def __init__(self, model_config, model_type, dataset, modules, *args, **kwargs):
super(BaseMinkowski, self).__init__(model_config, model_type, dataset, modules)
self.weight_initialization()
default_output_nc = kwargs.get("default_output_nc", None)
if not default_output_nc:
default_output_nc = extract_output_nc(model_config)
self._output_nc = default_output_nc
self._has_mlp_head = False
if "output_nc" in kwargs:
self._has_mlp_head = True
self._output_nc = kwargs["output_nc"]
self.mlp = MLP([default_output_nc, self.output_nc], activation=torch.nn.LeakyReLU(0.2), bias=False)
def __init__(self, model_config, model_type, dataset, modules, *args, **kwargs):
super(BaseKPConv, self).__init__(model_config, model_type, dataset, modules)
try:
default_output_nc = extract_output_nc(model_config)
except:
default_output_nc = -1
log.warning("Could not resolve number of output channels")
self._output_nc = default_output_nc
self._has_mlp_head = False
if "output_nc" in kwargs:
self._has_mlp_head = True
self._output_nc = kwargs["output_nc"]
self.mlp = MLP([default_output_nc, self.output_nc], activation=torch.nn.LeakyReLU(0.2), bias=False)