forked from PulseFocusPlatform/PulseFocusPlatform
421 lines
16 KiB
Python
421 lines
16 KiB
Python
# Copyright (c) 2019 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__, *(['..'] * 3)))
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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 time
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import numpy as np
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import datetime
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from collections import deque
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from paddle import fluid
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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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try:
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from ppdet.experimental import mixed_precision_context
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from ppdet.core.workspace import load_config, merge_config, create, register
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from ppdet.data.reader import create_reader
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from ppdet.utils import dist_utils
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from ppdet.utils.eval_utils import parse_fetches, eval_run
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from ppdet.utils.stats import TrainingStats
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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, enable_static_mode
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import ppdet.utils.checkpoint as checkpoint
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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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from paddleslim.analysis import flops, TableLatencyEvaluator
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from paddleslim.nas import SANAS
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@register
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class Constraint(object):
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"""
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Constraint for nas
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"""
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def __init__(self,
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ctype,
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max_constraint=None,
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min_constraint=None,
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table_file=None):
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super(Constraint, self).__init__()
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self.ctype = ctype
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self.max_constraint = max_constraint
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self.min_constraint = min_constraint
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self.table_file = table_file
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def compute_constraint(self, program):
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if self.ctype == 'flops':
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model_status = flops(program)
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elif self.ctype == 'latency':
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assert os.path.exists(
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self.table_file
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), "latency constraint must have latency table, please check whether table file exist!"
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model_latency = TableLatencyEvaluator(self.table_file)
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model_status = model_latency.latency(program, only_conv=True)
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else:
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raise NotImplementedError(
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"{} constraint is NOT support!!! Now PaddleSlim support flops constraint and latency constraint".
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format(self.ctype))
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return model_status
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def get_bboxes_scores(result):
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bboxes = result['bbox'][0]
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gt_bbox = result['gt_bbox'][0]
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bbox_lengths = result['bbox'][1][0]
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gt_lengths = result['gt_bbox'][1][0]
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bbox_list = []
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gt_box_list = []
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for i in range(len(bbox_lengths)):
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num = bbox_lengths[i]
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for j in range(num):
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dt = bboxes[j]
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clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
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im_shape = result['im_shape'][0][i].tolist()
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im_height, im_width = int(im_shape[0]), int(im_shape[1])
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xmin *= im_width
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ymin *= im_height
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xmax *= im_width
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ymax *= im_height
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bbox_list.append([xmin, ymin, xmax, ymax, score])
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faces_num_gt = 0
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for i in range(len(gt_lengths)):
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num = gt_lengths[i]
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for j in range(num):
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gt = gt_bbox[j]
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xmin, ymin, xmax, ymax = gt.tolist()
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im_shape = result['im_shape'][0][i].tolist()
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im_height, im_width = int(im_shape[0]), int(im_shape[1])
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xmin *= im_width
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ymin *= im_height
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xmax *= im_width
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ymax *= im_height
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gt_box_list.append([xmin, ymin, xmax, ymax])
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faces_num_gt += 1
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return gt_box_list, bbox_list, faces_num_gt
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def calculate_ap_py(results):
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def cal_iou(rect1, rect2):
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lt_x = max(rect1[0], rect2[0])
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lt_y = max(rect1[1], rect2[1])
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rb_x = min(rect1[2], rect2[2])
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rb_y = min(rect1[3], rect2[3])
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if (rb_x > lt_x) and (rb_y > lt_y):
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intersection = (rb_x - lt_x) * (rb_y - lt_y)
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else:
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return 0
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area1 = (rect1[2] - rect1[0]) * (rect1[3] - rect1[1])
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area2 = (rect2[2] - rect2[0]) * (rect2[3] - rect2[1])
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intersection = min(intersection, area1, area2)
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union = area1 + area2 - intersection
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return float(intersection) / union
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def is_same_face(face_gt, face_pred):
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iou = cal_iou(face_gt, face_pred)
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return iou >= 0.5
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def eval_single_image(faces_gt, faces_pred):
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pred_is_true = [False] * len(faces_pred)
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gt_been_pred = [False] * len(faces_gt)
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for i in range(len(faces_pred)):
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isface = False
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for j in range(len(faces_gt)):
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if gt_been_pred[j] == 0:
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isface = is_same_face(faces_gt[j], faces_pred[i])
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if isface == 1:
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gt_been_pred[j] = True
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break
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pred_is_true[i] = isface
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return pred_is_true
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score_res_pair = {}
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faces_num_gt = 0
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for t in results:
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gt_box_list, bbox_list, face_num_gt = get_bboxes_scores(t)
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faces_num_gt += face_num_gt
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pred_is_true = eval_single_image(gt_box_list, bbox_list)
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for i in range(0, len(pred_is_true)):
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now_score = bbox_list[i][-1]
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if now_score in score_res_pair:
