with open(name_path, "r") as f:
classes = [i.strip() for i in f.readlines()]
try:
im_list = [
osp.join(osp.realpath('.'), images, img)
for img in os.listdir(images)
]
except NotADirectoryError:
im_list = []
im_list.append(osp.join(osp.realpath('.'), images))
except FileNotFoundError:
print("No file or directory with the name {}".format(images))
exit()
net = Yolov3("test", input_wh, anchors, cfg["anchors_mask"], num_classes)
state_dict = torch.load(weightsfile)
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
if cuda:
net.cuda()
cudnn.benchmark = True
net.load_state_dict(new_state_dict)
print("load weights successfully.....")
cfg = voc_config
test_dataset = VOCDetection(cfg["root"], datasets_dict["VOC2007"],
input_wh)
elif dataset[0] == "C":
cfg = coco_config
test_dataset = COCODetection(cfg["root"], datasets_dict["COCOval"],
input_wh)
else:
print("only support VOC and COCO datasets !!!")
print("load test_dataset successfully.....")
with open(cfg["name_path"], "r") as f:
classes = [i.strip() for i in f.readlines()]
net = Yolov3("test", input_wh, cfg["anchors"], cfg["anchors_mask"],
cfg["num_classes"])
state_dict = torch.load(weightsfile)
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
if cuda:
net.cuda()
cudnn.benchmark = True
net.load_state_dict(new_state_dict)
pass
else:
bn = layer[i].bn
conv = layer[i].conv
ptr = copy_weights(bn, conv, ptr, weights)
fp.close()
if __name__ == '__main__':
args = arg_parse()
weightfile = args.weights
input_wh = args.input_wh
version = args.version
save_name = args.save_name
if version == "voc":
cfg = voc_config
yolov3 = Yolov3("train", input_wh, cfg["anchors"], cfg["anchors_mask"], cfg["num_classes"])
elif version == "coco":
cfg = coco_config
yolov3 = Yolov3("train", input_wh, cfg["anchors"], cfg["anchors_mask"], cfg["num_classes"])
elif version == "darknet53":
cfg = voc_config
num_blocks = [1,2,8,8,4]
yolov3 = Darknet53(num_blocks)
else:
print("Unkown version !!!")
import sys
sys.exit()
load_weights(weightfile, yolov3, version)
# name = "convert_yolo_" + version + ".pth"
# save_path = os.path.join("./weights", name)