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test_RFB.py
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test_RFB.py
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from __future__ import print_function
import sys
import os
import pickle
import argparse
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import numpy as np
from torch.autograd import Variable
from data import VOCroot,COCOroot
from data import AnnotationTransform, COCODetection, VOCDetection, BaseTransform, VOC_300,VOC_512,COCO_300,COCO_512, COCO_mobile_300
import torch.utils.data as data
from layers.functions import Detect,PriorBox
from utils.nms_wrapper import nms
from utils.timer import Timer
parser = argparse.ArgumentParser(description='Receptive Field Block Net')
parser.add_argument('-v', '--version', default='RFB_vgg',
help='RFB_vgg ,RFB_E_vgg or RFB_mobile version.')
parser.add_argument('-s', '--size', default='300',
help='300 or 512 input size.')
parser.add_argument('-d', '--dataset', default='VOC',
help='VOC or COCO version')
parser.add_argument('-m', '--trained_model', default='weights/RFB300_80_5.pth',
type=str, help='Trained state_dict file path to open')
parser.add_argument('--save_folder', default='eval/', type=str,
help='Dir to save results')
parser.add_argument('--cuda', default=True, type=bool,
help='Use cuda to train model')
parser.add_argument('--cpu', default=False, type=bool,
help='Use cpu nms')
parser.add_argument('--retest', default=False, type=bool,
help='test cache results')
args = parser.parse_args()
if not os.path.exists(args.save_folder):
os.mkdir(args.save_folder)
if args.dataset == 'VOC':
cfg = (VOC_300, VOC_512)[args.size == '512']
else:
cfg = (COCO_300, COCO_512)[args.size == '512']
if args.version == 'RFB_vgg':
from models.RFB_Net_vgg import build_net
elif args.version == 'RFB_E_vgg':
from models.RFB_Net_E_vgg import build_net
elif args.version == 'RFB_mobile':
from models.RFB_Net_mobile import build_net
cfg = COCO_mobile_300
else:
print('Unkown version!')
priorbox = PriorBox(cfg)
with torch.no_grad():
priors = priorbox.forward()
if args.cuda:
priors = priors.cuda()
def test_net(save_folder, net, detector, cuda, testset, transform, max_per_image=300, thresh=0.005):
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# dump predictions and assoc. ground truth to text file for now
num_images = len(testset)
num_classes = (21, 81)[args.dataset == 'COCO']
all_boxes = [[[] for _ in range(num_images)]
for _ in range(num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
det_file = os.path.join(save_folder, 'detections.pkl')
if args.retest:
f = open(det_file,'rb')
all_boxes = pickle.load(f)
print('Evaluating detections')
testset.evaluate_detections(all_boxes, save_folder)
return
for i in range(num_images):
img = testset.pull_image(i)
scale = torch.Tensor([img.shape[1], img.shape[0],
img.shape[1], img.shape[0]])
with torch.no_grad():
x = transform(img).unsqueeze(0)
if cuda:
x = x.cuda()
scale = scale.cuda()
_t['im_detect'].tic()
out = net(x) # forward pass
boxes, scores = detector.forward(out,priors)
detect_time = _t['im_detect'].toc()
boxes = boxes[0]
scores=scores[0]
boxes *= scale
boxes = boxes.cpu().numpy()
scores = scores.cpu().numpy()
# scale each detection back up to the image
_t['misc'].tic()
for j in range(1, num_classes):
inds = np.where(scores[:, j] > thresh)[0]
if len(inds) == 0:
all_boxes[j][i] = np.empty([0, 5], dtype=np.float32)
continue
c_bboxes = boxes[inds]
c_scores = scores[inds, j]
c_dets = np.hstack((c_bboxes, c_scores[:, np.newaxis])).astype(
np.float32, copy=False)
keep = nms(c_dets, 0.45, force_cpu=args.cpu)
c_dets = c_dets[keep, :]
all_boxes[j][i] = c_dets
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1] for j in range(1,num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
nms_time = _t['misc'].toc()
if i % 20 == 0:
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s'
.format(i + 1, num_images, detect_time, nms_time))
_t['im_detect'].clear()
_t['misc'].clear()
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
testset.evaluate_detections(all_boxes, save_folder)
if __name__ == '__main__':
# load net
img_dim = (300,512)[args.size=='512']
num_classes = (21, 81)[args.dataset == 'COCO']
net = build_net('test', img_dim, num_classes) # initialize detector
state_dict = torch.load(args.trained_model)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
net.eval()
print('Finished loading model!')
print(net)
# load data
if args.dataset == 'VOC':
testset = VOCDetection(
VOCroot, [('2007', 'test')], None, AnnotationTransform())
elif args.dataset == 'COCO':
testset = COCODetection(
COCOroot, [('2014', 'minival')], None)
#COCOroot, [('2015', 'test-dev')], None)
else:
print('Only VOC and COCO dataset are supported now!')
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
else:
net = net.cpu()
# evaluation
#top_k = (300, 200)[args.dataset == 'COCO']
top_k = 200
detector = Detect(num_classes,0,cfg)
save_folder = os.path.join(args.save_folder,args.dataset)
rgb_means = ((104, 117, 123),(103.94,116.78,123.68))[args.version == 'RFB_mobile']
test_net(save_folder, net, detector, args.cuda, testset,
BaseTransform(net.size, rgb_means, (2, 0, 1)),
top_k, thresh=0.01)