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score_res_pair[now_score].append(int(pred_is_true[i]))
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else:
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score_res_pair[now_score] = [int(pred_is_true[i])]
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keys = score_res_pair.keys()
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keys = sorted(keys, reverse=True)
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tp_num = 0
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predict_num = 0
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precision_list = []
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recall_list = []
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for i in range(len(keys)):
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k = keys[i]
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v = score_res_pair[k]
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predict_num += len(v)
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tp_num += sum(v)
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recall = float(tp_num) / faces_num_gt
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precision_list.append(float(tp_num) / predict_num)
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recall_list.append(recall)
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ap = precision_list[0] * recall_list[0]
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for i in range(1, len(precision_list)):
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ap += precision_list[i] * (recall_list[i] - recall_list[i - 1])
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return ap
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def main():
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env = os.environ
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FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
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if FLAGS.dist:
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trainer_id = int(env['PADDLE_TRAINER_ID'])
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import random
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local_seed = (99 + trainer_id)
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random.seed(local_seed)
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np.random.seed(local_seed)
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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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main_arch = cfg.architecture
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if cfg.use_gpu:
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devices_num = fluid.core.get_cuda_device_count()
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else:
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devices_num = int(os.environ.get('CPU_NUM', 1))
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if 'FLAGS_selected_gpus' in env:
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device_id = int(env['FLAGS_selected_gpus'])
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else:
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device_id = 0
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place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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lr_builder = create('LearningRate')
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optim_builder = create('OptimizerBuilder')
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# add NAS
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config = ([(cfg.search_space)])
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server_address = (cfg.server_ip, cfg.server_port)
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load_checkpoint = FLAGS.resume_checkpoint if FLAGS.resume_checkpoint else None
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sa_nas = SANAS(
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config,
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server_addr=server_address,
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init_temperature=cfg.init_temperature,
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reduce_rate=cfg.reduce_rate,
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search_steps=cfg.search_steps,
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save_checkpoint=cfg.save_dir,
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load_checkpoint=load_checkpoint,
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is_server=cfg.is_server)
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start_iter = 0
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train_reader = create_reader(cfg.TrainReader, (cfg.max_iters - start_iter) *
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devices_num, cfg)
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eval_reader = create_reader(cfg.EvalReader)
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constraint = create('Constraint')
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for step in range(cfg.search_steps):
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logger.info('----->>> search step: {} <<<------'.format(step))
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archs = sa_nas.next_archs()[0]
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# build program
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startup_prog = fluid.Program()
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train_prog = fluid.Program()
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with fluid.program_guard(train_prog, startup_prog):
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with fluid.unique_name.guard():
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model = create(main_arch)
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if FLAGS.fp16:
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assert (getattr(model.backbone, 'norm_type', None)
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!= 'affine_channel'), \
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'--fp16 currently does not support affine channel, ' \
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' please modify backbone settings to use batch norm'
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with mixed_precision_context(FLAGS.loss_scale,
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FLAGS.fp16) as ctx:
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inputs_def = cfg['TrainReader']['inputs_def']
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feed_vars, train_loader = model.build_inputs(**inputs_def)
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train_fetches = archs(feed_vars, 'train', cfg)
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loss = train_fetches['loss']
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if FLAGS.fp16:
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loss *= ctx.get_loss_scale_var()
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lr = lr_builder()
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optimizer = optim_builder(lr)
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optimizer.minimize(loss)
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if FLAGS.fp16:
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loss /= ctx.get_loss_scale_var()
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current_constraint = constraint.compute_constraint(train_prog)
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logger.info('current steps: {}, constraint {}'.format(
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step, current_constraint))
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if (constraint.max_constraint != None and
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current_constraint > constraint.max_constraint) or (
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constraint.min_constraint != None and
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current_constraint < constraint.min_constraint):
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continue
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# parse train fetches
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train_keys, train_values, _ = parse_fetches(train_fetches)
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train_values.append(lr)
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if FLAGS.eval:
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eval_prog = fluid.Program()
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with fluid.program_guard(eval_prog, startup_prog):
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with fluid.unique_name.guard():
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model = create(main_arch)
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inputs_def = cfg['EvalReader']['inputs_def']
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feed_vars, eval_loader = model.build_inputs(**inputs_def)
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fetches = archs(feed_vars, 'eval', cfg)
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eval_prog = eval_prog.clone(True)
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# When iterable mode, set set_sample_list_generator(eval_reader, place)
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eval_loader.set_sample_list_generator(eval_reader)
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extra_keys = ['im_id', 'im_shape', 'gt_bbox']
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eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
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extra_keys)
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# compile program for multi-devices
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build_strategy = fluid.BuildStrategy()
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build_strategy.fuse_all_optimizer_ops = False
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build_strategy.fuse_elewise_add_act_ops = True
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exec_strategy = fluid.ExecutionStrategy()
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# iteration number when CompiledProgram tries to drop local execution scopes.
