def initialize(self):
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.layer_stride = {'vggconv1': 2, 'conv1': 2, 'layer1': 4, 'layer2': 8, 'layer3': 16, 'layer4': 32,
'fc': None}
self.layer_dim = {'vggconv1': 96, 'conv1': 64, 'layer1': 64, 'layer2': 128, 'layer3': 256, 'layer4': 512,
'fc': None}
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)
if os.path.isabs(self.net_path):
net_path_full = [self.net_path]
else:
root_paths = env_settings().network_path
if isinstance(root_paths, str):
root_paths = [root_paths]
net_path_full = [os.path.join(root, self.net_path) for root in root_paths]
self.net = None
for net_path in net_path_full:
try:
self.net = resnet18_vggmconv1(self.output_layers, path=net_path)
break
except:
pass
if self.net is None:
raise Exception('Did not find network file {}'.format(self.net_path))
if self.use_gpu:
self.net.cuda()
self.net.eval()
def initialize(self, im):
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)
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.layer_stride = {
'vggconv1': 2,
'conv1': 2,
'layer1': 4,
'layer2': 8,
'layer3': 16,
'layer4': 32,
'fc': None
}
self.layer_dim = {
'vggconv1': 96,
'conv1': 64,
'layer1': 64,
'layer2': 128,
'layer3': 256,
'layer4': 512,
'fc': None
}
#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)
im = im / 255
self.mean = torch.Tensor([
torch.mean(im[:, 0, ...]),
torch.mean(im[:, 1, ...]),
torch.mean(im[:, 2, ...])
]).view(1, -1, 1, 1)
self.std = torch.Tensor([
torch.std(im[:, 0, ...]),
torch.std(im[:, 1, ...]),
torch.std(im[:, 2, ...])
]).view(1, -1, 1, 1)
self.net = resnet18_vggmconv1(self.output_layers, path=net_path_full)
if self.use_gpu:
self.net.cuda(self.gpu_device) if not (
self.gpu_device is None) else self.net.cuda()
self.net.eval()