pytorch------retinaface(テスト)

Retinafaceテスト

from __future__ import print_function
import os
import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from data import cfg_mnet, cfg_re50
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from models.retinaface import RetinaFace
from utils.box_utils import decode, decode_landm
from utils.timer import Timer

# 
parser = argparse.ArgumentParser(description='Retinaface')
parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth',
                   type=str, help='Trained state_dict file path to open')
parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50')
parser.add_argument('--origin_size', default=True, type=str, help='Whether use origin image size to evaluate')
parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results')
parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
parser.add_argument('--dataset_folder', default='./data/widerface/val/images/', type=str, help='dataset path')
parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
parser.add_argument('--top_k', default=5000, type=int, help='top_k')
parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
parser.add_argument('-s', '--save_image', action="store_true", default=False, help='show detection results')
parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold')
args = parser.parse_args()

#
def check_keys(model, pretrained_state_dict):
   ckpt_keys = set(pretrained_state_dict.keys())
   model_keys = set(model.state_dict().keys())
   used_pretrained_keys = model_keys & ckpt_keys
   unused_pretrained_keys = ckpt_keys - model_keys
   missing_keys = model_keys - ckpt_keys
   print('Missing keys:{}'.format(len(missing_keys)))
   print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
   print('Used keys:{}'.format(len(used_pretrained_keys)))
   assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
   return True


def remove_prefix(state_dict, prefix):
   ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
   print('remove prefix \'{}\''.format(prefix))
   f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
   return {f(key): value for key, value in state_dict.items()}


def load_model(model, pretrained_path, load_to_cpu):
   print('Loading pretrained model from {}'.format(pretrained_path))
   if load_to_cpu:
       pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
   else:
       device = torch.cuda.current_device()
       pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
   if "state_dict" in pretrained_dict.keys():
       pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
   else:
       pretrained_dict = remove_prefix(pretrained_dict, 'module.')
   check_keys(model, pretrained_dict)
   model.load_state_dict(pretrained_dict, strict=False)
   return model


if __name__ == '__main__':
   torch.set_grad_enabled(False)

   cfg = None
   if args.network == "mobile0.25":
       cfg = cfg_mnet
   elif args.network == "resnet50":
       cfg = cfg_re50
   # net and model
   net = RetinaFace(cfg=cfg, phase = 'test')
   net = load_model(net, args.trained_model, args.cpu)
   net.eval()# 
   print('Finished loading model!')
   print(net)
   cudnn.benchmark = True
   device = torch.device("cpu" if args.cpu else "cuda")
   net = net.to(device)

   # testing dataset
   testset_folder = args.dataset_folder
   testset_list = args.dataset_folder[:-7] + "wider_val.txt"
   # 
   with open(testset_list, 'r') as fr:
       test_dataset = fr.read().split()
   num_images = len(test_dataset)
   # 
   _t = {'forward_pass': Timer(), 'misc': Timer()}

   # testing begin
   for i, img_name in enumerate(test_dataset):
       image_path = testset_folder + img_name
       img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
       img = np.float32(img_raw)

       # testing scale
       target_size = 1600
       max_size = 2150
       im_shape = img.shape
       im_size_min = np.min(im_shape[0:2])
       im_size_max = np.max(im_shape[0:2])
       resize = float(target_size) / float(im_size_min)
       # prevent bigger axis from being more than max_size: 
       if np.round(resize * im_size_max) > max_size:
           resize = float(max_size) / float(im_size_max)
       if args.origin_size:
           resize = 1

       if resize != 1:
           img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
       im_height, im_width, _ = img.shape
       scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
       img -= (104, 117, 123)# 
       img = img.transpose(2, 0, 1)# 
       img = torch.from_numpy(img).unsqueeze(0)# 
       img = img.to(device)
       scale = scale.to(device)

       _t['forward_pass'].tic()
       loc, conf, landms = net(img)  # forward pass
       _t['forward_pass'].toc()
       _t['misc'].tic()
       # 
       priorbox = PriorBox(cfg, image_size=(im_height, im_width))
       priors = priorbox.forward()
       priors = priors.to(device)
       prior_data = priors.data
       boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
       boxes = boxes * scale / resize
       boxes = boxes.cpu().numpy()
       scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
       landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
       scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                              img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                              img.shape[3], img.shape[2]])
       scale1 = scale1.to(device)
       landms = landms * scale1 / resize
       landms = landms.cpu().numpy()

       # ignore low scores
       inds = np.where(scores > args.confidence_threshold)[0]
       boxes = boxes[inds]
       landms = landms[inds]
       scores = scores[inds]

       # keep top-K before NMS
       order = scores.argsort()[::-1]# 
       # order = scores.argsort()[::-1][:args.top_k]
       boxes = boxes[order]
       landms = landms[order]
       scores = scores[order]

       # do NMS
       dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
       keep = py_cpu_nms(dets, args.nms_threshold)
       # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
       dets = dets[keep, :]
       landms = landms[keep]

       # keep top-K faster NMS
       # dets = dets[:args.keep_top_k, :]
       # landms = landms[:args.keep_top_k, :]

       dets = np.concatenate((dets, landms), axis=1)# 
       _t['misc'].toc()

       # --------------------------------------------------------------------
       # 
       save_name = args.save_folder + img_name[:-4] + ".txt"
       dirname = os.path.dirname(save_name)
       if not os.path.isdir(dirname):
           os.makedirs(dirname)
       with open(save_name, "w") as fd:
           bboxs = dets
           file_name = os.path.basename(save_name)[:-4] + "
"
           bboxs_num = str(len(bboxs)) + "
"
           fd.write(file_name)
           fd.write(bboxs_num)
           for box in bboxs:
               x = int(box[0])
               y = int(box[1])
               w = int(box[2]) - int(box[0])
               h = int(box[3]) - int(box[1])
               confidence = str(box[4])
               line = str(x) + " " + str(y) + " " + str(w) + " " + str(h) + " " + confidence + " 
"
               fd.write(line)

       print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time))

       # save image
       if args.save_image:
           for b in dets:
               if b[4] < args.vis_thres:
                   continue
               text = "{:.4f}".format(b[4])
               b = list(map(int, b))
               cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
               cx = b[0]
               cy = b[1] + 12
               cv2.putText(img_raw, text, (cx, cy),
                           cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))

               # landms
               cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
               cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
               cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
               cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
               cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
           # save image
           if not os.path.exists("./results/"):
               os.makedirs("./results/")
           name  = "./results/" + str(i) + ".jpg"
           cv2.imwrite(name, img_raw)
           return name