def __init__(self, in_planes, encode_dims=200, silhou_thres=0):
super().__init__()
self.encoder = ImageEncoder(in_planes, encode_dims=encode_dims)
state_dict = torch.load('downloads/models/shapehd.pt')['nets'][0]
temp_dict = {}
for key in state_dict.keys():
if key.startswith("marrnet2.encoder."):
temp_dict[key[17:len(key)]] = state_dict[key]
self.encoder.load_state_dict(temp_dict)
#!!!!! load and freeze
self.vp_generator = render4cnn(weights='lua',
weights_path='models/render4cnn.pth',
num_classes=1)
for param in self.vp_generator.parameters():
param.requires_grad = False
self.fuser = VectorFuserMultiLayerA(1080, 1080)
self.silhou_thres = silhou_thres
self.fc_fuser = nn.Sequential(nn.Linear(1480, 2048), nn.ReLU(),
nn.Linear(2048, 1024), nn.ReLU())
self.decoder = VoxelDecoder(n_dims=1024, nf=512)
def __init__(self, in_planes, encode_dims=200, silhou_thres=0):
super().__init__()
self.encoder = ImageEncoder(in_planes, encode_dims=encode_dims)
self.decoder = VoxelDecoder(n_dims=encode_dims, nf=512)
self.silhou_thres = silhou_thres
def __init__(self, in_planes, encode_dims=200, silhou_thres=0):
super().__init__()
self.encoder = ImageEncoder(in_planes, encode_dims=encode_dims)
self.fuser = VectorFuserSingleLayer(200, 200)
self.decoder = VoxelDecoder(n_dims=encode_dims, nf=512)
self.silhou_thres = silhou_thres