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
