def __init__(self, kernel_size, dataset): super(InferenNet_fast, self).__init__() model = createModel().cuda() print('Loading pose model from {}'.format('./models/sppe/duc_se.pth')) model.load_state_dict(torch.load('./models/sppe/duc_se.pth')) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet, self).__init__() model = createModel().cuda() #print('Loading POSE model..') sys.stdout.flush() model.load_state_dict(torch.load('./models/sppe/duc_se.pth')) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet, self).__init__() model = createModel().cuda() print('Loading pose model from {}'.format(opt.pathModel)) sys.stdout.flush() model.load_state_dict(torch.load(opt.pathModel)) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet, self).__init__() model = createModel().cuda() print('Loading pose model from {}'.format(os.getcwd()+ '/Alphapose/models/sppe/duc_se.pth')) sys.stdout.flush() model.load_state_dict(torch.load(os.getcwd()+ '/Alphapose/models/sppe/duc_se.pth')) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet_fast, self).__init__() model = createModel().cuda() path_mian = os.path.dirname(os.path.abspath(__file__)) + '/../..' model_path = path_mian + '/models/sppe/duc_se.pth' print('Loading pose model from {}'.format(model_path)) model.load_state_dict(torch.load(model_path)) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet_fast, self).__init__() model = createModel().cuda() print('Loading pose model from {}'.format( './train_sppe/exp/coco/v100_exp2/model_63.pkl')) sys.stdout.flush() model.load_state_dict( torch.load('./train_sppe/exp/coco/v100_exp2/model_63.pkl')) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet_fast, self).__init__() model = createModel().cpu() print('Loading pose model from {}'.format('./models/sppe/duc_se.pth')) # model.load_state_dict(torch.load('./models/sppe/duc_se.pth')) model.load_state_dict( torch.load( '/home/a/roborts_project/src/alpha_pose/src/models/sppe/duc_se.pth', map_location=lambda storage, loc: storage)) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet, self).__init__() model = createModel().cuda() print('Loading pose model from {}'.format('./models/sppe/duc_se.pth')) sys.stdout.flush() dir_path = os.path.dirname(os.path.realpath(__file__)).split('/')[:-2] dir_path = '/'.join(dir_path) model.load_state_dict( torch.load(os.path.join(dir_path, 'models/sppe/duc_se.pth'))) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet_fast, self).__init__() model = createModel().cuda() print('加载模型 {}'.format('./models/sppe/duc_se.pth')) # model.load_state_dict(torch.load('./models/sppe/duc_se.pth')) model.load_state_dict( torch.load( '/media/ubuntu/文档/code/Pose/train_sppe/exp/coco/exp5/model_4.pkl' )) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet, self).__init__() model = createModel().cuda() if os.path.exists('./models/sppe/duc_se.pth'): print(' loading pose model from {}'.format( './models/sppe/duc_se.pth')) model.load_state_dict(torch.load('./models/sppe/duc_se.pth')) else: print(' loading pose model from {}'.format( './AlphaPose/models/sppe/duc_se.pth')) model.load_state_dict( torch.load('./AlphaPose/models/sppe/duc_se.pth')) model.eval() self.pyranet = model self.dataset = dataset
def __init__(self, kernel_size, dataset): super(InferenNet_fast, self).__init__() model = createModel() if torch.cuda.is_available(): model = model.cuda() print('Loading pose model from {}'.format('models/sppe/duc_se.pth')) # model.load_state_dict(torch.load('models/sppe/duc_se.pth')) if torch.cuda.is_available(): model.load_state_dict(torch.load('models/sppe/duc_se.pth')) else: model.load_state_dict( torch.load('models/sppe/duc_se.pth', map_location='cpu')) model.eval() self.pyranet = model self.dataset = dataset