def load_network(self):
print("net_path: {}".format(self.net_path))
self.net = load_network(self.net_path)
if self.use_gpu:
self.cuda()
self.eval()
def initialize(self):
# if os.path.isabs(self.net_path):
# net_path_full = self.net_path
# else:
# net_path_full = os.path.join(env_settings().network_path, self.net_path)
self.net = load_network(self.net_path)
if self.use_gpu:
self.net.cuda()
self.net.eval()
self.iou_predictor = self.net.bb_regressor
self.layer_stride = {
'conv1': 2,
'layer1': 4,
'layer2': 8,
'layer3': 16,
'layer4': 32,
'classification': 16,
'fc': None
}
self.layer_dim = {
'conv1': 64,
'layer1': 64,
'layer2': 128,
'layer3': 256,
'layer4': 512,
'classification': 256,
'fc': None
}
self.iounet_feature_layers = self.net.bb_regressor_layer
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.feature_layers = sorted(
list(set(self.output_layers + self.iounet_feature_layers)))
self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1)
self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1)
def load_network(self):
print(['loading net --', self.net_path])
self.net = load_network(self.net_path)
if self.use_gpu:
self.cuda()
self.eval()
def load_network(self):
self.net = load_network(self.net_path)
if self.use_gpu:
self.cuda()
self.eval()
def get_network(self, checkpoint_dir):
network = load_network(checkpoint_dir)
network.cuda()
network.eval()
return network
def load_network(self):
self.net = load_network(self.net_path, **self.net_kwargs)
if self.use_gpu:
self.cuda()
self.eval()