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evaluate.py
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evaluate.py
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import argparse
import time
import os
import numpy as np
import json
import cv2
import random
import torch
from ACID_test import test
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common detectron2 utilities
from detectron2.model_zoo import model_zoo
from detectron2.engine import DefaultTrainer, DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer, ColorMode
from detectron2.data import MetadataCatalog, DatasetCatalog, build_detection_test_loader
from detectron2.data.datasets import register_coco_instances
from detectron2.structures import BoxMode
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.modeling import build_model
parser = argparse.ArgumentParser(description='ACID_Object_Detection_Train')
parser.add_argument('--dataset', default='ACID_dataset', type=str, help='name of dataset')
parser.add_argument('--file', default='/home/hteam/Documents/hao/Research/Dataset/ACID/ACID_train_augmentation', type=str, help='data file')
parser.add_argument('--label', default='/home/hteam/Documents/hao/Research/Dataset/ACID/ACID_train_augmentation.json', type=str, help='COCO format json')
parser.add_argument('--test_dataset', default='ACID_testing', type=str, help='name of testing dataset')
parser.add_argument('--test_file', default='/home/hteam/Documents/hao/Research/Dataset/ACID/ACID_testing', type=str, help='testing data file')
parser.add_argument('--test_label', default='/home/hteam/Documents/hao/Research/Dataset/ACID/ACID_test.json', type=str, help='testing json')
parser.add_argument('--model', default='COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml', type=str, help='model')
parser.add_argument('--weight', default='./output/model_final.pth', type=str, help='model weight')
parser.add_argument('--num_class', default=3, type=int, help='num of classes')
parser.add_argument('--iter', default=30000, type=int, help='max iter')
def main():
args = parser.parse_args()
register_coco_instances(args.dataset, {}, args.label, args.file) # training dataset
register_coco_instances(args.test_dataset, {}, args.test_label, args.test_file) # testing dataset
### set metadata
MetadataCatalog.get(args.test_dataset).evaluator_type="coco"
DatasetCatalog.get(args.test_dataset)
### cfg setting
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(args.model))
cfg.DATASETS.TRAIN = (args.dataset,)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = args.num_class # excavator, dump_truck, cement_truck
cfg.MODEL.WEIGHTS = args.weight
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set the testing threshold for this model
cfg.DATASETS.TEST = (args.test_dataset,)
### trainner setting
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(cfg.MODEL.WEIGHTS)
### evaluation setting
evaluator = COCOEvaluator(args.test_dataset, cfg, False, output_dir="./output/")
val_loader = build_detection_test_loader(cfg, args.test_dataset)
inference_on_dataset(trainer.model, val_loader, evaluator)
if __name__ == '__main__':
main()