def test_net(sess,
net,
imdb,
weights_filename,
max_per_image=100,
thresh=0.05):
np.random.seed(cfg.FLAGS.rng_seed)
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
output_dir = get_output_dir(imdb, weights_filename)
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in range(num_images):
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes = im_detect(sess, net, im)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in range(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, 0.3)
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack(
[all_boxes[j][i][:, -1] for j in range(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time))
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
def __init__(self, i_net):
# Create network
self.netname = i_net
print('Train: cfg.FLAGS.network', net)
if i_net == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
self.pre_model = cfg.FLAGS.pretrained_model
elif i_net == 'res50':
self.net = resnetv1(num_layers=50)
self.pre_model = cfg.FLAGS.pretrained_resnet50_model
elif i_net == 'res101':
self.net = resnetv1(num_layers=101)
self.pre_model = cfg.FLAGS.pretrained_resnet101_model
elif i_net == 'res152':
self.net = resnetv1(num_layers=152)
self.pre_model = cfg.FLAGS.pretrained_resnet152_model
else:
raise NotImplementedError
print('self.net', self.net)
self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self, dataset):
# Create network
if cfg.FLAGS.network == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
if dataset == "tego":
self.imdb, self.roidb = load_db("tego_train")
elif dataset == "tego_wholeBB":
self.imdb, self.roidb = load_db("tego_train-wholeBB")
elif dataset == "tego_blind":
self.imdb, self.roidb = load_db("tego_train-blind")
elif dataset == "tego_sighted":
self.imdb, self.roidb = load_db("tego_train-sighted")
elif dataset == "tego_blind_wholeBB":
self.imdb, self.roidb = load_db("tego_train-blind-wholeBB")
elif dataset == "tego_sighted_wholeBB":
self.imdb, self.roidb = load_db("tego_train-sighted-wholeBB")
else:
self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, dataset)
def __init__(self):
# 初始化网络
if cfg.FLAGS.network == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch) # 1
else:
raise NotImplementedError
self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
# 输入输出
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self):
# create network
# 初始化，准备网络与数据载入
if cfg.FLAGS.net == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
self.imdb, self.roidb = combined_roidb("DIY_dataset")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self):
# Create network
if cfg.FLAGS.network == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
#self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
self.imdb, self.roidb = combined_roidb("geetcodechinese_2019_train")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self):
# Create network
if cfg.FLAGS.network == 'resnet_v1':
self.net = resnetv1(batch_size=cfg.FLAGS.ims_per_batch,
num_layers=50)
else:
raise NotImplementedError
self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self):
# Create network
if cfg.FLAGS.net == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
elif cfg.FLAGS.net == 'resnetv1':
self.net = resnetv1(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
self.imdb, self.roidb = combined_roidb("Columbia")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self):
# Create network
if cfg.FLAGS.network == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
# 获取数据集， 并对roidb做数据增强和数据补充
self.imdb, self.roidb = combined_roidb('jinnan2_round1_train_20190222')
print(len(self.imdb.roidb))
# self.roidb是合并了了水平翻转后的roi集合,数量为原先的2倍
print(len(self.roidb))
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self):
# Create network
if cfg.FLAGS.network == 'vgg16':
# 创建vgg(16)网络
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
# 加载数据
self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
# 模型保存的位置
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self, canting, data_riqi):
self.canting = canting
self.data_riqi = data_riqi
# Create network
if cfg.FLAGS.network == 'ghostnet':
self.net = GhostNet(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
self.imdb, self.roidb = combined_roidb("voc_2007_trainval", canting,
data_riqi)
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(
self.imdb, 'default_{}_{}'.format(self.canting, self.data_riqi))
def __init__(self):
# Create network
if cfg.FLAGS.network == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
'''
这里应用factory.py中的get_imdb(name)函数，
然后值为 __sets[name]()，是一个字典， __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))，值为一个函数，也即实例化一个pascal_voc(split, year)对象
也就确定了imbd的name，这里只是跟imbd的name有关，可以不用管
'''
self.imdb, self.roidb = combined_roidb("voc_2007_trainval")
# 对原始image和gt_boxes进行平移缩放等处理，得到network input
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
# 模型保存的路径
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
def __init__(self, dataset):
# Create network
if cfg.FLAGS.network == 'vgg16':
self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch)
elif cfg.FLAGS.network == 'RESNET_v1_50':
self.net = resnetv1(batch_size=cfg.FLAGS.ims_per_batch)
else:
raise NotImplementedError
#The database
#self.imdb, self.roidb = combined_roidb("voc_2007_trainval+test+Isabel")
self.imdb, self.roidb = combined_roidb(dataset)
#self.imdb, self.roidb = combined_roidb("Isabel")
print(self.imdb.name)
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.output_dir = cfg.get_output_dir(self.imdb, 'default')
print(self.output_dir)
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time))
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
if __name__ == '__main__':
imdb, roidb = combined_roidb("voc_2007_trainval")
data_layer = RoIDataLayer(roidb, imdb.num_classes)
output_dir = cfg.get_output_dir(imdb, 'default')
args = parse_args()
# model path
demonet = args.demo_net
dataset = args.dataset
# tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0])
tfmodel = os.path.join('default', DATASETS[dataset][0], 'default',
NETS[demonet][0])
if not os.path.isfile(tfmodel + '.meta'):
print(tfmodel)
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta'))
