def __init__(self, cfg, weight, batchSize=1):
self.det_model = Darknet(cfg)
# self.det_model.load_state_dict(torch.load('models/yolo/yolov3-spp.weights', map_location="cuda:0")['model'])
self.det_model.load_weights(weight)
self.det_model.net_info['height'] = input_size
self.det_inp_dim = int(self.det_model.net_info['height'])
assert self.det_inp_dim % 32 == 0
assert self.det_inp_dim > 32
if device != "cpu":
self.det_model.cuda()
inf_time = get_inference_time(self.det_model,
height=input_size,
width=input_size)
flops = print_model_param_flops(self.det_model,
input_width=input_size,
input_height=input_size)
params = print_model_param_nums(self.det_model)
print("Detection: Inference time {}s, Params {}, FLOPs {}".format(
inf_time, params, flops))
if libtorch:
example = torch.rand(2, 3, 224, 224)
traced_model = torch.jit.trace(self.det_model, example)
traced_model.save("det_lib.pt")
self.det_model.eval()
self.im_dim_list = []
self.batchSize = batchSize
self.mul_img = False
def __init__(self, batchSize=1):
self.det_model = Darknet("src/yolo/cfg/yolov3-spp.cfg")
self.det_model.load_weights('models/yolo/yolov3-spp.weights')
self.det_model.net_info['height'] = config.input_size
self.det_inp_dim = int(self.det_model.net_info['height'])
assert self.det_inp_dim % 32 == 0
assert self.det_inp_dim > 32
self.det_model.cuda()
self.det_model.eval()
self.stopped = False
self.batchSize = batchSize
def __init__(self, batchSize=1):
self.det_model = Darknet(config.yolo_cfg)
# self.det_model.load_state_dict(torch.load('models/yolo/yolov3-spp.weights', map_location="cuda:0")['model'])
self.det_model.load_weights(config.yolo_model)
self.det_model.net_info['height'] = config.input_size
self.det_inp_dim = int(self.det_model.net_info['height'])
assert self.det_inp_dim % 32 == 0
assert self.det_inp_dim > 32
if device != "cpu":
self.det_model.cuda()
self.det_model.eval()
self.stopped = False
self.batchSize = batchSize
def __init__(self, cfg, weight, batchSize=1):
self.det_model = Darknet(cfg)
self.det_model.load_weights(weight)
self.det_model.net_info['height'] = opt.input_size
self.det_inp_dim = int(self.det_model.net_info['height'])
assert self.det_inp_dim % 32 == 0
assert self.det_inp_dim > 32
if device != "cpu":
self.det_model.cuda()
inf_time = get_inference_time(self.det_model, height=opt.input_size, width=opt.input_size)
flops = print_model_param_flops(self.det_model, input_width=opt.input_size, input_height=opt.input_size)
params = print_model_param_nums(self.det_model)
print("Detection: Inference time {}s, Params {}, FLOPs {}".format(inf_time, params, flops))
self.det_model.eval()
self.im_dim_list = []
self.batchSize = batchSize
def __init__(self, cfg, weight, batchSize=1):
self.det_model = Darknet(cfg)
# self.det_model.load_state_dict(torch.load('models/yolo/yolov3-spp.weights', map_location="cuda:0")['model'])
self.det_model.load_weights(weight)
self.det_model.net_info['height'] = config.input_size
self.det_inp_dim = int(self.det_model.net_info['height'])
assert self.det_inp_dim % 32 == 0
assert self.det_inp_dim > 32
if device != "cpu":
self.det_model.cuda()
inf_time = get_inference_time(self.det_model,
height=config.input_size,
width=config.input_size)
flops = print_model_param_flops(self.det_model,
input_width=config.input_size,
input_height=config.input_size)
params = print_model_param_nums(self.det_model)
print("Detection: Inference time {}s, Params {}, FLOPs {}".format(
inf_time, params, flops))
self.det_model.eval()
self.im_dim_list = []
self.batchSize = batchSize