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test.py
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import utils.gpu as gpu
from modelR.lodet import LODet
from tensorboardX import SummaryWriter
from evalR.evaluator import Evaluator
import argparse
import os
import config.cfg_lodet as cfg
from utils.visualize import *
import time
import logging
from utils.utils_coco import *
from utils.log import Logger
import cv2
from evalR.coco_eval import COCOEvaluator
import torch.backends.cudnn as cudnn
class Tester(object):
def __init__(self, weight_path=None, gpu_id=0, visiual=None, eval=False):
self.img_size = cfg.TEST["TEST_IMG_SIZE"]
self.__num_class = cfg.DATA["NUM"]
self.__conf_threshold = cfg.TEST["CONF_THRESH"]
self.__nms_threshold = cfg.TEST["NMS_THRESH"]
self.__device = gpu.select_device(gpu_id, force_cpu=False)
self.__multi_scale_test = cfg.TEST["MULTI_SCALE_TEST"]
self.__flip_test = cfg.TEST["FLIP_TEST"]
self.__classes = cfg.DATA["CLASSES"]
self.__visiual = visiual
self.__eval = eval
self.__model = LODet().to(self.__device) # Single GPU
net_model = LODet()
if torch.cuda.device_count() >1: ## Multi GPUs
print("Let's use", torch.cuda.device_count(), "GPUs!")
net_model = torch.nn.DataParallel(net_model) ## Multi GPUs
self.__model = net_model.to(self.__device)
elif torch.cuda.device_count() ==1:
self.__model = net_model.to(self.__device)
self.__load_model_weights(weight_path)
self.__evalter = Evaluator(self.__model, visiual=False)
def __load_model_weights(self, weight_path):
print("loading weight file from : {}".format(weight_path))
weight = os.path.join(weight_path)
chkpt = torch.load(weight, map_location=self.__device)
self.__model.load_state_dict(chkpt) #['model']
#print("loading weight file is done")
del chkpt
def test(self):
global logger
logger.info("***********Start Evaluation****************")
if self.__visiual:
imgs = os.listdir(self.__visiual)
for v in imgs:
path = os.path.join(self.__visiual, v)
#print("test images : {}".format(path))
img = cv2.imread(path)
assert img is not None
bboxes_prd = self.__evalter.get_bbox(img)
if bboxes_prd.shape[0] != 0:
boxes = bboxes_prd[..., :4]
class_inds = bboxes_prd[..., 5].astype(np.int32)
scores = bboxes_prd[..., 4]
visualize_boxes(image=img, boxes=boxes, labels=class_inds, probs=scores, class_labels=self.__classes)
path = os.path.join(cfg.PROJECT_PATH, "prediction/imgs_all/{}".format(v))
cv2.imwrite(path, img)
#print("saved images : {}".format(path))
mAP = 0
if self.__eval and cfg.TEST["EVAL_TYPE"] == 'VOC':
with torch.no_grad():
start = time.time()
APs, inference_time = Evaluator(self.__model).APs_voc(self.__multi_scale_test, self.__flip_test)
for i in APs:
print("{} --> AP : {}".format(i, APs[i]))
mAP += APs[i]
mAP = mAP / self.__num_class
logger.info('mAP:{}'.format(mAP))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('test/VOCmAP', mAP)
end = time.time()
logger.info("Test cost time:{:.4f}s".format(end - start))
#print('mAP:%g' % (mAP))
#print("inference time : {:.2f} ms".format(inference_time))
elif self.__eval and cfg.TEST["EVAL_TYPE"] == 'COCO':
with torch.no_grad():
start = time.time()
evaluator = COCOEvaluator(data_dir=cfg.DATA_PATH,
img_size=cfg.TEST["TEST_IMG_SIZE"],
confthre=cfg.TEST["CONF_THRESH"],
nmsthre=cfg.TEST["NMS_THRESH"])
ap50_95, ap50, inference_time = evaluator.evaluate(self.__model)
logger.info('ap50_95:{} | ap50:{}'.format(ap50_95, ap50))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('test/COCOAP50', ap50)
writer.add_scalar('test/COCOAP50_95', ap50_95)
end = time.time()
logger.info("Test cost time:{:.4f}s".format(end - start))
elif self.__eval and cfg.TEST["EVAL_TYPE"] == 'BOTH':
with torch.no_grad():
start = time.time()
APs, inference_time = Evaluator(self.__model).APs_voc(self.__multi_scale_test, self.__flip_test)
for i in APs:
print("{} --> mAP : {}".format(i, APs[i]))
mAP += APs[i]
mAP = mAP / self.__num_class
logger.info('mAP:{}'.format(mAP))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('test/VOCmAP', mAP)
end = time.time()
logger.info("Test cost time:{:.4f}s".format(end - start))
start = time.time()
evaluator = COCOEvaluator(data_dir=cfg.DATA_PATH,
img_size=cfg.TEST["TEST_IMG_SIZE"],
confthre=cfg.TEST["CONF_THRESH"],
nmsthre=cfg.TEST["NMS_THRESH"])
ap50_95, ap50, inference_time = evaluator.evaluate(self.__model)
logger.info('ap50_95:{} | ap50:{}'.format(ap50_95, ap50))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('test/COCOAP50', ap50)
writer.add_scalar('test/COCOAP50_95', ap50_95)
end = time.time()
logger.info("Test cost time:{:.4f}s".format(end - start))
if __name__ == "__main__":
global logger
parser = argparse.ArgumentParser()
parser.add_argument('--weight_path', type=str, default='weight/dota608_66.17.pt', help='weight file path')
parser.add_argument('--log_val_path', type=str, default='log/', help='weight file path')
parser.add_argument('--visiual', type=str, default=None, help='test data path or None')
parser.add_argument('--eval', action='store_true', default=True, help='eval flag')
parser.add_argument('--gpu_id', type=int, default=0, help='gpu id')
parser.add_argument('--log_path', type=str, default='log/', help='log path')
opt = parser.parse_args()
writer = SummaryWriter(logdir=opt.log_path + '/event')
logger = Logger(log_file_name=opt.log_val_path + '/log_coco_test.txt', log_level=logging.DEBUG,
logger_name='NPMMRDet').get_log()
Tester(weight_path=opt.weight_path, gpu_id=opt.gpu_id, eval=opt.eval, visiual=opt.visiual).test()