): {}, "
"array_percentile high({}): {}, array_percentile 100: {}".format(
array_p0, low_percent, array_plow, high_percent, array_phigh, array_p100))
return array_plow, array_phigh
def sniper_anno_stats(architecture, anno_file):
"""
sniper_anno_stats
:param anno_file:
:return:
"""
anchor_range, default_crop_size, default_max_bbox_size = get_default_params(architecture)
box_ratios = get_box_ratios(anno_file)
box_ratio_p8, box_ratio_p92 = get_percentile(box_ratios, 8, 92)
image_target_sizes, valid_box_ratios = get_target_size_and_valid_box_ratios(anchor_range, box_ratio_p8, box_ratio_p92)
valid_ranges = get_valid_ranges(valid_box_ratios)
crop_size = min(default_crop_size, min([item for item in image_target_sizes]))
crop_size = int(np.ceil(crop_size / 32.) * 32.)
crop_stride = max(min(default_max_bbox_size, crop_size), crop_size - default_max_bbox_size)
logger.info("Result".center(100, '-'))
logger.info("image_target_sizes: {}".format(image_target_sizes))
logger.info("valid_box_ratio_ranges: {}".format(valid_ranges))
logger.info("chip_target_size: {}, chip_target_stride: {}".format(crop_size, crop_stride))
return {
"image_target_sizes": image_target_sizes,
"valid_box_ratio_ranges": valid_ranges,
"chip_target_size": crop_size,
"chip_target_stride": crop_stride
}
if __name__=="__main__":
architecture, anno_file = sys.argv[1], sys.argv[2]
sniper_anno_stats(architecture, anno_file)
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