def __init__(self, option, model_type, dataset, modules):
BaseModel.__init__(self, option)
self.mode = option.loss_mode
self.normalize_feature = option.normalize_feature
self.loss_names = ["loss_reg", "loss"]
self.metric_loss_module, self.miner_module = BaseModel.get_metric_loss_and_miner(
getattr(option, "metric_loss", None), getattr(option, "miner", None)
)
# Last Layer
if option.mlp_cls is not None:
last_mlp_opt = option.mlp_cls
in_feat = last_mlp_opt.nn[0]
self.FC_layer = Seq()
for i in range(1, len(last_mlp_opt.nn)):
self.FC_layer.append(
str(i),
Sequential(
*[
Linear(in_feat, last_mlp_opt.nn[i], bias=False),
FastBatchNorm1d(last_mlp_opt.nn[i], momentum=last_mlp_opt.bn_momentum),
LeakyReLU(0.2),
]
),
)
in_feat = last_mlp_opt.nn[i]
if last_mlp_opt.dropout:
self.FC_layer.append(Dropout(p=last_mlp_opt.dropout))
self.FC_layer.append(Linear(in_feat, in_feat, bias=False))
else:
self.FC_layer = torch.nn.Identity()
def __init__(self, option, model_type, dataset, modules):
BaseModel.__init__(self, option)
option_unet = option.option_unet
self.normalize_feature = option.normalize_feature
self.grid_size = option_unet.grid_size
self.unet = UnetMSparseConv3d(
option_unet.backbone,
input_nc=option_unet.input_nc,
pointnet_nn=option_unet.pointnet_nn,
post_mlp_nn=option_unet.post_mlp_nn,
pre_mlp_nn=option_unet.pre_mlp_nn,
add_pos=option_unet.add_pos,
add_pre_x=option_unet.add_pre_x,
aggr=option_unet.aggr,
backend=option.backend,
)
if option.mlp_cls is not None:
last_mlp_opt = option.mlp_cls
self.FC_layer = Seq()
for i in range(1, len(last_mlp_opt.nn)):
self.FC_layer.append(
nn.Sequential(*[
nn.Linear(last_mlp_opt.nn[i - 1],
last_mlp_opt.nn[i],
bias=False),
FastBatchNorm1d(last_mlp_opt.nn[i],
momentum=last_mlp_opt.bn_momentum),
nn.LeakyReLU(0.2),
]))
if last_mlp_opt.dropout:
self.FC_layer.append(nn.Dropout(p=last_mlp_opt.dropout))
else:
self.FC_layer = torch.nn.Identity()
self.head = nn.Sequential(
nn.Linear(option.output_nc, dataset.num_classes))
self.loss_names = ["loss_seg"]
def __init__(self, option):
BaseModel.__init__(self, option)