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# Set it to be 1 to save memory usages, so that unused variables in
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# local execution scopes can be deleted after each iteration.
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exec_strategy.num_iteration_per_drop_scope = 1
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if FLAGS.dist:
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dist_utils.prepare_for_multi_process(exe, build_strategy,
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startup_prog, train_prog)
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exec_strategy.num_threads = 1
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exe.run(startup_prog)
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compiled_train_prog = fluid.CompiledProgram(
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train_prog).with_data_parallel(
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loss_name=loss.name,
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build_strategy=build_strategy,
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exec_strategy=exec_strategy)
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if FLAGS.eval:
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compiled_eval_prog = fluid.CompiledProgram(eval_prog)
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# When iterable mode, set set_sample_list_generator(train_reader, place)
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train_loader.set_sample_list_generator(train_reader)
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train_stats = TrainingStats(cfg.log_iter, train_keys)
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train_loader.start()
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end_time = time.time()
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cfg_name = os.path.basename(FLAGS.config).split('.')[0]
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save_dir = os.path.join(cfg.save_dir, cfg_name)
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time_stat = deque(maxlen=cfg.log_iter)
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ap = 0
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for it in range(start_iter, cfg.max_iters):
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start_time = end_time
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end_time = time.time()
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time_stat.append(end_time - start_time)
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time_cost = np.mean(time_stat)
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eta_sec = (cfg.max_iters - it) * time_cost
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eta = str(datetime.timedelta(seconds=int(eta_sec)))
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outs = exe.run(compiled_train_prog, fetch_list=train_values)
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stats = {
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k: np.array(v).mean()
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for k, v in zip(train_keys, outs[:-1])
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}
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train_stats.update(stats)
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logs = train_stats.log()
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if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
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strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
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it, np.mean(outs[-1]), logs, time_cost, eta)
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logger.info(strs)
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if (it > 0 and it == cfg.max_iters - 1) and (not FLAGS.dist or
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trainer_id == 0):
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save_name = str(
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it) if it != cfg.max_iters - 1 else "model_final"
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checkpoint.save(exe, train_prog,
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os.path.join(save_dir, save_name))
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if FLAGS.eval:
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# evaluation
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results = eval_run(exe, compiled_eval_prog, eval_loader,
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eval_keys, eval_values, eval_cls)
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ap = calculate_ap_py(results)
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train_loader.reset()
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eval_loader.reset()
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logger.info('rewards: ap is {}'.format(ap))
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sa_nas.reward(float(ap))
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current_best_tokens = sa_nas.current_info()['best_tokens']
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logger.info("All steps end, the best BlazeFace-NAS structure is: ")
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sa_nas.tokens2arch(current_best_tokens)
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if __name__ == '__main__':
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enable_static_mode()
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parser = ArgsParser()
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parser.add_argument(
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"-r",
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"--resume_checkpoint",
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default=None,
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type=str,
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help="Checkpoint path for resuming training.")
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parser.add_argument(
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"--fp16",
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action='store_true',
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default=False,
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help="Enable mixed precision training.")
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parser.add_argument(
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"--loss_scale",
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default=8.,
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type=float,
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help="Mixed precision training loss scale.")
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parser.add_argument(
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"--eval",
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action='store_true',
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default=True,
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help="Whether to perform evaluation in train")
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FLAGS = parser.parse_args()
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main()
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